19188 lines
5.9 MiB
19188 lines
5.9 MiB
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from pathlib import Path\n",
|
||
"from shutil import copyfile\n",
|
||
"import pickle"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Load the general math/data manipulation packages"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Load the packages related to the Gaussian Process Regressor:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
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||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"import gpflow\n",
|
||
"import tensorflow as tf"
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||
]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
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||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"from gpflow.utilities import print_summary"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tf.config.set_visible_devices([], 'GPU')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Load the CasADi package used for optimization:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import casadi"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Import MATLAB engine and start it in the background since this takes a while:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"import matlab.engine"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"eng = matlab.engine.start_matlab()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matlab.engine.futureresult.FutureResult at 0x7f5ea0620190>"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"eng.load_system(\"../Simulink/polydome\", background = True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
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||
"source": [
|
||
"Copy the experimental data set to the CARNOT input location: "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"exp_id = 'Exp6'"
|
||
]
|
||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'../Data/input_WDB.mat'"
|
||
]
|
||
},
|
||
"execution_count": 12,
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||
"metadata": {},
|
||
"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
|
||
"copyfile(f\"../Data/Experimental_data_WDB/{exp_id}_WDB.mat\", \"../Data/input_WDB.mat\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Load the existing GP model"
|
||
]
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 13,
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||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"df_trainset = pd.read_pickle(\"gp_trainset.pkl\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": 14,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"x_scaler = pickle.load(open('x_scaler.pkl', 'rb'))"
|
||
]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"df_input = df_trainset.drop(columns = ['y'])\n",
|
||
"df_output = df_trainset['y']"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 16,
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||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"np_input = df_input.to_numpy()\n",
|
||
"np_output = df_output.to_numpy().reshape(-1, 1)"
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||
]
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||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": 17,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"np_input_sc = x_scaler.transform(np_input)"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 18,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"n_states = np_input_sc.shape[1]"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"╒═════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤════════════════╕\n",
|
||
"│ name │ class │ transform │ prior │ trainable │ shape │ dtype │ value │\n",
|
||
"╞═════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪════════════════╡\n",
|
||
"│ Sum.kernels[0].variance │ Parameter │ Softplus │ │ True │ () │ float64 │ 1.0 │\n",
|
||
"├─────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────────┤\n",
|
||
"│ Sum.kernels[0].lengthscales │ Parameter │ Softplus │ │ True │ (7,) │ float64 │ [1., 1., 1.... │\n",
|
||
"├─────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────────┤\n",
|
||
"│ Sum.kernels[1].variance │ Parameter │ Softplus │ │ True │ () │ float64 │ 1.0 │\n",
|
||
"╘═════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧════════════════╛\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"k = gpflow.kernels.SquaredExponential(lengthscales=([1] * np_input.shape[1])) + gpflow.kernels.Constant()\n",
|
||
"print_summary(k)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"╒════════════════════════════════════╤═══════════╤══════════════════╤═════════╤═════════════╤═════════╤═════════╤════════════════╕\n",
|
||
"│ name │ class │ transform │ prior │ trainable │ shape │ dtype │ value │\n",
|
||
"╞════════════════════════════════════╪═══════════╪══════════════════╪═════════╪═════════════╪═════════╪═════════╪════════════════╡\n",
|
||
"│ GPR.kernel.kernels[0].variance │ Parameter │ Softplus │ │ True │ () │ float64 │ 1.0 │\n",
|
||
"├────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────┼─────────┼────────────────┤\n",
|
||
"│ GPR.kernel.kernels[0].lengthscales │ Parameter │ Softplus │ │ True │ (7,) │ float64 │ [1., 1., 1.... │\n",
|
||
"├────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────┼─────────┼────────────────┤\n",
|
||
"│ GPR.kernel.kernels[1].variance │ Parameter │ Softplus │ │ True │ () │ float64 │ 1.0 │\n",
|
||
"├────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────┼─────────┼────────────────┤\n",
|
||
"│ GPR.likelihood.variance │ Parameter │ Softplus + Shift │ │ True │ () │ float64 │ 1.0 │\n",
|
||
"╘════════════════════════════════════╧═══════════╧══════════════════╧═════════╧═════════════╧═════════╧═════════╧════════════════╛\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model = gpflow.models.GPR(\n",
|
||
" data = (np_input_sc, np_output), \n",
|
||
" kernel = k, \n",
|
||
" mean_function = None\n",
|
||
" )\n",
|
||
"print_summary(model)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Load exported params over the model \"skeleton\":"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"model_params_loaded = pickle.load(open(Path(Path.cwd(), 'gp_params.gpf'), 'rb'))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"gpflow.utilities.multiple_assign(model, model_params_loaded)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"╒════════════════════════════════════╤═══════════╤══════════════════╤═════════╤═════════════╤═════════╤═════════╤══════════════════════════════════════════════╕\n",
|
||
"│ name │ class │ transform │ prior │ trainable │ shape │ dtype │ value │\n",
|
||
"╞════════════════════════════════════╪═══════════╪══════════════════╪═════════╪═════════════╪═════════╪═════════╪══════════════════════════════════════════════╡\n",
|
||
"│ GPR.kernel.kernels[0].variance │ Parameter │ Softplus │ │ True │ () │ float64 │ 522.3146176324312 │\n",
|
||
"├────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────┼─────────┼──────────────────────────────────────────────┤\n",
|
||
"│ GPR.kernel.kernels[0].lengthscales │ Parameter │ Softplus │ │ True │ (7,) │ float64 │ [398.28296795, 262.16471714, 1574.2697205... │\n",
|
||
"├────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────┼─────────┼──────────────────────────────────────────────┤\n",
|
||
"│ GPR.kernel.kernels[1].variance │ Parameter │ Softplus │ │ True │ () │ float64 │ 600.9888765600585 │\n",
|
||
"├────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────┼─────────┼──────────────────────────────────────────────┤\n",
|
||
"│ GPR.likelihood.variance │ Parameter │ Softplus + Shift │ │ True │ () │ float64 │ 0.005945197285215412 │\n",
|
||
"╘════════════════════════════════════╧═══════════╧══════════════════╧═════════╧═════════════╧═════════╧═════════╧══════════════════════════════════════════════╛\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print_summary(model)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Set up the CasADi optimization problem"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Package the regression model in a CasADi callback\n",
|
||
"class GPR(casadi.Callback):\n",
|
||
" def __init__(self, name, opts={}):\n",
|
||
" casadi.Callback.__init__(self)\n",
|
||
" self.construct(name, opts)\n",
|
||
" \n",
|
||
" # Number of inputs and outputs\n",
|
||
" def get_n_in(self): return 1\n",
|
||
" def get_n_out(self): return 1\n",
|
||
"\n",
|
||
" def get_sparsity_in(self,i):\n",
|
||
" return casadi.Sparsity.dense(n_states,1)\n",
|
||
"\n",
|
||
" def eval(self, arg):\n",
|
||
" x_scaled = x_scaler.transform(np.array(arg[0]).reshape(1, -1))\n",
|
||
" [mean, _] = model.predict_y(x_scaled)\n",
|
||
" return [mean.numpy()]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"GPR:(i0[7])->(o0) CallbackInternal\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Instantiate the Callback (make sure to keep a reference to it!)\n",
|
||
"gpr = GPR('GPR', {\"enable_fd\":True})\n",
|
||
"print(gpr)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 219,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"T_set = 20\n",
|
||
"N_horizon = 5"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Define optimization variables"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 220,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X = casadi.MX.sym(\"X\", N_horizon, n_states)\n",
|
||
"W = casadi.MX.sym(\"W\", N_horizon, 2)\n",
|
||
"x0_lags = casadi.MX.sym(\"lags\", 1, n_states - 3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Impose initial lags:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 221,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"g = casadi.vec(X[0,3:] - x0_lags)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Impose disturbances:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 222,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"g = casadi.vertcat(\n",
|
||
" g,\n",
|
||
" casadi.vec(X[:, :2] - W)\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Compute objective:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 223,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"J = casadi.norm_2(X.reshape((N_horizon, -1))[:,4] - T_set)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Fix input/output lags between time steps (equality constraints):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 224,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"for idx in range(1, N_horizon):\n",
|
||
" g = casadi.vertcat(\n",
|
||
" g,\n",
|
||
" X[idx, 3] - X[idx-1, 2],\n",
|
||
" X[idx, 4] - gpr(X[idx-1,:]),\n",
|
||
" X[idx, 5] - X[idx-1, 4],\n",
|
||
" X[idx, 6] - X[idx-1, 5]\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Impose input inequality constraints:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 225,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"g = casadi.vertcat(\n",
|
||
" g,\n",
|
||
" X[:, 2]\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Compile the optimization problem"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Compile the parameter vector:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 226,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"p = casadi.vertcat(\n",
|
||
" casadi.vec(W),\n",
|
||
" casadi.vec(x0_lags)\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 227,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"prob = {\"x\": casadi.vec(X), \"f\": J, \"p\": p, \"g\": g}\n",
|
||
"options = {\"ipopt\": {\"hessian_approximation\": \"limited-memory\", \"max_iter\": 100, \n",
|
||
" \"acceptable_tol\": 1e-8, \"acceptable_obj_change_tol\": 1e-6}}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Get the solver object:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 228,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"solver = casadi.nlpsol(\"solver\",\"ipopt\",prob, options)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Compute the `lbg` `ubg` since they are always the same:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 250,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"Pel_max = 6300\n",
|
||
"COP_heat = 5\n",
|
||
"COP_cool = 5\n",
|
||
"u_min = - COP_cool * Pel_max\n",
|
||
"u_max = COP_heat * Pel_max"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 251,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"real_lbg = [0] * (4 + 2 * N_horizon + 4 * (N_horizon - 1)) + [u_min] * (N_horizon)\n",
|
||
"real_ubg = [0] * (4 + 2 * N_horizon + 4 * (N_horizon - 1)) + [u_max] * (N_horizon)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Load CARNOT building with MATLAB backend"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 149,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Power</th>\n",
|
||
" <th>Setpoint</th>\n",
|
||
" <th>OutsideTemp</th>\n",
|
||
" <th>SupplyTemp</th>\n",
|
||
" <th>InsideTemp</th>\n",
|
||
" <th>SolRad</th>\n",
|
||
" <th>Heat</th>\n",
|
||
" <th>SimulatedTemp</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>timestamp</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:00:00+02:00</th>\n",
|
||
" <td>4651.034483</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>14.9</td>\n",
|
||
" <td>23.100000</td>\n",
|
||
" <td>143.479467</td>\n",
|
||
" <td>-23255.172414</td>\n",
|
||
" <td>22.258988</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:05:00+02:00</th>\n",
|
||
" <td>4634.896552</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>14.6</td>\n",
|
||
" <td>23.016667</td>\n",
|
||
" <td>133.344633</td>\n",
|
||
" <td>-23174.482759</td>\n",
|
||
" <td>21.514439</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:10:00+02:00</th>\n",
|
||
" <td>4620.620690</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>14.6</td>\n",
|
||
" <td>22.900000</td>\n",
|
||
" <td>122.100633</td>\n",
|
||
" <td>-23103.103448</td>\n",
|
||
" <td>21.488865</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:15:00+02:00</th>\n",
|
||
" <td>4449.233333</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>29.5</td>\n",
|
||
" <td>14.3</td>\n",
|
||
" <td>22.733333</td>\n",
|
||
" <td>111.456233</td>\n",
|
||
" <td>-22246.166667</td>\n",
|
||
" <td>21.907265</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:20:00+02:00</th>\n",
|
||
" <td>27.068966</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>29.5</td>\n",
|
||
" <td>18.5</td>\n",
|
||
" <td>22.700000</td>\n",
|
||
" <td>100.605500</td>\n",
|
||
" <td>-135.344828</td>\n",
|
||
" <td>23.201910</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Power Setpoint OutsideTemp SupplyTemp \\\n",
|
||
"timestamp \n",
|
||
"2017-07-07 20:00:00+02:00 4651.034483 22.5 30.0 14.9 \n",
|
||
"2017-07-07 20:05:00+02:00 4634.896552 22.5 30.0 14.6 \n",
|
||
"2017-07-07 20:10:00+02:00 4620.620690 22.5 30.0 14.6 \n",
|
||
"2017-07-07 20:15:00+02:00 4449.233333 22.5 29.5 14.3 \n",
|
||
"2017-07-07 20:20:00+02:00 27.068966 22.5 29.5 18.5 \n",
|
||
"\n",
|
||
" InsideTemp SolRad Heat SimulatedTemp \n",
|
||
"timestamp \n",
|
||
"2017-07-07 20:00:00+02:00 23.100000 143.479467 -23255.172414 22.258988 \n",
|
||
"2017-07-07 20:05:00+02:00 23.016667 133.344633 -23174.482759 21.514439 \n",
|
||
"2017-07-07 20:10:00+02:00 22.900000 122.100633 -23103.103448 21.488865 \n",
|
||
"2017-07-07 20:15:00+02:00 22.733333 111.456233 -22246.166667 21.907265 \n",
|
||
"2017-07-07 20:20:00+02:00 22.700000 100.605500 -135.344828 23.201910 "
|
||
]
|
||
},
|
||
"execution_count": 149,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df = pd.read_pickle(f\"../Data/CARNOT_output/{exp_id}_full.pkl\")\n",
|
||
"df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 150,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>time</th>\n",
|
||
" <th>timestamp</th>\n",
|
||
" <th>zenith</th>\n",
|
||
" <th>azimuth</th>\n",
|
||
" <th>dni</th>\n",
|
||
" <th>dhi</th>\n",
|
||
" <th>OutsideTemp</th>\n",
|
||
" <th>Tsky_rad</th>\n",
|
||
" <th>relative_humidity</th>\n",
|
||
" <th>precipitation</th>\n",
|
||
" <th>cloud_index</th>\n",
|
||
" <th>pressure</th>\n",
|
||
" <th>wind_speed</th>\n",
|
||
" <th>wind_direction</th>\n",
|
||
" <th>aoi</th>\n",
|
||
" <th>incidence_main</th>\n",
|
||
" <th>incidence_second</th>\n",
|
||
" <th>poa_direct</th>\n",
|
||
" <th>poa_diffuse</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>201707072000</td>\n",
|
||
" <td>77.301874</td>\n",
|
||
" <td>289.483783</td>\n",
|
||
" <td>219.244645</td>\n",
|
||
" <td>95.527995</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>24.0</td>\n",
|
||
" <td>50</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>0.5</td>\n",
|
||
" <td>96300</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>77.301874</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>48.193107</td>\n",
|
||
" <td>95.527995</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>300</td>\n",
|
||
" <td>201707072005</td>\n",
|
||
" <td>78.106572</td>\n",
|
||
" <td>290.326990</td>\n",
|
||
" <td>212.109089</td>\n",
|
||
" <td>89.880399</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>24.0</td>\n",
|
||
" <td>50</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>0.5</td>\n",
|
||
" <td>96300</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>78.106572</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>43.713976</td>\n",
|
||
" <td>89.880399</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>600</td>\n",
|
||
" <td>201707072010</td>\n",
|
||
" <td>78.906360</td>\n",
|
||
" <td>291.172101</td>\n",
|
||
" <td>196.448414</td>\n",
|
||
" <td>84.549236</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>24.0</td>\n",
|
||
" <td>50</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>0.5</td>\n",
|
||
" <td>96300</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>78.906360</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>37.799238</td>\n",
|
||
" <td>84.549236</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>900</td>\n",
|
||
" <td>201707072015</td>\n",
|
||
" <td>79.700825</td>\n",
|
||
" <td>292.019421</td>\n",
|
||
" <td>183.166538</td>\n",
|
||
" <td>78.957097</td>\n",
|
||
" <td>29.5</td>\n",
|
||
" <td>23.5</td>\n",
|
||
" <td>50</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>0.5</td>\n",
|
||
" <td>96300</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>79.700825</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>32.747988</td>\n",
|
||
" <td>78.957097</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1200</td>\n",
|
||
" <td>201707072020</td>\n",
|
||
" <td>80.489912</td>\n",
|
||
" <td>292.869248</td>\n",
|
||
" <td>166.672243</td>\n",
|
||
" <td>73.312077</td>\n",
|
||
" <td>29.5</td>\n",
|
||
" <td>23.5</td>\n",
|
||
" <td>50</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>0.5</td>\n",
|
||
" <td>96300</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>80.489912</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>-9999</td>\n",
|
||
" <td>27.537799</td>\n",
|
||
" <td>73.312077</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" time timestamp zenith azimuth dni dhi \\\n",
|
||
"0 0 201707072000 77.301874 289.483783 219.244645 95.527995 \n",
|
||
"1 300 201707072005 78.106572 290.326990 212.109089 89.880399 \n",
|
||
"2 600 201707072010 78.906360 291.172101 196.448414 84.549236 \n",
|
||
"3 900 201707072015 79.700825 292.019421 183.166538 78.957097 \n",
|
||
"4 1200 201707072020 80.489912 292.869248 166.672243 73.312077 \n",
|
||
"\n",
|
||
" OutsideTemp Tsky_rad relative_humidity precipitation cloud_index \\\n",
|
||
"0 30.0 24.0 50 -9999 0.5 \n",
|
||
"1 30.0 24.0 50 -9999 0.5 \n",
|
||
"2 30.0 24.0 50 -9999 0.5 \n",
|
||
"3 29.5 23.5 50 -9999 0.5 \n",
|
||
"4 29.5 23.5 50 -9999 0.5 \n",
|
||
"\n",
|
||
" pressure wind_speed wind_direction aoi incidence_main \\\n",
|
||
"0 96300 0 -9999 77.301874 -9999 \n",
|
||
"1 96300 0 -9999 78.106572 -9999 \n",
|
||
"2 96300 0 -9999 78.906360 -9999 \n",
|
||
"3 96300 0 -9999 79.700825 -9999 \n",
|
||
"4 96300 0 -9999 80.489912 -9999 \n",
|
||
"\n",
|
||
" incidence_second poa_direct poa_diffuse \n",
|
||
"0 -9999 48.193107 95.527995 \n",
|
||
"1 -9999 43.713976 89.880399 \n",
|
||
"2 -9999 37.799238 84.549236 \n",
|
||
"3 -9999 32.747988 78.957097 \n",
|
||
"4 -9999 27.537799 73.312077 "
|
||
]
|
||
},
|
||
"execution_count": 150,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_wdb = pd.read_pickle(f\"../Data/Experimental_python/{exp_id}_WDB.pkl\")\n",
|
||
"df_wdb.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 151,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Experiment runtime: 208500\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"runtime = df_wdb['time'].iloc[-1]\n",
|
||
"print(f\"Experiment runtime: {runtime}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 152,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"eng.workspace['t0'] = float(df['InsideTemp'][0])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 153,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"day_air_exchange_rate = 2.0\n",
|
||
"night_air_exchange_rate = 0.5"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 154,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"air_exchange_rate = np.zeros((df_wdb.shape[0], 2))\n",
|
||
"air_exchange_rate[:, 0] = df_wdb['time']\n",
|
||
"air_exchange_rate[:, 1] = np.where(df['Power'] < 100, day_air_exchange_rate, night_air_exchange_rate)\n",
|
||
"eng.workspace['air_exchange_rate'] = matlab.double(air_exchange_rate.tolist())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 155,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"power = np.array([df_wdb['time'], df['Heat']]).T\n",
|
||
"eng.workspace['power'] = matlab.double(power.tolist())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 156,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Power</th>\n",
|
||
" <th>Setpoint</th>\n",
|
||
" <th>OutsideTemp</th>\n",
|
||
" <th>SupplyTemp</th>\n",
|
||
" <th>InsideTemp</th>\n",
|
||
" <th>SolRad</th>\n",
|
||
" <th>Heat</th>\n",
|
||
" <th>SimulatedTemp</th>\n",
|
||
" <th>time</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>timestamp</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:00:00+02:00</th>\n",
|
||
" <td>4651.034483</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>14.9</td>\n",
|
||
" <td>23.100000</td>\n",
|
||
" <td>143.479467</td>\n",
|
||
" <td>-23255.172414</td>\n",
|
||
" <td>22.258988</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:05:00+02:00</th>\n",
|
||
" <td>4634.896552</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>14.6</td>\n",
|
||
" <td>23.016667</td>\n",
|
||
" <td>133.344633</td>\n",
|
||
" <td>-23174.482759</td>\n",
|
||
" <td>21.514439</td>\n",
|
||
" <td>300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:10:00+02:00</th>\n",
|
||
" <td>4620.620690</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>14.6</td>\n",
|
||
" <td>22.900000</td>\n",
|
||
" <td>122.100633</td>\n",
|
||
" <td>-23103.103448</td>\n",
|
||
" <td>21.488865</td>\n",
|
||
" <td>600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:15:00+02:00</th>\n",
|
||
" <td>4449.233333</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>29.5</td>\n",
|
||
" <td>14.3</td>\n",
|
||
" <td>22.733333</td>\n",
|
||
" <td>111.456233</td>\n",
|
||
" <td>-22246.166667</td>\n",
|
||
" <td>21.907265</td>\n",
|
||
" <td>900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:20:00+02:00</th>\n",
|
||
" <td>27.068966</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>29.5</td>\n",
|
||
" <td>18.5</td>\n",
|
||
" <td>22.700000</td>\n",
|
||
" <td>100.605500</td>\n",
|
||
" <td>-135.344828</td>\n",
|
||
" <td>23.201910</td>\n",
|
||
" <td>1200</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Power Setpoint OutsideTemp SupplyTemp \\\n",
|
||
"timestamp \n",
|
||
"2017-07-07 20:00:00+02:00 4651.034483 22.5 30.0 14.9 \n",
|
||
"2017-07-07 20:05:00+02:00 4634.896552 22.5 30.0 14.6 \n",
|
||
"2017-07-07 20:10:00+02:00 4620.620690 22.5 30.0 14.6 \n",
|
||
"2017-07-07 20:15:00+02:00 4449.233333 22.5 29.5 14.3 \n",
|
||
"2017-07-07 20:20:00+02:00 27.068966 22.5 29.5 18.5 \n",
|
||
"\n",
|
||
" InsideTemp SolRad Heat \\\n",
|
||
"timestamp \n",
|
||
"2017-07-07 20:00:00+02:00 23.100000 143.479467 -23255.172414 \n",
|
||
"2017-07-07 20:05:00+02:00 23.016667 133.344633 -23174.482759 \n",
|
||
"2017-07-07 20:10:00+02:00 22.900000 122.100633 -23103.103448 \n",
|
||
"2017-07-07 20:15:00+02:00 22.733333 111.456233 -22246.166667 \n",
|
||
"2017-07-07 20:20:00+02:00 22.700000 100.605500 -135.344828 \n",
|
||
"\n",
|
||
" SimulatedTemp time \n",
|
||
"timestamp \n",
|
||
"2017-07-07 20:00:00+02:00 22.258988 0 \n",
|
||
"2017-07-07 20:05:00+02:00 21.514439 300 \n",
|
||
"2017-07-07 20:10:00+02:00 21.488865 600 \n",
|
||
"2017-07-07 20:15:00+02:00 21.907265 900 \n",
|
||
"2017-07-07 20:20:00+02:00 23.201910 1200 "
|
||
]
|
||
},
|
||
"execution_count": 156,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df = pd.DataFrame(df).assign(time = df_wdb['time'].values)\n",
|
||
"df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Control loop"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Initiation setup"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 277,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"current_timestamp = 1500"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 278,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_power = df['Heat']\n",
|
||
"df_power = pd.DataFrame(df_power).assign(time = df_wdb['time'].values)\n",
|
||
"df_power.loc[df_power['time'] >= current_timestamp, 'Heat'] = np.NaN"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Compute input to apply at current time step"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 281,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"u_1 = float(df_power.loc[df['time'] == (current_timestamp - 300 * 1), 'Heat'])\n",
|
||
"\n",
|
||
"y_1 = float(df.loc[df['time'] == (current_timestamp - 300 * 1), 'SimulatedTemp'])\n",
|
||
"y_2 = float(df.loc[df['time'] == (current_timestamp - 300 * 2), 'SimulatedTemp'])\n",
|
||
"y_3 = float(df.loc[df['time'] == (current_timestamp - 300 * 3), 'SimulatedTemp'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 282,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"real_x0 = np.array([u_1, y_1, y_2, y_3])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 283,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"iter_idx = (df['time'] >= current_timestamp)\n",
|
||
"real_W = df[iter_idx].iloc[:N_horizon, [5, 2]].to_numpy()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 284,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"real_p = casadi.vertcat(\n",
|
||
" casadi.vec(real_W),\n",
|
||
" casadi.vec(real_x0)\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 285,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.35e+02 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9271395e+01 1.22e+01 1.35e+02 -1.5 1.35e+02 - 9.90e-01 1.00e+00f 1\n",
|
||
" 2 7.1279166e+00 3.92e+00 9.23e+00 0.4 1.22e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 5.9675618e+00 8.77e-01 7.27e-01 -1.6 6.78e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 6.6214434e+00 1.45e-03 7.84e-02 -3.4 1.22e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 6.6221581e+00 1.97e-07 1.10e-04 -5.3 1.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 6.6221582e+00 1.76e-07 2.36e-05 -11.0 5.79e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 6.6221584e+00 9.69e-09 1.58e-04 -11.0 9.49e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 6.6221583e+00 5.30e-08 1.08e-04 -11.0 4.31e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 6.6221583e+00 9.40e-08 1.75e-04 -11.0 3.65e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 6.6221582e+00 5.21e-08 4.63e-05 -11.0 5.31e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 6.6221584e+00 8.26e-11 2.02e-05 -11.0 3.31e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 12 6.6221584e+00 1.56e-08 1.49e-05 -11.0 1.54e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 6.6221584e+00 1.09e-08 1.88e-04 -11.0 1.02e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 6.6221578e+00 3.34e-07 1.06e-04 -11.0 9.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 6.6221575e+00 6.06e-07 1.49e-04 -11.0 2.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 6.6221578e+00 4.65e-07 4.50e-05 -11.0 1.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 6.6221584e+00 4.82e-08 3.51e-05 -11.0 3.10e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 6.6221584e+00 9.61e-09 6.94e-05 -11.0 1.10e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 6.6221584e+00 1.81e-08 2.21e-05 -11.0 6.77e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 6.6221584e+00 2.32e-09 1.10e-04 -11.0 6.40e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 6.6221584e+00 7.28e-09 5.11e-05 -11.0 3.87e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 6.6221584e+00 1.58e-08 5.64e-05 -11.0 1.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 6.6221584e+00 1.50e-08 4.24e-05 -11.0 8.53e-05 - 1.00e+00 6.25e-02h 5\n",
|
||
" 24 6.6221584e+00 1.64e-08 3.85e-05 -11.0 1.49e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 6.6221584e+00 1.53e-08 7.41e-05 -11.0 6.91e-05 - 1.00e+00 6.25e-02h 5\n",
|
||
" 26 6.6221584e+00 2.08e-08 8.48e-05 -11.0 8.84e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 6.6221584e+00 1.85e-08 8.93e-05 -11.0 9.58e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 6.6221584e+00 2.56e-08 3.13e-04 -11.0 9.32e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 6.6221576e+00 5.41e-07 5.71e-05 -11.0 3.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 6.6221584e+00 1.10e-07 1.07e-04 -11.0 7.82e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 6.6221585e+00 1.81e-08 2.70e-05 -11.0 3.37e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 6.6221585e+00 3.51e-09 7.75e-05 -11.0 1.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 6.6221584e+00 1.17e-07 6.02e-05 -11.0 5.59e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 6.6221584e+00 4.22e-08 3.49e-05 -11.0 2.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 6.6221585e+00 1.65e-10 1.45e-04 -11.0 4.20e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 36 6.6221581e+00 1.97e-07 5.01e-05 -11.0 1.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 6.6221579e+00 5.73e-07 1.70e-03 -11.0 4.56e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 6.6221486e+00 4.56e-06 7.36e-03 -11.0 7.98e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 6.6220426e+00 1.38e-04 7.62e-03 -11.0 1.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 6.6218031e+00 1.59e-04 1.85e-03 -11.0 8.14e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 6.6221225e+00 5.53e-05 2.31e-03 -11.0 2.21e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 6.6221175e+00 3.18e-05 2.99e-03 -11.0 2.38e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 6.6221162e+00 5.22e-05 1.32e-03 -11.0 1.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 6.6221488e+00 2.12e-05 1.38e-03 -11.0 6.41e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 6.6221332e+00 1.94e-05 1.99e-03 -11.0 8.63e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 6.6219925e+00 9.12e-05 2.58e-03 -11.0 4.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 6.6221160e+00 1.65e-05 1.61e-03 -11.0 1.60e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 6.6221330e+00 1.06e-05 1.17e-03 -11.0 6.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 6.6221326e+00 1.16e-05 1.04e-03 -11.0 3.97e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 6.6221530e+00 5.24e-09 1.70e-04 -11.0 9.62e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 51 6.6221343e+00 8.37e-06 1.18e-03 -11.0 4.75e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 6.6221372e+00 7.64e-06 1.53e-03 -11.0 4.36e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 6.6221499e+00 7.38e-07 1.31e-03 -11.0 1.86e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 6.6221313e+00 3.46e-05 1.47e-03 -11.0 1.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 6.6221445e+00 6.48e-06 1.25e-03 -11.0 2.68e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 6.6221440e+00 4.92e-06 1.54e-03 -11.0 3.34e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 6.6221489e+00 1.44e-06 1.88e-03 -11.0 1.74e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 6.6221448e+00 3.58e-06 1.52e-03 -11.0 1.43e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 6.6221508e+00 3.68e-10 1.84e-04 -11.0 3.36e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 6.6221422e+00 1.02e-05 1.44e-03 -11.0 1.65e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 6.6221472e+00 1.82e-06 2.59e-03 -11.0 1.90e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 6.6221114e+00 9.18e-05 2.50e-03 -11.0 2.52e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 6.6221427e+00 4.59e-06 1.65e-03 -11.0 7.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 6.6221281e+00 2.37e-05 9.19e-04 -11.0 6.06e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 6.6221433e+00 2.67e-06 9.30e-04 -11.0 1.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 6.6221449e+00 1.21e-06 1.25e-03 -11.0 8.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 6.6221458e+00 4.06e-07 1.21e-03 -11.0 3.96e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 6.6221371e+00 4.43e-06 3.11e-03 -11.0 2.43e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 6.6221425e+00 4.65e-06 1.83e-03 -11.0 3.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 6.6219486e+00 9.33e-05 5.94e-03 -11.0 2.02e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 6.6221220e+00 1.19e-05 1.25e-03 -11.0 8.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 6.6220685e+00 4.93e-05 1.57e-03 -11.0 2.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 6.6218628e+00 2.83e-04 3.02e-03 -11.0 5.16e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 6.6221145e+00 1.78e-06 1.00e-03 -11.0 4.36e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 6.6221086e+00 1.04e-05 7.87e-04 -11.0 3.92e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 6.6221179e+00 8.17e-10 1.21e-04 -11.0 6.69e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 77 6.6219927e+00 6.91e-05 1.42e-03 -11.0 1.15e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 78 6.6220997e+00 2.14e-08 1.03e-04 -11.0 9.62e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 79 6.6218281e+00 1.11e-04 3.35e-03 -11.0 2.17e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 6.6218183e+00 2.30e-04 9.74e-04 -11.0 9.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 6.6220460e+00 9.60e-05 8.43e-04 -11.0 5.69e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 6.6218423e+00 3.69e-04 1.57e-03 -11.0 2.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 6.6217385e+00 1.17e-04 6.44e-04 -11.0 7.27e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 6.6199458e+00 1.71e-03 3.34e-03 -11.0 1.60e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 6.6132002e+00 3.65e-03 2.18e-03 -11.0 2.88e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 6.6193511e+00 1.24e-03 1.85e-03 -11.0 1.52e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 6.6166566e+00 2.96e-03 3.14e-03 -11.0 3.01e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 6.6086150e+00 8.69e-03 1.60e-03 -11.0 1.71e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 6.6204717e+00 1.16e-03 2.15e-03 -11.0 1.40e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 6.6091813e+00 1.62e-02 1.89e-03 -11.0 3.88e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 6.5946950e+00 4.15e-02 2.13e-03 -11.0 4.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 6.2909497e+00 8.25e-01 1.03e-01 -11.0 1.72e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 93 6.1837306e+00 8.35e-01 6.68e-02 -9.0 2.09e+04 - 1.00e+00 4.79e-01h 1\n",
|
||
" 94 6.5760130e+00 1.56e-05 7.52e-01 -10.8 8.60e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 6.5760251e+00 6.42e-07 6.82e-05 -11.0 9.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 6.5760234e+00 2.02e-06 2.40e-03 -11.0 5.68e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 6.5760247e+00 5.15e-07 4.86e-05 -11.0 2.14e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 6.5760214e+00 3.00e-06 2.36e-03 -11.0 1.87e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 6.5760200e+00 2.75e-06 1.19e-03 -11.0 3.33e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 6.5760200e+00 6.66e-06 2.24e-03 -11.0 6.90e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 6.5760200008798533e+00 6.5760200008798533e+00\n",
|
||
"Dual infeasibility......: 2.2442159118026481e-03 2.2442159118026481e-03\n",
|
||
"Constraint violation....: 6.6578930102423328e-06 6.6578930102423328e-06\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 2.2442159118026481e-03 2.2442159118026481e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 115\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 115\n",
|
||
"Number of inequality constraint evaluations = 115\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.441\n",
|
||
"Total CPU secs in NLP function evaluations = 138.114\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 552.00us ( 4.80us) 542.47us ( 4.72us) 115\n",
|
||
" nlp_g | 5.33 s ( 46.31ms) 5.09 s ( 44.29ms) 115\n",
|
||
" nlp_grad | 1.60 s ( 1.60 s) 1.55 s ( 1.55 s) 1\n",
|
||
" nlp_grad_f | 384.00us ( 3.76us) 379.34us ( 3.72us) 102\n",
|
||
" nlp_jac_g | 135.80 s ( 1.33 s) 129.99 s ( 1.27 s) 102\n",
|
||
" total | 142.87 s (142.87 s) 136.77 s (136.77 s) 1\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"res = solver(p = real_p, lbg = real_lbg, ubg = real_ubg)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Apply the first computed input as the next input:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 295,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_power.loc[df_power['time'] == current_timestamp, 'Heat'] = res['x'].reshape((N_horizon, -1))[0, 2]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Simulate the building including the current input:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 299,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"power = np.array(df_power[['time', 'Heat']].dropna())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 303,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"eng.workspace['power'] = matlab.double(power.tolist())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 304,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"eng.set_param('polydome', 'StopTime', str(current_timestamp + 300), nargout = 0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 305,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"eng.workspace['result'] = eng.sim('polydome')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Interpret the MATLAB results as python:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 306,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dict_simulation = {}\n",
|
||
"dict_simulation['values'] = np.asarray(eng.eval('result.SimulatedTemp.Data')).reshape(-1)\n",
|
||
"dict_simulation['time'] = np.asarray(eng.eval('result.SimulatedTemp.Time')).reshape(-1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 307,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_simulation = pd.DataFrame(dict_simulation)\n",
|
||
"#df_simulation['time'] = df_simulation['time'].astype(int)\n",
|
||
"df_simulation.set_index('time', inplace = True, drop = True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 308,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_simulation['timestamp'] = df.index[0] + df_simulation.index.map(lambda x: pd.Timedelta(seconds = x))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 309,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_simulation = df_simulation.reset_index().set_index('timestamp')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 313,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_resampled_5 = df_simulation['values'].resample('5min').mean().pad()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 327,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_simulation = pd.concat([df['time'], df_resampled_5], axis = 1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Update the simulated temperature (output data) with the new info from this step:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 331,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df.loc[:, 'SimulatedTemp'] = df_simulation['values']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 332,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Power</th>\n",
|
||
" <th>Setpoint</th>\n",
|
||
" <th>OutsideTemp</th>\n",
|
||
" <th>SupplyTemp</th>\n",
|
||
" <th>InsideTemp</th>\n",
|
||
" <th>SolRad</th>\n",
|
||
" <th>Heat</th>\n",
|
||
" <th>SimulatedTemp</th>\n",
|
||
" <th>time</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>timestamp</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:00:00+02:00</th>\n",
|
||
" <td>4651.034483</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>14.9</td>\n",
|
||
" <td>23.100000</td>\n",
|
||
" <td>143.479467</td>\n",
|
||
" <td>-23255.172414</td>\n",
|
||
" <td>22.186572</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:05:00+02:00</th>\n",
|
||
" <td>4634.896552</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>14.6</td>\n",
|
||
" <td>23.016667</td>\n",
|
||
" <td>133.344633</td>\n",
|
||
" <td>-23174.482759</td>\n",
|
||
" <td>21.512325</td>\n",
|
||
" <td>300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:10:00+02:00</th>\n",
|
||
" <td>4620.620690</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>14.6</td>\n",
|
||
" <td>22.900000</td>\n",
|
||
" <td>122.100633</td>\n",
|
||
" <td>-23103.103448</td>\n",
|
||
" <td>21.489900</td>\n",
|
||
" <td>600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:15:00+02:00</th>\n",
|
||
" <td>4449.233333</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>29.5</td>\n",
|
||
" <td>14.3</td>\n",
|
||
" <td>22.733333</td>\n",
|
||
" <td>111.456233</td>\n",
|
||
" <td>-22246.166667</td>\n",
|
||
" <td>22.073906</td>\n",
|
||
" <td>900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:20:00+02:00</th>\n",
|
||
" <td>27.068966</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>29.5</td>\n",
|
||
" <td>18.5</td>\n",
|
||
" <td>22.700000</td>\n",
|
||
" <td>100.605500</td>\n",
|
||
" <td>-135.344828</td>\n",
|
||
" <td>23.232675</td>\n",
|
||
" <td>1200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-10 05:35:00+02:00</th>\n",
|
||
" <td>28.206897</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>18.0</td>\n",
|
||
" <td>23.0</td>\n",
|
||
" <td>22.333333</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>141.034483</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>207300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-10 05:40:00+02:00</th>\n",
|
||
" <td>27.965517</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>18.0</td>\n",
|
||
" <td>23.0</td>\n",
|
||
" <td>22.383333</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>139.827586</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>207600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-10 05:45:00+02:00</th>\n",
|
||
" <td>30.413793</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>18.0</td>\n",
|
||
" <td>23.0</td>\n",
|
||
" <td>22.366667</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>152.068966</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>207900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-10 05:50:00+02:00</th>\n",
|
||
" <td>29.800000</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>18.0</td>\n",
|
||
" <td>23.0</td>\n",
|
||
" <td>22.333333</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>149.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>208200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-10 05:55:00+02:00</th>\n",
|
||
" <td>31.931034</td>\n",
|
||
" <td>22.5</td>\n",
|
||
" <td>18.0</td>\n",
|
||
" <td>23.0</td>\n",
|
||
" <td>22.350000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>159.655172</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>208500</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>696 rows × 9 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Power Setpoint OutsideTemp SupplyTemp \\\n",
|
||
"timestamp \n",
|
||
"2017-07-07 20:00:00+02:00 4651.034483 22.5 30.0 14.9 \n",
|
||
"2017-07-07 20:05:00+02:00 4634.896552 22.5 30.0 14.6 \n",
|
||
"2017-07-07 20:10:00+02:00 4620.620690 22.5 30.0 14.6 \n",
|
||
"2017-07-07 20:15:00+02:00 4449.233333 22.5 29.5 14.3 \n",
|
||
"2017-07-07 20:20:00+02:00 27.068966 22.5 29.5 18.5 \n",
|
||
"... ... ... ... ... \n",
|
||
"2017-07-10 05:35:00+02:00 28.206897 22.5 18.0 23.0 \n",
|
||
"2017-07-10 05:40:00+02:00 27.965517 22.5 18.0 23.0 \n",
|
||
"2017-07-10 05:45:00+02:00 30.413793 22.5 18.0 23.0 \n",
|
||
"2017-07-10 05:50:00+02:00 29.800000 22.5 18.0 23.0 \n",
|
||
"2017-07-10 05:55:00+02:00 31.931034 22.5 18.0 23.0 \n",
|
||
"\n",
|
||
" InsideTemp SolRad Heat \\\n",
|
||
"timestamp \n",
|
||
"2017-07-07 20:00:00+02:00 23.100000 143.479467 -23255.172414 \n",
|
||
"2017-07-07 20:05:00+02:00 23.016667 133.344633 -23174.482759 \n",
|
||
"2017-07-07 20:10:00+02:00 22.900000 122.100633 -23103.103448 \n",
|
||
"2017-07-07 20:15:00+02:00 22.733333 111.456233 -22246.166667 \n",
|
||
"2017-07-07 20:20:00+02:00 22.700000 100.605500 -135.344828 \n",
|
||
"... ... ... ... \n",
|
||
"2017-07-10 05:35:00+02:00 22.333333 0.000000 141.034483 \n",
|
||
"2017-07-10 05:40:00+02:00 22.383333 0.000000 139.827586 \n",
|
||
"2017-07-10 05:45:00+02:00 22.366667 0.000000 152.068966 \n",
|
||
"2017-07-10 05:50:00+02:00 22.333333 0.000000 149.000000 \n",
|
||
"2017-07-10 05:55:00+02:00 22.350000 0.000000 159.655172 \n",
|
||
"\n",
|
||
" SimulatedTemp time \n",
|
||
"timestamp \n",
|
||
"2017-07-07 20:00:00+02:00 22.186572 0 \n",
|
||
"2017-07-07 20:05:00+02:00 21.512325 300 \n",
|
||
"2017-07-07 20:10:00+02:00 21.489900 600 \n",
|
||
"2017-07-07 20:15:00+02:00 22.073906 900 \n",
|
||
"2017-07-07 20:20:00+02:00 23.232675 1200 \n",
|
||
"... ... ... \n",
|
||
"2017-07-10 05:35:00+02:00 NaN 207300 \n",
|
||
"2017-07-10 05:40:00+02:00 NaN 207600 \n",
|
||
"2017-07-10 05:45:00+02:00 NaN 207900 \n",
|
||
"2017-07-10 05:50:00+02:00 NaN 208200 \n",
|
||
"2017-07-10 05:55:00+02:00 NaN 208500 \n",
|
||
"\n",
|
||
"[696 rows x 9 columns]"
|
||
]
|
||
},
|
||
"execution_count": 332,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Keep track of the all the prediction horizon, to add to the graph:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 340,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[22.79605454, 22.91609847, 23.08086156, 23.18446746, 23.81619112]])"
|
||
]
|
||
},
|
||
"execution_count": 340,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"T_sim_horizon = np.array(gpr(res['x'].reshape((N_horizon, -1)).T))\n",
|
||
"T_sim_horizon"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 352,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"simul_idx = (df_simulation['time'] >= current_timestamp) & (df_simulation['time'] < (current_timestamp + N_horizon * 300))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 367,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
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|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
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|
||
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|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>time</th>\n",
|
||
" <th>values</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>timestamp</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:25:00+02:00</th>\n",
|
||
" <td>1500</td>\n",
|
||
" <td>22.796055</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:30:00+02:00</th>\n",
|
||
" <td>1800</td>\n",
|
||
" <td>22.916098</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:35:00+02:00</th>\n",
|
||
" <td>2100</td>\n",
|
||
" <td>23.080862</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:40:00+02:00</th>\n",
|
||
" <td>2400</td>\n",
|
||
" <td>23.184467</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-07-07 20:45:00+02:00</th>\n",
|
||
" <td>2700</td>\n",
|
||
" <td>23.816191</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" time values\n",
|
||
"timestamp \n",
|
||
"2017-07-07 20:25:00+02:00 1500 22.796055\n",
|
||
"2017-07-07 20:30:00+02:00 1800 22.916098\n",
|
||
"2017-07-07 20:35:00+02:00 2100 23.080862\n",
|
||
"2017-07-07 20:40:00+02:00 2400 23.184467\n",
|
||
"2017-07-07 20:45:00+02:00 2700 23.816191"
|
||
]
|
||
},
|
||
"execution_count": 367,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_T_sim_horizon = df_simulation[simul_idx].copy()\n",
|
||
"df_T_sim_horizon.loc[:, 'values'] = T_sim_horizon.reshape((-1, ))\n",
|
||
"df_T_sim_horizon"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 384,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize = (15, 5))\n",
|
||
"plt.plot(df_simulation.index, df_simulation['values'], label = 'Simulated Temperature')\n",
|
||
"plt.plot(df_T_sim_horizon.index, df_T_sim_horizon['values'], label = 'Prediction Horizon', color = 'red', marker = 'x')\n",
|
||
"#plt.plot(df.index, df['InsideTemp'], label = 'Inside Temperature')\n",
|
||
"#plt.plot(df.index, df['OutsideTemp'], label = 'Outside Temperature')\n",
|
||
"plt.title(f'Temperatures step {current_timestamp}')\n",
|
||
"plt.legend()\n",
|
||
"plt.savefig(f\"sim_{current_timestamp}.png\")\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 386,
|
||
"metadata": {
|
||
"scrolled": true,
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Timestamp 1500\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.35e+02 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9008580e+01 1.15e+01 1.35e+02 -1.5 1.35e+02 - 9.90e-01 1.00e+00f 1\n",
|
||
" 2 6.5354360e+00 3.49e+00 9.25e+00 0.4 1.15e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 4.8629661e+00 7.32e-01 8.05e-01 -1.6 6.32e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 5.4187951e+00 8.35e-04 7.94e-02 -3.4 1.00e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 5.4192199e+00 1.17e-07 2.90e-04 -5.3 8.34e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 5.4192196e+00 2.90e-07 4.90e-05 -11.0 5.34e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 5.4192201e+00 9.87e-09 2.15e-04 -11.0 1.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 5.4192198e+00 3.11e-07 1.16e-04 -11.0 5.22e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 5.4192200e+00 7.42e-08 1.06e-04 -11.0 2.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 5.4192199e+00 1.00e-07 1.97e-04 -11.0 4.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 5.4192135e+00 4.70e-06 5.87e-03 -11.0 1.24e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 5.4192202e+00 9.70e-08 1.06e-04 -11.0 1.35e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 5.4192201e+00 4.80e-07 1.58e-03 -11.0 2.72e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 5.4192196e+00 5.19e-07 5.13e-05 -11.0 1.73e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 5.4192200e+00 5.08e-07 1.10e-04 -11.0 2.73e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 5.4192170e+00 2.09e-06 3.73e-04 -11.0 3.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 5.4191939e+00 2.12e-05 7.08e-04 -11.0 3.04e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 5.4187453e+00 2.70e-04 1.94e-03 -11.0 1.30e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 5.4187779e+00 1.29e-04 2.25e-03 -11.0 1.93e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 5.4184988e+00 1.47e-03 1.26e-03 -11.0 6.59e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 5.4189082e+00 1.19e-04 1.17e-03 -11.0 2.87e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 5.4178790e+00 2.02e-03 1.93e-03 -11.0 6.99e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 5.4190012e+00 5.30e-04 1.15e-03 -11.0 2.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 5.4182808e+00 6.65e-04 1.19e-03 -11.0 9.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 5.4136499e+00 3.95e-03 3.33e-03 -11.0 2.17e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 5.4189092e+00 2.34e-04 3.33e-03 -11.0 1.19e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 4.9937366e+00 6.64e-01 1.16e-01 -11.0 4.80e+04 - 6.57e-01 6.56e-01f 1\n",
|
||
" 28 4.7340545e+00 4.29e-01 7.57e-02 -11.0 1.78e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 4.9807860e+00 1.90e-01 1.87e-02 -1.4 2.65e+03 - 1.00e+00 6.63e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 3.9169304e+00 6.26e-01 1.20e-01 -3.5 2.88e+03 - 9.16e-01 1.00e+00f 1\n",
|
||
" 31 4.9903652e+00 9.40e-02 2.37e-01 -8.5 1.69e+03 - 7.67e-01 1.00e+00h 1\n",
|
||
" 32 3.9317172e+00 1.89e+00 1.39e-01 -1.9 2.54e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 33 4.7641980e+00 1.00e+00 3.21e-01 -2.0 1.90e+04 - 7.05e-01 6.64e-01H 1\n",
|
||
" 34 3.0833272e+00 2.01e+00 6.23e-01 -2.0 6.35e+04 - 3.97e-01 5.45e-01f 1\n",
|
||
" 35 3.1243745e+00 1.57e+00 3.31e-01 -1.9 2.70e+04 - 1.00e+00 2.24e-01h 1\n",
|
||
" 36 3.3276507e+00 1.12e+00 1.38e-01 -1.6 9.58e+03 - 1.42e-01 3.03e-01H 1\n",
|
||
" 37 4.0012733e+00 1.00e+00 1.58e-01 -1.4 3.38e+03 - 1.00e+00 8.95e-01h 1\n",
|
||
" 38 3.6278263e+00 6.02e-01 2.36e-01 -1.5 2.76e+03 - 4.52e-01 1.00e+00f 1\n",
|
||
" 39 3.5004064e+00 1.22e+00 1.79e-01 -2.1 1.91e+04 - 6.25e-01 1.88e-01f 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 3.4372226e+00 1.22e+00 5.19e-01 -1.9 4.86e+04 - 3.89e-01 7.47e-02f 3\n",
|
||
" 41 3.4445574e+00 1.18e+00 5.19e-01 -1.9 1.58e+05 - 3.02e-01 4.44e-02h 4\n",
|
||
" 42 3.4937683e+00 7.10e-01 1.74e-01 -1.9 6.48e+04 - 2.31e-01 7.60e-01h 1\n",
|
||
" 43 3.5152800e+00 6.65e-01 1.77e-01 -1.7 1.34e+04 - 1.00e+00 2.87e-01H 1\n",
|
||
" 44 3.0822994e+00 1.55e+00 5.12e-01 -2.8 4.19e+03 - 7.84e-01 1.00e+00h 1\n",
|
||
" 45 3.1965413e+00 1.11e+00 9.39e-01 -3.1 8.59e+04 - 4.72e-02 2.83e-01H 1\n",
|
||
" 46 3.4475635e+00 7.87e-01 1.11e+00 -1.5 6.59e+04 - 1.00e+00 2.91e-01h 1\n",
|
||
" 47 3.8996410e+00 9.61e-01 1.81e-01 -2.4 1.09e+03 - 9.47e-01 1.00e+00h 1\n",
|
||
" 48 4.3440658e+00 5.43e-01 2.57e-01 -2.1 6.19e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 4.6734167e+00 7.57e-01 2.08e-01 -2.1 1.12e+04 - 7.98e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 3.8432031e+00 2.18e+00 5.99e-01 -1.9 1.60e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 51 4.0669190e+00 9.93e-01 2.86e-01 -2.0 2.60e+04 - 3.98e-01 6.22e-01h 1\n",
|
||
" 52 4.0379080e+00 1.33e+00 2.04e-01 -2.0 9.50e+03 - 6.80e-02 5.00e-01h 2\n",
|
||
" 53 3.9875410e+00 1.96e+00 4.72e-02 -2.0 2.01e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 54 3.7105292e+00 3.30e+00 3.27e-01 -2.0 1.42e+05 - 1.15e-01 1.56e-01h 1\n",
|
||
" 55 3.6923658e+00 2.55e+00 3.18e-01 -2.0 1.29e+04 - 2.25e-01 1.00e+00h 1\n",
|
||
" 56 3.5262167e+00 1.20e+00 9.34e-02 -2.0 1.40e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 3.5749207e+00 1.78e+00 1.75e-01 -2.0 2.57e+04 - 4.73e-01 5.00e-01h 2\n",
|
||
" 58 3.5861385e+00 3.66e-01 6.06e-01 -2.0 1.04e+05 - 1.00e+00 3.71e-01h 1\n",
|
||
" 59 3.5271298e+00 1.13e+00 2.56e-01 -2.2 7.90e+03 - 9.91e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 3.3689735e+00 8.60e-01 1.50e-01 -2.3 2.90e+04 - 1.00e+00 2.42e-01h 1\n",
|
||
" 61r 3.3689735e+00 8.60e-01 9.99e+02 -0.1 0.00e+00 - 0.00e+00 3.14e-07R 22\n",
|
||
" 62r 3.9296482e+00 6.29e-01 9.80e+02 -2.2 2.94e+02 - 9.99e-01 4.05e-03f 1\n",
|
||
" 63 4.5508462e+00 2.79e-01 2.53e-01 -3.0 4.61e+03 - 7.89e-01 1.00e+00H 1\n",
|
||
" 64 4.5431038e+00 5.03e-01 3.37e-02 -3.3 4.95e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 4.2589358e+00 2.86e+00 2.69e-01 -3.5 2.40e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 4.1963461e+00 3.69e+00 5.25e-01 -3.5 1.38e+04 - 4.52e-01 1.00e+00h 1\n",
|
||
" 67 4.1659930e+00 3.57e+00 4.07e-01 -3.5 1.66e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 68 4.1268015e+00 2.54e+00 8.91e-02 -3.5 7.10e+03 - 6.86e-01 1.00e+00h 1\n",
|
||
" 69 4.0145483e+00 1.23e+00 3.18e-01 -3.5 7.11e+04 - 7.78e-01 1.22e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 3.8677643e+00 1.08e+00 2.97e-01 -3.5 4.00e+04 - 5.40e-02 6.48e-02h 1\n",
|
||
" 71r 3.8677643e+00 1.08e+00 9.99e+02 0.0 0.00e+00 - 0.00e+00 3.73e-07R 20\n",
|
||
" 72r 4.3819102e+00 4.42e-01 8.85e+02 -2.1 5.61e+02 - 1.00e+00 2.26e-03f 1\n",
|
||
" 73 4.6386635e+00 1.03e+00 1.28e-01 -2.5 4.79e+03 - 8.35e-01 8.35e-01s 22\n",
|
||
" 74 5.0977558e+00 5.94e-02 5.51e-02 -2.8 3.49e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 5.0092578e+00 5.46e-02 4.00e-02 -2.9 7.37e+02 - 1.00e+00 5.23e-01h 1\n",
|
||
" 76 5.0258966e+00 1.70e-02 1.48e-02 -4.3 1.52e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 5.0523498e+00 9.13e-04 4.98e-03 -10.1 5.05e+00 - 2.55e-01 1.00e+00h 1\n",
|
||
" 78 5.0047783e+00 8.20e-02 3.75e-02 -10.3 5.60e+02 - 8.61e-03 1.00e+00f 1\n",
|
||
" 79 5.0413335e+00 4.37e-03 1.70e-02 -3.2 9.97e+01 - 1.00e+00 9.86e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 5.0236246e+00 1.98e-01 1.97e-02 -3.2 8.01e+02 - 7.53e-01 1.00e+00h 1\n",
|
||
" 81 4.3642185e+00 1.04e+00 1.02e-01 -3.3 4.54e+03 - 4.07e-01 1.00e+00f 1\n",
|
||
" 82 4.5043111e+00 3.28e-01 1.36e-01 -3.3 1.94e+04 - 1.00e+00 3.21e-01h 1\n",
|
||
" 83 4.8534076e+00 1.35e-01 1.10e-01 -3.3 2.53e+03 - 7.36e-01 6.26e-01h 1\n",
|
||
" 84 5.0556366e+00 1.48e-02 5.97e-02 -2.8 3.27e+02 - 8.98e-01 1.00e+00h 1\n",
|
||
" 85 4.9242600e+00 4.01e-01 3.13e-02 -2.9 8.38e+03 - 2.73e-01 1.00e+00h 1\n",
|
||
" 86 4.9723731e+00 2.19e-01 1.77e-02 -2.9 4.84e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 4.7410340e+00 4.69e-01 6.79e-02 -2.9 8.99e+03 - 1.41e-01 5.93e-01h 1\n",
|
||
" 88 4.4911675e+00 6.38e-01 8.99e-02 -2.9 6.67e+03 - 1.00e+00 3.73e-01f 1\n",
|
||
" 89 4.9885566e+00 1.69e-02 1.43e-01 -2.9 6.81e+02 - 1.19e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 4.5114481e+00 1.35e+00 1.50e-01 -3.9 4.94e+03 - 3.81e-01 1.00e+00f 1\n",
|
||
" 91 4.8914833e+00 2.11e-01 2.25e-01 -3.1 1.36e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 4.1318919e+00 7.95e-01 2.11e-01 -3.1 8.84e+03 - 1.00e+00 7.09e-01f 1\n",
|
||
" 93 3.6743764e+00 1.83e+00 3.46e-01 -3.1 1.16e+04 - 1.00e+00 3.45e-01f 1\n",
|
||
" 94 5.0376577e+00 1.16e-01 1.06e-01 -3.1 3.04e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 4.7674122e+00 1.15e+00 1.81e-01 -3.1 3.69e+03 - 9.38e-01 1.00e+00h 1\n",
|
||
" 96 4.5860113e+00 5.39e-01 1.55e-01 -3.1 4.33e+03 - 8.75e-01 5.99e-01h 1\n",
|
||
" 97 4.8916918e+00 1.10e-01 1.18e-01 -3.1 3.24e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 98 5.0366477e+00 4.66e-02 1.88e-02 -3.1 3.65e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 5.0591970e+00 1.66e-05 3.32e-02 -3.1 3.84e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 5.0592047e+00 3.54e-07 1.26e-04 -5.0 1.39e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 5.0592047179397319e+00 5.0592047179397319e+00\n",
|
||
"Dual infeasibility......: 1.2617056781843167e-04 1.2617056781843167e-04\n",
|
||
"Constraint violation....: 3.5413911447790269e-07 3.5413911447790269e-07\n",
|
||
"Complementarity.........: 9.3193329713301685e-06 9.3193329713301685e-06\n",
|
||
"Overall NLP error.......: 1.2617056781843167e-04 1.2617056781843167e-04\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 202\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 202\n",
|
||
"Number of inequality constraint evaluations = 202\n",
|
||
"Number of equality constraint Jacobian evaluations = 103\n",
|
||
"Number of inequality constraint Jacobian evaluations = 103\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.486\n",
|
||
"Total CPU secs in NLP function evaluations = 141.341\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 882.00us ( 4.37us) 878.77us ( 4.35us) 202\n",
|
||
" nlp_g | 9.13 s ( 45.18ms) 8.71 s ( 43.11ms) 202\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 354.00us ( 3.47us) 343.48us ( 3.37us) 102\n",
|
||
" nlp_jac_g | 135.06 s ( 1.30 s) 128.94 s ( 1.24 s) 104\n",
|
||
" total | 145.68 s (145.68 s) 139.07 s (139.07 s) 1\n",
|
||
"Timestamp 1800\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.13e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0569164e+01 1.41e+01 3.13e+04 -1.5 3.13e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.9378752e+00 5.97e+00 6.30e+00 1.3 1.47e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 3.5992903e+00 9.32e-01 7.02e-01 -0.8 5.74e+02 - 9.96e-01 1.00e+00f 1\n",
|
||
" 4 4.5004465e+00 1.33e-02 2.96e-01 -2.6 3.50e+01 - 9.94e-01 1.00e+00h 1\n",
|
||
" 5 4.5071826e+00 1.17e-04 9.85e-03 -4.1 2.25e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 4.5070096e+00 2.42e-04 1.66e-03 -5.9 5.47e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 4.4961479e+00 1.20e-02 1.75e-02 -8.0 3.34e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 4.5034861e+00 1.41e-03 7.19e-03 -9.9 9.87e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 4.5057624e+00 1.54e-03 2.48e-03 -11.0 4.89e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 4.5039943e+00 1.03e-03 2.55e-03 -11.0 1.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 4.4181740e+00 6.32e-02 6.81e-03 -11.0 4.04e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 12 4.4816467e+00 1.56e-02 2.43e-02 -11.0 2.02e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 4.4662710e+00 2.66e-02 7.25e-03 -11.0 2.85e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 4.4993936e+00 2.38e-04 3.12e-03 -11.0 1.75e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 15 3.8904852e+00 2.49e+00 1.01e+00 -9.4 1.23e+06 - 1.00e+00 1.30e-02f 2\n",
|
||
" 16 3.8788894e+00 7.74e-01 3.78e-01 -9.5 2.05e+04 - 1.00e+00 7.90e-01h 1\n",
|
||
" 17 4.8138115e+00 1.62e-01 1.78e-01 -8.8 2.97e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 4.6525308e+00 2.59e-02 2.52e-02 -2.1 7.29e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 4.4179787e+00 2.56e-01 1.13e-01 -3.1 7.80e+02 - 2.70e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 4.6672185e+00 5.50e-03 4.23e-02 -4.1 1.37e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 21 4.6184995e+00 5.36e-02 3.75e-02 -4.2 8.74e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 4.5918880e+00 6.01e-02 1.61e-02 -4.2 1.50e+03 - 1.00e+00 2.42e-01h 1\n",
|
||
" 23 4.5687926e+00 2.51e-01 4.14e-02 -4.2 2.65e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 4.3745911e+00 1.77e-01 9.14e-02 -4.2 9.99e+03 - 5.14e-01 1.00e+00h 1\n",
|
||
" 25 4.5554191e+00 1.60e-01 3.31e-02 -4.9 2.86e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 4.1773084e+00 5.91e-01 7.15e-02 -5.1 1.54e+04 - 1.00e+00 8.21e-01h 1\n",
|
||
" 27 4.5955911e+00 2.47e-01 1.28e-02 -5.4 2.45e+03 - 9.01e-01 1.00e+00h 1\n",
|
||
" 28 4.3476155e+00 2.35e-01 5.71e-02 -4.2 2.04e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 4.7655994e+00 7.01e-02 5.50e-02 -3.3 3.51e+02 - 3.44e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 4.7609167e+00 7.68e-02 5.32e-02 -3.8 2.08e+04 - 1.00e+00 6.99e-03h 1\n",
|
||
" 31 4.7792599e+00 9.47e-03 2.05e-02 -3.8 1.25e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 4.7969075e+00 7.62e-04 6.73e-03 -3.8 9.93e+02 - 2.39e-02 1.00e+00H 1\n",
|
||
" 33 4.7361564e+00 7.87e-02 1.22e-02 -3.8 6.95e+03 - 1.00e+00 2.00e-01f 1\n",
|
||
" 34 4.6624452e+00 1.13e-01 2.98e-02 -3.8 1.07e+03 - 8.11e-02 1.00e+00f 1\n",
|
||
" 35 4.5940645e+00 2.08e-01 3.63e-02 -3.8 2.19e+04 - 1.00e+00 4.59e-02h 1\n",
|
||
" 36 4.1357296e+00 8.09e-01 1.13e-01 -3.8 1.24e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 37 4.7816004e+00 1.05e-02 7.85e-01 -7.7 7.96e-01 - 9.90e-01 1.00e+00h 1\n",
|
||
" 38 4.7863769e+00 2.77e-07 1.52e-05 -3.8 1.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 4.7863772e+00 1.06e-08 1.62e-04 -9.8 1.39e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 4.7863760e+00 7.91e-07 1.41e-03 -11.0 1.48e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 4.7863764e+00 5.02e-07 3.39e-05 -11.0 8.50e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 4.7863772e+00 2.35e-08 6.46e-05 -11.0 1.19e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 4.7863772e+00 2.71e-08 4.27e-05 -11.0 2.78e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 4.7863772e+00 2.12e-08 5.02e-05 -11.0 1.33e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 4.7863772e+00 1.18e-08 1.54e-04 -11.0 7.23e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 4.7863771e+00 4.33e-08 1.69e-04 -11.0 2.33e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 4.7863769e+00 1.18e-07 1.54e-04 -11.0 3.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 4.7863771e+00 6.26e-08 4.05e-05 -11.0 2.31e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 4.7863772e+00 6.82e-09 4.57e-05 -11.0 3.92e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 4.7863772e+00 1.19e-09 8.93e-05 -11.0 2.32e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 4.7863769e+00 1.27e-07 6.73e-05 -11.0 1.64e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 4.7863759e+00 5.37e-07 4.14e-03 -11.0 3.58e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 4.7863751e+00 9.25e-07 5.65e-03 -11.0 4.39e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 4.7863767e+00 1.06e-07 9.99e-05 -11.0 2.62e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 4.7863764e+00 3.45e-07 1.63e-04 -11.0 9.34e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 4.7863764e+00 3.01e-07 5.16e-05 -11.0 1.14e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 4.7863767e+00 7.33e-08 5.44e-05 -11.0 4.65e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 4.7863767e+00 1.42e-07 1.09e-04 -11.0 4.20e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 4.7863767e+00 6.16e-08 3.75e-05 -11.0 2.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 4.7863768e+00 1.72e-08 2.23e-04 -11.0 8.51e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 4.7863762e+00 3.09e-07 7.53e-05 -11.0 6.77e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 4.7863768e+00 2.85e-08 7.54e-05 -11.0 1.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 4.7863768e+00 4.40e-08 2.57e-04 -11.0 2.32e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 4.7863762e+00 4.72e-07 5.01e-05 -11.0 9.07e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 4.7863768e+00 3.51e-08 5.07e-05 -11.0 2.00e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 4.7863765e+00 1.35e-07 1.20e-04 -11.0 2.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 4.7863766e+00 1.25e-07 6.28e-05 -11.0 6.76e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 4.7863767e+00 4.53e-08 5.47e-05 -11.0 5.03e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 4.7863768e+00 1.11e-08 1.15e-04 -11.0 9.42e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 4.7863767e+00 5.82e-08 2.25e-04 -11.0 3.52e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 4.7863764e+00 1.95e-07 9.20e-05 -11.0 9.15e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 4.7863767e+00 2.85e-08 1.83e-04 -11.0 1.38e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 4.7858288e+00 2.04e-04 2.54e-02 -11.0 1.14e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 74 4.7863246e+00 2.44e-05 2.35e-03 -11.0 3.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 4.7855610e+00 5.18e-04 1.98e-03 -11.0 1.60e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 4.7863533e+00 6.29e-08 1.05e-04 -11.0 8.15e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 77 4.7861438e+00 1.66e-04 1.57e-03 -11.0 3.48e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 4.7858591e+00 1.37e-04 2.33e-03 -11.0 6.52e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 4.7863077e+00 3.44e-05 1.50e-03 -11.0 1.29e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 4.7863230e+00 2.01e-05 9.22e-04 -11.0 8.75e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 4.7863250e+00 1.01e-05 1.78e-03 -11.0 8.02e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 4.7863256e+00 7.38e-06 1.37e-03 -11.0 5.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 4.7860632e+00 1.29e-04 5.15e-03 -11.0 8.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 4.7862507e+00 1.64e-04 9.65e-04 -11.0 7.59e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 4.7837325e+00 3.13e-03 3.86e-03 -11.0 2.96e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 4.7863866e+00 1.22e-06 1.05e-03 -11.0 1.37e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 87 4.7859813e+00 3.32e-04 9.15e-04 -11.0 5.61e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 4.7858610e+00 3.91e-04 2.95e-03 -11.0 1.03e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 4.6470493e+00 1.86e+00 2.51e-01 -11.0 1.71e+05 - 3.25e-02 3.97e-02f 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 4.5855605e+00 3.14e+00 5.94e-01 -11.0 7.87e+04 - 1.33e-01 3.79e-01F 1\n",
|
||
" 91 4.5737780e+00 4.51e+00 1.05e+00 -11.0 9.51e+03 - 2.02e-10 1.00e+00f 1\n",
|
||
" 92 4.3161969e+00 2.59e+00 3.09e-01 -11.0 1.39e+04 - 9.05e-01 5.00e-01h 2\n",
|
||
" 93 3.9204073e+00 1.91e+00 1.41e-01 -11.0 4.00e+04 - 1.00e+00 2.79e-01h 1\n",
|
||
" 94 4.1982948e+00 8.73e-01 3.55e-01 -11.0 9.61e+03 - 3.54e-10 5.00e-01h 2\n",
|
||
" 95 3.0716460e+00 1.42e+00 3.10e-01 -11.0 3.70e+04 - 8.81e-01 8.42e-01f 1\n",
|
||
" 96 3.2152538e+00 1.10e+00 4.42e-01 -11.0 5.73e+05 - 2.49e-11 1.77e-02h 3\n",
|
||
" 97 3.0825076e+00 1.44e+00 4.28e-01 -8.3 1.76e+06 - 2.25e-02 1.74e-02f 1\n",
|
||
" 98 4.0465174e+00 1.92e+00 5.82e-01 -7.8 4.02e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 99 3.6698179e+00 2.09e+00 4.33e-01 -6.9 4.01e+04 - 6.44e-01 6.43e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 3.8791461e+00 8.63e-01 2.37e-01 -6.9 3.24e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 3.8791460524994053e+00 3.8791460524994053e+00\n",
|
||
"Dual infeasibility......: 2.3721171664478957e-01 2.3721171664478957e-01\n",
|
||
"Constraint violation....: 8.6330986912132346e-01 8.6330986912132346e-01\n",
|
||
"Complementarity.........: 8.6916062763976953e-02 8.6916062763976953e-02\n",
|
||
"Overall NLP error.......: 8.6330986912132346e-01 8.6330986912132346e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 123\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 123\n",
|
||
"Number of inequality constraint evaluations = 123\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.404\n",
|
||
"Total CPU secs in NLP function evaluations = 135.637\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 577.00us ( 4.69us) 547.87us ( 4.45us) 123\n",
|
||
" nlp_g | 5.54 s ( 45.06ms) 5.29 s ( 42.99ms) 123\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 378.00us ( 3.71us) 371.27us ( 3.64us) 102\n",
|
||
" nlp_jac_g | 132.75 s ( 1.30 s) 126.76 s ( 1.24 s) 102\n",
|
||
" total | 139.77 s (139.77 s) 133.46 s (133.46 s) 1\n",
|
||
"Timestamp 2100\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 4.61e+02 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9201712e+01 1.23e+01 4.61e+02 -1.5 4.61e+02 - 9.90e-01 1.00e+00f 1\n",
|
||
" 2 7.0287083e+00 3.93e+00 9.41e+00 0.4 1.23e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 6.2837776e+00 8.88e-01 7.69e-01 -1.6 6.94e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 6.9576193e+00 1.36e-03 7.73e-02 -3.4 1.23e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 6.9583011e+00 7.93e-08 3.64e-05 -5.3 1.36e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 6.9583013e+00 4.53e-08 1.23e-04 -11.0 8.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 6.9583002e+00 4.57e-07 1.01e-04 -11.0 2.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 6.9583010e+00 2.61e-07 8.60e-05 -11.0 8.85e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 6.9583012e+00 1.71e-07 2.17e-04 -11.0 5.35e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 6.9582968e+00 3.53e-06 3.24e-03 -11.0 1.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 6.9583002e+00 9.95e-07 1.92e-03 -11.0 6.39e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 6.9582701e+00 5.24e-05 4.08e-03 -11.0 1.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 6.9583009e+00 6.41e-06 1.48e-03 -11.0 4.71e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 6.9582984e+00 6.80e-06 1.17e-03 -11.0 3.41e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 6.9583031e+00 3.14e-07 1.21e-04 -11.0 4.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 6.9582959e+00 4.74e-06 2.69e-03 -11.0 4.50e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 6.9583015e+00 3.61e-06 1.82e-03 -11.0 1.31e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 6.9583025e+00 4.44e-07 6.41e-05 -11.0 1.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 6.9583017e+00 5.60e-07 1.63e-03 -11.0 1.20e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 6.9582743e+00 1.52e-05 4.64e-03 -11.0 1.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 6.9583033e+00 4.38e-09 5.26e-05 -11.0 5.39e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 6.9583033e+00 2.37e-08 1.11e-04 -11.0 1.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 6.9583033e+00 6.97e-09 2.47e-05 -11.0 6.09e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 6.9583033e+00 3.60e-09 4.67e-04 -11.0 5.77e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 6.9583033e+00 2.66e-08 1.40e-04 -11.0 4.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 6.9583032e+00 7.60e-08 1.89e-04 -11.0 3.90e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 6.9583033e+00 5.28e-09 6.63e-05 -11.0 6.25e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 6.9583032e+00 3.72e-08 6.18e-05 -11.0 3.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 6.9583032e+00 9.49e-08 1.03e-04 -11.0 7.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 6.9583028e+00 3.70e-07 1.79e-04 -11.0 1.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 6.9583031e+00 1.61e-07 8.26e-05 -11.0 6.73e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 6.9583033e+00 1.57e-09 7.82e-05 -11.0 6.52e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 6.9583016e+00 5.20e-06 1.44e-03 -11.0 4.14e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 6.9582992e+00 5.04e-06 9.76e-04 -11.0 3.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 6.9582975e+00 3.45e-06 1.30e-03 -11.0 2.11e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 6.9583005e+00 5.53e-06 2.73e-03 -11.0 2.42e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 6.9582956e+00 4.20e-06 2.76e-03 -11.0 9.23e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 6.9583030e+00 8.31e-10 2.66e-05 -11.0 5.62e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 39 6.9582990e+00 3.19e-06 1.54e-03 -11.0 8.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 6.9582970e+00 7.62e-06 8.44e-04 -11.0 7.75e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 6.9583023e+00 2.99e-08 1.89e-05 -11.0 2.01e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 42 6.9582972e+00 5.32e-06 1.00e-03 -11.0 1.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 6.9582866e+00 1.63e-05 1.76e-03 -11.0 2.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 6.9582848e+00 9.30e-06 2.75e-03 -11.0 4.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 6.8806436e+00 3.07e-01 3.00e-02 -11.0 2.18e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 46 6.6741019e+00 1.93e-01 4.56e-02 -11.0 4.01e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 6.5061054e+00 9.75e-01 8.52e-02 -11.0 2.12e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 6.7378212e+00 1.01e-01 9.15e-02 -11.0 2.69e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 5.3909528e+00 3.08e+00 4.27e-01 -10.3 1.33e+05 - 1.00e+00 2.53e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 5.3600259e+00 3.11e+00 4.40e-01 -8.4 1.59e+05 - 1.00e+00 2.11e-03h 1\n",
|
||
" 51 7.0065566e+00 6.92e-02 5.59e-01 -7.7 1.44e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 5.8984371e+00 4.46e+00 1.88e-01 -7.7 6.26e+04 - 6.47e-02 4.52e-01f 1\n",
|
||
" 53r 5.8984371e+00 4.46e+00 9.99e+02 0.6 0.00e+00 - 0.00e+00 3.63e-09R 4\n",
|
||
" 54r 5.3090032e+00 1.25e+00 9.94e+02 -0.9 8.20e+02 - 1.60e-01 5.51e-03f 1\n",
|
||
" 55 7.4649978e+00 8.33e-02 2.66e-01 -3.6 5.62e+02 - 1.64e-01 1.00e+00h 1\n",
|
||
" 56 7.4653498e+00 7.91e-02 2.58e-01 -4.6 3.60e+02 - 1.00e+00 2.97e-02h 1\n",
|
||
" 57 7.5171788e+00 1.34e-02 4.84e-02 -4.6 6.96e+02 - 7.95e-02 1.00e+00H 1\n",
|
||
" 58 6.8018037e+00 9.78e-01 1.36e-01 -4.6 4.94e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 59 7.1593235e+00 5.07e-01 2.75e-02 -4.6 1.95e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 7.5238877e+00 3.07e-02 7.83e-02 -4.6 4.87e+02 - 8.98e-01 1.00e+00h 1\n",
|
||
" 61 7.6072445e+00 2.79e-02 1.53e-02 -4.6 1.60e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 7.6041550e+00 2.88e-02 1.52e-02 -4.6 8.18e+05 - 5.98e-02 3.38e-04f 5\n",
|
||
" 63 7.5867285e+00 1.95e-02 8.73e-03 -4.6 1.18e+03 - 8.42e-01 1.00e+00h 1\n",
|
||
" 64 7.4220525e+00 1.33e-01 1.33e-02 -4.6 1.28e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 7.3139898e+00 3.39e-01 3.35e-02 -4.6 2.11e+04 - 1.00e+00 1.91e-01h 1\n",
|
||
" 66 7.6389208e+00 1.48e-02 2.53e-02 -4.6 1.49e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 7.6310401e+00 2.35e-03 2.37e-03 -4.6 1.27e+03 - 3.63e-01 1.00e+00H 1\n",
|
||
" 68 5.1280805e+00 2.42e+00 7.20e-01 -4.6 1.39e+05 - 1.85e-02 4.38e-01f 1\n",
|
||
" 69 7.8331844e+00 6.97e-01 3.88e-01 -4.2 4.72e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 7.3770420e+00 1.28e+00 7.07e-02 -1.9 2.27e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 7.2111747e+00 3.37e+00 3.11e-01 -1.9 2.86e+04 - 1.46e-01 1.00e+00h 1\n",
|
||
" 72 6.5501483e+00 4.95e+00 6.24e-01 -1.9 3.13e+05 - 2.38e-01 9.46e-02f 1\n",
|
||
" 73 7.8838767e+00 1.22e-01 5.69e-01 -1.9 4.00e+03 - 1.00e+00 7.94e-01h 1\n",
|
||
" 74 6.8164648e+00 3.49e-01 5.09e-02 -1.9 5.55e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 75 5.4943056e+00 3.83e+00 5.32e-01 -1.9 2.99e+05 - 5.41e-02 7.79e-02f 2\n",
|
||
" 76 7.8389750e+00 5.20e-01 3.42e-01 -1.9 8.22e+03 - 5.25e-01 1.00e+00h 1\n",
|
||
" 77 5.2715226e+00 4.78e+00 6.78e-01 -1.9 6.04e+04 - 3.80e-02 1.00e+00f 1\n",
|
||
" 78 4.7318257e+00 4.02e+00 4.77e-01 -1.9 5.98e+04 - 5.80e-01 2.52e-01h 1\n",
|
||
" 79 5.4814161e+00 4.08e+00 4.05e-01 -0.7 6.13e+05 - 1.57e-01 3.82e-02h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 5.1744749e+00 3.00e+00 2.82e-01 -1.3 1.56e+05 - 7.32e-01 1.51e-01h 1\n",
|
||
" 81 7.6260001e+00 1.74e-01 3.20e+00 -1.3 3.20e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 7.8808175e+00 3.10e-04 1.49e-02 -1.3 3.50e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 7.8809828e+00 2.20e-06 1.21e-03 -2.0 4.40e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 7.8809833e+00 8.73e-07 1.36e-03 -3.0 2.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 7.8809854e+00 9.73e-08 7.66e-05 -4.5 4.58e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 7.8809833e+00 1.27e-06 7.06e-03 -6.7 9.51e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 7.8809778e+00 3.21e-06 1.21e-02 -6.7 1.78e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 7.8809817e+00 1.61e-06 4.66e-03 -6.7 3.05e-03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 89 7.8809733e+00 1.21e-05 5.51e-03 -6.7 1.16e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 7.8807552e+00 4.88e-04 1.92e-02 -6.7 2.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 7.8794270e+00 1.36e-03 7.95e-03 -6.7 1.89e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 7.8807693e+00 1.52e-04 8.37e-03 -6.7 2.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 7.8809069e+00 6.31e-05 4.55e-03 -6.7 5.13e-01 - 1.00e+00 5.00e-01h 2\n",
|
||
" 94 7.8809438e+00 3.33e-05 2.63e-03 -6.7 3.57e-01 - 1.00e+00 5.00e-01h 2\n",
|
||
" 95 7.8764953e+00 1.71e-03 5.24e-03 -6.7 1.55e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 7.8786631e+00 1.76e-03 1.06e-03 -6.7 1.76e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 7.8545564e+00 3.68e-02 3.69e-03 -6.7 1.08e+06 - 5.81e-02 1.97e-04f 1\n",
|
||
" 98 7.8729393e+00 8.11e-03 1.11e-03 -6.7 4.20e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 7.8302315e+00 3.13e-02 1.19e-02 -6.7 2.71e+02 - 6.51e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 7.8111503e+00 3.63e-02 8.36e-03 -6.7 1.00e+03 - 3.46e-03 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 7.8111502570168803e+00 7.8111502570168803e+00\n",
|
||
"Dual infeasibility......: 8.3612296815835374e-03 8.3612296815835374e-03\n",
|
||
"Constraint violation....: 3.6338305992032360e-02 3.6338305992032360e-02\n",
|
||
"Complementarity.........: 2.3446929797151881e-07 2.3446929797151881e-07\n",
|
||
"Overall NLP error.......: 3.6338305992032360e-02 3.6338305992032360e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 125\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 125\n",
|
||
"Number of inequality constraint evaluations = 125\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.467\n",
|
||
"Total CPU secs in NLP function evaluations = 137.634\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 560.00us ( 4.48us) 552.39us ( 4.42us) 125\n",
|
||
" nlp_g | 5.64 s ( 45.11ms) 5.38 s ( 43.05ms) 125\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 358.00us ( 3.51us) 357.25us ( 3.50us) 102\n",
|
||
" nlp_jac_g | 134.86 s ( 1.31 s) 128.82 s ( 1.25 s) 103\n",
|
||
" total | 141.98 s (141.98 s) 135.63 s (135.63 s) 1\n",
|
||
"Timestamp 2400\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.88e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9632897e+01 1.46e+01 2.88e+04 -1.5 2.88e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.2603338e+01 5.73e+00 1.67e+01 0.8 2.78e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.9320844e+01 2.04e+00 8.37e-01 -1.3 5.49e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 2.0528006e+01 1.44e-04 8.23e-02 -3.0 2.25e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 2.0528189e+01 1.37e-05 7.14e-03 -4.9 1.12e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.0528225e+01 8.30e-10 1.63e-04 -7.0 1.15e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 7 2.0528191e+01 1.38e-05 2.98e-03 -9.1 5.84e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.0528216e+01 5.76e-06 2.47e-03 -11.0 3.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.0528210e+01 6.94e-06 1.95e-03 -11.0 2.29e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.0528175e+01 2.30e-05 2.82e-03 -11.0 6.38e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.0528212e+01 6.31e-06 1.55e-03 -11.0 4.56e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.0528177e+01 7.82e-05 1.94e-03 -11.0 2.51e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.0527969e+01 1.37e-04 4.64e-03 -11.0 1.54e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.0527880e+01 1.89e-04 1.96e-03 -11.0 1.06e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.0527215e+01 4.85e-04 6.86e-03 -11.0 3.03e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.0526425e+01 3.08e-03 8.78e-03 -11.0 4.40e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.8457286e+01 1.38e+00 8.17e-02 -11.0 5.59e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 18 1.5421877e+01 2.68e+00 1.10e-01 -11.0 8.48e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 19 1.9558377e+01 4.47e-01 1.21e-01 -11.0 4.49e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.9456652e+01 6.07e-01 5.89e-02 -11.0 3.85e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.2669321e+01 4.24e+00 4.33e-01 -10.6 3.40e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 22 1.3016702e+01 3.84e+00 3.17e-01 -8.8 3.25e+04 - 1.00e+00 1.21e-01h 1\n",
|
||
" 23 1.3012714e+01 3.84e+00 3.17e-01 -6.9 3.14e+04 - 1.00e+00 1.25e-03h 1\n",
|
||
" 24 2.0834551e+01 8.26e-01 4.77e-01 -4.9 6.95e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.9375384e+01 6.94e-01 4.92e-01 -4.3 7.32e+03 - 4.72e-01 4.54e-01f 1\n",
|
||
" 26 2.0912422e+01 4.80e-02 2.75e-02 -5.6 1.83e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.0603588e+01 2.75e-01 3.26e-02 -5.1 6.92e+03 - 5.21e-01 3.14e-01f 1\n",
|
||
" 28 2.0132177e+01 1.45e+00 4.33e-02 -5.1 1.33e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.0166760e+01 5.36e-01 1.44e-02 -5.1 6.46e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.0169615e+01 5.34e-01 1.43e-02 -5.1 3.92e+03 - 1.00e+00 5.19e-03h 8\n",
|
||
" 31 2.1016600e+01 5.66e-02 1.79e-02 -5.1 4.46e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 32 1.8080223e+01 6.66e-01 8.76e-02 -5.1 1.12e+04 - 9.34e-02 1.00e+00f 1\n",
|
||
" 33 2.1054691e+01 4.84e-03 2.20e+00 -6.8 2.23e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 2.1064064e+01 4.26e-06 2.29e-03 -6.9 1.90e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.1064068e+01 1.73e-06 1.99e-03 -6.9 8.92e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.1064055e+01 5.09e-06 2.74e-03 -6.9 1.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.1064071e+01 7.58e-07 9.82e-04 -6.9 3.70e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.1064071e+01 6.27e-07 1.33e-03 -6.9 4.64e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.1064072e+01 4.34e-10 1.70e-04 -6.9 3.44e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.1064071e+01 1.41e-06 1.48e-03 -6.9 3.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.1064071e+01 5.10e-07 2.19e-04 -6.9 2.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.1064064e+01 2.40e-06 3.83e-03 -6.9 1.33e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.1064071e+01 5.11e-07 2.79e-05 -6.9 2.50e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.1064072e+01 3.85e-08 7.23e-05 -6.9 9.49e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.1064070e+01 1.71e-09 6.82e-05 -6.9 3.41e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 46 2.1064041e+01 1.04e-05 5.68e-03 -6.9 1.38e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.1064009e+01 1.71e-05 5.00e-03 -6.9 3.71e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.1063674e+01 1.65e-04 8.30e-03 -6.9 9.48e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.1062566e+01 5.24e-04 1.64e-02 -6.9 1.90e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.1063943e+01 1.70e-07 1.04e-04 -6.9 1.21e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.1063943e+01 4.47e-08 1.79e-04 -6.9 1.56e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.1063943e+01 3.58e-08 2.02e-04 -6.9 8.13e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.1063942e+01 1.74e-07 1.29e-04 -6.9 1.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.1063941e+01 7.72e-07 3.53e-03 -6.9 2.47e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.1063942e+01 8.16e-08 1.26e-04 -6.9 1.07e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.1063942e+01 7.38e-08 3.98e-04 -6.9 5.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 2.1063940e+01 9.96e-07 8.21e-03 -6.9 2.12e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.1063942e+01 8.45e-09 1.01e-04 -6.9 1.12e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 2.1063942e+01 3.54e-08 9.98e-05 -6.9 4.05e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.1063942e+01 2.87e-10 3.42e-05 -6.9 4.34e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 61 2.1063942e+01 7.55e-07 1.10e-03 -11.0 4.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.1063942e+01 7.56e-08 1.58e-04 -11.0 8.10e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.1063930e+01 9.89e-06 9.43e-03 -11.0 2.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.1063936e+01 3.68e-06 2.15e-03 -11.0 2.31e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.1063886e+01 1.38e-05 8.54e-03 -11.0 7.49e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.1063914e+01 1.51e-05 4.28e-03 -11.0 6.91e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.1063902e+01 1.95e-05 6.57e-03 -11.0 8.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.1063931e+01 6.45e-07 8.20e-05 -11.0 6.46e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.1063931e+01 1.20e-06 1.93e-03 -11.0 3.18e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.1063652e+01 6.11e-04 1.02e-03 -11.0 1.36e+02 - 1.00e+00 6.62e-02h 1\n",
|
||
" 71 2.1062868e+01 3.57e-04 1.30e-03 -11.0 9.32e+00 - 1.43e-06 1.00e+00f 1\n",
|
||
"In iteration 71, 1 Slack too small, adjusting variable bound\n",
|
||
" 72 2.1062782e+01 3.93e-04 2.96e-03 -11.0 3.21e+01 - 1.00e+00 8.09e-03h 1\n",
|
||
" 73 2.1063899e+01 4.22e-06 3.21e-03 -11.0 3.19e-01 - 2.16e-06 1.00e+00h 1\n",
|
||
" 74 2.1063856e+01 2.82e-05 1.75e-03 -11.0 2.15e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 2.1063887e+01 1.70e-05 1.31e-03 -11.0 1.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.1063499e+01 1.79e-04 3.95e-03 -11.0 7.78e+00 - 1.65e-01 1.00e+00h 1\n",
|
||
" 77 2.1063303e+01 4.21e-04 2.79e-03 -11.0 1.03e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.0429863e+01 2.59e-01 1.54e-02 -11.0 1.81e+04 - 1.10e-03 1.00e+00f 1\n",
|
||
" 79 2.0127855e+01 1.86e+00 5.70e-02 -9.1 2.45e+04 - 1.00e+00 6.35e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.0625843e+01 8.68e-01 1.03e-02 -9.2 7.70e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.0503401e+01 1.07e+00 3.55e-03 -9.2 3.75e+04 - 1.20e-01 1.11e-01h 1\n",
|
||
" 82 2.1177696e+01 3.10e-01 2.81e-02 -9.2 1.48e+03 - 7.59e-01 1.00e+00h 1\n",
|
||
" 83 2.0621074e+01 2.28e-01 8.77e-03 -9.2 2.57e+03 - 3.05e-01 1.00e+00h 1\n",
|
||
" 84 2.1067670e+01 1.47e-04 3.10e-01 -9.2 3.10e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 2.1067906e+01 2.39e-07 1.13e-04 -9.2 1.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 2.1067843e+01 6.53e-05 2.57e-03 -9.2 6.06e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.1067672e+01 7.19e-05 1.49e-03 -9.2 4.55e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 2.1067865e+01 4.46e-05 1.89e-03 -9.2 1.66e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 2.1067874e+01 1.89e-05 3.24e-03 -9.2 2.38e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.0885871e+01 6.96e-02 2.91e-03 -9.2 4.28e+08 - 1.44e-05 3.36e-06f 1\n",
|
||
" 91 2.1029903e+01 2.24e-02 1.91e-03 -9.2 3.23e+02 - 1.86e-08 1.00e+00h 1\n",
|
||
" 92 2.0577810e+01 1.49e-01 1.04e-02 -9.2 1.51e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 93 2.1088637e+01 1.09e-08 6.63e-05 -9.2 2.19e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 94 2.1004773e+01 8.73e-02 2.39e-03 -9.2 1.83e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 95 2.0409655e+01 4.49e-01 1.96e-02 -9.2 1.04e+04 - 4.18e-01 3.28e-01f 1\n",
|
||
" 96 2.0517516e+01 4.22e-01 1.48e-02 -9.2 1.76e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 97 2.0998518e+01 4.02e-02 1.86e-02 -9.2 2.59e+02 - 3.02e-01 1.00e+00h 1\n",
|
||
" 98 1.5248038e+01 6.27e+00 5.79e-01 -9.2 3.54e+04 - 1.90e-01 1.00e+00f 1\n",
|
||
" 99 1.8944805e+01 5.25e-01 3.31e-01 -9.2 3.83e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.4917558e+01 4.11e+00 1.39e-01 -7.2 7.34e+05 - 1.00e+00 4.11e-02f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.4917557680695772e+01 1.4917557680695772e+01\n",
|
||
"Dual infeasibility......: 1.3936109495518667e-01 1.3936109495518667e-01\n",
|
||
"Constraint violation....: 4.1094163919324949e+00 4.1094163919324949e+00\n",
|
||
"Complementarity.........: 1.0451939489127514e-07 1.0451939489127514e-07\n",
|
||
"Overall NLP error.......: 4.1094163919324949e+00 4.1094163919324949e+00\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 121\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 121\n",
|
||
"Number of inequality constraint evaluations = 121\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.421\n",
|
||
"Total CPU secs in NLP function evaluations = 135.470\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 531.00us ( 4.39us) 516.21us ( 4.27us) 121\n",
|
||
" nlp_g | 5.44 s ( 44.95ms) 5.18 s ( 42.81ms) 121\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 350.00us ( 3.43us) 350.39us ( 3.44us) 102\n",
|
||
" nlp_jac_g | 132.82 s ( 1.30 s) 126.78 s ( 1.24 s) 102\n",
|
||
" total | 139.73 s (139.73 s) 133.37 s (133.37 s) 1\n",
|
||
"Timestamp 2700\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.13e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9613368e+01 1.39e+01 3.13e+04 -1.5 3.13e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.1570073e+01 5.22e+00 1.26e+01 0.8 3.57e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.6464638e+01 1.93e+00 8.67e-01 -1.3 7.08e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 1.7589424e+01 1.24e-04 8.67e-02 -3.0 2.17e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
" 5 1.7589521e+01 4.83e-06 3.29e-03 -4.9 4.28e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.7589506e+01 1.20e-05 2.72e-03 -7.0 7.71e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.7589411e+01 4.24e-05 5.99e-03 -9.1 1.95e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.7589237e+01 1.48e-04 2.12e-03 -11.0 9.16e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.7589188e+01 1.60e-04 3.53e-03 -11.0 2.12e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.7589096e+01 1.96e-04 1.21e-03 -11.0 1.28e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.7589486e+01 5.10e-08 9.82e-05 -11.0 5.76e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 12 1.7589447e+01 1.97e-05 2.10e-03 -11.0 1.91e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.7587608e+01 1.35e-03 5.05e-03 -11.0 2.79e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.7588442e+01 8.75e-04 1.38e-03 -11.0 1.66e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.7587425e+01 6.71e-04 4.90e-03 -11.0 4.31e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.7589450e+01 1.21e-04 3.25e-03 -11.0 8.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.7588309e+01 7.59e-04 2.96e-03 -11.0 2.48e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.7589397e+01 1.80e-04 1.85e-03 -11.0 7.97e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.7589450e+01 3.91e-05 9.91e-04 -11.0 5.93e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.7586111e+01 2.17e-03 3.22e-03 -11.0 5.46e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.7588446e+01 7.48e-04 3.22e-03 -11.0 6.03e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.7589334e+01 6.93e-05 2.27e-03 -11.0 1.61e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.7589259e+01 1.30e-04 2.19e-03 -11.0 9.65e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.7582092e+01 5.63e-03 8.75e-03 -11.0 4.98e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.7589277e+01 1.39e-06 1.56e-03 -11.0 1.34e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 26 1.7502440e+01 7.14e-02 6.83e-03 -11.0 3.02e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 27 1.4419080e+01 9.90e+00 8.25e-01 -9.0 9.23e+05 - 1.00e+00 3.33e-02f 1\n",
|
||
" 28 1.4389171e+01 9.91e+00 8.23e-01 -9.2 5.54e+04 - 7.24e-01 5.60e-03h 1\n",
|
||
" 29 1.7373832e+01 9.94e-02 2.19e-01 -9.2 7.28e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.7531793e+01 8.63e-03 1.20e-02 -8.2 8.77e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.7517659e+01 2.68e-02 1.01e-02 -4.0 1.57e+02 - 1.00e+00 4.02e-01h 1\n",
|
||
" 32 1.7532198e+01 6.04e-03 1.66e-03 -5.7 4.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.7546289e+01 3.61e-05 1.90e-03 -7.5 2.37e+01 - 1.00e+00 9.96e-01H 1\n",
|
||
" 34 1.7542030e+01 2.67e-03 1.67e-03 -9.5 1.61e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.7530667e+01 1.19e-02 1.71e-03 -6.6 4.04e+01 - 1.47e-01 1.00e+00h 1\n",
|
||
" 36 1.7539256e+01 5.60e-03 4.63e-03 -5.5 9.20e+01 - 2.20e-03 1.00e+00h 1\n",
|
||
" 37 1.7525739e+01 6.49e-03 5.19e-03 -7.4 1.72e+02 - 1.00e+00 5.35e-01h 1\n",
|
||
" 38 1.7522046e+01 1.94e-02 1.51e-03 -6.9 1.79e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 39 1.7545755e+01 7.09e-03 2.13e-03 -4.1 5.46e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.7541318e+01 1.70e-02 1.79e-03 -4.1 2.04e+02 - 1.00e+00 2.98e-01h 1\n",
|
||
" 41 1.7543624e+01 1.37e-02 1.37e-03 -4.1 6.46e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.7537148e+01 1.03e-02 1.94e-03 -4.1 4.25e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.7513035e+01 1.43e-02 1.13e-03 -3.8 6.64e+02 - 1.00e+00 8.59e-02h 1\n",
|
||
" 44 1.7544452e+01 4.47e-03 1.83e-03 -9.9 2.29e+01 - 9.36e-01 1.00e+00h 1\n",
|
||
" 45 1.7520261e+01 2.47e-02 1.52e-03 -5.2 5.03e+04 - 8.64e-01 3.23e-03f 1\n",
|
||
" 46 1.7546436e+01 3.32e-03 1.57e-03 -5.2 1.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.7538696e+01 1.52e-02 2.08e-03 -5.2 5.51e+01 - 3.23e-02 1.00e+00h 1\n",
|
||
" 48 1.7538330e+01 1.44e-02 2.19e-03 -5.2 1.82e+03 - 1.00e+00 2.90e-03h 1\n",
|
||
" 49 1.7430228e+01 1.01e-01 6.85e-03 -5.2 2.88e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.5416364e+01 8.09e-01 5.86e-02 -9.7 1.59e+04 - 2.50e-01 3.30e-01f 1\n",
|
||
" 51 1.5439755e+01 8.02e-01 5.56e-02 -3.1 3.52e+03 - 1.80e-01 1.49e-02h 1\n",
|
||
" 52 1.7586558e+01 3.52e-02 8.90e-02 -3.5 1.25e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.7604047e+01 9.82e-03 4.39e-03 -3.1 1.04e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.7605736e+01 5.26e-03 1.82e-03 -3.1 1.81e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.7599706e+01 1.02e-02 4.90e-03 -4.7 7.57e+01 - 1.19e-01 1.00e+00h 1\n",
|
||
" 56 1.7208246e+01 2.10e-01 1.50e-02 -4.7 2.44e+03 - 9.04e-02 1.00e+00f 1\n",
|
||
" 57 1.5677429e+01 1.10e+00 4.15e-02 -3.9 9.37e+03 - 6.41e-01 1.00e+00f 1\n",
|
||
" 58 1.5564252e+01 1.33e+00 3.64e-02 -3.3 2.00e+04 - 1.00e+00 8.14e-02h 1\n",
|
||
" 59 1.7472525e+01 1.66e-02 6.51e-02 -3.3 1.47e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.7409488e+01 4.69e-02 2.52e-02 -3.7 3.86e+02 - 8.48e-01 1.00e+00h 1\n",
|
||
" 61 1.7495210e+01 8.87e-04 2.54e-02 -3.0 7.01e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 62 1.6765249e+01 1.03e+00 9.97e-02 -2.5 2.34e+03 - 7.72e-01 1.00e+00f 1\n",
|
||
" 63 1.6152537e+01 7.47e-01 6.38e-02 -2.6 1.13e+04 - 9.53e-01 1.00e+00h 1\n",
|
||
" 64 1.4264082e+01 3.04e+00 1.13e-01 -2.3 2.16e+04 - 7.47e-01 1.00e+00f 1\n",
|
||
" 65 1.3684462e+01 2.75e+00 1.29e-01 -2.6 1.97e+04 - 9.99e-01 1.00e+00h 1\n",
|
||
" 66 1.5179176e+01 2.24e+00 7.23e-02 -3.1 7.33e+03 - 9.96e-01 1.00e+00h 1\n",
|
||
" 67 1.7696900e+01 2.02e-01 1.42e-01 -3.9 7.90e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 68 1.4406328e+01 1.73e+00 7.82e-02 -4.0 5.79e+06 - 4.91e-03 3.98e-03f 1\n",
|
||
" 69 1.3471250e+01 1.44e+00 9.46e-02 -3.3 1.99e+05 - 1.00e+00 4.15e-02f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.5661977e+01 8.07e-01 2.77e-02 -2.7 4.02e+03 - 9.54e-01 1.00e+00h 1\n",
|
||
" 71 1.7572234e+01 4.89e-01 9.26e-02 -8.7 5.28e+03 - 6.73e-02 1.00e+00H 1\n",
|
||
" 72 1.7560534e+01 5.93e-01 8.79e-02 -2.9 8.88e+04 - 5.56e-01 4.54e-02h 1\n",
|
||
" 73 1.7708659e+01 1.09e-01 1.81e-02 -2.9 2.91e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.7463178e+01 8.79e-02 2.38e-02 -2.9 7.93e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.7753626e+01 9.61e-05 2.17e-01 -2.9 2.21e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.7753712e+01 7.68e-07 1.93e-03 -2.9 4.99e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.7753710e+01 1.18e-06 9.88e-04 -4.3 4.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.7753713e+01 1.89e-07 3.16e-05 -4.3 1.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.7753713e+01 1.71e-07 9.78e-05 -6.5 1.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.7753712e+01 1.70e-07 3.79e-04 -6.5 1.95e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.7753694e+01 9.03e-06 7.29e-03 -6.5 1.42e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.7753689e+01 1.35e-05 3.03e-03 -6.5 3.66e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.7753701e+01 6.85e-06 1.74e-03 -6.5 7.34e-03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 84 1.7753708e+01 3.54e-06 1.65e-03 -6.5 2.03e-03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 85 1.7753711e+01 1.71e-06 2.10e-03 -6.5 4.18e-03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 86 1.7753706e+01 3.47e-06 5.72e-03 -6.5 1.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.7753712e+01 6.96e-07 1.63e-03 -6.5 7.96e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.7753712e+01 7.31e-07 8.82e-04 -6.5 5.88e-03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 89 1.7753714e+01 8.19e-11 1.54e-04 -6.5 1.15e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.7753714e+01 2.74e-07 3.29e-04 -8.6 3.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.7753713e+01 6.23e-07 8.96e-05 -8.7 1.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.7753714e+01 9.96e-08 3.97e-05 -8.7 2.10e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.7753714e+01 1.68e-10 4.16e-05 -8.7 4.67e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 94 1.7753711e+01 6.97e-06 1.65e-03 -11.0 7.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.7750798e+01 2.97e-03 6.92e-03 -11.0 4.85e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.7749637e+01 3.25e-03 8.86e-04 -11.0 3.34e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.7751593e+01 1.16e-03 1.45e-03 -11.0 1.49e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.7753243e+01 1.14e-04 2.18e-03 -11.0 2.63e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.7752944e+01 2.81e-04 1.64e-03 -11.0 3.84e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.7373246e+01 5.55e-01 1.83e-02 -11.0 2.62e+04 - 4.78e-01 8.67e-02f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.7373245746090067e+01 1.7373245746090067e+01\n",
|
||
"Dual infeasibility......: 1.8331011377166306e-02 1.8331011377166306e-02\n",
|
||
"Constraint violation....: 5.5456471468778901e-01 5.5456471468778901e-01\n",
|
||
"Complementarity.........: 1.6246817796750790e-11 1.6246817796750790e-11\n",
|
||
"Overall NLP error.......: 5.5456471468778901e-01 5.5456471468778901e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 114\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 114\n",
|
||
"Number of inequality constraint evaluations = 114\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.418\n",
|
||
"Total CPU secs in NLP function evaluations = 134.477\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 512.00us ( 4.49us) 509.17us ( 4.47us) 114\n",
|
||
" nlp_g | 5.08 s ( 44.58ms) 4.84 s ( 42.48ms) 114\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 345.00us ( 3.38us) 335.85us ( 3.29us) 102\n",
|
||
" nlp_jac_g | 132.07 s ( 1.29 s) 126.02 s ( 1.24 s) 102\n",
|
||
" total | 138.63 s (138.63 s) 132.28 s (132.28 s) 1\n",
|
||
"Timestamp 3000\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.88e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9720453e+01 1.39e+01 2.88e+04 -1.5 2.88e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.1168744e+01 4.85e+00 1.44e+01 1.1 9.85e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.5265701e+01 1.92e+00 8.71e-01 -1.0 2.09e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.6474839e+01 1.40e-03 1.02e-01 -2.9 3.59e+00 - 9.94e-01 1.00e+00h 1\n",
|
||
" 5 1.6477356e+01 9.47e-04 4.88e-02 -4.6 3.39e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
" 6 1.6479122e+01 7.90e-06 2.12e-03 -6.4 1.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.6479115e+01 5.17e-06 1.52e-03 -8.5 9.23e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.6478978e+01 5.96e-05 4.85e-03 -11.0 4.02e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.6479034e+01 3.28e-05 1.33e-03 -11.0 2.09e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.6479077e+01 2.97e-05 2.46e-03 -11.0 2.22e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.6479106e+01 1.73e-05 1.49e-03 -11.0 7.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.6478680e+01 4.41e-04 7.09e-03 -11.0 1.87e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.6393132e+01 7.93e-02 2.45e-02 -11.0 4.28e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 14 1.6324167e+01 3.98e-02 8.07e-03 -11.0 1.20e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.6430748e+01 7.55e-05 3.45e-03 -11.0 1.01e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 16 1.5725189e+01 3.86e-01 3.07e-02 -11.0 3.74e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 17 1.5875292e+01 2.66e-01 5.81e-03 -11.0 1.83e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.6302418e+01 1.25e-01 1.24e-02 -11.0 8.48e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.6167995e+01 2.68e-01 1.06e-02 -11.0 1.12e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.6133506e+01 1.94e-01 8.16e-03 -11.0 6.22e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.3370812e+01 1.31e+00 8.20e-02 -11.0 1.19e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 22 1.1250249e+01 3.51e+00 3.75e-01 -9.0 1.12e+05 - 1.00e+00 1.37e-01f 1\n",
|
||
" 23 1.1430548e+01 2.12e+00 6.38e-02 -9.2 9.27e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.5316347e+01 3.66e-01 2.78e-01 -10.2 3.99e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.4881407e+01 7.43e-01 5.90e-02 -8.2 1.35e+04 - 1.00e+00 6.25e-01h 1\n",
|
||
" 26 1.4880264e+01 7.34e-01 5.90e-02 -6.3 7.09e+03 - 1.00e+00 8.02e-03h 1\n",
|
||
" 27 1.5376345e+01 5.39e-01 4.92e-02 -4.5 4.76e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.4213256e+01 1.11e+00 1.04e-01 -4.1 1.04e+04 - 2.86e-01 1.00e+00f 1\n",
|
||
" 29 1.2595245e+01 2.79e+00 1.64e-01 -4.5 1.20e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.4885362e+01 1.65e+00 1.55e-01 -4.7 1.73e+04 - 8.42e-01 1.00e+00h 1\n",
|
||
" 31 1.4359481e+01 3.10e+00 1.93e-01 -4.7 6.55e+05 - 4.66e-02 1.75e-02f 2\n",
|
||
" 32 1.3708301e+01 2.88e+00 2.64e-01 -4.7 4.78e+04 - 1.00e+00 2.40e-01f 1\n",
|
||
" 33 1.6444640e+01 1.07e-01 1.80e-01 -4.7 6.69e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.6217345e+01 1.25e-02 2.04e-02 -4.7 2.15e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.6193690e+01 5.68e-02 1.35e-02 -4.3 5.85e+02 - 1.00e+00 2.49e-01h 1\n",
|
||
" 36 1.6186369e+01 3.05e-02 1.11e-02 -6.0 1.17e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.6060555e+01 1.21e-01 3.63e-02 -4.0 3.92e+03 - 1.00e+00 3.90e-01h 1\n",
|
||
" 38 1.6125600e+01 4.21e-02 2.62e-02 -3.3 5.25e+02 - 1.82e-01 1.00e+00h 1\n",
|
||
" 39 1.6122232e+01 5.83e-02 1.39e-02 -3.4 1.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.5826038e+01 1.20e-01 2.40e-02 -2.1 8.71e+02 - 3.24e-01 1.00e+00h 1\n",
|
||
" 41 1.6017578e+01 1.79e-01 1.64e-02 -8.6 9.55e+02 - 1.99e-01 1.00e+00h 1\n",
|
||
" 42 1.6196422e+01 3.53e-02 1.17e-02 -3.8 1.95e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.6102494e+01 5.07e-02 5.93e-03 -3.9 6.54e+02 - 4.89e-01 1.00e+00h 1\n",
|
||
" 44 1.6082931e+01 1.37e-01 7.70e-03 -2.8 6.14e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.5797858e+01 2.03e-01 1.11e-02 -2.5 1.05e+03 - 1.00e+00 7.16e-01h 1\n",
|
||
" 46 1.6199971e+01 2.80e-02 1.30e-02 -8.6 2.16e+02 - 3.84e-01 1.00e+00h 1\n",
|
||
" 47 1.5924563e+01 3.11e-01 1.08e-02 -2.9 2.40e+05 - 9.35e-04 8.42e-03f 1\n",
|
||
" 48 1.5677495e+01 2.14e-01 1.62e-02 -2.9 1.59e+03 - 5.77e-02 1.00e+00f 1\n",
|
||
" 49 1.5648503e+01 4.66e-01 1.43e-02 -3.9 1.48e+03 - 9.83e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.6164127e+01 1.06e-01 2.61e-02 -3.8 9.64e+02 - 9.46e-01 1.00e+00h 1\n",
|
||
" 51 1.6239623e+01 2.81e-02 9.76e-03 -4.0 5.37e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.5952788e+01 5.28e-01 2.99e-02 -4.0 2.56e+03 - 7.88e-01 1.00e+00f 1\n",
|
||
" 53 1.4969418e+01 7.49e-01 6.39e-02 -4.0 3.92e+04 - 7.27e-01 1.00e+00f 1\n",
|
||
" 54 1.4449424e+01 5.66e-01 5.01e-02 -4.0 7.00e+06 - 5.83e-03 3.14e-03f 1\n",
|
||
" 55 1.5865871e+01 5.96e-01 8.92e-02 -3.9 8.09e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.5954131e+01 1.80e-01 3.60e-02 -1.7 1.85e+03 - 8.62e-01 1.00e+00h 1\n",
|
||
" 57 1.5475486e+01 1.02e+00 4.31e-02 -2.5 1.15e+06 - 4.39e-02 8.54e-03f 1\n",
|
||
" 58 1.5452670e+01 9.92e-01 3.86e-02 -2.5 5.53e+03 - 1.00e+00 3.95e-02h 1\n",
|
||
" 59 1.5825674e+01 1.47e-01 5.26e-02 -2.5 1.28e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.6071666e+01 5.58e-02 1.09e-02 -2.3 3.16e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.6015025e+01 8.58e-02 1.39e-02 -3.7 6.36e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 62 1.6038296e+01 7.19e-02 4.88e-03 -2.5 1.74e+03 - 2.57e-01 1.00e+00h 1\n",
|
||
" 63 1.5919213e+01 9.85e-02 6.66e-03 -4.5 1.66e+03 - 9.94e-01 1.00e+00h 1\n",
|
||
" 64 1.6117922e+01 8.04e-03 3.92e-03 -2.8 3.92e+02 - 1.00e+00 9.07e-01H 1\n",
|
||
" 65 1.5869021e+01 1.13e-01 9.64e-03 -3.9 8.33e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 66 1.6133556e+01 1.82e-02 5.88e-03 -9.9 2.02e+02 - 6.18e-01 1.00e+00h 1\n",
|
||
" 67 1.5970229e+01 1.16e-01 6.40e-03 -4.0 6.40e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 68 1.3627617e+01 9.79e-01 5.74e-02 -9.9 8.94e+03 - 2.62e-01 1.00e+00f 1\n",
|
||
" 69 1.2791111e+01 2.48e+00 1.14e-01 -10.2 1.23e+04 - 8.04e-03 6.23e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.3636536e+01 1.76e+00 2.17e-02 -4.3 6.89e+03 - 1.00e+00 4.31e-01h 1\n",
|
||
" 71 1.0147717e+01 4.68e+00 3.40e-01 -4.3 1.06e+04 - 1.09e-01 1.00e+00f 1\n",
|
||
" 72 1.3561156e+01 3.67e+00 3.03e-01 -1.1 8.85e+03 - 3.97e-01 1.00e+00h 1\n",
|
||
" 73 1.3300419e+01 1.67e+00 1.79e-01 -1.7 1.82e+04 - 1.00e+00 5.51e-01h 1\n",
|
||
" 74 1.5313932e+01 4.40e-01 4.99e-02 -2.7 4.73e+03 - 9.96e-01 1.00e+00h 1\n",
|
||
" 75 1.4460963e+01 6.27e-01 2.58e-02 -8.5 3.77e+03 - 6.86e-01 1.00e+00h 1\n",
|
||
" 76 1.4470489e+01 6.20e-01 2.50e-02 -3.3 6.99e+03 - 1.00e+00 9.48e-03h 1\n",
|
||
" 77 1.5871491e+01 1.25e-01 1.04e-01 -3.3 1.08e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.6067202e+01 6.08e-03 3.93e-02 -3.3 1.28e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 79 1.6060548e+01 3.22e-02 5.44e-03 -9.3 3.46e+02 - 2.20e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.5830937e+01 2.71e-01 2.36e-02 -3.5 1.93e+03 - 3.86e-01 1.00e+00f 1\n",
|
||
" 81 1.5281295e+01 8.38e-01 6.06e-02 -3.6 3.74e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.5287439e+01 2.07e-01 2.47e-02 -3.2 3.95e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.4581178e+01 1.38e+01 1.12e+00 -9.2 1.20e+06 - 5.96e-03 4.61e-02f 1\n",
|
||
" 84 1.4511758e+01 1.37e+01 1.10e+00 -3.4 2.50e+04 - 9.92e-01 2.23e-02h 1\n",
|
||
" 85 1.5143230e+01 5.40e-02 1.05e+00 -3.4 5.16e+02 - 5.18e-01 1.00e+00h 1\n",
|
||
" 86 1.5131878e+01 8.07e-02 7.23e-03 -1.9 5.52e+02 - 9.17e-01 1.00e+00h 1\n",
|
||
" 87 1.5074598e+01 9.44e-02 5.32e-03 -8.0 2.31e+04 - 1.21e-01 2.79e-02f 1\n",
|
||
" 88 1.5093092e+01 7.73e-02 1.40e-02 -2.5 1.65e+03 - 1.00e+00 2.91e-01h 1\n",
|
||
" 89 1.5187112e+01 5.72e-03 7.15e-03 -3.9 2.30e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.5200309e+01 5.04e-04 8.00e-03 -3.7 5.36e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 91 1.4362805e+01 1.33e+00 1.58e-01 -2.6 1.10e+05 - 1.16e-01 5.60e-01f 1\n",
|
||
" 92 1.3421493e+01 7.23e+00 4.61e-01 -3.5 4.90e+04 - 6.05e-02 1.00e+00f 1\n",
|
||
" 93 1.3672039e+01 4.58e+00 1.12e-01 -3.5 3.96e+04 - 1.00e+00 3.42e-01h 1\n",
|
||
" 94 1.4913884e+01 1.21e-01 2.46e-01 -3.5 1.06e+04 - 6.70e-01 1.00e+00h 1\n",
|
||
" 95 1.4807035e+01 4.55e-01 1.07e-01 -3.0 1.95e+04 - 1.00e+00 5.37e-01h 1\n",
|
||
" 96 1.5224605e+01 6.38e-02 5.42e-02 -2.1 1.71e+02 - 5.37e-01 1.00e+00h 1\n",
|
||
" 97 1.5223282e+01 2.47e-02 1.78e-02 -2.5 7.27e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.5222198e+01 2.69e-02 3.10e-02 -3.7 9.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.5229813e+01 1.43e-02 1.48e-02 -3.7 8.05e+01 - 1.00e+00 5.80e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.5245820e+01 8.57e-04 1.41e-02 -3.7 1.12e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.5245820205239601e+01 1.5245820205239601e+01\n",
|
||
"Dual infeasibility......: 1.4149784608908200e-02 1.4149784608908200e-02\n",
|
||
"Constraint violation....: 8.5662517117768289e-04 8.5662517117768289e-04\n",
|
||
"Complementarity.........: 1.8983430936528984e-04 1.8983430936528984e-04\n",
|
||
"Overall NLP error.......: 1.4149784608908200e-02 1.4149784608908200e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 109\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 109\n",
|
||
"Number of inequality constraint evaluations = 109\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.408\n",
|
||
"Total CPU secs in NLP function evaluations = 134.594\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 475.00us ( 4.36us) 470.36us ( 4.32us) 109\n",
|
||
" nlp_g | 4.88 s ( 44.76ms) 4.65 s ( 42.62ms) 109\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 323.00us ( 3.17us) 316.59us ( 3.10us) 102\n",
|
||
" nlp_jac_g | 132.41 s ( 1.30 s) 126.37 s ( 1.24 s) 102\n",
|
||
" total | 138.78 s (138.78 s) 132.43 s (132.43 s) 1\n",
|
||
"Timestamp 3300\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.67e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0676248e+01 1.16e+01 2.67e+04 -1.5 2.67e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.0950847e+00 3.81e+00 4.33e+00 0.8 2.88e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.6163456e+00 3.91e-01 1.36e-01 -1.3 1.01e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 2.1666784e+00 1.98e-03 2.05e-01 -7.2 2.68e+00 - 9.90e-01 1.00e+00h 1\n",
|
||
" 5 2.1672257e+00 1.92e-04 7.19e-03 -4.8 1.07e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.1668474e+00 4.25e-04 1.12e-03 -6.9 8.66e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.1674901e+00 4.11e-07 2.18e-04 -9.0 4.63e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 8 2.1647000e+00 1.64e-03 4.67e-03 -11.0 3.87e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 9 2.1672282e+00 3.36e-04 1.35e-03 -11.0 1.60e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.1666546e+00 7.77e-04 6.50e-03 -11.0 5.84e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.1657130e+00 7.25e-04 1.71e-03 -11.0 4.35e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.1657921e+00 8.99e-04 3.73e-03 -11.0 7.18e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.1669727e+00 4.43e-04 1.80e-03 -11.0 2.96e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.1673062e+00 1.62e-04 1.11e-03 -11.0 1.82e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.1542900e+00 2.15e-02 6.87e-03 -11.0 1.16e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.1543687e+00 6.27e-03 3.60e-03 -11.0 1.17e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.1031850e+00 8.46e-01 1.20e+00 -9.0 7.96e+04 - 1.00e+00 3.89e-01F 1\n",
|
||
" 18 2.0649424e+00 8.46e-01 1.18e+00 -7.0 8.17e+03 - 1.00e+00 2.38e-02h 1\n",
|
||
" 19 2.0626663e+00 8.46e-01 1.18e+00 -5.0 4.36e+03 - 1.00e+00 1.32e-03h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.5460125e+00 3.93e-01 2.29e-01 -4.8 2.70e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 21 2.0196007e+00 9.46e-03 3.07e-01 -6.1 1.21e+03 - 9.92e-01 1.00e+00h 1\n",
|
||
" 22 2.0141163e+00 3.20e-03 2.02e-03 -4.6 6.76e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.0086892e+00 4.77e-03 2.54e-03 -6.7 7.30e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.0114420e+00 3.27e-03 5.19e-03 -8.6 1.27e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.9640036e+00 1.18e-01 2.62e-02 -5.4 9.75e+03 - 2.95e-01 1.00e+00h 1\n",
|
||
" 26 2.0046322e+00 1.71e-01 2.92e-02 -4.6 6.75e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 27 1.9939051e+00 4.74e-02 8.56e-02 -2.5 3.68e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.8307067e+00 3.28e-01 2.13e-01 -2.6 1.09e+04 - 9.59e-02 1.00e+00h 1\n",
|
||
" 29 1.7787246e+00 2.04e-01 7.11e-02 -3.8 1.57e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.8202045e+00 1.48e-01 7.20e-02 -3.4 9.68e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.7367399e+00 3.98e-01 2.46e-01 -9.4 1.44e+04 - 7.25e-02 1.00e+00h 1\n",
|
||
" 32 1.6324950e+00 5.78e-01 5.28e-01 -3.5 6.48e+06 - 2.05e-04 6.17e-04f 4\n",
|
||
" 33 1.5835173e+00 1.01e+00 6.88e-01 -3.5 4.97e+05 - 6.68e-02 3.02e-02f 2\n",
|
||
" 34 1.5687120e+00 1.12e+00 6.81e-01 -3.5 2.13e+05 - 2.04e-02 7.04e-02h 1\n",
|
||
" 35 2.0076898e+00 3.79e-01 6.96e-01 -3.5 1.13e+04 - 1.99e-03 1.00e+00h 1\n",
|
||
" 36 1.6889786e+00 9.49e-02 2.48e-01 -3.5 5.91e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.6341766e+00 5.17e-01 4.12e-01 -3.7 2.19e+04 - 1.00e+00 2.68e-01f 1\n",
|
||
" 38 1.5873959e+00 1.77e-01 9.13e-02 -3.0 1.36e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.5822841e+00 5.45e-01 2.75e-01 -1.4 6.96e+05 - 1.06e-01 3.35e-03f 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.4362278e+00 3.64e-01 2.99e-01 -2.2 1.76e+03 - 6.27e-01 1.00e+00h 1\n",
|
||
" 41 1.6669434e+00 4.42e-01 5.15e-01 -2.8 4.11e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 42 1.6303145e+00 4.37e-01 4.87e-01 -3.0 1.40e+04 - 1.00e+00 2.24e-02h 1\n",
|
||
" 43 1.4148316e+00 4.07e-01 1.02e-01 -3.0 1.88e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.6082144e+00 2.52e-01 1.67e-01 -9.0 1.41e+03 - 7.26e-01 1.00e+00h 1\n",
|
||
" 45 1.4503421e+00 2.36e-01 3.90e-01 -3.7 1.05e+03 - 5.39e-02 1.00e+00h 1\n",
|
||
" 46 1.6153974e+00 1.13e-01 1.34e-01 -4.6 1.30e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.5600754e+00 3.33e-01 4.05e-02 -2.6 3.63e+03 - 1.00e+00 5.41e-01h 1\n",
|
||
" 48 1.3837576e+00 5.14e-01 4.53e-01 -2.7 2.73e+03 - 5.58e-02 5.00e-01h 2\n",
|
||
" 49 1.3731585e+00 7.04e-01 2.42e-01 -1.6 6.51e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.8240220e+00 2.85e-01 6.77e-01 -2.6 5.51e+03 - 9.96e-01 1.00e+00h 1\n",
|
||
" 51 1.7712265e+00 5.36e-01 2.34e-01 -2.7 2.06e+03 - 9.46e-01 1.00e+00h 1\n",
|
||
" 52 1.5925524e+00 2.52e-01 1.19e-01 -2.7 1.31e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.8072683e+00 1.61e-01 2.01e-01 -2.7 1.78e+03 - 8.14e-01 1.00e+00h 1\n",
|
||
" 54 1.7243908e+00 4.03e-02 2.20e-01 -2.7 1.63e+03 - 1.00e+00 2.45e-01h 1\n",
|
||
" 55 1.6399857e+00 8.81e-02 5.56e-02 -4.2 6.07e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.5852383e+00 7.77e-02 3.58e-02 -4.2 1.12e+03 - 7.85e-01 1.00e+00h 1\n",
|
||
" 57 1.6892275e+00 8.87e-02 6.21e-02 -4.7 3.67e+03 - 7.12e-03 1.00e+00H 1\n",
|
||
" 58 1.6371880e+00 1.28e+00 5.28e-01 -3.1 8.03e+05 - 1.00e+00 8.63e-03f 1\n",
|
||
" 59 1.8115614e+00 3.49e-01 2.92e-01 -3.2 8.79e+03 - 6.99e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.7693910e+00 4.40e-01 3.54e-01 -3.2 9.95e+03 - 1.05e-01 1.00e+00H 1\n",
|
||
" 61 1.5672271e+00 2.24e-01 1.53e-01 -3.2 2.77e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.5261324e+00 1.84e-01 9.45e-02 -2.4 1.42e+04 - 1.00e+00 2.39e-01h 2\n",
|
||
" 63 1.5217613e+00 4.22e-01 1.84e-01 -2.5 5.36e+04 - 1.35e-01 5.08e-02h 5\n",
|
||
" 64 1.4740122e+00 3.38e-01 2.00e-01 -2.5 1.80e+04 - 1.00e+00 1.72e-01h 1\n",
|
||
" 65 1.5074254e+00 1.85e-01 6.63e-02 -2.5 1.44e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 66 1.6630717e+00 2.84e-02 9.71e-02 -2.5 3.39e+03 - 9.00e-01 1.00e+00h 1\n",
|
||
" 67 1.4642405e+00 5.47e-01 2.10e-01 -2.8 2.44e+04 - 6.73e-01 1.00e+00f 1\n",
|
||
" 68 1.6049212e+00 1.77e-01 1.48e-01 -2.7 3.11e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.4706845e+00 2.56e-01 1.65e-01 -2.7 1.31e+05 - 1.67e-01 8.94e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.5380061e+00 2.06e-01 1.36e-01 -2.7 4.32e+03 - 9.01e-01 1.00e+00h 1\n",
|
||
" 71 1.5165426e+00 3.93e-01 2.15e-01 -2.0 4.67e+04 - 8.28e-01 4.61e-02h 2\n",
|
||
" 72 1.6636298e+00 9.24e-02 4.71e-02 -2.2 3.30e+03 - 8.85e-01 1.00e+00h 1\n",
|
||
" 73 1.6130165e+00 1.16e-01 1.04e-01 -2.2 1.21e+04 - 1.00e+00 4.52e-01h 1\n",
|
||
" 74 1.6100613e+00 4.07e-01 4.82e-01 -2.2 1.51e+04 - 1.76e-01 2.30e-01f 2\n",
|
||
" 75 1.5762160e+00 3.02e-01 4.04e-01 -2.2 2.37e+04 - 1.00e+00 1.35e-01H 1\n",
|
||
" 76 1.4719898e+00 2.89e-01 1.87e-01 -2.2 2.74e+03 - 4.40e-01 5.00e-01f 2\n",
|
||
" 77 1.4039705e+00 2.79e-01 1.39e-01 -2.2 5.54e+03 - 5.41e-01 1.23e-01h 1\n",
|
||
" 78r 1.4039705e+00 2.79e-01 9.99e+02 -0.6 0.00e+00 - 0.00e+00 4.77e-07R 22\n",
|
||
" 79r 1.5354957e+00 6.46e-02 4.47e+02 -2.7 2.27e+02 - 1.00e+00 1.21e-03f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.6293380e+00 3.36e-02 1.30e-02 -5.5 6.06e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.4885314e+00 1.29e+00 3.31e-01 -4.0 2.50e+03 - 8.65e-01 1.00e+00f 1\n",
|
||
" 82 1.4864980e+00 1.26e+00 3.18e-01 -4.3 6.76e+02 - 1.00e+00 2.49e-02h 1\n",
|
||
" 83 1.6015344e+00 6.65e-02 3.78e-01 -4.3 7.30e+02 - 1.15e-01 1.00e+00h 1\n",
|
||
" 84 1.6370419e+00 9.49e-02 2.17e-01 -4.3 1.87e+03 - 1.00e+00 8.21e-01H 1\n",
|
||
" 85 1.6570821e+00 5.64e-03 1.82e-02 -4.3 1.12e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 86 1.6508276e+00 4.05e-02 1.59e-02 -8.3 2.01e+02 - 6.80e-01 1.00e+00h 1\n",
|
||
" 87 1.6554956e+00 2.52e-02 3.01e-02 -2.5 3.66e+03 - 3.73e-01 4.59e-01H 1\n",
|
||
" 88 1.6476651e+00 2.65e-02 1.72e-02 -9.1 1.10e+02 - 6.77e-01 1.00e+00h 1\n",
|
||
" 89 1.6368781e+00 1.55e-01 4.24e-01 -2.3 1.87e+04 - 1.00e+00 1.00e+00F 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.6006297e+00 1.56e-01 3.67e-01 -1.7 1.34e+04 - 1.00e+00 5.21e-02h 2\n",
|
||
" 91 1.6308278e+00 2.58e-02 9.48e-02 -1.7 3.89e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 92 1.5895579e+00 1.52e-01 1.76e-01 -2.3 1.39e+04 - 9.67e-01 2.49e-01h 1\n",
|
||
" 93 1.5195401e+00 1.44e+00 8.08e-01 -2.2 8.09e+04 - 1.17e-01 1.61e-01f 3\n",
|
||
" 94 2.4268404e+00 8.62e-01 5.89e-01 -2.2 8.95e+04 - 7.25e-01 4.38e-01h 1\n",
|
||
" 95 2.1238042e+00 7.93e-01 7.84e-01 -2.2 1.99e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 96 1.3558183e+00 5.21e-01 6.20e-01 -2.2 9.50e+03 - 6.23e-01 1.00e+00h 1\n",
|
||
" 97 1.2155221e+00 3.70e-01 2.24e-01 -2.7 6.05e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 98 1.4475522e+00 1.53e-01 3.85e-02 -2.0 8.19e+03 - 9.09e-01 1.00e+00h 1\n",
|
||
" 99 1.4294520e+00 2.51e-01 2.88e-01 -8.1 1.31e+06 - 3.24e-02 3.49e-03f 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.3597379e+00 8.48e-01 5.37e-01 -1.6 4.01e+05 - 1.00e+00 3.03e-02f 3\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.3597378639772628e+00 1.3597378639772628e+00\n",
|
||
"Dual infeasibility......: 5.3724964431282352e-01 5.3724964431282352e-01\n",
|
||
"Constraint violation....: 8.4846519975510759e-01 8.4846519975510759e-01\n",
|
||
"Complementarity.........: 1.0194093499663394e-01 1.0194093499663394e-01\n",
|
||
"Overall NLP error.......: 8.4846519975510759e-01 8.4846519975510759e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 201\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 201\n",
|
||
"Number of inequality constraint evaluations = 201\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.425\n",
|
||
"Total CPU secs in NLP function evaluations = 139.801\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 901.00us ( 4.48us) 894.95us ( 4.45us) 201\n",
|
||
" nlp_g | 9.00 s ( 44.80ms) 8.58 s ( 42.69ms) 201\n",
|
||
" nlp_grad | 1.32 s ( 1.32 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 328.00us ( 3.22us) 323.91us ( 3.18us) 102\n",
|
||
" nlp_jac_g | 133.83 s ( 1.30 s) 127.73 s ( 1.24 s) 103\n",
|
||
" total | 144.32 s (144.32 s) 137.72 s (137.72 s) 1\n",
|
||
"Timestamp 3600\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.99e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9988853e+01 1.54e+01 1.99e+04 -1.5 1.99e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.2149208e+01 5.50e+00 1.38e+01 0.6 1.15e+02 - 9.99e-01 1.00e+00f 1\n",
|
||
" 3 1.8296460e+01 2.17e+00 8.42e-01 -1.5 2.62e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 1.9676030e+01 9.63e-05 8.83e-02 -3.2 2.54e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.9676006e+01 1.12e-05 6.27e-03 -5.1 5.57e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.9676042e+01 2.23e-09 1.37e-04 -7.2 7.41e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 7 1.9675997e+01 1.69e-05 2.08e-03 -11.0 1.10e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.9676020e+01 7.11e-06 1.11e-03 -11.0 5.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.9676022e+01 7.44e-06 1.45e-03 -11.0 9.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.9676041e+01 3.18e-06 1.05e-03 -11.0 3.43e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.9676019e+01 3.65e-05 1.14e-03 -11.0 4.55e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.9675959e+01 7.73e-05 2.03e-03 -11.0 1.22e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.9676014e+01 3.32e-05 1.57e-03 -11.0 8.53e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.9675979e+01 4.83e-05 7.83e-04 -11.0 6.25e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.9676046e+01 2.07e-06 1.45e-03 -11.0 1.78e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.9675553e+01 1.86e-04 2.28e-03 -11.0 2.63e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.9674855e+01 4.38e-04 6.43e-04 -11.0 2.10e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.9674781e+01 6.19e-04 2.17e-03 -11.0 2.23e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.9675276e+01 3.69e-04 1.44e-03 -11.0 1.12e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.9675821e+01 1.00e-04 2.91e-03 -11.0 9.33e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.9675792e+01 1.59e-04 1.80e-03 -11.0 8.89e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.9675984e+01 2.67e-05 2.79e-03 -11.0 1.99e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.9675932e+01 7.34e-05 1.26e-03 -11.0 5.86e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.9675808e+01 9.35e-04 2.41e-03 -11.0 3.73e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.9674356e+01 1.08e-03 1.99e-03 -11.0 3.18e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.9673347e+01 1.66e-03 3.09e-03 -11.0 3.10e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.9675696e+01 2.28e-04 2.10e-03 -11.0 6.65e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.9674442e+01 5.62e-04 5.56e-03 -11.0 4.00e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.9675856e+01 2.80e-05 3.65e-03 -11.0 8.27e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.9675778e+01 1.08e-04 2.82e-03 -11.0 6.06e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.9363126e+01 1.30e-01 1.53e-02 -11.0 1.14e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 32 1.9472958e+01 1.17e-01 1.12e-02 -11.0 1.32e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.6316536e+01 1.70e+00 1.15e-01 -11.0 2.64e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 34 1.7231086e+01 1.27e+00 6.09e-02 -9.1 2.35e+04 - 1.00e+00 4.03e-01h 1\n",
|
||
" 35 1.7225189e+01 1.27e+00 6.08e-02 -7.1 8.60e+04 - 1.00e+00 4.55e-04h 1\n",
|
||
" 36 1.7225529e+01 1.27e+00 6.08e-02 -5.2 4.96e+03 - 1.00e+00 1.79e-04h 1\n",
|
||
" 37 1.9457138e+01 1.45e-02 5.88e-02 -6.5 4.86e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.9460900e+01 4.53e-03 2.67e-03 -8.3 9.81e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.9453318e+01 9.87e-03 5.43e-03 -6.2 1.77e+02 - 2.38e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.9432474e+01 3.85e-02 1.99e-02 -4.9 8.20e+02 - 3.45e-03 1.00e+00h 1\n",
|
||
" 41 1.9430871e+01 3.98e-02 1.96e-02 -5.2 9.71e+04 - 1.00e+00 2.19e-04h 1\n",
|
||
" 42 1.9341116e+01 2.62e-01 3.98e-02 -4.5 1.91e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.9475612e+01 2.64e-07 3.05e-05 -6.4 2.61e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.9475612e+01 9.49e-08 8.62e-05 -8.3 1.21e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.9475611e+01 5.25e-07 1.57e-04 -11.0 3.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.9475612e+01 5.01e-07 5.21e-05 -11.0 2.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.9475612e+01 8.56e-08 7.80e-05 -11.0 4.55e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.9475612e+01 4.08e-07 4.56e-05 -11.0 3.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.9475612e+01 2.79e-07 8.89e-05 -11.0 9.56e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.9475612e+01 3.19e-07 2.06e-04 -11.0 1.93e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.9475603e+01 5.35e-06 3.69e-03 -11.0 1.14e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.9475612e+01 8.65e-08 1.80e-04 -11.0 2.78e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.9475611e+01 4.26e-07 6.79e-05 -11.0 1.72e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.9475612e+01 8.69e-11 1.58e-04 -11.0 2.52e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 55 1.9475611e+01 3.74e-07 5.45e-05 -11.0 1.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.9475612e+01 8.22e-08 1.23e-04 -11.0 7.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.9475611e+01 2.14e-07 1.75e-04 -11.0 1.30e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.9475611e+01 5.23e-07 5.24e-03 -11.0 2.46e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.9475612e+01 6.94e-08 1.59e-04 -11.0 1.90e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.9475612e+01 2.48e-08 9.51e-05 -11.0 8.09e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.9475612e+01 1.70e-08 7.72e-05 -11.0 1.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.9475612e+01 8.71e-09 1.82e-04 -11.0 3.20e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.9475612e+01 3.24e-08 1.58e-04 -11.0 1.20e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.9475612e+01 5.67e-08 2.44e-05 -11.0 3.96e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.9475612e+01 1.28e-08 2.86e-04 -11.0 1.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.9475611e+01 3.99e-07 1.65e-04 -11.0 8.96e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.9475592e+01 8.34e-06 1.30e-02 -10.2 1.20e-01 - 1.00e+00 2.53e-01h 1\n",
|
||
" 68 1.9475609e+01 1.34e-06 3.51e-03 -10.5 9.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.9475268e+01 2.33e-04 1.88e-02 -8.2 1.27e+00 - 9.18e-03 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.9475540e+01 2.56e-05 3.37e-03 -10.3 5.83e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.9475480e+01 2.53e-04 2.51e-03 -11.0 2.06e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.9475370e+01 9.17e-05 5.42e-03 -11.0 3.23e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.9475594e+01 3.92e-08 5.27e-05 -11.0 9.28e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 74 1.9475574e+01 1.57e-05 1.47e-03 -11.0 4.10e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.9475367e+01 2.02e-04 1.65e-03 -9.0 1.45e+01 - 1.00e+00 2.98e-01h 1\n",
|
||
" 76 1.9475364e+01 2.02e-04 2.24e-03 -7.7 1.65e+02 - 1.00e+00 4.24e-05h 1\n",
|
||
" 77 1.9475528e+01 1.36e-05 1.27e-03 -9.2 4.61e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.9475530e+01 1.21e-05 7.87e-04 -11.0 1.70e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.9475528e+01 6.35e-06 2.36e-03 -8.7 1.37e-01 - 1.99e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.9475354e+01 3.53e-05 1.79e-03 -7.5 4.94e+00 - 3.58e-04 1.00e+00h 1\n",
|
||
" 81 1.9475352e+01 1.55e-04 4.00e-03 -11.0 3.19e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.9475218e+01 5.84e-04 1.40e-03 -8.9 6.06e+00 - 1.00e+00 9.03e-01h 1\n",
|
||
" 83 1.9144265e+01 3.25e-01 2.20e-02 -7.0 6.89e+03 - 9.30e-06 1.00e+00f 1\n",
|
||
" 84 1.8710226e+01 1.11e+00 2.93e-02 -8.7 1.15e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.9040727e+01 7.75e-01 4.14e-02 -9.2 1.07e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.9048282e+01 7.44e-01 3.82e-02 -7.2 1.98e+04 - 1.00e+00 1.38e-01h 1\n",
|
||
" 87 1.9048063e+01 7.47e-01 3.81e-02 -5.3 5.25e+04 - 1.00e+00 5.95e-04h 1\n",
|
||
" 88 1.9058670e+01 7.27e-01 3.77e-02 -3.3 1.15e+03 - 3.18e-02 3.12e-02h 6\n",
|
||
" 89 1.9449440e+01 5.67e-03 2.89e-02 -6.4 1.41e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.9438721e+01 1.10e-02 2.93e-02 -5.7 1.05e+04 - 1.00e+00 1.57e-02f 1\n",
|
||
" 91 1.9441126e+01 1.19e-02 5.82e-03 -5.3 7.63e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.9337362e+01 1.98e-01 3.18e-02 -6.9 2.86e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.9450918e+01 3.07e-03 5.23e-03 -5.5 6.84e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.9061756e+01 7.57e-01 6.80e-02 -3.6 3.13e+03 - 2.62e-01 1.00e+00f 1\n",
|
||
" 95 1.9429435e+01 2.15e-02 3.05e-02 -3.6 5.86e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.9445313e+01 5.04e-03 1.27e-02 -5.2 3.04e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.9440326e+01 2.57e-02 1.95e-03 -5.5 4.26e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.9429474e+01 4.82e-02 3.86e-03 -5.6 3.04e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.9384491e+01 5.18e-02 8.92e-03 -5.6 1.84e+03 - 1.00e+00 2.25e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.8930375e+01 6.29e-01 4.78e-02 -6.5 2.50e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.8930374897410008e+01 1.8930374897410008e+01\n",
|
||
"Dual infeasibility......: 4.7760135255395464e-02 4.7760135255395464e-02\n",
|
||
"Constraint violation....: 6.2919560116365503e-01 6.2919560116365503e-01\n",
|
||
"Complementarity.........: 4.2318741304821873e-07 4.2318741304821873e-07\n",
|
||
"Overall NLP error.......: 6.2919560116365503e-01 6.2919560116365503e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 109\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 109\n",
|
||
"Number of inequality constraint evaluations = 109\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.430\n",
|
||
"Total CPU secs in NLP function evaluations = 134.537\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 478.00us ( 4.39us) 471.65us ( 4.33us) 109\n",
|
||
" nlp_g | 4.87 s ( 44.69ms) 4.64 s ( 42.59ms) 109\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 358.00us ( 3.51us) 351.69us ( 3.45us) 102\n",
|
||
" nlp_jac_g | 132.51 s ( 1.30 s) 126.47 s ( 1.24 s) 102\n",
|
||
" total | 138.89 s (138.89 s) 132.55 s (132.55 s) 1\n",
|
||
"Timestamp 3900\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.25e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9684668e+01 1.39e+01 1.25e+04 -1.5 1.25e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.6836600e+00 4.78e+00 1.10e+01 0.6 1.54e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.2046106e+01 1.62e+00 9.12e-01 -1.4 3.55e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 1.3113384e+01 9.92e-04 9.33e-02 -3.2 2.05e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.3113758e+01 4.19e-06 2.18e-03 -5.1 3.11e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.3113762e+01 3.45e-06 1.30e-03 -7.2 2.11e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.3113758e+01 7.88e-06 2.81e-03 -9.2 7.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.3113766e+01 9.13e-10 6.75e-05 -11.0 4.82e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 9 1.3113744e+01 1.05e-05 2.46e-03 -11.0 8.21e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.3113747e+01 5.78e-06 2.97e-03 -11.0 6.87e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.3113760e+01 2.13e-06 1.12e-03 -11.0 2.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.3113763e+01 3.25e-10 5.68e-05 -11.0 1.56e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 13 1.3113762e+01 1.43e-06 1.71e-03 -11.0 8.30e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.3113685e+01 9.36e-05 4.67e-03 -11.0 9.64e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.3112324e+01 6.08e-04 1.96e-02 -11.0 4.16e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.3113361e+01 1.50e-04 2.20e-03 -11.0 1.60e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.3113257e+01 2.19e-04 2.48e-03 -11.0 9.33e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.3113195e+01 3.17e-04 2.44e-03 -11.0 5.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.3113545e+01 4.22e-05 3.19e-03 -11.0 7.12e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.3111968e+01 1.38e-03 3.54e-03 -11.0 3.06e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.3110590e+01 2.51e-03 3.78e-03 -11.0 2.19e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.3113068e+01 2.74e-04 1.45e-03 -11.0 1.26e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.3110525e+01 2.38e-03 2.17e-03 -11.0 1.19e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.3112798e+01 4.37e-04 1.76e-03 -11.0 4.05e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.3113722e+01 4.86e-08 7.88e-05 -11.0 2.85e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 26 1.3113102e+01 7.27e-04 2.56e-03 -11.0 4.31e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 27 1.3102255e+01 2.23e-02 2.17e-03 -11.0 1.09e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.3112697e+01 1.38e-04 2.35e-03 -11.0 1.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.3111730e+01 1.34e-03 1.47e-03 -11.0 1.39e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.3099325e+01 1.48e-02 1.44e-03 -11.0 6.65e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.3109665e+01 4.28e-03 2.07e-03 -11.0 3.40e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.3108389e+01 9.29e-03 1.03e-03 -11.0 2.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.3096019e+01 1.39e-02 2.22e-03 -11.0 2.44e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.2888511e+01 9.41e-01 4.47e-02 -9.0 6.28e+05 - 1.00e+00 4.76e-02f 1\n",
|
||
" 35 1.1394920e+01 1.80e+00 1.73e-01 -8.8 2.81e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 36 1.2858594e+01 1.66e-01 1.48e-01 -7.3 2.28e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.2642818e+01 5.43e-01 1.36e-01 -5.4 1.36e+06 - 1.00e+00 8.61e-03f 1\n",
|
||
" 38 1.3150468e+01 9.53e-02 2.87e-02 -5.2 1.07e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 39 1.2478946e+01 3.16e+00 9.26e-02 -4.7 1.13e+04 - 1.00e+00 6.74e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.0789224e+01 1.90e+00 1.37e-01 -4.7 9.46e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 41 8.1386304e+00 4.43e+00 8.73e-01 -2.3 7.16e+04 - 1.00e+00 3.10e-01f 1\n",
|
||
" 42 1.1750283e+01 1.66e+00 7.16e-01 -1.7 4.67e+03 - 9.07e-01 1.00e+00h 1\n",
|
||
" 43 1.2045469e+01 5.04e-01 8.01e-02 -2.4 3.14e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.1736976e+01 5.23e-01 8.52e-02 -2.7 3.83e+04 - 9.48e-01 4.58e-02f 1\n",
|
||
" 45 1.2382921e+01 1.66e-01 4.34e-02 -2.6 2.46e+03 - 9.14e-01 1.00e+00h 1\n",
|
||
" 46 1.1855385e+01 8.22e-01 1.39e-01 -2.7 4.01e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 47 1.2609073e+01 5.34e-03 8.77e-01 -2.8 9.22e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.2611810e+01 1.20e-07 2.12e-05 -4.7 5.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.2611810e+01 3.43e-08 1.32e-04 -10.7 6.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.2611809e+01 1.34e-06 1.03e-03 -11.0 3.80e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.2611810e+01 2.64e-10 1.18e-04 -11.0 3.17e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 52 1.2611810e+01 2.08e-07 2.44e-04 -11.0 1.15e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.2611809e+01 4.42e-07 1.66e-03 -11.0 5.35e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.2611790e+01 2.04e-05 5.06e-03 -11.0 9.67e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.2611790e+01 1.26e-05 3.45e-03 -11.0 9.56e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.2611803e+01 9.54e-06 1.75e-03 -11.0 3.21e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.2611803e+01 5.34e-06 1.50e-03 -11.0 6.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.2611795e+01 3.52e-05 1.31e-03 -11.0 9.14e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.2611807e+01 4.06e-06 1.15e-03 -11.0 3.99e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.2611800e+01 6.72e-06 1.19e-03 -11.0 2.96e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.2611734e+01 5.99e-05 2.22e-03 -11.0 1.12e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.2609952e+01 1.39e-03 1.52e-03 -10.6 4.72e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.2560933e+01 8.96e-02 1.34e-02 -9.4 1.64e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.2436565e+01 4.67e-01 1.77e-02 -7.6 8.43e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.2436285e+01 3.05e-01 3.03e-02 -7.6 1.29e+04 - 4.74e-01 1.00e+00h 1\n",
|
||
" 66 1.2647454e+01 7.12e-03 1.74e-02 -7.6 7.37e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 1.2278121e+01 4.46e-01 3.98e-02 -7.6 1.35e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 68 1.2217117e+01 7.10e-01 4.85e-02 -5.7 2.28e+04 - 1.00e+00 9.07e-02h 1\n",
|
||
" 69 1.2609447e+01 1.32e-05 6.83e-01 -5.9 7.26e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.2609484e+01 9.55e-07 7.91e-04 -7.7 2.12e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.2609477e+01 7.42e-06 1.74e-03 -9.8 3.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.2609485e+01 2.04e-09 7.15e-05 -11.0 3.97e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 73 1.2609480e+01 3.69e-06 1.24e-03 -11.0 2.56e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.2609467e+01 6.47e-06 2.97e-03 -11.0 2.50e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.2609479e+01 3.20e-06 1.92e-03 -11.0 1.92e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.2608713e+01 1.02e-03 2.25e-02 -11.0 3.09e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.2609270e+01 4.98e-04 2.38e-03 -11.0 2.05e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.2609346e+01 5.02e-04 1.68e-03 -11.0 3.68e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.2609361e+01 5.52e-04 1.88e-03 -11.0 4.37e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.2594642e+01 9.70e-03 1.13e-02 -10.7 7.76e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.2611476e+01 3.13e-05 1.32e-02 -11.0 7.90e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 82 1.2610186e+01 4.94e-03 1.02e-03 -11.0 3.00e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.2609932e+01 4.83e-03 8.67e-04 -11.0 2.45e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.2145960e+01 2.75e-01 2.59e-02 -11.0 1.16e+03 - 5.61e-02 1.00e+00f 1\n",
|
||
" 85 1.2615047e+01 1.79e-02 2.76e-02 -11.0 8.38e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.2602722e+01 2.03e-02 1.36e-02 -11.0 1.43e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.2560756e+01 5.35e-02 8.15e-03 -11.0 7.92e+02 - 1.00e+00 8.04e-01h 1\n",
|
||
" 88 1.2446108e+01 1.20e-01 7.96e-03 -10.1 4.30e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 89 9.9989838e+00 2.03e+00 1.37e-01 -3.7 4.37e+03 - 2.04e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.2518079e+01 2.81e-01 1.69e-01 -4.2 1.71e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.2165703e+01 6.23e+00 3.45e-01 -4.4 4.81e+05 - 7.77e-03 6.16e-02f 2\n",
|
||
" 92 1.1678484e+01 5.84e+00 1.79e-01 -4.4 1.12e+05 - 3.68e-01 2.64e-01h 1\n",
|
||
" 93 1.2158725e+01 7.20e-01 2.67e-01 -4.4 5.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.1819287e+01 4.32e-01 2.26e-01 -4.4 5.67e+03 - 4.98e-01 1.00e+00h 1\n",
|
||
" 95 1.0973965e+01 1.47e+00 1.24e-01 -4.4 1.05e+05 - 1.63e-01 4.91e-01f 1\n",
|
||
" 96 1.1889014e+01 1.32e+00 1.31e-01 -4.4 2.17e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.1420978e+01 1.79e+00 4.05e-02 -4.4 2.76e+04 - 4.40e-01 1.00e+00h 1\n",
|
||
" 98 1.0387262e+01 3.46e+00 1.61e-01 -2.7 9.77e+05 - 1.00e+00 2.09e-02f 1\n",
|
||
" 99 1.0407729e+01 3.37e+00 1.53e-01 -2.9 3.12e+04 - 9.47e-01 2.05e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.2027175e+01 4.85e-01 1.95e-01 -2.9 2.90e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.2027175358925469e+01 1.2027175358925469e+01\n",
|
||
"Dual infeasibility......: 1.9537004685706635e-01 1.9537004685706635e-01\n",
|
||
"Constraint violation....: 4.8531992247455236e-01 4.8531992247455236e-01\n",
|
||
"Complementarity.........: 5.3217701342184098e-03 5.3217701342184098e-03\n",
|
||
"Overall NLP error.......: 4.8531992247455236e-01 4.8531992247455236e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 112\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 112\n",
|
||
"Number of inequality constraint evaluations = 112\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.421\n",
|
||
"Total CPU secs in NLP function evaluations = 133.966\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 499.00us ( 4.46us) 507.58us ( 4.53us) 112\n",
|
||
" nlp_g | 5.00 s ( 44.67ms) 4.77 s ( 42.56ms) 112\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 336.00us ( 3.29us) 337.05us ( 3.30us) 102\n",
|
||
" nlp_jac_g | 131.85 s ( 1.29 s) 125.83 s ( 1.23 s) 102\n",
|
||
" total | 138.32 s (138.32 s) 132.00 s (132.00 s) 1\n",
|
||
"Timestamp 4200\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.15e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0881280e+01 1.26e+01 3.15e+04 -1.5 3.15e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.1003819e+00 4.45e+00 4.08e+00 0.8 1.28e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.8275265e+00 5.01e-01 3.52e-01 -1.3 4.60e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 1.1375967e+00 4.96e-03 4.68e-01 -3.3 1.62e+00 - 9.97e-01 1.00e+00h 1\n",
|
||
" 5 1.1366029e+00 1.27e-05 2.59e-03 -5.0 6.87e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.1366203e+00 4.20e-06 8.56e-04 -7.1 2.98e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.1366201e+00 2.52e-06 5.92e-04 -9.2 2.10e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 9.3708906e-01 1.20e-01 1.93e-01 -10.4 1.85e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 9 1.0365258e+00 6.14e-02 1.36e-01 -11.0 1.57e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 9.5519766e-01 5.55e-02 8.96e-02 -11.0 1.75e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 9.1798358e-01 2.06e-01 2.28e-01 -11.0 1.48e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.1274542e+00 3.06e-02 3.04e-01 -11.0 3.61e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.0088323e+00 1.35e-01 1.35e-01 -11.0 1.48e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 9.0393444e-01 2.37e-01 1.13e-01 -11.0 1.85e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 8.7214639e-01 3.79e-01 2.02e-01 -11.0 1.52e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 16 8.7875685e-01 1.85e-01 1.50e-01 -11.0 6.75e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 17 8.7883329e-01 1.85e-01 1.49e-01 -11.0 2.71e+03 - 1.00e+00 1.95e-03h 10\n",
|
||
" 18 8.7591171e-01 3.01e-01 4.01e-02 -11.0 3.02e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 19 9.1410628e-01 2.18e-01 2.06e-01 -11.0 1.50e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 8.8223704e-01 5.10e-01 6.74e-01 -11.0 6.23e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 21 9.5165240e-01 3.36e-01 3.69e-01 -11.0 1.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 8.4574592e-01 6.89e-01 3.93e-01 -11.0 3.02e+04 - 8.43e-01 1.00e+00h 1\n",
|
||
" 23 9.3935602e-01 4.41e-01 2.51e-01 -10.7 3.76e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 24 9.0634444e-01 2.93e-01 1.93e-01 -10.9 1.94e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 25 8.7443586e-01 3.04e-01 1.67e-01 -10.9 7.63e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 26 9.9817177e-01 3.80e-01 4.21e-01 -10.9 1.81e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 27 9.6580588e-01 2.21e-02 4.18e-01 -10.9 4.76e+06 - 6.52e-03 6.70e-03f 1\n",
|
||
" 28 9.1010163e-01 3.72e-01 4.37e-01 -11.0 2.93e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 29 8.9288524e-01 2.07e-01 2.24e-01 -3.3 6.11e+03 - 1.00e+00 4.80e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 9.8391350e-01 5.45e-02 3.98e-01 -3.3 1.22e+03 - 4.32e-01 1.00e+00h 1\n",
|
||
" 31 9.7187352e-01 1.67e-01 4.22e-01 -3.3 2.07e+04 - 1.00e+00 6.25e-02h 5\n",
|
||
" 32 8.8191338e-01 4.62e-01 4.30e-01 -3.3 1.04e+04 - 1.06e-01 1.00e+00H 1\n",
|
||
" 33 8.5361521e-01 1.84e-01 1.42e-01 -3.3 4.10e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 8.3098116e-01 2.70e-01 2.72e-01 -4.4 3.39e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 35 8.2905360e-01 1.85e-01 4.74e-02 -5.5 2.36e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 36 8.8644470e-01 1.76e-01 3.40e-01 -5.8 3.68e+03 - 7.36e-01 1.00e+00H 1\n",
|
||
" 37 8.6732017e-01 2.34e-01 3.16e-01 -6.3 1.06e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 38 1.2086609e+00 2.17e-01 5.82e-01 -6.4 1.60e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 39 1.0856486e+00 1.21e-01 3.87e-01 -6.4 2.73e+04 - 4.35e-01 1.25e-01h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.0378554e+00 2.21e-01 4.24e-01 -6.4 1.87e+06 - 1.51e-02 3.81e-04f 7\n",
|
||
" 41 1.0156739e+00 2.54e-01 3.68e-01 -6.4 2.22e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 9.7379330e-01 2.15e-01 3.00e-01 -6.4 1.44e+05 - 1.12e-01 4.06e-02h 4\n",
|
||
" 43 9.5603005e-01 2.01e-01 2.42e-01 -6.4 2.55e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 44 9.6711822e-01 1.82e-01 2.10e-01 -6.4 1.46e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 45 9.6696640e-01 1.82e-01 2.10e-01 -6.4 7.64e+05 - 3.03e-02 5.06e-05h 11\n",
|
||
" 46 9.6354107e-01 1.45e-01 1.87e-01 -6.4 1.87e+06 - 2.14e-02 1.26e-02h 1\n",
|
||
" 47 9.4911459e-01 1.93e-01 1.89e-01 -6.4 9.70e+05 - 2.46e-01 8.12e-03f 4\n",
|
||
" 48 1.0968090e+00 1.71e-01 2.02e-01 -6.4 7.19e+03 - 5.23e-01 1.00e+00h 1\n",
|
||
" 49 1.1365555e+00 3.75e-06 2.55e-02 -6.4 1.77e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.1365569e+00 4.76e-08 6.85e-05 -8.2 1.98e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.1365570e+00 1.23e-08 3.82e-05 -11.0 5.10e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.1365570e+00 1.95e-11 2.19e-05 -11.0 9.44e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 53 1.1365570e+00 3.54e-09 4.86e-05 -11.0 4.09e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.1365570e+00 2.40e-08 1.33e-04 -11.0 1.80e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.1365569e+00 4.60e-08 1.20e-04 -11.0 4.78e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.1365569e+00 5.57e-08 4.37e-05 -11.0 5.99e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.1365563e+00 8.78e-07 7.46e-06 -11.0 5.35e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.1365568e+00 2.95e-08 1.96e-04 -11.0 1.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.1365567e+00 1.78e-07 6.46e-05 -11.0 1.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.1365569e+00 8.70e-11 1.05e-04 -11.0 6.32e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 61 1.1365565e+00 1.88e-07 1.56e-05 -11.0 2.82e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.1365568e+00 1.55e-10 3.18e-05 -11.0 5.55e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 63 1.1365559e+00 1.27e-06 2.05e-03 -11.0 2.55e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.1365549e+00 2.72e-06 4.97e-04 -11.0 1.54e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.1365526e+00 4.34e-06 2.28e-03 -11.0 1.92e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.1365573e+00 1.49e-09 6.93e-05 -11.0 2.96e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 1.1365535e+00 4.81e-06 9.67e-04 -11.0 1.87e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.1365571e+00 5.16e-10 2.60e-05 -11.0 2.81e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 69 1.1365559e+00 4.56e-06 1.19e-03 -11.0 3.93e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.1365373e+00 4.84e-05 1.10e-03 -11.0 1.24e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.1365454e+00 1.04e-05 8.60e-04 -11.0 3.99e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.1364976e+00 2.25e-04 1.46e-03 -11.0 1.50e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.1364941e+00 4.48e-05 1.07e-03 -11.0 4.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.1355876e+00 8.59e-04 2.59e-03 -11.0 1.71e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.1365170e+00 3.20e-05 1.06e-03 -11.0 3.14e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.1363756e+00 7.47e-04 1.91e-03 -11.0 4.52e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.1328307e+00 3.98e-03 1.18e-02 -11.0 2.57e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.1363753e+00 8.36e-07 2.77e-03 -11.0 3.51e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 79 1.1242849e+00 3.34e-02 2.86e-02 -11.0 7.30e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.1254641e+00 1.11e-02 4.50e-02 -11.0 9.08e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.1272791e+00 7.64e-03 7.27e-03 -11.0 6.62e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.1125850e+00 9.30e-03 9.71e-03 -11.0 1.72e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.1330339e+00 8.08e-03 1.29e-02 -11.0 5.54e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.1253048e+00 4.14e-03 4.69e-02 -11.0 2.70e+03 - 1.00e+00 1.00e+00F 1\n",
|
||
" 85 9.9998452e-01 2.61e-01 2.51e-01 -11.0 2.28e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 86 9.2332780e-01 4.52e-01 2.40e-01 -11.0 2.48e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 9.6723185e-01 1.68e-01 4.78e-01 -11.0 3.73e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.1479911e+00 1.25e-01 2.20e-01 -11.0 2.73e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 89 1.1116994e+00 1.34e-01 1.88e-01 -11.0 1.41e+05 - 1.33e-01 3.75e-03f 7\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.1401269e+00 3.56e-02 4.34e-02 -11.0 1.77e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.1240341e+00 1.71e-01 1.48e-01 -11.0 3.18e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 92 1.1400937e+00 9.15e-02 6.66e-02 -11.0 4.66e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.0804233e+00 1.24e-01 1.15e-01 -11.0 7.28e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 94 1.0565139e+00 1.58e-01 1.17e-01 -11.0 1.28e+04 - 1.00e+00 1.56e-02h 7\n",
|
||
" 95r 1.0565139e+00 1.58e-01 9.99e+02 -0.8 0.00e+00 - 0.00e+00 4.77e-07R 22\n",
|
||
" 96r 1.1479124e+00 2.60e-02 3.43e+02 -6.9 1.40e+02 - 1.00e+00 1.51e-03f 1\n",
|
||
" 97 1.1176914e+00 5.15e-02 2.25e-02 -11.0 3.21e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.1172478e+00 6.80e-02 3.49e-02 -11.0 2.27e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 99 8.5678668e-01 4.72e-01 8.82e-01 -11.0 1.24e+04 - 7.60e-01 8.80e-01F 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.6947708e-01 4.02e-01 5.80e-01 -11.0 2.11e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.6947708063539586e-01 8.6947708063539586e-01\n",
|
||
"Dual infeasibility......: 5.7972904729471830e-01 5.7972904729471830e-01\n",
|
||
"Constraint violation....: 4.0172082469793224e-01 4.0172082469793224e-01\n",
|
||
"Complementarity.........: 2.3816012892970206e-02 2.3816012892970206e-02\n",
|
||
"Overall NLP error.......: 5.7972904729471830e-01 5.7972904729471830e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 256\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 256\n",
|
||
"Number of inequality constraint evaluations = 256\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.426\n",
|
||
"Total CPU secs in NLP function evaluations = 142.732\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 1.12ms ( 4.39us) 1.12ms ( 4.36us) 256\n",
|
||
" nlp_g | 11.56 s ( 45.17ms) 11.03 s ( 43.07ms) 256\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 353.00us ( 3.46us) 341.83us ( 3.35us) 102\n",
|
||
" nlp_jac_g | 134.04 s ( 1.30 s) 127.95 s ( 1.24 s) 103\n",
|
||
" total | 147.11 s (147.11 s) 140.41 s (140.41 s) 1\n",
|
||
"Timestamp 4500\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.93e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0340847e+01 1.64e+01 1.93e+04 -1.5 1.93e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.3636234e+01 6.09e+00 1.51e+01 0.8 3.24e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.1240697e+01 2.54e+00 8.29e-01 -1.3 7.59e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 2.2802727e+01 2.52e-04 9.23e-02 -3.0 2.88e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
" 5 2.2802522e+01 6.88e-05 1.23e-02 -4.9 2.53e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.2802620e+01 1.96e-05 1.59e-03 -6.8 1.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.2802549e+01 4.94e-05 1.13e-03 -8.9 5.60e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.2802606e+01 2.10e-05 1.35e-03 -9.0 2.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.2802574e+01 3.51e-05 1.51e-03 -11.0 1.22e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.2802641e+01 7.89e-09 1.17e-04 -11.0 1.55e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 2.2802513e+01 3.90e-04 3.52e-03 -11.0 5.86e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.2802603e+01 1.96e-05 1.18e-03 -11.0 1.22e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.2802623e+01 1.67e-05 1.17e-03 -11.0 1.16e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.2802593e+01 2.31e-05 2.07e-03 -11.0 3.37e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.2802528e+01 2.49e-04 8.18e-03 -11.0 1.32e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.2802339e+01 9.80e-04 4.96e-03 -11.0 4.72e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.2802193e+01 2.49e-04 1.20e-03 -11.0 3.40e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.2800481e+01 3.29e-03 3.36e-03 -11.0 1.36e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.2799225e+01 3.06e-03 1.97e-03 -11.0 2.47e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.2801666e+01 2.02e-03 1.25e-03 -11.0 3.91e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.2602991e+01 1.54e-01 3.01e-03 -11.0 5.23e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 22 2.2180686e+01 1.14e+00 3.62e-02 -11.0 8.96e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.1460792e+01 1.52e+00 4.06e-02 -9.0 8.02e+04 - 1.00e+00 2.13e-01f 1\n",
|
||
" 24 2.2582110e+01 1.26e-01 4.79e-02 -9.7 3.28e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.8960833e+01 1.16e+01 5.91e-01 -8.7 8.60e+04 - 1.00e+00 6.85e-01f 1\n",
|
||
" 26 1.8956317e+01 1.15e+01 5.79e-01 -8.9 7.67e+04 - 1.00e+00 7.76e-03h 1\n",
|
||
" 27 2.0179617e+01 2.63e+00 5.15e-01 -8.9 6.88e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.2768753e+01 8.79e-02 7.36e-02 -3.6 1.36e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.2727565e+01 6.99e-02 2.65e-02 -3.7 1.25e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.2665912e+01 1.01e-01 8.88e-03 -3.3 8.35e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.2835594e+01 3.01e-02 2.99e-03 -3.4 3.31e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.2906577e+01 2.11e-02 5.87e-02 -3.4 1.37e+03 - 2.25e-01 1.00e+00H 1\n",
|
||
" 33 2.1636091e+01 6.28e-01 2.26e-02 -3.4 1.39e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 34 2.1667853e+01 4.72e-01 4.30e-02 -1.8 2.10e+03 - 1.00e+00 9.91e-01h 1\n",
|
||
" 35 2.2408755e+01 2.36e-01 2.33e-02 -7.9 1.03e+03 - 8.64e-01 1.00e+00h 1\n",
|
||
" 36 1.8680279e+01 3.54e+00 2.08e-01 -2.8 1.16e+04 - 3.64e-02 1.00e+00f 1\n",
|
||
" 37 2.1611022e+01 5.58e-01 8.18e-02 -4.0 1.46e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.1268605e+01 8.56e-01 6.95e-02 -3.4 4.89e+05 - 1.00e+00 2.01e-03f 1\n",
|
||
" 39 2.2504559e+01 1.69e-01 6.32e-02 -2.1 8.24e+02 - 7.08e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.5820563e+01 6.47e+00 2.28e-01 -1.8 2.46e+05 - 9.54e-02 1.61e-01f 1\n",
|
||
" 41 1.4196649e+01 3.04e+00 1.42e-01 -2.1 2.86e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.0653231e+01 2.19e+00 1.63e-01 -1.4 2.15e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.0326336e+01 2.13e+00 1.26e-01 -1.6 1.06e+05 - 4.19e-01 9.06e-02h 1\n",
|
||
" 44 2.0916869e+01 5.09e-01 8.70e-02 -1.6 1.24e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.1395917e+01 5.68e-01 4.00e-02 -1.8 8.63e+03 - 1.00e+00 8.65e-01h 1\n",
|
||
" 46 1.8716765e+01 1.25e+00 7.41e-02 -7.8 1.15e+05 - 1.80e-01 3.24e-01f 1\n",
|
||
" 47 2.1579098e+01 6.27e-01 4.04e-02 -0.0 1.11e+06 - 1.04e-01 2.80e-02f 2\n",
|
||
" 48 1.8048732e+01 1.62e+00 8.69e-02 -0.6 1.86e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 49 2.1129088e+01 4.31e-01 1.58e-01 -6.3 5.08e+03 - 7.59e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.0145264e+01 3.52e+00 6.58e-02 -1.9 1.50e+04 - 3.42e-01 1.00e+00f 1\n",
|
||
" 51 2.0802788e+01 2.54e+00 1.95e-02 -1.5 1.14e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.9590043e+01 2.84e+00 8.64e-02 -1.5 1.30e+05 - 1.72e-01 7.96e-02f 1\n",
|
||
" 53 1.9997017e+01 7.75e-01 1.07e-01 -1.5 7.62e+03 - 1.00e+00 9.25e-01h 1\n",
|
||
" 54 2.1971995e+01 1.26e-01 3.20e-02 -2.0 5.84e+02 - 9.08e-01 1.00e+00h 1\n",
|
||
" 55 2.1748647e+01 5.87e-01 4.66e-02 -2.4 6.72e+03 - 1.00e+00 5.61e-01h 1\n",
|
||
" 56 1.9262020e+01 9.67e-01 5.06e-02 -2.5 1.22e+04 - 1.75e-01 1.00e+00f 1\n",
|
||
" 57 2.2134569e+01 1.63e-03 2.47e+00 -4.3 2.53e+00 - 9.95e-01 1.00e+00h 1\n",
|
||
" 58 2.2136371e+01 1.03e-05 1.51e-03 -5.9 2.81e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 2.2136361e+01 1.85e-05 3.41e-03 -7.9 7.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.2136336e+01 1.23e-05 2.13e-03 -10.0 1.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.2136380e+01 4.90e-06 1.17e-03 -11.0 3.20e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.2136351e+01 1.52e-05 1.85e-03 -11.0 1.62e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.2136385e+01 1.92e-05 1.13e-03 -11.0 6.29e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.2136381e+01 3.83e-06 1.42e-03 -11.0 2.52e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.2136390e+01 9.86e-07 1.11e-03 -11.0 1.83e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.2136210e+01 1.10e-04 3.84e-03 -11.0 1.35e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.2136422e+01 5.86e-06 1.84e-03 -11.0 2.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.2135967e+01 4.36e-04 2.56e-03 -11.0 2.47e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.2135697e+01 3.86e-04 2.66e-03 -11.0 3.32e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.2136577e+01 3.09e-07 1.50e-04 -11.0 7.28e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.2136577e+01 1.14e-07 9.43e-05 -11.0 7.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 2.2136574e+01 1.70e-06 6.58e-03 -11.0 9.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 2.2136576e+01 7.99e-07 1.09e-03 -11.0 4.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.2136577e+01 3.81e-07 1.68e-04 -11.0 1.59e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 2.2136576e+01 3.16e-07 1.59e-04 -11.0 1.10e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.2136577e+01 1.81e-10 1.99e-04 -11.0 1.44e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 77 2.2136577e+01 2.81e-07 1.55e-04 -11.0 9.34e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.2136577e+01 8.21e-08 2.15e-04 -11.0 2.78e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 2.2136576e+01 1.75e-07 1.78e-04 -11.0 1.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.2136577e+01 1.37e-07 8.53e-05 -11.0 5.60e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.2136574e+01 7.01e-06 2.65e-03 -11.0 1.39e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.2136575e+01 2.56e-06 2.26e-03 -11.0 6.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 2.2136574e+01 1.78e-06 3.24e-03 -11.0 1.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.2136577e+01 7.54e-07 1.13e-04 -11.0 3.85e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 2.2136576e+01 1.04e-06 1.95e-03 -11.0 6.29e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 2.2136576e+01 7.76e-07 1.79e-03 -11.0 7.33e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.2136577e+01 2.14e-08 1.17e-04 -11.0 3.05e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 2.2136577e+01 5.28e-08 3.94e-05 -11.0 3.67e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 2.2136576e+01 7.12e-07 3.18e-03 -11.0 9.81e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.2136570e+01 5.73e-06 6.24e-03 -11.0 2.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 2.2136573e+01 2.00e-06 2.90e-03 -11.0 9.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 2.2136560e+01 1.68e-05 4.32e-03 -11.0 1.04e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 2.2136559e+01 2.04e-05 1.12e-03 -11.0 6.22e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 2.2136575e+01 1.84e-06 1.94e-03 -11.0 2.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.2136577e+01 7.20e-11 3.10e-04 -11.0 2.74e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 96 2.2136546e+01 2.60e-05 2.87e-03 -11.0 7.75e-02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 97 2.2136576e+01 5.27e-09 8.59e-05 -11.0 4.91e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 2.2136576e+01 2.32e-08 2.19e-04 -11.0 1.29e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 2.2136567e+01 5.18e-06 6.94e-03 -11.0 3.41e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.2136563e+01 7.78e-06 1.56e-03 -11.0 2.31e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.2136562600842783e+01 2.2136562600842783e+01\n",
|
||
"Dual infeasibility......: 1.5616490927137794e-03 1.5616490927137794e-03\n",
|
||
"Constraint violation....: 7.7807066318769103e-06 7.7807066318769103e-06\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 1.5616490927137794e-03 1.5616490927137794e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 108\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 108\n",
|
||
"Number of inequality constraint evaluations = 108\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.414\n",
|
||
"Total CPU secs in NLP function evaluations = 134.073\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 474.00us ( 4.39us) 475.85us ( 4.41us) 108\n",
|
||
" nlp_g | 4.82 s ( 44.66ms) 4.60 s ( 42.58ms) 108\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 355.00us ( 3.48us) 342.28us ( 3.36us) 102\n",
|
||
" nlp_jac_g | 132.09 s ( 1.29 s) 126.06 s ( 1.24 s) 102\n",
|
||
" total | 138.40 s (138.40 s) 132.09 s (132.09 s) 1\n",
|
||
"Timestamp 4800\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.44e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0513347e+01 1.31e+01 2.44e+04 -1.5 2.44e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.5898524e+00 4.70e+00 6.46e+00 0.8 2.11e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.3189588e+00 7.13e-01 5.81e-01 -1.3 6.81e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 2.5992337e+00 5.38e-03 4.46e-01 -3.1 2.88e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 2.6011454e+00 3.01e-06 1.58e-03 -4.9 4.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.6011443e+00 4.19e-06 8.77e-04 -7.0 3.85e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.6011184e+00 3.50e-05 6.72e-04 -9.1 2.97e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.6011381e+00 2.83e-05 1.46e-03 -11.0 9.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.6011345e+00 9.29e-06 1.88e-03 -11.0 3.07e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.6011515e+00 1.08e-07 3.93e-05 -11.0 1.64e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 2.6011486e+00 3.37e-06 7.54e-04 -11.0 5.06e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.6011415e+00 7.93e-06 8.17e-04 -11.0 6.66e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.6010870e+00 3.17e-05 2.05e-03 -11.0 1.50e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.6011449e+00 1.84e-06 1.38e-03 -11.0 1.77e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.6010632e+00 1.18e-04 1.47e-03 -11.0 3.23e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.6011400e+00 8.30e-06 1.40e-03 -11.0 5.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.6009949e+00 1.43e-04 1.31e-03 -11.0 5.50e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.6010660e+00 4.28e-05 8.68e-04 -11.0 2.55e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.6011093e+00 3.12e-05 6.87e-04 -11.0 1.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.6011312e+00 7.33e-06 6.58e-04 -11.0 5.51e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.6011287e+00 8.00e-06 6.70e-04 -11.0 2.58e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.6011174e+00 1.92e-05 1.55e-03 -11.0 1.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.6011042e+00 1.74e-05 1.40e-03 -11.0 7.47e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.6011272e+00 1.41e-05 1.36e-03 -11.0 4.77e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.6010778e+00 4.11e-05 3.29e-03 -11.0 3.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.6010466e+00 5.23e-05 2.16e-03 -11.0 3.71e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.6011018e+00 2.90e-05 1.03e-03 -11.0 1.94e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.6011133e+00 1.90e-05 1.21e-03 -11.0 1.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.6011340e+00 6.53e-09 4.64e-05 -11.0 1.90e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.6010953e+00 2.75e-05 1.27e-03 -11.0 2.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.6011271e+00 3.94e-06 1.65e-03 -11.0 4.00e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.6010744e+00 4.06e-05 9.22e-04 -11.0 3.03e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.6011072e+00 1.30e-05 1.12e-03 -11.0 2.09e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 2.6010574e+00 7.75e-05 1.13e-03 -11.0 2.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.6009688e+00 8.99e-05 1.27e-03 -11.0 5.37e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.6010590e+00 1.10e-04 2.38e-03 -11.0 7.74e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.6010073e+00 1.37e-04 1.19e-03 -11.0 3.96e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.6011130e+00 1.40e-05 1.53e-03 -11.0 1.65e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.6010173e+00 2.27e-04 9.39e-04 -11.0 8.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.6009966e+00 1.62e-04 6.47e-04 -11.0 7.77e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.6010817e+00 8.76e-05 1.19e-03 -11.0 4.31e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.6009612e+00 2.02e-04 1.80e-03 -11.0 1.16e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.6010515e+00 4.94e-05 1.17e-03 -11.0 9.35e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.6011124e+00 5.09e-06 1.36e-03 -11.0 2.21e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.6007990e+00 1.08e-03 1.78e-03 -11.0 1.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.5996382e+00 1.08e-03 3.82e-03 -11.0 2.72e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.5953857e+00 1.52e-02 4.94e-03 -11.0 5.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.6014336e+00 2.63e-07 1.04e-04 -11.0 1.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.6014335e+00 1.07e-07 8.36e-05 -11.0 4.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.6014333e+00 1.76e-07 9.01e-05 -11.0 1.15e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.6014277e+00 2.91e-06 3.50e-03 -11.0 2.59e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.6014309e+00 1.70e-06 6.06e-04 -11.0 8.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.6013987e+00 3.52e-05 3.53e-03 -11.0 1.77e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.6014210e+00 9.65e-06 1.19e-03 -11.0 8.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.6014300e+00 4.72e-06 2.13e-03 -11.0 3.60e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.5864148e+00 1.07e-02 3.36e-02 -11.0 1.14e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 57 2.4698194e+00 1.40e-01 3.68e-02 -11.0 1.15e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.4477423e+00 1.14e-01 2.91e-02 -11.0 7.77e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 2.3996292e+00 1.84e-01 3.63e-02 -11.0 1.54e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.5513651e+00 5.41e-02 6.24e-02 -11.0 1.96e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.5233664e+00 8.43e-02 3.40e-02 -11.0 1.45e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.1115927e+00 7.64e-01 2.60e-01 -11.0 2.26e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 63 2.8105553e+00 1.69e-01 3.51e-01 -11.0 6.59e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.3423399e+00 2.63e-01 1.58e-01 -11.0 2.76e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.3865376e+00 2.50e-01 1.13e-01 -11.0 1.45e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.5697527e+00 8.73e-02 1.50e-01 -11.0 2.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.4325163e+00 2.08e-01 4.71e-02 -11.0 3.64e+07 - 9.84e-04 7.45e-04f 1\n",
|
||
" 68 2.3708745e+00 1.02e+00 4.00e-01 -11.0 4.96e+03 - 1.00e+00 1.00e+00F 1\n",
|
||
" 69 2.8612139e+00 3.75e-01 3.61e-01 -11.0 9.19e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.5634832e+00 3.46e-02 9.89e-02 -11.0 5.76e+02 - 4.72e-01 1.00e+00h 1\n",
|
||
" 71 2.4088435e+00 7.30e-01 1.70e-01 -11.0 2.32e+06 - 4.95e-04 3.34e-03f 4\n",
|
||
" 72 2.3167761e+00 1.17e+00 3.86e-01 -11.0 2.34e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 73 2.3625371e+00 1.03e+00 2.98e-01 -11.0 2.03e+04 - 1.00e+00 3.61e-01h 2\n",
|
||
" 74 2.3615730e+00 6.10e-01 4.13e-02 -11.0 1.52e+04 - 1.00e+00 4.84e-01h 1\n",
|
||
" 75 2.4990674e+00 5.06e-01 3.79e-01 -11.0 1.97e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 76 2.5662210e+00 4.40e-01 2.23e-01 -11.0 2.83e+03 - 7.45e-01 1.00e+00H 1\n",
|
||
" 77 2.5534649e+00 4.33e-01 2.24e-01 -11.0 2.58e+04 - 1.00e+00 1.58e-02h 1\n",
|
||
" 78 2.6352400e+00 3.77e-02 1.19e-01 -11.0 4.00e+02 - 5.97e-08 1.00e+00h 1\n",
|
||
" 79 2.7112642e+00 2.13e-02 1.49e-01 -11.0 3.89e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.5021026e+00 3.05e-01 7.93e-02 -11.0 2.54e+04 - 1.26e-01 1.25e-01f 4\n",
|
||
" 81 2.3429378e+00 3.43e-01 3.43e-01 -11.0 3.63e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.4743630e+00 1.69e-01 6.10e-02 -11.0 1.00e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 83 2.7668398e+00 2.87e-02 1.53e-01 -11.0 3.95e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 84 2.7353736e+00 2.74e-02 1.52e-01 -11.0 1.03e+05 - 3.08e-01 6.87e-03f 5\n",
|
||
" 85 2.6159424e+00 1.50e-01 3.11e-02 -11.0 1.24e+03 - 8.94e-01 1.00e+00h 1\n",
|
||
" 86 2.7575314e+00 2.41e-02 9.77e-03 -11.0 1.07e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 87 2.7548756e+00 2.34e-02 1.02e-02 -11.0 5.95e+03 - 1.00e+00 5.01e-03h 7\n",
|
||
" 88 2.7723582e+00 2.25e-03 2.14e-02 -11.0 1.16e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 89 2.7329033e+00 4.62e-02 1.93e-02 -11.0 1.02e+03 - 1.00e+00 2.50e-01f 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.7716387e+00 1.50e-02 1.33e-02 -11.0 1.22e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 91 2.7583817e+00 1.63e-02 8.46e-03 -11.0 3.16e+03 - 1.00e+00 3.12e-02h 6\n",
|
||
" 92 2.7712752e+00 3.53e-04 9.01e-03 -11.0 1.53e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 93 2.7674211e+00 5.94e-03 3.21e-03 -11.0 1.11e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 2.7636336e+00 1.18e-02 3.51e-03 -11.0 8.36e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.6540374e+00 1.52e-01 7.78e-02 -11.0 1.54e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 2.7313188e+00 1.51e-02 4.78e-02 -11.0 6.53e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.6564771e+00 9.49e-02 4.87e-02 -11.0 1.55e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 2.6834281e+00 6.33e-02 3.01e-02 -11.0 4.53e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 2.5721049e+00 1.89e+00 6.07e-01 -11.0 4.75e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.5080526e+00 9.09e-01 8.50e-02 -11.0 7.43e+03 - 1.00e+00 5.37e-01h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.5080526003773507e+00 2.5080526003773507e+00\n",
|
||
"Dual infeasibility......: 8.5027674793480806e-02 8.5027674793480806e-02\n",
|
||
"Constraint violation....: 9.0934890974098082e-01 9.0934890974098082e-01\n",
|
||
"Complementarity.........: 1.0124877436431087e-11 1.0124877436431087e-11\n",
|
||
"Overall NLP error.......: 9.0934890974098082e-01 9.0934890974098082e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 161\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 161\n",
|
||
"Number of inequality constraint evaluations = 161\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.394\n",
|
||
"Total CPU secs in NLP function evaluations = 136.668\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 742.00us ( 4.61us) 734.55us ( 4.56us) 161\n",
|
||
" nlp_g | 7.28 s ( 45.20ms) 6.94 s ( 43.12ms) 161\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 337.00us ( 3.30us) 327.26us ( 3.21us) 102\n",
|
||
" nlp_jac_g | 132.29 s ( 1.30 s) 126.22 s ( 1.24 s) 102\n",
|
||
" total | 141.05 s (141.05 s) 134.57 s (134.57 s) 1\n",
|
||
"Timestamp 5100\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 5.78e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0076041e+01 1.52e+01 5.78e+03 -1.5 5.78e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.0412985e+01 5.67e+00 1.05e+01 0.6 2.22e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.3757260e+01 1.89e+00 7.45e-01 -1.5 1.05e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.5099582e+01 1.26e-03 8.99e-02 -3.3 2.48e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.5100396e+01 6.95e-08 8.29e-05 -5.1 1.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.5100395e+01 6.34e-07 1.83e-04 -11.0 3.05e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.5100388e+01 3.75e-06 1.61e-03 -11.0 2.68e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.5088065e+01 1.67e-02 5.14e-02 -11.0 4.16e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 9 1.5096278e+01 1.56e-03 2.86e-03 -11.0 2.52e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.5083986e+01 1.61e-02 2.64e-03 -11.0 4.96e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.5090482e+01 6.35e-03 2.12e-03 -11.0 2.48e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.5089393e+01 4.50e-03 3.17e-03 -11.0 2.84e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.5095901e+01 1.08e-03 1.27e-03 -11.0 1.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.5093928e+01 3.72e-03 3.02e-03 -11.0 1.51e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.5092193e+01 3.05e-03 1.50e-03 -11.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.5084260e+01 1.50e-02 2.16e-03 -11.0 9.47e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.5053368e+01 2.14e-02 1.77e-03 -11.0 3.94e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.5043988e+01 2.03e-02 2.15e-03 -11.0 6.86e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.5023012e+01 2.25e-02 4.31e-03 -11.0 1.10e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.5058408e+01 1.61e-02 1.36e-03 -11.0 1.48e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.5087744e+01 1.83e-03 1.85e-03 -11.0 2.21e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.4252841e+01 3.78e-01 2.78e-02 -9.4 1.14e+06 - 1.00e+00 2.73e-02f 1\n",
|
||
" 23 1.4250914e+01 3.78e-01 2.79e-02 -7.5 1.44e+06 - 1.00e+00 3.98e-05h 1\n",
|
||
" 24 1.4250901e+01 3.78e-01 2.77e-02 -5.6 2.07e+04 - 1.00e+00 2.78e-05h 1\n",
|
||
" 25 1.4964819e+01 3.13e-03 6.90e-01 -7.5 6.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.4967039e+01 6.28e-06 2.31e-03 -8.5 1.28e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.4967044e+01 4.49e-07 1.03e-04 -11.0 6.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.4967044e+01 4.19e-07 8.94e-05 -11.0 3.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.4967041e+01 2.20e-06 2.30e-03 -11.0 1.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.4967045e+01 5.09e-07 7.28e-05 -11.0 5.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.4967040e+01 3.05e-06 6.44e-03 -11.0 2.25e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.4967040e+01 3.72e-06 2.25e-03 -11.0 1.48e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.4967044e+01 1.08e-06 2.41e-03 -11.0 6.19e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.4967033e+01 6.87e-06 6.74e-03 -10.7 3.28e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.4967042e+01 3.13e-06 7.17e-04 -11.0 1.69e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.4967045e+01 1.53e-06 2.27e-03 -11.0 1.15e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.4967044e+01 1.54e-06 1.08e-03 -11.0 7.06e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.4967046e+01 5.50e-10 1.61e-04 -11.0 1.03e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 39 1.4967043e+01 3.41e-06 1.30e-03 -11.0 8.20e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.4967044e+01 8.61e-07 1.92e-03 -11.0 9.92e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.4967041e+01 3.72e-06 1.10e-03 -11.0 1.18e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.4967004e+01 4.38e-05 4.38e-03 -11.0 1.20e-01 - 1.25e-01 1.00e+00h 1\n",
|
||
" 43 1.4967036e+01 2.15e-05 9.32e-04 -11.0 9.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.4967045e+01 3.93e-06 1.66e-03 -11.0 2.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.4967038e+01 6.86e-06 1.72e-03 -11.0 1.68e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.4967021e+01 2.81e-05 2.10e-03 -11.0 1.55e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.4967014e+01 1.60e-05 1.60e-03 -10.8 8.42e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.4967049e+01 2.01e-09 9.85e-05 -11.0 1.00e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 49 1.2066149e+01 6.04e+00 5.64e-01 -11.0 1.59e+05 - 8.52e-06 1.50e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.3998483e+01 6.43e-01 3.44e-01 -10.7 3.68e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.4323272e+01 1.83e-01 7.20e-02 -3.0 1.59e+03 - 3.99e-01 1.00e+00h 1\n",
|
||
" 52 1.3879700e+01 4.09e-01 4.39e-02 -1.6 4.34e+05 - 1.97e-01 7.59e-03f 1\n",
|
||
" 53 1.3573847e+01 5.52e-01 5.77e-02 -2.0 5.97e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.4666216e+01 5.81e-02 3.99e-02 -3.4 1.02e+03 - 9.52e-01 1.00e+00h 1\n",
|
||
" 55 1.4634853e+01 5.85e-02 2.25e-02 -2.6 9.02e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.4463192e+01 1.37e-01 4.11e-02 -8.6 1.60e+03 - 4.55e-01 1.00e+00h 1\n",
|
||
" 57 1.4401399e+01 4.62e-01 1.93e-02 -2.3 4.42e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.4468805e+01 1.01e-01 1.94e-02 -2.3 3.37e+03 - 4.14e-01 1.00e+00h 1\n",
|
||
" 59 1.2251332e+01 2.09e+00 1.52e-01 -2.3 1.63e+06 - 8.84e-04 3.71e-03f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.3928902e+01 1.42e+00 1.36e-01 -2.9 3.17e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.3674213e+01 1.19e+00 1.27e-01 -3.1 1.41e+05 - 1.78e-02 1.26e-02f 1\n",
|
||
" 62 1.4227631e+01 2.25e-01 8.14e-02 -3.1 1.50e+03 - 1.27e-02 1.00e+00h 1\n",
|
||
" 63 1.3755057e+01 5.05e-01 3.83e-02 -3.6 2.48e+04 - 9.92e-01 1.49e-01f 1\n",
|
||
" 64 1.4134948e+01 4.06e-01 3.56e-02 -4.3 2.30e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.4172363e+01 2.13e-01 7.43e-02 -5.6 2.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.4539284e+01 9.15e-03 2.30e-02 -3.9 2.86e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 1.4256746e+01 2.55e-01 1.92e-02 -5.2 2.32e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 68 1.4080470e+01 3.87e-01 1.79e-02 -4.0 1.64e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.4407540e+01 2.11e-01 1.97e-02 -5.0 1.46e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.4181522e+01 2.07e-01 1.90e-02 -4.5 9.72e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.3855081e+01 3.00e-01 3.17e-02 -5.8 2.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.3401534e+01 4.62e-01 4.51e-02 -3.7 6.94e+03 - 1.00e+00 1.33e-01f 1\n",
|
||
" 73 1.4202681e+01 1.88e-01 2.04e-02 -3.4 6.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.4437634e+01 4.56e-02 2.06e-02 -3.5 2.11e+02 - 4.20e-01 1.00e+00h 1\n",
|
||
" 75 1.4419629e+01 1.93e-02 1.46e-02 -2.4 6.39e+01 - 1.00e+00 8.79e-01h 1\n",
|
||
" 76 1.4456994e+01 8.07e-03 2.08e-03 -3.8 1.04e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.4427673e+01 2.67e-02 2.83e-03 -5.6 3.52e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.4440038e+01 1.30e-02 2.11e-03 -5.0 1.91e+02 - 9.83e-01 6.25e-01h 1\n",
|
||
" 79 1.4293427e+01 1.10e-01 7.83e-03 -6.8 7.80e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.4429320e+01 9.20e-02 3.28e-03 -8.3 2.90e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.4421253e+01 3.65e-02 9.59e-03 -8.1 5.14e+02 - 1.38e-01 1.00e+00h 1\n",
|
||
" 82r 1.4421253e+01 3.65e-02 9.99e+02 -1.4 0.00e+00 - 0.00e+00 3.63e-07R 5\n",
|
||
" 83r 1.4461385e+01 6.47e-03 1.86e+02 -7.5 6.12e+01 - 1.00e+00 1.05e-03f 1\n",
|
||
" 84 1.4425577e+01 5.85e-02 3.16e-03 -8.1 3.75e+02 - 2.31e-01 1.00e+00h 1\n",
|
||
" 85 1.4389573e+01 7.77e-02 2.17e-03 -8.1 5.24e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.4340647e+01 1.40e-01 2.33e-03 -6.1 4.60e+05 - 8.10e-03 2.68e-03f 1\n",
|
||
" 87 1.4340663e+01 1.40e-01 2.59e-03 -5.9 5.87e+03 - 1.00e+00 3.04e-04h 1\n",
|
||
" 88 1.4450520e+01 9.71e-05 1.51e-03 -6.0 1.78e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.4448998e+01 3.57e-03 8.95e-03 -7.5 6.52e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.4450619e+01 1.00e-03 1.66e-03 -8.5 4.18e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.4448692e+01 2.82e-03 3.56e-03 -8.4 2.95e+03 - 1.01e-01 1.37e-02h 1\n",
|
||
" 92 1.4429788e+01 5.34e-02 8.79e-03 -11.0 1.04e+03 - 5.34e-02 1.00e+00h 1\n",
|
||
" 93 1.4450012e+01 7.95e-04 1.89e-03 -8.7 2.59e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.4451634e+01 1.74e-05 1.70e-03 -5.9 1.33e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 95 1.4281454e+01 7.22e-02 4.14e-03 -3.9 1.61e+06 - 8.87e-04 2.10e-02f 1\n",
|
||
" 96 1.2501393e+01 1.94e+00 1.55e-01 -5.8 3.17e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 97 1.2520198e+01 1.92e+00 1.51e-01 -6.0 7.61e+03 - 1.00e+00 1.10e-02h 1\n",
|
||
" 98 1.4448129e+01 1.64e-02 1.52e-01 -6.0 6.17e+02 - 5.65e-01 1.00e+00h 1\n",
|
||
" 99 1.4448127e+01 1.64e-02 1.52e-01 -6.0 1.52e+04 - 2.68e-01 1.14e-05h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.4454531e+01 9.12e-04 3.09e-03 -6.0 3.96e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.4454531004998962e+01 1.4454531004998962e+01\n",
|
||
"Dual infeasibility......: 3.0943419918352810e-03 3.0943419918352810e-03\n",
|
||
"Constraint violation....: 9.1180575875426939e-04 9.1180575875426939e-04\n",
|
||
"Complementarity.........: 1.5442067510572753e-06 1.5442067510572753e-06\n",
|
||
"Overall NLP error.......: 3.0943419918352810e-03 3.0943419918352810e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 112\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 112\n",
|
||
"Number of inequality constraint evaluations = 112\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.449\n",
|
||
"Total CPU secs in NLP function evaluations = 135.184\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 503.00us ( 4.49us) 494.25us ( 4.41us) 112\n",
|
||
" nlp_g | 4.99 s ( 44.55ms) 4.75 s ( 42.42ms) 112\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 350.00us ( 3.43us) 340.07us ( 3.33us) 102\n",
|
||
" nlp_jac_g | 133.13 s ( 1.29 s) 127.07 s ( 1.23 s) 103\n",
|
||
" total | 139.59 s (139.59 s) 133.23 s (133.23 s) 1\n",
|
||
"Timestamp 5400\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.65e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9886614e+01 1.41e+01 2.65e+03 -1.5 2.65e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.2135917e+00 5.05e+00 9.88e+00 0.4 1.41e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.0225312e+01 1.49e+00 6.51e-01 -1.6 8.72e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.1266168e+01 1.95e-03 8.32e-02 -3.4 2.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.1267143e+01 9.16e-08 2.39e-05 -5.3 1.98e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.1267143e+01 3.75e-08 1.62e-04 -11.0 3.73e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.1267142e+01 8.02e-07 8.56e-05 -11.0 2.76e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.1267143e+01 1.93e-07 2.20e-05 -11.0 7.81e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.1267143e+01 4.93e-08 2.49e-05 -11.0 2.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.1267143e+01 4.89e-08 1.30e-04 -11.0 3.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.1267142e+01 1.01e-06 1.60e-03 -11.0 2.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.1267143e+01 5.06e-07 7.32e-05 -11.0 1.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.1267143e+01 2.05e-07 3.09e-05 -11.0 1.09e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.1267143e+01 2.66e-08 2.56e-05 -11.0 2.31e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.1267143e+01 2.33e-11 6.03e-05 -11.0 3.03e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 16 1.1267143e+01 6.27e-08 1.39e-04 -11.0 1.27e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.1267142e+01 7.90e-07 5.23e-03 -11.0 3.55e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.1267143e+01 4.39e-09 8.63e-05 -11.0 1.65e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.1267143e+01 6.60e-08 9.24e-05 -11.0 1.29e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.1267144e+01 3.82e-11 3.73e-05 -11.0 7.17e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 21 1.1267143e+01 2.38e-08 2.10e-04 -11.0 2.37e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.1267143e+01 5.79e-08 1.25e-04 -11.0 1.46e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.1267143e+01 5.42e-08 1.32e-04 -11.0 2.27e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.1267143e+01 2.23e-08 4.81e-05 -11.0 1.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.1267143e+01 2.38e-09 1.32e-04 -11.0 2.31e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.1267144e+01 4.54e-11 8.31e-05 -11.0 2.44e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 27 1.1267144e+01 2.14e-11 2.99e-04 -11.0 3.54e-04 - 1.00e+00 4.88e-04h 12\n",
|
||
" 28 1.1267144e+01 3.60e-11 6.40e-05 -11.0 5.37e-05 - 1.00e+00 1.22e-04h 14\n",
|
||
" 29 1.1267144e+01 3.68e-11 9.98e-05 -11.0 2.40e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.1267144e+01 1.17e-11 8.03e-05 -11.0 2.92e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 31 1.1267143e+01 1.33e-08 3.52e-05 -11.0 1.71e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.1267143e+01 1.03e-07 3.29e-05 -11.0 4.07e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.1267143e+01 1.78e-09 2.08e-05 -11.0 2.93e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.1267143e+01 2.76e-09 1.90e-04 -11.0 5.88e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.1267143e+01 1.61e-07 1.69e-04 -11.0 4.96e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.1267143e+01 8.43e-08 1.06e-05 -11.0 5.44e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.1267142e+01 6.53e-06 2.09e-03 -11.0 1.63e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.1267121e+01 2.54e-05 2.06e-03 -11.0 1.85e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.1266567e+01 3.92e-04 5.39e-03 -11.0 1.82e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.1266788e+01 3.47e-04 8.62e-03 -11.0 1.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.1190801e+01 5.67e-02 4.41e-03 -11.0 1.19e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 42 1.1266130e+01 2.23e-04 4.12e-03 -11.0 3.15e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 43 1.0562117e+01 2.01e+00 1.53e-01 -10.2 1.24e+05 - 1.00e+00 2.37e-01f 1\n",
|
||
" 44 1.0562454e+01 2.00e+00 1.51e-01 -10.4 3.27e+04 - 1.00e+00 3.73e-03h 1\n",
|
||
" 45 1.1003534e+01 6.87e-02 1.12e-01 -10.4 8.46e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.0600986e+01 1.92e+00 1.12e-02 -9.6 2.83e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 47 9.2723741e+00 1.98e+00 1.28e-01 -4.6 1.92e+04 - 3.98e-01 8.06e-01f 1\n",
|
||
" 48 8.8785568e+00 1.73e+00 1.78e-01 -4.5 2.41e+04 - 3.76e-03 1.00e+00h 1\n",
|
||
" 49 8.8739733e+00 1.77e+00 1.82e-01 -2.5 2.60e+04 - 1.00e+00 1.42e-02h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.0631141e+01 2.93e-01 1.78e-01 -1.5 1.49e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.0381542e+01 3.95e-01 1.87e-01 -1.9 1.80e+04 - 9.12e-01 5.15e-01h 1\n",
|
||
" 52 1.0714368e+01 3.24e-02 3.93e-03 -2.6 5.80e+02 - 5.12e-01 1.00e+00h 1\n",
|
||
" 53 1.0569803e+01 5.73e-01 2.93e-02 -2.2 6.60e+03 - 8.76e-01 1.67e-01f 1\n",
|
||
" 54 9.4451633e+00 1.18e+00 1.33e-01 -2.4 1.08e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 55 1.0215378e+01 1.02e+00 1.97e-01 -2.3 1.15e+04 - 5.54e-01 1.00e+00h 1\n",
|
||
" 56 1.0738189e+01 6.29e-01 4.64e-02 -8.3 6.61e+03 - 4.98e-01 1.00e+00h 1\n",
|
||
" 57 9.9982986e+00 6.65e+00 4.97e-01 -2.8 1.31e+05 - 3.52e-03 2.09e-01f 1\n",
|
||
" 58 8.6581240e+00 3.05e+00 3.27e-01 -2.8 1.91e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 59 9.0431142e+00 1.97e+00 1.62e-01 -2.3 1.09e+05 - 6.93e-01 3.72e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 7.7390204e+00 5.66e+00 8.32e-01 -2.2 3.95e+04 - 1.28e-01 1.00e+00f 1\n",
|
||
" 61 9.1686870e+00 2.72e+00 3.47e-01 -0.4 4.06e+04 - 1.00e+00 7.09e-01h 1\n",
|
||
" 62 9.3453471e+00 7.58e-01 1.40e-01 -0.5 1.19e+04 - 8.04e-01 1.00e+00f 1\n",
|
||
" 63 8.5795875e+00 4.20e+00 7.21e-01 -1.7 5.02e+04 - 4.43e-01 4.75e-01f 1\n",
|
||
" 64 8.6721681e+00 5.15e+00 9.95e-01 -1.3 3.05e+04 - 9.85e-01 1.00e+00h 1\n",
|
||
" 65 8.8110772e+00 5.33e+00 9.31e-01 -1.3 3.12e+04 - 1.00e+00 9.65e-02h 4\n",
|
||
" 66 9.3165129e+00 1.53e+00 6.52e-01 -1.3 1.53e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.0894009e+01 1.13e+00 1.96e-01 -1.5 1.60e+04 - 4.96e-01 1.00e+00h 1\n",
|
||
" 68 1.0805951e+01 9.14e-01 1.18e-01 -1.7 1.24e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.1037500e+01 2.25e-01 9.63e-02 -1.7 1.53e+04 - 6.26e-01 7.52e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 9.9497693e+00 9.20e-01 7.64e-02 -1.7 8.94e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 71 9.8666284e+00 7.79e-01 5.84e-02 -1.9 2.85e+04 - 1.00e+00 3.15e-01h 1\n",
|
||
" 72 1.1455619e+01 5.55e-02 9.15e-02 -2.8 1.50e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.1231114e+01 1.78e-01 3.68e-02 -2.9 1.93e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.0679860e+01 1.20e+00 1.18e-01 -2.9 2.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.0777239e+01 9.54e-01 8.98e-02 -2.9 2.44e+03 - 1.00e+00 1.69e-01h 1\n",
|
||
" 76 1.0777972e+01 9.55e-01 8.97e-02 -2.9 5.42e+03 - 1.00e+00 1.90e-03h 9\n",
|
||
" 77 1.0780459e+01 1.05e+00 8.97e-02 -2.9 7.22e+03 - 1.00e+00 4.54e-02h 4\n",
|
||
" 78 1.0522202e+01 1.09e+00 6.80e-02 -2.9 1.36e+04 - 1.00e+00 1.69e-01h 1\n",
|
||
" 79 1.1576661e+01 1.42e-03 1.45e+00 -2.9 1.54e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.1578492e+01 1.37e-06 1.17e-03 -2.9 5.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.1578492e+01 6.89e-07 1.01e-03 -4.4 8.55e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.1578464e+01 2.05e-05 2.56e-03 -4.4 2.59e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.1578475e+01 2.61e-05 1.60e-03 -4.4 1.16e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.1578363e+01 1.34e-04 3.03e-03 -4.4 3.75e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.1578482e+01 6.68e-06 1.90e-03 -4.4 8.93e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.1578483e+01 4.70e-06 1.31e-03 -4.4 6.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.1578424e+01 4.46e-05 3.24e-03 -4.4 1.93e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.1578478e+01 1.13e-05 1.91e-03 -4.4 1.28e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.1578490e+01 1.62e-09 4.60e-05 -4.4 9.05e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.1578478e+01 8.27e-06 2.42e-03 -6.5 3.09e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.1578455e+01 6.11e-05 2.38e-03 -6.5 4.32e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.1577404e+01 5.64e-04 2.58e-03 -6.5 4.08e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.1578435e+01 3.22e-07 1.32e-04 -6.5 1.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.1578435e+01 1.71e-07 8.50e-05 -6.5 6.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.1578434e+01 1.58e-07 1.64e-04 -6.5 1.56e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.1578434e+01 3.12e-07 1.36e-04 -6.5 9.52e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.1578434e+01 2.23e-07 2.00e-04 -6.5 3.42e-04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 98 1.1577335e+01 3.95e-04 2.90e-02 -6.5 3.52e+00 - 1.00e+00 5.24e-01f 1\n",
|
||
" 99 1.1577697e+01 2.58e-04 1.34e-02 -6.5 2.67e+00 - 1.00e+00 5.06e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.1578068e+01 1.30e-04 6.68e-03 -6.5 4.81e-01 - 1.00e+00 5.00e-01h 2\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.1578068382443226e+01 1.1578068382443226e+01\n",
|
||
"Dual infeasibility......: 6.6789526214592262e-03 6.6789526214592262e-03\n",
|
||
"Constraint violation....: 1.3033917486282576e-04 1.3033917486282576e-04\n",
|
||
"Complementarity.........: 3.5916163049427814e-05 3.5916163049427814e-05\n",
|
||
"Overall NLP error.......: 6.6789526214592262e-03 6.6789526214592262e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 169\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 169\n",
|
||
"Number of inequality constraint evaluations = 169\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.392\n",
|
||
"Total CPU secs in NLP function evaluations = 137.601\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 749.00us ( 4.43us) 740.76us ( 4.38us) 169\n",
|
||
" nlp_g | 7.60 s ( 44.96ms) 7.24 s ( 42.85ms) 169\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 382.00us ( 3.75us) 372.78us ( 3.65us) 102\n",
|
||
" nlp_jac_g | 132.72 s ( 1.30 s) 126.67 s ( 1.24 s) 102\n",
|
||
" total | 141.80 s (141.80 s) 135.33 s (135.33 s) 1\n",
|
||
"Timestamp 5700\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.55e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9985907e+01 1.42e+01 2.55e+03 -1.5 2.55e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.2516496e+00 5.15e+00 9.73e+00 0.4 1.42e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.0011496e+01 1.48e+00 6.02e-01 -1.6 8.58e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.1058352e+01 2.33e-03 8.28e-02 -3.4 2.04e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.1059465e+01 1.03e-06 2.18e-03 -5.3 2.56e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.1059466e+01 6.74e-07 2.86e-03 -7.4 1.75e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.1059467e+01 2.98e-08 4.53e-05 -9.4 3.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.1059467e+01 3.83e-08 5.80e-05 -11.0 2.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.1059467e+01 5.25e-08 4.31e-05 -11.0 5.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.1059467e+01 2.34e-08 9.29e-05 -11.0 1.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.1059467e+01 2.11e-07 1.48e-04 -11.0 8.35e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.1059466e+01 4.38e-07 8.23e-05 -11.0 2.06e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.1059467e+01 6.12e-07 1.06e-04 -11.0 1.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.1059466e+01 2.94e-07 3.26e-05 -11.0 1.46e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.1059467e+01 4.50e-08 4.29e-05 -11.0 5.66e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.1059467e+01 1.05e-07 4.08e-05 -11.0 3.71e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.1059467e+01 2.56e-07 2.14e-04 -11.0 9.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.1059466e+01 4.00e-07 2.45e-04 -11.0 1.40e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.1059466e+01 7.91e-07 5.50e-05 -11.0 1.56e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.1059467e+01 1.51e-08 2.58e-05 -11.0 8.25e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.1059467e+01 8.42e-11 5.18e-05 -11.0 1.08e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 22 1.1059434e+01 5.20e-05 2.09e-02 -11.0 2.69e-01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 23 1.1059394e+01 5.44e-05 1.20e-03 -11.0 9.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.1059472e+01 1.54e-06 1.03e-03 -11.0 6.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.1059409e+01 3.95e-05 2.32e-03 -11.0 2.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.1059433e+01 2.74e-05 1.14e-03 -11.0 1.32e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.1059460e+01 4.67e-06 2.47e-03 -11.0 3.38e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.1059419e+01 4.45e-05 3.14e-03 -11.0 2.42e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.1059429e+01 2.08e-05 8.92e-04 -11.0 1.41e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.1056206e+01 2.94e-03 1.34e-02 -11.0 1.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.1044335e+01 3.08e-03 9.14e-03 -11.0 9.89e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.1001351e+01 3.62e-02 1.48e-02 -11.0 1.82e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.1061098e+01 7.00e-05 2.04e-03 -11.0 1.22e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 1.1059142e+01 1.14e-03 1.80e-03 -11.0 1.82e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.1056899e+01 2.20e-03 1.70e-03 -11.0 4.10e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.1001695e+01 6.65e-02 6.69e-03 -11.0 1.82e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.1032590e+01 1.57e-02 2.24e-03 -11.0 9.31e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.1055605e+01 1.54e-03 3.18e-03 -11.0 4.47e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.1045283e+01 5.40e-03 2.36e-03 -11.0 8.44e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.1051037e+01 4.23e-03 1.38e-03 -11.0 2.07e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.1054573e+01 5.24e-03 2.15e-03 -11.0 1.98e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.1045087e+01 9.50e-03 1.75e-03 -11.0 3.43e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.1062181e+01 8.92e-06 1.24e-03 -11.0 8.43e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 44 1.0974736e+01 1.18e-01 6.98e-03 -11.0 4.93e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 45 1.1045246e+01 4.28e-02 6.09e-03 -11.0 1.61e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.0415395e+01 1.32e+00 9.75e-02 -11.0 9.55e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 47 6.8867899e+00 3.65e+00 6.98e-01 -11.0 4.05e+04 - 1.00e+00 5.45e-01f 1\n",
|
||
" 48 7.2753872e+00 3.60e+00 9.10e-01 -10.3 1.27e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 49 1.0936788e+01 2.32e-01 9.36e-01 -2.0 2.48e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.1477924e+01 3.07e-02 4.15e-02 -3.2 3.55e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.1013716e+01 1.52e-01 2.38e-02 -3.3 1.91e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 52 1.1445770e+01 4.65e-02 2.22e-02 -3.3 5.45e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.1413096e+01 1.36e-01 2.03e-02 -3.3 2.41e+04 - 4.73e-01 1.25e-01h 4\n",
|
||
" 54 9.8436612e+00 3.30e+00 2.78e-01 -3.3 2.67e+05 - 1.49e-01 9.50e-02f 1\n",
|
||
" 55 9.5052187e+00 1.10e+01 9.48e-01 -9.5 5.66e+04 - 6.89e-04 1.00e+00f 1\n",
|
||
" 56 9.3483382e+00 1.08e+01 9.21e-01 -3.3 2.60e+05 - 3.72e-01 1.66e-02h 1\n",
|
||
" 57 8.1309472e+00 9.09e+00 7.51e-01 -3.3 1.41e+05 - 6.69e-01 1.58e-01f 1\n",
|
||
" 58 6.0998937e+00 3.80e+00 2.93e-01 -3.3 2.76e+04 - 6.10e-04 3.65e-01f 1\n",
|
||
" 59 7.3475152e+00 1.83e+00 9.61e-02 -1.5 2.06e+04 - 3.30e-01 4.85e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 7.7320278e+00 1.68e+00 1.02e-01 -1.5 2.35e+04 - 1.00e+00 1.37e-01h 1\n",
|
||
" 61 5.6222471e+00 2.02e+00 2.80e-01 -1.7 7.88e+03 - 8.78e-01 4.70e-01f 1\n",
|
||
" 62 1.0048809e+01 1.97e-01 6.66e-02 -2.0 3.62e+03 - 1.00e+00 9.94e-01h 1\n",
|
||
" 63 1.0226751e+01 1.55e-01 3.82e-02 -8.0 9.93e+02 - 4.12e-02 1.00e+00h 1\n",
|
||
" 64 9.0183177e+00 1.46e+00 9.38e-02 -2.9 3.58e+04 - 3.65e-01 1.00e+00f 1\n",
|
||
" 65 8.0351068e+00 5.22e+00 3.90e-01 -2.0 1.85e+04 - 1.00e+00 1.00e+00F 1\n",
|
||
" 66 7.9473408e+00 4.14e+00 2.47e-01 -2.2 7.29e+03 - 1.00e+00 2.08e-01h 1\n",
|
||
" 67 9.3775247e+00 5.15e-01 3.43e-01 -2.2 5.14e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.0263110e+01 7.33e-02 7.26e-02 -8.2 1.15e+03 - 4.51e-01 1.00e+00h 1\n",
|
||
" 69 1.0279131e+01 9.05e-02 2.96e-02 -2.2 6.11e+02 - 1.00e+00 6.50e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.0243724e+01 5.47e-02 4.38e-02 -3.9 8.83e+02 - 9.92e-01 1.00e+00h 1\n",
|
||
" 71 1.0118282e+01 5.06e-01 7.57e-02 -3.7 1.06e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.0339393e+01 3.21e-02 4.20e-02 -3.8 8.60e+02 - 9.35e-01 1.00e+00h 1\n",
|
||
" 73 1.0016263e+01 5.64e-01 3.05e-02 -5.9 3.93e+04 - 1.68e-01 4.14e-01f 1\n",
|
||
" 74 1.0673839e+01 5.34e-02 7.08e-02 -2.5 3.85e+04 - 2.28e-02 1.00e+00H 1\n",
|
||
" 75 1.0595655e+01 4.67e-01 3.99e-02 -2.7 2.10e+05 - 1.97e-01 2.27e-02f 1\n",
|
||
" 76 1.0032100e+01 1.65e+00 1.02e-01 -2.7 1.19e+04 - 1.00e+00 6.54e-01f 1\n",
|
||
" 77 1.0443357e+01 9.43e-01 1.41e-02 -2.7 4.31e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.0496763e+01 1.61e-01 3.84e-02 -2.7 4.09e+03 - 5.81e-01 1.00e+00h 1\n",
|
||
" 79 1.0386110e+01 3.84e-01 2.62e-02 -2.7 5.37e+04 - 8.33e-01 6.82e-02f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.0452885e+01 3.15e-01 2.17e-02 -2.7 1.41e+04 - 1.00e+00 7.77e-01H 1\n",
|
||
" 81 1.0437934e+01 3.33e-01 2.56e-02 -2.7 6.48e+03 - 1.00e+00 4.84e-02h 1\n",
|
||
" 82 1.0556845e+01 7.27e-02 3.28e-02 -2.7 2.95e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.0520650e+01 1.03e-01 1.31e-02 -2.7 2.38e+02 - 1.00e+00 8.58e-01h 1\n",
|
||
" 84 1.0378165e+01 2.92e-01 1.28e-02 -2.7 1.16e+04 - 4.48e-02 4.35e-02f 1\n",
|
||
" 85 1.0556055e+01 7.16e-02 3.52e-02 -2.7 4.51e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.0560005e+01 1.50e-02 1.05e-02 -2.7 9.14e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.0499774e+01 1.05e-01 7.59e-03 -8.5 5.54e+02 - 3.28e-01 1.00e+00h 1\n",
|
||
" 88 1.0407884e+01 2.36e-01 2.74e-02 -3.0 3.83e+03 - 1.51e-01 2.59e-01h 1\n",
|
||
" 89 1.0616946e+01 1.00e-01 6.30e-03 -3.0 1.20e+03 - 1.00e+00 8.81e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.0487844e+01 9.45e-02 1.48e-02 -3.0 1.02e+03 - 1.00e+00 7.83e-01f 1\n",
|
||
" 91 1.0456551e+01 1.23e-01 2.89e-02 -3.0 1.47e+03 - 3.59e-01 1.00e+00h 1\n",
|
||
" 92 1.0621334e+01 2.25e-02 1.93e-02 -3.0 9.89e+02 - 3.78e-01 1.00e+00H 1\n",
|
||
" 93 1.0603258e+01 3.73e-03 5.88e-03 -4.0 7.32e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.0550988e+01 1.83e-02 5.31e-03 -3.2 1.54e+04 - 5.82e-01 7.58e-03f 1\n",
|
||
" 95 1.0602903e+01 1.66e-03 1.01e-02 -9.3 7.63e+01 - 7.70e-01 1.00e+00h 1\n",
|
||
" 96 1.0468003e+01 5.50e-02 5.78e-03 -3.3 8.53e+02 - 4.60e-03 1.00e+00f 1\n",
|
||
" 97 1.0462805e+01 5.59e-02 6.45e-03 -4.2 1.60e+03 - 1.00e+00 1.45e-02h 1\n",
|
||
" 98 1.0587019e+01 4.82e-03 6.95e-03 -4.2 2.34e+01 - 7.17e-01 1.00e+00h 1\n",
|
||
" 99 1.0592624e+01 4.10e-03 1.63e-03 -4.4 8.72e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.0593988e+01 6.32e-04 1.60e-03 -6.1 2.66e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.0593987655890079e+01 1.0593987655890079e+01\n",
|
||
"Dual infeasibility......: 1.5991722917135243e-03 1.5991722917135243e-03\n",
|
||
"Constraint violation....: 6.3232085636855118e-04 6.3232085636855118e-04\n",
|
||
"Complementarity.........: 6.2726797014683059e-06 6.2726797014683059e-06\n",
|
||
"Overall NLP error.......: 1.5991722917135243e-03 1.5991722917135243e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 122\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 122\n",
|
||
"Number of inequality constraint evaluations = 122\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.421\n",
|
||
"Total CPU secs in NLP function evaluations = 135.766\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 546.00us ( 4.48us) 539.83us ( 4.42us) 122\n",
|
||
" nlp_g | 5.46 s ( 44.78ms) 5.21 s ( 42.72ms) 122\n",
|
||
" nlp_grad | 1.38 s ( 1.38 s) 1.32 s ( 1.32 s) 1\n",
|
||
" nlp_grad_f | 357.00us ( 3.50us) 347.76us ( 3.41us) 102\n",
|
||
" nlp_jac_g | 133.13 s ( 1.31 s) 127.11 s ( 1.25 s) 102\n",
|
||
" total | 140.12 s (140.12 s) 133.78 s (133.78 s) 1\n",
|
||
"Timestamp 6000\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.15e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0784481e+01 1.25e+01 3.15e+04 -1.5 3.15e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.3289344e+00 4.63e+00 5.07e+00 1.2 1.26e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.8676672e+00 5.49e-01 2.62e-01 -0.9 4.48e+02 - 9.96e-01 1.00e+00f 1\n",
|
||
" 4 1.0171605e+00 7.89e-03 5.11e-01 -6.6 1.56e+01 - 9.90e-01 1.00e+00f 1\n",
|
||
" 5 1.0055854e+00 8.42e-03 5.22e-02 -4.1 2.11e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.0143528e+00 6.91e-04 6.98e-03 -6.0 5.95e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 9.9576039e-01 1.68e-02 6.86e-02 -7.9 1.18e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.0151399e+00 2.72e-03 2.01e-02 -9.7 2.36e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.0088525e+00 4.50e-02 2.44e-02 -9.8 2.15e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 9.2924490e-01 3.57e-01 4.50e-01 -9.8 1.97e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.0058303e+00 4.73e-02 3.02e-01 -11.0 9.24e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 9.9812525e-01 5.08e-01 1.53e-01 -11.0 2.11e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 9.5498040e-01 2.86e-02 1.37e-01 -11.0 3.85e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 9.1366572e-01 9.82e-02 1.11e-01 -11.0 7.84e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 8.5353956e-01 3.16e-01 3.09e-01 -11.0 2.82e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 16 9.9413628e-01 1.29e-01 2.12e-01 -11.0 7.09e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 17 9.9391625e-01 5.65e-01 3.13e-01 -11.0 2.64e+06 - 1.46e-02 4.63e-03f 2\n",
|
||
" 18 8.6098827e-01 4.68e-01 3.62e-01 -11.0 2.45e+04 - 1.00e+00 1.42e-01h 3\n",
|
||
" 19 8.8813924e-01 2.37e-01 4.35e-01 -11.0 2.07e+05 - 2.57e-01 2.22e-02h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 8.6170569e-01 2.39e-01 1.44e-01 -11.0 2.08e+05 - 2.16e-02 7.01e-02h 3\n",
|
||
" 21 8.2743632e-01 5.79e-01 3.79e-01 -9.1 1.80e+06 - 2.51e-02 5.28e-03f 2\n",
|
||
" 22 9.8495907e-01 4.26e-01 3.67e-01 -10.8 1.51e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 8.2353351e-01 4.23e-01 1.51e-01 -11.0 4.88e+04 - 6.18e-01 5.07e-01h 1\n",
|
||
" 24 7.5332129e-01 6.04e-01 5.94e-01 -11.0 8.38e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 25 9.4634651e-01 4.13e-01 5.71e-01 -1.8 1.67e+03 - 6.85e-01 1.00e+00h 1\n",
|
||
" 26 9.2798870e-01 8.46e-01 2.68e-01 -2.1 3.77e+04 - 1.00e+00 5.00e-01f 2\n",
|
||
" 27 1.0141913e+00 2.08e-01 3.06e-01 -2.1 7.59e+03 - 8.57e-01 1.00e+00h 1\n",
|
||
" 28 8.5324594e-01 2.70e-01 3.60e-01 -2.1 2.29e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.6147929e-01 2.44e-01 2.89e-01 -2.2 2.32e+04 - 9.94e-01 1.25e-01h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.1219260e+00 1.85e-01 3.65e-01 -2.8 5.73e+03 - 7.05e-01 1.00e+00H 1\n",
|
||
" 31 9.1905623e-01 3.98e-01 3.63e-01 -2.7 7.66e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 9.1647545e-01 2.62e-01 3.49e-01 -2.7 4.67e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.0510218e+00 2.08e-02 1.67e-01 -2.7 1.48e+04 - 1.85e-01 1.00e+00H 1\n",
|
||
" 34 8.9155810e-01 7.21e-01 5.75e-01 -2.7 4.29e+04 - 1.00e+00 5.00e-01f 2\n",
|
||
" 35 9.1583172e-01 5.53e-01 6.32e-01 -2.7 6.88e+04 - 3.96e-01 1.99e-01h 2\n",
|
||
" 36 9.1290169e-01 2.69e-01 6.43e-01 -2.7 1.15e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 8.8632978e-01 3.04e-01 3.96e-01 -2.7 4.92e+04 - 5.23e-02 6.25e-02h 5\n",
|
||
" 38 1.0062699e+00 1.48e-01 5.50e-01 -2.7 1.87e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 39 9.8419688e-01 1.03e-01 5.24e-01 -2.7 3.54e+04 - 9.15e-01 6.25e-02h 5\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.5846743e-01 1.15e-01 1.62e-01 -2.7 1.02e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 8.2021573e-01 2.04e-01 3.25e-01 -1.4 5.76e+05 - 3.90e-01 1.19e-02f 3\n",
|
||
" 42 1.0458642e+00 6.13e-01 3.66e-01 -2.1 5.84e+03 - 9.27e-01 9.27e-01s 22\n",
|
||
" 43 9.8794327e-01 3.92e-01 6.81e-01 -2.2 5.97e+04 - 9.12e-01 1.53e-01h 3\n",
|
||
" 44 9.7379313e-01 3.32e-01 7.51e-01 -2.2 2.12e+05 - 1.65e-01 3.23e-02h 3\n",
|
||
" 45 8.8864864e-01 7.24e-01 7.13e-01 -2.2 1.92e+05 - 1.00e-01 1.08e-01f 2\n",
|
||
" 46 1.6958696e+00 2.16e-01 1.19e+00 -2.2 4.17e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.6194287e+00 2.49e-01 1.14e+00 -2.2 6.80e+04 - 3.56e-01 6.80e-02h 4\n",
|
||
" 48 1.6113701e+00 2.73e-01 1.13e+00 -2.2 5.35e+04 - 2.67e-01 4.95e-03h 8\n",
|
||
" 49 7.2147086e-01 2.58e-01 3.15e-01 -2.2 2.61e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 7.0395503e-01 3.65e-01 3.67e-01 -2.7 1.18e+04 - 9.99e-01 6.25e-02h 5\n",
|
||
" 51 7.6626674e-01 3.14e-01 4.09e-01 -3.5 6.80e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 52 7.1970495e-01 3.15e-01 1.17e-01 -3.4 1.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 7.1596436e-01 2.68e-01 1.39e-01 -3.1 2.68e+04 - 1.00e+00 3.12e-02h 6\n",
|
||
" 54 7.2278248e-01 2.25e-01 1.74e-01 -3.4 2.31e+04 - 9.99e-01 2.50e-01h 3\n",
|
||
" 55 7.7389419e-01 3.36e-01 1.79e-01 -3.5 9.11e+03 - 8.33e-01 1.00e+00h 1\n",
|
||
" 56 7.6791180e-01 3.11e-01 1.65e-01 -3.5 8.24e+05 - 3.39e-02 1.06e-02h 3\n",
|
||
" 57 7.1963360e-01 2.60e-01 3.30e-01 -3.5 5.23e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 58 7.1500675e-01 2.93e-01 2.57e-01 -3.5 2.19e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 59 7.2667981e-01 2.43e-01 4.89e-01 -3.2 1.66e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 6.9678318e-01 3.55e-01 4.30e-01 -3.3 5.37e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 7.9679364e-01 1.89e-01 6.86e-01 -3.7 1.62e+04 - 4.26e-01 1.00e+00H 1\n",
|
||
" 62 7.2980858e-01 2.71e-01 1.49e-01 -3.6 1.95e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 7.2356622e-01 2.62e-01 1.57e-01 -3.6 1.51e+04 - 1.00e+00 2.79e-02h 1\n",
|
||
" 64 7.0713211e-01 2.50e-01 4.24e-01 -3.6 3.80e+04 - 1.00e+00 1.37e-01h 3\n",
|
||
" 65 9.2167073e-01 1.32e-01 6.95e-01 -5.3 3.48e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 7.2584644e-01 1.32e-01 1.91e-01 -4.9 1.37e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 7.4515906e-01 1.93e-01 1.06e-01 -5.9 1.95e+03 - 8.41e-01 7.86e-01H 1\n",
|
||
" 68 7.6547411e-01 1.30e-01 7.63e-02 -6.4 4.21e+03 - 1.00e+00 3.32e-01H 1\n",
|
||
" 69 7.5228659e-01 2.15e-01 4.31e-02 -6.7 1.30e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.5705691e-01 1.64e-02 1.80e-01 -6.9 2.95e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 7.7072692e-01 2.25e-01 3.05e-01 -7.0 1.30e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 7.3093140e-01 2.90e-01 1.63e-01 -7.1 4.64e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 73 8.8639878e-01 2.03e-01 3.64e-01 -7.1 2.70e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 74 8.8396183e-01 2.01e-01 3.60e-01 -7.1 6.75e+04 - 4.41e-01 4.14e-03h 5\n",
|
||
" 75 8.8327546e-01 2.05e-01 3.61e-01 -7.1 1.31e+04 - 1.00e+00 1.40e-03h 10\n",
|
||
" 76 9.3235413e-01 1.27e-01 4.74e-02 -7.1 4.47e+03 - 3.72e-01 1.00e+00H 1\n",
|
||
" 77 7.6725310e-01 2.74e-01 1.86e-01 -7.1 1.84e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 7.6997684e-01 1.58e-01 1.42e-01 -7.1 1.34e+04 - 6.37e-01 6.25e-02h 5\n",
|
||
" 79 7.4265982e-01 1.82e-01 7.62e-02 -7.1 1.16e+04 - 1.00e+00 3.36e-02h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 7.3045799e-01 3.62e-01 1.83e-01 -7.1 2.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 7.4006028e-01 2.21e-01 7.26e-02 -7.1 3.00e+03 - 6.26e-01 5.00e-01h 2\n",
|
||
" 82 7.4142764e-01 2.09e-01 9.77e-02 -7.1 3.87e+03 - 1.00e+00 2.01e-01h 3\n",
|
||
" 83 7.6556558e-01 2.89e-01 3.30e-01 -7.1 4.16e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 84 7.6530756e-01 2.90e-01 3.30e-01 -7.1 1.17e+06 - 4.82e-02 3.26e-05h 8\n",
|
||
" 85 7.6509062e-01 2.91e-01 3.30e-01 -7.1 1.09e+07 - 4.24e-03 3.02e-06h 10\n",
|
||
" 86 7.6507633e-01 2.91e-01 3.30e-01 -7.1 7.96e+04 - 1.30e-01 1.06e-05h 14\n",
|
||
" 87 7.3667327e-01 3.40e-01 2.44e-01 -7.1 1.77e+05 - 8.65e-02 9.20e-02H 1\n",
|
||
" 88 1.0455109e+00 1.61e-01 1.77e-01 -7.1 1.47e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 89 8.8139171e-01 1.61e-01 1.83e-01 -7.1 6.53e+03 - 8.64e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 8.8326385e-01 9.90e-02 1.16e-01 -7.1 3.36e+03 - 9.21e-01 1.00e+00h 1\n",
|
||
" 91 8.7695541e-01 1.52e-01 1.71e-01 -7.1 1.02e+04 - 9.78e-01 4.48e-01H 1\n",
|
||
" 92 8.8248655e-01 1.32e-01 1.35e-01 -7.1 1.50e+03 - 6.38e-06 1.25e-01h 4\n",
|
||
" 93 7.3091405e-01 2.69e-01 3.82e-01 -7.1 7.19e+03 - 1.14e-01 2.50e-01f 3\n",
|
||
" 94 7.1138867e-01 2.22e-01 4.46e-01 -7.1 3.55e+04 - 2.55e-01 8.47e-03h 1\n",
|
||
" 95 1.2746100e+00 1.02e-01 5.68e-01 -7.8 1.93e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 9.6442444e-01 4.79e-02 1.46e-01 -3.9 1.11e+03 - 4.54e-01 1.00e+00h 1\n",
|
||
" 97 9.5527791e-01 4.65e-02 1.47e-01 -3.9 2.09e+03 - 1.00e+00 2.86e-02h 1\n",
|
||
" 98 8.2946012e-01 1.06e-01 2.39e-01 -3.9 6.50e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 99 1.0089684e+00 2.07e-02 1.16e-01 -3.9 1.51e+04 - 7.19e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 7.7177765e-01 6.65e-01 7.30e-01 -3.9 9.21e+04 - 2.50e-03 1.18e-01f 3\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 7.7177765049788349e-01 7.7177765049788349e-01\n",
|
||
"Dual infeasibility......: 7.3030714622163018e-01 7.3030714622163018e-01\n",
|
||
"Constraint violation....: 6.6518778871342121e-01 6.6518778871342121e-01\n",
|
||
"Complementarity.........: 1.6947305014870475e-04 1.6947305014870475e-04\n",
|
||
"Overall NLP error.......: 7.3030714622163018e-01 7.3030714622163018e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 341\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 341\n",
|
||
"Number of inequality constraint evaluations = 341\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.406\n",
|
||
"Total CPU secs in NLP function evaluations = 144.699\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 1.55ms ( 4.54us) 1.54ms ( 4.52us) 341\n",
|
||
" nlp_g | 15.38 s ( 45.10ms) 14.67 s ( 43.03ms) 341\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 348.00us ( 3.41us) 339.23us ( 3.33us) 102\n",
|
||
" nlp_jac_g | 132.08 s ( 1.29 s) 126.04 s ( 1.24 s) 102\n",
|
||
" total | 148.96 s (148.96 s) 142.14 s (142.14 s) 1\n",
|
||
"Timestamp 6300\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.02e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0433308e+01 1.67e+01 3.02e+04 -1.5 3.02e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.5576249e+01 6.67e+00 1.83e+01 1.2 1.19e+03 - 9.99e-01 1.00e+00f 1\n",
|
||
" 3 2.5311211e+01 2.52e+00 7.87e-01 -0.9 2.53e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 2.6784822e+01 1.64e-04 8.10e-02 -6.8 2.70e+00 - 9.90e-01 1.00e+00h 1\n",
|
||
" 5 2.6784941e+01 1.06e-05 4.44e-03 -4.4 2.79e-01 - 9.99e-01 1.00e+00h 1\n",
|
||
" 6 2.6784858e+01 6.84e-05 1.48e-03 -6.5 4.50e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.6782932e+01 1.25e-03 9.82e-04 -8.6 4.40e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.6772619e+01 7.97e-03 2.22e-03 -11.0 2.74e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.6781372e+01 1.24e-03 3.50e-03 -11.0 1.72e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.6774742e+01 6.36e-03 1.69e-03 -11.0 3.07e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.6782045e+01 1.41e-05 3.07e-03 -11.0 2.92e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 12 2.6772314e+01 1.29e-02 3.13e-03 -11.0 4.20e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 13 2.6750459e+01 1.32e-02 4.74e-03 -11.0 8.06e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.6778592e+01 1.49e-05 2.29e-02 -11.0 2.31e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.6778584e+01 8.03e-09 1.12e-04 -11.0 8.91e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.6778584e+01 1.50e-08 9.24e-05 -11.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.6778584e+01 1.26e-08 2.54e-05 -11.0 1.39e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.6778584e+01 5.49e-09 3.77e-04 -11.0 4.52e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.6778584e+01 2.07e-07 1.72e-04 -11.0 5.80e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.6778578e+01 9.59e-06 1.11e-02 -11.0 3.62e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.6778577e+01 4.03e-06 1.80e-03 -11.0 2.49e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.6778582e+01 3.12e-06 2.51e-03 -11.0 1.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.6778571e+01 8.23e-06 3.59e-03 -11.0 3.95e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.6778545e+01 6.88e-05 3.19e-03 -11.0 2.02e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.6778540e+01 2.65e-05 3.59e-03 -11.0 3.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.6778528e+01 1.92e-05 2.26e-03 -11.0 3.15e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.6778145e+01 1.67e-04 1.39e-03 -11.0 2.22e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.6768442e+01 5.61e-02 1.02e-03 -11.0 1.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.6654970e+01 3.62e-02 3.16e-03 -11.0 5.77e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.6751877e+01 1.13e-02 2.88e-03 -11.0 1.70e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.6761777e+01 5.10e-03 2.32e-03 -11.0 5.22e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.6645218e+01 7.41e-02 4.48e-03 -11.0 6.50e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 33 2.6750576e+01 1.58e-03 3.73e-03 -11.0 2.69e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 2.6746632e+01 1.26e-03 2.38e-03 -11.0 4.62e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.6632454e+01 1.20e-01 3.61e-03 -11.0 2.17e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 36 2.6490784e+01 4.13e-01 1.07e-02 -9.0 5.67e+04 - 1.00e+00 1.44e-01h 1\n",
|
||
" 37 2.6489464e+01 4.14e-01 1.07e-02 -7.1 7.36e+04 - 1.00e+00 1.11e-03h 1\n",
|
||
" 38 2.6752744e+01 9.76e-06 4.57e-01 -9.0 4.70e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.6752728e+01 1.96e-06 1.43e-03 -11.0 6.55e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.6752729e+01 1.08e-06 1.28e-03 -11.0 9.78e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.6752729e+01 1.40e-06 2.24e-03 -11.0 1.11e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.6752721e+01 3.03e-06 2.06e-03 -11.0 2.15e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.6752732e+01 2.47e-09 2.36e-04 -11.0 4.41e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.6752732e+01 7.23e-09 2.53e-05 -11.0 5.71e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.6752732e+01 1.13e-08 1.01e-04 -11.0 1.81e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.6752732e+01 3.77e-08 1.02e-04 -11.0 1.40e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.6752732e+01 2.75e-08 6.05e-05 -11.0 6.82e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.6752732e+01 9.24e-09 1.21e-04 -11.0 5.23e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.6752732e+01 7.57e-09 7.06e-05 -11.0 2.38e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.6752732e+01 2.21e-09 1.02e-04 -11.0 1.38e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.6752732e+01 1.89e-07 1.11e-04 -11.0 5.05e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.6752732e+01 1.76e-07 1.59e-04 -11.0 9.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.6752732e+01 1.95e-07 1.85e-04 -11.0 6.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.6751059e+01 7.59e-04 6.28e-02 -11.0 2.44e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 55 2.6752628e+01 2.57e-05 2.06e-03 -11.0 4.37e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.6752390e+01 1.25e-04 3.23e-03 -11.0 3.69e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 2.6752666e+01 4.85e-06 4.29e-03 -11.0 1.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.6752665e+01 1.29e-05 1.66e-03 -11.0 8.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 2.6752689e+01 1.25e-06 2.32e-03 -11.0 2.66e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.6752690e+01 1.68e-06 1.28e-03 -11.0 1.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.6752667e+01 2.32e-05 4.33e-03 -11.0 1.41e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.6752671e+01 1.09e-05 1.18e-03 -11.0 7.99e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.6752685e+01 3.82e-06 2.47e-03 -11.0 7.48e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.6752663e+01 1.68e-05 4.73e-03 -11.0 1.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.6752684e+01 8.77e-06 2.19e-03 -11.0 3.32e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.6752686e+01 4.58e-06 1.70e-03 -11.0 2.21e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.6752584e+01 8.26e-05 7.33e-03 -11.0 5.00e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.6752688e+01 1.40e-08 9.95e-05 -11.0 5.60e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 69 2.6752626e+01 5.72e-05 1.13e-03 -11.0 2.11e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.6752683e+01 8.94e-06 1.52e-03 -11.0 1.10e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.6752658e+01 1.66e-05 1.33e-03 -11.0 8.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 2.6752622e+01 1.09e-04 1.78e-03 -11.0 3.31e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 2.6752639e+01 4.42e-05 1.02e-03 -11.0 4.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.6752623e+01 7.12e-05 7.22e-04 -11.0 7.57e-01 - 1.00e+00 4.96e-01h 1\n",
|
||
" 75 2.6752623e+01 7.09e-05 7.31e-04 -11.0 5.21e-02 - 1.00e+00 3.91e-03h 9\n",
|
||
" 76 2.6752687e+01 6.29e-06 1.27e-03 -8.0 2.53e-01 - 3.99e-02 1.00e+00h 1\n",
|
||
" 77 2.6752684e+01 5.65e-05 1.44e-03 -9.5 3.87e-01 - 3.01e-01 1.00e+00h 1\n",
|
||
" 78 2.6733342e+01 1.44e-02 2.19e-02 -9.5 2.39e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 79 2.6728841e+01 1.28e-02 2.87e-02 -7.4 3.94e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.6744271e+01 3.17e-03 6.36e-03 -5.4 3.36e+01 - 1.00e+00 8.29e-01h 1\n",
|
||
" 81 2.6753423e+01 6.13e-04 1.64e-03 -6.2 4.61e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.6721136e+01 1.67e-02 1.87e-03 -6.3 9.05e+02 - 6.52e-03 1.00e+00f 1\n",
|
||
" 83 2.6140163e+01 5.89e-01 2.21e-02 -7.0 4.56e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 84 2.5811164e+01 4.08e-01 1.26e-02 -4.8 1.69e+04 - 1.00e+00 3.82e-01h 1\n",
|
||
" 85 2.6879578e+01 1.46e-03 2.79e-02 -4.3 9.03e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 86 2.4949127e+01 1.86e+00 5.26e-02 -3.7 7.97e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 87 2.5952103e+01 5.97e-01 7.73e-02 -1.6 5.46e+03 - 1.00e+00 9.07e-01h 1\n",
|
||
" 88 2.6419232e+01 2.03e-01 3.24e-02 -1.8 2.68e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 2.0882236e+01 5.12e+00 1.61e-01 -7.7 8.98e+03 - 1.53e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.6397709e+01 2.50e-01 1.57e-01 -3.1 1.37e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.9443082e+01 3.79e+00 2.66e-01 -3.8 1.33e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 92 2.5161140e+01 7.68e-01 1.50e-01 -4.5 8.72e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 2.5020601e+01 9.07e-01 1.22e-01 -2.4 4.35e+04 - 1.00e+00 5.91e-02h 1\n",
|
||
" 94 2.6128185e+01 5.14e-02 2.73e-02 -2.7 1.55e+03 - 5.79e-01 1.00e+00h 1\n",
|
||
" 95 2.5232752e+01 2.24e+00 6.20e-02 -3.7 1.25e+04 - 3.23e-01 1.00e+00f 1\n",
|
||
" 96 2.1451386e+01 1.21e+01 3.99e-01 -2.9 1.47e+05 - 4.47e-03 4.22e-01f 1\n",
|
||
" 97 2.1440120e+01 1.20e+01 3.98e-01 -2.9 4.77e+04 - 1.00e+00 1.75e-03h 1\n",
|
||
" 98 2.6025253e+01 1.46e+00 2.62e-01 -2.9 3.93e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 2.5330446e+01 6.39e-01 1.59e-01 -2.9 6.25e+03 - 9.96e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.6481655e+01 2.11e-01 1.21e-02 -8.9 2.62e+03 - 3.04e-01 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.6481654805528912e+01 2.6481654805528912e+01\n",
|
||
"Dual infeasibility......: 1.2080934020166301e-02 1.2080934020166301e-02\n",
|
||
"Constraint violation....: 2.1057416442343424e-01 2.1057416442343424e-01\n",
|
||
"Complementarity.........: 9.4951226303991647e-04 9.4951226303991647e-04\n",
|
||
"Overall NLP error.......: 2.1057416442343424e-01 2.1057416442343424e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 112\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 112\n",
|
||
"Number of inequality constraint evaluations = 112\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.407\n",
|
||
"Total CPU secs in NLP function evaluations = 134.778\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 510.00us ( 4.55us) 499.41us ( 4.46us) 112\n",
|
||
" nlp_g | 5.04 s ( 45.00ms) 4.81 s ( 42.94ms) 112\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 360.00us ( 3.53us) 351.63us ( 3.45us) 102\n",
|
||
" nlp_jac_g | 132.49 s ( 1.30 s) 126.49 s ( 1.24 s) 102\n",
|
||
" total | 139.00 s (139.00 s) 132.71 s (132.71 s) 1\n",
|
||
"Timestamp 6600\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.24e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9990787e+01 1.21e+01 1.24e+04 -1.5 1.24e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.1067712e+00 4.18e+00 6.76e+00 0.8 3.89e+02 - 9.99e-01 1.00e+00f 1\n",
|
||
" 3 2.4629140e+00 6.85e-01 6.42e-01 -1.2 1.08e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 2.7821829e+00 3.20e-03 3.43e-01 -3.0 3.52e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 5 2.7829292e+00 2.77e-05 6.33e-03 -4.9 3.43e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.7829252e+00 4.61e-05 6.51e-04 -6.9 2.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.7829610e+00 1.49e-05 5.87e-04 -9.0 1.02e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.7829554e+00 1.40e-05 2.11e-03 -11.0 1.83e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.7824430e+00 4.56e-04 7.88e-03 -11.0 4.59e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.7822209e+00 4.34e-04 6.38e-03 -11.0 5.07e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.7708883e+00 7.25e-03 3.06e-02 -11.0 2.19e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.7822342e+00 1.13e-06 9.96e-03 -11.0 9.96e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.7822341e+00 6.40e-07 1.39e-04 -11.0 1.76e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.7822346e+00 2.74e-07 3.85e-05 -11.0 7.57e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.7822349e+00 5.77e-08 1.55e-04 -11.0 1.60e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.7822343e+00 7.53e-07 8.81e-05 -11.0 1.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.7822345e+00 3.50e-07 8.06e-05 -11.0 1.13e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.7822343e+00 8.76e-07 9.20e-05 -11.0 2.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.7822346e+00 2.75e-07 1.41e-04 -11.0 1.30e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.7822346e+00 2.72e-07 1.19e-04 -11.0 8.20e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.7822347e+00 1.76e-07 5.49e-05 -11.0 5.81e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.7822350e+00 8.48e-11 2.27e-05 -11.0 7.43e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 23 2.7822350e+00 1.66e-08 1.24e-04 -11.0 3.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.7822348e+00 1.76e-07 1.04e-04 -11.0 3.30e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.7822349e+00 3.77e-08 1.21e-05 -11.0 6.21e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.7822349e+00 3.31e-08 5.43e-05 -11.0 7.32e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.7822348e+00 2.18e-07 4.40e-05 -11.0 4.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.7822328e+00 2.74e-06 3.99e-03 -11.0 1.67e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.7822339e+00 9.69e-07 9.59e-04 -11.0 7.36e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.7822337e+00 9.91e-07 1.22e-03 -11.0 4.94e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.7822332e+00 4.53e-06 1.97e-03 -11.0 2.49e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.7822299e+00 4.51e-06 3.38e-03 -11.0 4.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.7822343e+00 1.39e-10 5.38e-05 -11.0 3.13e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 2.7822215e+00 2.76e-05 2.03e-03 -11.0 1.57e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.7822288e+00 1.81e-05 4.84e-04 -11.0 9.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.7821886e+00 2.81e-05 1.00e-03 -11.0 2.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.7822145e+00 1.75e-05 1.09e-03 -11.0 1.38e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.7822305e+00 5.68e-07 8.45e-04 -11.0 1.49e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.7822286e+00 7.86e-06 1.19e-03 -11.0 3.08e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.7818861e+00 1.65e-03 9.12e-03 -11.0 4.10e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.7820625e+00 1.56e-04 9.96e-04 -11.0 1.86e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.7821615e+00 6.23e-05 2.09e-03 -11.0 9.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.7820637e+00 3.27e-04 7.62e-04 -11.0 5.58e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.7795459e+00 2.11e-03 5.73e-03 -11.0 1.50e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.7808343e+00 8.20e-04 1.07e-03 -11.0 1.33e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.7816469e+00 1.82e-03 1.49e-03 -11.0 5.29e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.7775577e+00 1.17e-02 3.76e-03 -11.0 2.01e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.7707062e+00 2.13e-02 4.40e-03 -11.0 4.71e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.7691390e+00 1.06e-02 3.38e-03 -11.0 4.31e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.7789207e+00 1.36e-03 4.45e-03 -11.0 1.81e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.7767698e+00 1.26e-03 2.26e-03 -11.0 2.06e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.7634701e+00 2.01e-02 8.28e-03 -11.0 1.22e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.7775312e+00 1.28e-05 2.08e-02 -11.0 2.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.7775444e+00 1.93e-08 7.27e-05 -11.0 1.80e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.7775444e+00 1.91e-08 8.33e-05 -11.0 7.90e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.7775444e+00 2.57e-08 4.15e-05 -11.0 1.81e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 2.7775442e+00 5.28e-07 6.88e-05 -11.0 2.34e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.7775441e+00 1.89e-07 3.96e-05 -11.0 2.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 2.7775436e+00 1.16e-06 1.17e-03 -11.0 7.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.7775443e+00 4.93e-08 1.65e-04 -11.0 1.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.7775404e+00 3.76e-06 8.90e-04 -11.0 1.02e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.7775443e+00 1.15e-07 1.47e-05 -11.0 1.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.7775444e+00 9.14e-09 3.85e-05 -11.0 5.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.7775372e+00 1.06e-05 3.48e-03 -11.0 1.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.7774915e+00 2.80e-05 1.38e-02 -11.0 4.07e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.7774574e+00 1.38e-04 1.04e-03 -11.0 3.48e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.7766397e+00 1.28e-03 2.11e-03 -11.0 1.14e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.7766154e+00 2.20e-03 8.77e-04 -11.0 7.36e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.7732758e+00 8.45e-03 2.59e-03 -11.0 6.02e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.7771863e+00 3.57e-04 2.46e-03 -11.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.7755472e+00 2.09e-03 2.87e-03 -11.0 3.85e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 2.7765028e+00 8.15e-04 1.10e-03 -11.0 5.76e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 2.7764023e+00 3.42e-03 1.38e-03 -11.0 3.66e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.7753085e+00 4.03e-03 1.73e-03 -11.0 5.78e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 2.7634719e+00 4.40e-02 1.30e-02 -11.0 8.21e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.7282340e+00 9.69e-03 4.66e-02 -11.0 2.39e+04 - 1.00e+00 1.00e+00F 1\n",
|
||
" 77 2.5020390e+00 6.11e-01 4.16e-01 -10.8 1.69e+04 - 1.00e+00 5.00e-01f 2\n",
|
||
" 78 3.0878584e+00 3.33e-01 5.05e-01 -10.9 3.67e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 2.6391891e+00 2.68e-01 1.03e-01 -10.9 4.46e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.4531903e+00 6.16e-01 8.04e-01 -8.9 5.12e+04 - 1.00e+00 7.91e-01F 1\n",
|
||
" 81 2.4430115e+00 6.19e-01 7.90e-01 -7.0 2.22e+04 - 1.00e+00 1.16e-02h 1\n",
|
||
" 82 2.4430041e+00 6.18e-01 7.89e-01 -5.0 2.79e+03 - 1.00e+00 9.24e-04h 1\n",
|
||
" 83 2.7382103e+00 8.82e-02 3.19e-01 -5.3 5.04e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.4446048e+00 4.50e-01 2.87e-01 -5.3 9.67e+03 - 7.23e-01 1.00e+00f 1\n",
|
||
" 85 2.4443587e+00 4.49e-01 2.87e-01 -5.3 5.62e+04 - 9.82e-01 1.02e-04h 1\n",
|
||
" 86 2.4940295e+00 2.59e-01 1.15e-01 -5.3 2.04e+04 - 4.62e-02 2.93e-01h 1\n",
|
||
" 87 2.6632689e+00 1.43e-01 1.04e-01 -6.4 1.58e+03 - 9.92e-01 1.00e+00H 1\n",
|
||
" 88 2.6119523e+00 4.70e-01 8.61e-02 -1.8 2.17e+03 - 4.81e-01 1.00e+00f 1\n",
|
||
" 89 2.5814525e+00 6.24e-01 1.68e-01 -2.3 4.23e+04 - 1.00e+00 5.05e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.6874933e+00 4.56e-02 2.04e-01 -2.3 2.56e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 2.6842898e+00 1.02e-01 3.65e-01 -2.9 1.25e+03 - 9.99e-01 1.00e+00h 1\n",
|
||
" 92 2.4713808e+00 9.01e-01 4.68e-01 -3.0 9.92e+04 - 4.09e-02 2.81e-01f 2\n",
|
||
" 93 2.4467284e+00 8.24e-01 4.39e-01 -3.0 1.06e+06 - 3.56e-02 1.30e-02h 1\n",
|
||
" 94 2.4494753e+00 8.37e-01 4.28e-01 -3.0 5.10e+04 - 2.92e-01 1.50e-02f 7\n",
|
||
" 95 2.3534597e+00 3.33e-01 1.19e-01 -3.0 9.33e+03 - 1.88e-01 1.00e+00h 1\n",
|
||
" 96 2.7470217e+00 8.42e-03 6.69e-01 -4.8 8.65e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.7505244e+00 4.99e-07 2.59e-04 -6.7 1.04e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 2.7505237e+00 4.15e-07 9.55e-05 -8.8 1.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 2.7505244e+00 1.10e-07 6.90e-05 -11.0 7.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.7505191e+00 3.34e-06 4.26e-03 -11.0 1.57e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.7505190794731500e+00 2.7505190794731500e+00\n",
|
||
"Dual infeasibility......: 4.2632499478917063e-03 4.2632499478917063e-03\n",
|
||
"Constraint violation....: 3.3377553734226240e-06 3.3377553734226240e-06\n",
|
||
"Complementarity.........: 1.0000000001032024e-11 1.0000000001032024e-11\n",
|
||
"Overall NLP error.......: 4.2632499478917063e-03 4.2632499478917063e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 125\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 125\n",
|
||
"Number of inequality constraint evaluations = 125\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.435\n",
|
||
"Total CPU secs in NLP function evaluations = 135.123\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 586.00us ( 4.69us) 570.16us ( 4.56us) 125\n",
|
||
" nlp_g | 5.63 s ( 45.06ms) 5.38 s ( 43.02ms) 125\n",
|
||
" nlp_grad | 1.40 s ( 1.40 s) 1.34 s ( 1.34 s) 1\n",
|
||
" nlp_grad_f | 404.00us ( 3.96us) 362.31us ( 3.55us) 102\n",
|
||
" nlp_jac_g | 132.28 s ( 1.30 s) 126.25 s ( 1.24 s) 102\n",
|
||
" total | 139.46 s (139.46 s) 133.11 s (133.11 s) 1\n",
|
||
"Timestamp 6900\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.24e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0013212e+01 1.45e+01 2.24e+03 -1.5 2.24e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.4013325e+00 5.33e+00 9.76e+00 0.4 1.45e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.0759054e+01 1.56e+00 6.06e-01 -1.6 8.95e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.1886859e+01 2.59e-03 8.55e-02 -3.4 2.15e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.1888132e+01 7.69e-08 1.09e-04 -5.3 2.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.1888132e+01 1.77e-07 5.83e-05 -11.0 1.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.1888132e+01 7.12e-08 8.10e-05 -11.0 1.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.1888132e+01 7.15e-11 2.57e-05 -11.0 4.85e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 9 1.1888132e+01 7.10e-09 2.38e-05 -11.0 2.95e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.1888132e+01 1.62e-08 8.95e-05 -11.0 1.62e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.1888132e+01 1.37e-07 1.08e-04 -11.0 1.15e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.1888131e+01 1.02e-06 3.46e-03 -11.0 1.68e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.1888132e+01 4.98e-08 2.85e-05 -11.0 1.67e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.1888132e+01 3.24e-09 3.33e-05 -11.0 4.93e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.1888132e+01 4.42e-09 2.42e-04 -11.0 6.61e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.1888132e+01 2.48e-08 6.77e-05 -11.0 6.14e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.1888132e+01 1.01e-08 3.87e-04 -11.0 8.60e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.1888132e+01 1.68e-07 5.36e-05 -11.0 3.36e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.1888132e+01 3.08e-08 3.23e-05 -11.0 1.22e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.1888132e+01 4.79e-09 7.92e-05 -11.0 7.79e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.1888132e+01 2.84e-08 1.19e-04 -11.0 1.95e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.1888132e+01 2.44e-07 1.41e-04 -11.0 8.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.1888132e+01 3.01e-08 8.89e-05 -11.0 1.80e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.1888132e+01 2.13e-08 5.97e-05 -11.0 1.71e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.1888132e+01 1.75e-09 1.45e-05 -11.0 1.87e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.1888132e+01 1.02e-10 1.88e-04 -11.0 7.91e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 27 1.1888132e+01 5.39e-08 5.45e-05 -11.0 5.09e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.1888132e+01 1.67e-10 3.32e-05 -11.0 2.90e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 29 1.1888132e+01 2.68e-08 3.18e-05 -11.0 3.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.1888132e+01 9.64e-08 1.24e-04 -11.0 6.55e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.1888131e+01 1.82e-06 6.17e-03 -11.0 6.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.1888130e+01 1.05e-06 8.49e-03 -11.0 7.37e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.1888131e+01 9.88e-08 2.94e-05 -11.0 6.51e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.1888131e+01 1.59e-07 1.01e-04 -11.0 3.54e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.1888131e+01 9.11e-07 5.32e-03 -11.0 5.72e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.1888132e+01 6.37e-09 5.59e-05 -11.0 4.43e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.1888132e+01 2.03e-09 1.88e-04 -11.0 2.66e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.1888132e+01 1.80e-08 2.08e-05 -11.0 3.42e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.1888132e+01 4.16e-09 7.62e-05 -11.0 3.73e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.1888132e+01 8.28e-09 1.26e-04 -11.0 2.77e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.1888132e+01 3.62e-08 1.26e-04 -11.0 1.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.1888132e+01 9.69e-09 5.78e-05 -11.0 4.41e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.1888132e+01 7.78e-09 8.13e-05 -11.0 5.03e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.1888132e+01 2.39e-08 3.05e-05 -11.0 2.85e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.1888132e+01 9.04e-09 4.55e-05 -11.0 1.78e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.1858333e+01 1.15e-02 1.47e-01 -11.0 4.05e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 47 1.1774558e+01 6.29e-02 4.02e-03 -11.0 3.33e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.1683777e+01 7.17e-02 6.78e-03 -11.0 2.46e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.1863730e+01 1.29e-02 6.19e-03 -11.0 5.32e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.1880485e+01 1.23e-04 3.58e-03 -11.0 7.11e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 51 1.1871427e+01 1.29e-02 5.34e-03 -11.0 1.11e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.1832378e+01 8.31e-02 5.26e-03 -11.0 1.24e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.1871824e+01 4.78e-03 3.98e-03 -11.0 5.91e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.1858695e+01 2.08e-02 3.04e-03 -11.0 5.45e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.1835667e+01 2.84e-02 2.93e-03 -11.0 1.25e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.1876094e+01 9.64e-05 3.53e-03 -11.0 5.05e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.1856504e+01 1.57e-02 1.99e-03 -11.0 9.44e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 58 1.1844730e+01 1.19e-02 1.58e-03 -11.0 5.64e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.0849160e+01 6.02e-01 5.19e-02 -11.0 2.25e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.1864259e+01 7.52e-02 7.48e-02 -11.0 4.49e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.1577586e+01 2.10e-01 1.07e-02 -11.0 1.30e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.1442634e+01 3.56e-01 2.71e-03 -11.0 1.45e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.1526817e+01 3.47e-01 3.01e-02 -11.0 1.33e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.1724329e+01 1.38e-01 2.21e-02 -11.0 1.59e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.1675253e+01 1.29e-01 3.49e-02 -11.0 5.28e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.1894244e+01 4.76e-02 1.12e-02 -11.0 2.16e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 9.5084894e+00 2.65e+00 2.39e-01 -11.0 1.52e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 68 8.9860162e+00 2.55e+00 3.72e-01 -9.0 6.69e+04 - 1.00e+00 3.01e-01h 1\n",
|
||
" 69 1.2060597e+01 1.41e-01 3.57e-01 -9.9 2.74e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.6013401e+00 6.59e+00 2.09e-01 -10.0 1.25e+05 - 2.37e-02 4.80e-01f 1\n",
|
||
" 71 1.1060363e+01 1.05e+00 3.78e-01 -10.2 7.29e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.0243768e+01 2.14e+00 6.37e-02 -4.4 1.10e+04 - 4.39e-01 1.00e+00h 1\n",
|
||
" 73 1.0239262e+01 2.13e+00 6.29e-02 -2.7 4.03e+04 - 1.00e+00 3.27e-03h 1\n",
|
||
" 74 1.1406034e+01 6.88e-01 1.37e-01 -3.5 7.49e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.1288865e+01 6.05e-01 4.75e-02 -3.6 4.47e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.1654081e+01 4.16e-01 8.59e-02 -2.9 2.87e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.1089712e+01 3.52e+00 2.12e-01 -3.0 1.13e+04 - 3.50e-01 1.00e+00f 1\n",
|
||
" 78 1.1083294e+01 3.49e+00 2.10e-01 -3.0 1.01e+05 - 5.52e-01 1.79e-03h 1\n",
|
||
" 79 1.1411013e+01 5.41e-01 1.45e-01 -3.0 4.08e+03 - 5.60e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 9.7037411e+00 3.82e+00 2.49e-01 -9.0 7.17e+04 - 8.08e-02 4.18e-01f 1\n",
|
||
" 81 9.6579740e+00 3.91e+00 2.63e-01 -3.3 1.29e+06 - 1.77e-03 2.35e-04f 1\n",
|
||
" 82 1.0791154e+01 2.16e+00 5.33e-02 -3.3 2.70e+03 - 1.00e+00 4.52e-01h 1\n",
|
||
" 83 1.2084684e+01 2.28e-01 1.66e-01 -3.3 1.20e+03 - 4.90e-01 1.00e+00h 1\n",
|
||
" 84 1.2173239e+01 1.48e-01 8.86e-02 -3.3 1.82e+03 - 4.06e-01 1.00e+00h 1\n",
|
||
" 85 1.2074309e+01 3.08e-01 4.57e-02 -3.3 4.94e+04 - 7.27e-01 6.11e-02h 1\n",
|
||
" 86 1.2282264e+01 9.22e-02 1.14e-01 -3.3 2.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.2426169e+01 4.75e-02 4.15e-02 -3.3 8.68e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.2376259e+01 5.60e-02 4.46e-02 -3.3 3.74e+02 - 8.25e-01 1.00e+00h 1\n",
|
||
" 89 1.2162577e+01 1.28e-01 1.25e-02 -3.3 1.99e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90r 1.2162577e+01 1.28e-01 9.99e+02 -0.9 0.00e+00 - 0.00e+00 3.95e-07R 20\n",
|
||
" 91r 1.2444383e+01 4.97e-02 8.37e+02 -3.0 1.68e+02 - 1.00e+00 1.77e-03f 1\n",
|
||
" 92r 1.2533480e+01 1.08e-02 9.66e-01 -1.1 4.91e-01 - 1.00e+00 3.48e-01f 1\n",
|
||
" 93 1.2510390e+01 1.64e-02 4.18e-01 -3.3 3.85e+02 - 1.00e+00 1.95e-01h 1\n",
|
||
" 94 1.2475607e+01 3.81e-03 1.43e-02 -3.3 6.32e+03 - 5.92e-01 1.00e+00F 1\n",
|
||
" 95 1.2475032e+01 3.82e-03 1.42e-02 -3.3 1.45e+04 - 1.00e+00 4.25e-04h 2\n",
|
||
" 96 1.2463835e+01 1.81e-02 1.52e-03 -3.3 1.03e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.2476678e+01 1.17e-03 2.62e-03 -3.3 8.98e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 98 1.2470893e+01 1.00e-02 2.11e-03 -4.9 5.22e+02 - 3.45e-01 1.22e-01h 1\n",
|
||
" 99 1.2262542e+01 4.01e-01 2.09e-02 -4.9 3.28e+03 - 1.15e-02 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.2371348e+01 1.86e-01 6.55e-03 -4.9 5.28e+02 - 1.00e+00 5.00e-01h 2\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.2371347882143215e+01 1.2371347882143215e+01\n",
|
||
"Dual infeasibility......: 6.5508768732365219e-03 6.5508768732365219e-03\n",
|
||
"Constraint violation....: 1.8649714782083393e-01 1.8649714782083393e-01\n",
|
||
"Complementarity.........: 1.8325498153168711e-05 1.8325498153168711e-05\n",
|
||
"Overall NLP error.......: 1.8649714782083393e-01 1.8649714782083393e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 132\n",
|
||
"Number of objective gradient evaluations = 100\n",
|
||
"Number of equality constraint evaluations = 132\n",
|
||
"Number of inequality constraint evaluations = 132\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.395\n",
|
||
"Total CPU secs in NLP function evaluations = 136.654\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 581.00us ( 4.40us) 576.99us ( 4.37us) 132\n",
|
||
" nlp_g | 5.86 s ( 44.40ms) 5.59 s ( 42.32ms) 132\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 387.00us ( 3.83us) 378.69us ( 3.75us) 101\n",
|
||
" nlp_jac_g | 133.51 s ( 1.30 s) 127.46 s ( 1.24 s) 103\n",
|
||
" total | 140.84 s (140.84 s) 134.45 s (134.45 s) 1\n",
|
||
"Timestamp 7200\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 4.97e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9875913e+01 1.43e+01 4.97e+03 -1.5 4.97e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.5211848e+00 5.14e+00 1.02e+01 0.6 5.12e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.1293767e+01 1.60e+00 8.63e-01 -1.5 1.30e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.2414975e+01 1.68e-03 9.00e-02 -3.3 2.13e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.2415820e+01 7.42e-06 3.31e-03 -5.1 1.83e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.2415606e+01 9.51e-05 5.30e-03 -7.2 3.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.2415803e+01 7.68e-06 1.93e-03 -9.3 6.88e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.2415614e+01 8.23e-05 1.67e-03 -11.0 2.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.2403316e+01 5.05e-03 1.23e-02 -11.0 1.79e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.2416371e+01 2.43e-04 1.61e-03 -11.0 1.49e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.2413453e+01 6.61e-04 7.23e-03 -11.0 3.87e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.2410960e+01 1.91e-03 1.02e-02 -11.0 4.45e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.2401945e+01 1.48e-02 1.70e-02 -11.0 7.58e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.2410645e+01 2.37e-06 1.21e-02 -11.0 1.28e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.2410645e+01 6.67e-07 1.35e-04 -11.0 2.87e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.2410644e+01 1.63e-06 1.01e-03 -11.0 4.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.2410645e+01 2.29e-07 5.53e-05 -11.0 2.07e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.2410645e+01 2.85e-07 1.07e-04 -11.0 1.51e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.2410645e+01 2.80e-07 1.81e-04 -11.0 8.93e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.2410644e+01 1.63e-06 3.17e-03 -11.0 3.98e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.2410645e+01 1.14e-07 1.67e-04 -11.0 2.14e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.2410640e+01 4.63e-06 3.48e-03 -11.0 1.02e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.2410645e+01 6.27e-07 8.86e-05 -11.0 4.83e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.2410644e+01 1.15e-06 1.35e-03 -11.0 6.72e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.2410644e+01 6.93e-07 2.64e-03 -11.0 5.55e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.2410644e+01 7.52e-07 1.62e-03 -11.0 9.15e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.2410645e+01 1.86e-10 2.54e-04 -11.0 1.05e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 28 1.2410637e+01 6.81e-06 5.63e-04 -11.0 9.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.2410645e+01 6.16e-10 8.12e-05 -11.0 1.21e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.2410644e+01 8.69e-07 1.14e-03 -11.0 3.37e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.2410644e+01 5.13e-07 8.30e-05 -11.0 2.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.2410645e+01 3.67e-07 2.13e-04 -11.0 1.82e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.2410644e+01 4.55e-07 1.69e-04 -11.0 1.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.2410644e+01 1.41e-06 2.95e-03 -11.0 3.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.2410641e+01 3.28e-06 8.44e-03 -11.0 1.27e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.2410645e+01 1.86e-07 3.97e-05 -11.0 2.54e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.2410644e+01 8.51e-07 2.86e-03 -11.0 8.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.2410629e+01 1.26e-05 4.92e-03 -11.0 2.59e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.2410645e+01 1.60e-08 9.62e-05 -11.0 9.82e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.2410645e+01 6.33e-09 3.71e-05 -11.0 5.52e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.2410645e+01 9.76e-10 6.05e-05 -11.0 1.40e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.2410645e+01 8.93e-09 1.62e-04 -11.0 2.35e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.2410645e+01 6.05e-09 3.95e-05 -11.0 2.57e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.2410645e+01 2.34e-09 1.49e-04 -11.0 3.06e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.2410645e+01 5.40e-08 4.42e-05 -11.0 1.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.2410645e+01 1.29e-08 3.91e-05 -11.0 1.75e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.2410645e+01 1.52e-09 6.00e-05 -11.0 2.02e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.2410645e+01 2.86e-09 1.00e-04 -11.0 4.64e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.2410645e+01 7.65e-09 1.30e-04 -11.0 5.00e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.2410645e+01 8.58e-11 1.02e-04 -11.0 4.40e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 51 1.2410645e+01 4.08e-09 1.50e-04 -11.0 2.34e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.2410645e+01 3.49e-09 5.25e-05 -11.0 1.46e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.2410645e+01 1.22e-10 1.53e-04 -11.0 1.21e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 54 1.2410645e+01 9.59e-09 1.89e-04 -11.0 3.68e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.2410645e+01 1.83e-08 3.92e-05 -11.0 5.43e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.2410645e+01 3.20e-09 6.41e-05 -11.0 5.87e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.2410644e+01 2.94e-07 9.93e-06 -11.0 5.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.2410645e+01 1.57e-07 2.30e-04 -11.0 6.41e-04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 59 1.2410636e+01 3.21e-06 2.75e-03 -11.0 1.33e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.2410313e+01 3.71e-04 1.71e-02 -11.0 5.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.2410572e+01 4.20e-05 3.37e-03 -11.0 2.71e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.2410532e+01 3.68e-05 1.22e-03 -11.0 1.88e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.2410622e+01 3.13e-06 3.49e-03 -11.0 1.98e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.2410613e+01 7.71e-06 1.36e-03 -11.0 1.44e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.2410612e+01 2.51e-05 1.24e-03 -11.0 1.70e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.2410628e+01 4.23e-09 3.86e-05 -11.0 9.38e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 1.2410628e+01 6.87e-07 1.76e-03 -11.0 1.02e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.2410627e+01 8.89e-07 1.06e-03 -11.0 1.66e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.2410599e+01 3.59e-05 3.47e-03 -11.0 1.55e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.2410423e+01 1.19e-04 2.37e-03 -11.0 5.77e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.2410434e+01 4.67e-05 1.08e-03 -11.0 3.28e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.2410629e+01 3.11e-06 2.14e-03 -11.0 1.49e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.2410106e+01 1.15e-04 4.42e-03 -11.0 4.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.2410391e+01 1.41e-04 2.60e-03 -11.0 4.81e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.2410617e+01 4.26e-05 2.47e-03 -11.0 2.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.2410607e+01 5.35e-05 2.40e-03 -11.0 5.79e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.2410523e+01 1.22e-04 3.18e-03 -11.0 1.31e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.2408883e+01 1.16e-03 5.28e-03 -11.0 4.82e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.2410668e+01 7.45e-08 4.98e-05 -11.0 1.78e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.2410667e+01 1.78e-07 1.16e-04 -11.0 1.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.2410654e+01 2.53e-05 6.60e-03 -11.0 6.59e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.2410666e+01 1.50e-06 1.19e-03 -11.0 2.03e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.2410602e+01 4.32e-05 3.02e-03 -11.0 1.68e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.2410660e+01 7.41e-06 1.75e-03 -11.0 6.28e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.2410660e+01 3.30e-06 6.58e-04 -11.0 3.82e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.2410591e+01 1.15e-04 9.23e-03 -11.0 5.03e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.2410637e+01 3.08e-05 1.03e-03 -11.0 1.42e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.2410199e+01 2.34e-04 1.04e-03 -11.0 3.47e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.2349500e+01 3.49e-02 6.78e-03 -11.0 5.38e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.2407594e+01 2.26e-03 3.17e-03 -11.0 3.80e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.2396465e+01 6.63e-03 2.41e-03 -11.0 3.33e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.2318672e+01 2.21e-01 1.73e-02 -11.0 7.15e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.2424178e+01 7.75e-03 2.75e-02 -11.0 1.17e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 94 1.2397033e+01 6.58e-02 4.52e-03 -11.0 7.48e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.1888714e+01 5.28e-01 2.88e-02 -11.0 2.54e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 96 1.2377841e+01 3.66e-02 5.57e-02 -11.0 6.83e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.2332462e+01 5.67e-02 2.85e-02 -11.0 3.32e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.2392804e+01 1.03e-02 4.01e-03 -11.0 2.67e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.2338321e+01 8.56e-02 1.53e-02 -11.0 9.36e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.1863362e+01 3.01e+00 1.64e-01 -11.0 1.43e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.1863362310740611e+01 1.1863362310740611e+01\n",
|
||
"Dual infeasibility......: 1.6408154030262617e-01 1.6408154030262617e-01\n",
|
||
"Constraint violation....: 3.0095294544231344e+00 3.0095294544231344e+00\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 3.0095294544231344e+00 3.0095294544231344e+00\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 108\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 108\n",
|
||
"Number of inequality constraint evaluations = 108\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.426\n",
|
||
"Total CPU secs in NLP function evaluations = 134.461\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 507.00us ( 4.69us) 495.64us ( 4.59us) 108\n",
|
||
" nlp_g | 4.87 s ( 45.11ms) 4.64 s ( 43.00ms) 108\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 396.00us ( 3.88us) 386.72us ( 3.79us) 102\n",
|
||
" nlp_jac_g | 132.33 s ( 1.30 s) 126.34 s ( 1.24 s) 102\n",
|
||
" total | 138.68 s (138.68 s) 132.40 s (132.40 s) 1\n",
|
||
"Timestamp 7500\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 8.73e+02 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9980014e+01 1.41e+01 8.73e+02 -1.5 8.73e+02 - 9.90e-01 1.00e+00f 1\n",
|
||
" 2 9.0993293e+00 5.11e+00 9.49e+00 0.4 1.41e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 9.5275454e+00 1.43e+00 6.06e-01 -1.6 8.33e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.0543945e+01 2.49e-03 8.25e-02 -3.4 2.00e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.0545136e+01 2.49e-07 1.21e-04 -5.3 2.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.0545136e+01 1.39e-07 8.48e-05 -11.0 8.15e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.0545136e+01 8.04e-08 5.73e-05 -11.0 3.46e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.0545136e+01 2.81e-08 1.11e-04 -11.0 1.33e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.0545136e+01 2.92e-08 1.24e-04 -11.0 1.58e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.0545136e+01 1.78e-08 4.60e-05 -11.0 7.56e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.0545136e+01 8.54e-09 1.18e-04 -11.0 3.18e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.0545136e+01 2.12e-07 3.72e-05 -11.0 7.21e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.0545136e+01 4.55e-08 6.16e-05 -11.0 8.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.0545136e+01 6.34e-07 4.25e-03 -11.0 6.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.0545133e+01 1.73e-06 1.03e-03 -11.0 5.34e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.0545136e+01 1.96e-07 1.86e-05 -11.0 2.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.0545136e+01 1.51e-07 6.92e-05 -11.0 1.48e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.0545136e+01 4.33e-07 1.31e-04 -11.0 3.96e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.0545135e+01 5.69e-07 2.16e-03 -11.0 2.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.0544969e+01 8.38e-05 9.02e-03 -11.0 3.97e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.0544623e+01 2.05e-04 7.75e-03 -11.0 1.62e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.0544919e+01 1.31e-04 2.42e-03 -11.0 6.60e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.0545108e+01 3.80e-09 1.28e-04 -11.0 2.08e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 24 1.0544436e+01 6.54e-04 4.41e-03 -11.0 1.93e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 25 1.0544724e+01 6.58e-04 2.44e-03 -11.0 2.76e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.0545052e+01 3.67e-05 1.92e-03 -11.0 7.23e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.0544965e+01 1.17e-04 2.34e-03 -11.0 9.18e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.0544727e+01 2.45e-04 1.49e-03 -11.0 3.12e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.0545104e+01 3.19e-08 7.43e-05 -11.0 3.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.0545104e+01 5.39e-08 9.32e-05 -11.0 1.12e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.0545103e+01 5.41e-07 6.28e-05 -11.0 2.20e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.0545104e+01 2.43e-08 2.80e-04 -11.0 2.08e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.0545103e+01 8.98e-08 4.62e-05 -11.0 2.07e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.0545103e+01 1.49e-07 3.54e-05 -11.0 9.61e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.0545103e+01 4.40e-08 1.36e-04 -11.0 6.20e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.0545103e+01 4.68e-08 3.65e-04 -11.0 2.27e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.0545103e+01 1.81e-07 1.32e-04 -11.0 6.24e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.0545103e+01 7.73e-08 1.77e-04 -11.0 5.25e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.0545103e+01 9.30e-08 7.90e-05 -11.0 8.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.0545103e+01 6.13e-08 4.77e-05 -11.0 3.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.0545103e+01 8.35e-08 9.79e-05 -11.0 1.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.0545103e+01 4.22e-07 1.61e-04 -11.0 2.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.0545100e+01 1.92e-06 4.20e-03 -11.0 2.88e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.0545104e+01 1.40e-08 3.96e-05 -11.0 4.61e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.0545102e+01 3.28e-06 1.92e-03 -11.0 1.50e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.0545044e+01 1.16e-04 4.68e-03 -11.0 5.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.0545070e+01 4.74e-05 1.97e-03 -11.0 6.71e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.0545045e+01 7.66e-05 2.36e-03 -11.0 4.60e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.0545087e+01 3.74e-05 2.61e-03 -11.0 3.91e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.0545063e+01 4.92e-05 1.14e-03 -11.0 1.71e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.0545006e+01 7.90e-05 1.93e-03 -11.0 1.22e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.0544537e+01 3.82e-04 2.76e-03 -11.0 8.42e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.0544734e+01 1.78e-04 1.46e-03 -11.0 7.60e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.0535718e+01 1.57e-02 2.50e-03 -11.0 3.06e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.0521191e+01 9.53e-02 6.43e-03 -11.0 1.04e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.0194385e+01 3.46e-01 3.00e-02 -11.0 1.73e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.0389120e+01 4.11e-02 3.58e-02 -11.0 2.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.0217586e+01 3.13e-01 2.01e-02 -11.0 4.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.0395677e+01 1.44e-01 1.16e-02 -11.0 1.08e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 9.2552474e+00 8.40e-01 1.06e-01 -11.0 5.54e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 61 9.7926806e+00 9.49e-01 9.39e-02 -11.0 3.12e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 9.2242004e+00 1.91e+00 1.22e-01 -10.7 2.51e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 9.8582800e+00 1.57e+00 9.15e-02 -10.9 1.35e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 9.4925377e+00 4.59e+00 1.93e-01 -11.0 3.24e+04 - 1.00e+00 9.79e-01h 1\n",
|
||
" 65 9.4655701e+00 4.53e+00 1.88e-01 -11.0 6.46e+04 - 5.13e-11 2.81e-02h 1\n",
|
||
" 66 9.0441714e+00 2.25e+00 3.38e-01 -11.0 7.45e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 8.5857953e+00 2.99e+00 2.32e-01 -9.4 7.64e+03 - 8.15e-01 1.00e+00h 1\n",
|
||
" 68 8.4874827e+00 2.95e+00 2.37e-01 -3.3 1.00e+04 - 2.58e-02 1.31e-01h 1\n",
|
||
" 69 1.0069077e+01 8.40e-01 2.31e-01 -4.4 1.58e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 9.7563282e+00 3.36e-01 1.95e-01 -4.0 3.11e+04 - 1.00e+00 4.27e-01h 1\n",
|
||
" 71 9.7158084e+00 4.06e-01 1.82e-01 -3.3 2.00e+04 - 1.00e+00 2.01e-01h 1\n",
|
||
" 72 1.0493179e+01 1.25e-01 3.77e-02 -3.6 1.44e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.0623650e+01 1.13e-02 8.08e-03 -3.4 6.92e+02 - 7.02e-01 1.00e+00h 1\n",
|
||
" 74 1.0361468e+01 9.32e+00 7.90e-01 -3.4 1.29e+05 - 1.43e-03 4.71e-01f 1\n",
|
||
" 75 1.0357184e+01 9.31e+00 7.89e-01 -3.4 2.31e+04 - 7.52e-01 1.11e-03h 1\n",
|
||
" 76 1.0464820e+01 9.68e-02 9.03e-01 -3.4 3.28e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.0414059e+01 5.41e-02 7.23e-02 -3.4 5.10e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.0465822e+01 3.96e-02 6.42e-03 -3.4 9.76e+02 - 5.79e-01 1.00e+00h 1\n",
|
||
" 79 1.0461185e+01 4.40e-02 5.71e-03 -3.5 3.26e+04 - 9.76e-01 1.46e-03h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.0424793e+01 6.63e-02 8.85e-03 -4.5 5.66e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 9.8571040e+00 3.27e-01 2.76e-02 -4.6 1.58e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 82 1.0095598e+01 1.00e-01 5.51e-03 -4.6 8.73e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.0377548e+01 4.11e-02 1.29e-02 -5.6 1.11e+03 - 1.00e+00 7.21e-01H 1\n",
|
||
" 84 8.1927119e+00 1.08e+00 2.68e-01 -4.7 8.46e+03 - 3.53e-01 1.00e+00f 1\n",
|
||
" 85 8.1008165e+00 8.71e-01 1.88e-01 -3.1 1.28e+04 - 1.00e+00 1.42e-01h 1\n",
|
||
" 86 8.5915439e+00 1.07e+00 5.84e-02 -3.0 2.32e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 9.9733091e+00 3.64e-01 1.27e-01 -2.9 9.80e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 9.6686039e+00 4.47e-01 4.42e-02 -1.9 3.20e+03 - 7.82e-01 7.45e-01h 1\n",
|
||
" 89 9.9492522e+00 2.05e-01 4.30e-02 -2.1 1.30e+03 - 5.58e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.0264746e+01 8.06e-02 1.79e-02 -3.0 5.30e+02 - 9.97e-01 1.00e+00h 1\n",
|
||
" 91 1.0389823e+01 1.87e-02 8.57e-03 -4.6 2.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.0369784e+01 3.48e-02 2.51e-02 -5.0 3.59e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 9.7104870e+00 4.06e-01 8.51e-02 -4.8 1.97e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 94 8.5700757e+00 1.17e+00 1.01e-01 -4.5 4.93e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 95 6.3173445e+00 4.43e+00 4.74e-01 -4.6 8.97e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 96 8.3751879e+00 1.57e+00 5.00e-01 -4.4 1.02e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 97 7.1122092e+00 3.60e+00 2.21e-01 -4.5 2.18e+04 - 7.02e-01 1.00e+00f 1\n",
|
||
" 98 6.6140711e+00 4.41e+00 3.11e-01 -4.5 5.59e+04 - 1.00e+00 3.82e-01f 1\n",
|
||
" 99 5.3457038e+00 2.35e+00 4.89e-01 -4.5 3.21e+04 - 1.52e-01 3.99e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.1992689e+00 1.70e+00 5.28e-01 -4.7 1.50e+04 - 1.30e-01 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.1992689086962915e+00 8.1992689086962915e+00\n",
|
||
"Dual infeasibility......: 5.2811854542878767e-01 5.2811854542878767e-01\n",
|
||
"Constraint violation....: 1.7030895856018091e+00 1.7030895856018091e+00\n",
|
||
"Complementarity.........: 9.1430750521755610e-04 9.1430750521755610e-04\n",
|
||
"Overall NLP error.......: 1.7030895856018091e+00 1.7030895856018091e+00\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 106\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 106\n",
|
||
"Number of inequality constraint evaluations = 106\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.445\n",
|
||
"Total CPU secs in NLP function evaluations = 134.741\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 472.00us ( 4.45us) 471.13us ( 4.44us) 106\n",
|
||
" nlp_g | 4.81 s ( 45.37ms) 4.59 s ( 43.31ms) 106\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 362.00us ( 3.55us) 361.74us ( 3.55us) 102\n",
|
||
" nlp_jac_g | 132.72 s ( 1.30 s) 126.71 s ( 1.24 s) 102\n",
|
||
" total | 139.01 s (139.01 s) 132.72 s (132.72 s) 1\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<ipython-input-386-844f88ed3e07>:51: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||
" plt.figure(figsize = (15, 5))\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Timestamp 7800\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.10e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9981840e+01 1.47e+01 1.10e+04 -1.5 1.10e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.0445731e+01 5.35e+00 1.02e+01 0.6 1.14e+02 - 9.99e-01 1.00e+00f 1\n",
|
||
" 3 1.3473671e+01 1.85e+00 8.79e-01 -1.5 2.80e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 1.4738282e+01 1.10e-03 9.57e-02 -3.2 2.38e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.4738752e+01 9.55e-06 4.45e-03 -5.1 4.35e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.4738767e+01 1.34e-06 1.29e-03 -7.2 1.84e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.4738761e+01 5.50e-06 2.05e-03 -9.3 5.83e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.4738756e+01 7.55e-06 1.39e-03 -11.0 3.55e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.4738741e+01 2.68e-05 2.37e-03 -11.0 1.23e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.4738447e+01 1.68e-04 7.91e-03 -11.0 3.91e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.4738783e+01 1.86e-05 2.52e-03 -11.0 5.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.4738790e+01 9.75e-07 1.11e-03 -11.0 3.06e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.4738752e+01 3.46e-05 2.59e-03 -11.0 1.16e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.4738672e+01 6.69e-05 3.14e-03 -11.0 4.70e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.4738676e+01 5.07e-05 1.75e-03 -11.0 2.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.4738769e+01 7.22e-06 4.21e-03 -11.0 5.20e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.4738718e+01 2.25e-05 2.27e-03 -11.0 4.97e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.4738766e+01 3.55e-06 1.68e-03 -11.0 1.73e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.4738774e+01 3.04e-06 2.14e-03 -11.0 1.54e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.4738773e+01 1.95e-06 3.07e-03 -11.0 1.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.4738762e+01 1.21e-05 1.91e-03 -11.0 4.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.4738772e+01 6.75e-06 1.58e-03 -11.0 2.48e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.4738768e+01 8.08e-06 2.03e-03 -11.0 2.82e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.4738688e+01 4.77e-05 9.54e-03 -11.0 2.22e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.4738772e+01 5.43e-06 1.87e-03 -11.0 5.29e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.4738765e+01 3.96e-06 1.63e-03 -11.0 3.60e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.4738773e+01 1.78e-06 1.99e-03 -11.0 2.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.4738775e+01 1.34e-06 1.44e-03 -11.0 1.56e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.4738773e+01 7.34e-06 1.20e-03 -11.0 1.43e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.4738630e+01 7.78e-05 2.90e-03 -11.0 5.07e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.4737856e+01 5.59e-04 6.47e-03 -11.0 3.61e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.4738582e+01 1.22e-07 1.35e-04 -11.0 1.59e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 33 1.4738071e+01 5.77e-04 1.27e-03 -11.0 1.97e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.4738243e+01 1.32e-04 9.94e-04 -11.0 1.36e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.4738539e+01 4.24e-05 1.47e-03 -11.0 5.53e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.4737006e+01 5.94e-04 1.64e-03 -11.0 1.75e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.4738619e+01 6.71e-04 1.35e-03 -11.0 3.15e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.4738742e+01 1.48e-04 1.90e-03 -11.0 2.54e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.4735725e+01 2.27e-03 3.42e-03 -11.0 1.68e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.4738806e+01 3.59e-04 2.55e-03 -11.0 5.56e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.4330278e+01 9.32e+00 5.10e-01 -11.0 3.25e+04 - 9.67e-01 9.69e-01f 1\n",
|
||
" 42r 1.4330278e+01 9.32e+00 9.99e+02 1.0 0.00e+00 - 0.00e+00 3.43e-11R 2\n",
|
||
" 43r 1.3833960e+01 1.22e+00 8.64e+02 -5.1 1.22e+03 - 1.00e+00 6.66e-03f 1\n",
|
||
" 44 1.4297337e+01 4.00e-04 1.73e-02 -4.6 9.57e+00 - 1.25e-01 1.00e+00h 1\n",
|
||
" 45 1.4297111e+01 4.57e-04 1.64e-02 -6.2 6.18e+01 - 1.00e+00 2.48e-02h 1\n",
|
||
" 46 1.4297534e+01 1.09e-04 4.80e-03 -5.8 7.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.4297792e+01 2.17e-05 1.15e-03 -6.8 5.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.4295667e+01 2.99e-03 7.39e-03 -8.7 8.95e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.4291128e+01 5.51e-03 4.52e-03 -9.4 1.53e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.4275668e+01 4.44e-02 8.67e-03 -7.4 9.54e+02 - 5.71e-01 1.00e+00h 1\n",
|
||
" 51 1.4259352e+01 6.00e-02 8.49e-03 -5.7 2.28e+03 - 8.07e-03 1.00e+00h 1\n",
|
||
" 52 1.4046444e+01 2.40e-01 1.02e-02 -11.0 5.24e+06 - 1.43e-05 2.28e-03f 1\n",
|
||
" 53 1.4065059e+01 2.22e-01 9.02e-03 -5.5 1.66e+03 - 1.00e+00 7.21e-02h 1\n",
|
||
" 54 1.4314497e+01 2.48e-03 1.34e-02 -4.3 6.98e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.4272490e+01 1.86e-02 2.78e-03 -3.8 1.40e+02 - 4.58e-01 1.00e+00h 1\n",
|
||
" 56 1.4322456e+01 9.32e-06 5.38e-02 -5.9 5.61e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.4322464e+01 1.03e-06 1.27e-03 -7.8 4.27e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.4322450e+01 6.70e-06 1.88e-03 -9.9 5.48e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.4322441e+01 7.73e-06 1.61e-03 -11.0 6.43e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.4322439e+01 1.41e-05 2.52e-03 -11.0 2.00e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.4322344e+01 8.82e-05 3.18e-03 -11.0 9.07e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.4322442e+01 5.92e-05 2.53e-03 -11.0 1.71e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.4322437e+01 5.06e-05 1.62e-03 -11.0 2.73e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.4322417e+01 6.61e-05 2.67e-03 -11.0 1.84e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.4322358e+01 9.65e-05 2.42e-03 -11.0 4.84e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.4322452e+01 9.89e-09 2.86e-05 -11.0 2.28e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 1.4322413e+01 2.36e-05 4.36e-03 -11.0 3.84e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.4322235e+01 2.24e-04 1.48e-03 -11.0 2.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.4321959e+01 1.10e-03 1.52e-03 -11.0 1.42e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.4322158e+01 8.54e-04 2.58e-03 -11.0 5.80e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.4318932e+01 2.02e-03 1.78e-03 -11.0 7.01e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.4313065e+01 7.81e-03 2.38e-03 -11.0 1.17e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.4296633e+01 1.15e-02 2.69e-03 -11.0 1.90e+02 - 9.90e-01 1.00e+00h 1\n",
|
||
" 74 1.4322396e+01 2.08e-06 2.94e-02 -11.0 3.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.4322393e+01 2.50e-06 1.04e-03 -11.0 7.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.4322397e+01 1.45e-07 2.11e-04 -11.0 1.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.4322397e+01 4.65e-07 2.60e-04 -11.0 1.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.4322397e+01 5.70e-07 1.78e-03 -11.0 2.76e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.4322396e+01 1.24e-06 2.87e-03 -11.0 4.94e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.4322397e+01 2.36e-07 5.32e-05 -11.0 2.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.4322398e+01 2.89e-11 6.91e-05 -11.0 1.98e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 82 1.4322397e+01 6.97e-08 7.04e-05 -11.0 6.53e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.4322397e+01 1.27e-07 5.03e-05 -11.0 2.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.4322397e+01 1.61e-07 1.44e-04 -11.0 1.80e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.4322397e+01 5.02e-07 9.91e-05 -11.0 1.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.4322397e+01 2.06e-07 1.79e-04 -11.0 8.42e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.4322397e+01 2.10e-07 6.13e-05 -11.0 6.10e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.4322397e+01 9.52e-08 4.60e-05 -11.0 5.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.4322398e+01 1.15e-07 8.91e-05 -11.0 4.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.4322397e+01 3.78e-07 8.94e-05 -11.0 1.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.4322396e+01 8.30e-07 2.34e-03 -11.0 5.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.4322397e+01 9.07e-08 2.98e-04 -11.0 1.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.4322397e+01 4.84e-07 2.66e-05 -11.0 6.22e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.4322398e+01 6.96e-08 1.80e-04 -11.0 4.76e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.4322392e+01 1.16e-06 6.21e-03 -11.0 7.92e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.4322396e+01 7.23e-07 2.08e-03 -11.0 3.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.4322395e+01 1.93e-06 2.34e-03 -11.0 1.60e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.4322398e+01 2.84e-10 1.60e-04 -11.0 5.52e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 99 1.4322397e+01 9.27e-07 1.23e-03 -11.0 2.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.4322397e+01 3.65e-07 7.46e-05 -11.0 2.54e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.4322397342410332e+01 1.4322397342410332e+01\n",
|
||
"Dual infeasibility......: 7.4635599901387334e-05 7.4635599901387334e-05\n",
|
||
"Constraint violation....: 3.6482396836845510e-07 3.6482396836845510e-07\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 7.4635599901387334e-05 7.4635599901387334e-05\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 107\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 107\n",
|
||
"Number of inequality constraint evaluations = 107\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.444\n",
|
||
"Total CPU secs in NLP function evaluations = 136.157\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 486.00us ( 4.54us) 478.50us ( 4.47us) 107\n",
|
||
" nlp_g | 4.82 s ( 45.00ms) 4.59 s ( 42.90ms) 107\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 367.00us ( 3.60us) 360.60us ( 3.54us) 102\n",
|
||
" nlp_jac_g | 134.15 s ( 1.30 s) 128.06 s ( 1.24 s) 103\n",
|
||
" total | 140.43 s (140.43 s) 134.06 s (134.06 s) 1\n",
|
||
"Timestamp 8100\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.79e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0049803e+01 1.37e+01 3.79e+03 -1.5 3.79e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.6472942e+00 4.92e+00 8.72e+00 0.4 2.28e+01 - 9.97e-01 1.00e+00f 1\n",
|
||
" 3 7.2013449e+00 1.19e+00 7.75e-01 -1.6 7.21e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 8.0317799e+00 3.47e-03 9.06e-02 -3.3 1.74e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 8.0333998e+00 1.04e-06 8.04e-04 -5.2 3.52e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 8.0334012e+00 3.38e-07 1.02e-04 -7.3 2.39e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 8.0333975e+00 4.79e-06 1.30e-03 -11.0 2.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 8.0333857e+00 1.18e-05 4.56e-03 -11.0 4.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 8.0330471e+00 2.03e-04 9.45e-03 -11.0 1.06e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 8.0322494e+00 7.15e-04 2.06e-02 -11.0 1.93e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 8.0334673e+00 7.22e-06 2.48e-03 -11.0 2.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 8.0334633e+00 9.87e-06 1.59e-03 -11.0 1.31e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 8.0334303e+00 3.80e-05 3.79e-03 -11.0 2.55e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 8.0334630e+00 2.08e-05 5.33e-03 -11.0 2.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 8.0334564e+00 2.12e-05 2.39e-03 -11.0 1.44e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 8.0334626e+00 1.59e-05 1.85e-03 -11.0 6.01e-02 - 1.00e+00 2.50e-01h 3\n",
|
||
" 17 8.0333904e+00 6.22e-05 2.59e-03 -11.0 1.86e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 7.4242443e+00 3.26e-01 3.37e-02 -11.0 1.22e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 19 7.9756273e+00 3.83e-02 2.28e-02 -11.0 3.53e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 8.0018609e+00 2.50e-02 1.97e-02 -11.0 2.15e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 8.0420120e+00 2.57e-02 3.65e-03 -11.0 1.46e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 8.0602291e+00 8.64e-03 2.31e-03 -11.0 1.31e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 8.0701609e+00 4.69e-05 2.98e-03 -11.0 3.86e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 24 7.7492419e+00 1.16e-01 1.74e-02 -11.0 8.72e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 25 7.5690204e+00 2.47e-01 5.62e-03 -11.0 9.63e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 8.0127056e+00 1.71e-03 4.04e-01 -11.0 4.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.0142527e+00 1.07e-06 1.54e-03 -11.0 5.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 8.0142553e+00 2.48e-07 1.00e-04 -11.0 1.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.0142556e+00 1.82e-07 1.35e-04 -11.0 9.28e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.0091332e+00 2.94e-03 7.00e-02 -11.0 1.24e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 31 8.0130135e+00 5.18e-04 1.03e-03 -11.0 3.75e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 8.0127635e+00 1.40e-03 1.01e-03 -11.0 3.80e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 8.0114430e+00 1.76e-03 2.45e-03 -11.0 3.95e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 8.0113187e+00 3.02e-03 1.94e-03 -11.0 3.96e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 8.0131181e+00 8.70e-04 2.16e-03 -11.0 3.15e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 8.0096328e+00 1.72e-03 1.76e-03 -11.0 6.36e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 8.0135493e+00 1.33e-04 1.18e-03 -11.0 2.67e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 8.0123861e+00 2.22e-03 3.55e-03 -11.0 1.54e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 8.0062604e+00 5.16e-03 2.18e-03 -11.0 4.03e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.0036283e+00 1.86e-02 1.85e-03 -11.0 3.84e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 8.0116810e+00 1.35e-03 2.46e-03 -11.0 1.11e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 8.0100024e+00 1.67e-03 1.19e-03 -11.0 8.66e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 8.0133888e+00 3.43e-04 2.23e-03 -11.0 2.52e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 8.0129362e+00 9.65e-04 8.86e-04 -11.0 7.56e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 8.0127509e+00 1.97e-03 7.84e-04 -11.0 8.52e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 8.0132821e+00 4.79e-04 1.27e-03 -11.0 3.52e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 8.0136307e+00 3.87e-04 1.04e-03 -11.0 1.78e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 8.0103542e+00 3.26e-03 2.44e-03 -11.0 2.58e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 8.0141440e+00 6.77e-07 5.26e-05 -11.0 1.51e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.0048324e+00 6.62e-03 2.73e-03 -11.0 3.94e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 51 8.0128136e+00 3.21e-03 1.30e-03 -11.0 1.52e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 8.0106574e+00 2.40e-03 1.48e-03 -11.0 1.03e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 7.9803136e+00 4.30e-02 5.17e-03 -11.0 1.10e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 7.9931536e+00 6.68e-03 1.53e-03 -11.0 6.64e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 7.9646853e+00 2.37e-02 5.60e-03 -11.0 1.13e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 8.0099339e+00 9.07e-03 2.68e-03 -11.0 4.32e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 8.0137507e+00 1.91e-03 1.21e-03 -11.0 8.94e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 8.0127103e+00 7.96e-04 1.53e-03 -11.0 9.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 8.0122447e+00 1.06e-03 1.95e-03 -11.0 7.99e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 8.0148040e+00 5.66e-06 1.21e-03 -11.0 2.14e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 61 8.0144905e+00 1.94e-04 1.29e-03 -11.0 1.07e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 8.0138619e+00 5.88e-04 8.12e-04 -11.0 8.20e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 8.0147899e+00 4.17e-08 6.91e-05 -11.0 1.10e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 64 8.0122768e+00 5.91e-03 1.14e-03 -11.0 1.25e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 65 7.3531385e+00 5.59e+00 7.27e-01 -11.0 1.37e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 66 8.2016676e+00 1.02e+00 2.78e-01 -11.0 1.89e+05 - 2.49e-01 9.92e-02h 1\n",
|
||
" 67 7.2159071e+00 6.57e-01 2.10e-01 -11.0 4.37e+03 - 1.34e-10 1.00e+00f 1\n",
|
||
" 68 6.4063354e+00 2.36e+00 8.95e-02 -11.0 3.33e+04 - 1.00e+00 2.50e-01f 3\n",
|
||
" 69 5.9141041e+00 2.78e+00 3.56e-01 -9.7 6.91e+04 - 1.00e+00 4.74e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 5.9111026e+00 2.72e+00 3.41e-01 -7.8 3.02e+04 - 1.00e+00 2.02e-02h 1\n",
|
||
" 71 5.9108043e+00 2.72e+00 3.41e-01 -5.8 5.34e+06 - 3.19e-02 2.35e-06h 1\n",
|
||
" 72 7.7552774e+00 1.48e-01 1.43e-01 -5.1 4.73e+02 - 4.97e-03 1.00e+00h 1\n",
|
||
" 73 7.5601263e+00 1.04e+00 7.58e-02 -7.1 1.28e+04 - 8.37e-04 1.00e+00h 1\n",
|
||
" 74 6.7493823e+00 2.21e+00 3.40e-01 -7.1 1.91e+06 - 1.59e-01 2.63e-02f 1\n",
|
||
" 75 6.6675290e+00 8.60e-01 1.93e-01 -7.1 3.06e+03 - 8.74e-01 1.00e+00h 1\n",
|
||
" 76 7.1612702e+00 4.43e-01 1.30e-01 -4.4 3.16e+03 - 8.79e-01 1.00e+00h 1\n",
|
||
" 77 6.9723479e+00 8.92e-01 9.25e-02 -3.4 5.57e+05 - 1.00e+00 5.28e-03f 1\n",
|
||
" 78 7.5461774e+00 4.65e-01 8.81e-02 -3.5 9.26e+02 - 1.00e+00 6.23e-01h 1\n",
|
||
" 79 6.8948534e+00 4.33e+00 8.28e-01 -3.5 1.24e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 6.3247146e+00 3.80e+00 5.63e-01 -3.5 3.08e+04 - 1.00e+00 1.74e-01f 1\n",
|
||
" 81 5.8841560e+00 2.07e+00 1.28e-01 -1.5 1.24e+04 - 1.00e+00 5.10e-01f 1\n",
|
||
" 82 6.3151338e+00 1.25e+00 5.58e-01 -2.3 1.31e+04 - 1.32e-02 1.00e+00h 1\n",
|
||
" 83 7.5108872e+00 4.58e-01 2.94e-01 -2.5 1.76e+04 - 5.59e-01 1.00e+00H 1\n",
|
||
" 84 6.7351386e+00 3.10e+00 3.58e-01 -2.0 9.02e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 85 6.9080293e+00 1.12e+00 2.04e-01 -2.0 1.20e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 6.1660334e+00 2.21e+00 2.98e-01 -2.0 1.79e+04 - 5.98e-01 5.86e-01F 1\n",
|
||
" 87 6.9742931e+00 1.74e+00 6.11e-01 -1.0 7.23e+04 - 4.15e-01 8.64e-01H 1\n",
|
||
" 88 6.8148296e+00 1.55e+00 5.48e-01 -1.1 8.82e+03 - 8.46e-01 8.94e-02h 1\n",
|
||
" 89 5.9472570e+00 3.53e+00 5.16e-01 -1.1 1.04e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 7.6181302e+00 3.80e-01 4.41e+00 -1.1 4.74e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 7.7988827e+00 1.96e-06 2.94e-02 -1.1 3.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 7.7988839e+00 5.28e-07 1.40e-04 -3.2 9.99e-04 - 9.98e-01 1.00e+00h 1\n",
|
||
" 93 7.7988840e+00 1.63e-07 8.49e-05 -9.1 5.06e-04 - 9.99e-01 1.00e+00h 1\n",
|
||
" 94 7.7988841e+00 7.43e-08 1.33e-04 -11.0 8.76e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 7.7988837e+00 4.69e-07 2.68e-05 -11.0 1.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 7.7988842e+00 9.07e-08 9.19e-05 -11.0 3.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 7.7988839e+00 9.34e-08 5.46e-05 -11.0 1.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 7.7988838e+00 1.80e-07 3.54e-05 -11.0 1.58e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 7.7988841e+00 2.45e-08 2.12e-04 -11.0 6.98e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 7.7988799e+00 2.59e-06 3.63e-03 -11.0 7.65e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 7.7988799294079856e+00 7.7988799294079856e+00\n",
|
||
"Dual infeasibility......: 3.6340867604441884e-03 3.6340867604441884e-03\n",
|
||
"Constraint violation....: 2.5873325846248463e-06 2.5873325846248463e-06\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 3.6340867604441884e-03 3.6340867604441884e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 113\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 113\n",
|
||
"Number of inequality constraint evaluations = 113\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.384\n",
|
||
"Total CPU secs in NLP function evaluations = 134.482\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 510.00us ( 4.51us) 502.04us ( 4.44us) 113\n",
|
||
" nlp_g | 5.08 s ( 45.00ms) 4.85 s ( 42.92ms) 113\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 352.00us ( 3.45us) 343.03us ( 3.36us) 102\n",
|
||
" nlp_jac_g | 132.15 s ( 1.30 s) 126.11 s ( 1.24 s) 102\n",
|
||
" total | 138.71 s (138.71 s) 132.36 s (132.36 s) 1\n",
|
||
"Timestamp 8400\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.26e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0547327e+01 1.36e+01 2.26e+04 -1.5 2.26e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.7876208e+00 5.30e+00 5.50e+00 1.0 6.87e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 3.4929470e+00 8.57e-01 6.64e-01 -1.1 2.39e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 4.0702445e+00 7.50e-03 2.93e-01 -3.0 9.41e+00 - 9.94e-01 1.00e+00h 1\n",
|
||
" 5 4.0717492e+00 4.13e-05 5.75e-03 -4.4 6.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 4.0711262e+00 1.01e-03 9.90e-03 -6.5 6.94e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 4.0616537e+00 9.23e-03 3.56e-02 -8.4 3.04e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 4.0687429e+00 3.08e-03 7.24e-04 -10.2 1.43e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 4.0705857e+00 1.63e-03 1.31e-03 -11.0 1.36e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 4.0592500e+00 9.36e-03 3.68e-03 -11.0 3.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 4.0644215e+00 7.83e-03 2.70e-03 -11.0 3.80e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 4.0696068e+00 1.82e-03 1.84e-03 -11.0 1.88e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 4.0470621e+00 1.49e-02 4.94e-03 -11.0 8.91e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 4.0609276e+00 8.88e-03 4.20e-03 -11.0 5.62e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 3.9941926e+00 9.38e-02 1.41e-02 -11.0 1.82e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 4.0617663e+00 5.82e-03 2.08e-02 -11.0 2.46e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 4.0617010e+00 1.66e-03 1.04e-02 -11.0 3.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 4.0488696e+00 2.27e-02 2.76e-02 -11.0 2.83e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 3.7891694e+00 8.01e-01 2.00e-01 -11.0 9.92e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 3.0824947e+00 1.40e+00 5.05e-01 -9.0 3.79e+04 - 1.00e+00 5.24e-01F 1\n",
|
||
" 21 3.0763492e+00 1.37e+00 4.82e-01 -7.0 9.86e+03 - 1.00e+00 2.10e-02h 1\n",
|
||
" 22 3.9369481e+00 2.08e-01 5.22e-01 -6.1 8.75e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 3.8330099e+00 3.92e-01 4.49e-01 -3.3 2.32e+04 - 1.00e+00 6.37e-02h 1\n",
|
||
" 24 3.1887463e+00 1.14e+00 3.75e-01 -2.4 1.30e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 25 3.3511881e+00 1.35e+00 3.27e-01 -2.4 9.18e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 3.2684042e+00 3.76e-01 2.47e-01 -2.6 1.34e+04 - 9.96e-01 1.00e+00h 1\n",
|
||
" 27 3.4267729e+00 2.30e-01 1.46e-01 -1.6 7.19e+03 - 1.00e+00 8.13e-01h 1\n",
|
||
" 28 3.3414287e+00 5.88e-01 1.02e-01 -2.3 2.34e+04 - 6.73e-01 1.00e+00h 1\n",
|
||
" 29 3.1199416e+00 8.57e-01 1.11e-01 -1.7 2.54e+04 - 1.00e+00 2.58e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 3.2279570e+00 6.81e-01 1.95e-01 -2.8 2.86e+03 - 9.70e-01 1.00e+00h 1\n",
|
||
" 31 3.2099080e+00 6.38e-01 1.49e-01 -1.7 6.19e+03 - 3.98e-01 5.00e-01h 2\n",
|
||
" 32 4.5076513e+00 3.21e-01 4.62e-01 -2.1 2.07e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 33 3.2714326e+00 1.96e+00 4.52e-01 -1.8 3.49e+04 - 8.54e-01 6.92e-01f 1\n",
|
||
" 34 3.7148145e+00 2.25e+00 1.05e+00 -1.8 9.86e+03 - 1.08e-01 1.00e+00h 1\n",
|
||
" 35 3.6584623e+00 8.45e-01 8.06e-02 -1.8 1.39e+04 - 5.99e-01 5.29e-01h 1\n",
|
||
" 36 4.2903524e+00 1.29e+00 3.10e-01 -1.8 4.21e+03 - 9.94e-01 1.00e+00H 1\n",
|
||
" 37 4.0756793e+00 9.20e-01 1.64e-01 -1.8 6.37e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 3.6364952e+00 9.84e-01 2.62e-01 -1.8 1.21e+05 - 5.72e-02 1.31e-01f 1\n",
|
||
" 39 3.6925109e+00 1.06e+00 2.15e-01 -1.8 1.00e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 3.5502808e+00 1.41e+00 3.20e-01 -1.8 9.64e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 3.4821612e+00 1.24e+00 1.32e-01 -1.8 2.68e+04 - 1.00e+00 1.17e-01h 1\n",
|
||
" 42 3.8482381e+00 4.87e-01 2.96e-01 -1.8 8.73e+03 - 6.55e-01 1.00e+00h 1\n",
|
||
" 43 3.3364286e+00 6.89e-01 3.32e-01 -1.8 7.15e+05 - 7.24e-02 1.40e-02f 1\n",
|
||
" 44 5.4388333e+00 3.26e-01 2.60e-01 -1.8 1.67e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 45 3.7378699e+00 5.04e-01 2.17e-01 -1.8 9.75e+03 - 9.02e-01 1.00e+00f 1\n",
|
||
" 46 3.0412206e+00 1.32e+00 2.57e-01 -1.8 9.73e+04 - 5.94e-01 5.11e-02f 1\n",
|
||
" 47 3.1293689e+00 1.13e+00 1.62e-01 -1.2 2.65e+03 - 5.21e-01 1.21e-01h 4\n",
|
||
" 48 4.2144332e+00 2.93e-01 3.39e-01 -1.5 6.80e+03 - 7.87e-01 1.00e+00h 1\n",
|
||
" 49 4.0601921e+00 5.70e-01 2.64e-01 -1.5 1.77e+04 - 8.23e-01 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 3.9533459e+00 9.00e-01 2.00e-01 -1.5 2.16e+04 - 3.31e-01 2.50e-01h 3\n",
|
||
" 51 3.6429149e+00 1.40e+00 1.79e-01 -1.5 6.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 3.8844768e+00 4.81e-01 1.84e-01 -1.5 2.20e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 4.6591126e+00 8.37e-02 1.18e-01 -1.5 3.59e+03 - 7.58e-01 1.00e+00h 1\n",
|
||
" 54 4.7129596e+00 4.83e-02 3.98e-02 -2.3 4.42e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 4.5980927e+00 5.50e-01 1.01e-01 -3.4 1.17e+04 - 8.52e-01 8.53e-01h 1\n",
|
||
" 56 4.3118932e+00 1.16e+00 1.54e-01 -3.4 3.49e+03 - 5.20e-01 1.00e+00h 1\n",
|
||
" 57 4.0678539e+00 3.22e-01 8.27e-02 -3.4 1.32e+03 - 1.30e-01 1.00e+00h 1\n",
|
||
" 58 4.0895531e+00 3.07e-01 6.77e-02 -3.4 7.60e+03 - 6.59e-02 6.59e-02s 19\n",
|
||
" 59 4.7690201e+00 6.46e-02 1.30e-01 -3.4 3.40e+03 - 1.00e+00 0.00e+00S 19\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 4.8269794e+00 1.73e-02 6.48e-02 -3.4 1.49e+03 - 6.95e-01 1.00e+00H 1\n",
|
||
" 61 4.0130680e+00 1.18e+00 2.29e-01 -3.4 7.48e+05 - 6.48e-03 2.54e-02f 1\n",
|
||
" 62 4.0311792e+00 1.14e+00 2.08e-01 -3.4 5.75e+03 - 1.00e+00 3.68e-02h 1\n",
|
||
" 63 4.6112342e+00 4.11e-01 3.26e-01 -3.4 5.30e+03 - 7.74e-02 1.00e+00h 1\n",
|
||
" 64 4.5224843e+00 4.12e-01 2.97e-01 -3.4 1.36e+05 - 2.63e-03 2.69e-02f 4\n",
|
||
" 65 4.4847042e+00 4.49e-01 2.85e-01 -3.4 2.41e+06 - 1.53e-02 1.33e-03f 4\n",
|
||
" 66 4.6514487e+00 2.33e-01 1.92e-01 -3.4 5.77e+02 - 1.00e+00 5.00e-01h 2\n",
|
||
" 67 4.5695024e+00 4.00e-01 1.41e-01 -3.4 2.62e+05 - 2.80e-02 2.12e-02f 3\n",
|
||
" 68 4.7213497e+00 6.61e-02 5.94e-02 -3.4 2.95e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 4.6711959e+00 1.34e-01 3.93e-02 -3.4 8.20e+04 - 1.68e-01 8.13e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 4.6708070e+00 1.34e-01 3.95e-02 -3.4 4.58e+03 - 1.00e+00 1.52e-03h 2\n",
|
||
" 71 4.8507387e+00 5.99e-02 5.73e-02 -3.4 5.15e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 4.8765552e+00 4.88e-03 4.32e-02 -3.4 4.54e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 73 4.8595322e+00 4.12e-02 3.78e-02 -3.4 1.02e+04 - 1.00e+00 5.94e-02h 3\n",
|
||
" 74 4.8824025e+00 3.79e-03 1.58e-02 -3.4 7.76e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 75 4.6365442e+00 1.65e-01 3.24e-02 -3.4 2.36e+05 - 1.82e-01 2.94e-02f 1\n",
|
||
" 76 4.6989780e+00 9.63e-02 2.04e-02 -3.4 3.46e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 77 4.8509900e+00 3.57e-02 2.06e-02 -3.4 1.84e+03 - 3.32e-01 1.00e+00h 1\n",
|
||
" 78 4.8513933e+00 8.12e-03 1.11e-01 -3.4 3.76e+04 - 1.80e-03 1.00e+00F 1\n",
|
||
" 79 4.8279683e+00 5.48e-02 1.09e-01 -3.4 4.48e+05 - 1.11e-01 6.57e-04f 6\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 4.6836251e+00 1.17e-01 7.12e-03 -3.4 1.73e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 4.5917780e+00 4.01e-01 5.94e-02 -3.4 1.01e+04 - 1.00e+00 3.66e-01h 2\n",
|
||
" 82 4.6200325e+00 3.57e-01 4.43e-02 -3.4 7.21e+02 - 1.00e+00 1.25e-01h 4\n",
|
||
" 83 4.9160653e+00 1.28e-03 3.28e-02 -3.4 1.49e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 84 3.3366485e+00 1.27e+00 8.40e-01 -3.4 1.83e+04 - 2.83e-01 1.00e+00f 1\n",
|
||
" 85 3.3148799e+00 1.63e+00 4.80e-01 -3.4 4.85e+05 - 8.32e-02 2.22e-02f 2\n",
|
||
" 86 2.9144222e+00 1.46e+00 3.49e-01 -3.4 1.19e+05 - 1.38e-01 2.54e-01h 1\n",
|
||
" 87 4.7073888e+00 7.75e-02 3.82e-01 -3.4 8.01e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 4.5356438e+00 5.44e-01 4.02e-01 -3.5 2.50e+04 - 1.00e+00 1.15e-01f 1\n",
|
||
" 89 4.8289605e+00 4.59e-02 3.63e-02 -3.5 2.29e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 4.5653982e+00 1.29e-01 2.02e-01 -3.5 3.95e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 4.3534275e+00 7.61e-01 3.64e-01 -3.5 2.63e+05 - 1.04e-01 1.94e-02f 1\n",
|
||
" 92 4.0233684e+00 2.19e+00 7.54e-01 -3.5 2.40e+05 - 3.51e-01 1.47e-02f 1\n",
|
||
" 93 4.2926494e+00 8.37e-01 3.31e-01 -3.5 4.26e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 4.7057040e+00 9.02e-02 1.11e-01 -3.5 1.75e+03 - 5.31e-01 1.00e+00h 1\n",
|
||
" 95 4.4361199e+00 4.53e-01 9.45e-02 -3.5 1.31e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 4.5645128e+00 7.75e-02 4.96e-02 -3.5 5.12e+02 - 1.35e-01 1.00e+00h 1\n",
|
||
" 97 4.6206200e+00 4.74e-02 4.57e-02 -3.5 6.17e+02 - 5.20e-01 1.00e+00h 1\n",
|
||
" 98 4.4014570e+00 1.69e-01 6.31e-02 -3.5 9.28e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 4.5196199e+00 8.35e-02 4.84e-02 -3.5 5.29e+02 - 1.00e+00 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 4.6372258e+00 1.80e-02 4.31e-02 -3.5 3.20e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 4.6372258065881411e+00 4.6372258065881411e+00\n",
|
||
"Dual infeasibility......: 4.3081426200565986e-02 4.3081426200565986e-02\n",
|
||
"Constraint violation....: 1.7973750612213735e-02 1.7973750612213735e-02\n",
|
||
"Complementarity.........: 3.6626713511885500e-04 3.6626713511885500e-04\n",
|
||
"Overall NLP error.......: 4.3081426200565986e-02 4.3081426200565986e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 193\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 193\n",
|
||
"Number of inequality constraint evaluations = 193\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.416\n",
|
||
"Total CPU secs in NLP function evaluations = 137.727\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 857.00us ( 4.44us) 853.32us ( 4.42us) 193\n",
|
||
" nlp_g | 8.63 s ( 44.72ms) 8.23 s ( 42.63ms) 193\n",
|
||
" nlp_grad | 1.38 s ( 1.38 s) 1.32 s ( 1.32 s) 1\n",
|
||
" nlp_grad_f | 322.00us ( 3.16us) 324.39us ( 3.18us) 102\n",
|
||
" nlp_jac_g | 131.90 s ( 1.29 s) 125.88 s ( 1.23 s) 102\n",
|
||
" total | 142.06 s (142.06 s) 135.57 s (135.57 s) 1\n",
|
||
"Timestamp 8700\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.93e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0472107e+01 1.66e+01 2.93e+04 -1.5 2.93e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.5353043e+01 6.63e+00 1.26e+01 0.8 1.67e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.4568271e+01 2.53e+00 7.81e-01 -1.3 3.56e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 2.6003283e+01 2.08e-04 7.67e-02 -3.0 2.69e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 2.6003184e+01 2.19e-05 5.26e-03 -4.9 7.38e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.6002857e+01 1.73e-04 2.49e-03 -7.0 8.53e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.6002712e+01 2.51e-04 8.99e-04 -9.1 5.80e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.3279795e+01 1.70e+00 8.95e-02 -10.1 3.01e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 9 1.9256315e+01 1.51e+01 1.15e+00 -8.1 1.24e+06 - 1.00e+00 2.28e-02f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.9150171e+01 1.50e+01 1.14e+00 -8.2 3.52e+04 - 8.84e-01 8.14e-03h 1\n",
|
||
" 11 1.8983085e+01 6.07e+00 6.25e-01 -8.2 8.92e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 12 2.4556196e+01 2.93e-02 3.18e-01 -1.7 3.37e+03 - 9.61e-01 1.00e+00h 1\n",
|
||
" 13 2.4437277e+01 1.21e-01 2.11e-01 -3.3 1.33e+03 - 9.98e-01 3.54e-01h 1\n",
|
||
" 14 2.4534823e+01 3.90e-02 7.85e-03 -2.7 5.66e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.3871194e+01 9.43e-01 2.16e-02 -8.7 5.11e+03 - 1.25e-01 1.00e+00f 1\n",
|
||
" 16 1.4126006e+01 3.85e+00 2.14e-01 -2.9 2.34e+04 - 5.03e-01 1.00e+00f 1\n",
|
||
" 17 2.4534801e+01 4.16e-01 1.02e+01 -4.5 1.05e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.4920438e+01 1.61e-04 1.93e-02 -4.6 6.03e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.4920351e+01 2.35e-06 3.56e-03 -6.5 7.54e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.4920346e+01 4.56e-06 3.91e-03 -8.6 2.57e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.4920354e+01 2.30e-06 2.33e-03 -11.0 7.01e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.4920346e+01 6.29e-06 2.42e-03 -11.0 7.20e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.4920354e+01 1.59e-06 1.69e-03 -11.0 1.79e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.4920339e+01 1.02e-05 6.06e-03 -11.0 1.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.4919999e+01 1.60e-04 3.60e-02 -11.0 8.00e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.4914049e+01 3.42e-03 4.37e-02 -11.0 1.90e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.4917755e+01 2.85e-03 1.55e-03 -11.0 1.79e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.4919610e+01 2.11e-04 1.93e-03 -11.0 6.26e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.4919219e+01 1.66e-03 1.08e-03 -11.0 1.48e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.3578736e+01 8.99e-01 2.30e-02 -9.0 1.26e+08 - 6.12e-05 2.10e-04f 1\n",
|
||
" 31 2.0288795e+01 2.27e+00 1.46e-01 -10.9 2.18e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 32 2.4582451e+01 1.24e-01 1.18e-01 -11.0 8.79e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.3825309e+01 2.06e+00 5.66e-02 -10.6 1.96e+06 - 1.10e-02 1.16e-02f 1\n",
|
||
" 34 2.4415676e+01 7.65e-01 6.42e-02 -10.6 3.65e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.4394560e+01 6.84e-01 5.75e-02 -10.6 4.31e+03 - 1.00e+00 2.24e-01h 1\n",
|
||
" 36 2.4774554e+01 1.03e-01 1.04e-02 -10.6 3.00e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.4427077e+01 7.07e-01 2.07e-02 -10.6 7.82e+05 - 5.66e-02 2.40e-02f 1\n",
|
||
" 38 2.5019372e+01 3.55e-01 3.00e-02 -10.1 1.16e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.4845884e+01 1.04e-02 7.37e-03 -1.9 2.33e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.4836918e+01 2.83e-07 6.52e-05 -7.9 1.16e-02 - 9.90e-01 1.00e+00h 1\n",
|
||
" 41 2.4836917e+01 1.23e-06 1.12e-03 -9.9 3.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.4836918e+01 1.54e-07 7.59e-05 -9.8 1.36e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.4836918e+01 2.65e-07 6.02e-05 -11.0 1.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.4836917e+01 7.84e-07 2.70e-04 -11.0 5.56e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.4836895e+01 8.32e-06 1.01e-02 -11.0 2.63e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.4836917e+01 2.75e-06 2.74e-03 -11.0 6.30e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.4836902e+01 1.25e-05 6.99e-03 -11.0 4.99e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.4836917e+01 7.00e-07 1.97e-03 -11.0 8.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.4836909e+01 4.79e-06 2.80e-03 -11.0 3.57e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.4836878e+01 3.52e-05 2.49e-03 -11.0 1.29e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.4836910e+01 5.11e-06 4.31e-03 -11.0 5.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.4836913e+01 4.76e-06 1.06e-03 -11.0 2.75e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.4836914e+01 1.14e-06 1.15e-03 -11.0 1.46e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.4836894e+01 1.74e-05 3.99e-03 -11.0 7.69e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.4836866e+01 9.35e-05 1.84e-03 -11.0 4.31e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.4836880e+01 2.62e-05 2.69e-03 -11.0 2.18e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 2.4836770e+01 5.93e-05 2.73e-03 -11.0 1.03e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.4774429e+01 9.78e-02 8.50e-03 -11.0 4.97e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 59 2.4816420e+01 1.01e-02 2.22e-03 -11.0 2.08e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.4810295e+01 1.87e-02 5.87e-03 -11.0 4.41e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.4791122e+01 2.74e-02 5.47e-03 -9.0 1.57e+03 - 1.00e+00 1.57e-01h 1\n",
|
||
" 62 2.4838062e+01 1.19e-05 2.58e-03 -9.1 3.15e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 63 2.4798821e+01 2.76e-02 1.13e-03 -9.3 2.50e+04 - 4.95e-02 3.75e-02f 1\n",
|
||
" 64 2.4779426e+01 1.22e-01 3.20e-03 -9.3 3.87e+03 - 1.24e-08 1.00e+00f 1\n",
|
||
" 65 1.8039764e+01 9.55e+00 5.98e-01 -9.3 1.72e+05 - 1.27e-01 3.31e-01f 1\n",
|
||
" 66r 1.8039764e+01 9.55e+00 9.99e+02 1.0 0.00e+00 - 0.00e+00 4.21e-10R 2\n",
|
||
" 67r 1.8868398e+01 2.76e+00 9.89e+02 -5.1 9.83e+02 - 4.33e-02 9.62e-03f 1\n",
|
||
" 68 2.4855759e+01 1.90e-01 2.51e-01 -6.5 7.90e+02 - 7.40e-02 1.00e+00h 1\n",
|
||
" 69 2.4855826e+01 1.88e-01 2.49e-01 -7.7 2.93e+02 - 1.00e+00 7.09e-03h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.4956600e+01 4.05e-03 3.43e-03 -7.7 1.90e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.4954463e+01 1.76e-02 6.67e-03 -7.7 5.41e+01 - 2.29e-01 1.00e+00h 1\n",
|
||
" 72 2.4783817e+01 4.47e-01 9.62e-03 -7.7 1.85e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 73 2.4146800e+01 9.35e-01 3.47e-02 -7.7 1.66e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.4422003e+01 6.46e-01 8.87e-03 -7.7 5.19e+03 - 1.00e+00 3.77e-01h 1\n",
|
||
" 75 2.4928878e+01 2.89e-02 2.63e-02 -9.1 2.21e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"In iteration 75, 1 Slack too small, adjusting variable bound\n",
|
||
" 76 2.4853176e+01 4.52e-02 2.44e-02 -9.2 5.17e+02 - 1.00e+00 5.67e-01h 1\n",
|
||
" 77 2.4878056e+01 3.95e-02 1.83e-02 -9.2 2.85e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.2772945e+01 1.04e+00 3.74e-02 -9.2 7.23e+07 - 1.23e-04 1.04e-04f 1\n",
|
||
" 79 2.1421013e+01 1.42e+00 8.27e-02 -7.7 3.55e+04 - 1.00e+00 1.66e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.3626064e+01 4.87e-01 3.48e-02 -5.9 1.61e+03 - 7.79e-01 1.00e+00h 1\n",
|
||
" 81 2.4907192e+01 2.04e-02 2.59e-02 -7.8 3.43e+02 - 7.73e-01 1.00e+00h 1\n",
|
||
" 82 2.4810397e+01 1.11e-01 8.99e-03 -2.5 3.06e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 83 2.4917842e+01 9.48e-02 5.63e-03 -2.5 4.87e+02 - 9.87e-01 1.00e+00h 1\n",
|
||
" 84 2.4498184e+01 9.67e-02 5.46e-03 -2.5 4.21e+03 - 1.00e+00 7.32e-01f 1\n",
|
||
" 85 2.4662605e+01 1.02e-01 4.18e-03 -2.5 7.83e+02 - 2.75e-01 1.00e+00h 1\n",
|
||
" 86 2.3525422e+01 1.77e+00 9.46e-02 -2.5 1.24e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 87 2.1349906e+01 1.19e+01 4.33e-01 -2.5 6.91e+04 - 1.55e-01 6.59e-01f 1\n",
|
||
" 88 2.2378563e+01 5.94e+00 4.81e-02 -2.5 1.75e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 89 2.4192784e+01 1.24e+00 4.11e-02 -2.5 1.13e+03 - 1.00e+00 7.87e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.4223228e+01 2.91e-01 1.38e-01 -2.5 2.77e+03 - 9.05e-01 1.00e+00h 1\n",
|
||
" 91r 2.4223228e+01 2.91e-01 9.99e+02 -0.5 0.00e+00 - 0.00e+00 3.48e-07R 19\n",
|
||
" 92r 2.4686749e+01 7.80e-02 9.87e+02 1.2 1.60e+02 - 9.94e-01 2.90e-03f 1\n",
|
||
" 93 2.4988382e+01 2.69e-03 3.68e-03 -2.3 2.56e+02 - 8.36e-01 1.00e+00h 1\n",
|
||
" 94 2.4885270e+01 1.90e-01 1.00e-02 -3.4 4.01e+03 - 3.22e-02 1.00e+00f 1\n",
|
||
" 95 2.4719669e+01 4.27e-01 2.96e-02 -3.4 3.59e+03 - 7.83e-01 1.00e+00h 1\n",
|
||
" 96 2.4473195e+01 7.42e-01 2.16e-02 -3.4 5.85e+05 - 2.95e-02 4.35e-02f 1\n",
|
||
" 97 2.4344184e+01 8.14e-01 1.97e-02 -3.4 7.36e+04 - 2.59e-01 5.45e-01h 1\n",
|
||
" 98 2.4337442e+01 8.25e-01 2.00e-02 -3.4 7.19e+05 - 1.43e-02 3.10e-04h 1\n",
|
||
" 99 2.4689024e+01 2.45e-01 8.61e-03 -3.4 2.12e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.5006783e+01 1.22e-04 4.09e-01 -3.4 4.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.5006783200726296e+01 2.5006783200726296e+01\n",
|
||
"Dual infeasibility......: 4.0907098215054072e-01 4.0907098215054072e-01\n",
|
||
"Constraint violation....: 1.2180986058041299e-04 1.2180986058041299e-04\n",
|
||
"Complementarity.........: 4.3736045897552564e-04 4.3736045897552564e-04\n",
|
||
"Overall NLP error.......: 4.0907098215054072e-01 4.0907098215054072e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 127\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 127\n",
|
||
"Number of inequality constraint evaluations = 127\n",
|
||
"Number of equality constraint Jacobian evaluations = 103\n",
|
||
"Number of inequality constraint Jacobian evaluations = 103\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.422\n",
|
||
"Total CPU secs in NLP function evaluations = 137.238\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 566.00us ( 4.46us) 566.25us ( 4.46us) 127\n",
|
||
" nlp_g | 5.74 s ( 45.19ms) 5.47 s ( 43.09ms) 127\n",
|
||
" nlp_grad | 1.38 s ( 1.38 s) 1.32 s ( 1.32 s) 1\n",
|
||
" nlp_grad_f | 342.00us ( 3.35us) 337.66us ( 3.31us) 102\n",
|
||
" nlp_jac_g | 134.28 s ( 1.29 s) 128.14 s ( 1.23 s) 104\n",
|
||
" total | 141.54 s (141.54 s) 135.06 s (135.06 s) 1\n",
|
||
"Timestamp 9000\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.26e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0397852e+01 1.17e+01 2.26e+04 -1.5 2.26e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.7551138e+00 3.89e+00 5.52e+00 0.8 3.66e+02 - 9.99e-01 1.00e+00f 1\n",
|
||
" 3 1.8351690e+00 4.62e-01 2.24e-01 -1.3 1.00e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 1.1544151e+00 2.93e-03 3.59e-01 -7.1 3.13e+00 - 9.90e-01 1.00e+00h 1\n",
|
||
" 5 1.1536184e+00 2.45e-04 4.85e-03 -4.8 1.07e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.1535094e+00 2.84e-04 1.25e-03 -6.9 9.95e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.1537121e+00 1.48e-04 1.96e-03 -9.0 5.60e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.1538204e+00 1.69e-05 1.19e-03 -11.0 1.81e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.1536554e+00 1.35e-04 2.92e-03 -11.0 4.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.1538226e+00 3.05e-05 8.21e-04 -11.0 1.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.1538169e+00 5.08e-05 8.51e-04 -11.0 1.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.1538433e+00 1.44e-05 1.24e-03 -11.0 1.23e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.1536593e+00 7.32e-05 2.29e-03 -11.0 3.99e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.1536127e+00 1.70e-04 1.46e-03 -11.0 4.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.1538782e+00 2.38e-08 1.06e-04 -11.0 2.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.1538782e+00 5.56e-08 1.07e-04 -11.0 2.38e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.1538782e+00 3.50e-08 7.13e-05 -11.0 1.47e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.1538782e+00 4.86e-08 4.76e-05 -11.0 2.12e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.1538782e+00 1.22e-08 9.71e-05 -11.0 8.60e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.1538782e+00 1.70e-08 4.73e-05 -11.0 6.22e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.1538782e+00 4.69e-09 5.20e-05 -11.0 5.77e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.1538782e+00 3.25e-09 4.75e-05 -11.0 2.14e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.1538782e+00 3.04e-09 4.76e-05 -11.0 3.03e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.1538782e+00 1.53e-09 2.37e-04 -11.0 2.25e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.1538782e+00 9.34e-09 6.27e-05 -11.0 1.84e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.1538782e+00 1.09e-10 5.18e-05 -11.0 4.42e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 27 1.1538782e+00 8.46e-09 7.21e-05 -11.0 3.62e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.1538782e+00 1.07e-08 4.80e-05 -11.0 4.18e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.1538782e+00 1.14e-08 7.49e-05 -11.0 1.00e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.1538782e+00 6.95e-09 1.31e-04 -11.0 6.58e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.1538782e+00 1.66e-08 6.57e-05 -11.0 9.72e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.1538782e+00 9.72e-09 4.75e-05 -11.0 4.57e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.1538782e+00 1.92e-09 8.31e-05 -11.0 1.97e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.1538782e+00 1.94e-08 5.74e-05 -11.0 1.11e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.1538782e+00 8.24e-09 9.48e-05 -11.0 9.39e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.1538781e+00 9.01e-08 3.20e-05 -11.0 2.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.1538782e+00 1.05e-08 3.52e-05 -11.0 6.66e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.1538782e+00 2.68e-08 8.25e-05 -11.0 1.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.1538781e+00 8.06e-08 1.46e-04 -11.0 5.88e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.1538780e+00 2.09e-07 5.10e-05 -11.0 3.29e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.1538782e+00 7.23e-09 4.48e-05 -11.0 7.40e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.1538782e+00 8.50e-09 1.03e-04 -11.0 1.27e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.1538781e+00 9.49e-08 6.53e-05 -11.0 3.63e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.1538782e+00 1.79e-08 9.48e-05 -11.0 9.80e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.1538782e+00 1.31e-08 5.96e-05 -11.0 5.67e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.1538782e+00 1.74e-08 1.18e-04 -11.0 1.35e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.1538781e+00 4.91e-08 3.96e-05 -11.0 2.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.1538782e+00 1.71e-08 8.30e-05 -11.0 5.51e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.1538782e+00 5.70e-09 2.27e-05 -11.0 4.67e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.1538782e+00 4.79e-11 8.45e-05 -11.0 8.76e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 51 1.1538782e+00 3.04e-08 7.29e-05 -11.0 1.88e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.1538780e+00 1.20e-07 1.33e-04 -11.0 8.34e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.1538774e+00 4.23e-07 4.28e-05 -11.0 1.78e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.1538781e+00 3.93e-08 5.17e-05 -11.0 2.12e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.1538781e+00 1.45e-08 1.09e-04 -11.0 1.47e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.1538780e+00 6.23e-08 5.85e-05 -11.0 3.15e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.1538778e+00 1.27e-07 1.17e-04 -11.0 1.20e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.1538779e+00 1.56e-07 8.79e-05 -11.0 6.08e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.1538331e+00 5.06e-05 8.89e-03 -11.0 1.64e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.1538783e+00 4.12e-06 9.05e-04 -11.0 3.90e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.1538137e+00 3.37e-05 2.00e-03 -11.0 1.77e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.1538712e+00 4.88e-06 9.00e-04 -11.0 4.33e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.1538622e+00 9.51e-06 7.69e-04 -11.0 3.37e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.1538753e+00 8.44e-07 9.46e-04 -11.0 1.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.1538740e+00 3.25e-06 1.07e-03 -11.0 1.51e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.1538754e+00 8.91e-07 5.78e-04 -11.0 7.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.1538765e+00 3.52e-07 6.46e-04 -11.0 3.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.1538693e+00 5.10e-06 9.22e-04 -11.0 1.20e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.1538745e+00 2.26e-06 7.83e-04 -11.0 5.52e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.1538775e+00 3.34e-08 1.37e-04 -11.0 4.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.1538749e+00 1.20e-06 1.96e-03 -11.0 6.11e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.1538777e+00 3.57e-10 3.99e-05 -11.0 4.80e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 73 1.1538775e+00 1.72e-07 1.78e-04 -11.0 1.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.1538771e+00 9.09e-07 1.72e-03 -11.0 3.78e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.1538770e+00 4.62e-07 1.45e-03 -11.0 2.33e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.1538777e+00 8.24e-11 2.33e-05 -11.0 3.43e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 77 1.1538754e+00 7.23e-06 5.86e-03 -11.0 3.14e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.1538764e+00 8.29e-07 1.83e-03 -11.0 1.39e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.1538520e+00 1.05e-04 9.60e-03 -11.0 4.14e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.1538804e+00 5.00e-06 1.86e-03 -11.0 4.97e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.1538743e+00 1.95e-05 1.58e-03 -11.0 4.83e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.1538773e+00 4.34e-06 6.67e-04 -11.0 2.41e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.1538771e+00 2.58e-06 7.20e-04 -11.0 1.43e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.1538800e+00 3.26e-07 1.05e-04 -11.0 1.74e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.1538807e+00 9.23e-10 1.86e-04 -11.0 1.43e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 86 1.1538520e+00 1.70e-05 1.71e-03 -11.0 2.25e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.1538799e+00 3.27e-09 6.20e-05 -11.0 1.36e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 88 1.1538790e+00 1.88e-06 7.15e-04 -11.0 1.51e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.1538090e+00 1.53e-04 1.56e-03 -11.0 9.67e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.1536853e+00 1.48e-04 1.02e-03 -11.0 1.30e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.1538635e+00 4.04e-08 6.82e-05 -11.0 1.50e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.1538635e+00 2.72e-08 7.18e-05 -11.0 1.02e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.1538624e+00 1.09e-06 9.12e-03 -11.0 4.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.1538634e+00 3.44e-07 8.91e-05 -11.0 1.05e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.1538632e+00 3.38e-07 7.35e-05 -11.0 4.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.1538630e+00 4.37e-07 4.97e-05 -11.0 2.65e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.1538634e+00 3.87e-08 6.86e-05 -11.0 5.88e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.1538621e+00 9.83e-07 3.39e-03 -11.0 5.07e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.1538631e+00 3.89e-07 6.32e-05 -11.0 2.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.1535952e+00 2.05e-04 3.98e-02 -11.0 1.65e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.1535951630310792e+00 1.1535951630310792e+00\n",
|
||
"Dual infeasibility......: 3.9801067062598658e-02 3.9801067062598658e-02\n",
|
||
"Constraint violation....: 2.0493802367838043e-04 2.0493802367838043e-04\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 3.9801067062598658e-02 3.9801067062598658e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 107\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 107\n",
|
||
"Number of inequality constraint evaluations = 107\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.429\n",
|
||
"Total CPU secs in NLP function evaluations = 134.094\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 487.00us ( 4.55us) 480.19us ( 4.49us) 107\n",
|
||
" nlp_g | 4.79 s ( 44.74ms) 4.56 s ( 42.63ms) 107\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 418.00us ( 4.10us) 408.18us ( 4.00us) 102\n",
|
||
" nlp_jac_g | 132.24 s ( 1.30 s) 126.18 s ( 1.24 s) 102\n",
|
||
" total | 138.50 s (138.50 s) 132.15 s (132.15 s) 1\n",
|
||
"Timestamp 9300\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 4.65e+02 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0225306e+01 1.51e+01 4.65e+02 -1.5 4.65e+02 - 9.90e-01 1.00e+00f 1\n",
|
||
" 2 9.8882713e+00 5.79e+00 9.83e+00 0.4 1.51e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.1739508e+01 1.73e+00 5.21e-01 -1.6 9.43e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.3026722e+01 2.64e-03 9.16e-02 -3.4 2.41e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.3028029e+01 2.80e-07 1.02e-04 -5.3 2.64e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.3028029e+01 2.74e-07 1.02e-04 -11.0 1.07e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.3028028e+01 9.76e-07 1.80e-03 -11.0 4.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.3028018e+01 6.68e-06 2.89e-03 -11.0 1.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.3028028e+01 7.36e-07 2.05e-03 -11.0 3.91e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.3028029e+01 4.08e-07 3.13e-05 -11.0 1.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.3028030e+01 9.86e-08 1.36e-05 -11.0 4.93e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.3028030e+01 1.47e-08 5.92e-05 -11.0 2.05e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.3028030e+01 1.40e-07 4.36e-05 -11.0 1.52e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.3028029e+01 3.22e-07 8.82e-05 -11.0 2.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.3028030e+01 2.18e-08 2.70e-04 -11.0 1.32e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.3028030e+01 8.43e-08 1.26e-04 -11.0 1.42e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.3028030e+01 7.00e-08 8.56e-05 -11.0 4.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.3028029e+01 7.71e-07 4.95e-03 -11.0 2.88e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.3028027e+01 1.33e-06 8.93e-03 -11.0 5.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.3028030e+01 3.25e-07 2.61e-04 -11.0 2.85e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.3028023e+01 4.30e-06 4.02e-03 -11.0 1.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.3027618e+01 2.29e-04 1.47e-02 -11.0 6.04e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.3027974e+01 3.53e-05 9.92e-04 -11.0 1.87e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.3027985e+01 1.83e-05 3.82e-03 -11.0 2.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.3027941e+01 3.64e-05 9.37e-04 -11.0 1.99e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.3028029e+01 3.38e-06 1.58e-03 -11.0 4.96e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.3028028e+01 2.90e-06 1.34e-03 -11.0 1.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.3027988e+01 2.35e-05 1.02e-03 -11.0 5.83e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.3028008e+01 1.20e-05 1.92e-03 -11.0 8.32e-02 - 1.00e+00 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.3028026e+01 8.75e-06 2.68e-03 -11.0 7.91e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.3028001e+01 4.36e-05 2.97e-03 -11.0 2.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.3018822e+01 1.05e-02 3.54e-02 -11.0 1.35e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.3028710e+01 5.95e-06 6.68e-03 -11.0 4.21e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 1.2960381e+01 1.68e-01 8.48e-03 -11.0 7.35e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 35 1.2598448e+01 3.30e-01 2.65e-02 -11.0 6.44e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.3011336e+01 9.29e-03 3.05e-02 -11.0 1.36e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.2801803e+01 1.35e-01 7.62e-03 -11.0 4.68e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 38 1.1870747e+01 1.18e+00 1.01e-01 -11.0 1.65e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 39 1.2398368e+01 4.52e-01 1.76e-02 -10.7 4.43e+04 - 1.00e+00 9.72e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.2384523e+01 4.88e-01 1.53e-02 -8.7 1.60e+05 - 1.00e+00 2.68e-03h 1\n",
|
||
" 41 1.2384310e+01 4.88e-01 1.53e-02 -6.8 2.38e+05 - 1.00e+00 1.81e-05h 1\n",
|
||
" 42 1.3043903e+01 1.37e-03 9.30e-01 -8.7 9.83e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.3044585e+01 9.21e-07 1.46e-03 -8.8 2.75e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.3044561e+01 1.81e-05 1.59e-03 -8.8 5.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.3044584e+01 4.38e-06 2.01e-03 -8.8 1.43e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.3044295e+01 1.38e-04 1.67e-02 -8.8 1.63e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.3044303e+01 1.13e-04 2.40e-03 -8.8 9.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.3044552e+01 1.45e-05 3.72e-03 -8.8 1.13e-01 - 1.00e+00 8.95e-01h 1\n",
|
||
" 49 1.3044385e+01 8.94e-05 3.69e-03 -8.8 7.81e-01 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.3044596e+01 2.44e-08 4.59e-05 -8.8 8.47e-01 - 3.91e-01 1.00e+00H 1\n",
|
||
" 51 1.3044596e+01 6.22e-09 4.91e-05 -8.8 6.89e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.3044596e+01 6.02e-09 8.58e-05 -11.0 2.15e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.3044596e+01 5.04e-09 1.75e-04 -11.0 1.91e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.3044596e+01 2.29e-08 1.21e-04 -11.0 4.50e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.3044596e+01 6.13e-09 1.45e-04 -11.0 1.86e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.3044596e+01 1.21e-08 7.12e-05 -11.0 7.19e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.3044596e+01 7.48e-09 7.35e-05 -11.0 2.88e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.3044596e+01 2.94e-09 1.73e-05 -11.0 1.57e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.3044596e+01 1.15e-09 2.91e-04 -11.0 5.83e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.3044596e+01 1.03e-09 1.72e-04 -11.0 2.21e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.3044596e+01 5.24e-09 4.48e-05 -11.0 2.97e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.3044596e+01 1.31e-09 2.05e-04 -11.0 9.11e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.3044596e+01 8.26e-09 1.86e-04 -11.0 4.24e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.3044596e+01 1.81e-09 1.74e-04 -11.0 7.14e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.3044596e+01 8.28e-08 2.72e-05 -11.0 1.56e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.3044596e+01 1.95e-08 1.75e-05 -11.0 1.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.3044596e+01 3.57e-09 1.27e-04 -11.0 3.32e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.3044596e+01 9.74e-09 5.41e-05 -11.0 4.31e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.3044596e+01 2.54e-08 4.26e-05 -11.0 1.35e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.3044596e+01 1.33e-09 1.12e-04 -11.0 5.66e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.3044596e+01 4.75e-09 3.19e-04 -11.0 1.20e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.3044596e+01 3.07e-08 3.07e-04 -11.0 9.00e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"In iteration 72, 1 Slack too small, adjusting variable bound\n",
|
||
" 73 1.3044593e+01 9.20e-06 2.09e-03 -11.0 2.17e-01 - 1.00e+00 2.55e-01h 1\n",
|
||
" 74 1.3044595e+01 4.41e-06 1.22e-03 -11.0 6.27e-03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 75 1.3044596e+01 1.01e-07 1.43e-04 -11.0 2.31e-03 - 5.75e-01 1.00e+00h 1\n",
|
||
" 76 1.3044596e+01 6.66e-08 9.55e-05 -11.0 2.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.3044596e+01 4.16e-08 6.79e-05 -11.0 9.52e-05 - 1.00e+00 5.00e-01h 2\n",
|
||
" 78 1.3044596e+01 4.05e-08 5.07e-05 -11.0 1.22e-04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 79 1.3044596e+01 3.92e-08 1.15e-04 -11.0 1.21e-05 - 1.00e+00 3.12e-02h 6\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.3044596e+01 6.87e-10 5.16e-05 -11.0 9.91e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.3044596e+01 5.79e-11 9.35e-05 -11.0 6.91e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 82 1.3044596e+01 2.95e-07 8.46e-05 -11.0 1.20e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.3044596e+01 1.09e-07 6.99e-05 -11.0 9.89e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.3044596e+01 1.09e-08 3.89e-05 -11.0 1.75e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.3044596e+01 1.21e-07 1.60e-04 -11.0 6.49e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.3044594e+01 8.92e-07 7.18e-03 -11.0 4.19e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.3044596e+01 2.51e-09 8.65e-05 -11.0 2.26e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.3044596e+01 2.23e-10 3.72e-05 -11.0 2.17e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 89 1.3044595e+01 3.11e-07 5.02e-05 -11.0 6.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.3044596e+01 5.59e-11 1.59e-04 -11.0 2.01e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 91 1.3044595e+01 4.46e-06 1.83e-03 -11.0 5.48e-01 - 1.00e+00 2.05e-02h 1\n",
|
||
" 92 1.3044595e+01 4.32e-07 4.42e-05 -11.0 3.66e-03 - 5.23e-04 1.00e+00h 1\n",
|
||
" 93 1.3044595e+01 1.54e-07 5.52e-05 -11.0 1.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.3044595e+01 1.87e-07 5.82e-05 -11.0 1.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"In iteration 94, 1 Slack too small, adjusting variable bound\n",
|
||
" 95 1.3044595e+01 1.96e-06 1.67e-03 -11.0 2.06e-01 - 1.00e+00 2.50e-02h 1\n",
|
||
"In iteration 95, 1 Slack too small, adjusting variable bound\n",
|
||
" 96 1.3044595e+01 1.96e-06 1.61e-03 -11.0 1.43e-02 - 1.00e+00 7.55e-04h 1\n",
|
||
" 97 1.3044595e+01 8.94e-08 3.55e-04 -11.0 6.33e-04 - 8.32e-01 1.00e+00h 1\n",
|
||
" 98 1.3044580e+01 8.50e-06 9.70e-03 -11.0 3.00e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.3044588e+01 4.53e-06 5.22e-03 -11.0 6.43e-03 - 8.24e-01 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.3044594e+01 5.88e-06 1.33e-03 -11.0 3.69e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.3044594072804648e+01 1.3044594072804648e+01\n",
|
||
"Dual infeasibility......: 1.3317205945152083e-03 1.3317205945152083e-03\n",
|
||
"Constraint violation....: 5.8845067165691489e-06 5.8845067165691489e-06\n",
|
||
"Complementarity.........: 2.1102920220507009e-11 2.1102920220507009e-11\n",
|
||
"Overall NLP error.......: 1.3317205945152083e-03 1.3317205945152083e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 118\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 118\n",
|
||
"Number of inequality constraint evaluations = 118\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.418\n",
|
||
"Total CPU secs in NLP function evaluations = 134.159\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 524.00us ( 4.44us) 519.13us ( 4.40us) 118\n",
|
||
" nlp_g | 5.28 s ( 44.76ms) 5.03 s ( 42.65ms) 118\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 529.00us ( 5.19us) 408.35us ( 4.00us) 102\n",
|
||
" nlp_jac_g | 131.78 s ( 1.29 s) 125.74 s ( 1.23 s) 102\n",
|
||
" total | 138.55 s (138.55 s) 132.20 s (132.20 s) 1\n",
|
||
"Timestamp 9600\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.69e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9837809e+01 1.41e+01 2.69e+03 -1.5 2.69e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.2090624e+00 5.04e+00 1.00e+01 0.4 1.41e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.0473676e+01 1.50e+00 6.51e-01 -1.6 8.86e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.1525845e+01 1.83e-03 8.35e-02 -3.4 2.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.1526772e+01 2.97e-07 6.19e-05 -5.3 1.85e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.1526772e+01 1.32e-07 1.60e-04 -11.0 7.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.1526772e+01 1.00e-07 3.06e-05 -11.0 3.88e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.1526772e+01 8.53e-09 1.99e-05 -11.0 2.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.1526772e+01 3.85e-11 2.54e-04 -11.0 1.85e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.1526772e+01 4.73e-08 2.03e-04 -11.0 1.81e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.1526601e+01 1.42e-04 7.19e-02 -11.0 2.78e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.1526758e+01 1.33e-05 1.17e-03 -11.0 9.98e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.1526773e+01 7.80e-06 1.46e-03 -11.0 3.23e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.1254951e+01 2.24e-01 6.18e-02 -11.0 3.30e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 15 1.1500613e+01 3.42e-04 9.10e-03 -11.0 3.36e-01 -4.0 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.1494536e+01 4.81e-03 1.58e-02 -11.0 1.04e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.1500282e+01 3.45e-04 1.59e-03 -11.0 3.66e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.0671933e+01 5.61e-01 5.63e-02 -11.0 6.15e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 19 1.1561083e+01 8.93e-02 5.11e-02 -11.0 6.63e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.0348400e+01 4.80e-01 6.00e-02 -11.0 3.16e+07 - 8.97e-04 1.09e-03f 1\n",
|
||
" 21 9.7508206e+00 1.33e+00 7.76e-02 -10.4 1.25e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 22 8.7682092e+00 5.06e+00 7.70e-01 -2.3 5.53e+04 - 1.00e+00 3.03e-01f 1\n",
|
||
" 23 1.1225113e+01 5.49e-01 7.01e-02 -1.3 1.97e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.1119917e+01 4.86e-01 1.29e-01 -1.3 4.04e+03 - 6.33e-01 1.00e+00h 1\n",
|
||
" 25 9.6325297e+00 1.49e+00 1.62e-01 -7.4 1.42e+04 - 1.12e-01 6.03e-01f 1\n",
|
||
" 26 9.1573625e+00 3.47e+00 2.56e-01 -2.0 5.09e+04 - 1.00e+00 7.45e-01F 1\n",
|
||
" 27 1.4348350e+01 1.25e+00 2.08e-01 0.1 2.21e+05 - 1.03e-01 5.61e-02f 3\n",
|
||
" 28 1.1626411e+01 7.45e-01 2.66e-01 -0.5 5.98e+04 - 8.07e-01 1.00e+00f 1\n",
|
||
" 29 1.1116914e+01 1.02e-04 7.82e-01 -0.5 8.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.1117063e+01 1.25e-06 1.29e-03 -2.6 4.44e-03 - 9.98e-01 1.00e+00h 1\n",
|
||
" 31 1.1117065e+01 1.11e-07 2.99e-04 -8.5 1.15e-03 - 9.97e-01 1.00e+00h 1\n",
|
||
" 32 1.1117054e+01 5.60e-06 2.18e-03 -11.0 1.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.1117062e+01 1.49e-06 1.40e-03 -11.0 7.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.1117064e+01 3.94e-07 3.82e-05 -11.0 3.50e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.1117065e+01 1.46e-07 6.01e-05 -11.0 1.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.1117065e+01 1.20e-10 1.34e-04 -11.0 1.61e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 37 1.1117064e+01 1.87e-06 1.59e-03 -11.0 6.83e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.1117062e+01 1.89e-06 2.31e-03 -11.0 1.50e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.1117055e+01 4.04e-06 4.35e-03 -11.0 7.66e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.1117047e+01 1.28e-05 7.88e-04 -11.0 5.34e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.1117050e+01 1.30e-05 2.39e-03 -11.0 6.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.1117062e+01 2.22e-06 2.15e-03 -11.0 1.55e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.1117056e+01 3.42e-06 1.61e-03 -11.0 1.21e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.1116969e+01 4.62e-05 5.79e-03 -11.0 2.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.1117050e+01 4.41e-05 1.51e-03 -11.0 9.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.1117066e+01 2.62e-06 7.30e-04 -11.0 3.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.1117067e+01 2.70e-06 1.19e-03 -11.0 9.35e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.1116941e+01 6.58e-05 3.38e-03 -11.0 5.14e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.1117062e+01 6.44e-09 5.85e-05 -11.0 5.45e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.1117021e+01 2.58e-05 1.52e-03 -11.0 1.71e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.1117054e+01 1.03e-05 1.28e-03 -11.0 1.69e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.1117012e+01 3.07e-05 1.36e-03 -11.0 4.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.1099362e+01 1.05e-02 6.27e-03 -11.0 1.46e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 54 1.1084670e+01 2.15e-02 5.87e-03 -11.0 2.02e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.1080546e+01 1.98e-02 8.90e-03 -11.0 2.19e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.1122139e+01 1.91e-05 4.28e-02 -11.0 4.51e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.1122143e+01 4.84e-06 1.31e-03 -11.0 1.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.1122151e+01 1.45e-06 1.53e-03 -11.0 3.87e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.1122152e+01 6.97e-07 2.17e-04 -11.0 1.21e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.0898404e+01 9.73e-02 1.17e-01 -11.0 3.82e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 61 1.0840573e+01 1.46e-01 1.30e-02 -11.0 7.53e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.0285876e+01 4.65e-01 3.05e-02 -11.0 6.03e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 63 1.1001201e+01 7.49e-02 2.89e-02 -11.0 1.75e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.1080145e+01 1.66e-02 1.54e-02 -11.0 8.31e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.0847840e+01 1.12e-01 1.28e-02 -11.0 2.99e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 66 1.1052079e+01 3.66e-02 2.08e-02 -11.0 1.75e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.1023559e+01 4.66e-02 9.20e-03 -11.0 3.83e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.1079937e+01 3.86e-02 6.01e-03 -11.0 1.82e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.1115972e+01 7.83e-03 4.55e-03 -11.0 1.27e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.1090472e+01 1.79e-02 2.56e-03 -11.0 8.80e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 9.9545508e+00 3.99e-01 5.66e-02 -11.0 3.38e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 72 9.7227293e+00 9.80e-01 9.38e-02 -11.0 3.63e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.0074086e+01 6.69e-01 4.51e-02 -11.0 6.36e+02 - 1.00e+00 3.13e-01h 1\n",
|
||
" 74 1.1022594e+01 2.83e-02 4.72e-02 -11.0 1.98e+02 - 6.32e-08 1.00e+00h 1\n",
|
||
" 75 1.1011387e+01 2.49e-02 4.91e-02 -9.0 7.70e+02 - 1.00e+00 4.76e-02h 1\n",
|
||
" 76 1.1011751e+01 2.43e-02 4.81e-02 -7.1 3.67e+01 - 1.00e+00 2.12e-02h 1\n",
|
||
" 77 1.0910763e+01 2.86e-01 2.15e-02 -6.3 6.53e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.1015882e+01 1.59e-02 1.10e-02 -6.3 1.98e+02 - 9.92e-01 1.00e+00h 1\n",
|
||
" 79 1.0977076e+01 1.63e-01 2.28e-02 -6.7 1.49e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.0955615e+01 9.74e-02 8.25e-03 -5.6 9.57e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.0908498e+01 1.07e-01 5.14e-03 -5.6 1.15e+04 - 1.00e+00 4.60e-02h 1\n",
|
||
" 82 1.0997102e+01 3.98e-02 6.63e-03 -5.6 1.49e+02 - 1.25e-01 1.00e+00h 1\n",
|
||
" 83 1.0952091e+01 9.34e-02 9.76e-03 -7.0 7.46e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.0136127e+01 5.42e-01 9.10e-02 -4.2 5.30e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 85 9.8577602e+00 1.70e+00 6.77e-02 -4.4 6.23e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.1242559e+01 4.43e-03 2.25e+00 -4.4 2.43e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.1245300e+01 3.79e-07 1.35e-04 -4.4 5.10e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.1245298e+01 1.28e-06 1.20e-03 -6.5 5.05e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.1245298e+01 1.05e-06 8.67e-04 -6.5 6.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.1245299e+01 1.06e-06 9.94e-04 -6.5 4.14e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.1245299e+01 1.07e-06 1.38e-03 -6.5 2.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.1245300e+01 9.14e-08 1.17e-04 -6.5 3.55e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.1245300e+01 5.05e-10 8.18e-05 -6.5 5.40e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 94 1.1245280e+01 1.12e-05 5.89e-03 -6.5 8.29e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.1245250e+01 2.02e-05 1.73e-03 -6.5 3.00e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.1245291e+01 5.84e-06 2.17e-03 -6.5 3.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.1245099e+01 1.10e-04 3.73e-03 -6.5 6.62e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.1244924e+01 5.29e-04 6.55e-03 -6.5 1.64e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.1245214e+01 5.38e-05 3.36e-03 -6.5 3.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.1186022e+01 6.23e-02 8.27e-03 -6.5 4.84e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.1186022104613029e+01 1.1186022104613029e+01\n",
|
||
"Dual infeasibility......: 8.2675092816427154e-03 8.2675092816427154e-03\n",
|
||
"Constraint violation....: 6.2305635224102929e-02 6.2305635224102929e-02\n",
|
||
"Complementarity.........: 2.9818758062648628e-07 2.9818758062648628e-07\n",
|
||
"Overall NLP error.......: 6.2305635224102929e-02 6.2305635224102929e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 111\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 111\n",
|
||
"Number of inequality constraint evaluations = 111\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.402\n",
|
||
"Total CPU secs in NLP function evaluations = 134.351\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 499.00us ( 4.50us) 494.96us ( 4.46us) 111\n",
|
||
" nlp_g | 4.95 s ( 44.63ms) 4.72 s ( 42.52ms) 111\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 358.00us ( 3.51us) 354.42us ( 3.47us) 102\n",
|
||
" nlp_jac_g | 132.23 s ( 1.30 s) 126.21 s ( 1.24 s) 102\n",
|
||
" total | 138.67 s (138.67 s) 132.36 s (132.36 s) 1\n",
|
||
"Timestamp 9900\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.26e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0154345e+01 1.34e+01 1.26e+04 -1.5 1.26e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.3053027e+00 4.85e+00 7.44e+00 0.6 7.12e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 5.0500816e+00 9.65e-01 8.14e-01 -1.5 2.06e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 5.7347489e+00 4.20e-03 1.26e-01 -3.2 1.52e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 5.7364488e+00 7.07e-07 1.52e-03 -5.1 8.90e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 5.7364501e+00 2.41e-07 9.80e-05 -7.2 3.19e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 5.7364458e+00 4.16e-06 1.78e-03 -11.0 5.49e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 5.7364510e+00 1.78e-08 1.25e-04 -11.0 2.97e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 9 5.7364422e+00 6.28e-06 9.08e-04 -11.0 2.30e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 5.7364298e+00 9.35e-06 3.12e-03 -11.0 3.63e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 5.7364503e+00 2.09e-06 1.30e-03 -11.0 1.21e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 5.7364503e+00 1.51e-06 1.73e-03 -11.0 7.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 5.7012885e+00 1.36e-02 5.74e-02 -11.0 1.21e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 14 5.7316982e+00 1.18e-02 2.42e-03 -11.0 2.83e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 5.6544844e+00 4.40e-03 3.47e-02 -11.0 1.57e+04 - 1.00e+00 1.00e+00F 1\n",
|
||
" 16 5.1096338e+00 4.62e+00 9.87e-01 -9.1 5.54e+04 - 1.00e+00 2.83e-01f 1\n",
|
||
" 17 5.0884499e+00 4.58e+00 9.73e-01 -9.3 1.73e+04 - 1.00e+00 9.17e-03h 1\n",
|
||
" 18 5.2770274e+00 3.28e-01 1.79e-01 -9.3 1.54e+03 - 7.47e-01 1.00e+00h 1\n",
|
||
" 19 5.4874353e+00 8.06e-02 6.98e-02 -3.6 5.17e+02 - 7.42e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 5.5517632e+00 4.28e-02 2.01e-02 -2.7 2.40e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 5.5498173e+00 1.69e-02 1.33e-02 -3.4 1.57e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 5.2247195e+00 2.61e-01 2.55e-02 -4.0 1.32e+04 - 9.96e-01 2.21e-01f 1\n",
|
||
" 23 5.5204401e+00 3.21e-02 2.95e-02 -3.3 2.44e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 5.2403221e+00 1.65e-01 6.42e-02 -3.4 7.52e+02 - 3.99e-01 1.00e+00f 1\n",
|
||
" 25 5.5509527e+00 2.24e-02 2.53e-02 -4.0 3.18e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 5.5611688e+00 1.01e-02 9.51e-03 -9.8 1.17e+02 - 1.79e-01 1.00e+00h 1\n",
|
||
" 27 5.5717550e+00 2.19e-02 3.45e-02 -2.8 1.08e+03 - 3.30e-02 2.50e-01f 3\n",
|
||
" 28 5.5848632e+00 5.70e-03 2.64e-03 -2.9 7.29e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 5.5192518e+00 3.27e-02 6.47e-03 -8.9 3.05e+03 - 9.15e-01 6.11e-02f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 5.5757723e+00 6.60e-03 1.21e-02 -9.4 3.46e+01 - 7.88e-01 1.00e+00h 1\n",
|
||
" 31 5.5822960e+00 1.18e-03 7.93e-03 -3.7 2.04e+01 - 1.00e+00 8.77e-01H 1\n",
|
||
" 32 5.5799848e+00 2.59e-03 1.81e-03 -3.4 2.23e+01 - 8.10e-01 1.00e+00f 1\n",
|
||
" 33 5.2034021e+00 5.23e-01 9.96e-02 -3.6 1.26e+03 - 2.56e-01 1.00e+00f 1\n",
|
||
" 34 4.4626987e+00 1.59e+00 3.47e-01 -3.1 7.98e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 35 5.7836650e+00 1.97e-01 4.75e-01 -2.3 9.97e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 36 3.9620904e+00 1.48e+00 2.58e-01 -2.4 5.73e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 37 5.9747354e+00 6.78e-01 4.13e-01 -3.1 5.58e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 3.6919122e+00 1.33e+00 8.45e-02 -3.2 1.62e+04 - 4.21e-01 1.00e+00f 1\n",
|
||
" 39 3.7328869e+00 1.35e+00 7.35e-02 -3.0 5.73e+04 - 7.43e-01 8.22e-02h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 3.7283761e+00 1.55e+00 1.14e-01 -2.9 3.00e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 41 5.1168101e+00 2.99e-01 3.59e-01 -2.9 5.93e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 5.0720073e+00 2.73e-01 3.71e-01 -3.0 2.73e+04 - 1.36e-01 9.27e-02F 1\n",
|
||
" 43 4.8523395e+00 5.77e-01 1.94e-01 -2.0 1.55e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 4.5761260e+00 5.32e-01 2.63e-01 -2.0 1.59e+04 - 9.11e-01 3.68e-01h 1\n",
|
||
" 45 5.4492374e+00 1.82e-02 1.12e+00 -4.0 1.34e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
" 46 5.4585589e+00 1.16e-07 6.27e-05 -5.9 2.18e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 5.4585588e+00 9.20e-08 1.71e-04 -11.0 6.36e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 5.4534489e+00 4.07e-03 1.78e-02 -11.0 2.18e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 49 5.4492459e+00 7.71e-03 4.20e-03 -11.0 5.83e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 5.4352381e+00 1.75e-02 1.94e-03 -11.0 4.49e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 5.4573539e+00 1.39e-03 1.74e-03 -11.0 1.06e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 5.4594208e+00 5.66e-04 1.06e-03 -11.0 2.75e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 5.4591712e+00 6.26e-04 9.15e-04 -11.0 2.21e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 5.4571605e+00 1.73e-03 2.41e-03 -11.0 8.26e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 5.4599441e+00 3.66e-06 2.12e-03 -11.0 1.72e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 56 5.4576903e+00 1.13e-03 1.09e-03 -11.0 1.05e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 5.4470345e+00 1.67e-02 3.18e-03 -10.7 7.34e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 5.2627462e+00 1.16e-01 2.04e-02 -9.9 1.32e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 59 4.8307260e+00 5.98e-01 1.09e-01 -11.0 1.11e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 5.3577134e+00 8.34e-02 7.40e-02 -11.0 2.60e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 4.4264406e+00 1.69e+00 4.47e-01 -10.0 8.40e+03 - 1.00e+00 7.50e-01f 1\n",
|
||
" 62 4.4284718e+00 1.68e+00 4.39e-01 -10.2 5.87e+03 - 1.00e+00 8.61e-03h 1\n",
|
||
" 63 4.6410069e+00 7.94e-01 5.97e-01 -10.2 9.57e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 64 4.5320158e+00 2.21e+00 3.42e-01 -10.3 1.78e+04 - 1.00e+00 2.50e-01f 3\n",
|
||
" 65 4.9397882e+00 3.04e-01 3.67e-01 -5.3 8.66e+03 - 9.86e-02 1.00e+00h 1\n",
|
||
" 66 4.9260284e+00 3.46e-01 3.59e-01 -4.4 6.67e+03 - 1.00e+00 2.59e-02h 1\n",
|
||
" 67 4.7344223e+00 5.80e-01 3.31e-01 -3.9 1.87e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 4.7262495e+00 2.06e+00 4.29e-01 -3.9 8.59e+03 - 6.12e-01 1.00e+00H 1\n",
|
||
" 69 4.4633179e+00 1.45e+00 3.47e-01 -4.1 5.22e+03 - 1.00e+00 1.61e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 4.2868742e+00 1.36e+00 1.45e-01 -4.1 4.66e+03 - 8.81e-01 1.00e+00h 1\n",
|
||
" 71 4.7915630e+00 3.88e-01 3.37e-01 -4.9 3.68e+03 - 9.75e-01 1.00e+00h 1\n",
|
||
" 72 4.3283531e+00 5.63e-01 2.10e-01 -2.2 3.85e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 4.3002373e+00 1.84e+00 4.74e-02 -1.6 1.61e+05 - 8.27e-01 3.02e-02f 2\n",
|
||
" 74 4.4868666e+00 1.71e+00 5.42e-02 -1.8 5.30e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 4.1003873e+00 2.49e+00 5.40e-01 -1.8 2.49e+05 - 1.77e-01 4.49e-02f 1\n",
|
||
" 76 4.0109790e+00 1.27e+00 1.36e-01 -1.8 1.15e+04 - 1.00e+00 5.46e-01h 1\n",
|
||
" 77 4.2826977e+00 6.25e-01 1.89e-01 -2.7 2.63e+03 - 9.68e-01 1.00e+00h 1\n",
|
||
" 78 4.8974442e+00 1.89e-01 8.76e-02 -4.7 1.92e+03 - 9.87e-01 1.00e+00h 1\n",
|
||
" 79 5.0236803e+00 5.53e-02 1.31e-01 -3.3 8.25e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 4.9282800e+00 3.46e-01 3.53e-02 -2.4 1.72e+03 - 9.50e-01 1.00e+00h 1\n",
|
||
" 81 4.7994860e+00 1.53e+00 2.23e-01 -2.5 2.90e+04 - 3.46e-01 2.50e-01f 3\n",
|
||
" 82 4.4330426e+00 1.21e+00 3.34e-01 -2.5 3.03e+04 - 1.00e+00 3.17e-01h 1\n",
|
||
" 83 5.0658545e+00 8.64e-02 3.56e-01 -2.5 1.70e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 4.9834711e+00 5.69e-01 1.11e-01 -2.5 2.14e+03 - 5.57e-01 1.00e+00h 1\n",
|
||
" 85 4.9401842e+00 3.41e-01 7.68e-02 -2.5 4.87e+03 - 5.61e-01 1.00e+00h 1\n",
|
||
" 86 4.9038773e+00 4.73e-01 2.65e-02 -2.5 6.80e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 4.5231638e+00 9.16e-01 1.90e-01 -2.5 1.35e+05 - 8.59e-02 9.74e-02f 1\n",
|
||
" 88 4.6202523e+00 4.98e-01 1.28e-01 -2.5 5.22e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 5.0076035e+00 1.94e-01 4.86e-02 -2.5 2.90e+04 - 5.66e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 4.8078375e+00 5.23e-01 2.67e-02 -2.1 1.73e+05 - 6.04e-01 5.31e-02f 1\n",
|
||
" 91 4.7087057e+00 2.13e-01 1.23e-01 -2.4 1.63e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 5.0641092e+00 1.06e-01 4.19e-02 -3.2 8.85e+03 - 1.97e-01 1.00e+00H 1\n",
|
||
" 93 4.9972245e+00 3.90e-01 6.37e-02 -2.7 3.45e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 4.8625440e+00 1.43e+00 2.83e-01 -2.7 6.82e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 4.9633873e+00 4.91e-01 3.22e-01 -2.7 5.52e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 4.9369554e+00 8.49e-02 1.03e-01 -2.7 1.92e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 4.7098624e+00 1.12e+00 4.13e-01 -2.7 1.04e+04 - 9.83e-01 1.00e+00f 1\n",
|
||
" 98 4.6523065e+00 9.70e-01 3.23e-01 -2.9 3.18e+03 - 1.00e+00 1.50e-01h 1\n",
|
||
" 99 5.0923104e+00 8.07e-02 2.70e-01 -2.9 3.25e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 5.0186051e+00 6.05e-02 2.11e-01 -2.9 3.61e+03 - 1.00e+00 2.69e-01h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 5.0186050633553547e+00 5.0186050633553547e+00\n",
|
||
"Dual infeasibility......: 2.1131757318960864e-01 2.1131757318960864e-01\n",
|
||
"Constraint violation....: 6.0513063218142094e-02 6.0513063218142094e-02\n",
|
||
"Complementarity.........: 1.5367972504610637e-03 1.5367972504610637e-03\n",
|
||
"Overall NLP error.......: 2.1131757318960864e-01 2.1131757318960864e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 142\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 142\n",
|
||
"Number of inequality constraint evaluations = 142\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.422\n",
|
||
"Total CPU secs in NLP function evaluations = 135.850\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 646.00us ( 4.55us) 629.43us ( 4.43us) 142\n",
|
||
" nlp_g | 6.40 s ( 45.04ms) 6.10 s ( 42.97ms) 142\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 336.00us ( 3.29us) 331.64us ( 3.25us) 102\n",
|
||
" nlp_jac_g | 132.25 s ( 1.30 s) 126.23 s ( 1.24 s) 102\n",
|
||
" total | 140.13 s (140.13 s) 133.76 s (133.76 s) 1\n",
|
||
"Timestamp 10200\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 8.69e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0064039e+01 1.40e+01 8.69e+03 -1.5 8.69e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.5440964e+00 5.13e+00 9.50e+00 0.6 4.03e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 7.4272016e+00 1.20e+00 8.52e-01 -1.5 1.14e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 8.3407567e+00 3.71e-03 1.02e-01 -3.3 1.81e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 8.3425331e+00 1.63e-07 5.26e-05 -5.1 3.76e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 8.3425325e+00 3.81e-07 1.21e-04 -11.0 3.10e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 8.3425308e+00 1.10e-06 2.12e-03 -11.0 5.78e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 8.3425314e+00 6.92e-07 9.66e-04 -11.0 7.13e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 8.3425297e+00 1.48e-06 1.21e-03 -11.0 1.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 8.3425309e+00 1.07e-06 1.69e-03 -11.0 1.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 8.3425252e+00 3.57e-06 8.11e-04 -11.0 8.64e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 8.3425286e+00 2.25e-06 2.84e-03 -11.0 1.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 8.3425320e+00 5.46e-07 3.41e-05 -11.0 3.40e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 8.3425310e+00 6.00e-07 2.34e-05 -11.0 1.61e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 8.3425316e+00 5.52e-07 6.13e-05 -11.0 4.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 8.3363361e+00 4.89e-03 3.22e-02 -11.0 5.00e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 17 8.3044212e+00 3.09e-02 4.06e-03 -11.0 4.90e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 7.0093200e+00 7.99e-01 1.21e-01 -11.0 1.87e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 19 6.0609379e+00 1.50e+00 2.66e-01 -9.0 3.33e+04 - 1.00e+00 3.64e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 6.0209718e+00 1.53e+00 2.71e-01 -7.1 7.91e+04 - 1.00e+00 1.53e-03h 1\n",
|
||
" 21 8.8694817e+00 1.40e-01 2.99e+00 -9.0 3.32e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 8.9684800e+00 1.58e-05 1.14e-02 -9.1 1.76e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 8.9684881e+00 1.91e-07 6.13e-05 -9.1 1.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 8.9684882e+00 8.05e-08 8.12e-05 -9.1 4.42e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 8.9684881e+00 1.31e-07 4.49e-05 -11.0 1.27e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 8.9684883e+00 5.00e-08 1.40e-04 -11.0 2.99e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.9684882e+00 1.51e-07 7.96e-05 -11.0 4.80e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 8.9684883e+00 3.39e-08 4.91e-05 -11.0 2.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.9684884e+00 2.12e-08 8.51e-05 -11.0 2.74e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.9684881e+00 3.19e-07 1.80e-04 -11.0 8.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 8.9684881e+00 1.96e-07 2.18e-04 -11.0 5.47e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 8.9684881e+00 1.69e-07 1.91e-04 -11.0 4.46e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 8.9683918e+00 5.88e-05 2.62e-02 -11.0 2.29e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 8.9684849e+00 8.68e-07 1.40e-03 -11.0 2.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 8.9684648e+00 8.11e-06 2.67e-03 -11.0 9.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 8.9684259e+00 4.53e-05 1.72e-03 -11.0 8.71e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 8.9682727e+00 1.17e-04 3.59e-03 -11.0 1.18e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 8.9683878e+00 1.18e-04 2.05e-03 -11.0 5.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 8.9684484e+00 9.51e-06 2.07e-03 -11.0 1.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.9684923e+00 3.52e-09 3.54e-05 -11.0 5.95e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 41 8.9684840e+00 3.25e-05 8.56e-04 -11.0 2.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 8.9684548e+00 3.83e-05 1.41e-03 -11.0 2.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 8.9682767e+00 1.44e-04 1.43e-03 -11.0 9.37e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 8.9681632e+00 3.54e-04 7.42e-04 -11.0 6.67e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 8.9683215e+00 1.35e-04 1.54e-03 -11.0 6.42e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 8.9681624e+00 3.62e-04 1.55e-03 -11.0 1.33e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 8.9504155e+00 1.08e-02 3.26e-03 -11.0 7.73e+01 - 7.67e-02 1.00e+00h 1\n",
|
||
" 48 8.9661205e+00 1.15e-03 1.60e-03 -11.0 1.72e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 8.9675483e+00 5.28e-04 1.68e-03 -11.0 8.48e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.9673005e+00 7.92e-04 2.30e-03 -11.0 9.37e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 8.9673390e+00 3.87e-04 7.28e-04 -11.0 8.38e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 8.9670441e+00 1.10e-03 7.57e-04 -11.0 2.11e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 8.9644363e+00 5.39e-03 1.66e-03 -11.0 2.34e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 8.9603769e+00 8.22e-03 2.78e-03 -11.0 7.80e+02 - 1.00e+00 1.66e-01h 1\n",
|
||
" 55 8.9535697e+00 2.28e-02 1.71e-03 -11.0 2.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 8.9036481e+00 9.75e-02 9.04e-03 -11.0 5.23e+02 - 1.00e+00 7.61e-01h 1\n",
|
||
" 57 8.5373024e+00 8.15e-01 4.18e-02 -11.0 4.90e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 58 8.3682363e+00 7.30e-01 9.54e-02 -11.0 9.75e+03 - 9.31e-01 1.00e+00h 1\n",
|
||
" 59 8.4434194e+00 8.51e-01 4.61e-03 -11.0 6.30e+03 - 4.04e-10 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 7.2855814e+00 5.79e-01 9.51e-02 -11.0 7.38e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 61 6.1291661e+00 1.51e+00 2.13e-01 -9.6 9.86e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 62 5.9059823e+00 1.61e+00 4.21e-01 -7.7 4.29e+04 - 1.00e+00 1.27e-01h 1\n",
|
||
" 63 5.9184883e+00 1.58e+00 4.07e-01 -5.7 4.77e+03 - 1.00e+00 1.26e-02h 1\n",
|
||
" 64 7.3745218e+00 2.14e+00 3.19e-01 -4.2 3.15e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 7.6869390e+00 3.15e-01 2.56e-01 -3.9 3.80e+03 - 3.58e-01 1.00e+00h 1\n",
|
||
" 66 6.8167469e+00 2.11e+00 3.57e-01 -10.1 4.56e+04 - 8.01e-04 3.73e-01f 1\n",
|
||
" 67 6.8013858e+00 2.11e+00 3.54e-01 -4.2 1.58e+04 - 1.00e+00 1.09e-02h 1\n",
|
||
" 68 5.8631719e+00 1.08e+00 1.45e-01 -4.2 1.52e+04 - 1.00e+00 7.64e-01h 1\n",
|
||
" 69 8.6208279e+00 7.49e-02 2.21e+00 -4.2 2.21e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.7412386e+00 1.12e-04 1.06e-02 -4.3 1.52e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 8.7412918e+00 1.51e-07 7.58e-05 -4.3 1.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 8.7412914e+00 1.09e-07 1.83e-04 -6.4 1.01e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 8.7412915e+00 1.86e-07 1.30e-04 -6.4 5.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 8.7412917e+00 7.20e-08 1.27e-04 -8.5 2.55e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 8.7412918e+00 3.22e-08 5.12e-05 -11.0 1.94e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 8.7412907e+00 7.60e-07 4.10e-05 -11.0 5.21e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 8.7412915e+00 2.66e-07 9.46e-05 -11.0 2.70e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 8.7412916e+00 1.20e-10 7.27e-05 -11.0 8.37e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 79 8.7412914e+00 5.40e-07 7.90e-05 -11.0 4.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 8.7412913e+00 8.18e-07 1.10e-03 -11.0 3.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 8.7412903e+00 1.90e-06 2.60e-03 -11.0 3.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 8.7412862e+00 3.21e-06 3.93e-03 -11.0 2.35e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 8.7369626e+00 4.31e-03 2.51e-02 -11.0 2.64e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 84 8.7367596e+00 1.74e-03 2.57e-03 -11.0 4.49e+01 - 4.37e-02 1.00e+00h 1\n",
|
||
" 85 8.7354788e+00 4.94e-03 2.39e-03 -11.0 1.37e+02 - 1.00e+00 2.64e-01h 1\n",
|
||
" 86 8.7393686e+00 6.56e-04 1.32e-03 -11.0 4.07e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 8.7391739e+00 4.93e-04 1.45e-03 -11.0 5.61e+00 - 6.97e-01 1.00e+00h 1\n",
|
||
" 88 8.7366709e+00 3.26e-03 1.82e-03 -11.0 7.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 8.7300638e+00 1.65e-02 2.46e-03 -11.0 1.96e+02 - 2.90e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 8.7292532e+00 2.85e-02 2.09e-03 -11.0 7.02e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 8.7191679e+00 1.11e-02 1.40e-03 -11.0 7.90e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 8.7379284e+00 1.98e-03 3.58e-03 -11.0 2.57e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 8.6968918e+00 1.49e-01 1.08e-02 -11.0 1.22e+03 - 1.91e-01 1.00e+00h 1\n",
|
||
" 94 8.6433030e+00 1.62e-01 1.13e-02 -11.0 4.93e+03 - 1.00e+00 8.77e-02h 1\n",
|
||
" 95 8.6918952e+00 8.70e-02 3.06e-03 -11.0 4.05e+02 - 2.64e-08 5.00e-01h 2\n",
|
||
" 96 8.6766182e+00 1.04e-01 3.53e-03 -11.0 4.02e+06 - 5.01e-04 8.85e-05f 1\n",
|
||
" 97 7.2847101e+00 7.50e-01 1.02e-01 -11.0 1.68e+07 - 1.63e-03 4.90e-04f 1\n",
|
||
" 98 8.0139778e+00 3.72e-01 5.54e-03 -11.0 6.93e+02 - 9.21e-01 5.00e-01h 2\n",
|
||
" 99r 8.0139778e+00 3.72e-01 9.99e+02 -0.4 0.00e+00 - 0.00e+00 2.85e-07R 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100r 8.2664577e+00 2.70e-01 9.88e+02 -2.6 2.06e+02 - 9.65e-01 1.53e-03f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.2664577352619819e+00 8.2664577352619819e+00\n",
|
||
"Dual infeasibility......: 1.3930056670231855e+02 1.3930056670231855e+02\n",
|
||
"Constraint violation....: 2.7045916952828719e-01 2.7045916952828719e-01\n",
|
||
"Complementarity.........: 1.5374873119264549e+01 1.5374873119264549e+01\n",
|
||
"Overall NLP error.......: 1.3930056670231855e+02 1.3930056670231855e+02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 108\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 108\n",
|
||
"Number of inequality constraint evaluations = 108\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.407\n",
|
||
"Total CPU secs in NLP function evaluations = 136.093\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 478.00us ( 4.43us) 475.13us ( 4.40us) 108\n",
|
||
" nlp_g | 4.85 s ( 44.93ms) 4.63 s ( 42.88ms) 108\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 373.00us ( 3.66us) 367.25us ( 3.60us) 102\n",
|
||
" nlp_jac_g | 134.01 s ( 1.30 s) 127.92 s ( 1.24 s) 103\n",
|
||
" total | 140.32 s (140.32 s) 133.95 s (133.95 s) 1\n",
|
||
"Timestamp 10500\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.15e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0309249e+01 1.60e+01 3.15e+04 -1.5 3.15e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.4772044e+01 6.49e+00 1.28e+01 1.0 6.69e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.3120946e+01 2.52e+00 7.98e-01 -1.1 1.40e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 2.4626763e+01 2.58e-04 8.55e-02 -3.0 2.76e+00 - 9.95e-01 1.00e+00h 1\n",
|
||
" 5 2.4626534e+01 1.17e-06 1.64e-03 -4.7 1.02e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.4625904e+01 2.90e-04 2.44e-02 -6.8 1.32e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.4626189e+01 2.14e-04 2.45e-03 -8.7 7.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.4626429e+01 2.80e-05 2.03e-03 -11.0 2.25e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.4622870e+01 7.65e-04 8.60e-03 -11.0 1.08e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.4625164e+01 6.02e-04 1.44e-03 -11.0 3.29e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.4620910e+01 2.94e-03 2.61e-03 -11.0 7.96e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.4626198e+01 6.15e-04 1.87e-03 -11.0 3.50e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.4626090e+01 1.72e-04 1.10e-03 -11.0 2.58e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.4625801e+01 7.06e-04 2.13e-03 -11.0 3.30e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.4607811e+01 7.23e-03 9.50e-03 -11.0 8.52e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.4603846e+01 9.20e-03 1.02e-03 -11.0 5.45e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.4616146e+01 3.52e-03 1.89e-03 -11.0 2.94e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.4605698e+01 6.60e-03 2.44e-03 -11.0 5.51e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.4369742e+01 1.02e-01 8.34e-03 -11.0 8.54e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.4455097e+01 8.06e-02 6.77e-03 -11.0 9.55e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.1503306e+01 8.31e+00 3.17e-01 -9.0 1.20e+05 - 1.00e+00 2.52e-01f 1\n",
|
||
" 22 2.1529888e+01 8.14e+00 3.04e-01 -7.0 1.36e+04 - 1.00e+00 2.21e-02h 1\n",
|
||
" 23 2.3583034e+01 3.68e-01 3.18e-01 -6.3 4.39e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.2943842e+01 7.48e-01 2.88e-01 -3.7 2.34e+05 - 1.40e-02 3.97e-02f 1\n",
|
||
" 25 2.2752898e+01 2.05e+00 1.68e-02 -5.3 1.15e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.2587689e+01 1.14e+00 1.29e-02 -2.8 2.59e+04 - 1.00e+00 1.97e-01h 1\n",
|
||
" 27 2.2625099e+01 5.63e-01 2.12e-02 -1.8 6.70e+03 - 1.00e+00 4.50e-01h 1\n",
|
||
" 28 2.3378524e+01 8.67e-01 7.55e-03 -3.4 1.83e+03 - 9.36e-01 1.00e+00h 1\n",
|
||
" 29 2.1449001e+01 3.31e+00 8.67e-02 -8.9 2.65e+04 - 2.86e-01 5.68e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.2897698e+01 6.65e-01 3.87e-02 -2.7 7.66e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.3896829e+01 4.88e-02 5.20e-02 -2.0 4.61e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 32 2.1868465e+01 1.90e+01 4.93e-01 -8.0 1.32e+05 - 6.54e-02 3.62e-01f 1\n",
|
||
" 33 2.1809707e+01 1.87e+01 4.81e-01 -2.3 6.04e+04 - 1.00e+00 1.66e-02h 1\n",
|
||
" 34 2.1166337e+01 1.21e+01 8.49e-02 -2.3 1.17e+04 - 4.06e-01 3.94e-01h 1\n",
|
||
" 35 2.3189925e+01 6.06e-01 1.31e-01 -2.2 5.36e+03 - 4.90e-02 1.00e+00h 1\n",
|
||
" 36 2.3189440e+01 6.56e-01 1.19e-01 -1.7 7.29e+03 - 1.00e+00 1.13e-01f 2\n",
|
||
" 37 2.3621818e+01 1.07e-01 1.05e-02 -2.3 4.24e+02 - 9.02e-01 1.00e+00h 1\n",
|
||
" 38 2.3418145e+01 3.59e-01 1.23e-02 -8.3 6.09e+03 - 4.92e-01 2.05e-01f 1\n",
|
||
" 39 2.3595405e+01 3.91e-02 1.20e-02 -2.7 7.15e+02 - 1.00e+00 9.06e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.3585161e+01 7.93e-02 6.07e-03 -3.9 3.29e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.3616609e+01 5.44e-02 3.32e-03 -9.1 6.60e+02 - 6.86e-01 1.00e+00h 1\n",
|
||
" 42 2.3465383e+01 1.48e-01 4.53e-03 -10.3 1.43e+05 - 6.08e-03 2.70e-02f 1\n",
|
||
" 43 2.3623037e+01 1.66e-01 1.88e-03 -2.8 8.10e+03 - 1.29e-01 4.31e-01h 1\n",
|
||
" 44 2.3619487e+01 1.69e-01 1.85e-03 -2.9 4.21e+04 - 7.79e-02 4.02e-04h 2\n",
|
||
" 45 2.3500851e+01 5.12e-02 6.14e-03 -2.9 2.05e+03 - 1.98e-01 1.00e+00h 1\n",
|
||
" 46 2.3336327e+01 3.40e-01 8.73e-03 -3.4 9.82e+03 - 2.22e-01 1.00e+00h 1\n",
|
||
" 47 2.3183553e+01 9.94e-01 1.68e-02 -1.9 5.01e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.3694412e+01 1.43e-02 3.20e-02 -7.8 5.03e+02 - 7.01e-01 1.00e+00h 1\n",
|
||
" 49 2.3543964e+01 3.48e-01 6.56e-03 -2.5 2.00e+03 - 9.92e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.3454522e+01 1.89e-01 1.02e-02 -2.6 4.90e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.2628762e+01 9.19e-01 3.14e-02 -3.0 1.00e+05 - 1.74e-01 1.50e-01f 1\n",
|
||
" 52 2.3343613e+01 8.96e-01 1.14e-02 -2.9 6.54e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.2545357e+01 1.82e+00 4.83e-02 -2.9 1.22e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.3573661e+01 1.62e-01 5.25e-02 -3.0 2.59e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.3583181e+01 5.97e-02 1.23e-02 -3.2 4.71e+03 - 3.63e-01 1.00e+00h 1\n",
|
||
" 56 2.2741200e+01 1.13e+00 7.17e-02 -3.2 9.14e+04 - 1.97e-01 4.35e-01f 1\n",
|
||
" 57r 2.2741200e+01 1.13e+00 9.99e+02 0.1 0.00e+00 - 0.00e+00 2.89e-07R 13\n",
|
||
" 58r 2.3056987e+01 4.31e-01 6.98e+02 -2.1 4.40e+02 - 1.00e+00 2.05e-03f 1\n",
|
||
" 59 2.3631758e+01 2.68e-02 8.48e-03 -2.7 2.42e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.3517680e+01 7.95e-02 3.20e-02 -3.5 7.16e+02 - 9.24e-01 1.00e+00h 1\n",
|
||
" 61 2.3080632e+01 8.56e-01 1.82e-02 -3.5 1.84e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 62 2.3579426e+01 4.94e-02 1.05e-02 -3.5 4.75e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.3640958e+01 1.48e-02 5.02e-03 -5.2 1.83e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.3279494e+01 1.23e-01 3.78e-02 -6.4 2.17e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 65 2.3468864e+01 1.49e-01 4.91e-03 -4.1 9.25e+02 - 1.38e-01 1.00e+00h 1\n",
|
||
" 66 2.3188874e+01 3.07e-01 1.14e-02 -5.1 9.29e+03 - 2.64e-03 1.22e-01f 1\n",
|
||
" 67 2.3609605e+01 5.36e-03 1.14e-02 -6.0 8.22e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.3619315e+01 2.71e-03 3.44e-03 -5.6 2.49e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.3621928e+01 2.60e-05 1.59e-03 -4.8 2.89e+01 - 6.59e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.3531913e+01 1.03e-01 3.03e-03 -3.6 8.01e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 71 2.3498811e+01 9.59e-02 2.95e-03 -3.7 1.73e+04 - 3.79e-02 1.41e-02h 1\n",
|
||
" 72 2.3356143e+01 2.33e-01 2.07e-02 -3.7 2.03e+03 - 1.71e-01 1.00e+00f 1\n",
|
||
" 73 2.3346767e+01 2.33e-01 2.23e-02 -3.7 3.17e+05 - 6.29e-02 2.70e-04f 1\n",
|
||
" 74 2.0596452e+01 2.16e+00 6.80e-02 -3.7 6.38e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 75 2.4034877e+01 1.22e-02 2.60e+00 -7.8 2.64e+00 - 9.90e-01 1.00e+00h 1\n",
|
||
" 76 2.4028418e+01 1.12e-05 1.65e-03 -3.9 4.63e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 2.4028410e+01 1.21e-05 1.05e-03 -9.9 8.06e-02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 78 2.4028328e+01 6.34e-05 3.88e-03 -8.8 4.87e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 2.4028440e+01 4.56e-08 6.54e-05 -8.9 3.05e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.4028363e+01 4.48e-05 2.01e-03 -11.0 3.00e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.4028401e+01 1.82e-05 2.15e-03 -11.0 1.20e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.4028319e+01 3.40e-05 2.64e-03 -11.0 3.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 2.4027182e+01 1.14e-03 3.34e-03 -11.0 5.67e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.4027738e+01 3.91e-04 1.49e-03 -11.0 2.78e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 2.4027707e+01 4.48e-04 1.56e-03 -11.0 1.52e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 2.4024088e+01 3.54e-03 2.33e-03 -11.0 6.27e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.4027855e+01 5.22e-04 2.43e-03 -11.0 4.19e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 2.4028049e+01 2.18e-04 2.70e-03 -11.0 1.11e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 2.4028221e+01 7.71e-08 1.27e-04 -11.0 2.79e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.3942444e+01 1.29e-01 4.03e-03 -11.0 2.95e+03 - 1.00e+00 1.92e-01f 1\n",
|
||
"In iteration 90, 1 Slack too small, adjusting variable bound\n",
|
||
" 91 2.3942440e+01 1.29e-01 4.03e-03 -11.0 1.91e+03 - 1.00e+00 3.75e-05h 1\n",
|
||
" 92 2.4027985e+01 6.78e-05 3.77e-03 -11.0 1.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 2.4027497e+01 6.28e-04 2.87e-03 -11.0 7.41e+00 - 3.88e-01 1.00e+00h 1\n",
|
||
" 94 2.4027138e+01 5.31e-04 2.61e-03 -11.0 5.07e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.4027235e+01 1.74e-04 3.92e-03 -11.0 2.86e+00 - 7.03e-01 1.00e+00h 1\n",
|
||
" 96 2.4027105e+01 6.22e-04 3.10e-03 -11.0 1.33e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.4000128e+01 2.83e-02 3.69e-03 -11.0 5.31e+02 - 3.57e-02 1.00e+00h 1\n",
|
||
" 98 2.0995864e+01 3.03e+00 1.10e-01 -11.0 5.14e+04 - 6.34e-01 1.00e+00f 1\n",
|
||
" 99 2.2434998e+01 1.32e+00 1.82e-02 -11.0 9.72e+03 - 8.54e-01 6.00e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.3354599e+01 6.92e-02 4.84e-02 -11.0 3.46e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.3354599261349446e+01 2.3354599261349446e+01\n",
|
||
"Dual infeasibility......: 4.8366118277069492e-02 4.8366118277069492e-02\n",
|
||
"Constraint violation....: 6.9166731715164076e-02 6.9166731715164076e-02\n",
|
||
"Complementarity.........: 5.1803463578119751e-03 5.1803463578119751e-03\n",
|
||
"Overall NLP error.......: 6.9166731715164076e-02 6.9166731715164076e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 124\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 124\n",
|
||
"Number of inequality constraint evaluations = 124\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.425\n",
|
||
"Total CPU secs in NLP function evaluations = 136.145\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 560.00us ( 4.52us) 552.72us ( 4.46us) 124\n",
|
||
" nlp_g | 5.52 s ( 44.49ms) 5.26 s ( 42.39ms) 124\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 339.00us ( 3.32us) 335.81us ( 3.29us) 102\n",
|
||
" nlp_jac_g | 133.29 s ( 1.29 s) 127.21 s ( 1.24 s) 103\n",
|
||
" total | 140.28 s (140.28 s) 133.88 s (133.88 s) 1\n",
|
||
"Timestamp 10800\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.16e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0546741e+01 1.26e+01 2.16e+04 -1.5 2.16e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.7944382e+00 4.43e+00 6.29e+00 0.6 6.00e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.9354537e+00 6.19e-01 6.04e-01 -1.5 1.86e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 1.7986608e+00 4.66e-03 5.65e-01 -3.3 1.11e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.7995711e+00 1.04e-07 2.64e-05 -5.1 4.76e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.7995711e+00 1.44e-07 3.30e-05 -11.0 7.46e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.7995710e+00 1.46e-07 3.26e-05 -11.0 7.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.7995708e+00 1.01e-06 2.92e-04 -11.0 4.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.7995710e+00 1.03e-07 2.42e-05 -11.0 2.51e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.7995692e+00 2.11e-05 1.40e-03 -11.0 4.99e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.7995665e+00 8.33e-06 3.40e-03 -11.0 7.39e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.7995537e+00 2.45e-05 6.07e-03 -11.0 1.51e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.7995704e+00 2.99e-08 2.28e-05 -11.0 8.86e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.7995704e+00 7.91e-09 2.52e-05 -11.0 5.35e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.7995704e+00 7.51e-11 3.43e-05 -11.0 2.25e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 16 1.7995704e+00 1.56e-08 1.78e-05 -11.0 6.10e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.7995704e+00 2.30e-09 2.61e-05 -11.0 2.48e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.7995704e+00 2.68e-09 1.64e-05 -11.0 9.39e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.7995704e+00 7.05e-10 3.07e-05 -11.0 9.98e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.7995704e+00 4.63e-11 4.91e-05 -11.0 6.67e-06 - 1.00e+00 1.00e+00H 1\n",
|
||
" 21 1.7995704e+00 3.84e-09 5.10e-05 -11.0 1.12e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.7995703e+00 3.03e-08 2.36e-05 -11.0 1.39e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.7995704e+00 7.59e-09 9.11e-06 -11.0 1.55e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.7995704e+00 7.31e-09 3.59e-05 -11.0 1.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.7995704e+00 1.73e-08 1.90e-05 -11.0 3.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.7995704e+00 2.63e-08 2.70e-05 -11.0 1.97e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.7995704e+00 1.11e-08 4.28e-05 -11.0 9.64e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.7995703e+00 1.45e-07 1.04e-05 -11.0 4.93e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.7995704e+00 3.64e-09 3.36e-05 -11.0 1.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.7995703e+00 9.92e-08 1.00e-05 -11.0 4.28e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.7988171e+00 3.70e-03 1.56e-02 -11.0 2.03e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 32 1.7973247e+00 1.61e-03 2.02e-03 -11.0 1.54e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.7883628e+00 2.97e-02 2.26e-02 -11.0 1.43e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.7999026e+00 1.97e-03 1.83e-02 -11.0 1.82e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.7994102e+00 4.04e-03 9.87e-03 -11.0 2.59e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.7976938e+00 9.03e-03 1.42e-02 -11.0 3.84e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.7986122e+00 5.46e-03 1.36e-02 -11.0 1.93e+01 - 1.00e+00 5.00e-01h 2\n",
|
||
" 38 1.8009319e+00 1.62e-03 2.86e-03 -11.0 1.11e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.8012563e+00 1.70e-04 1.37e-03 -11.0 1.85e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.8011958e+00 1.76e-04 9.56e-04 -11.0 4.85e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.7917065e+00 1.90e-02 7.85e-03 -11.0 7.30e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.7902837e+00 2.28e-02 1.84e-02 -11.0 6.46e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.7986458e+00 1.67e-02 1.57e-02 -11.0 4.92e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.8011578e+00 7.01e-04 9.35e-03 -11.0 5.85e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.8008549e+00 2.18e-03 6.29e-03 -11.0 9.71e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.7876703e+00 4.94e-02 3.73e-02 -11.0 3.38e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.8063097e+00 7.77e-03 3.71e-02 -11.0 3.65e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 48 1.7809617e+00 1.64e-01 6.00e-02 -11.0 2.85e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.7917620e+00 4.74e-02 1.31e-01 -11.0 1.01e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.7478497e+00 1.94e-01 1.01e-01 -11.0 4.17e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.8107620e+00 3.07e-02 1.12e-01 -11.0 1.59e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.7629855e+00 1.40e-01 8.59e-02 -11.0 9.24e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.7790148e+00 7.01e-02 5.58e-02 -11.0 3.80e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.7710584e+00 5.24e-01 2.85e-01 -11.0 1.72e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.8523034e+00 1.35e-01 3.50e-01 -11.0 2.00e+04 - 9.97e-01 1.00e+00h 1\n",
|
||
" 56 1.8540236e+00 1.33e-01 3.86e-01 -11.0 9.78e+04 - 3.61e-01 1.77e-02h 5\n",
|
||
" 57 1.7453874e+00 2.79e-01 1.62e-01 -11.0 1.94e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.7435446e+00 3.15e-01 1.25e-01 -10.8 3.55e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 59 1.7579878e+00 3.79e-01 1.11e-01 -11.0 3.13e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.7509629e+00 4.81e-01 1.13e-01 -11.0 4.13e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.7767507e+00 2.28e-01 3.04e-01 -11.0 2.03e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.7718083e+00 2.54e-01 1.75e-01 -11.0 1.09e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 63 1.7659384e+00 3.45e-01 1.02e-01 -11.0 1.11e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.7585354e+00 3.67e-01 1.85e-01 -11.0 1.45e+05 - 1.58e-01 4.32e-03h 7\n",
|
||
" 65 1.7580063e+00 3.05e-01 8.72e-02 -11.0 5.16e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 66 1.8231348e+00 1.06e-01 2.54e-01 -11.0 5.00e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 1.7667427e+00 1.67e-01 1.29e-01 -11.0 8.31e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 68 1.7566766e+00 4.03e-02 3.16e-02 -11.0 7.67e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.7566060e+00 6.81e-02 2.03e-02 -11.0 1.78e+04 - 1.00e+00 3.12e-02h 6\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.7569809e+00 8.85e-02 1.18e-02 -11.0 2.00e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 71 1.7664940e+00 2.98e-02 3.94e-02 -11.0 5.19e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 72 1.7656594e+00 3.78e-02 3.66e-02 -11.0 6.37e+04 - 1.66e-01 2.63e-02h 6\n",
|
||
" 73 1.7649395e+00 5.59e-02 5.85e-02 -11.0 8.48e+04 - 1.00e+00 9.57e-03h 7\n",
|
||
" 74 1.7601462e+00 1.50e-05 3.26e-02 -11.0 3.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.7601478e+00 1.38e-09 1.67e-05 -11.0 4.72e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.7601478e+00 6.90e-09 4.63e-06 -11.0 2.96e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.7601478e+00 6.02e-10 1.63e-05 -11.0 5.38e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.7601478e+00 1.17e-09 3.35e-05 -11.0 4.35e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.7601478e+00 1.36e-09 3.23e-05 -11.0 2.13e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.7601478e+00 9.24e-10 1.83e-05 -11.0 2.56e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.7601478e+00 2.01e-10 7.38e-06 -11.0 1.68e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.7601478e+00 2.88e-10 2.86e-05 -11.0 1.27e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.7601478e+00 2.71e-11 1.30e-05 -11.0 1.48e-06 - 1.00e+00 1.00e+00H 1\n",
|
||
" 84 1.7601478e+00 1.84e-10 2.51e-05 -11.0 1.69e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.7601478e+00 5.40e-10 3.75e-05 -11.0 2.81e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.7601478e+00 1.77e-09 8.17e-06 -11.0 7.11e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.7601478e+00 1.21e-09 1.48e-05 -11.0 1.45e-05 - 1.00e+00 5.00e-01h 2\n",
|
||
" 88 1.7601478e+00 7.91e-11 1.26e-05 -11.0 4.17e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 89 1.7601478e+00 5.37e-08 1.12e-05 -11.0 1.88e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.7601478e+00 2.80e-09 2.41e-05 -11.0 3.80e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.7601478e+00 1.23e-08 6.41e-06 -11.0 4.34e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.7601476e+00 2.24e-06 1.89e-03 -11.0 1.87e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.7601478e+00 5.56e-07 1.32e-05 -11.0 7.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.7601476e+00 2.25e-06 6.19e-04 -11.0 1.86e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.7601478e+00 4.53e-08 2.21e-05 -11.0 7.99e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.7601476e+00 2.39e-06 6.49e-04 -11.0 5.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.7601476e+00 4.04e-06 1.50e-04 -11.0 4.25e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.7601476e+00 5.36e-06 4.19e-04 -11.0 4.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.7601292e+00 2.57e-04 3.78e-04 -11.0 1.55e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.7601077e+00 1.11e-03 5.78e-04 -11.0 1.06e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.7601077114945023e+00 1.7601077114945023e+00\n",
|
||
"Dual infeasibility......: 5.7771432419334579e-04 5.7771432419334579e-04\n",
|
||
"Constraint violation....: 1.1137916193710851e-03 1.1137916193710851e-03\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 1.1137916193710851e-03 1.1137916193710851e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 165\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 165\n",
|
||
"Number of inequality constraint evaluations = 165\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.429\n",
|
||
"Total CPU secs in NLP function evaluations = 136.665\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 736.00us ( 4.46us) 723.67us ( 4.39us) 165\n",
|
||
" nlp_g | 7.38 s ( 44.71ms) 7.03 s ( 42.63ms) 165\n",
|
||
" nlp_grad | 1.32 s ( 1.32 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 393.00us ( 3.85us) 381.87us ( 3.74us) 102\n",
|
||
" nlp_jac_g | 132.06 s ( 1.29 s) 126.05 s ( 1.24 s) 102\n",
|
||
" total | 140.90 s (140.90 s) 134.49 s (134.49 s) 1\n",
|
||
"Timestamp 11100\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.15e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9952190e+01 1.43e+01 2.15e+03 -1.5 2.15e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.2721912e+00 5.24e+00 1.00e+01 0.4 1.43e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.0530740e+01 1.52e+00 6.22e-01 -1.6 8.85e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.1622151e+01 2.34e-03 8.48e-02 -3.4 2.08e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.1623275e+01 4.73e-08 1.43e-04 -5.3 2.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.1623275e+01 1.07e-07 5.11e-05 -11.0 8.08e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.1623275e+01 2.86e-07 9.00e-05 -11.0 2.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.1623275e+01 1.89e-07 8.88e-05 -11.0 7.17e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.1623275e+01 9.22e-08 2.52e-05 -11.0 9.36e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.1623275e+01 6.13e-08 7.50e-05 -11.0 1.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.1623275e+01 4.61e-07 1.58e-04 -11.0 2.92e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.1623275e+01 3.31e-07 4.42e-05 -11.0 7.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.1623275e+01 6.36e-08 2.69e-04 -11.0 4.17e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.1623275e+01 9.95e-08 7.68e-05 -11.0 7.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.1623275e+01 1.01e-07 7.24e-05 -11.0 4.27e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.1623267e+01 6.99e-06 5.54e-03 -11.0 3.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.1623274e+01 1.12e-06 1.96e-03 -11.0 1.98e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.1623276e+01 1.10e-07 1.29e-04 -11.0 3.58e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.1623276e+01 2.82e-07 1.77e-04 -11.0 1.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.1623275e+01 1.89e-07 7.46e-05 -11.0 2.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.1623275e+01 2.75e-06 1.18e-03 -11.0 7.55e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.1623252e+01 9.10e-06 2.44e-03 -11.0 4.93e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.1623274e+01 1.10e-09 5.60e-05 -11.0 1.20e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 24 1.1623260e+01 1.41e-05 1.27e-03 -11.0 1.84e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.1623233e+01 2.60e-05 1.17e-03 -11.0 2.11e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.1608115e+01 6.76e-03 9.68e-03 -11.0 1.70e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 27 1.1497469e+01 7.74e-02 1.43e-02 -11.0 5.20e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.1607860e+01 5.35e-03 8.36e-03 -11.0 3.77e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.1601403e+01 5.08e-03 7.78e-03 -11.0 4.77e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.1566647e+01 1.85e-01 1.27e-02 -11.0 4.29e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.0335094e+01 2.91e+00 2.62e-01 -11.0 3.07e+04 - 1.00e+00 9.13e-01f 1\n",
|
||
" 32 1.0763364e+01 2.59e-01 1.76e-01 -10.5 8.53e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.1106269e+01 1.71e-01 9.51e-02 -3.0 8.67e+03 - 1.00e+00 9.73e-01h 1\n",
|
||
" 34 1.1027236e+01 2.64e-01 4.00e-02 -2.7 1.45e+04 - 4.18e-01 1.00e+00h 1\n",
|
||
" 35 9.8788143e+00 1.48e+00 1.61e-01 -2.1 9.71e+06 - 3.83e-03 4.93e-03f 1\n",
|
||
" 36 1.0639932e+01 7.04e-01 5.82e-02 -3.3 1.25e+04 - 9.94e-01 1.00e+00h 1\n",
|
||
" 37 9.9399530e+00 1.61e+00 1.25e-01 -2.9 1.67e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 9.5139240e+00 4.75e+00 5.25e-01 -3.0 2.47e+06 - 5.96e-03 9.29e-03f 1\n",
|
||
" 39 1.2336782e+01 1.44e+00 3.81e-01 -3.0 3.28e+04 - 9.55e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.0251810e+01 6.76e-01 1.48e-01 -3.1 2.49e+04 - 1.67e-01 1.00e+00f 1\n",
|
||
" 41 8.8724616e+00 1.33e+00 4.40e-01 -1.7 6.94e+05 - 9.23e-01 4.76e-02f 1\n",
|
||
" 42 1.3163710e+01 5.54e+00 4.74e-01 0.1 8.02e+05 - 2.66e-02 3.87e-02f 2\n",
|
||
" 43 9.4862119e+00 2.01e+00 9.48e-02 -0.7 4.17e+04 - 1.00e+00 6.58e-01f 1\n",
|
||
" 44 1.1310636e+01 5.73e-02 2.06e+00 -0.7 2.06e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.1366331e+01 4.84e-06 2.99e-03 -2.8 9.15e-02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 46 1.1366328e+01 2.54e-06 1.66e-03 -8.7 6.66e-03 - 9.97e-01 1.00e+00h 1\n",
|
||
" 47 1.1366332e+01 7.84e-07 7.66e-04 -7.3 3.82e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.1366333e+01 2.76e-07 3.05e-05 -9.4 1.96e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.1366333e+01 5.74e-08 1.24e-04 -11.0 6.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.1366332e+01 1.84e-06 1.99e-03 -11.0 2.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.1366333e+01 2.13e-07 3.43e-04 -11.0 1.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.1366332e+01 3.98e-07 3.20e-05 -11.0 1.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.1366333e+01 1.02e-07 1.83e-05 -11.0 5.41e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.1366333e+01 1.21e-07 8.35e-05 -11.0 5.99e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.1366333e+01 2.10e-07 4.31e-05 -11.0 4.54e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.1366333e+01 8.42e-08 1.82e-04 -11.0 7.89e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.1366331e+01 1.84e-06 3.33e-03 -11.0 3.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.1366333e+01 3.02e-07 6.48e-05 -11.0 2.55e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.1366332e+01 1.17e-06 3.03e-03 -11.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.1366331e+01 1.29e-06 9.77e-04 -11.0 3.77e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.1366333e+01 4.31e-07 4.37e-04 -11.0 2.50e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.1366324e+01 3.53e-06 2.91e-03 -11.0 7.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.1366333e+01 1.51e-07 8.17e-05 -11.0 6.51e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.1366333e+01 1.81e-07 1.14e-04 -11.0 1.13e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.1366332e+01 6.04e-07 3.07e-04 -11.0 2.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.1366331e+01 1.24e-06 3.90e-03 -11.0 6.36e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.1366333e+01 1.75e-07 1.15e-04 -11.0 1.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.1366333e+01 3.89e-07 4.06e-05 -11.0 1.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.1366333e+01 7.49e-08 1.70e-04 -11.0 5.19e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.1366333e+01 1.78e-07 1.02e-04 -11.0 4.36e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.1366329e+01 6.58e-06 3.74e-03 -11.0 1.60e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.1363100e+01 2.49e-03 2.49e-02 -11.0 1.10e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.1353557e+01 1.71e-02 8.18e-03 -11.0 7.35e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.1366547e+01 2.84e-04 1.78e-03 -11.0 1.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.1366052e+01 1.86e-03 2.21e-03 -11.0 3.95e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.1283797e+01 4.82e-02 1.93e-02 -11.0 5.98e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 77 1.1224502e+01 1.03e-01 8.59e-03 -9.0 2.75e+03 - 1.00e+00 7.26e-01h 1\n",
|
||
" 78 1.1224045e+01 1.05e-01 8.69e-03 -7.1 1.93e+03 - 1.00e+00 5.53e-03h 1\n",
|
||
" 79 1.1148421e+01 1.41e-01 1.50e-02 -5.7 1.63e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.1361057e+01 2.69e-02 1.51e-02 -4.9 3.42e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.1367322e+01 9.78e-04 6.33e-03 -5.0 1.38e+02 - 9.26e-01 1.00e+00h 1\n",
|
||
" 82 1.1265496e+01 1.35e-01 6.63e-03 -5.0 1.94e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 83 1.1247483e+01 9.91e-02 4.37e-03 -5.0 4.15e+03 - 8.72e-01 2.60e-01h 1\n",
|
||
" 84 1.1205636e+01 2.31e-01 2.59e-02 -5.0 9.40e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.1314884e+01 3.90e-02 1.11e-02 -5.0 3.36e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.1324877e+01 2.07e-02 1.09e-02 -5.4 1.96e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.1304510e+01 2.79e-02 2.64e-03 -6.3 2.63e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.0866834e+01 2.53e-01 3.11e-02 -3.7 1.27e+03 - 8.13e-02 1.00e+00f 1\n",
|
||
" 89 1.0611018e+01 1.89e+00 1.20e-01 -3.2 1.70e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 9.2868759e+00 3.47e+00 2.52e-01 -3.3 6.93e+04 - 2.76e-01 6.58e-01f 1\n",
|
||
" 91 9.2863518e+00 3.47e+00 2.52e-01 -3.3 6.18e+04 - 1.00e+00 1.89e-04h 2\n",
|
||
" 92 8.6228653e+00 3.90e+00 4.64e-01 -3.3 3.66e+04 - 1.00e+00 1.73e-01f 1\n",
|
||
" 93 1.0184441e+01 6.51e-01 5.22e-01 -2.8 2.93e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.0483675e+01 2.96e-01 1.08e-01 -4.0 1.52e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.0140029e+01 3.35e-01 1.56e-01 -5.6 1.78e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.0869089e+01 5.78e-02 4.07e-02 -2.5 4.94e+02 - 5.73e-01 1.00e+00h 1\n",
|
||
" 97 1.0880581e+01 2.38e-02 6.05e-02 -2.8 2.05e+02 - 1.00e+00 9.86e-01h 1\n",
|
||
" 98 1.0627082e+01 1.27e-01 1.58e-01 -8.9 2.17e+03 - 7.89e-02 1.00e+00f 1\n",
|
||
" 99 9.0789837e+00 7.59e+00 8.96e-01 -2.6 1.93e+05 - 6.94e-03 6.57e-02f 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.0607636e+00 2.37e+00 1.98e-01 -3.0 2.12e+04 - 6.82e-03 5.65e-01f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.0607636235363049e+00 8.0607636235363049e+00\n",
|
||
"Dual infeasibility......: 1.9833761698523900e-01 1.9833761698523900e-01\n",
|
||
"Constraint violation....: 2.3733666903513608e+00 2.3733666903513608e+00\n",
|
||
"Complementarity.........: 4.3086498841157072e-03 4.3086498841157072e-03\n",
|
||
"Overall NLP error.......: 2.3733666903513608e+00 2.3733666903513608e+00\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 111\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 111\n",
|
||
"Number of inequality constraint evaluations = 111\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.432\n",
|
||
"Total CPU secs in NLP function evaluations = 134.005\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 489.00us ( 4.41us) 491.09us ( 4.42us) 111\n",
|
||
" nlp_g | 4.93 s ( 44.43ms) 4.70 s ( 42.31ms) 111\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 379.00us ( 3.72us) 376.78us ( 3.69us) 102\n",
|
||
" nlp_jac_g | 131.80 s ( 1.29 s) 125.79 s ( 1.23 s) 102\n",
|
||
" total | 138.23 s (138.23 s) 131.92 s (131.92 s) 1\n",
|
||
"Timestamp 11400\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.01e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9833247e+01 1.39e+01 2.01e+03 -1.5 2.01e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.0075872e+00 4.95e+00 9.75e+00 0.4 1.39e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 9.7466487e+00 1.42e+00 6.24e-01 -1.6 8.48e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.0737729e+01 2.18e-03 8.15e-02 -3.4 1.93e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.0738770e+01 1.41e-07 6.14e-05 -5.3 2.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.0738770e+01 2.13e-07 1.26e-04 -11.0 8.77e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.0738770e+01 1.43e-07 8.75e-05 -11.0 6.67e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.0738770e+01 1.09e-10 1.07e-04 -11.0 2.98e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 9 1.0738770e+01 6.41e-08 1.05e-04 -11.0 2.44e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.0738740e+01 6.51e-05 1.16e-02 -11.0 9.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.0738736e+01 5.26e-05 4.10e-03 -11.0 1.91e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.0738744e+01 1.32e-05 2.11e-03 -11.0 1.17e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.0738742e+01 9.00e-06 2.78e-03 -11.0 1.63e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.0738737e+01 5.02e-05 5.75e-04 -11.0 1.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.0738682e+01 1.02e-04 3.19e-03 -11.0 4.28e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.0738689e+01 7.97e-05 1.17e-03 -11.0 8.66e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.0738736e+01 1.92e-05 9.27e-04 -11.0 1.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.0683226e+01 4.25e-02 2.00e-02 -11.0 2.09e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 19 1.0638148e+01 4.31e-02 6.51e-03 -11.0 6.25e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.0552017e+01 8.97e-02 1.07e-02 -11.0 2.77e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.0797377e+01 8.43e-04 9.63e-03 -11.0 3.16e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 22 1.0507422e+01 1.83e-01 1.77e-02 -11.0 2.22e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 23 1.0647461e+01 3.93e-02 2.12e-02 -11.0 1.69e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 9.0037438e+00 2.75e+00 2.98e-01 -11.0 2.53e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 25 9.2390294e+00 1.17e+00 4.73e-02 -11.0 5.05e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 9.7742496e+00 5.03e-01 1.32e-01 -10.7 1.25e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 7.4567612e+00 1.79e+00 1.93e-01 -10.9 2.48e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 28 7.3528837e+00 2.18e+00 4.07e-01 -9.0 3.96e+06 - 1.00e+00 2.95e-03f 1\n",
|
||
" 29 7.3497042e+00 2.18e+00 4.08e-01 -7.0 9.80e+05 - 1.00e+00 1.19e-04h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 9.3614797e+00 1.69e+00 4.16e-01 -5.0 1.34e+04 - 5.81e-04 5.00e-01h 2\n",
|
||
" 31 9.2548012e+00 1.75e+00 4.13e-01 -6.9 2.91e+04 - 8.44e-01 8.25e-02h 1\n",
|
||
" 32 9.2691088e+00 1.07e+00 2.05e-01 -6.9 3.92e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 7.5737335e+00 2.19e+00 6.48e-01 -4.8 1.79e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 34 9.7777353e+00 2.77e-01 2.74e-01 -3.6 1.81e+03 - 4.12e-01 1.00e+00h 1\n",
|
||
" 35 8.7474242e+00 2.04e+00 1.09e-01 -3.6 3.15e+04 - 9.54e-01 1.00e+00f 1\n",
|
||
" 36 8.2809134e+00 2.10e+00 1.13e-01 -3.1 4.72e+04 - 1.00e+00 6.78e-01h 1\n",
|
||
" 37 9.2660709e+00 5.29e-01 2.20e-01 -2.9 7.72e+03 - 1.00e+00 9.74e-01h 1\n",
|
||
" 38 9.7572442e+00 1.63e-01 2.84e-02 -2.4 4.71e+03 - 9.57e-01 1.00e+00h 1\n",
|
||
" 39 1.0058632e+01 4.28e-01 8.98e-02 -0.5 2.97e+05 - 8.17e-02 2.03e-01F 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 9.7975869e+00 1.72e+00 1.43e-01 -1.3 5.94e+04 - 1.00e+00 1.59e-01f 1\n",
|
||
" 41 1.0081696e+01 3.01e-01 1.21e-01 -1.3 3.65e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 9.4591694e+00 5.71e-01 8.93e-02 -2.0 2.83e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 8.5056138e+00 1.82e+00 1.73e-01 -1.8 4.26e+03 - 3.59e-01 9.64e-01f 1\n",
|
||
" 44 9.5544232e+00 9.27e-01 1.72e-01 -8.0 3.11e+03 - 2.27e-01 1.00e+00h 1\n",
|
||
" 45 9.1543813e+00 5.47e-01 4.27e-02 -2.3 7.21e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 8.2488617e+00 9.34e-01 1.31e-01 -2.8 1.87e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 8.3216633e+00 9.27e-01 7.67e-02 -1.8 3.61e+03 - 1.00e+00 4.05e-01h 1\n",
|
||
" 48 9.9652113e+00 5.04e-02 7.67e-02 -7.8 1.51e+03 - 6.67e-01 1.00e+00h 1\n",
|
||
" 49 9.8172839e+00 4.65e-01 2.58e-02 -8.3 9.12e+03 - 8.86e-02 4.83e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 9.7363357e+00 3.56e-01 3.07e-02 -2.5 5.03e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 9.8208338e+00 6.30e-02 2.81e-02 -3.9 5.06e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 9.9243172e+00 4.28e-02 1.62e-02 -2.7 3.41e+03 - 7.45e-01 1.00e+00h 1\n",
|
||
" 53 9.8016196e+00 2.82e-01 4.66e-03 -3.4 2.71e+04 - 5.32e-01 4.88e-02f 1\n",
|
||
" 54 9.8355371e+00 3.71e-02 1.71e-02 -3.0 2.10e+02 - 8.35e-01 1.00e+00h 1\n",
|
||
" 55 8.8481934e+00 6.85e-01 1.12e-01 -9.1 3.09e+03 - 1.51e-01 1.00e+00f 1\n",
|
||
" 56 8.5541680e+00 2.46e+00 1.51e-01 -9.2 7.02e+04 - 7.90e-03 3.51e-01h 1\n",
|
||
" 57 8.5592017e+00 2.45e+00 1.48e-01 -3.3 3.68e+04 - 1.00e+00 8.40e-03h 1\n",
|
||
" 58 9.9370221e+00 2.47e-01 2.68e-01 -3.3 8.82e+03 - 3.77e-01 1.00e+00h 1\n",
|
||
" 59 9.9332081e+00 2.46e-01 2.67e-01 -3.3 1.83e+04 - 1.00e+00 4.87e-03h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 9.9327517e+00 7.45e-02 1.54e-02 -3.3 1.39e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 61 9.9662402e+00 4.61e-02 2.65e-02 -3.3 1.08e+03 - 9.00e-01 1.00e+00h 1\n",
|
||
" 62 9.8431453e+00 1.65e+00 1.65e-01 -3.6 6.13e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 63 9.8647053e+00 9.03e-01 6.63e-02 -3.7 6.74e+03 - 1.00e+00 3.89e-01h 1\n",
|
||
" 64 9.9724115e+00 3.08e-02 2.35e-02 -3.7 7.51e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 9.5611082e+00 2.01e-01 6.05e-02 -3.7 2.67e+03 - 2.00e-01 1.00e+00f 1\n",
|
||
" 66 9.9801447e+00 1.80e-03 4.16e-01 -5.7 4.42e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 9.9813143e+00 6.01e-07 9.13e-05 -7.6 2.77e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 9.9813149e+00 4.24e-07 7.33e-05 -11.0 1.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 9.9813154e+00 3.82e-08 5.39e-05 -11.0 6.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 9.9807850e+00 1.89e-04 5.24e-02 -11.0 1.89e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 71 9.9812586e+00 1.75e-07 7.66e-05 -11.0 1.31e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 72 9.9805513e+00 5.06e-04 2.34e-03 -11.0 1.68e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 9.9809098e+00 1.61e-04 1.99e-03 -11.0 8.72e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 9.9812423e+00 8.17e-08 3.06e-05 -11.0 9.67e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 75 9.9812120e+00 3.24e-05 1.44e-03 -11.0 2.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 9.9811105e+00 5.54e-05 2.33e-03 -11.0 3.86e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 9.9812236e+00 1.40e-05 1.40e-03 -11.0 2.96e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 9.9811698e+00 1.57e-04 2.03e-03 -11.0 1.04e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 9.9798794e+00 4.03e-03 3.20e-03 -11.0 2.37e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 9.9736221e+00 5.51e-03 8.07e-03 -11.0 5.48e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 9.9717024e+00 6.11e-03 2.45e-03 -11.0 4.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 9.9772850e+00 5.39e-03 3.03e-03 -11.0 1.74e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 9.9813966e+00 6.07e-04 1.66e-03 -11.0 4.22e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 9.9819446e+00 1.89e-04 1.30e-03 -11.0 2.48e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 9.9820824e+00 5.73e-07 3.95e-05 -11.0 2.23e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 86 9.9818200e+00 3.52e-04 1.39e-03 -11.0 3.41e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 9.9775002e+00 5.01e-03 3.36e-03 -11.0 1.95e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 9.9811044e+00 1.49e-03 8.83e-04 -11.0 1.18e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 9.9781793e+00 5.83e-03 1.30e-03 -11.0 3.09e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 9.9773258e+00 3.93e-03 1.31e-03 -11.0 1.79e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 9.9716094e+00 1.27e-02 2.62e-03 -11.0 3.69e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 9.9172204e+00 1.24e-01 1.06e-02 -11.0 3.53e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 9.8792485e+00 1.23e-01 6.63e-03 -11.0 5.55e+02 - 1.00e+00 4.04e-01h 1\n",
|
||
" 94 9.7995077e+00 3.41e-01 1.70e-02 -11.0 1.10e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 9.9819002e+00 1.73e-02 3.60e-02 -11.0 4.31e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.0009838e+01 1.51e-03 6.34e-03 -11.0 2.93e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.0008551e+01 5.31e-03 5.16e-03 -11.0 8.69e+01 - 1.00e+00 2.32e-01h 1\n",
|
||
" 98 9.9748444e+00 3.85e-02 2.91e-03 -11.0 7.07e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"In iteration 98, 1 Slack too small, adjusting variable bound\n",
|
||
" 99 9.9833099e+00 3.02e-02 1.43e-03 -11.0 6.27e+02 - 1.00e+00 4.25e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 9.9892022e+00 4.23e-04 2.51e-03 -11.0 5.99e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 9.9892021707594072e+00 9.9892021707594072e+00\n",
|
||
"Dual infeasibility......: 2.5132200475362354e-03 2.5132200475362354e-03\n",
|
||
"Constraint violation....: 4.2310921883625952e-04 4.2310921883625952e-04\n",
|
||
"Complementarity.........: 1.1365473061775987e-11 1.1365473061775987e-11\n",
|
||
"Overall NLP error.......: 2.5132200475362354e-03 2.5132200475362354e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 111\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 111\n",
|
||
"Number of inequality constraint evaluations = 111\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.399\n",
|
||
"Total CPU secs in NLP function evaluations = 134.320\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 493.00us ( 4.44us) 482.50us ( 4.35us) 111\n",
|
||
" nlp_g | 4.95 s ( 44.64ms) 4.72 s ( 42.54ms) 111\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 344.00us ( 3.37us) 341.77us ( 3.35us) 102\n",
|
||
" nlp_jac_g | 132.01 s ( 1.29 s) 126.01 s ( 1.24 s) 102\n",
|
||
" total | 138.44 s (138.44 s) 132.14 s (132.14 s) 1\n",
|
||
"Timestamp 11700\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.12e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0355454e+01 1.27e+01 2.12e+04 -1.5 2.12e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.3542590e+00 4.45e+00 6.60e+00 0.8 2.91e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.0795879e+00 6.62e-01 5.82e-01 -1.3 8.68e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 2.3172825e+00 4.87e-03 4.69e-01 -3.0 3.43e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
" 5 2.3195709e+00 2.23e-05 2.97e-03 -4.9 1.12e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.3194814e+00 9.84e-05 7.60e-03 -7.0 3.51e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.3195669e+00 1.30e-08 4.16e-05 -9.1 2.42e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 8 2.3194897e+00 6.46e-05 4.57e-04 -11.0 6.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.3195104e+00 4.19e-05 6.30e-04 -11.0 3.72e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.3194818e+00 1.41e-04 4.01e-04 -11.0 2.64e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.3194954e+00 6.45e-05 7.54e-04 -11.0 1.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.3193905e+00 4.05e-04 9.69e-04 -11.0 7.96e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.3195420e+00 8.54e-06 1.02e-03 -11.0 4.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.3195327e+00 1.66e-05 7.11e-04 -11.0 2.26e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.3195257e+00 8.36e-05 9.48e-04 -11.0 6.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.3195089e+00 3.43e-05 1.06e-03 -11.0 4.64e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.3194562e+00 9.43e-05 1.28e-03 -11.0 2.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.3195506e+00 2.28e-06 1.54e-03 -11.0 5.38e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.3195374e+00 8.42e-06 1.69e-03 -11.0 3.73e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.3194540e+00 2.06e-04 1.11e-03 -11.0 5.32e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.3194887e+00 7.51e-05 6.30e-04 -11.0 3.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.3194970e+00 6.40e-05 6.76e-04 -11.0 1.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.3193883e+00 1.43e-04 1.63e-03 -11.0 6.09e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.3195318e+00 7.07e-06 1.85e-03 -11.0 2.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.3195285e+00 1.87e-05 1.19e-03 -11.0 9.50e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.3188897e+00 7.19e-04 1.15e-02 -11.0 4.56e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.3196097e+00 3.02e-07 4.98e-05 -11.0 7.58e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 28 2.2881114e+00 4.54e-02 2.01e-02 -11.0 4.53e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 29 2.3117444e+00 1.04e-02 7.39e-03 -11.0 1.18e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.2191065e+00 4.81e-01 1.92e-01 -11.0 1.05e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.1792229e+00 5.95e-01 8.46e-02 -11.0 9.06e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.3142974e+00 1.25e-05 3.34e-01 -11.0 5.77e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.3143054e+00 4.11e-09 5.15e-05 -11.0 8.10e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 2.3143054e+00 7.35e-09 2.11e-05 -11.0 3.64e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.3143054e+00 2.20e-09 1.46e-04 -11.0 3.00e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.3143053e+00 9.27e-08 6.38e-05 -11.0 2.44e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.3143054e+00 6.57e-08 1.96e-05 -11.0 1.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.3143054e+00 3.85e-09 3.57e-05 -11.0 3.35e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.3143054e+00 8.33e-09 5.44e-05 -11.0 4.10e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.3143040e+00 3.37e-06 1.66e-02 -11.0 1.18e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.3143047e+00 5.06e-07 4.72e-05 -11.0 1.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.3143052e+00 3.18e-07 6.31e-05 -11.0 2.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.3143049e+00 1.02e-06 9.89e-04 -11.0 5.37e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.3143053e+00 6.40e-07 3.44e-05 -11.0 5.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.3143051e+00 3.30e-07 5.03e-05 -11.0 1.85e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.2926404e+00 5.03e-01 1.21e-01 -11.0 3.16e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 47 2.1160385e+00 5.06e-01 1.96e-01 -11.0 3.86e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.9742117e+00 8.54e-01 3.83e-01 -11.0 3.40e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.8662470e+00 6.26e-01 7.11e-01 -11.0 4.86e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.2623231e+00 9.46e-01 2.79e-01 -11.0 1.80e+05 - 1.98e-01 7.61e-02f 2\n",
|
||
" 51 2.0993681e+00 2.95e-01 3.08e-01 -11.0 2.72e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.9752614e+00 3.94e-01 1.83e-01 -11.0 5.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.0009827e+00 5.80e-01 2.44e-01 -9.0 8.90e+04 - 1.00e+00 7.56e-02h 1\n",
|
||
" 54 1.9967850e+00 5.07e-01 2.18e-01 -7.1 7.85e+03 - 1.00e+00 5.07e-02h 1\n",
|
||
" 55 1.9967474e+00 5.08e-01 2.18e-01 -5.1 3.79e+04 - 1.00e+00 2.80e-04h 1\n",
|
||
" 56 2.0056621e+00 3.29e-01 1.97e-01 -11.0 7.71e+03 - 2.44e-01 1.00e+00h 1\n",
|
||
" 57 2.2282994e+00 2.02e-01 2.70e-01 -5.4 1.01e+04 - 9.67e-01 1.00e+00H 1\n",
|
||
" 58 2.1982488e+00 1.63e-01 2.56e-01 -4.4 4.82e+03 - 9.45e-01 5.94e-02h 1\n",
|
||
" 59 1.9722527e+00 2.67e-01 2.73e-01 -10.5 6.62e+03 - 2.13e-03 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.9550218e+00 2.78e-01 7.43e-02 -4.2 1.69e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 61 2.0371552e+00 3.35e-02 1.38e-01 -2.3 1.26e+04 - 4.15e-01 1.00e+00H 1\n",
|
||
" 62 2.0023867e+00 9.08e-02 9.82e-02 -3.0 1.29e+04 - 9.97e-01 1.00e+00h 1\n",
|
||
" 63 1.9602595e+00 3.60e-01 1.55e-01 -2.8 4.19e+04 - 1.00e+00 2.77e-01h 1\n",
|
||
" 64r 1.9602595e+00 3.60e-01 9.99e+02 -0.4 0.00e+00 - 0.00e+00 4.77e-07R 22\n",
|
||
" 65r 1.9703937e+00 1.53e-01 6.12e+02 -2.6 3.03e+02 - 1.00e+00 1.18e-03f 1\n",
|
||
" 66 1.9685081e+00 1.88e-01 6.39e-01 -2.8 1.25e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 67 2.0054317e+00 1.20e-01 8.19e-02 -1.9 3.16e+03 - 7.80e-01 1.00e+00h 1\n",
|
||
" 68 2.0084023e+00 1.99e-01 8.71e-02 -2.1 6.37e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 69 1.9631386e+00 1.43e-01 7.07e-02 -2.1 1.45e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.9559302e+00 3.72e-01 1.09e-01 -2.1 5.67e+04 - 8.04e-01 9.10e-02h 3\n",
|
||
" 71r 1.9559302e+00 3.72e-01 9.99e+02 -0.4 0.00e+00 - 0.00e+00 4.77e-07R 22\n",
|
||
" 72r 1.9823694e+00 1.25e-01 4.87e+02 -2.6 3.04e+02 - 1.00e+00 1.21e-03f 1\n",
|
||
" 73 2.0044298e+00 1.02e-01 8.12e-02 -3.3 8.66e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.0022402e+00 2.47e-02 3.42e-02 -3.4 1.04e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.9981432e+00 2.14e-01 7.50e-02 -3.6 1.04e+04 - 9.99e-01 1.25e-01h 4\n",
|
||
" 76 1.9895801e+00 4.73e-01 1.37e-01 -3.7 1.98e+04 - 1.00e+00 2.26e-01h 3\n",
|
||
" 77 2.0302834e+00 2.24e-01 1.40e-01 -3.7 1.34e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.9970443e+00 8.59e-02 4.27e-02 -3.7 4.38e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.9830516e+00 3.94e-01 1.87e-01 -4.3 5.25e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.0104958e+00 1.13e-01 1.45e-01 -4.4 4.26e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.0010796e+00 6.68e-02 2.35e-02 -4.4 6.21e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.0004332e+00 2.48e-02 2.95e-02 -4.4 9.29e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.9613250e+00 4.94e-01 1.87e-01 -3.8 1.38e+05 - 1.00e+00 2.75e-02f 3\n",
|
||
" 84 1.9764031e+00 2.04e-01 2.52e-01 -3.9 7.66e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 2.0141284e+00 9.35e-02 1.42e-01 -3.9 1.41e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 2.0234689e+00 1.16e-01 1.87e-01 -3.9 2.39e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 87 2.0091029e+00 1.08e-01 1.83e-01 -3.9 2.44e+04 - 6.06e-01 6.25e-02h 5\n",
|
||
" 88 2.0033854e+00 1.29e-01 1.82e-01 -3.9 9.02e+05 - 4.17e-02 1.82e-03h 5\n",
|
||
" 89 1.9686068e+00 2.80e-01 1.47e-01 -3.9 1.85e+04 - 6.93e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.9631283e+00 3.42e-01 8.16e-02 -3.9 3.92e+04 - 1.00e+00 5.05e-01h 1\n",
|
||
" 91 2.0114247e+00 7.45e-02 1.06e-01 -3.9 1.06e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 92 2.0136320e+00 5.29e-02 7.98e-02 -3.9 3.61e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.9979749e+00 2.44e-01 3.30e-02 -3.9 5.30e+04 - 1.00e+00 9.37e-02h 1\n",
|
||
" 94 1.9937879e+00 1.45e-01 1.88e-01 -3.9 1.04e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 95 2.0195550e+00 6.01e-04 1.66e-01 -3.9 1.66e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 2.0194751e+00 2.33e-08 4.70e-06 -5.7 8.13e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.0194751e+00 3.53e-09 9.77e-06 -11.0 1.50e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 2.0194751e+00 3.82e-09 9.49e-06 -11.0 3.16e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 2.0194750e+00 6.70e-08 1.32e-05 -11.0 2.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.0194732e+00 7.91e-06 4.68e-03 -11.0 4.36e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.0194731798308969e+00 2.0194731798308969e+00\n",
|
||
"Dual infeasibility......: 4.6795012396601025e-03 4.6795012396601025e-03\n",
|
||
"Constraint violation....: 7.9068530425274730e-06 7.9068530425274730e-06\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 4.6795012396601025e-03 4.6795012396601025e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 203\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 203\n",
|
||
"Number of inequality constraint evaluations = 203\n",
|
||
"Number of equality constraint Jacobian evaluations = 103\n",
|
||
"Number of inequality constraint Jacobian evaluations = 103\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.418\n",
|
||
"Total CPU secs in NLP function evaluations = 141.490\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 934.00us ( 4.60us) 918.41us ( 4.52us) 203\n",
|
||
" nlp_g | 9.22 s ( 45.42ms) 8.80 s ( 43.37ms) 203\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 399.00us ( 3.91us) 369.60us ( 3.62us) 102\n",
|
||
" nlp_jac_g | 135.18 s ( 1.30 s) 129.00 s ( 1.24 s) 104\n",
|
||
" total | 145.89 s (145.89 s) 139.23 s (139.23 s) 1\n",
|
||
"Timestamp 12000\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 9.22e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0046409e+01 1.40e+01 9.22e+03 -1.5 9.22e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.5942910e+00 5.21e+00 8.81e+00 0.6 1.55e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 7.5678879e+00 1.25e+00 9.03e-01 -1.4 4.45e+01 - 9.97e-01 1.00e+00f 1\n",
|
||
" 4 8.5139695e+00 3.64e-03 1.12e-01 -3.2 1.86e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 8.5161048e+00 8.45e-06 1.85e-03 -5.1 3.61e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 8.5160935e+00 1.63e-05 1.27e-03 -7.2 1.06e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 8.5160549e+00 5.32e-05 3.29e-03 -9.3 2.20e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 8.5159388e+00 6.61e-05 5.87e-03 -11.0 3.86e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 8.5161078e+00 5.27e-08 8.68e-05 -11.0 1.65e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 8.5161078e+00 2.18e-08 2.84e-05 -11.0 1.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 8.5161078e+00 9.44e-09 3.84e-05 -11.0 7.71e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 8.5161078e+00 5.44e-09 2.01e-04 -11.0 3.05e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 8.5161077e+00 9.64e-08 9.40e-05 -11.0 5.40e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 8.5161074e+00 3.89e-07 1.47e-04 -11.0 1.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 8.5161077e+00 1.93e-07 7.30e-05 -11.0 4.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 8.5161074e+00 4.01e-07 9.07e-05 -11.0 1.95e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 8.5161077e+00 1.06e-07 4.75e-05 -11.0 5.75e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 8.5161077e+00 2.37e-07 4.55e-05 -11.0 9.33e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 8.5161078e+00 4.51e-08 9.10e-05 -11.0 3.07e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 8.5161073e+00 4.62e-07 1.03e-04 -11.0 1.59e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 8.5161076e+00 1.37e-07 5.01e-05 -11.0 8.80e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 8.5161078e+00 6.22e-08 3.15e-05 -11.0 6.46e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 8.5161078e+00 7.94e-08 5.41e-05 -11.0 5.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 8.5161075e+00 3.65e-07 2.19e-04 -11.0 1.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 8.5159365e+00 1.53e-04 1.72e-02 -11.0 6.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 8.5131929e+00 3.42e-03 3.28e-03 -11.0 1.78e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.5152455e+00 4.63e-04 2.10e-03 -11.0 7.60e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 8.5162324e+00 7.85e-07 2.05e-03 -11.0 9.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.5161219e+00 2.22e-04 1.44e-03 -11.0 2.39e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.5145790e+00 1.41e-03 3.04e-03 -11.0 9.40e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 8.5160863e+00 1.54e-07 1.06e-04 -11.0 1.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 8.5160861e+00 1.28e-07 4.21e-05 -11.0 7.10e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 8.5160863e+00 2.36e-08 6.71e-05 -11.0 1.71e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 8.5160863e+00 5.52e-08 3.13e-05 -11.0 3.09e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 8.5160863e+00 3.73e-08 9.16e-05 -11.0 1.89e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 8.5160863e+00 1.20e-07 1.05e-04 -11.0 4.17e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 8.5160863e+00 5.31e-08 6.99e-05 -11.0 2.74e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 8.5160862e+00 9.52e-08 5.98e-05 -11.0 5.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 8.5160862e+00 7.11e-08 9.59e-05 -11.0 7.47e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.5160862e+00 5.14e-08 9.88e-05 -11.0 2.93e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 8.5160854e+00 7.89e-07 1.10e-04 -11.0 3.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 8.5160859e+00 3.33e-07 1.01e-04 -11.0 2.64e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 8.5160857e+00 3.10e-07 2.19e-04 -11.0 1.90e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 8.5160856e+00 2.75e-07 2.42e-04 -11.0 1.37e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 8.5160846e+00 9.68e-07 4.72e-03 -11.0 5.78e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 8.5160860e+00 3.28e-07 8.73e-05 -11.0 2.44e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 8.5160844e+00 2.03e-06 1.73e-03 -11.0 7.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 8.5160830e+00 1.76e-06 1.79e-03 -11.0 7.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 8.5160859e+00 1.73e-07 7.09e-05 -11.0 1.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.5160859e+00 4.20e-08 8.10e-05 -11.0 6.67e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 8.5160857e+00 3.84e-07 3.28e-05 -11.0 1.59e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 8.5160858e+00 2.42e-07 7.42e-05 -11.0 1.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 8.5160856e+00 2.33e-07 1.64e-04 -11.0 1.59e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 8.5160854e+00 4.68e-07 5.89e-05 -11.0 9.62e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 8.5160858e+00 5.86e-08 8.47e-05 -11.0 4.33e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 8.5160859e+00 3.49e-11 1.06e-04 -11.0 3.24e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 57 8.5160855e+00 3.19e-07 5.91e-05 -11.0 1.15e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 8.5160855e+00 6.52e-07 2.58e-03 -11.0 1.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 8.5160857e+00 1.82e-07 1.37e-04 -11.0 7.56e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 8.5160820e+00 5.55e-06 7.30e-03 -11.0 6.91e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 8.5160850e+00 4.77e-07 1.00e-04 -11.0 2.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 8.5160857e+00 2.67e-10 7.23e-05 -11.0 1.16e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 63 8.5160854e+00 1.70e-07 4.29e-05 -11.0 1.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 8.5160856e+00 1.61e-08 5.70e-05 -11.0 3.09e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 8.5160855e+00 1.75e-07 9.64e-05 -11.0 1.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 8.5160853e+00 4.54e-07 8.70e-05 -11.0 9.11e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 8.5160855e+00 9.17e-08 6.10e-05 -11.0 5.90e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 8.5160855e+00 1.06e-07 4.37e-05 -11.0 6.97e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 8.5160855e+00 4.74e-08 7.96e-05 -11.0 3.67e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.5160855e+00 1.29e-07 3.47e-05 -11.0 8.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 8.5160855e+00 3.36e-07 1.26e-04 -11.0 1.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 8.5160818e+00 3.07e-06 4.22e-03 -11.0 3.80e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 8.5160817e+00 4.79e-06 3.30e-03 -11.0 4.36e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 8.5160699e+00 1.21e-05 5.83e-03 -11.0 8.10e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 8.5160906e+00 1.30e-08 1.58e-04 -11.0 8.63e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 8.5160905e+00 9.09e-08 9.91e-05 -11.0 7.00e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 8.5160906e+00 3.52e-08 5.18e-05 -11.0 2.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 8.5160906e+00 2.49e-08 4.90e-05 -11.0 5.65e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 8.5160906e+00 2.09e-08 3.84e-05 -11.0 3.54e-04 - 1.00e+00 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 8.5160906e+00 8.30e-08 3.83e-05 -11.0 4.19e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 8.5160905e+00 5.37e-08 1.11e-04 -11.0 1.13e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 8.5160906e+00 2.74e-08 2.18e-04 -11.0 1.66e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 8.5160906e+00 4.33e-08 4.38e-05 -11.0 1.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 8.5160906e+00 1.98e-09 6.64e-05 -11.0 3.41e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 8.5160882e+00 1.09e-06 3.94e-03 -11.0 1.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 8.5160376e+00 3.16e-05 1.73e-02 -11.0 5.47e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 8.5160757e+00 7.90e-06 2.29e-03 -11.0 2.92e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 8.5160841e+00 2.11e-06 9.38e-04 -11.0 1.36e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 8.5160859e+00 1.29e-06 1.36e-03 -11.0 7.35e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 8.5160883e+00 1.22e-07 1.07e-04 -11.0 1.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 8.5160877e+00 8.97e-07 2.18e-03 -11.0 5.86e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 8.5160798e+00 4.16e-06 3.29e-03 -11.0 7.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 8.4315117e+00 1.63e-01 5.22e-02 -11.0 9.17e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 94 8.4596614e+00 1.50e-01 9.48e-03 -11.0 2.59e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 8.3870963e+00 2.33e-01 1.24e-02 -11.0 1.98e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 6.4727787e+00 2.86e+00 4.09e-01 -10.4 1.06e+05 - 1.00e+00 3.08e-01f 1\n",
|
||
" 97 6.4539602e+00 2.84e+00 4.07e-01 -8.5 4.73e+05 - 1.00e+00 6.90e-04h 1\n",
|
||
" 98 6.4543145e+00 2.84e+00 4.07e-01 -6.5 9.83e+03 - 1.00e+00 5.13e-04h 1\n",
|
||
" 99 8.2267880e+00 1.94e-01 4.15e-01 -6.1 4.64e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.2203406e+00 4.52e-02 2.27e-02 -3.9 9.99e+02 - 8.61e-01 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.2203405984400515e+00 8.2203405984400515e+00\n",
|
||
"Dual infeasibility......: 2.2674045123126874e-02 2.2674045123126874e-02\n",
|
||
"Constraint violation....: 4.5226433210459049e-02 4.5226433210459049e-02\n",
|
||
"Complementarity.........: 1.2631439551897807e-04 1.2631439551897807e-04\n",
|
||
"Overall NLP error.......: 4.5226433210459049e-02 4.5226433210459049e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 104\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 104\n",
|
||
"Number of inequality constraint evaluations = 104\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.425\n",
|
||
"Total CPU secs in NLP function evaluations = 134.003\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 463.00us ( 4.45us) 456.31us ( 4.39us) 104\n",
|
||
" nlp_g | 4.64 s ( 44.59ms) 4.42 s ( 42.52ms) 104\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 414.00us ( 4.06us) 411.77us ( 4.04us) 102\n",
|
||
" nlp_jac_g | 132.12 s ( 1.30 s) 126.10 s ( 1.24 s) 102\n",
|
||
" total | 138.23 s (138.23 s) 131.93 s (131.93 s) 1\n",
|
||
"Timestamp 12300\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 4.36e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9821991e+01 1.43e+01 4.36e+03 -1.5 4.36e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.5000972e+00 5.10e+00 1.05e+01 0.4 1.43e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.1451976e+01 1.60e+00 5.90e-01 -1.6 9.36e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.2559785e+01 1.45e-03 8.44e-02 -3.4 2.11e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.2560504e+01 2.61e-06 3.34e-03 -5.3 6.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.2560509e+01 4.74e-08 1.12e-04 -7.4 6.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.2560509e+01 7.85e-08 5.44e-05 -11.0 4.81e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.2560509e+01 1.98e-07 2.15e-04 -11.0 1.11e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.2560508e+01 9.62e-07 2.57e-05 -11.0 5.97e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.2560509e+01 1.61e-07 5.71e-05 -11.0 2.94e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.2560509e+01 1.24e-07 6.87e-05 -11.0 1.40e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.2560509e+01 2.93e-07 6.65e-05 -11.0 1.55e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.2560489e+01 4.65e-05 2.47e-02 -11.0 1.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.2560499e+01 3.97e-06 1.68e-03 -11.0 7.82e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.2560460e+01 4.45e-05 3.06e-03 -11.0 2.13e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.2560508e+01 1.90e-06 1.80e-03 -11.0 1.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.2560421e+01 1.71e-04 4.19e-03 -11.0 8.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.2560067e+01 1.80e-04 2.80e-03 -11.0 7.11e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.2559922e+01 4.77e-04 5.95e-03 -11.0 2.48e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.2560360e+01 1.12e-04 1.63e-03 -11.0 7.91e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.2560506e+01 1.73e-08 1.74e-04 -11.0 7.74e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 22 1.2559726e+01 5.56e-04 1.34e-03 -11.0 1.65e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 23 1.2560464e+01 2.86e-05 1.41e-03 -11.0 2.61e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.2560322e+01 3.52e-04 2.49e-03 -11.0 2.80e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.2560427e+01 5.84e-05 1.71e-03 -11.0 9.18e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.2560360e+01 1.28e-04 1.49e-03 -11.0 5.15e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.2553483e+01 3.77e-03 6.79e-03 -11.0 2.71e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.2558262e+01 3.54e-03 1.76e-03 -11.0 2.32e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.2556209e+01 2.70e-03 2.30e-03 -11.0 1.81e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.2552643e+01 2.59e-03 5.03e-03 -11.0 1.58e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.2535947e+01 1.51e-02 2.91e-03 -11.0 1.42e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.2558159e+01 9.12e-06 1.95e-03 -11.0 2.04e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 33 1.2551840e+01 2.94e-03 3.16e-03 -11.0 2.25e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 34 1.2516385e+01 1.63e-02 3.54e-03 -11.0 4.66e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.2549106e+01 3.46e-03 2.04e-03 -11.0 5.75e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 9.7490399e+00 2.18e+00 1.98e-01 -9.0 1.21e+08 - 2.58e-04 2.68e-04f 1\n",
|
||
" 37 1.0524336e+01 1.69e+00 1.35e-01 -10.8 1.86e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 8.7340287e+00 2.21e+00 1.38e-01 -11.0 1.55e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 39 9.7260983e+00 2.28e+00 1.78e-01 -8.7 1.75e+04 - 1.00e+00 7.64e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 9.7026616e+00 2.28e+00 1.78e-01 -6.7 2.23e+04 - 1.00e+00 5.47e-03h 1\n",
|
||
" 41 9.7046962e+00 2.28e+00 1.78e-01 -4.8 1.06e+03 - 1.00e+00 8.51e-04h 1\n",
|
||
" 42 1.2929753e+01 6.14e-02 3.03e-01 -6.5 1.65e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.2891123e+01 4.89e-02 1.92e-02 -6.5 3.40e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.2915241e+01 3.46e-02 9.37e-03 -6.5 3.04e+02 - 1.00e+00 3.80e-01h 1\n",
|
||
" 45 1.2914610e+01 5.56e-02 9.18e-03 -6.5 1.05e+03 - 5.74e-01 1.25e-01h 4\n",
|
||
" 46 1.2914971e+01 5.49e-02 9.26e-03 -6.5 8.23e+03 - 1.00e+00 5.40e-04h 11\n",
|
||
" 47 1.2966589e+01 6.23e-03 4.43e-03 -6.5 2.20e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.2936069e+01 1.59e-02 6.98e-03 -6.5 7.01e+02 - 1.00e+00 2.92e-01h 1\n",
|
||
" 49 1.2960715e+01 2.19e-02 1.53e-03 -6.5 9.69e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.2753892e+01 6.00e-01 3.90e-02 -6.5 2.18e+04 - 4.50e-03 7.20e-02f 1\n",
|
||
" 51 1.2753905e+01 6.00e-01 3.90e-02 -6.5 1.22e+03 - 1.00e+00 4.96e-05h 1\n",
|
||
" 52 1.2966565e+01 1.27e-04 2.87e-02 -6.5 3.01e+00 - 4.13e-01 1.00e+00h 1\n",
|
||
" 53 1.2966416e+01 2.48e-04 2.11e-02 -6.5 2.23e+00 - 1.00e+00 2.78e-01h 1\n",
|
||
" 54 1.2964292e+01 2.23e-03 2.42e-03 -6.5 1.35e+01 - 7.04e-02 1.00e+00h 1\n",
|
||
" 55 1.2958089e+01 5.06e-03 4.19e-03 -6.5 2.72e+01 - 5.75e-01 1.00e+00h 1\n",
|
||
" 56 1.2961452e+01 2.60e-03 8.04e-03 -6.5 2.93e+01 - 1.00e+00 5.00e-01h 2\n",
|
||
" 57 1.2965234e+01 1.12e-03 1.21e-03 -6.5 6.42e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.2963839e+01 1.07e-03 1.29e-03 -6.5 4.67e+01 - 3.61e-01 2.14e-01h 1\n",
|
||
" 59 1.2965630e+01 7.42e-04 4.89e-03 -6.5 2.40e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.2944759e+01 2.06e-02 7.28e-03 -6.5 3.00e+02 - 4.30e-02 4.59e-01h 1\n",
|
||
" 61 1.2947112e+01 1.82e-02 7.03e-03 -6.5 2.56e+01 - 1.00e+00 1.25e-01h 4\n",
|
||
" 62 1.2964432e+01 1.22e-05 1.16e-03 -6.5 2.65e+00 - 3.99e-01 1.00e+00h 1\n",
|
||
" 63 1.2964431e+01 1.24e-05 9.31e-04 -6.5 9.54e+02 - 1.00e+00 9.04e-05h 1\n",
|
||
" 64 1.2962642e+01 1.57e-03 5.12e-03 -6.5 2.71e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 65 1.2963773e+01 4.90e-04 2.34e-03 -6.5 1.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.2963474e+01 9.77e-04 1.57e-03 -6.5 7.45e+00 - 7.07e-02 1.00e+00h 1\n",
|
||
" 67 1.2963305e+01 7.62e-04 1.57e-03 -6.5 2.63e+02 - 5.09e-01 2.82e-02h 1\n",
|
||
" 68 1.2723094e+01 2.33e-01 9.90e-03 -6.5 1.87e+03 - 1.09e-03 1.00e+00f 1\n",
|
||
" 69 8.4973116e+00 1.39e+00 2.01e-01 -6.5 2.31e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.2822981e+01 1.37e-01 1.67e-01 -5.3 6.13e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.2729675e+01 5.45e-01 8.25e-02 -4.7 5.55e+03 - 8.39e-01 1.00e+00h 1\n",
|
||
" 72 1.0671101e+01 8.18e-01 1.84e-01 -4.7 1.34e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 73 9.4196486e+00 1.74e+00 1.55e-01 -4.0 1.22e+05 - 1.00e+00 3.51e-01f 1\n",
|
||
" 74 9.4244532e+00 1.66e+00 1.52e-01 -4.2 9.95e+04 - 1.00e+00 4.36e-03h 1\n",
|
||
" 75 1.2555808e+01 7.67e-01 2.09e-01 -4.2 4.14e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.3004942e+01 1.31e-01 7.53e-02 -4.2 5.55e+03 - 6.87e-01 1.00e+00H 1\n",
|
||
" 77 1.2996865e+01 1.27e-01 7.55e-02 -4.2 1.61e+04 - 1.00e+00 5.96e-03h 1\n",
|
||
" 78 9.3883604e+00 2.93e+00 1.44e-01 -4.2 8.56e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 79 8.5982711e+00 2.25e+00 2.35e-01 -1.3 2.67e+04 - 6.82e-01 3.40e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.2292954e+01 3.66e+00 1.72e-01 -1.7 1.16e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.2188284e+01 1.38e+00 2.19e-01 -1.7 4.38e+03 - 6.34e-01 1.00e+00h 1\n",
|
||
" 82 1.2649100e+01 7.03e-01 1.74e-01 -1.7 4.79e+03 - 8.26e-01 1.00e+00h 1\n",
|
||
" 83 1.1523712e+01 2.94e+00 1.31e-01 -1.7 1.85e+05 - 4.58e-02 1.14e-01f 2\n",
|
||
" 84 8.1239878e+00 6.00e+00 5.09e-01 -1.7 5.13e+05 - 9.35e-02 2.67e-02f 1\n",
|
||
" 85 9.9926814e+00 4.81e+00 8.09e-01 0.1 8.53e+05 - 6.64e-02 3.06e-02f 2\n",
|
||
" 86 1.3488184e+01 9.76e-01 8.27e-01 -0.9 4.62e+04 - 5.73e-01 7.78e-01h 1\n",
|
||
" 87 8.9190409e+00 4.79e+00 3.52e-01 -1.6 1.55e+04 - 1.11e-01 8.00e-01f 1\n",
|
||
" 88 9.0449722e+00 3.28e+00 2.91e-01 -1.6 6.94e+04 - 6.23e-01 2.47e-01h 1\n",
|
||
" 89 1.0123541e+01 1.97e+00 2.63e-01 -1.6 3.04e+04 - 5.19e-01 6.74e-01H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.2456279e+01 3.27e-01 3.03e-01 -1.6 5.89e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.2398029e+01 3.04e-01 1.83e-01 -2.3 1.55e+03 - 1.00e+00 9.64e-01h 1\n",
|
||
" 92 1.1887272e+01 1.55e+00 4.29e-02 -3.1 2.75e+03 - 8.03e-01 1.00e+00h 1\n",
|
||
" 93 9.1037814e+00 2.89e+00 2.94e-01 -2.4 1.43e+04 - 1.31e-01 1.00e+00f 1\n",
|
||
" 94 9.3847555e+00 1.78e+00 8.88e-02 -2.6 5.35e+03 - 1.00e+00 4.74e-01h 1\n",
|
||
" 95 1.3014273e+01 4.09e-01 1.67e-01 -1.9 6.02e+03 - 3.09e-01 1.00e+00h 1\n",
|
||
" 96 1.2311703e+01 5.11e-01 1.74e-01 -2.4 1.13e+04 - 3.85e-01 1.00e+00f 1\n",
|
||
" 97 1.2835179e+01 1.59e-01 2.57e-02 -2.1 4.14e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.2606085e+01 4.67e-01 2.97e-02 -2.1 2.76e+04 - 1.66e-01 1.90e-01f 1\n",
|
||
" 99 1.2773711e+01 3.35e-01 3.96e-02 -2.1 4.70e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.2579137e+01 1.37e-01 2.22e-02 -2.1 1.36e+04 - 5.16e-01 2.03e-01h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.2579137084519342e+01 1.2579137084519342e+01\n",
|
||
"Dual infeasibility......: 2.2246331797988017e-02 2.2246331797988017e-02\n",
|
||
"Constraint violation....: 1.3748209569977732e-01 1.3748209569977732e-01\n",
|
||
"Complementarity.........: 8.1369827976847230e-03 8.1369827976847230e-03\n",
|
||
"Overall NLP error.......: 1.3748209569977732e-01 1.3748209569977732e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 134\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 134\n",
|
||
"Number of inequality constraint evaluations = 134\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.404\n",
|
||
"Total CPU secs in NLP function evaluations = 135.894\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 605.00us ( 4.51us) 595.38us ( 4.44us) 134\n",
|
||
" nlp_g | 6.03 s ( 45.02ms) 5.75 s ( 42.90ms) 134\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 336.00us ( 3.29us) 330.61us ( 3.24us) 102\n",
|
||
" nlp_jac_g | 132.55 s ( 1.30 s) 126.49 s ( 1.24 s) 102\n",
|
||
" total | 140.06 s (140.06 s) 133.66 s (133.66 s) 1\n",
|
||
"Timestamp 12600\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 6.80e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9983879e+01 1.35e+01 6.80e+03 -1.5 6.80e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.3800900e+00 4.84e+00 8.73e+00 0.6 7.95e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 6.5561096e+00 1.10e+00 8.75e-01 -1.5 2.17e+01 - 9.97e-01 1.00e+00f 1\n",
|
||
" 4 7.3473480e+00 3.34e-03 1.06e-01 -3.2 1.65e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 7.3488003e+00 1.20e-07 6.95e-05 -5.1 3.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 7.3487993e+00 6.91e-07 7.08e-05 -11.0 3.88e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 7.3488003e+00 1.49e-10 4.56e-05 -11.0 1.86e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 8 7.3487997e+00 2.86e-07 5.53e-05 -11.0 3.09e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 7.3487994e+00 5.51e-07 7.94e-05 -11.0 4.52e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 7.3488002e+00 4.04e-07 8.75e-05 -11.0 1.67e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 7.3488003e+00 2.27e-07 6.73e-05 -11.0 8.97e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 7.3488000e+00 2.76e-07 1.85e-04 -11.0 2.94e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 7.3486935e+00 1.17e-04 2.81e-02 -11.0 5.38e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 7.3487369e+00 6.75e-05 2.01e-03 -11.0 6.50e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 7.3488044e+00 1.45e-05 1.20e-03 -11.0 2.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 7.3486025e+00 1.43e-04 3.01e-03 -11.0 7.10e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 7.3487776e+00 4.53e-05 1.53e-03 -11.0 2.32e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 7.3487526e+00 4.52e-05 1.33e-03 -11.0 2.78e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 7.3487839e+00 1.19e-05 2.58e-03 -11.0 2.83e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 7.3485523e+00 1.53e-04 1.16e-03 -11.0 1.10e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 7.3487717e+00 2.55e-05 1.35e-03 -11.0 3.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 7.3487453e+00 3.73e-05 9.66e-04 -11.0 1.12e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 7.3484721e+00 7.80e-04 3.57e-03 -11.0 2.35e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 7.3485027e+00 1.96e-04 7.62e-04 -11.0 1.66e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 7.3483349e+00 8.43e-04 1.33e-03 -11.0 5.90e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 7.3474522e+00 2.41e-03 1.96e-03 -11.0 1.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 7.3486296e+00 2.98e-04 1.36e-03 -11.0 7.44e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 7.3483720e+00 1.47e-04 2.77e-03 -11.0 4.90e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 7.3489614e+00 1.52e-04 2.42e-03 -11.0 1.94e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 7.3480851e+00 2.59e-03 3.46e-03 -11.0 2.64e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 7.3371406e+00 1.87e-02 6.44e-03 -11.0 1.00e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 7.3250592e+00 1.26e-02 2.15e-03 -11.0 1.24e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 7.3431929e+00 1.10e-02 3.46e-03 -11.0 1.71e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 7.3383066e+00 8.08e-03 4.07e-03 -11.0 1.93e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 7.3477275e+00 1.78e-05 2.33e-03 -11.0 2.91e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 36 7.3465174e+00 1.55e-03 1.36e-03 -11.0 1.59e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 7.2007003e+00 1.29e-01 1.53e-02 -11.0 2.47e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 38 6.6391700e+00 1.23e+00 1.77e-01 -9.0 1.43e+06 - 1.00e+00 2.07e-02f 1\n",
|
||
" 39 6.6608265e+00 1.21e+00 1.66e-01 -7.0 8.40e+03 - 1.00e+00 3.53e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 6.6609343e+00 1.21e+00 1.65e-01 -5.1 1.33e+04 - 1.00e+00 2.23e-04h 1\n",
|
||
" 41 7.3720947e+00 1.24e-02 1.20e-01 -6.1 8.84e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 7.3846609e+00 4.12e-06 1.66e-02 -6.3 1.84e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 7.3846645e+00 9.28e-07 1.99e-03 -6.3 1.69e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 7.3846650e+00 7.24e-08 1.14e-04 -6.3 5.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 7.3846651e+00 1.17e-10 1.68e-04 -6.3 2.45e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 46 7.3845473e+00 1.06e-04 1.14e-02 -6.3 2.52e-01 - 1.39e-01 1.00e+00f 1\n",
|
||
" 47 7.3846546e+00 7.25e-06 1.44e-03 -6.3 3.74e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 7.3846588e+00 3.65e-06 7.74e-04 -6.3 1.70e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 7.3846609e+00 1.35e-06 7.04e-04 -6.3 8.98e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 7.3846588e+00 1.86e-06 1.09e-03 -6.3 6.94e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 7.3846604e+00 1.26e-06 1.27e-03 -6.3 9.29e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 7.3846602e+00 7.45e-07 1.61e-03 -6.3 1.97e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 7.3846560e+00 4.53e-06 1.09e-03 -6.3 1.31e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 7.3846524e+00 6.88e-06 2.72e-03 -6.3 3.35e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 7.3846601e+00 2.57e-07 1.03e-04 -6.3 3.50e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 7.3846166e+00 1.74e-05 5.60e-03 -6.3 9.22e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 7.3846530e+00 7.61e-06 2.10e-03 -6.3 5.33e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 7.3846547e+00 7.29e-06 1.97e-03 -6.3 4.29e-02 - 1.00e+00 5.00e-01h 2\n",
|
||
" 59 7.3831339e+00 2.21e-03 1.59e-03 -6.3 6.94e+00 - 1.99e-02 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 7.2673612e+00 1.14e-01 1.53e-02 -6.3 1.74e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 61 7.0376552e+00 2.79e-01 4.38e-02 -6.3 5.66e+03 - 2.36e-01 1.00e+00h 1\n",
|
||
" 62 6.6156169e+00 4.38e-01 5.45e-02 -7.3 4.71e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 7.3625326e+00 1.41e-02 6.19e-02 -9.1 7.75e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 7.3130955e+00 7.64e-02 6.51e-02 -9.0 7.58e+03 - 1.00e+00 2.52e-01h 1\n",
|
||
" 65 7.0477894e+00 1.57e-01 6.24e-02 -10.5 7.74e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 7.3875597e+00 9.17e-03 6.50e-03 -3.9 7.87e+02 - 6.19e-01 1.00e+00H 1\n",
|
||
" 67 6.7301529e+00 5.42e-01 6.09e-02 -4.3 2.05e+03 - 4.47e-02 1.00e+00f 1\n",
|
||
" 68 6.7827657e+00 4.78e-01 4.71e-02 -4.3 5.33e+03 - 1.00e+00 1.13e-01h 1\n",
|
||
" 69 6.7073755e+00 4.58e+00 5.62e-01 -4.6 2.04e+04 - 8.68e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 7.7950737e+00 1.41e-01 3.41e-01 -3.1 4.43e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 6.6353688e+00 1.25e+00 1.07e-01 -3.1 1.91e+04 - 3.09e-01 1.00e+00f 1\n",
|
||
" 72 6.3167030e+00 1.85e+00 1.19e-01 -3.1 1.75e+04 - 9.95e-01 1.00e+00h 1\n",
|
||
" 73 5.6620616e+00 1.26e+00 1.04e-01 -9.2 4.36e+05 - 9.12e-04 4.97e-02f 1\n",
|
||
" 74 7.0221678e+00 2.43e-01 3.49e-01 -2.7 3.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 6.4371100e+00 4.40e-01 1.39e-01 -2.3 2.43e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 5.9743462e+00 9.58e-01 6.49e-02 -8.3 5.07e+04 - 1.30e-01 4.80e-01h 1\n",
|
||
" 77 5.9888909e+00 1.02e+00 6.57e-02 -2.1 3.30e+04 - 1.00e+00 3.81e-02h 5\n",
|
||
" 78 5.8843859e+00 8.20e-01 3.61e-02 -2.1 4.17e+04 - 7.20e-01 6.45e-02h 1\n",
|
||
" 79 7.4997498e+00 4.38e-01 9.32e-02 -2.2 6.12e+04 - 2.04e-01 9.97e-01H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 7.4684217e+00 4.21e-01 9.10e-02 -2.4 3.46e+04 - 1.00e+00 1.77e-02h 1\n",
|
||
" 81 7.3820247e+00 3.90e-01 8.35e-02 -2.4 3.92e+03 - 5.67e-01 1.03e-01h 1\n",
|
||
" 82 5.6131135e+00 2.74e+00 1.98e-01 -2.4 3.44e+04 - 1.00e+00 7.59e-01f 1\n",
|
||
" 83 6.8844950e+00 1.83e-01 2.89e-01 -1.7 5.92e+02 - 9.05e-01 1.00e+00h 1\n",
|
||
" 84 6.5625171e+00 1.63e-01 6.44e-02 -2.4 1.20e+03 - 9.56e-01 5.84e-01h 1\n",
|
||
" 85 6.7161730e+00 1.38e-01 3.44e-02 -3.1 1.84e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 6.8796253e+00 9.63e-03 2.95e-02 -4.8 1.52e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 6.7520013e+00 9.96e-02 1.07e-02 -5.2 1.47e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 6.7492401e+00 6.86e-02 6.39e-03 -3.3 4.46e+03 - 7.50e-01 1.08e-01h 1\n",
|
||
" 89 6.8498915e+00 2.01e-02 3.17e-03 -3.8 1.61e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 6.3253600e+00 8.57e-01 1.29e-01 -9.6 3.01e+03 - 4.06e-02 1.00e+00f 1\n",
|
||
" 91 5.5144368e+00 1.07e+00 1.48e-01 -3.2 1.53e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 7.6546212e+00 1.51e+00 3.10e-01 -4.1 5.76e+03 - 8.56e-01 1.00e+00H 1\n",
|
||
" 93 6.8938470e+00 9.37e-01 1.28e-01 -3.9 1.61e+04 - 1.00e+00 2.69e-01f 1\n",
|
||
" 94 6.8922045e+00 9.36e-01 1.28e-01 -3.9 7.26e+04 - 6.33e-01 4.78e-05h 1\n",
|
||
" 95 6.8451004e+00 5.50e-03 7.35e-02 -3.9 1.78e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 6.8387930e+00 4.69e-02 3.26e-02 -5.5 6.74e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 6.6066886e+00 3.80e-01 5.54e-02 -3.9 1.40e+05 - 5.55e-01 1.17e-01f 1\n",
|
||
" 98 6.3847326e+00 1.41e+00 1.53e-01 -4.3 1.17e+04 - 3.75e-02 1.00e+00h 1\n",
|
||
" 99 6.8015156e+00 3.02e-01 1.85e-01 -4.3 8.81e+03 - 3.15e-02 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 6.5527825e+00 2.94e-01 2.16e-01 -4.3 1.41e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 6.5527824892550157e+00 6.5527824892550157e+00\n",
|
||
"Dual infeasibility......: 2.1611365774953822e-01 2.1611365774953822e-01\n",
|
||
"Constraint violation....: 2.9449580823694532e-01 2.9449580823694532e-01\n",
|
||
"Complementarity.........: 1.0972631140301599e-04 1.0972631140301599e-04\n",
|
||
"Overall NLP error.......: 2.9449580823694532e-01 2.9449580823694532e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 114\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 114\n",
|
||
"Number of inequality constraint evaluations = 114\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.448\n",
|
||
"Total CPU secs in NLP function evaluations = 134.121\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 531.00us ( 4.66us) 521.76us ( 4.58us) 114\n",
|
||
" nlp_g | 5.07 s ( 44.49ms) 4.83 s ( 42.39ms) 114\n",
|
||
" nlp_grad | 1.32 s ( 1.32 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 340.00us ( 3.33us) 336.78us ( 3.30us) 102\n",
|
||
" nlp_jac_g | 131.86 s ( 1.29 s) 125.82 s ( 1.23 s) 102\n",
|
||
" total | 138.39 s (138.39 s) 132.05 s (132.05 s) 1\n",
|
||
"Timestamp 12900\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 4.58e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9802645e+01 1.40e+01 4.58e+03 -1.5 4.58e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.1881449e+00 4.97e+00 1.00e+01 0.4 1.40e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.0564491e+01 1.48e+00 6.82e-01 -1.6 8.90e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.1596488e+01 1.86e-03 8.18e-02 -3.4 1.98e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.1597355e+01 4.11e-07 1.12e-04 -5.3 1.82e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.1597354e+01 1.26e-06 2.02e-03 -11.0 4.15e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.1597354e+01 7.13e-07 6.97e-05 -11.0 2.93e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.1597355e+01 1.34e-10 3.85e-05 -11.0 3.02e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 9 1.1597355e+01 7.95e-08 2.99e-05 -11.0 9.17e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.1597355e+01 2.41e-07 9.51e-05 -11.0 1.14e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.1597355e+01 1.19e-07 1.05e-04 -11.0 6.71e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.1597355e+01 1.01e-07 8.03e-05 -11.0 5.48e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.1597355e+01 8.93e-11 9.13e-05 -11.0 4.65e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 14 1.1597346e+01 4.43e-06 1.36e-02 -11.0 3.30e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.1597354e+01 1.97e-06 2.96e-03 -11.0 1.84e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.1597352e+01 1.43e-06 1.56e-03 -11.0 8.81e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.1597353e+01 5.95e-07 1.29e-03 -11.0 7.09e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.1597309e+01 1.27e-05 3.40e-03 -11.0 1.97e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.1597353e+01 4.12e-06 9.05e-04 -11.0 2.23e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.1597247e+01 4.90e-05 1.81e-03 -11.0 2.62e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.1597353e+01 3.64e-06 1.71e-03 -11.0 6.32e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.1597336e+01 2.28e-05 1.47e-03 -11.0 1.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.1597332e+01 1.16e-05 8.05e-04 -11.0 1.00e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.1597351e+01 3.82e-06 1.12e-03 -11.0 3.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.1597355e+01 1.37e-06 1.41e-03 -11.0 1.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.1597351e+01 3.13e-06 1.46e-03 -11.0 6.28e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.1597353e+01 2.10e-06 9.97e-04 -11.0 1.71e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.1597209e+01 5.85e-05 7.32e-03 -11.0 2.91e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.1597349e+01 2.11e-05 2.06e-03 -11.0 1.20e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.1597045e+01 2.89e-04 2.17e-03 -11.0 4.44e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.1597326e+01 6.04e-05 9.85e-04 -11.0 2.65e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.1597312e+01 5.39e-05 2.90e-03 -11.0 5.57e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.1597361e+01 2.54e-06 9.39e-04 -11.0 8.58e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.0224158e+01 1.66e+00 1.47e-01 -11.0 1.76e+06 - 1.79e-02 1.79e-02f 1\n",
|
||
" 35 1.1424867e+01 2.81e-01 1.00e-01 -11.0 1.25e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.1272570e+01 2.09e-01 1.39e-01 -2.3 2.44e+03 - 5.34e-01 1.00e+00h 1\n",
|
||
" 37 9.1382233e+00 1.66e+00 1.31e-01 -3.6 2.33e+03 - 9.99e-01 1.00e+00f 1\n",
|
||
" 38 1.1075741e+01 5.09e-01 9.43e-02 -2.8 8.04e+03 - 9.74e-01 1.00e+00h 1\n",
|
||
" 39 1.1131238e+01 2.65e-01 6.00e-02 -3.8 5.42e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.0119216e+01 1.06e+00 8.34e-02 -4.8 6.61e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 41 1.0697134e+01 8.61e-01 7.39e-02 -4.7 4.08e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.1354081e+01 2.72e-01 5.02e-02 -6.0 9.36e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.1544436e+01 1.87e-02 2.01e-02 -6.1 3.89e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.1492846e+01 4.04e-02 8.06e-03 -7.8 4.39e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 9.2246765e+00 1.30e+00 3.00e-01 -8.4 4.66e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 46 1.1280007e+01 1.99e-01 1.67e-01 -8.4 1.63e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.0997065e+01 8.28e-01 1.43e-01 -6.4 1.18e+07 - 3.37e-03 3.31e-04f 1\n",
|
||
" 48 1.0995233e+01 8.27e-01 1.43e-01 -6.4 3.21e+04 - 1.00e+00 1.21e-03h 1\n",
|
||
" 49 1.0994838e+01 2.42e-01 1.37e-02 -5.2 1.29e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.1368352e+01 7.51e-02 1.07e-02 -5.5 9.80e+02 - 3.23e-01 1.00e+00h 1\n",
|
||
" 51 1.0583499e+01 1.15e+00 4.21e-02 -4.0 4.81e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 52 1.0985698e+01 8.90e-01 1.10e-02 -4.9 2.38e+03 - 8.88e-01 1.00e+00h 1\n",
|
||
" 53 1.1057640e+01 2.61e-01 8.33e-02 -3.1 2.34e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 9.6015794e+00 1.63e+00 1.72e-01 -3.2 1.23e+04 - 1.00e+00 5.18e-01f 1\n",
|
||
" 55 1.1159372e+01 4.37e-01 2.50e-01 -3.2 2.09e+03 - 9.65e-01 1.00e+00h 1\n",
|
||
" 56 1.0583620e+01 1.14e+00 6.92e-02 -3.2 1.44e+04 - 1.59e-03 5.91e-01f 1\n",
|
||
" 57 9.2619816e+00 2.12e+00 1.30e-01 -3.3 7.55e+03 - 7.75e-03 1.00e+00f 1\n",
|
||
" 58 8.6946991e+00 2.73e+00 5.02e-01 -3.4 1.80e+06 - 2.34e-02 4.26e-03f 1\n",
|
||
" 59 1.1076218e+01 2.04e+00 5.05e-01 -4.0 9.82e+03 - 8.89e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.0111613e+01 6.13e-01 2.72e-01 -3.6 1.63e+04 - 1.00e+00 6.68e-01h 1\n",
|
||
" 61 1.0979608e+01 7.60e-01 9.35e-02 -9.6 1.19e+04 - 5.81e-01 1.00e+00H 1\n",
|
||
" 62 1.0806495e+01 1.35e+00 7.43e-02 -4.4 1.04e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.0176663e+01 8.07e-01 2.00e-01 -4.5 1.22e+04 - 1.87e-01 1.00e+00h 1\n",
|
||
" 64 1.0174511e+01 8.09e-01 1.99e-01 -4.5 7.87e+05 - 7.89e-02 9.22e-05h 1\n",
|
||
" 65 9.4557990e+00 1.19e+00 1.70e-01 -4.5 3.88e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 66 7.2727139e+00 3.18e+00 5.45e-01 -3.5 3.87e+05 - 9.92e-01 3.62e-02f 1\n",
|
||
" 67 1.0051768e+01 1.22e+00 9.28e-02 -1.4 1.56e+04 - 1.00e+00 9.45e-01h 1\n",
|
||
" 68 1.0066929e+01 1.09e+00 1.03e-01 -1.6 5.19e+03 - 1.00e+00 1.35e-01h 1\n",
|
||
" 69 1.1683562e+01 2.67e-02 1.05e-01 -1.6 4.33e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.1516734e+01 1.24e-01 6.57e-02 -2.5 3.73e+02 - 7.74e-01 1.00e+00h 1\n",
|
||
" 71 1.1566967e+01 9.11e-02 2.18e-02 -2.5 5.94e+02 - 1.00e+00 9.99e-01h 1\n",
|
||
" 72 1.1635125e+01 9.50e-03 1.83e-02 -2.5 1.11e+02 - 6.77e-01 1.00e+00h 1\n",
|
||
" 73 5.8133198e+00 2.27e+00 6.53e-01 -3.7 1.92e+04 - 2.96e-03 1.00e+00f 1\n",
|
||
" 74 9.5250848e+00 1.51e+00 5.59e-01 -1.3 1.85e+04 - 7.25e-01 1.00e+00H 1\n",
|
||
" 75 9.1728819e+00 1.70e+00 3.24e-01 -1.5 9.08e+04 - 2.36e-01 4.23e-01h 1\n",
|
||
" 76 1.0339221e+01 2.08e+00 1.94e-01 -1.5 4.69e+04 - 1.20e-02 8.16e-01H 1\n",
|
||
" 77 8.5707351e+00 1.12e+00 2.36e-01 -1.5 1.38e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 78 8.1422170e+00 1.44e+00 2.34e-01 -1.6 1.60e+04 - 9.64e-01 1.64e-01h 1\n",
|
||
" 79 1.0895138e+01 7.36e-01 2.45e-01 -1.7 5.11e+03 - 4.79e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.1278140e+01 2.70e-01 1.07e-01 -1.8 4.64e+03 - 1.00e+00 7.05e-01h 1\n",
|
||
" 81 1.1192884e+01 1.34e-01 7.93e-02 -1.8 3.41e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 82 8.2862098e+00 9.40e-01 1.17e-01 -7.6 7.32e+03 - 3.46e-01 1.00e+00f 1\n",
|
||
" 83 1.1230963e+01 4.59e-02 2.74e+00 -4.0 2.92e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.1291440e+01 1.16e-05 5.36e-03 -5.8 8.75e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.1291448e+01 2.43e-07 7.92e-05 -7.9 1.35e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.1291446e+01 5.40e-06 1.52e-03 -11.0 2.04e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.1291444e+01 5.21e-06 1.40e-03 -11.0 4.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.1291442e+01 1.43e-05 1.17e-03 -11.0 2.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.1291445e+01 1.61e-06 2.18e-03 -11.0 2.85e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.1291442e+01 4.55e-06 1.93e-03 -11.0 5.11e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.1291446e+01 2.53e-06 1.35e-03 -11.0 3.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.1291442e+01 2.58e-06 1.58e-03 -11.0 1.98e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.1291445e+01 2.59e-06 3.47e-03 -11.0 3.22e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.1290860e+01 2.06e-04 1.39e-02 -11.0 3.23e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.1289650e+01 7.53e-04 2.49e-02 -11.0 5.74e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.1291227e+01 2.05e-04 3.42e-03 -11.0 4.07e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.1284104e+01 8.81e-03 3.90e-03 -11.0 6.61e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.1281599e+01 1.31e-02 1.91e-03 -11.0 5.13e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.1063761e+01 2.75e-01 1.53e-02 -11.0 1.63e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.1281384e+01 1.13e-02 1.20e-02 -11.0 3.52e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.1281384277578443e+01 1.1281384277578443e+01\n",
|
||
"Dual infeasibility......: 1.2021316222307454e-02 1.2021316222307454e-02\n",
|
||
"Constraint violation....: 1.1252990312421218e-02 1.1252990312421218e-02\n",
|
||
"Complementarity.........: 1.2386632124267694e-11 1.2386632124267694e-11\n",
|
||
"Overall NLP error.......: 1.2021316222307454e-02 1.2021316222307454e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 108\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 108\n",
|
||
"Number of inequality constraint evaluations = 108\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.422\n",
|
||
"Total CPU secs in NLP function evaluations = 135.095\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 507.00us ( 4.69us) 498.55us ( 4.62us) 108\n",
|
||
" nlp_g | 4.89 s ( 45.24ms) 4.66 s ( 43.18ms) 108\n",
|
||
" nlp_grad | 1.37 s ( 1.37 s) 1.31 s ( 1.31 s) 1\n",
|
||
" nlp_grad_f | 354.00us ( 3.47us) 349.90us ( 3.43us) 102\n",
|
||
" nlp_jac_g | 133.17 s ( 1.31 s) 127.23 s ( 1.25 s) 102\n",
|
||
" total | 139.56 s (139.56 s) 133.34 s (133.34 s) 1\n",
|
||
"Timestamp 13200\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 8.32e+02 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9798549e+01 1.34e+01 8.32e+02 -1.5 8.32e+02 - 9.90e-01 1.00e+00f 1\n",
|
||
" 2 8.4410014e+00 4.71e+00 9.27e+00 0.4 1.34e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 7.8435306e+00 1.20e+00 6.49e-01 -1.6 7.55e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 8.6855317e+00 2.35e-03 8.21e-02 -3.4 1.69e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 8.6866216e+00 2.02e-07 1.33e-04 -5.3 2.35e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 8.6866209e+00 7.90e-07 3.59e-03 -11.0 3.30e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 8.6865008e+00 8.86e-05 2.20e-02 -11.0 3.96e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 8.6865857e+00 1.50e-05 9.26e-04 -11.0 1.63e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 8.6866105e+00 1.86e-05 9.58e-04 -11.0 7.00e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 8.6865391e+00 7.68e-05 1.63e-03 -11.0 4.84e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 8.6862533e+00 1.70e-04 3.97e-03 -11.0 1.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 8.6844412e+00 1.91e-03 1.33e-02 -11.0 6.98e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 8.6857193e+00 5.01e-04 1.01e-03 -11.0 3.56e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 8.6843172e+00 1.19e-03 8.82e-04 -11.0 2.60e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 8.6865909e+00 1.12e-04 1.06e-03 -11.0 7.50e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 8.6868543e+00 4.83e-06 1.66e-03 -11.0 1.01e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 8.6868367e+00 1.23e-05 2.75e-03 -11.0 5.09e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 8.6868339e+00 2.08e-05 9.44e-04 -11.0 2.13e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 8.6867896e+00 6.47e-05 3.07e-03 -11.0 8.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 8.6839199e+00 5.25e-03 9.94e-03 -11.0 5.31e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 8.6805851e+00 3.07e-03 6.41e-03 -11.0 7.15e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 8.6746046e+00 1.08e-02 1.49e-03 -11.0 7.47e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 8.6872029e+00 1.24e-04 1.00e-03 -11.0 1.03e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 8.6869423e+00 4.13e-04 1.94e-03 -11.0 1.68e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 8.6700969e+00 8.85e-03 3.30e-03 -11.0 6.16e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 8.6869613e+00 2.16e-06 1.38e-02 -11.0 1.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.6869630e+00 4.35e-07 3.72e-05 -11.0 2.46e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 8.6869624e+00 6.52e-07 3.10e-03 -11.0 6.44e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.6869629e+00 2.32e-07 6.01e-05 -11.0 2.70e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.6869630e+00 1.21e-07 1.61e-04 -11.0 2.19e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 8.6869596e+00 4.58e-06 3.63e-03 -11.0 1.23e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 8.6869608e+00 1.39e-06 3.10e-03 -11.0 1.25e-02 - 1.00e+00 5.00e-01h 2\n",
|
||
" 33 8.6869640e+00 5.28e-10 9.41e-05 -11.0 1.26e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 8.6869529e+00 7.35e-06 1.64e-02 -11.0 3.96e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 8.6869596e+00 3.77e-06 3.83e-03 -11.0 4.57e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 8.6869161e+00 3.11e-05 3.70e-03 -11.0 4.57e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 8.6869386e+00 2.75e-05 1.91e-03 -11.0 2.06e-01 - 1.00e+00 5.00e-01h 2\n",
|
||
" 38 8.6869714e+00 2.44e-06 1.20e-03 -11.0 1.54e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 8.6867793e+00 2.02e-04 2.67e-03 -11.0 9.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.6868814e+00 9.86e-05 2.52e-03 -11.0 5.22e-02 - 1.00e+00 5.00e-01h 2\n",
|
||
" 41 8.6868518e+00 4.07e-05 2.05e-03 -11.0 1.23e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 8.6865276e+00 3.08e-04 2.49e-03 -11.0 1.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 8.6867365e+00 1.53e-04 2.60e-03 -11.0 4.49e-01 - 1.00e+00 5.00e-01h 2\n",
|
||
" 44 8.6869094e+00 5.22e-05 9.83e-04 -11.0 1.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 8.6869809e+00 4.30e-09 1.06e-04 -11.0 5.82e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 46 8.6869797e+00 5.42e-09 1.46e-04 -11.0 3.03e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 47 8.6869233e+00 2.67e-05 1.25e-03 -11.0 1.58e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 8.6869595e+00 8.69e-06 6.49e-04 -11.0 4.57e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 8.6869856e+00 1.61e-11 2.59e-05 -11.0 4.53e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.6869617e+00 3.26e-05 1.09e-03 -11.0 3.51e-01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 51 8.6869510e+00 7.71e-05 9.61e-04 -11.0 9.58e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 8.6869900e+00 7.13e-06 8.00e-04 -11.0 3.25e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 8.6869897e+00 9.26e-06 1.20e-03 -11.0 1.88e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 8.6869420e+00 6.26e-05 2.21e-03 -11.0 2.99e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 8.6868565e+00 1.56e-04 2.45e-03 -11.0 4.62e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 8.6870141e+00 2.85e-08 1.95e-04 -11.0 1.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 8.6870139e+00 9.82e-08 3.44e-05 -11.0 2.93e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 8.6870140e+00 1.73e-09 2.53e-04 -11.0 6.45e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 8.6870140e+00 2.64e-08 2.37e-04 -11.0 5.57e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 8.6870140e+00 8.52e-09 1.01e-04 -11.0 2.71e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 8.6870140e+00 1.43e-09 6.49e-05 -11.0 8.84e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 8.6870140e+00 5.18e-10 9.77e-05 -11.0 6.64e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 8.6870140e+00 5.95e-11 8.32e-05 -11.0 1.20e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 64 8.6870098e+00 1.53e-06 2.01e-02 -11.0 2.28e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 8.6870045e+00 5.31e-06 2.74e-02 -11.0 3.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 8.6870137e+00 4.52e-07 1.96e-03 -11.0 6.74e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 8.6870138e+00 2.84e-07 8.83e-05 -11.0 4.09e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 8.6870133e+00 7.45e-07 1.39e-03 -11.0 2.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 8.6870137e+00 3.66e-07 1.53e-05 -11.0 4.84e-04 - 1.00e+00 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.6870139e+00 2.74e-07 5.38e-05 -11.0 1.46e-04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 71 8.6870139e+00 2.70e-07 9.12e-05 -11.0 7.97e-05 - 1.00e+00 1.56e-02h 7\n",
|
||
" 72 8.6870139e+00 2.70e-07 1.08e-04 -11.0 2.48e-05 - 1.00e+00 2.44e-04h 13\n",
|
||
" 73 8.6870139e+00 2.70e-07 5.39e-05 -11.0 3.16e-06 - 1.00e+00 6.10e-05h 15\n",
|
||
" 74 8.6870143e+00 5.96e-10 1.06e-04 -11.0 9.40e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 8.6870142e+00 7.62e-09 1.41e-04 -11.0 3.34e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 8.6870142e+00 6.61e-08 1.45e-04 -11.0 2.12e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 8.6870105e+00 1.63e-06 3.23e-03 -11.0 8.54e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 8.6870136e+00 2.37e-07 1.28e-04 -11.0 2.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 8.6870135e+00 3.65e-07 7.24e-05 -11.0 1.62e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 8.6870139e+00 8.89e-08 7.31e-05 -11.0 5.95e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 8.6870141e+00 9.92e-09 1.28e-04 -11.0 3.87e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 8.6869300e+00 1.45e-04 5.65e-02 -11.0 4.21e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 8.6850789e+00 2.09e-03 4.76e-03 -11.0 1.29e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 8.6840653e+00 2.90e-03 2.33e-03 -11.0 1.02e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 8.6852605e+00 1.19e-03 3.46e-03 -11.0 1.12e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 8.6831167e+00 3.62e-03 5.10e-03 -11.0 3.32e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 8.6540679e+00 1.69e-02 7.66e-03 -11.0 9.42e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 8.6841563e+00 3.68e-04 3.99e-03 -11.0 3.27e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 8.6763516e+00 7.57e-03 1.94e-03 -11.0 4.96e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 8.6735638e+00 2.56e-02 4.85e-03 -11.0 6.11e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 8.6861931e+00 1.51e-06 2.55e-02 -11.0 2.80e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 8.6861946e+00 8.78e-08 6.94e-05 -11.0 5.66e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 8.6861916e+00 1.46e-06 2.14e-03 -11.0 1.52e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 8.6861917e+00 1.83e-06 1.56e-03 -11.0 7.29e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 8.6861941e+00 3.16e-07 7.15e-05 -11.0 2.52e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 8.6861938e+00 5.54e-07 1.32e-03 -11.0 4.70e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 8.6861935e+00 2.32e-06 8.76e-04 -11.0 7.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 8.6861831e+00 8.58e-06 5.19e-03 -11.0 2.11e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 8.6861922e+00 8.60e-07 2.48e-03 -11.0 5.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.6861930e+00 3.21e-07 8.06e-05 -11.0 4.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.6861930389438573e+00 8.6861930389438573e+00\n",
|
||
"Dual infeasibility......: 8.0559161867340846e-05 8.0559161867340846e-05\n",
|
||
"Constraint violation....: 3.2081532097549825e-07 3.2081532097549825e-07\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 8.0559161867340846e-05 8.0559161867340846e-05\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 146\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 146\n",
|
||
"Number of inequality constraint evaluations = 146\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.433\n",
|
||
"Total CPU secs in NLP function evaluations = 137.955\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 658.00us ( 4.51us) 659.62us ( 4.52us) 146\n",
|
||
" nlp_g | 6.62 s ( 45.36ms) 6.32 s ( 43.30ms) 146\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 372.00us ( 3.65us) 375.90us ( 3.69us) 102\n",
|
||
" nlp_jac_g | 134.13 s ( 1.31 s) 128.24 s ( 1.26 s) 102\n",
|
||
" total | 142.21 s (142.21 s) 135.96 s (135.96 s) 1\n",
|
||
"Timestamp 13500\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.80e+01 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9865765e+01 1.37e+01 3.80e+01 -1.5 3.80e+01 - 9.90e-01 1.00e+00f 1\n",
|
||
" 2 8.6947803e+00 4.87e+00 9.11e+00 0.4 1.37e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 8.5668399e+00 1.29e+00 5.00e-01 -1.6 7.89e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 9.4782162e+00 2.40e-03 8.53e-02 -3.4 1.81e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 9.4793433e+00 2.40e-07 3.40e-05 -5.3 2.40e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 9.4793436e+00 6.07e-08 1.75e-04 -11.0 7.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 9.4793434e+00 2.35e-07 8.55e-05 -11.0 1.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 9.4793436e+00 1.76e-08 2.98e-05 -11.0 9.66e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 9.4793436e+00 6.10e-11 3.90e-05 -11.0 2.24e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 9.4793434e+00 2.75e-07 8.87e-05 -11.0 2.10e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 9.4793435e+00 8.93e-08 1.02e-04 -11.0 1.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 9.4793401e+00 2.55e-06 8.17e-03 -11.0 9.52e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 9.4793437e+00 1.90e-08 1.09e-04 -11.0 1.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 9.4793433e+00 1.18e-06 3.03e-03 -11.0 1.96e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 9.4793371e+00 9.97e-06 6.14e-03 -11.0 3.30e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 9.4793117e+00 3.55e-05 1.92e-03 -11.0 2.57e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 9.4792634e+00 5.53e-05 5.16e-03 -11.0 1.51e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 9.4793053e+00 3.46e-05 3.45e-03 -11.0 1.74e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 9.4792562e+00 8.30e-05 3.68e-03 -11.0 2.80e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 9.4793336e+00 1.06e-05 2.91e-03 -11.0 1.42e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 9.4793315e+00 3.37e-05 1.18e-03 -11.0 1.24e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 9.4771536e+00 7.15e-03 1.72e-03 -11.0 8.97e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 9.4318082e+00 6.48e-02 7.11e-03 -11.0 4.25e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 9.4749911e+00 1.49e-02 2.09e-03 -11.0 1.67e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 9.4802574e+00 3.15e-03 2.10e-03 -11.0 6.88e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 9.3713708e+00 1.62e-01 5.17e-03 -11.0 8.08e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 27 9.4853663e+00 4.55e-03 7.96e-03 -11.0 1.71e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 9.4463952e+00 2.92e-02 8.16e-03 -11.0 3.20e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.3494296e+00 1.14e+00 1.32e-01 -11.0 6.14e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.6950179e+00 7.45e-01 1.71e-01 -11.0 1.61e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 8.4450436e+00 1.87e+00 1.35e-01 -9.0 7.51e+05 - 1.00e+00 1.23e-02f 1\n",
|
||
" 32 8.4426543e+00 1.87e+00 1.35e-01 -9.2 1.86e+06 - 3.36e-02 5.02e-05h 1\n",
|
||
" 33 9.5915567e+00 1.64e-01 1.48e-01 -9.2 3.15e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 9.5548998e+00 1.29e-01 1.81e-01 -9.2 1.24e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 9.5370671e+00 2.12e-01 1.88e-01 -9.2 1.07e+03 - 1.00e+00 3.75e-01h 1\n",
|
||
" 36 9.4479889e+00 3.66e-01 3.57e-02 -9.2 1.28e+03 - 5.51e-01 1.00e+00h 1\n",
|
||
" 37 9.2683843e+00 5.23e-01 6.47e-02 -9.2 4.06e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 9.5327467e+00 2.84e-01 6.02e-02 -9.2 3.23e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 8.4283786e+00 6.28e-01 1.39e-01 -9.2 1.04e+04 - 1.00e+00 5.11e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 9.1397981e+00 6.96e-01 1.05e-01 -7.2 4.60e+03 - 5.25e-01 1.00e+00h 1\n",
|
||
" 41 8.0021918e+00 4.24e+00 7.27e-01 -5.5 1.29e+04 - 4.75e-03 1.00e+00f 1\n",
|
||
" 42 8.0002695e+00 4.24e+00 7.27e-01 -5.4 9.55e+04 - 1.00e+00 8.49e-05h 1\n",
|
||
" 43 7.3155736e+00 3.39e+00 5.87e-01 -3.4 3.62e+04 - 7.84e-01 9.87e-02f 1\n",
|
||
" 44 8.0869536e+00 7.54e-01 8.68e-01 -2.4 3.87e+04 - 8.25e-03 1.00e+00H 1\n",
|
||
" 45 7.6750995e+00 5.16e+00 1.05e+00 -2.3 1.72e+04 - 1.00e+00 7.67e-01f 1\n",
|
||
" 46 7.4765465e+00 5.11e+00 1.04e+00 -2.3 6.26e+04 - 1.62e-01 1.21e-02h 1\n",
|
||
" 47 7.1877628e+00 5.60e+00 1.77e-01 -2.3 1.17e+04 - 6.99e-01 1.00e+00f 1\n",
|
||
" 48 7.2251846e+00 3.63e+00 2.65e-01 -2.8 1.22e+04 - 7.32e-01 1.00e+00h 1\n",
|
||
" 49 6.8658458e+00 3.70e+00 2.08e-01 -2.6 2.06e+04 - 9.91e-01 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.7391602e+00 6.28e-01 3.67e-01 -3.2 8.64e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 51 6.0522305e+00 2.74e+00 3.76e-01 -2.0 1.30e+05 - 1.00e+00 1.92e-01f 1\n",
|
||
" 52r 6.0522305e+00 2.74e+00 9.99e+02 0.4 0.00e+00 - 0.00e+00 4.67e-07R 22\n",
|
||
" 53r 6.4980025e+00 2.47e+00 9.89e+02 2.3 2.50e+02 - 7.91e-01 9.25e-03f 1\n",
|
||
" 54r 6.6043849e+00 2.42e+00 3.75e+02 -4.5 5.63e+01 - 9.88e-01 2.01e-03f 1\n",
|
||
" 55 5.2419656e+00 2.68e+00 1.31e-01 -1.0 9.86e+03 - 6.69e-01 1.00e+00f 1\n",
|
||
" 56 5.7660157e+00 3.78e+00 7.94e-02 -2.8 4.38e+03 - 8.88e-01 8.88e-01s 22\n",
|
||
" 57 5.7592509e+00 3.80e+00 7.48e-02 -2.2 3.42e+04 - 1.00e+00 4.48e-02h 2\n",
|
||
" 58 5.7597347e+00 3.79e+00 7.47e-02 -2.2 2.62e+04 - 7.16e-01 2.47e-03h 8\n",
|
||
" 59 7.4332473e+00 1.95e+00 1.44e-01 -2.2 1.94e+04 - 1.64e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 7.6056431e+00 2.04e+00 2.91e-01 -2.2 2.94e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 61 6.5935986e+00 2.66e+00 1.43e-01 -2.2 1.59e+04 - 1.00e+00 5.00e-01f 2\n",
|
||
" 62 6.5043079e+00 3.82e+00 5.00e-01 -1.9 3.53e+05 - 9.35e-01 8.05e-02F 1\n",
|
||
" 63 8.3349442e+00 1.25e+00 3.02e-01 -2.1 4.94e+03 - 7.31e-02 1.00e+00h 1\n",
|
||
" 64 9.4176159e+00 2.68e-01 2.35e-01 -2.1 4.99e+04 - 2.46e-01 6.52e-01H 1\n",
|
||
" 65 9.3738098e+00 2.91e-01 2.34e-01 -2.1 5.36e+05 - 1.41e-02 2.31e-03f 1\n",
|
||
" 66 9.1397271e+00 7.88e-01 2.15e-01 -2.1 3.75e+05 - 7.99e-03 1.45e-01f 1\n",
|
||
" 67 9.1063209e+00 8.63e-01 2.13e-01 -2.1 3.34e+04 - 1.00e+00 1.33e-02h 5\n",
|
||
" 68 9.1226708e+00 8.36e-01 2.16e-01 -2.1 4.03e+03 - 1.86e-01 1.00e+00h 1\n",
|
||
" 69 7.9079331e+00 1.28e+00 1.87e-01 -2.1 6.55e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.0051664e+01 1.08e-01 1.38e-01 -1.4 2.44e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 9.4758436e+00 5.21e-01 1.54e-01 -1.6 1.66e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 72 9.9056171e+00 1.97e-01 8.87e-02 -1.6 1.36e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 9.6285892e+00 2.72e-01 1.87e-02 -2.3 1.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 9.4112238e+00 3.80e-01 1.43e-02 -2.3 3.47e+04 - 2.93e-02 4.79e-02f 1\n",
|
||
" 75 1.0274121e+01 1.91e-01 7.73e-02 -2.3 4.10e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 76 7.7127941e+00 1.43e+00 1.58e-01 -2.3 1.21e+05 - 2.26e-01 2.29e-01f 1\n",
|
||
" 77 1.0426778e+01 6.33e-02 9.39e-02 -3.2 4.27e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 78 1.0311304e+01 6.67e-02 8.64e-02 -2.9 7.07e+03 - 1.00e+00 1.64e-01f 1\n",
|
||
" 79 1.0303017e+01 3.70e-02 2.55e-03 -2.9 1.41e+02 - 3.20e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.0360648e+01 2.63e-04 8.22e-03 -2.9 5.20e+02 - 4.80e-01 1.00e+00H 1\n",
|
||
" 81 1.0349654e+01 1.14e-02 6.01e-03 -4.4 2.20e+03 - 6.96e-01 1.69e-01f 1\n",
|
||
" 82 9.7972241e+00 4.46e-01 3.72e-02 -4.4 9.65e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 83 9.5716957e+00 5.31e-01 5.64e-02 -4.4 3.75e+03 - 5.03e-01 1.00e+00h 1\n",
|
||
" 84 5.6672590e+00 2.51e+00 7.45e-01 -4.4 4.02e+05 - 7.77e-02 7.93e-02f 1\n",
|
||
" 85 5.6654014e+00 2.51e+00 7.43e-01 -4.4 7.80e+04 - 1.00e+00 4.86e-04h 1\n",
|
||
" 86 9.1835777e+00 5.48e-01 3.46e-01 -4.4 2.61e+02 - 1.00e+00 9.28e-01h 1\n",
|
||
" 87 9.8102877e+00 9.18e-02 2.33e-02 -4.4 5.56e+02 - 1.00e+00 8.83e-01h 1\n",
|
||
" 88 1.0018084e+01 4.84e-02 3.92e-02 -4.4 2.36e+02 - 7.00e-01 1.00e+00h 1\n",
|
||
" 89 1.0134232e+01 2.60e-04 8.91e-03 -4.4 1.65e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.0102526e+01 1.32e-02 2.44e-03 -4.4 1.89e+02 - 4.73e-02 2.19e-01f 1\n",
|
||
" 91 9.7765845e+00 1.59e-01 7.02e-02 -4.4 7.51e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 92 1.0120819e+01 2.72e-02 5.54e-03 -4.4 2.67e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 9.3996223e+00 2.77e-01 2.82e-02 -4.4 7.40e+02 - 5.80e-01 1.00e+00f 1\n",
|
||
" 94 9.6192918e+00 1.84e-01 1.15e-02 -4.4 4.59e+02 - 1.00e+00 3.42e-01h 1\n",
|
||
" 95 1.0117713e+01 1.61e-02 2.39e-02 -4.4 4.62e+01 - 9.08e-01 1.00e+00h 1\n",
|
||
" 96 1.0084144e+01 4.09e-03 6.38e-03 -4.4 1.85e+03 - 1.00e+00 1.00e+00F 1\n",
|
||
" 97 1.0048851e+01 5.00e-02 1.33e-02 -4.4 5.53e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 9.7961069e+00 1.41e-01 1.67e-02 -4.4 5.12e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 9.9931759e+00 5.50e-02 6.68e-03 -4.4 5.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.0101919e+01 3.16e-03 1.12e-02 -4.4 1.20e+03 - 5.69e-01 1.00e+00H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.0101919143268807e+01 1.0101919143268807e+01\n",
|
||
"Dual infeasibility......: 1.1198145880060184e-02 1.1198145880060184e-02\n",
|
||
"Constraint violation....: 3.1561011719993814e-03 3.1561011719993814e-03\n",
|
||
"Complementarity.........: 4.1095663680282286e-05 4.1095663680282286e-05\n",
|
||
"Overall NLP error.......: 1.1198145880060184e-02 1.1198145880060184e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 183\n",
|
||
"Number of objective gradient evaluations = 100\n",
|
||
"Number of equality constraint evaluations = 183\n",
|
||
"Number of inequality constraint evaluations = 183\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.410\n",
|
||
"Total CPU secs in NLP function evaluations = 139.208\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 824.00us ( 4.50us) 825.75us ( 4.51us) 183\n",
|
||
" nlp_g | 8.19 s ( 44.76ms) 7.82 s ( 42.71ms) 183\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 350.00us ( 3.47us) 340.32us ( 3.37us) 101\n",
|
||
" nlp_jac_g | 133.93 s ( 1.30 s) 127.89 s ( 1.24 s) 103\n",
|
||
" total | 143.61 s (143.61 s) 137.13 s (137.13 s) 1\n",
|
||
"Timestamp 13800\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.67e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9860171e+01 1.48e+01 2.67e+04 -1.5 2.67e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.2284239e+01 5.22e+00 1.52e+01 1.1 1.01e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.7845305e+01 2.17e+00 8.50e-01 -1.0 2.15e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.9330109e+01 2.91e-04 9.93e-02 -2.9 2.64e+00 - 9.94e-01 1.00e+00h 1\n",
|
||
" 5 1.9328561e+01 4.65e-04 3.49e-02 -4.6 2.30e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
" 6 1.9327060e+01 6.60e-04 2.43e-03 -6.4 5.94e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.9328625e+01 3.66e-04 1.58e-03 -8.5 1.45e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.9325366e+01 3.99e-03 3.01e-03 -11.0 1.19e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.9326370e+01 9.15e-04 1.40e-03 -11.0 5.46e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.9301383e+01 1.36e-02 5.83e-03 -11.0 6.64e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.9328532e+01 9.62e-06 1.19e-03 -11.0 3.60e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 12 1.9314818e+01 8.91e-03 1.02e-03 -11.0 2.29e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 13 1.9286952e+01 1.54e-02 3.60e-03 -11.0 4.48e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.9316793e+01 5.33e-03 1.91e-03 -11.0 2.04e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.9322147e+01 2.15e-03 1.69e-03 -11.0 1.93e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.9321140e+01 4.89e-03 2.37e-03 -11.0 2.74e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.9323427e+01 1.65e-03 1.31e-03 -11.0 7.76e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.9325010e+01 1.15e-03 1.92e-03 -11.0 1.43e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.9318890e+01 2.37e-02 2.97e-03 -11.0 5.57e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.9324618e+01 9.59e-03 1.16e-03 -11.0 2.88e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.9323531e+01 4.74e-03 2.71e-03 -11.0 8.42e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.9325921e+01 1.51e-03 1.53e-03 -11.0 9.63e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.9303382e+01 2.10e-02 2.73e-03 -11.0 3.29e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.9324632e+01 2.02e-03 1.89e-03 -11.0 1.98e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.9232073e+01 1.21e-01 4.12e-03 -11.0 4.32e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 26 1.9324898e+01 1.35e-03 3.01e-03 -11.0 6.82e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.9171624e+01 1.12e-01 8.23e-03 -11.0 5.81e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 28 1.9295805e+01 4.45e-02 2.17e-03 -11.0 1.81e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.9299949e+01 3.15e-02 2.91e-03 -11.0 4.36e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.9196271e+01 5.46e-02 2.74e-03 -11.0 3.00e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.7544525e+01 1.30e+00 4.41e-02 -11.0 9.93e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 32 1.4317669e+01 2.75e+00 3.06e-01 -11.0 1.81e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 33 1.3992027e+01 2.94e+00 3.13e-01 -9.1 6.42e+04 - 1.00e+00 5.41e-02h 1\n",
|
||
" 34 1.8153363e+01 4.83e-01 2.89e-01 -9.2 1.29e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.7971621e+01 1.88e-01 1.70e-02 -8.1 1.03e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.8129362e+01 7.06e-02 4.34e-02 -9.1 1.69e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.7785257e+01 4.00e-01 1.98e-02 -9.2 6.53e+04 - 1.00e+00 3.83e-01f 1\n",
|
||
" 38 1.7802676e+01 3.87e-01 1.78e-02 -7.2 4.49e+03 - 1.00e+00 5.57e-02h 1\n",
|
||
" 39 1.7709164e+01 2.86e+00 7.46e-02 -5.3 1.92e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.1363196e+01 4.87e+00 7.43e-01 -4.8 8.93e+04 - 1.66e-01 4.90e-01f 1\n",
|
||
" 41 1.7874601e+01 3.12e+00 3.85e-01 -4.3 1.41e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.2325519e+01 4.46e+00 2.35e-01 -1.2 1.05e+04 - 6.64e-01 1.00e+00f 1\n",
|
||
" 43 1.2245523e+01 4.15e+00 1.81e-01 -0.8 1.86e+05 - 6.07e-01 1.18e-01h 1\n",
|
||
" 44 1.6548809e+01 5.91e-01 1.71e-01 -1.7 8.80e+03 - 7.19e-01 9.96e-01h 1\n",
|
||
" 45 1.8098607e+01 3.73e-01 7.95e-02 -3.0 2.72e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.8041240e+01 3.09e-01 6.41e-02 -3.1 7.09e+02 - 1.00e+00 1.58e-01h 1\n",
|
||
" 47 1.8255165e+01 8.90e-03 1.11e-02 -3.1 1.20e+02 - 7.06e-01 1.00e+00h 1\n",
|
||
" 48 1.8125872e+01 4.46e-01 4.26e-02 -4.0 9.33e+03 - 9.44e-01 1.00e+00h 1\n",
|
||
" 49 1.8136353e+01 4.09e-01 3.30e-02 -3.8 4.93e+03 - 1.00e+00 1.73e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.8219249e+01 9.18e-02 7.04e-03 -3.8 2.56e+03 - 9.65e-01 1.00e+00h 1\n",
|
||
" 51 1.7505714e+01 3.59e-01 2.01e-02 -9.8 1.04e+04 - 3.81e-04 7.35e-01f 1\n",
|
||
" 52 1.8184801e+01 9.28e-02 1.44e-02 -3.4 4.71e+03 - 1.00e+00 9.39e-01H 1\n",
|
||
" 53 1.8026030e+01 2.27e-01 8.59e-03 -3.0 2.49e+03 - 1.00e+00 3.27e-01h 1\n",
|
||
" 54 1.8250108e+01 2.08e-02 7.98e-03 -2.9 2.68e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.6541313e+01 3.82e+00 1.78e-01 -2.6 5.92e+03 - 1.18e-01 1.00e+00f 1\n",
|
||
" 56 1.8347846e+01 1.34e-01 1.66e-01 -3.6 8.54e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.6819215e+01 2.82e+00 3.62e-02 -3.8 1.16e+04 - 3.15e-01 1.00e+00f 1\n",
|
||
" 58 1.6682772e+01 2.74e+00 2.85e-02 -3.8 1.23e+05 - 7.95e-04 2.85e-02h 1\n",
|
||
" 59 1.6849027e+01 2.68e+00 4.23e-02 -3.8 1.61e+04 - 1.00e+00 2.20e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.8275287e+01 6.12e-02 8.20e-02 -3.8 3.26e+03 - 6.39e-01 1.00e+00h 1\n",
|
||
" 61 1.7986331e+01 3.62e-01 8.83e-02 -3.8 6.73e+03 - 1.00e+00 4.84e-01f 1\n",
|
||
" 62 1.5623120e+01 4.46e+00 2.44e-01 -3.8 4.71e+04 - 4.06e-02 1.00e+00f 1\n",
|
||
" 63 1.4975887e+01 4.98e+00 2.21e-01 -3.8 3.14e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.4775758e+01 4.25e+00 2.09e-01 -3.8 6.44e+04 - 2.39e-01 1.94e-01h 1\n",
|
||
" 65 1.5660739e+01 1.85e+00 1.10e-01 -3.8 2.88e+04 - 5.87e-04 5.00e-01h 2\n",
|
||
" 66 1.4732621e+01 5.29e+00 1.98e-01 -3.5 1.42e+05 - 1.00e+00 3.30e-01f 1\n",
|
||
" 67 1.9228649e+01 1.10e+00 2.55e-01 -3.6 7.60e+04 - 6.50e-01 2.48e-01H 1\n",
|
||
" 68 1.9094826e+01 1.08e+00 2.38e-01 -3.6 5.97e+03 - 4.29e-03 1.46e-01h 1\n",
|
||
" 69 1.8685883e+01 1.37e-01 6.06e-02 -3.6 1.10e+04 - 2.63e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.8319664e+01 1.10e+00 1.05e-01 -3.6 1.71e+05 - 7.98e-02 4.23e-02f 1\n",
|
||
" 71 1.6391168e+01 9.48e-01 3.32e-01 -3.6 4.17e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 72 1.9148381e+01 1.85e-02 2.57e+00 -4.3 2.64e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.9161600e+01 3.09e-06 2.19e-03 -4.4 2.22e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.9161602e+01 4.35e-07 1.35e-04 -6.5 4.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.9161600e+01 2.24e-06 2.16e-03 -8.6 6.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.9161391e+01 1.06e-04 9.95e-03 -11.0 3.50e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.9161523e+01 2.70e-05 9.88e-04 -11.0 1.71e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.9161564e+01 2.15e-05 1.75e-03 -11.0 5.24e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.9161596e+01 5.58e-06 1.51e-03 -11.0 2.75e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.9161572e+01 2.13e-05 2.10e-03 -11.0 6.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.9161560e+01 2.04e-05 1.69e-03 -11.0 6.40e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.9161587e+01 8.37e-06 1.70e-03 -11.0 3.23e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.9161550e+01 2.28e-05 2.74e-03 -11.0 1.33e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.9161385e+01 1.23e-04 3.94e-03 -11.0 2.95e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.9113393e+01 2.12e-02 5.66e-03 -9.3 1.13e+03 - 1.00e+00 5.74e-02f 1\n",
|
||
" 86 1.9152757e+01 4.53e-03 1.88e-03 -9.4 2.40e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.9156508e+01 2.32e-03 2.20e-03 -11.0 1.27e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.9162689e+01 5.53e-04 1.44e-03 -9.0 3.42e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.9160143e+01 1.34e-03 3.64e-03 -9.1 7.41e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.9161487e+01 5.77e-04 3.62e-03 -9.5 7.64e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.9156796e+01 4.66e-03 4.78e-03 -9.6 3.75e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.9148081e+01 4.04e-03 4.95e-03 -9.6 5.81e+01 - 5.42e-01 1.00e+00h 1\n",
|
||
" 93 1.9140296e+01 5.30e-03 2.38e-03 -11.0 5.30e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.9134293e+01 9.04e-03 1.19e-03 -9.0 1.03e+03 - 1.00e+00 3.03e-02h 1\n",
|
||
" 95 1.9134946e+01 8.84e-03 1.42e-03 -9.1 2.75e+01 - 1.00e+00 2.62e-02h 1\n",
|
||
" 96 1.9135267e+01 8.66e-03 1.35e-03 -9.1 6.18e+01 - 1.00e+00 1.56e-02h 7\n",
|
||
"In iteration 96, 1 Slack too small, adjusting variable bound\n",
|
||
" 97 1.9146184e+01 4.93e-03 1.74e-03 -9.1 1.84e+00 - 1.00e+00 4.34e-01h 1\n",
|
||
" 98 1.9159236e+01 6.87e-04 3.98e-03 -10.6 7.76e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.9160101e+01 2.72e-04 3.56e-03 -8.6 1.42e+00 - 9.68e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.9159683e+01 3.23e-04 1.10e-03 -9.7 1.15e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.9159683274561100e+01 1.9159683274561100e+01\n",
|
||
"Dual infeasibility......: 1.0968449616542714e-03 1.0968449616542714e-03\n",
|
||
"Constraint violation....: 3.2264542241833283e-04 3.2264542241833283e-04\n",
|
||
"Complementarity.........: 2.4609543018873994e-10 2.4609543018873994e-10\n",
|
||
"Overall NLP error.......: 1.0968449616542714e-03 1.0968449616542714e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 111\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 111\n",
|
||
"Number of inequality constraint evaluations = 111\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.402\n",
|
||
"Total CPU secs in NLP function evaluations = 134.591\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 510.00us ( 4.59us) 509.84us ( 4.59us) 111\n",
|
||
" nlp_g | 4.97 s ( 44.75ms) 4.74 s ( 42.68ms) 111\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 338.00us ( 3.31us) 331.64us ( 3.25us) 102\n",
|
||
" nlp_jac_g | 132.39 s ( 1.30 s) 126.41 s ( 1.24 s) 102\n",
|
||
" total | 138.83 s (138.83 s) 132.56 s (132.56 s) 1\n",
|
||
"Timestamp 14100\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.44e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9829806e+01 1.30e+01 1.44e+03 -1.5 1.44e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.3862247e+00 4.44e+00 9.03e+00 0.4 1.30e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 6.6482827e+00 1.08e+00 6.51e-01 -1.6 6.94e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 7.3462851e+00 2.27e-03 8.03e-02 -3.4 1.54e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 7.3472814e+00 3.62e-07 6.51e-05 -5.3 2.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 7.3472819e+00 1.93e-07 2.81e-05 -11.0 4.92e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 7.3472821e+00 1.92e-08 1.57e-04 -11.0 1.65e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 7.3472820e+00 3.91e-08 8.04e-05 -11.0 7.12e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 7.3472814e+00 1.05e-06 3.47e-03 -11.0 4.50e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 7.3472812e+00 3.51e-07 7.55e-05 -11.0 4.72e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 7.3472808e+00 1.15e-06 1.70e-03 -11.0 5.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 7.3472816e+00 1.16e-09 5.12e-05 -11.0 2.48e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 7.3472816e+00 9.03e-09 5.72e-05 -11.0 7.68e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 7.3472816e+00 3.97e-09 1.13e-04 -11.0 3.36e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 7.3472816e+00 5.21e-09 1.06e-04 -11.0 2.12e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 7.3472816e+00 2.04e-09 1.75e-04 -11.0 1.03e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 7.3472816e+00 5.60e-09 2.64e-05 -11.0 1.52e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 7.3472817e+00 1.48e-09 1.25e-04 -11.0 5.48e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 7.3472816e+00 5.45e-09 8.24e-05 -11.0 3.11e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 7.3472816e+00 6.54e-08 2.15e-04 -11.0 8.58e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 7.3472803e+00 8.79e-07 8.10e-05 -11.0 3.35e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 7.3472811e+00 3.26e-07 4.22e-05 -11.0 1.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 7.3472814e+00 2.01e-07 7.89e-05 -11.0 1.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 7.3472816e+00 8.15e-08 4.99e-05 -11.0 6.66e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 7.3472815e+00 6.75e-08 1.62e-04 -11.0 8.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 7.3472799e+00 7.82e-07 3.30e-03 -11.0 3.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 7.3472816e+00 3.97e-09 4.62e-05 -11.0 4.02e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 7.3472816e+00 6.07e-09 3.76e-05 -11.0 4.31e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 7.3472817e+00 4.31e-11 1.82e-05 -11.0 3.04e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 7.3472816e+00 1.98e-09 7.28e-05 -11.0 5.12e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 7.3472816e+00 3.49e-08 3.37e-05 -11.0 1.90e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 7.3472816e+00 3.69e-09 3.32e-05 -11.0 1.76e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 7.3472816e+00 1.05e-09 1.62e-04 -11.0 1.89e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 7.3472814e+00 1.18e-07 1.43e-04 -11.0 3.92e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 7.3472815e+00 5.23e-08 7.47e-05 -11.0 1.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 7.3472816e+00 2.56e-10 5.34e-05 -11.0 2.97e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 37 7.3472816e+00 1.37e-08 6.87e-05 -11.0 1.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 7.3472813e+00 1.85e-07 2.18e-04 -11.0 2.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 7.3472809e+00 8.76e-07 4.81e-03 -11.0 2.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 7.3472802e+00 1.11e-06 3.21e-03 -11.0 9.07e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 7.3472785e+00 2.08e-06 1.31e-03 -11.0 6.80e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 7.3472811e+00 4.57e-07 5.29e-05 -11.0 1.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 7.3472814e+00 4.84e-08 8.99e-05 -11.0 4.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 7.3472764e+00 2.99e-06 3.32e-03 -11.0 4.88e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 7.3472791e+00 1.22e-06 2.31e-03 -11.0 1.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 7.3472789e+00 8.81e-07 3.79e-03 -11.0 1.81e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 7.3472776e+00 2.71e-06 1.20e-03 -11.0 2.23e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 7.3472681e+00 1.34e-05 1.34e-03 -11.0 4.74e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 7.3472113e+00 4.38e-05 8.39e-03 -11.0 1.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 7.3472802e+00 3.89e-07 6.81e-05 -11.0 4.97e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 7.3472790e+00 2.13e-06 1.33e-03 -11.0 1.34e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 7.3472749e+00 5.90e-06 1.30e-03 -11.0 3.58e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 7.3472580e+00 3.38e-05 1.90e-03 -11.0 2.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 7.3472654e+00 3.16e-05 3.00e-03 -11.0 2.22e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 7.3472682e+00 8.37e-06 1.01e-03 -11.0 1.01e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 7.3472410e+00 3.41e-05 1.79e-03 -11.0 1.78e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 7.3472667e+00 1.04e-05 2.40e-03 -11.0 1.38e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 7.3472722e+00 5.27e-06 1.28e-03 -11.0 7.56e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 7.3470809e+00 4.93e-04 2.41e-03 -11.0 2.84e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 7.3453269e+00 1.51e-03 4.15e-03 -11.0 4.87e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 7.3382971e+00 8.55e-03 2.21e-03 -11.0 4.23e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 7.3287604e+00 3.14e-02 4.88e-03 -11.0 3.09e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 7.3373537e+00 1.27e-02 1.67e-03 -11.0 4.92e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 7.3350004e+00 1.33e-02 6.12e-03 -11.0 9.60e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 7.3205624e+00 1.32e-02 1.04e-02 -11.0 1.88e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 7.3438410e+00 2.63e-04 5.58e-03 -11.0 3.31e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 7.3089140e+00 8.86e-02 6.07e-03 -11.0 2.04e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 68 6.5815951e+00 1.39e+00 2.80e-01 -11.0 1.64e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 69 6.6372394e+00 1.81e-01 7.93e-02 -11.0 4.27e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 6.3525522e+00 4.01e+00 4.32e-01 -9.1 1.44e+06 - 1.00e+00 9.66e-03f 2\n",
|
||
" 71 6.2643390e+00 4.38e+00 6.64e-01 -7.2 6.29e+05 - 1.00e+00 9.88e-03f 3\n",
|
||
" 72 6.6610996e+00 4.66e+00 4.00e-01 -5.2 1.77e+04 - 1.00e+00 4.78e-01h 1\n",
|
||
" 73 6.6577923e+00 4.64e+00 3.98e-01 -5.4 8.50e+05 - 7.21e-02 1.01e-04h 1\n",
|
||
" 74 6.1557161e+00 4.74e-01 4.99e-01 -5.4 4.79e+03 - 5.41e-03 1.00e+00h 1\n",
|
||
" 75 6.1324767e+00 1.14e+00 3.65e-01 -6.1 5.89e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 76 6.3333145e+00 1.02e+00 2.96e-01 -2.9 3.08e+03 - 1.00e+00 5.19e-01h 1\n",
|
||
" 77 6.3248687e+00 1.04e+00 2.92e-01 -3.0 1.58e+04 - 8.42e-01 4.58e-03F 1\n",
|
||
" 78 7.1886668e+00 5.53e-02 6.54e-02 -3.0 7.04e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 7.2289612e+00 2.43e-03 4.94e-02 -4.6 1.08e+03 - 9.76e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 7.0586956e+00 2.95e-01 4.22e-02 -2.4 3.22e+03 - 8.25e-02 1.00e+00f 1\n",
|
||
" 81 6.4787828e+00 3.24e+00 3.96e-01 -9.4 1.55e+06 - 2.05e-03 1.90e-02f 2\n",
|
||
" 82 6.1167503e+00 2.14e+00 2.54e-01 -3.6 2.88e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 6.0863800e+00 2.19e+00 2.94e-01 -1.6 8.22e+03 - 1.00e+00 8.95e-02H 1\n",
|
||
" 84 7.2681258e+00 5.96e-02 2.57e-01 -7.6 4.83e+02 - 6.43e-01 1.00e+00h 1\n",
|
||
" 85 6.5868400e+00 1.06e+00 1.17e-01 -2.2 3.71e+04 - 1.56e-02 1.00e+00f 1\n",
|
||
" 86 6.2726038e+00 2.37e+00 3.35e-01 -2.2 1.74e+06 - 3.98e-01 1.47e-02f 1\n",
|
||
" 87 5.8408205e+00 1.62e+00 2.53e-01 -2.2 1.28e+04 - 1.00e+00 3.28e-01h 1\n",
|
||
" 88 7.3232057e+00 2.71e-01 1.44e-01 -2.8 2.35e+03 - 9.94e-01 1.00e+00h 1\n",
|
||
" 89 6.3871355e+00 4.55e-01 7.61e-02 -3.0 2.34e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 7.4282255e+00 1.07e-02 5.02e-02 -3.9 3.22e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 7.4160489e+00 1.40e-02 5.05e-02 -4.0 4.70e+02 - 1.00e+00 1.93e-01h 1\n",
|
||
" 92 7.3964683e+00 6.57e-02 6.43e-03 -4.0 2.22e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 93 6.8527392e+00 2.80e-01 6.62e-02 -4.0 2.12e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 94 6.5356183e+00 4.68e-01 6.69e-02 -4.0 1.18e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 7.3853916e+00 4.79e-02 8.18e-02 -4.0 1.67e+02 - 1.68e-01 1.00e+00h 1\n",
|
||
" 96 7.2487257e+00 1.24e-01 6.92e-02 -4.0 6.12e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 7.3106690e+00 9.47e-02 4.57e-02 -4.0 5.75e+02 - 7.77e-01 1.00e+00h 1\n",
|
||
" 98 7.1346376e+00 1.77e-01 2.67e-02 -4.0 1.61e+03 - 1.00e+00 8.17e-01h 1\n",
|
||
" 99 6.9739572e+00 3.16e-01 3.79e-02 -4.0 1.12e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 7.4003731e+00 1.94e-03 3.61e-02 -4.0 1.56e+03 - 8.16e-01 1.00e+00H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 7.4003731061942934e+00 7.4003731061942934e+00\n",
|
||
"Dual infeasibility......: 3.6060091895407598e-02 3.6060091895407598e-02\n",
|
||
"Constraint violation....: 1.9410214391335501e-03 1.9410214391335501e-03\n",
|
||
"Complementarity.........: 9.1412842429938618e-05 9.1412842429938618e-05\n",
|
||
"Overall NLP error.......: 3.6060091895407598e-02 3.6060091895407598e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 121\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 121\n",
|
||
"Number of inequality constraint evaluations = 121\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.428\n",
|
||
"Total CPU secs in NLP function evaluations = 135.079\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 547.00us ( 4.52us) 550.40us ( 4.55us) 121\n",
|
||
" nlp_g | 5.42 s ( 44.83ms) 5.17 s ( 42.74ms) 121\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 376.00us ( 3.69us) 372.74us ( 3.65us) 102\n",
|
||
" nlp_jac_g | 132.40 s ( 1.30 s) 126.41 s ( 1.24 s) 102\n",
|
||
" total | 139.32 s (139.32 s) 133.01 s (133.01 s) 1\n",
|
||
"Timestamp 14400\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 4.61e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0067096e+01 1.37e+01 4.61e+03 -1.5 4.61e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.6042937e+00 4.96e+00 8.74e+00 0.6 4.67e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 7.0611663e+00 1.17e+00 8.38e-01 -1.5 1.29e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 7.8849166e+00 3.49e-03 9.84e-02 -3.3 1.73e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 7.8864318e+00 1.31e-07 9.30e-05 -5.1 3.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 7.8864313e+00 4.85e-07 9.38e-05 -11.0 2.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 6.8874348e+00 7.45e-01 1.81e-01 -10.1 3.80e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 8 7.9222007e+00 2.08e-02 7.02e-02 -11.0 7.61e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 7.1814355e+00 2.48e+00 1.99e-01 -11.0 3.23e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 7.2385066e+00 4.04e-01 6.60e-02 -11.0 1.72e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 7.9334583e+00 6.61e-02 7.20e-02 -11.0 8.24e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 8.0546659e+00 4.46e-03 1.34e-02 -11.0 9.87e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 13 7.8881631e+00 9.75e-02 2.75e-02 -11.0 9.42e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 14 7.9130930e+00 5.47e-02 3.63e-03 -11.0 8.24e+02 - 1.00e+00 5.00e-01h 2\n",
|
||
" 15 6.6822900e+00 6.52e-01 1.43e-01 -11.0 3.27e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 16 7.1277380e+00 3.71e-01 3.79e-02 -11.0 1.35e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 7.7256529e+00 1.86e-01 4.23e-02 -11.0 9.43e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 7.7193874e+00 2.04e-01 5.14e-02 -11.0 6.75e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 7.9084123e+00 1.30e-01 2.35e-02 -11.0 5.54e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 7.3166354e+00 7.44e-01 9.29e-02 -11.0 1.37e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 21 7.6076707e+00 2.95e-01 4.05e-02 -11.0 1.16e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 7.5256110e+00 1.74e-01 1.34e-02 -11.0 2.27e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 7.8249465e+00 2.03e-01 3.35e-02 -11.0 6.61e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 7.0605023e+00 9.63e-01 1.08e-01 -11.0 6.00e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 25 7.4810610e+00 4.10e-01 1.55e-01 -11.0 4.86e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 7.4452466e+00 8.01e-01 9.86e-02 -11.0 8.55e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 7.1504501e+00 2.03e+00 2.50e-01 -11.0 7.79e+05 - 6.80e-02 1.31e-02f 1\n",
|
||
" 28r 7.1504501e+00 2.03e+00 9.99e+02 0.3 0.00e+00 - 0.00e+00 3.39e-10R 2\n",
|
||
" 29r 7.4590366e+00 1.87e-01 6.31e+02 -5.8 7.65e+02 - 1.00e+00 2.63e-03f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 7.6520356e+00 2.60e-02 3.15e-02 -6.4 1.55e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 7.6835072e+00 1.06e-02 1.95e-02 -7.6 2.76e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 7.6373389e+00 6.50e-02 2.14e-02 -8.8 2.90e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 7.7048500e+00 1.11e-03 1.01e-02 -6.8 6.19e+01 - 6.80e-01 1.00e+00h 1\n",
|
||
" 34 7.6986337e+00 3.66e-03 2.49e-03 -5.0 5.46e+01 - 2.83e-02 1.00e+00f 1\n",
|
||
" 35 7.6606883e+00 7.19e-02 6.34e-02 -5.2 5.73e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 7.6879734e+00 9.71e-03 5.09e-03 -5.3 2.56e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 7.6679075e+00 2.21e-02 2.62e-03 -5.5 7.15e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 7.6636985e+00 2.05e-02 6.46e-03 -7.0 4.27e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 7.6922004e+00 8.57e-03 3.64e-03 -8.9 3.58e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 7.6733782e+00 4.62e-02 2.91e-03 -7.1 3.15e+06 - 1.00e+00 3.68e-04f 1\n",
|
||
" 41 7.6726567e+00 4.62e-02 1.90e-03 -7.0 1.44e+04 - 1.00e+00 7.53e-04h 1\n",
|
||
" 42 7.6966493e+00 1.21e-04 3.48e-03 -7.0 6.36e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 7.6903938e+00 1.39e-02 2.02e-03 -6.7 4.81e+02 - 6.04e-01 1.00e+00h 1\n",
|
||
" 44 7.6965860e+00 3.96e-04 2.41e-03 -6.9 6.71e+00 - 3.09e-03 1.00e+00h 1\n",
|
||
" 45 7.6947748e+00 2.93e-03 4.24e-03 -8.4 6.31e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 7.6117630e+00 8.75e-02 9.32e-03 -6.2 5.45e+05 - 1.35e-03 5.60e-02f 1\n",
|
||
" 47 7.6115747e+00 8.84e-02 9.37e-03 -6.4 1.22e+05 - 1.00e+00 1.05e-03h 1\n",
|
||
" 48 7.7000838e+00 9.43e-04 1.39e-02 -4.8 4.20e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 49 7.5424109e+00 4.48e-01 2.88e-02 -4.8 1.65e+04 - 5.88e-02 2.55e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 6.1399641e+00 1.47e+00 2.72e-01 -4.8 8.28e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 51 4.9793349e+00 3.93e+00 8.64e-01 -4.0 2.98e+04 - 3.86e-01 9.58e-01f 1\n",
|
||
" 52 4.9560074e+00 3.84e+00 8.32e-01 -2.1 1.20e+04 - 1.00e+00 2.37e-02h 1\n",
|
||
" 53 6.5185855e+00 4.84e-01 3.68e-01 -1.9 2.49e+03 - 9.24e-01 1.00e+00h 1\n",
|
||
" 54 6.7376141e+00 5.19e-01 2.96e-02 -3.3 7.86e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 6.4576553e+00 5.97e-01 6.85e-02 -2.1 1.11e+05 - 1.00e+00 1.59e-01f 1\n",
|
||
" 56 6.7265383e+00 4.19e-01 4.03e-02 -2.9 8.29e+03 - 9.17e-01 1.00e+00h 1\n",
|
||
" 57 6.8292853e+00 1.34e-01 4.84e-02 -3.4 3.23e+03 - 9.42e-01 1.00e+00h 1\n",
|
||
" 58 6.3942342e+00 2.56e+00 2.34e-01 -2.0 2.43e+04 - 4.83e-01 1.00e+00f 1\n",
|
||
" 59 5.9291749e+00 2.32e+00 2.84e-01 -2.4 2.11e+05 - 1.86e-01 8.65e-02f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 6.7014793e+00 1.07e+00 1.10e-01 -2.2 1.35e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 61 6.2233731e+00 6.84e-01 8.53e-02 -1.6 1.37e+05 - 1.00e+00 1.71e-01h 1\n",
|
||
" 62 6.0017863e+00 5.25e-01 2.44e-01 -1.7 2.50e+03 - 8.05e-01 8.70e-01h 1\n",
|
||
" 63 6.8203851e+00 4.58e-01 9.70e-02 -2.2 1.19e+03 - 2.96e-01 1.00e+00h 1\n",
|
||
" 64 6.5528314e+00 9.91e-01 1.71e-01 -2.0 2.64e+04 - 1.00e+00 1.25e-01f 1\n",
|
||
" 65 6.8326723e+00 6.74e-01 1.52e-02 -2.1 1.62e+03 - 9.47e-01 1.00e+00h 1\n",
|
||
" 66 6.9975530e+00 2.43e-02 6.95e-02 -2.1 4.54e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 6.2517914e+00 4.54e-01 9.17e-02 -7.5 6.61e+03 - 3.45e-01 1.00e+00f 1\n",
|
||
" 68 6.0644137e+00 1.87e+00 8.53e-01 -8.3 1.25e+05 - 2.97e-02 1.35e-01f 1\n",
|
||
" 69 6.0009226e+00 1.84e+00 8.03e-01 -2.5 3.86e+04 - 5.39e-01 3.35e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 6.7067912e+00 2.93e-01 1.74e-01 -2.5 2.10e+03 - 6.69e-02 1.00e+00h 1\n",
|
||
" 71 6.8278856e+00 4.92e-02 2.00e-02 -1.7 3.36e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 72 6.7421194e+00 3.96e-01 3.54e-02 -2.7 5.41e+03 - 9.72e-01 1.00e+00h 1\n",
|
||
" 73 6.5804946e+00 8.55e-01 8.51e-02 -2.7 3.31e+05 - 2.03e-01 1.50e-02f 1\n",
|
||
" 74 6.8549614e+00 4.75e-02 6.59e-02 -2.7 1.83e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 6.8021206e+00 2.38e-01 6.28e-02 -3.4 2.22e+03 - 1.00e+00 9.77e-01h 1\n",
|
||
" 76 6.5643527e+00 9.29e-01 1.82e-01 -2.7 3.87e+03 - 4.62e-01 1.00e+00h 1\n",
|
||
" 77 6.3849350e+00 1.07e+00 1.52e-01 -3.1 1.17e+04 - 7.59e-03 4.78e-01h 1\n",
|
||
" 78 6.5865926e+00 4.13e-01 1.96e-01 -3.1 7.74e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 6.6606237e+00 3.60e-01 7.60e-02 -2.9 2.41e+03 - 7.58e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 6.5815377e+00 1.15e+00 4.10e-02 -8.9 6.27e+04 - 6.02e-02 1.60e-01h 1\n",
|
||
" 81 6.7491961e+00 2.49e-01 1.39e-01 -3.2 1.82e+03 - 1.75e-01 1.00e+00h 1\n",
|
||
" 82 6.7194666e+00 1.06e+00 3.57e-02 -3.9 4.00e+04 - 8.31e-01 2.36e-01h 1\n",
|
||
" 83 6.5010472e+00 1.34e+00 1.60e-01 -3.6 4.21e+03 - 7.75e-03 1.00e+00h 1\n",
|
||
" 84 6.4369010e+00 4.44e+00 6.01e-01 -3.6 1.27e+06 - 5.94e-03 4.37e-02f 1\n",
|
||
" 85 7.8582720e+00 3.75e-01 5.06e-01 -3.6 3.27e+04 - 8.01e-04 1.00e+00h 1\n",
|
||
" 86 7.4985907e+00 4.72e-01 4.31e-01 -3.6 1.29e+04 - 6.54e-01 7.60e-01F 1\n",
|
||
" 87 7.2143747e+00 4.34e-01 3.11e-01 -3.6 7.48e+04 - 6.54e-01 2.16e-01h 1\n",
|
||
" 88 7.5632081e+00 1.33e-01 4.45e-02 -3.6 7.91e+03 - 4.93e-01 1.00e+00H 1\n",
|
||
" 89 7.3970002e+00 5.11e-01 2.23e-02 -3.6 4.69e+04 - 1.00e+00 1.67e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 7.1469406e+00 4.38e-01 3.86e-02 -3.6 5.53e+03 - 1.00e+00 4.03e-01h 1\n",
|
||
" 91 7.5384477e+00 1.08e-01 8.31e-02 -3.6 9.55e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 7.5355232e+00 9.85e-02 6.40e-02 -3.6 5.98e+02 - 8.43e-01 2.90e-01h 1\n",
|
||
" 93 7.6046278e+00 8.80e-03 3.01e-02 -3.6 1.60e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 94 7.5316004e+00 5.92e-01 8.92e-02 -2.7 3.04e+03 - 6.66e-01 1.00e+00h 1\n",
|
||
" 95 7.3546661e+00 2.09e-01 4.86e-02 -3.1 2.59e+03 - 2.36e-02 1.00e+00h 1\n",
|
||
" 96 7.2278370e+00 2.29e-01 5.75e-02 -3.1 2.79e+05 - 2.75e-02 1.23e-03f 1\n",
|
||
" 97 7.2766655e+00 2.79e-01 6.85e-02 -3.1 1.01e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 7.1921623e+00 1.39e-01 2.01e-02 -3.1 8.65e+02 - 4.03e-01 1.00e+00h 1\n",
|
||
" 99 7.2594225e+00 1.32e-01 8.13e-03 -3.1 4.35e+03 - 1.00e+00 4.45e-01H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 7.4800092e+00 5.71e-02 2.39e-02 -3.1 7.25e+02 - 5.24e-02 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 7.4800091967699966e+00 7.4800091967699966e+00\n",
|
||
"Dual infeasibility......: 2.3891667462602850e-02 2.3891667462602850e-02\n",
|
||
"Constraint violation....: 5.7109055717383228e-02 5.7109055717383228e-02\n",
|
||
"Complementarity.........: 1.3809685993276771e-03 1.3809685993276771e-03\n",
|
||
"Overall NLP error.......: 5.7109055717383228e-02 5.7109055717383228e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 115\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 115\n",
|
||
"Number of inequality constraint evaluations = 115\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.424\n",
|
||
"Total CPU secs in NLP function evaluations = 136.423\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 513.00us ( 4.46us) 501.94us ( 4.36us) 115\n",
|
||
" nlp_g | 5.14 s ( 44.72ms) 4.90 s ( 42.63ms) 115\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 336.00us ( 3.29us) 333.49us ( 3.27us) 102\n",
|
||
" nlp_jac_g | 133.99 s ( 1.30 s) 127.92 s ( 1.24 s) 103\n",
|
||
" total | 140.64 s (140.64 s) 134.26 s (134.26 s) 1\n",
|
||
"Timestamp 14700\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.10e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0773332e+01 1.30e+01 3.10e+04 -1.5 3.10e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.9744147e+00 4.84e+00 7.30e+00 1.2 1.20e+03 - 9.99e-01 1.00e+00f 1\n",
|
||
" 3 1.6157748e+00 6.16e-01 6.37e-01 -0.9 4.45e+02 - 9.96e-01 1.00e+00f 1\n",
|
||
" 4 1.3976208e+00 7.45e-03 7.81e-01 -6.7 2.87e+01 - 9.89e-01 1.00e+00h 1\n",
|
||
" 5 1.3969460e+00 1.21e-03 2.23e-02 -4.1 5.52e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.3968022e+00 5.54e-01 4.58e-01 -5.4 1.28e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 7 1.3122557e+00 1.31e-01 1.21e-01 -7.1 1.00e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.3080797e+00 2.42e-01 1.81e-01 -7.9 1.09e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.3454949e+00 9.44e-02 1.27e-01 -9.0 1.52e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.3377974e+00 2.21e-01 1.20e-01 -10.5 2.70e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 11 1.3311940e+00 2.76e-01 1.94e-01 -10.4 5.27e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 12 1.3250791e+00 2.79e-01 1.14e-01 -10.4 5.84e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 13 1.3231772e+00 3.16e-01 1.45e-01 -10.4 6.57e+03 - 1.00e+00 6.25e-02h 5\n",
|
||
" 14 1.3861137e+00 9.56e-02 2.42e-01 -10.4 1.91e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 15 1.3770096e+00 9.77e-02 2.22e-01 -10.4 2.76e+06 - 1.07e-02 1.52e-03h 4\n",
|
||
" 16 1.3430029e+00 5.14e-02 1.61e-01 -10.4 1.34e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.3156031e+00 2.17e-01 7.62e-02 -8.4 4.41e+06 - 2.96e-02 4.20e-04f 5\n",
|
||
" 18 1.3150507e+00 2.04e-01 9.10e-02 -9.9 3.70e+03 - 1.00e+00 6.25e-02h 5\n",
|
||
" 19 1.4455749e+00 1.38e-01 4.44e-01 -9.9 1.79e+05 - 9.11e-01 7.40e-01H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.3759667e+00 2.47e-01 2.41e-01 -9.9 3.78e+04 - 9.02e-10 9.30e-02f 4\n",
|
||
" 21 1.3327133e+00 1.01e-01 1.78e-01 -9.9 3.62e+05 - 3.90e-01 1.06e-02f 5\n",
|
||
" 22 1.3292788e+00 1.69e-01 5.43e-02 -11.0 3.67e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.3291113e+00 1.55e-01 4.20e-02 -8.9 2.22e+03 - 1.00e+00 2.19e-01h 3\n",
|
||
" 24 1.3277217e+00 1.62e-01 3.63e-02 -6.9 8.51e+03 - 1.00e+00 1.72e-01h 1\n",
|
||
" 25 1.3276542e+00 1.59e-01 3.44e-02 -5.0 1.34e+04 - 1.00e+00 1.09e-03h 1\n",
|
||
" 26 1.3421634e+00 7.35e-02 1.30e-01 -6.4 7.64e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.3402477e+00 1.49e-01 1.47e-01 -4.4 1.08e+03 - 1.44e-01 1.00e+00h 1\n",
|
||
" 28 1.3399080e+00 1.48e-01 1.44e-01 -5.4 3.78e+04 - 5.20e-01 3.01e-03h 1\n",
|
||
" 29 1.3385193e+00 2.23e-01 9.15e-02 -5.4 1.18e+03 - 1.71e-01 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.3382091e+00 2.20e-01 8.83e-02 -5.4 1.51e+03 - 1.00e+00 1.79e-02h 1\n",
|
||
" 31 1.3292151e+00 1.42e-01 8.64e-02 -5.4 1.59e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 32 1.3300837e+00 9.41e-02 6.23e-02 -6.6 1.77e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 33 1.3447678e+00 6.63e-02 5.35e-02 -5.6 3.79e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 1.3338529e+00 1.30e-01 4.76e-01 -5.8 5.60e+03 - 1.60e-01 1.00e+00H 1\n",
|
||
" 35 1.3202162e+00 1.64e-01 1.35e-01 -6.9 1.53e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.3237239e+00 1.92e-02 9.15e-02 -5.4 1.86e+03 - 3.06e-01 1.00e+00H 1\n",
|
||
" 37 1.3211178e+00 6.10e-02 5.95e-02 -6.9 6.37e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 38 1.3212538e+00 4.40e-02 8.70e-02 -7.8 7.35e+02 - 1.00e+00 2.50e-01h 3\n",
|
||
" 39 1.3188298e+00 1.81e-01 8.99e-02 -7.6 3.41e+04 - 1.00e+00 3.93e-02h 5\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.3336124e+00 1.79e-01 1.70e-01 -7.7 5.30e+03 - 9.56e-01 1.00e+00H 1\n",
|
||
" 41 1.3269584e+00 7.16e-02 6.56e-02 -7.7 4.90e+06 - 1.02e-02 3.43e-04f 6\n",
|
||
" 42 1.3269063e+00 9.30e-02 5.00e-02 -7.7 9.50e+03 - 1.00e+00 6.25e-02h 5\n",
|
||
" 43 1.3273112e+00 7.37e-02 6.62e-02 -7.7 1.74e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 44 1.3198270e+00 9.06e-02 6.31e-02 -7.7 8.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.2994148e+00 1.80e-01 1.46e-01 -7.4 1.27e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 46 1.3104054e+00 1.24e-01 6.20e-02 -7.9 5.45e+03 - 9.30e-01 1.00e+00H 1\n",
|
||
" 47 1.3272958e+00 7.79e-02 9.02e-02 -9.0 1.86e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.3120862e+00 2.22e-01 1.00e-01 -9.1 9.09e+05 - 3.48e-02 3.61e-03f 3\n",
|
||
" 49 1.4047405e+00 3.18e-01 2.61e-01 -9.1 4.84e+03 - 6.23e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.3936400e+00 2.99e-01 4.57e-01 -9.1 2.27e+05 - 3.70e-01 2.01e-01h 1\n",
|
||
" 51 1.3825488e+00 2.49e-01 3.89e-01 -9.1 2.39e+04 - 2.10e-08 1.21e-01h 3\n",
|
||
" 52 1.3696993e+00 1.89e-01 3.12e-01 -9.1 1.98e+04 - 1.00e+00 7.28e-02h 2\n",
|
||
" 53 1.3689951e+00 1.89e-01 2.62e-01 -9.1 3.84e+04 - 3.91e-02 1.79e-01h 1\n",
|
||
" 54 1.3094998e+00 5.15e-01 3.81e-01 -9.1 4.83e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 55 1.3078176e+00 3.92e-01 2.72e-01 -9.1 3.60e+03 - 4.54e-01 1.25e-01h 4\n",
|
||
" 56 1.2928191e+00 3.91e-01 2.78e-01 -9.1 4.93e+04 - 4.55e-01 3.59e-02h 5\n",
|
||
" 57 1.2960102e+00 3.54e-01 1.88e-01 -7.9 4.92e+05 - 1.00e+00 2.57e-02h 1\n",
|
||
" 58 1.2954226e+00 3.53e-01 1.91e-01 -7.4 3.73e+04 - 1.00e+00 3.61e-03H 1\n",
|
||
" 59 1.3982954e+00 1.80e-01 3.81e-01 -5.5 1.07e+04 - 2.84e-04 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.4521887e+00 1.95e-01 3.67e-01 -7.5 1.06e+04 - 5.11e-01 1.00e+00h 1\n",
|
||
" 61 1.3834835e+00 2.19e-01 3.00e-01 -7.5 1.40e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.3834036e+00 3.28e-01 2.18e-01 -7.5 6.12e+04 - 6.41e-01 3.33e-01H 1\n",
|
||
" 63 1.3664919e+00 2.76e-01 1.80e-01 -7.5 9.58e+03 - 3.39e-07 1.39e-01h 3\n",
|
||
" 64 1.3510687e+00 4.86e-01 1.86e-01 -7.5 1.93e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.3424245e+00 1.34e-01 1.47e-01 -7.5 3.47e+03 - 2.91e-01 8.26e-01h 1\n",
|
||
" 66 1.3368815e+00 1.47e-01 9.54e-02 -7.5 8.80e+04 - 1.00e+00 8.42e-02h 4\n",
|
||
" 67 1.3554873e+00 7.31e-02 4.22e-02 -7.5 9.37e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.3636743e+00 8.90e-02 6.27e-02 -7.5 9.08e+02 - 1.00e+00 9.92e-01h 1\n",
|
||
" 69 1.3531441e+00 4.77e-02 5.63e-02 -7.5 4.24e+03 - 2.33e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.3528180e+00 4.32e-02 2.92e-02 -7.5 7.16e+03 - 1.00e+00 9.72e-02h 1\n",
|
||
" 71 1.3411798e+00 1.10e-01 5.97e-02 -7.5 3.43e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 72 1.3709889e+00 1.64e-02 2.00e-01 -7.5 3.89e+04 - 2.91e-01 1.00e+00H 1\n",
|
||
" 73 1.3709879e+00 1.64e-02 2.00e-01 -6.6 7.66e+02 - 1.00e+00 3.40e-05h 1\n",
|
||
" 74 1.3622802e+00 3.32e-02 8.56e-02 -4.6 9.11e+03 - 1.00e+00 1.00e+00F 1\n",
|
||
" 75 1.3706089e+00 2.59e-01 2.75e-01 -2.7 3.05e+04 - 2.94e-02 1.00e+00F 1\n",
|
||
" 76 1.3658060e+00 2.22e-01 1.94e-01 -4.2 7.11e+05 - 2.70e-03 7.88e-04f 7\n",
|
||
" 77 1.3427520e+00 2.17e-01 1.65e-01 -4.2 6.77e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.3422240e+00 2.20e-01 1.42e-01 -4.2 4.32e+04 - 1.36e-01 5.55e-03h 8\n",
|
||
" 79 1.3500418e+00 1.32e-01 3.87e-02 -4.2 7.51e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.3664648e+00 1.08e-01 9.93e-02 -4.2 8.04e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 81 1.3221977e+00 3.75e-01 1.53e-01 -4.2 1.47e+04 - 1.00e+00 9.86e-01h 1\n",
|
||
" 82 1.3101827e+00 4.92e-01 3.26e-01 -4.2 6.03e+04 - 3.93e-01 5.77e-02h 4\n",
|
||
" 83 1.3066172e+00 4.58e-01 3.03e-01 -4.2 1.77e+03 - 1.00e+00 3.83e-02h 1\n",
|
||
" 84 1.3196688e+00 1.85e-01 1.76e-01 -4.2 7.02e+03 - 4.27e-01 5.00e-01h 2\n",
|
||
" 85 1.3191965e+00 2.49e-01 2.43e-01 -4.2 8.86e+04 - 4.63e-01 6.23e-02h 3\n",
|
||
" 86 1.3367906e+00 1.99e-01 2.53e-01 -4.2 2.09e+03 - 1.00e+00 2.46e-01h 3\n",
|
||
" 87 1.3436131e+00 1.24e-01 9.02e-02 -4.2 2.40e+04 - 5.21e-01 4.07e-01H 1\n",
|
||
" 88 1.3554779e+00 4.63e-02 8.89e-02 -4.2 2.78e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.3672872e+00 4.71e-02 9.08e-02 -4.2 1.60e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.3654507e+00 7.55e-02 8.58e-02 -4.2 2.27e+05 - 1.32e-01 1.13e-03h 7\n",
|
||
" 91 1.3654746e+00 1.06e-02 6.95e-02 -4.2 9.10e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.3635536e+00 8.72e-03 5.36e-02 -4.4 3.18e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.3610516e+00 2.50e-01 5.52e-02 -3.9 8.40e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.3824664e+00 2.12e-01 1.29e-01 -3.8 1.69e+04 - 1.00e+00 7.01e-01H 1\n",
|
||
" 95 1.3694852e+00 7.82e-02 3.89e-02 -3.8 2.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.3659925e+00 2.01e-02 6.85e-02 -3.8 4.39e+04 - 4.77e-01 1.16e-01h 4\n",
|
||
" 97 1.3612659e+00 6.53e-02 1.02e-01 -3.8 4.74e+04 - 6.35e-01 1.57e-02h 1\n",
|
||
" 98 1.3619325e+00 1.97e-02 1.13e-02 -3.8 9.92e+02 - 1.01e-01 5.00e-01f 2\n",
|
||
" 99 1.3599516e+00 3.45e-02 4.73e-02 -2.7 4.05e+03 - 1.00e+00 4.25e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.3793485e+00 2.87e-02 2.32e-01 -2.8 2.11e+04 - 1.00e+00 7.33e-01H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.3793484693440758e+00 1.3793484693440758e+00\n",
|
||
"Dual infeasibility......: 2.3232451155602829e-01 2.3232451155602829e-01\n",
|
||
"Constraint violation....: 2.8673533474254498e-02 2.8673533474254498e-02\n",
|
||
"Complementarity.........: 1.6306587398998303e-03 1.6306587398998303e-03\n",
|
||
"Overall NLP error.......: 2.3232451155602829e-01 2.3232451155602829e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 305\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 305\n",
|
||
"Number of inequality constraint evaluations = 305\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.416\n",
|
||
"Total CPU secs in NLP function evaluations = 144.486\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 1.43ms ( 4.69us) 1.43ms ( 4.68us) 305\n",
|
||
" nlp_g | 13.87 s ( 45.47ms) 13.25 s ( 43.44ms) 305\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 341.00us ( 3.34us) 335.92us ( 3.29us) 102\n",
|
||
" nlp_jac_g | 133.31 s ( 1.31 s) 127.29 s ( 1.25 s) 102\n",
|
||
" total | 148.69 s (148.69 s) 141.98 s (141.98 s) 1\n",
|
||
"Timestamp 15000\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.47e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9621771e+01 1.44e+01 1.47e+04 -1.5 1.47e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.0470688e+01 5.00e+00 1.20e+01 0.8 2.24e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.4728633e+01 1.80e+00 9.00e-01 -1.3 5.03e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.5910544e+01 2.45e-04 9.06e-02 -3.0 2.19e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.5910715e+01 4.19e-05 7.84e-03 -4.9 1.73e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.5910690e+01 3.96e-05 1.70e-03 -7.0 1.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.5910793e+01 9.68e-06 1.82e-03 -9.1 3.92e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.5909722e+01 7.59e-04 1.20e-02 -11.0 3.70e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.5908392e+01 1.60e-03 2.30e-02 -11.0 5.12e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.5907723e+01 1.33e-03 2.56e-02 -11.0 6.25e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.5910766e+01 2.35e-07 1.25e-04 -11.0 2.64e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.5910766e+01 2.10e-07 1.87e-04 -11.0 1.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.5910023e+01 3.58e-04 1.88e-02 -11.0 1.39e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.5910629e+01 5.28e-05 2.07e-03 -11.0 5.25e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.5910425e+01 1.86e-04 1.68e-03 -11.0 3.86e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.5910714e+01 2.15e-05 1.87e-03 -11.0 9.83e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.5910711e+01 2.49e-05 1.02e-03 -11.0 6.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.5910676e+01 2.83e-05 4.32e-03 -11.0 2.73e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.5910657e+01 7.60e-05 1.82e-03 -11.0 1.89e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.5910735e+01 1.42e-05 9.03e-04 -11.0 9.30e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.5910539e+01 9.82e-05 3.29e-03 -11.0 9.53e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.5910724e+01 2.84e-05 2.12e-03 -11.0 3.27e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.5910586e+01 6.07e-05 1.49e-03 -11.0 2.82e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.5910689e+01 2.99e-05 2.02e-03 -11.0 1.64e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.5910741e+01 5.96e-06 1.78e-03 -11.0 7.61e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.5910753e+01 1.41e-09 8.12e-05 -11.0 7.41e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 27 1.5910730e+01 1.10e-05 1.89e-03 -11.0 1.03e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.5910750e+01 2.07e-09 6.10e-05 -11.0 2.46e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 29 1.5910734e+01 4.72e-05 1.75e-03 -11.0 1.60e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.5910743e+01 1.38e-05 1.14e-03 -11.0 8.59e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.5910741e+01 8.48e-06 1.27e-03 -11.0 1.29e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.5910730e+01 3.66e-05 1.69e-03 -11.0 8.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.5910653e+01 4.72e-05 2.83e-03 -11.0 4.11e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.5910639e+01 9.96e-05 1.09e-03 -11.0 8.20e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.5910753e+01 1.73e-08 1.06e-04 -11.0 3.05e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 36 1.5910673e+01 5.75e-05 1.73e-03 -11.0 3.64e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.5910646e+01 6.24e-05 2.59e-03 -11.0 3.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.5910749e+01 8.36e-09 2.17e-04 -11.0 2.37e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 39 1.5908035e+01 1.83e-03 6.53e-03 -11.0 1.22e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.5904002e+01 5.98e-03 7.01e-03 -11.0 1.96e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.5909119e+01 1.70e-03 2.09e-03 -11.0 7.21e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.5910632e+01 3.19e-04 3.26e-03 -11.0 5.21e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.5910339e+01 2.85e-04 1.21e-03 -11.0 3.61e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.5910859e+01 2.97e-04 1.10e-03 -11.0 4.52e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.5907947e+01 1.54e-02 2.07e-03 -11.0 8.05e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.5771742e+01 1.76e-01 8.63e-03 -11.0 3.00e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.5884487e+01 3.60e-02 4.18e-03 -11.0 2.50e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.5864286e+01 3.52e-02 3.07e-03 -11.0 9.87e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.5816033e+01 6.46e-02 2.62e-03 -11.0 4.74e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.5553549e+01 2.40e-01 1.74e-02 -11.0 2.00e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.5888638e+01 1.47e-03 6.95e-03 -11.0 1.16e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 52 1.5654896e+01 1.18e-01 5.49e-03 -11.0 3.86e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 53 1.5089320e+01 1.51e+00 6.65e-02 -11.0 1.30e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 54 1.4291260e+01 1.35e+00 6.63e-02 -11.0 4.90e+04 - 1.00e+00 3.67e-01h 1\n",
|
||
" 55 1.4306850e+01 1.31e+00 6.64e-02 -10.2 1.13e+04 - 1.00e+00 3.12e-02h 6\n",
|
||
" 56 1.5594245e+01 4.65e-01 6.19e-02 -2.2 4.28e+03 - 3.11e-01 1.00e+00h 1\n",
|
||
" 57 1.3473793e+01 6.27e+00 2.87e-01 -2.9 1.52e+06 - 3.20e-03 3.84e-02f 1\n",
|
||
" 58 1.6111458e+01 7.52e-02 2.80e-01 -3.3 1.75e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.5934486e+01 1.97e-02 2.63e-01 -3.4 6.71e+02 - 1.00e+00 2.31e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.5901400e+01 2.76e-02 7.32e-03 -3.4 1.11e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 61 1.5879499e+01 3.67e-02 2.50e-02 -3.4 3.72e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.5746263e+01 1.78e-01 3.80e-02 -3.4 6.07e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.5954366e+01 2.04e-02 5.50e-03 -3.4 2.01e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.5949672e+01 2.11e-02 5.82e-03 -3.4 1.28e+03 - 1.00e+00 1.31e-02h 1\n",
|
||
" 65 1.5962977e+01 1.47e-02 2.51e-03 -3.4 6.14e+01 - 1.00e+00 5.00e-01h 2\n",
|
||
" 66 1.5941041e+01 2.98e-02 3.36e-02 -3.4 1.95e+02 - 1.82e-01 1.00e+00h 1\n",
|
||
" 67 1.5949476e+01 2.43e-02 2.69e-02 -3.4 1.28e+02 - 1.00e+00 1.96e-01h 1\n",
|
||
" 68 1.5950653e+01 2.26e-02 2.53e-02 -3.4 1.55e+02 - 1.00e+00 6.25e-02f 5\n",
|
||
" 69 1.5988017e+01 2.23e-03 1.68e-03 -3.4 1.04e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.5981454e+01 2.76e-03 2.69e-03 -5.1 1.79e+01 - 9.86e-01 1.00e+00h 1\n",
|
||
" 71 1.5920543e+01 4.68e-02 6.14e-03 -5.1 1.87e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.5972561e+01 1.14e-02 1.61e-03 -5.1 1.10e+02 - 1.00e+00 9.12e-01h 1\n",
|
||
" 73 1.5972003e+01 7.23e-03 1.41e-03 -5.1 2.62e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.5978713e+01 3.32e-03 2.80e-03 -5.1 2.43e+01 - 4.89e-01 1.00e+00h 1\n",
|
||
" 75 1.5972243e+01 9.90e-05 8.42e-03 -5.1 3.92e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 76 1.5969434e+01 4.37e-03 8.32e-03 -5.1 3.54e+02 - 1.00e+00 1.40e-01h 1\n",
|
||
" 77 1.5965793e+01 4.71e-03 1.80e-03 -5.1 4.56e+01 - 1.76e-01 1.00e+00h 1\n",
|
||
" 78 1.5950761e+01 1.65e-02 2.36e-03 -5.1 6.17e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.5752664e+01 1.06e-01 1.09e-02 -5.1 3.89e+02 - 7.18e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.5949972e+01 8.19e-03 6.00e-03 -6.7 6.74e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.5936166e+01 1.49e-02 1.73e-03 -6.8 1.76e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.5862191e+01 7.67e-02 9.84e-03 -6.8 4.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.5913494e+01 3.85e-02 5.09e-03 -6.8 2.08e+02 - 6.88e-01 5.00e-01h 2\n",
|
||
" 84 1.5973090e+01 3.41e-03 2.73e-03 -6.8 1.92e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.5972856e+01 4.89e-05 1.82e-03 -6.8 7.70e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 86 1.5963742e+01 6.53e-06 2.19e-03 -6.8 4.51e+02 - 5.33e-01 1.00e+00F 1\n",
|
||
" 87 1.5642809e+01 2.44e-01 1.09e-02 -6.8 2.99e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 88 1.5765754e+01 1.21e-01 4.72e-03 -6.8 7.16e+02 - 9.65e-01 5.00e-01h 2\n",
|
||
" 89 1.5515664e+01 2.32e-01 6.83e-03 -6.8 5.35e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.4486150e+01 7.12e+00 3.22e-01 -4.8 1.11e+05 - 7.34e-01 4.90e-01F 1\n",
|
||
" 91 1.4498960e+01 6.85e+00 2.95e-01 -5.1 1.42e+04 - 1.00e+00 3.99e-02h 1\n",
|
||
" 92 1.5656825e+01 2.13e-01 3.07e-01 -5.1 1.13e+03 - 1.09e-03 1.00e+00h 1\n",
|
||
" 93 1.4065543e+01 2.59e+00 4.12e-01 -5.1 4.41e+05 - 1.92e-01 1.42e-01f 1\n",
|
||
" 94 1.4065661e+01 2.59e+00 4.12e-01 -4.6 4.18e+03 - 1.00e+00 1.11e-04h 1\n",
|
||
" 95 1.5729365e+01 7.08e-02 8.87e-02 -2.6 1.14e+02 - 4.71e-01 1.00e+00h 1\n",
|
||
" 96 1.4410259e+01 2.19e+00 2.95e-01 -1.8 2.97e+04 - 2.41e-01 1.00e+00f 1\n",
|
||
" 97 1.3569704e+01 2.38e+00 1.72e-01 -2.5 1.03e+05 - 1.00e+00 3.22e-01f 1\n",
|
||
" 98 1.4631083e+01 1.34e+00 3.21e-02 -1.9 3.27e+04 - 6.13e-01 1.00e+00h 1\n",
|
||
" 99 1.3401231e+01 1.54e+00 1.35e-01 -2.2 2.77e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.4854124e+01 1.45e+00 9.56e-02 -1.2 3.75e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.4854123820233017e+01 1.4854123820233017e+01\n",
|
||
"Dual infeasibility......: 9.5613291895863595e-02 9.5613291895863595e-02\n",
|
||
"Constraint violation....: 1.4505788213850224e+00 1.4505788213850224e+00\n",
|
||
"Complementarity.........: 6.8348976849602880e-02 6.8348976849602880e-02\n",
|
||
"Overall NLP error.......: 1.4505788213850224e+00 1.4505788213850224e+00\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 126\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 126\n",
|
||
"Number of inequality constraint evaluations = 126\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.407\n",
|
||
"Total CPU secs in NLP function evaluations = 135.573\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 587.00us ( 4.66us) 571.33us ( 4.53us) 126\n",
|
||
" nlp_g | 5.68 s ( 45.08ms) 5.42 s ( 43.02ms) 126\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 349.00us ( 3.42us) 341.14us ( 3.34us) 102\n",
|
||
" nlp_jac_g | 132.57 s ( 1.30 s) 126.57 s ( 1.24 s) 102\n",
|
||
" total | 139.72 s (139.72 s) 133.40 s (133.40 s) 1\n",
|
||
"Timestamp 15300\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.36e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0048413e+01 1.44e+01 1.36e+03 -1.5 1.36e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.3897416e+00 5.31e+00 9.70e+00 0.4 1.44e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.0168420e+01 1.54e+00 5.88e-01 -1.6 8.67e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.1261283e+01 2.82e-03 8.52e-02 -3.4 2.14e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.1262627e+01 1.26e-06 1.57e-03 -5.3 4.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.1262629e+01 9.98e-07 1.77e-03 -7.4 3.62e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.1262629e+01 6.67e-07 1.24e-04 -9.4 5.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.1262628e+01 1.29e-06 2.13e-03 -11.0 8.47e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.1262628e+01 1.30e-06 1.49e-03 -11.0 7.78e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.1262578e+01 1.46e-05 9.69e-03 -11.0 1.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.1262608e+01 1.68e-05 8.34e-04 -11.0 4.83e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.1262625e+01 1.16e-06 1.94e-03 -11.0 1.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.1262555e+01 9.55e-05 7.86e-03 -11.0 1.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.1262631e+01 1.01e-06 1.84e-03 -11.0 1.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.1262263e+01 3.49e-04 3.27e-03 -11.0 5.25e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.1237444e+01 1.67e-02 1.87e-02 -11.0 4.32e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.1249856e+01 4.31e-03 2.37e-03 -11.0 3.95e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.1242138e+01 1.41e-02 1.47e-03 -11.0 5.04e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.1094366e+01 7.51e-02 5.29e-03 -11.0 4.77e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.1087790e+01 8.89e-02 4.05e-03 -11.0 2.85e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.1250660e+01 2.02e-03 7.18e-03 -11.0 8.51e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.0599052e+01 3.59e-01 7.48e-02 -9.1 1.17e+05 - 1.00e+00 2.67e-01f 1\n",
|
||
" 23 1.0561008e+01 3.69e-01 7.78e-02 -7.1 1.93e+06 - 1.00e+00 1.15e-04f 1\n",
|
||
" 24 1.0561302e+01 3.69e-01 7.77e-02 -5.2 2.55e+03 - 1.00e+00 8.76e-04h 1\n",
|
||
" 25 1.1009949e+01 1.25e-03 1.80e-02 -7.1 1.09e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.1011319e+01 1.11e-04 1.48e-03 -5.2 1.18e+00 - 1.14e-01 1.00e+00h 1\n",
|
||
" 27 1.1010953e+01 3.51e-04 2.52e-03 -6.3 7.12e+01 - 1.00e+00 1.61e-02h 1\n",
|
||
" 28 1.1011385e+01 8.37e-05 1.29e-03 -7.5 1.29e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.1010575e+01 6.15e-04 1.41e-03 -5.5 4.55e+00 - 3.09e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.1007376e+01 3.07e-03 1.34e-02 -5.4 1.41e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.0990050e+01 8.11e-03 3.17e-02 -4.0 3.33e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.1009288e+01 1.15e-03 2.71e-03 -4.9 4.90e+02 - 1.00e+00 9.54e-01H 1\n",
|
||
" 33 1.1009148e+01 1.49e-03 2.66e-03 -5.5 9.22e+02 - 1.00e+00 5.12e-03h 1\n",
|
||
" 34 1.1009579e+01 1.49e-03 1.83e-03 -7.7 6.44e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.1003219e+01 8.27e-03 4.04e-03 -5.6 4.96e+01 - 3.02e-02 1.00e+00h 1\n",
|
||
" 36 1.1003309e+01 8.18e-03 3.95e-03 -5.1 4.26e+01 - 1.00e+00 1.33e-02h 1\n",
|
||
" 37 1.0982173e+01 3.74e-02 4.08e-03 -6.1 1.04e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.0990365e+01 2.36e-02 5.91e-03 -6.5 1.45e+02 - 6.85e-01 1.00e+00h 1\n",
|
||
" 39 1.0619838e+01 3.82e-01 1.79e-02 -6.5 5.98e+02 - 5.44e-03 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.1011223e+01 1.35e-02 3.18e-02 -6.0 1.58e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.1004202e+01 1.92e-02 3.02e-02 -5.6 8.35e+03 - 1.00e+00 8.87e-03h 1\n",
|
||
" 42 9.6000051e+00 5.62e+00 1.30e+00 -5.6 2.80e+04 - 8.81e-01 1.00e+00f 1\n",
|
||
" 43 8.5839996e+00 2.79e+00 3.00e-01 -5.6 2.81e+04 - 1.00e+00 4.69e-01f 1\n",
|
||
" 44 1.1508205e+01 1.36e+00 3.60e-01 -5.3 6.26e+03 - 9.12e-01 1.00e+00h 1\n",
|
||
" 45 9.1726333e+00 1.35e+00 2.94e-01 -2.0 2.83e+03 - 9.78e-02 1.00e+00f 1\n",
|
||
" 46 7.5313836e+00 2.69e+00 2.14e-01 -2.2 1.06e+05 - 1.00e+00 1.18e-01f 1\n",
|
||
" 47 1.0317069e+01 6.19e-01 3.38e-01 -1.8 3.32e+04 - 7.02e-01 1.00e+00h 1\n",
|
||
" 48 1.0278741e+01 5.91e-01 3.05e-01 -1.6 2.71e+04 - 1.00e+00 1.34e-01H 1\n",
|
||
" 49 1.1072848e+01 1.58e-01 5.44e-02 -1.9 1.13e+04 - 3.48e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 9.7677329e+00 3.08e+00 2.52e-01 -1.9 5.01e+04 - 7.89e-01 1.00e+00f 1\n",
|
||
" 51 9.8908780e+00 3.80e+00 1.78e-01 -1.9 3.64e+04 - 2.49e-01 2.49e-01s 20\n",
|
||
" 52r 9.8908780e+00 3.80e+00 9.99e+02 0.6 0.00e+00 - 0.00e+00 0.00e+00R 1\n",
|
||
" 53r 1.1066620e+01 3.16e-01 9.95e+02 1.3 8.24e+02 - 9.28e-01 4.57e-03f 1\n",
|
||
" 54 1.0587599e+01 1.22e+00 9.96e-02 -1.9 1.28e+04 - 2.25e-01 1.00e+00h 1\n",
|
||
" 55 9.9937653e+00 2.88e+00 1.63e-01 -1.9 2.48e+04 - 8.45e-01 1.00e+00H 1\n",
|
||
" 56 9.7370952e+00 3.09e+00 1.44e-01 -1.9 2.25e+04 - 6.91e-02 2.13e-01h 1\n",
|
||
" 57 1.1156432e+01 3.36e-03 3.46e+00 -1.9 3.78e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.1158094e+01 2.03e-06 2.08e-03 -1.9 8.48e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.1158096e+01 3.56e-07 2.23e-04 -4.0 1.55e-03 - 9.98e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.1158085e+01 5.30e-06 1.92e-03 -9.9 1.26e-02 - 9.99e-01 1.00e+00h 1\n",
|
||
" 61 1.1158083e+01 5.13e-06 1.67e-03 -9.0 1.40e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.1158095e+01 7.19e-08 2.16e-04 -11.0 1.95e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.1158096e+01 4.20e-08 8.33e-05 -11.0 1.09e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.1158096e+01 1.54e-08 1.83e-04 -11.0 1.00e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.1158096e+01 2.93e-08 9.86e-05 -11.0 7.08e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.1158096e+01 6.32e-09 6.05e-05 -11.0 6.13e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.1158096e+01 4.75e-08 5.52e-05 -11.0 1.37e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.1158096e+01 3.72e-09 1.18e-04 -11.0 2.73e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.1158096e+01 7.68e-09 5.43e-05 -11.0 2.86e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.1158096e+01 4.82e-11 8.60e-05 -11.0 5.39e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 71 1.1158096e+01 1.41e-09 1.75e-04 -11.0 3.28e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.1158095e+01 1.29e-07 5.35e-05 -11.0 4.45e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.1158096e+01 1.66e-08 1.05e-04 -11.0 1.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.1158095e+01 3.38e-08 6.31e-05 -11.0 3.35e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.1158096e+01 1.84e-08 1.68e-04 -11.0 1.40e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.1158096e+01 2.43e-08 1.71e-05 -11.0 1.31e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.1158096e+01 5.18e-10 1.21e-04 -11.0 1.67e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.1158096e+01 1.89e-08 6.58e-05 -11.0 1.50e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.1158096e+01 8.27e-09 7.41e-05 -11.0 8.11e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.1157948e+01 4.50e-05 3.22e-02 -11.0 7.58e-01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 81 1.1158070e+01 2.57e-08 3.61e-05 -11.0 2.47e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 82 1.1157841e+01 1.03e-03 2.21e-03 -11.0 4.47e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.1156548e+01 5.93e-04 1.40e-02 -11.0 1.13e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.1151802e+01 5.96e-03 1.26e-02 -11.0 7.16e+01 - 9.10e-01 1.00e+00h 1\n",
|
||
" 85 1.1090139e+01 3.89e-02 4.04e-02 -11.0 2.42e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.1051987e+01 4.55e-02 4.31e-02 -11.0 3.15e+02 - 6.87e-01 7.30e-01h 1\n",
|
||
" 87 1.1142157e+01 3.61e-05 8.22e-02 -11.0 8.62e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.1142195e+01 1.62e-07 3.64e-04 -4.0 2.32e-03 - 9.91e-01 1.00e+00h 1\n",
|
||
" 89 1.1142187e+01 1.31e-05 2.78e-03 -5.2 1.31e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.1142193e+01 1.54e-06 9.06e-04 -7.3 5.51e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.1142195e+01 1.02e-07 1.08e-04 -9.2 2.05e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.1141458e+01 2.68e-03 1.27e-02 -8.6 5.77e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 93 1.1140556e+01 5.77e-04 1.54e-03 -8.6 4.16e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.1142096e+01 3.69e-05 2.84e-03 -8.6 6.75e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.1142141e+01 2.71e-05 2.28e-03 -11.0 7.15e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.1141993e+01 4.52e-05 2.31e-03 -9.4 4.17e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.1140182e+01 7.42e-04 1.18e-02 -9.6 5.31e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.1137546e+01 5.03e-03 5.42e-03 -9.7 2.10e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.1137404e+01 5.29e-03 1.93e-03 -9.7 2.99e+01 - 9.53e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.1138955e+01 1.60e-03 2.22e-03 -9.7 1.03e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.1138954682752315e+01 1.1138954682752315e+01\n",
|
||
"Dual infeasibility......: 2.2185695594880706e-03 2.2185695594880706e-03\n",
|
||
"Constraint violation....: 1.6009907982663663e-03 1.6009907982663663e-03\n",
|
||
"Complementarity.........: 1.9632553669924947e-10 1.9632553669924947e-10\n",
|
||
"Overall NLP error.......: 2.2185695594880706e-03 2.2185695594880706e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 127\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 127\n",
|
||
"Number of inequality constraint evaluations = 127\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.419\n",
|
||
"Total CPU secs in NLP function evaluations = 136.870\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 575.00us ( 4.53us) 562.99us ( 4.43us) 127\n",
|
||
" nlp_g | 5.69 s ( 44.80ms) 5.43 s ( 42.74ms) 127\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 372.00us ( 3.65us) 368.99us ( 3.62us) 102\n",
|
||
" nlp_jac_g | 134.02 s ( 1.30 s) 127.99 s ( 1.24 s) 103\n",
|
||
" total | 141.20 s (141.20 s) 134.84 s (134.84 s) 1\n",
|
||
"Timestamp 15600\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.53e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0175495e+01 1.30e+01 1.53e+04 -1.5 1.53e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.1267675e+00 4.56e+00 6.75e+00 0.6 8.25e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 3.5213838e+00 7.89e-01 7.25e-01 -1.5 2.39e+01 - 9.97e-01 1.00e+00f 1\n",
|
||
" 4 4.0215671e+00 3.79e-03 2.17e-01 -3.2 1.30e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 4.0231285e+00 4.29e-06 8.76e-04 -5.1 9.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 4.0231298e+00 2.85e-06 6.71e-04 -7.2 1.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 4.0231322e+00 9.33e-07 1.21e-03 -9.3 3.09e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 4.0231248e+00 2.46e-05 1.68e-03 -11.0 4.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 4.0231318e+00 1.53e-06 1.25e-03 -11.0 1.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 4.0231323e+00 9.62e-07 8.12e-04 -11.0 7.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 4.0231331e+00 1.91e-07 9.81e-05 -11.0 5.12e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 4.0231217e+00 1.49e-05 3.31e-03 -11.0 5.34e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 4.0231304e+00 6.58e-06 1.38e-03 -11.0 2.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 4.0231252e+00 1.05e-05 1.73e-03 -11.0 5.88e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 4.0230693e+00 1.67e-04 3.06e-03 -11.0 6.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 4.0228549e+00 3.56e-04 9.12e-04 -11.0 3.08e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 4.0230166e+00 1.04e-04 9.30e-04 -11.0 8.12e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 4.0215296e+00 3.78e-03 4.97e-03 -11.0 1.87e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 4.0222711e+00 5.15e-04 1.95e-03 -11.0 8.91e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 3.6842002e+00 1.82e+00 4.90e-01 -10.6 7.08e+04 - 1.00e+00 1.11e-01f 3\n",
|
||
" 21 4.7850818e+00 1.58e+00 4.27e-01 -10.8 6.92e+04 - 3.61e-01 4.60e-01H 1\n",
|
||
" 22 3.9910298e+00 4.14e-01 3.16e-01 -10.8 1.11e+04 - 1.09e-10 1.00e+00h 1\n",
|
||
" 23 3.8537757e+00 6.20e-01 7.65e-02 -8.8 3.06e+04 - 1.00e+00 3.60e-01h 1\n",
|
||
" 24 3.8537628e+00 6.23e-01 7.65e-02 -6.8 3.07e+05 - 1.00e+00 3.59e-04h 1\n",
|
||
" 25 3.9994330e+00 6.29e-02 1.49e-01 -5.7 1.16e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 3.9105456e+00 1.52e-01 1.02e-01 -4.3 1.29e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 3.5954803e+00 1.76e+00 5.88e-01 -3.1 1.73e+06 - 1.00e+00 1.73e-02f 2\n",
|
||
" 28 5.1994113e+00 8.98e-01 2.91e-01 -3.2 1.72e+05 - 2.36e-01 6.39e-02h 2\n",
|
||
" 29 4.2351362e+00 1.71e+00 1.90e-01 -3.2 1.65e+04 - 8.13e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 4.1016269e+00 1.45e+00 1.39e-01 -3.2 6.66e+04 - 7.33e-01 1.26e-01h 1\n",
|
||
" 31 3.3163200e+00 3.79e-01 2.30e-01 -3.2 3.40e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 4.3434040e+00 7.40e-02 2.24e-01 -1.8 1.82e+04 - 1.21e-01 1.00e+00H 1\n",
|
||
" 33 3.9113992e+00 1.40e+00 2.29e-01 -2.8 1.62e+06 - 1.26e-02 2.67e-02f 1\n",
|
||
" 34 3.7243806e+00 1.20e+00 1.46e-01 -2.8 2.43e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 3.5937762e+00 1.72e+00 3.42e-01 -2.8 1.16e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 36 3.1025059e+00 1.11e+00 3.59e-01 -2.8 7.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 3.2667889e+00 6.50e-01 3.22e-01 -1.4 1.31e+04 - 1.00e+00 9.73e-01h 1\n",
|
||
" 38 3.2856769e+00 1.69e+00 3.31e-01 -1.5 9.31e+03 - 7.06e-01 1.00e+00f 1\n",
|
||
" 39 2.9037679e+00 1.58e+00 3.67e-01 -1.6 2.29e+04 - 1.00e+00 4.38e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 3.1058034e+00 7.45e-01 1.69e-01 -3.6 1.22e+04 - 3.70e-01 1.00e+00h 1\n",
|
||
" 41 3.1153224e+00 7.25e-01 1.55e-01 -2.1 4.32e+04 - 1.42e-01 9.98e-01f 1\n",
|
||
" 42 3.2768932e+00 1.30e+00 1.31e-01 -2.2 1.54e+04 - 1.75e-01 1.00e+00H 1\n",
|
||
" 43 3.2679878e+00 1.33e+00 1.42e-01 -2.2 2.28e+05 - 2.94e-01 1.96e-02h 4\n",
|
||
" 44 3.9657735e+00 5.74e-01 5.37e-01 -2.2 8.83e+04 - 1.00e+00 3.54e-01h 1\n",
|
||
" 45 3.6694929e+00 9.06e-01 5.39e-01 -2.2 3.05e+04 - 4.72e-01 2.50e-01f 3\n",
|
||
" 46 3.0533996e+00 1.68e+00 7.28e-01 -2.2 1.78e+04 - 4.17e-01 1.00e+00F 1\n",
|
||
" 47 3.3600990e+00 2.09e+00 8.48e-01 -2.0 1.37e+05 - 1.00e+00 3.59e-01H 1\n",
|
||
" 48 3.1241378e+00 1.40e+00 6.22e-01 -2.1 1.94e+04 - 8.74e-01 3.22e-01h 1\n",
|
||
" 49 3.5472184e+00 5.50e-02 4.74e-01 -2.1 3.61e+03 - 2.69e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 3.4923488e+00 1.70e+00 3.10e-01 -3.3 2.96e+04 - 7.97e-01 5.00e-01f 2\n",
|
||
" 51 3.4259027e+00 1.67e+00 1.78e-01 -2.9 4.42e+04 - 1.04e-01 7.29e-01H 1\n",
|
||
" 52 3.1621371e+00 9.18e-01 3.88e-01 -2.9 4.73e+04 - 7.26e-03 6.31e-01h 1\n",
|
||
" 53 3.1458551e+00 2.19e+00 7.48e-01 -2.9 2.05e+05 - 3.16e-01 7.30e-02f 2\n",
|
||
" 54 3.5595080e+00 2.11e-01 7.25e-01 -2.9 1.57e+04 - 7.64e-01 1.00e+00h 1\n",
|
||
" 55 2.6181109e+00 1.45e+00 5.48e-01 -2.9 9.98e+03 - 5.78e-01 1.00e+00f 1\n",
|
||
" 56 2.6753344e+00 9.86e-01 1.49e-01 -1.4 3.16e+04 - 1.00e+00 6.47e-01h 1\n",
|
||
" 57 2.4716120e+00 9.89e-01 2.43e-01 -2.2 1.06e+04 - 3.52e-01 1.00e+00h 1\n",
|
||
" 58 2.8075941e+00 1.04e+00 1.36e-01 -1.6 1.29e+04 - 4.05e-01 5.91e-01H 1\n",
|
||
" 59 2.8998844e+00 7.29e-01 8.22e-02 -2.5 5.40e+03 - 9.07e-01 5.91e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 3.9904653e+00 1.21e+00 4.70e-01 -8.5 7.78e+03 - 3.19e-01 1.00e+00h 1\n",
|
||
" 61 3.7117769e+00 3.36e-01 3.75e-01 -2.8 9.69e+04 - 2.10e-02 1.27e-01f 1\n",
|
||
" 62 3.3412706e+00 1.22e+00 1.75e-01 -2.8 4.32e+04 - 8.44e-01 3.91e-01f 1\n",
|
||
" 63 3.2974263e+00 9.55e-01 1.64e-01 -2.8 2.45e+03 - 6.93e-01 1.00e+00h 1\n",
|
||
" 64 3.2123879e+00 1.20e+00 5.21e-01 -2.8 1.08e+04 - 1.00e+00 8.06e-01h 1\n",
|
||
" 65 2.9753680e+00 7.78e-01 1.70e-01 -2.8 1.04e+04 - 5.50e-01 1.00e+00h 1\n",
|
||
" 66 2.9769618e+00 7.49e-01 1.64e-01 -3.0 3.79e+03 - 3.62e-01 3.12e-02h 6\n",
|
||
" 67 2.9619500e+00 7.17e-01 1.58e-01 -2.0 9.23e+03 - 1.00e+00 5.25e-02h 1\n",
|
||
" 68 3.3184187e+00 1.37e-02 8.27e-01 -3.9 8.45e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 3.3252956e+00 3.19e-06 3.37e-03 -5.8 2.49e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 3.3252961e+00 1.18e-07 1.17e-05 -7.9 3.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 3.3252961e+00 1.72e-08 8.02e-05 -11.0 1.03e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 3.3252961e+00 2.19e-08 5.26e-05 -11.0 1.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 3.3252961e+00 6.01e-08 1.33e-04 -11.0 1.91e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 3.3252961e+00 1.08e-10 1.30e-04 -11.0 1.53e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 75 3.3252961e+00 1.33e-08 9.24e-05 -11.0 7.12e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 3.3252961e+00 9.87e-08 5.15e-05 -11.0 4.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 3.3252960e+00 1.64e-07 2.99e-05 -11.0 8.45e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 3.3252961e+00 6.42e-09 1.07e-05 -11.0 9.06e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 3.3252961e+00 8.46e-08 1.27e-04 -11.0 1.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 3.3252951e+00 7.70e-07 1.28e-02 -11.0 5.46e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 3.3252960e+00 6.57e-08 6.89e-05 -11.0 1.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 3.3252959e+00 1.87e-07 4.83e-05 -11.0 8.44e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 3.3252956e+00 6.23e-07 2.30e-03 -11.0 2.33e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 3.3252959e+00 1.04e-07 6.95e-05 -11.0 2.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 3.3252957e+00 2.88e-07 5.53e-05 -11.0 1.34e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 3.3252958e+00 8.04e-08 6.82e-05 -11.0 1.86e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 3.3252956e+00 2.46e-07 1.03e-04 -11.0 3.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 3.3252957e+00 1.42e-07 1.91e-05 -11.0 9.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 3.3252960e+00 3.92e-11 4.74e-05 -11.0 2.52e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 3.3252958e+00 5.08e-07 3.04e-05 -11.0 5.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 3.3252960e+00 1.34e-10 4.42e-05 -11.0 3.19e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 92 3.3252956e+00 1.21e-06 1.68e-03 -11.0 1.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 3.3252959e+00 1.19e-07 1.41e-04 -11.0 1.42e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 3.3252954e+00 2.94e-07 1.70e-05 -11.0 4.35e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 3.3252958e+00 3.95e-07 7.73e-05 -11.0 1.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 3.3252949e+00 3.19e-06 1.37e-03 -11.0 2.86e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 3.3252953e+00 1.87e-06 8.69e-04 -11.0 1.40e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 3.3252900e+00 2.36e-05 2.44e-03 -11.0 2.12e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 3.3252782e+00 1.27e-04 3.93e-03 -11.0 3.57e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 3.3252204e+00 2.52e-04 8.78e-03 -11.0 7.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 3.3252204146192668e+00 3.3252204146192668e+00\n",
|
||
"Dual infeasibility......: 8.7787792406257836e-03 8.7787792406257836e-03\n",
|
||
"Constraint violation....: 2.5158088865850914e-04 2.5158088865850914e-04\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 8.7787792406257836e-03 8.7787792406257836e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 146\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 146\n",
|
||
"Number of inequality constraint evaluations = 146\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.428\n",
|
||
"Total CPU secs in NLP function evaluations = 136.312\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 665.00us ( 4.55us) 664.30us ( 4.55us) 146\n",
|
||
" nlp_g | 6.55 s ( 44.86ms) 6.25 s ( 42.79ms) 146\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 370.00us ( 3.63us) 370.99us ( 3.64us) 102\n",
|
||
" nlp_jac_g | 132.44 s ( 1.30 s) 126.45 s ( 1.24 s) 102\n",
|
||
" total | 140.48 s (140.48 s) 134.12 s (134.12 s) 1\n",
|
||
"Timestamp 15900\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.15e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0580653e+01 1.26e+01 3.15e+04 -1.5 3.15e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.6566941e+00 4.50e+00 4.97e+00 1.0 5.78e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.2908760e+00 5.35e-01 5.05e-01 -1.1 1.91e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 8.2939578e-01 3.11e-03 9.26e-01 -3.1 1.52e+01 - 9.95e-01 1.00e+00h 1\n",
|
||
" 5 8.2965571e-01 6.07e-05 2.88e-03 -4.5 4.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 8.2962246e-01 1.22e-04 1.74e-03 -6.6 1.04e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 8.2964594e-01 9.15e-05 1.12e-03 -8.7 9.00e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 8.2906567e-01 1.75e-03 8.95e-03 -11.0 4.46e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 8.1668856e-01 7.71e-02 1.46e-01 -11.0 1.18e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 8.2986215e-01 8.71e-03 7.74e-02 -11.0 1.64e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 8.2522632e-01 5.72e-03 1.96e-02 -11.0 6.36e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 8.2922548e-01 4.37e-03 3.91e-02 -11.0 1.22e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 13 8.4378130e-01 5.14e-03 5.32e-02 -11.0 8.50e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 14 8.1344476e-01 6.17e-02 1.29e-01 -11.0 7.96e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 8.0595971e-01 1.09e-01 1.19e-01 -11.0 5.67e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 8.4210514e-01 1.54e-02 2.25e-01 -11.0 2.25e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 8.4168033e-01 1.03e-01 1.34e-01 -11.0 2.37e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 18 8.2713908e-01 1.22e-01 1.55e-01 -11.0 7.05e+05 - 4.32e-02 2.88e-03f 5\n",
|
||
" 19 7.9875414e-01 1.33e-01 1.12e-01 -11.0 3.10e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 8.3419714e-01 5.10e-02 1.73e-01 -11.0 2.19e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 8.2399230e-01 6.85e-02 1.55e-01 -11.0 5.29e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 22 8.5341327e-01 3.88e-02 3.62e-01 -11.0 9.47e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 23 8.5188285e-01 5.04e-02 3.60e-01 -11.0 6.75e+04 - 5.53e-01 3.11e-03h 8\n",
|
||
" 24 8.4374101e-01 5.59e-02 3.14e-01 -11.0 2.43e+03 - 1.00e+00 6.25e-02h 5\n",
|
||
" 25 8.1286559e-01 2.31e-01 2.53e-01 -11.0 6.84e+04 - 4.29e-01 1.32e-01h 3\n",
|
||
" 26 8.2733666e-01 8.41e-04 2.02e-01 -11.0 2.02e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.2732501e-01 3.87e-08 3.33e-06 -11.0 8.47e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 8.2732502e-01 4.16e-10 4.66e-05 -11.0 4.24e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.2732502e-01 4.17e-10 4.37e-05 -11.0 1.74e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.2732502e-01 2.37e-09 1.40e-05 -11.0 5.19e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 8.2732502e-01 7.98e-10 1.20e-05 -11.0 3.24e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 8.2732502e-01 1.65e-10 4.37e-06 -11.0 1.07e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 8.2732502e-01 1.17e-10 1.71e-05 -11.0 6.16e-07 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 8.2732203e-01 1.82e-05 2.28e-02 -11.0 2.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 8.2725697e-01 1.74e-04 1.09e-01 -11.0 1.19e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 8.2731253e-01 1.89e-05 3.89e-04 -11.0 3.04e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 8.2731939e-01 1.77e-05 4.27e-04 -11.0 9.65e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 8.2731415e-01 3.15e-05 3.67e-04 -11.0 2.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 8.2730243e-01 6.36e-05 2.02e-04 -11.0 1.84e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.2731935e-01 8.01e-07 3.06e-04 -11.0 3.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 8.2731849e-01 3.20e-06 2.27e-04 -11.0 2.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 8.2731695e-01 7.05e-06 1.89e-04 -11.0 4.39e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 8.2731790e-01 4.09e-06 2.77e-04 -11.0 3.50e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 8.2731989e-01 2.23e-09 4.08e-05 -11.0 2.18e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 45 8.2731892e-01 1.31e-06 1.55e-04 -11.0 1.02e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 8.2731975e-01 3.32e-07 3.06e-05 -11.0 2.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 8.2731838e-01 5.63e-06 6.53e-04 -11.0 1.46e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 8.2731969e-01 4.04e-07 6.80e-06 -11.0 3.77e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 8.2731979e-01 6.74e-08 4.92e-06 -11.0 1.20e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.2731983e-01 4.88e-11 1.38e-05 -11.0 1.95e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 51 8.2731973e-01 2.73e-07 8.86e-06 -11.0 1.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 8.2731982e-01 4.08e-08 2.46e-05 -11.0 5.94e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 8.2731973e-01 2.48e-07 1.01e-05 -11.0 1.97e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 8.2731970e-01 6.76e-07 2.22e-04 -11.0 4.09e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 8.2731977e-01 7.57e-08 4.78e-05 -11.0 2.59e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 8.2731965e-01 3.39e-07 4.15e-05 -11.0 2.34e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 8.2731976e-01 6.99e-08 2.50e-05 -11.0 6.25e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 8.2731978e-01 1.21e-07 3.74e-05 -11.0 3.74e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 8.2731977e-01 4.69e-08 2.96e-05 -11.0 4.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 8.2731974e-01 1.03e-07 1.61e-05 -11.0 1.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 8.2731980e-01 1.02e-10 2.13e-05 -11.0 1.79e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 62 8.2731829e-01 4.49e-06 2.34e-03 -11.0 6.49e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 8.2731944e-01 1.64e-06 4.82e-04 -11.0 4.81e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 8.2732005e-01 2.91e-10 1.36e-05 -11.0 7.23e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 65 8.2732014e-01 6.75e-11 9.10e-06 -11.0 1.37e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 66 8.2732010e-01 8.72e-11 2.21e-05 -11.0 8.94e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 8.2731935e-01 2.19e-06 1.62e-04 -11.0 3.33e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 8.2721485e-01 2.08e-04 4.47e-04 -11.0 1.70e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 8.2717656e-01 8.94e-04 1.58e-03 -11.0 1.23e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.2731490e-01 2.14e-04 1.52e-03 -11.0 3.56e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 8.2654138e-01 1.63e-02 1.07e-02 -11.0 4.51e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 8.2467715e-01 2.24e-02 4.11e-02 -11.0 3.01e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 8.2619675e-01 1.03e-02 3.92e-02 -11.0 3.26e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 8.2604271e-01 1.19e-02 1.48e-02 -9.0 5.81e+05 - 1.00e+00 3.16e-04h 8\n",
|
||
" 75 8.2595989e-01 2.52e-02 6.91e-03 -7.0 1.31e+06 - 1.00e+00 2.22e-03h 4\n",
|
||
" 76 8.2258645e-01 2.17e-02 2.68e-02 -7.1 8.62e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 8.1895952e-01 9.26e-02 5.29e-02 -8.4 1.28e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 8.1005909e-01 2.47e-01 2.10e-01 -8.5 1.98e+06 - 1.81e-02 3.54e-03f 3\n",
|
||
" 79 8.8010998e-01 2.97e-01 3.08e-01 -8.5 1.07e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 8.5375236e-01 3.26e-01 3.51e-01 -8.5 3.79e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 81 8.4832291e-01 3.53e-01 3.50e-01 -8.5 1.35e+06 - 8.28e-03 1.20e-03h 6\n",
|
||
" 82 8.4325456e-01 3.66e-01 3.40e-01 -8.5 1.29e+05 - 3.68e-01 1.56e-03h 7\n",
|
||
" 83 8.0552970e-01 2.68e-01 1.88e-01 -8.5 3.52e+05 - 5.01e-01 3.59e-02h 1\n",
|
||
" 84 8.2535107e-01 1.23e-01 6.37e-02 -8.5 5.78e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 8.0969100e-01 1.87e-02 5.33e-02 -8.5 1.37e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 8.0430991e-01 6.29e-02 8.27e-02 -4.1 2.20e+02 - 8.13e-01 1.00e+00h 1\n",
|
||
" 87 8.0610812e-01 3.42e-02 4.90e-02 -2.7 5.59e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 8.0870932e-01 2.98e-02 3.98e-02 -8.7 1.32e+02 - 8.89e-01 1.00e+00H 1\n",
|
||
" 89 8.0452653e-01 2.01e-01 1.48e-01 -4.1 1.09e+03 - 1.00e+00 4.80e-01H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 8.1172198e-01 5.40e-02 2.05e-01 -4.3 1.76e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 91 8.1175331e-01 4.73e-02 1.77e-01 -4.3 9.14e+03 - 1.00e+00 1.56e-02h 7\n",
|
||
" 92 8.1140903e-01 3.40e-02 1.09e-01 -4.3 4.67e+02 - 1.00e+00 2.50e-01h 3\n",
|
||
" 93 8.0337501e-01 5.08e-02 5.32e-02 -4.3 1.12e+02 - 6.81e-01 1.00e+00h 1\n",
|
||
" 94 8.0330314e-01 5.10e-02 5.35e-02 -3.7 9.36e+03 - 1.00e+00 3.37e-04h 3\n",
|
||
" 95 8.0553410e-01 1.80e-02 8.81e-02 -5.4 3.06e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 8.0662339e-01 2.82e-02 6.05e-02 -5.4 9.23e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 8.0678959e-01 2.06e-02 3.54e-02 -5.9 2.70e+02 - 1.00e+00 4.94e-01H 1\n",
|
||
" 98 8.0678059e-01 8.18e-03 1.80e-02 -4.5 1.98e+01 - 9.27e-01 1.00e+00h 1\n",
|
||
" 99 8.0786588e-01 5.45e-03 1.13e-02 -4.4 4.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.0656827e-01 9.40e-03 1.08e-02 -5.9 2.97e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.0656826907719037e-01 8.0656826907719037e-01\n",
|
||
"Dual infeasibility......: 1.0848653708706288e-02 1.0848653708706288e-02\n",
|
||
"Constraint violation....: 9.4046919403574236e-03 9.4046919403574236e-03\n",
|
||
"Complementarity.........: 3.0623399824778537e-04 3.0623399824778537e-04\n",
|
||
"Overall NLP error.......: 1.0848653708706288e-02 1.0848653708706288e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 195\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 195\n",
|
||
"Number of inequality constraint evaluations = 195\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.484\n",
|
||
"Total CPU secs in NLP function evaluations = 138.523\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 902.00us ( 4.63us) 902.05us ( 4.63us) 195\n",
|
||
" nlp_g | 8.85 s ( 45.36ms) 8.45 s ( 43.32ms) 195\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 372.00us ( 3.65us) 363.69us ( 3.57us) 102\n",
|
||
" nlp_jac_g | 132.63 s ( 1.30 s) 126.65 s ( 1.24 s) 102\n",
|
||
" total | 142.98 s (142.98 s) 136.52 s (136.52 s) 1\n",
|
||
"Timestamp 16200\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.29e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9584473e+01 1.37e+01 1.29e+03 -1.5 1.29e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.4197694e+00 4.79e+00 9.94e+00 0.4 1.37e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 9.5157749e+00 1.30e+00 6.42e-01 -1.6 8.45e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.0470792e+01 1.78e-03 8.11e-02 -3.4 1.78e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.0471676e+01 1.09e-07 1.54e-04 -5.3 1.78e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.0471674e+01 1.34e-06 1.78e-03 -11.0 8.82e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.0471674e+01 7.79e-07 2.01e-03 -11.0 4.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.0471676e+01 1.03e-07 5.70e-05 -11.0 1.30e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.0471638e+01 1.22e-05 5.20e-03 -11.0 1.93e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.0471624e+01 2.71e-05 2.26e-03 -11.0 1.26e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.0471673e+01 9.10e-07 1.70e-03 -11.0 1.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.0471486e+01 6.80e-05 9.48e-03 -11.0 2.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.0471390e+01 1.06e-04 1.80e-02 -11.0 5.60e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.0471624e+01 1.58e-05 2.41e-03 -11.0 1.04e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.0471578e+01 7.12e-05 2.58e-03 -11.0 4.55e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.0471572e+01 6.37e-05 4.62e-03 -11.0 4.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.0471603e+01 4.23e-05 3.62e-03 -11.0 7.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.0471570e+01 1.42e-04 1.04e-03 -11.0 1.05e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.0459620e+01 1.84e-02 8.80e-03 -11.0 1.15e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.0445673e+01 2.28e-02 1.36e-02 -11.0 1.69e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.0463883e+01 1.85e-02 1.77e-03 -11.0 1.24e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.0452594e+01 1.31e-02 1.63e-03 -11.0 9.32e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 8.5434682e+00 2.97e+00 2.71e-01 -11.0 1.09e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 24 7.6382495e+00 6.96e+00 8.24e-01 -11.0 1.70e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 25 8.0634593e+00 7.10e+00 5.87e-01 -9.1 8.38e+03 - 1.00e+00 4.68e-01h 1\n",
|
||
" 26 9.7806392e+00 1.42e+00 7.27e-01 -9.3 5.33e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.5479553e+00 5.34e+00 2.20e-01 -9.4 2.89e+04 - 1.09e-03 1.00e+00f 1\n",
|
||
" 28 7.8995150e+00 5.10e+00 4.78e-01 -9.4 5.49e+05 - 2.68e-02 5.00e-02f 2\n",
|
||
" 29 7.7372207e+00 4.05e+00 4.66e-01 -7.4 1.11e+05 - 1.00e+00 2.86e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 7.7349832e+00 4.06e+00 4.66e-01 -5.4 5.03e+06 - 2.41e-02 4.94e-05h 1\n",
|
||
" 31 7.7348510e+00 4.06e+00 4.66e-01 -4.9 3.86e+05 - 1.00e+00 2.38e-05h 1\n",
|
||
" 32 6.8101865e+00 3.90e+00 2.08e-01 -10.9 1.41e+06 - 2.46e-03 4.17e-02f 1\n",
|
||
" 33 7.8342419e+00 3.63e+00 4.75e-01 -11.0 8.59e+04 - 6.81e-03 7.19e-01h 1\n",
|
||
" 34 1.0173265e+01 8.25e-02 4.72e-01 -6.3 3.81e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 9.8495745e+00 7.94e-01 1.52e-01 -5.7 6.21e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 8.3017278e+00 1.84e+00 3.16e-01 -3.7 3.08e+04 - 3.04e-01 1.00e+00f 1\n",
|
||
" 37 1.0325037e+01 8.39e-01 1.13e-01 -10.1 5.65e+04 - 4.72e-03 1.00e+00H 1\n",
|
||
" 38 6.3577747e+00 2.26e+00 3.82e-01 -4.3 1.34e+04 - 6.98e-01 1.00e+00f 1\n",
|
||
" 39 1.1015821e+01 2.32e-01 3.84e-01 -4.4 5.50e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.2984262e+00 8.15e-01 3.82e-01 -4.6 4.37e+05 - 7.57e-02 5.35e-02f 1\n",
|
||
" 41 1.1299014e+01 2.38e-01 1.12e-01 -5.5 6.22e+04 - 8.60e-06 1.00e+00H 1\n",
|
||
" 42 8.5389147e+00 1.14e+00 1.30e-01 -4.5 1.17e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 43 8.1598701e+00 1.07e+00 1.18e-01 -4.5 6.31e+05 - 3.54e-02 4.45e-03f 1\n",
|
||
" 44 8.3787133e+00 1.20e+00 1.43e-01 -3.7 1.52e+05 - 1.00e+00 1.63e-01H 1\n",
|
||
" 45 7.6683331e+00 1.63e+00 1.94e-01 -2.3 8.11e+04 - 2.56e-01 7.69e-01f 1\n",
|
||
" 46 7.6462587e+00 4.41e+00 3.80e-01 -7.5 1.19e+05 - 2.24e-03 4.29e-01F 1\n",
|
||
" 47 8.0805883e+00 3.31e+00 1.48e-01 -2.9 2.54e+03 - 1.00e+00 2.46e-01h 1\n",
|
||
" 48 8.7490897e+00 8.87e-01 3.96e-01 -2.9 6.56e+03 - 6.74e-01 1.00e+00h 1\n",
|
||
" 49 7.7218669e+00 1.24e+00 2.59e-01 -2.9 6.05e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.0671442e+01 2.75e-01 7.32e-02 -1.5 5.19e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 51 1.0358231e+01 5.07e-01 1.66e-01 -1.6 3.32e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 8.9092172e+00 1.07e+00 8.26e-02 -1.6 2.06e+04 - 1.95e-01 1.00e+00f 1\n",
|
||
" 53 1.0923152e+01 2.87e-01 1.65e-01 -1.6 7.97e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 54 1.0275279e+01 1.39e+00 1.39e-01 -1.6 1.02e+04 - 1.00e+00 1.00e+00F 1\n",
|
||
" 55 1.0196178e+01 1.40e+00 1.37e-01 -1.6 1.34e+05 - 2.34e-01 4.15e-03f 5\n",
|
||
" 56 7.4468874e+00 1.62e+00 1.83e-01 -1.6 1.31e+05 - 1.00e+00 8.02e-02f 1\n",
|
||
" 57 1.0332740e+01 2.31e-01 1.09e-01 -2.3 2.78e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.0047719e+01 3.83e-01 1.03e-01 -2.4 1.95e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.0643524e+01 1.70e-01 2.62e-02 -2.7 9.82e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 5.5616956e+00 3.80e+00 5.40e-01 -2.9 1.09e+04 - 1.44e-01 1.00e+00f 1\n",
|
||
" 61 5.4511060e+00 3.54e+00 1.31e-01 -3.3 1.27e+04 - 6.68e-01 1.00e+00h 1\n",
|
||
" 62 8.6875897e+00 2.29e+00 4.54e-01 -1.1 1.00e+04 - 6.06e-02 1.00e+00h 1\n",
|
||
" 63 9.6485144e+00 1.34e+00 1.17e-01 -2.4 5.43e+04 - 2.13e-01 5.84e-01H 1\n",
|
||
" 64 1.2496500e+01 2.64e+00 3.75e-01 -2.4 1.49e+04 - 5.32e-01 1.00e+00H 1\n",
|
||
" 65 9.9176398e+00 8.65e-01 1.50e-01 -2.4 1.10e+05 - 1.11e-01 6.01e-02f 3\n",
|
||
" 66 8.7424206e+00 2.07e+00 4.44e-01 -2.4 1.15e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 6.7382124e+00 1.95e+00 3.13e-01 -2.4 1.40e+05 - 4.12e-01 6.04e-02f 1\n",
|
||
" 68 1.1019524e+01 1.16e+00 1.59e-01 -2.5 8.61e+03 - 2.37e-01 1.00e+00h 1\n",
|
||
" 69 1.1725015e+01 3.19e-01 1.44e-01 -2.5 6.25e+03 - 2.96e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.1252839e+01 3.19e-01 7.59e-02 -2.5 5.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 9.4205089e+00 1.67e+00 1.93e-01 -2.5 1.91e+05 - 3.74e-02 7.23e-02f 1\n",
|
||
" 72 9.5328881e+00 1.57e+00 1.68e-01 -2.5 4.68e+03 - 1.00e+00 6.70e-02h 1\n",
|
||
" 73 1.1646913e+01 9.85e-02 1.33e-01 -2.5 2.51e+02 - 5.88e-01 1.00e+00h 1\n",
|
||
" 74 1.1382089e+01 1.69e-02 2.50e-02 -2.5 1.05e+04 - 2.38e-02 1.00e+00F 1\n",
|
||
" 75 8.6974241e+00 2.52e+00 1.95e-01 -3.7 9.81e+03 - 2.95e-01 1.00e+00f 1\n",
|
||
" 76 8.4980056e+00 2.42e+00 2.07e-01 -3.7 9.54e+04 - 4.08e-01 7.86e-03f 1\n",
|
||
" 77 9.7494007e+00 1.53e+00 4.82e-02 -3.7 1.76e+04 - 5.09e-03 3.78e-01h 1\n",
|
||
" 78 9.8973073e+00 1.39e+00 3.39e-02 -3.7 5.87e+02 - 9.52e-01 1.06e-01h 1\n",
|
||
" 79 1.0343755e+01 9.99e-01 4.95e-02 -3.7 1.03e+03 - 2.75e-01 3.15e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.0346189e+01 9.97e-01 4.91e-02 -3.7 5.37e+02 - 1.00e+00 2.10e-03h 1\n",
|
||
" 81 1.1750388e+01 2.98e-02 4.34e-02 -3.7 1.21e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.1759045e+01 1.39e-03 2.91e-02 -3.7 1.35e+01 - 5.04e-01 1.00e+00h 1\n",
|
||
" 83 1.1752704e+01 3.41e-03 2.77e-02 -3.7 1.37e+03 - 2.61e-01 1.48e-02f 1\n",
|
||
" 84 1.1751102e+01 2.71e-03 2.71e-02 -3.7 3.04e+02 - 1.00e+00 1.40e-02h 1\n",
|
||
" 85 1.1763684e+01 3.64e-04 1.92e-03 -3.7 1.69e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.1752321e+01 4.76e-03 6.33e-03 -5.5 1.10e+01 - 2.74e-01 1.00e+00h 1\n",
|
||
" 87 1.1753391e+01 4.11e-03 4.78e-03 -5.5 5.32e+01 - 1.83e-01 1.69e-01h 1\n",
|
||
" 88 1.1762804e+01 4.06e-06 3.38e-03 -5.5 4.44e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 89 1.1758741e+01 1.91e-03 1.25e-03 -5.5 2.93e+01 - 7.61e-01 8.10e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.1763274e+01 3.02e-07 1.70e-04 -5.5 1.18e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 91 1.1756676e+01 2.47e-03 2.11e-03 -5.5 1.49e+02 - 2.54e-01 2.95e-01f 1\n",
|
||
" 92 1.1761846e+01 1.25e-03 2.89e-03 -5.5 1.59e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.1761735e+01 8.94e-04 2.98e-03 -5.5 1.89e+01 - 8.04e-01 8.40e-01H 1\n",
|
||
" 94 1.1762890e+01 7.62e-07 1.55e-03 -5.5 7.18e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 95 1.1761225e+01 2.49e-03 7.63e-04 -5.5 4.44e+00 - 4.09e-01 1.00e+00f 1\n",
|
||
" 96 1.1706875e+01 6.18e-02 8.28e-03 -5.5 1.89e+02 - 4.40e-02 1.00e+00h 1\n",
|
||
" 97 1.1455391e+01 3.99e-01 2.19e-02 -5.5 2.20e+03 - 6.93e-02 1.00e+00h 1\n",
|
||
" 98 1.1523611e+01 1.17e-01 1.07e-02 -5.5 8.84e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.1618815e+01 8.26e-02 9.87e-03 -5.5 1.50e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.1695540e+01 2.36e-02 1.02e-02 -5.5 1.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.1695539767043419e+01 1.1695539767043419e+01\n",
|
||
"Dual infeasibility......: 1.0177556334262539e-02 1.0177556334262539e-02\n",
|
||
"Constraint violation....: 2.3573674224600438e-02 2.3573674224600438e-02\n",
|
||
"Complementarity.........: 3.2160414767889149e-06 3.2160414767889149e-06\n",
|
||
"Overall NLP error.......: 2.3573674224600438e-02 2.3573674224600438e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 131\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 131\n",
|
||
"Number of inequality constraint evaluations = 131\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.400\n",
|
||
"Total CPU secs in NLP function evaluations = 135.990\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 613.00us ( 4.68us) 601.56us ( 4.59us) 131\n",
|
||
" nlp_g | 5.93 s ( 45.23ms) 5.65 s ( 43.14ms) 131\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 343.00us ( 3.36us) 337.86us ( 3.31us) 102\n",
|
||
" nlp_jac_g | 132.86 s ( 1.30 s) 126.87 s ( 1.24 s) 102\n",
|
||
" total | 140.27 s (140.27 s) 133.94 s (133.94 s) 1\n",
|
||
"Timestamp 16500\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.53e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9918637e+01 1.51e+01 2.53e+04 -1.5 2.53e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.2850940e+01 5.67e+00 1.33e+01 0.8 3.23e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.9292330e+01 2.27e+00 8.38e-01 -1.3 6.81e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 2.0581512e+01 2.58e-04 8.55e-02 -3.0 2.49e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 2.0581739e+01 2.29e-04 2.28e-02 -4.9 5.64e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.0581890e+01 1.57e-04 2.12e-03 -6.8 7.58e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.0582120e+01 5.91e-05 1.58e-03 -8.9 2.78e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.0581903e+01 1.16e-04 2.56e-03 -11.0 4.42e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.0582195e+01 1.45e-05 2.51e-03 -11.0 1.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.0582226e+01 1.14e-09 5.50e-05 -11.0 1.79e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 2.0578000e+01 1.32e-02 4.55e-03 -11.0 4.49e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 12 2.0567072e+01 2.29e-02 9.54e-03 -11.0 8.70e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.0581355e+01 4.11e-03 1.47e-03 -11.0 3.12e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.0565343e+01 1.44e-02 3.56e-03 -11.0 7.94e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.0574686e+01 5.97e-03 6.18e-03 -11.0 5.75e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.0568529e+01 2.21e-02 5.16e-03 -11.0 1.08e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.0556628e+01 1.95e-02 1.88e-03 -11.0 8.52e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.0577612e+01 2.12e-03 2.06e-03 -11.0 6.73e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.0547268e+01 1.63e-02 2.50e-03 -11.0 3.32e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.0565453e+01 1.41e-02 2.38e-03 -11.0 6.76e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.0575616e+01 2.82e-03 1.89e-03 -11.0 5.04e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.0539922e+01 1.34e-02 3.02e-03 -11.0 1.87e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.0553749e+01 1.37e-02 2.56e-03 -11.0 1.24e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.0559415e+01 7.40e-03 2.69e-03 -11.0 4.32e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.0552342e+01 1.20e-02 2.09e-03 -11.0 1.38e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.0559358e+01 6.46e-03 1.66e-03 -11.0 4.25e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.0569762e+01 2.65e-03 2.30e-03 -11.0 3.86e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.0571121e+01 1.75e-03 1.80e-03 -11.0 7.26e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.0491528e+01 4.67e-02 3.86e-03 -11.0 1.46e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.0542659e+01 1.99e-02 1.21e-03 -11.0 6.30e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.0572135e+01 8.15e-04 3.18e-03 -11.0 1.43e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.0570564e+01 1.39e-03 1.34e-03 -11.0 3.50e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.0567438e+01 2.61e-03 1.06e-03 -11.0 2.09e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 2.0569044e+01 4.26e-03 3.13e-03 -11.0 1.58e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.0561313e+01 5.61e-03 3.35e-03 -11.0 1.66e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.0571581e+01 9.09e-04 2.77e-03 -11.0 2.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.0570707e+01 2.10e-03 1.38e-03 -11.0 2.67e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.0570117e+01 1.33e-03 1.78e-03 -11.0 2.29e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.0571174e+01 1.64e-03 1.36e-03 -11.0 1.10e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.0561056e+01 4.91e-02 3.53e-03 -11.0 1.47e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.0566131e+01 1.17e-02 2.03e-03 -11.0 8.25e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.0434475e+01 4.15e-02 4.07e-03 -11.0 1.18e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 43 2.0552249e+01 1.45e-02 1.87e-03 -11.0 3.08e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.0547038e+01 3.89e-02 1.23e-03 -11.0 3.47e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.0481632e+01 5.03e-02 1.75e-03 -11.0 9.29e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.0507031e+01 3.76e-02 1.17e-03 -11.0 3.17e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.0554219e+01 1.05e-02 2.48e-03 -11.0 2.41e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.0533948e+01 3.94e-02 2.36e-03 -11.0 5.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.0088048e+01 8.29e-01 3.11e-02 -11.0 4.89e+04 - 8.46e-01 4.60e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.0567310e+01 1.36e-02 2.66e-02 -11.0 1.89e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.0436390e+01 1.96e-01 2.25e-02 -3.0 1.74e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.0429706e+01 1.59e-01 1.75e-02 -3.0 7.23e+02 - 1.00e+00 2.05e-01h 1\n",
|
||
" 53 2.0546304e+01 3.33e-03 5.04e-03 -4.9 4.19e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.0540737e+01 4.15e-02 2.40e-03 -7.0 1.06e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.8741763e+01 1.48e+00 1.16e-01 -6.6 1.65e+04 - 3.17e-01 1.00e+00f 1\n",
|
||
" 56 1.9816806e+01 1.04e+00 1.22e-02 -6.6 2.94e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.8057865e+01 1.33e+00 5.11e-02 -1.7 3.57e+05 - 1.03e-01 9.30e-02f 1\n",
|
||
" 58 1.9548549e+01 5.00e-01 3.54e-02 -3.4 6.78e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.6415450e+01 7.93e+00 4.14e-01 -3.0 9.32e+04 - 1.00e+00 5.93e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.6424064e+01 7.92e+00 4.20e-01 -1.1 3.02e+04 - 1.00e+00 1.83e-02h 1\n",
|
||
" 61 2.0280983e+01 4.68e-01 4.76e-01 -7.1 6.29e+03 - 3.51e-01 1.00e+00h 1\n",
|
||
" 62 1.9315129e+01 1.64e+00 4.42e-02 -1.4 2.62e+04 - 5.85e-02 1.00e+00f 1\n",
|
||
" 63 1.7114098e+01 6.06e+00 4.30e-01 -1.7 4.49e+04 - 6.07e-01 5.78e-01f 1\n",
|
||
" 64 1.9087962e+01 1.51e+00 1.63e-01 -2.2 8.87e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.4934125e+01 1.60e+00 1.82e-01 -2.3 3.61e+04 - 1.00e+00 1.14e-01f 1\n",
|
||
" 66 1.7461873e+01 5.79e-01 8.55e-02 -1.1 3.73e+03 - 1.00e+00 6.55e-01h 1\n",
|
||
" 67 1.9255328e+01 5.66e-02 1.47e-02 -7.2 7.65e+02 - 2.31e-01 1.00e+00h 1\n",
|
||
" 68 1.9001105e+01 1.18e+00 5.05e-02 -3.0 2.06e+03 - 9.29e-01 1.00e+00h 1\n",
|
||
" 69 1.8955831e+01 1.01e+00 3.96e-02 -2.4 5.43e+03 - 1.00e+00 1.59e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.9080383e+01 1.37e-01 2.59e-02 -3.0 1.17e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.7956306e+01 1.54e+00 6.50e-02 -3.7 4.10e+03 - 9.94e-01 1.00e+00f 1\n",
|
||
" 72 1.9149850e+01 8.48e-01 3.34e-02 -3.9 2.39e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.9303914e+01 1.49e-01 2.55e-02 -3.9 1.42e+03 - 2.08e-01 1.00e+00h 1\n",
|
||
" 74 1.9314186e+01 1.52e-01 2.60e-02 -3.9 7.63e+02 - 1.00e+00 9.61e-02h 1\n",
|
||
" 75 1.8976585e+01 3.60e-01 3.53e-02 -3.9 1.34e+03 - 3.53e-02 1.00e+00f 1\n",
|
||
" 76 1.9268797e+01 2.81e-01 3.93e-02 -4.9 2.06e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.9255537e+01 2.90e-01 9.96e-03 -4.8 1.33e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.8874234e+01 4.38e-01 1.27e-02 -4.8 3.16e+03 - 1.00e+00 2.28e-01f 1\n",
|
||
" 79 1.9274399e+01 1.01e-01 7.75e-03 -5.9 5.03e+02 - 9.95e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.9406194e+01 6.78e-02 1.21e-02 -5.0 4.02e+02 - 9.94e-01 1.00e+00h 1\n",
|
||
" 81 1.9272884e+01 2.80e-01 2.49e-02 -5.0 1.99e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.9439566e+01 8.36e-02 2.90e-03 -5.0 6.65e+02 - 5.25e-01 1.00e+00h 1\n",
|
||
" 83 1.8522053e+01 1.20e+00 6.78e-02 -5.0 6.30e+03 - 1.00e+00 7.97e-01f 1\n",
|
||
" 84 1.8522198e+01 1.20e+00 6.78e-02 -5.0 7.66e+02 - 1.00e+00 1.85e-04h 1\n",
|
||
" 85 1.9584789e+01 4.25e-04 5.95e-02 -5.0 1.38e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.9577337e+01 3.07e-03 7.52e-03 -6.1 2.43e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.9582772e+01 1.54e-03 5.24e-03 -4.1 2.12e+01 - 1.99e-01 1.00e+00h 1\n",
|
||
" 88 1.9575967e+01 1.31e-02 1.38e-02 -4.1 3.49e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.9564996e+01 6.48e-02 2.42e-03 -3.0 1.26e+03 - 6.10e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.9121125e+01 1.02e+00 6.82e-03 -3.2 6.10e+03 - 2.51e-01 1.00e+00f 1\n",
|
||
" 91 1.9449263e+01 1.04e-01 2.16e-02 -3.2 1.23e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.9366031e+01 4.23e-01 5.90e-03 -3.2 3.04e+04 - 1.00e+00 5.94e-02h 2\n",
|
||
" 93 1.9633745e+01 1.10e-01 1.81e-02 -3.2 9.61e+02 - 7.79e-01 1.00e+00h 1\n",
|
||
" 94 1.9480568e+01 7.08e-02 1.15e-02 -3.2 9.01e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.9647819e+01 8.63e-04 2.09e-03 -3.9 6.20e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 96 1.5853397e+01 1.49e+01 8.36e-01 -1.9 9.75e+06 - 4.36e-04 4.59e-03f 1\n",
|
||
" 97 1.5846985e+01 1.49e+01 8.34e-01 -4.1 9.97e+04 - 3.83e-01 8.95e-04h 1\n",
|
||
" 98 2.0089064e+01 2.01e-01 4.23e-01 -4.1 1.85e+04 - 8.22e-01 1.00e+00h 1\n",
|
||
" 99 1.8641067e+01 1.99e+00 2.18e-01 -4.1 2.77e+04 - 8.00e-01 7.84e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100r 1.8641067e+01 1.99e+00 9.99e+02 0.3 0.00e+00 - 0.00e+00 2.53e-07R 10\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.8641067153859375e+01 1.8641067153859375e+01\n",
|
||
"Dual infeasibility......: 4.9733349346245698e-01 4.9733349346245698e-01\n",
|
||
"Constraint violation....: 1.9859844470923917e+00 1.9859844470923917e+00\n",
|
||
"Complementarity.........: 2.8276229711386307e-04 2.8276229711386307e-04\n",
|
||
"Overall NLP error.......: 1.9859844470923917e+00 1.9859844470923917e+00\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 116\n",
|
||
"Number of objective gradient evaluations = 102\n",
|
||
"Number of equality constraint evaluations = 116\n",
|
||
"Number of inequality constraint evaluations = 116\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.416\n",
|
||
"Total CPU secs in NLP function evaluations = 136.638\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 522.00us ( 4.50us) 517.67us ( 4.46us) 116\n",
|
||
" nlp_g | 5.23 s ( 45.05ms) 4.98 s ( 42.97ms) 116\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 338.00us ( 3.28us) 335.75us ( 3.26us) 103\n",
|
||
" nlp_jac_g | 134.33 s ( 1.30 s) 128.34 s ( 1.25 s) 103\n",
|
||
" total | 141.06 s (141.06 s) 134.76 s (134.76 s) 1\n",
|
||
"Timestamp 16800\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.15e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0883206e+01 1.21e+01 3.15e+04 -1.5 3.15e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.7734249e+00 4.16e+00 6.03e+00 1.2 1.41e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.6439467e+00 5.09e-01 1.32e-01 -0.6 4.80e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 4 1.8496138e+00 5.48e-03 2.57e-01 -6.6 4.67e+00 - 9.90e-01 1.00e+00h 1\n",
|
||
" 5 1.8463820e+00 9.19e-04 1.49e-02 -4.1 1.23e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.8473602e+00 2.44e-06 1.39e-03 -6.0 7.79e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 7 1.8307773e+00 1.52e-02 1.29e-02 -8.1 8.41e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 8 1.8431162e+00 3.33e-03 2.48e-03 -10.0 1.72e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.8148884e+00 2.92e-02 1.18e-02 -11.0 1.04e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.7645030e+00 6.86e-02 2.94e-02 -11.0 4.44e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.7609877e+00 8.94e-02 5.44e-02 -11.0 1.20e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.8684822e+00 5.33e-03 4.09e-02 -11.0 7.52e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 13 1.8549237e+00 2.55e-02 1.59e-02 -11.0 2.20e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.8612583e+00 1.17e-02 3.33e-03 -11.0 7.79e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 8.6390899e-01 1.07e+00 9.06e-01 -11.0 3.04e+07 - 1.06e-03 2.50e-04f 3\n",
|
||
" 16 2.5043401e+00 9.81e-01 1.27e+00 -11.0 1.90e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 17 2.1912522e+00 9.88e-01 1.20e+00 -11.0 4.09e+05 - 8.82e-02 6.63e-02f 1\n",
|
||
" 18 1.7200460e+00 7.21e-01 1.12e+00 -11.0 1.40e+04 - 5.74e-01 1.00e+00F 1\n",
|
||
" 19 1.1093572e+00 1.10e+00 7.31e-01 -1.8 1.26e+04 - 1.00e+00 5.61e-01F 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 8.0719775e-01 1.06e+00 2.32e-01 -2.2 8.33e+03 - 1.00e+00 6.09e-01h 1\n",
|
||
" 21 1.4285980e+00 1.99e-01 7.06e-01 -2.7 1.64e+04 - 7.54e-01 1.00e+00h 1\n",
|
||
" 22 1.4001778e+00 1.10e+00 9.66e-01 -2.7 5.35e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.3276537e+00 3.98e-01 4.62e-01 -2.5 3.98e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.2814821e+00 6.92e-01 4.03e-01 -3.2 5.24e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 25 1.2851772e+00 7.16e-01 3.32e-01 -4.0 4.46e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.0782575e+00 5.75e-01 1.35e+00 -2.0 1.24e+04 - 8.99e-02 1.00e+00f 1\n",
|
||
" 27 8.1359450e-01 7.68e-01 1.39e+00 -2.2 1.81e+05 - 1.00e+00 1.14e-01f 1\n",
|
||
" 28 8.0086542e-01 7.23e-01 1.32e+00 -2.3 2.01e+04 - 4.78e-01 1.03e-02h 1\n",
|
||
" 29 1.5965281e+00 1.42e-01 8.43e-01 -0.8 4.07e+03 - 4.92e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.4166774e+00 1.85e+00 9.79e-01 -1.7 8.37e+03 - 9.78e-01 6.84e-01f 1\n",
|
||
" 31 1.5923051e+00 1.73e+00 9.43e-01 -1.8 1.28e+04 - 6.34e-02 2.50e-01h 3\n",
|
||
" 32 8.5882202e-01 8.28e-01 6.50e-01 -1.8 7.92e+04 - 2.33e-01 3.04e-01f 1\n",
|
||
" 33 2.0026311e+00 1.08e-01 8.66e-01 -2.0 5.33e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 1.4853093e+00 2.05e-01 7.67e-01 -2.2 1.94e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 35 1.3778727e+00 3.31e-01 3.91e-01 -2.2 4.57e+03 - 1.00e+00 3.33e-01h 1\n",
|
||
" 36 1.1647880e+00 1.10e+00 3.16e-01 -2.2 5.08e+03 - 2.18e-01 5.00e-01f 2\n",
|
||
" 37 1.1685583e+00 1.16e+00 4.48e-01 -2.2 2.94e+04 - 1.00e+00 6.92e-02h 4\n",
|
||
" 38 1.0636074e+00 1.41e+00 1.08e+00 -2.2 8.64e+04 - 2.76e-02 2.85e-01f 1\n",
|
||
" 39 1.4155158e+00 8.31e-01 7.37e-01 -2.2 3.47e+04 - 5.60e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.3557943e+00 7.83e-01 7.10e-01 -2.2 4.92e+04 - 4.77e-01 6.41e-02h 1\n",
|
||
" 41 1.0059548e+00 8.19e-01 7.28e-01 -2.2 1.95e+04 - 5.93e-02 1.00e-01f 1\n",
|
||
" 42 2.0194041e+00 1.44e-01 3.73e-01 -8.3 6.10e+03 - 1.49e-02 1.00e+00H 1\n",
|
||
" 43 1.3176645e+00 4.88e-01 1.46e-01 -2.4 5.19e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 44 1.1735305e+00 6.56e-01 6.80e-01 -2.8 2.56e+05 - 7.63e-02 1.16e-02f 2\n",
|
||
" 45 1.2077709e+00 8.19e-01 6.02e-01 -2.6 3.83e+04 - 1.00e+00 1.64e-01h 1\n",
|
||
" 46 2.0073370e+00 7.85e-02 2.33e-01 -2.6 2.46e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.7894174e+00 1.58e-01 2.26e-01 -2.6 1.60e+03 - 6.40e-01 1.00e+00h 1\n",
|
||
" 48 1.5978286e+00 5.31e-01 3.02e-01 -2.6 4.40e+03 - 1.00e+00 4.28e-01h 1\n",
|
||
" 49 1.6311005e+00 4.78e-01 2.39e-01 -2.6 5.64e+03 - 1.00e+00 8.77e-02H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 5.5232402e-01 4.87e-01 3.85e-01 -2.6 1.67e+04 - 2.44e-01 2.15e-01f 1\n",
|
||
" 51 4.4517496e-01 8.49e-01 9.33e-01 -8.7 2.01e+05 - 7.07e-04 7.61e-03f 6\n",
|
||
" 52 4.3203671e-01 8.32e-01 8.64e-01 -2.1 4.14e+04 - 1.00e+00 2.36e-02h 1\n",
|
||
" 53 2.7000827e+00 5.41e-01 3.27e-01 -3.4 1.69e+03 - 9.96e-01 1.00e+00h 1\n",
|
||
" 54 2.2764696e+00 4.45e-02 3.00e-01 -3.1 3.59e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.8786895e+00 2.43e-01 5.61e-01 -3.1 4.87e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 56 1.9390527e+00 2.11e-01 4.66e-01 -3.1 1.49e+03 - 1.00e+00 1.56e-01h 1\n",
|
||
" 57 2.2031762e+00 8.40e-02 1.23e-01 -3.1 4.10e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.1561621e+00 9.42e-02 1.47e-01 -3.1 6.92e+02 - 1.00e+00 4.55e-01h 1\n",
|
||
" 59 2.3589426e+00 3.10e-04 1.30e-01 -3.1 1.20e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.3582153e+00 6.31e-07 4.28e-03 -3.1 6.40e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.3582149e+00 2.88e-08 5.52e-05 -9.1 1.06e-04 - 9.96e-01 1.00e+00h 1\n",
|
||
" 62 2.3582150e+00 1.27e-08 1.31e-04 -11.0 8.28e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.3582150e+00 3.31e-08 4.84e-05 -11.0 1.18e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.3582150e+00 2.36e-09 4.36e-05 -11.0 3.78e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.3582150e+00 8.03e-11 1.49e-04 -11.0 3.34e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 66 2.3582150e+00 2.00e-08 1.84e-04 -11.0 6.71e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.3582149e+00 5.72e-08 1.60e-04 -11.0 1.17e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.3582148e+00 5.40e-08 2.25e-04 -11.0 6.32e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.3582149e+00 7.19e-08 1.04e-04 -11.0 3.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.3582150e+00 2.66e-08 2.58e-05 -11.0 2.28e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.3582150e+00 1.77e-08 1.63e-04 -11.0 3.77e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 2.3582148e+00 1.69e-07 5.84e-05 -11.0 7.91e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 2.3582147e+00 1.83e-07 1.38e-05 -11.0 1.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.3582149e+00 7.51e-08 8.69e-05 -11.0 1.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 2.3582134e+00 6.38e-06 2.19e-03 -10.7 3.48e-01 - 1.00e+00 1.86e-01h 1\n",
|
||
"In iteration 75, 1 Slack too small, adjusting variable bound\n",
|
||
" 76 2.3582134e+00 6.38e-06 2.66e-03 -10.9 2.08e-01 - 1.00e+00 2.71e-05h 1\n",
|
||
" 77 2.3582149e+00 6.49e-08 1.02e-04 -10.9 3.06e-04 - 5.23e-01 1.00e+00h 1\n",
|
||
" 78 2.3582148e+00 2.30e-07 1.23e-04 -11.0 2.53e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 2.3582143e+00 4.35e-07 1.45e-04 -8.3 1.68e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.3582147e+00 1.50e-07 2.88e-04 -7.7 8.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.3582147e+00 9.66e-08 4.44e-05 -11.0 4.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.3582144e+00 2.56e-07 4.40e-05 -9.5 6.82e-03 - 1.00e+00 1.82e-01h 1\n",
|
||
" 83 2.3582146e+00 1.67e-07 1.64e-04 -7.6 4.81e-03 - 1.74e-01 1.00e+00f 1\n",
|
||
" 84 2.3582118e+00 7.50e-06 8.85e-04 -10.3 7.71e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 2.3582136e+00 9.47e-06 2.22e-03 -10.2 7.62e-02 - 5.62e-01 1.00e+00h 1\n",
|
||
" 86 2.3578961e+00 3.52e-04 9.89e-03 -10.2 1.37e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.3581552e+00 2.81e-05 1.18e-03 -10.2 4.68e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 2.3582132e+00 9.01e-06 6.14e-04 -10.2 5.96e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 2.3582072e+00 2.21e-05 1.25e-03 -10.2 1.52e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.3577827e+00 1.18e-04 4.40e-03 -10.2 1.31e+00 - 1.00e+00 7.38e-01h 1\n",
|
||
" 91 2.3577831e+00 1.18e-04 3.77e-03 -10.2 4.17e-01 - 1.00e+00 9.77e-04h 11\n",
|
||
" 92 2.3581146e+00 3.51e-05 1.94e-03 -10.2 3.83e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 2.3581714e+00 1.47e-05 1.49e-03 -10.2 1.05e-01 - 7.56e-01 1.00e+00h 1\n",
|
||
" 94 2.3582135e+00 2.71e-07 6.41e-05 -10.2 8.01e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 95 2.3579312e+00 2.40e-04 1.42e-03 -6.1 3.03e+00 - 1.00e+00 8.00e-01h 1\n",
|
||
" 96 2.3581513e+00 5.44e-05 1.43e-03 -6.2 1.53e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.3581201e+00 5.04e-05 1.52e-03 -6.2 7.21e-01 - 6.81e-01 1.00e+00h 1\n",
|
||
" 98 2.3578543e+00 3.64e-04 5.78e-04 -6.2 1.23e+01 - 1.00e+00 1.49e-01h 1\n",
|
||
" 99 2.3466603e+00 4.28e-03 1.64e-02 -6.2 3.15e+01 - 2.46e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.3521826e+00 2.15e-03 7.52e-03 -6.2 8.46e+00 - 1.00e+00 5.48e-01h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.3521825653631345e+00 2.3521825653631345e+00\n",
|
||
"Dual infeasibility......: 7.5204318181116610e-03 7.5204318181116610e-03\n",
|
||
"Constraint violation....: 2.1541139769922779e-03 2.1541139769922779e-03\n",
|
||
"Complementarity.........: 5.7228315360935602e-07 5.7228315360935602e-07\n",
|
||
"Overall NLP error.......: 7.5204318181116610e-03 7.5204318181116610e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 155\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 155\n",
|
||
"Number of inequality constraint evaluations = 155\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.441\n",
|
||
"Total CPU secs in NLP function evaluations = 137.210\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 708.00us ( 4.57us) 702.00us ( 4.53us) 155\n",
|
||
" nlp_g | 6.97 s ( 44.96ms) 6.65 s ( 42.88ms) 155\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 367.00us ( 3.60us) 359.21us ( 3.52us) 102\n",
|
||
" nlp_jac_g | 133.08 s ( 1.30 s) 127.10 s ( 1.25 s) 102\n",
|
||
" total | 141.53 s (141.53 s) 135.17 s (135.17 s) 1\n",
|
||
"Timestamp 17100\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 4.48e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9686172e+01 1.32e+01 4.48e+03 -1.5 4.48e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.8369810e+00 4.58e+00 8.98e+00 0.4 2.01e+01 - 9.97e-01 1.00e+00f 1\n",
|
||
" 3 6.8404465e+00 1.03e+00 8.53e-01 -1.6 7.13e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 7.6207240e+00 2.12e-03 8.92e-02 -3.3 1.51e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 7.6216789e+00 1.68e-07 9.53e-05 -5.2 2.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 7.6216788e+00 3.31e-07 4.47e-05 -11.0 1.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 7.6216791e+00 8.72e-08 8.18e-05 -11.0 1.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 7.6216787e+00 4.77e-07 8.50e-05 -11.0 1.74e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 7.6216790e+00 1.87e-07 9.09e-05 -11.0 1.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 7.6216793e+00 1.21e-10 2.11e-04 -11.0 1.15e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 7.6216786e+00 3.65e-07 7.33e-05 -11.0 1.39e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 7.6216778e+00 1.11e-06 3.11e-03 -11.0 6.70e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 7.6216702e+00 7.42e-06 2.34e-03 -11.0 4.59e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 7.6216758e+00 1.85e-06 1.91e-03 -11.0 2.22e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 7.6216758e+00 8.65e-07 9.22e-04 -11.0 1.37e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 7.6216487e+00 1.84e-05 2.79e-03 -11.0 6.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 7.6216758e+00 1.15e-06 8.49e-04 -11.0 1.74e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 7.6216783e+00 4.40e-07 1.04e-04 -11.0 8.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 7.6216532e+00 1.56e-05 3.31e-03 -11.0 6.22e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 7.6216767e+00 1.04e-07 3.66e-05 -11.0 6.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 7.6216766e+00 1.23e-07 9.78e-05 -11.0 5.27e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 7.6178408e+00 1.87e-02 8.95e-03 -11.0 9.08e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 23 7.6084944e+00 4.38e-02 2.46e-03 -11.0 5.26e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 7.5292800e+00 8.55e-02 1.47e-02 -11.0 1.41e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 7.2071135e+00 4.75e-01 7.27e-02 -11.0 1.12e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 6.4236753e+00 5.56e-01 9.96e-02 -11.0 7.60e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.4753352e+00 1.00e+00 9.99e-02 -11.0 1.85e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 28 7.5120258e+00 1.27e-01 1.05e-01 -11.0 3.47e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 7.3091060e+00 2.46e+00 2.94e-01 -11.0 9.60e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 6.7530896e+00 5.53e+00 8.07e-01 -11.0 8.42e+04 - 5.18e-01 2.37e-01f 1\n",
|
||
" 31 6.7530896e+00 5.53e+00 8.07e-01 -11.0 2.08e+04 - 2.30e-10 2.30e-10s 2\n",
|
||
" 32r 6.7530896e+00 5.53e+00 9.99e+02 0.7 0.00e+00 - 0.00e+00 0.00e+00R 1\n",
|
||
" 33r 7.2453875e+00 3.31e-01 9.92e+02 -5.3 8.51e+02 - 4.28e-01 6.44e-03f 1\n",
|
||
" 34 7.5049387e+00 7.02e-02 4.32e-02 -11.0 2.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 7.4210446e+00 8.94e-02 5.92e-02 -3.9 1.03e+04 - 3.58e-01 1.00e+00h 1\n",
|
||
" 36 5.2007223e+00 1.06e+00 1.72e-01 -2.5 3.20e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 37 4.8636443e+00 1.60e+00 1.01e-01 -0.9 2.29e+04 - 1.00e+00 3.16e-01h 2\n",
|
||
" 38 6.8783686e+00 3.80e-01 3.92e-01 -6.9 4.91e+03 - 6.17e-01 1.00e+00h 1\n",
|
||
" 39 6.1753773e+00 5.17e+00 3.57e-01 -1.9 1.73e+04 - 9.33e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 5.9325052e+00 3.87e+00 2.93e-01 -1.9 2.57e+04 - 6.95e-01 2.58e-01h 1\n",
|
||
" 41 6.1194691e+00 2.15e+00 1.48e-01 -1.9 8.35e+04 - 6.73e-02 4.24e-01h 1\n",
|
||
" 42 6.1337937e+00 2.18e+00 1.50e-01 -1.9 1.74e+05 - 1.00e+00 1.75e-02h 3\n",
|
||
" 43 7.1064182e+00 4.47e-01 9.50e-02 -1.9 2.50e+03 - 2.59e-01 1.00e+00h 1\n",
|
||
" 44 7.1925425e+00 4.80e-01 1.26e-01 -1.9 8.02e+03 - 9.60e-01 4.71e-01H 1\n",
|
||
" 45 6.8577621e+00 4.55e-01 1.32e-01 -1.9 4.15e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 7.2741388e+00 1.52e-01 5.09e-02 -8.0 2.36e+03 - 7.56e-01 1.00e+00h 1\n",
|
||
" 47 5.8125143e+00 5.76e-01 1.38e-01 -8.6 1.23e+05 - 4.44e-03 1.19e-01f 1\n",
|
||
" 48 6.7136809e+00 4.11e-01 1.04e-01 -1.8 2.39e+04 - 1.00e+00 6.28e-01h 1\n",
|
||
" 49 6.6521631e+00 1.70e+00 1.61e-01 -1.7 1.77e+04 - 9.92e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 7.3772926e+00 5.54e-02 2.18e-01 -1.8 2.57e+03 - 3.91e-01 1.00e+00h 1\n",
|
||
" 51 7.3821773e+00 1.02e-01 2.19e-02 -2.8 3.59e+03 - 9.99e-01 1.00e+00h 1\n",
|
||
" 52 7.0572246e+00 2.62e-01 2.02e-02 -3.0 1.02e+05 - 8.69e-01 1.63e-01f 1\n",
|
||
" 53 7.3805471e+00 3.77e-02 4.98e-02 -3.5 9.57e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 7.2746409e+00 6.64e-01 6.89e-02 -3.8 5.93e+03 - 9.98e-01 1.00e+00h 1\n",
|
||
" 55 7.1189083e+00 8.69e-01 4.91e-02 -3.9 5.15e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 7.0807985e+00 8.48e-01 2.62e-02 -3.9 3.59e+03 - 5.34e-02 1.00e+00h 1\n",
|
||
" 57 6.6541525e+00 1.70e+00 1.64e-01 -3.9 2.17e+05 - 9.98e-02 2.96e-02f 1\n",
|
||
" 58 6.6179620e+00 1.25e+00 3.78e-02 -3.9 4.57e+03 - 1.00e+00 3.36e-01f 1\n",
|
||
" 59 7.1792674e+00 1.12e-01 1.15e-01 -3.9 1.03e+03 - 7.51e-03 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 7.0364294e+00 9.25e-02 1.44e-01 -3.2 8.37e+02 - 9.94e-01 1.00e+00h 1\n",
|
||
" 61 7.3428630e+00 5.62e-03 2.16e-02 -2.1 2.23e+03 - 7.48e-01 1.00e+00H 1\n",
|
||
" 62 7.1062268e+00 2.97e-01 3.79e-02 -8.2 3.25e+03 - 7.82e-01 1.00e+00f 1\n",
|
||
" 63 7.1610423e+00 1.23e-01 2.00e-02 -3.1 1.28e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 7.3130052e+00 4.95e-02 5.08e-02 -3.9 1.35e+03 - 9.71e-01 1.00e+00H 1\n",
|
||
" 65 7.1923972e+00 4.46e-01 2.10e-02 -4.8 4.36e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 7.1952149e+00 2.89e-01 4.15e-02 -4.8 8.06e+03 - 1.00e+00 3.93e-01h 1\n",
|
||
" 67 7.1592579e+00 3.97e-01 3.66e-02 -4.8 3.14e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 6.9732106e+00 1.81e-01 2.55e-02 -4.8 2.37e+04 - 9.47e-01 4.98e-02f 1\n",
|
||
" 69 5.8820702e+00 1.78e+00 3.12e-01 -3.4 2.00e+04 - 1.00e+00 4.02e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 7.2914557e+00 7.08e-03 2.16e+00 -3.5 2.47e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 7.2999807e+00 1.40e-06 1.25e-03 -5.4 1.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 7.2999824e+00 1.78e-07 1.28e-04 -7.5 1.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 7.2999812e+00 7.28e-07 1.33e-04 -11.0 2.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 7.2998906e+00 5.37e-05 1.94e-02 -11.0 2.11e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 7.2999634e+00 1.33e-05 2.33e-03 -11.0 8.30e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 7.2999451e+00 1.28e-05 5.48e-03 -11.0 1.86e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 7.2998924e+00 3.58e-05 8.63e-03 -11.0 1.99e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 7.2999718e+00 9.46e-06 1.64e-03 -11.0 6.33e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 7.2998747e+00 1.59e-04 5.15e-03 -11.0 1.25e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 7.2997444e+00 3.29e-04 4.39e-03 -11.0 3.24e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 7.2999444e+00 9.80e-05 2.03e-03 -11.0 2.24e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 7.2984215e+00 9.02e-04 1.04e-03 -11.0 2.46e+01 - 1.00e+00 2.21e-01h 1\n",
|
||
" 83 7.2984216e+00 9.02e-04 1.06e-03 -10.3 2.84e+00 - 1.00e+00 6.10e-05h 15\n",
|
||
" 84 7.2986223e+00 7.89e-04 5.56e-04 -8.0 2.54e-02 - 1.00e+00 1.25e-01h 1\n",
|
||
" 85 7.2989113e+00 6.06e-04 1.01e-03 -6.0 1.64e+00 - 5.30e-01 2.50e-01h 3\n",
|
||
" 86 7.2997359e+00 5.46e-04 1.06e-02 -6.0 2.45e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 7.2999015e+00 1.40e-04 1.36e-03 -7.0 1.08e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 7.2996980e+00 7.94e-04 1.79e-03 -6.0 1.93e+00 - 4.60e-01 1.00e+00h 1\n",
|
||
" 89 7.2997930e+00 9.68e-05 9.78e-04 -6.0 1.33e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 7.2994521e+00 3.03e-04 1.55e-03 -5.1 3.07e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 7.2977061e+00 2.03e-03 2.93e-03 -5.9 4.89e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 7.3000082e+00 7.59e-07 9.74e-05 -8.0 2.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 7.3000084e+00 2.29e-07 1.54e-04 -8.1 9.45e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 7.3000080e+00 5.71e-07 5.23e-05 -8.1 1.34e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 7.3000086e+00 1.88e-08 2.59e-04 -8.1 3.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 7.3000084e+00 1.23e-07 6.72e-05 -11.0 2.84e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 7.3000085e+00 9.57e-08 1.12e-04 -11.0 9.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 7.3000076e+00 1.67e-06 1.92e-03 -11.0 5.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 7.3000049e+00 3.45e-06 2.97e-03 -11.0 1.06e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 7.3000068e+00 1.17e-06 2.68e-03 -11.0 6.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 7.3000067688917492e+00 7.3000067688917492e+00\n",
|
||
"Dual infeasibility......: 2.6820718825956713e-03 2.6820718825956713e-03\n",
|
||
"Constraint violation....: 1.1714730625556058e-06 1.1714730625556058e-06\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 2.6820718825956713e-03 2.6820718825956713e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 131\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 131\n",
|
||
"Number of inequality constraint evaluations = 131\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.420\n",
|
||
"Total CPU secs in NLP function evaluations = 136.958\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 602.00us ( 4.60us) 585.89us ( 4.47us) 131\n",
|
||
" nlp_g | 5.90 s ( 45.07ms) 5.63 s ( 42.96ms) 131\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 362.00us ( 3.55us) 352.45us ( 3.46us) 102\n",
|
||
" nlp_jac_g | 133.82 s ( 1.30 s) 127.77 s ( 1.24 s) 103\n",
|
||
" total | 141.21 s (141.21 s) 134.83 s (134.83 s) 1\n",
|
||
"Timestamp 17400\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.15e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0444238e+01 1.19e+01 3.15e+04 -1.5 3.15e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.5361050e+00 3.98e+00 5.58e+00 1.0 5.78e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.6141783e+00 4.62e-01 1.54e-01 -1.1 1.82e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 9.9495169e-01 2.09e-03 4.40e-01 -7.0 2.71e+00 - 9.90e-01 1.00e+00h 1\n",
|
||
" 5 9.9521232e-01 1.23e-04 6.96e-03 -4.7 1.58e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 9.9546027e-01 8.00e-08 2.89e-05 -6.8 1.18e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 7 9.9490989e-01 3.32e-04 1.84e-03 -11.0 5.86e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 5.4838155e-01 7.68e-01 7.13e-01 -10.2 1.41e+05 - 1.00e+00 5.72e-02f 3\n",
|
||
" 9 5.0427429e-01 9.21e-01 1.37e+00 -8.0 2.35e+07 - 2.29e-03 2.55e-04f 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.3483111e+00 3.08e-01 3.74e-01 -11.0 1.42e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.2683463e+00 7.63e-01 5.85e-01 -10.9 3.54e+03 - 8.86e-01 1.00e+00H 1\n",
|
||
" 12 9.0492259e-01 1.17e+00 5.81e-01 -10.9 1.82e+04 - 1.00e+00 5.00e-01f 2\n",
|
||
" 13 1.0220436e+00 4.51e-01 4.67e-01 -10.9 1.50e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 8.0670009e-01 1.35e-01 3.77e-01 -10.9 4.76e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 6.4080039e-01 8.84e-01 1.02e+00 -11.0 4.74e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 16 4.2976772e-01 4.98e-01 9.45e-01 -11.0 9.06e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 3.6255653e-01 4.54e-01 1.34e+00 -9.0 6.33e+04 - 1.00e+00 6.30e-02f 4\n",
|
||
" 18 2.5339155e-01 4.15e-01 4.52e-01 -9.3 1.72e+04 - 1.00e+00 1.25e-01f 4\n",
|
||
" 19 1.6839772e-01 3.76e-01 4.09e-01 -9.0 1.43e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.6797585e-01 3.73e-01 4.61e-01 -9.9 6.57e+03 - 1.00e+00 1.56e-02h 7\n",
|
||
" 21 1.6784600e-01 3.66e-01 3.78e-01 -9.5 3.16e+04 - 1.00e+00 3.64e-03h 9\n",
|
||
" 22 1.6514948e-01 3.65e-01 3.16e-01 -7.6 1.19e+06 - 1.00e+00 3.86e-04h 7\n",
|
||
" 23 8.2421231e-01 7.24e-03 5.99e-01 -9.2 5.99e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 8.1525325e-01 9.18e-07 1.22e-02 -11.0 1.48e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 8.1525341e-01 9.38e-08 4.92e-05 -11.0 2.02e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 8.1525346e-01 9.14e-09 5.01e-05 -11.0 5.01e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.1525341e-01 2.94e-08 1.39e-04 -11.0 4.25e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 8.1525335e-01 9.26e-08 7.66e-05 -11.0 3.10e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.1525345e-01 2.06e-08 8.58e-05 -11.0 1.12e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.1525346e-01 7.97e-09 2.94e-05 -11.0 5.88e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 8.1525347e-01 2.83e-09 6.89e-05 -11.0 4.43e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 8.1525346e-01 1.33e-08 2.93e-05 -11.0 5.78e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 8.1525347e-01 3.46e-09 1.11e-04 -11.0 2.63e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 8.1525347e-01 5.62e-09 1.36e-04 -11.0 4.64e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 8.1525346e-01 5.28e-09 5.85e-05 -11.0 2.92e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 8.1525347e-01 4.22e-09 4.30e-05 -11.0 5.79e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 8.1525346e-01 1.26e-08 2.65e-05 -11.0 1.21e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 8.1525346e-01 8.62e-09 2.89e-05 -11.0 1.56e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 8.1525346e-01 1.39e-08 9.34e-05 -11.0 9.80e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.1525337e-01 1.43e-07 1.08e-04 -11.0 3.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 8.1525345e-01 2.15e-08 5.14e-05 -11.0 8.92e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 8.1525347e-01 9.58e-09 6.76e-05 -11.0 4.54e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 8.1525347e-01 2.67e-09 1.49e-04 -11.0 2.63e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 8.1525342e-01 4.15e-08 5.71e-05 -11.0 1.19e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 8.1525347e-01 4.04e-09 2.12e-05 -11.0 3.62e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 8.1525347e-01 1.87e-09 8.72e-05 -11.0 2.11e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 8.1525347e-01 2.93e-09 1.46e-05 -11.0 1.63e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 8.1525347e-01 8.24e-11 7.72e-06 -11.0 2.16e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 49 8.1525347e-01 2.13e-10 4.44e-05 -11.0 3.48e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.1525347e-01 5.37e-09 6.36e-05 -11.0 1.95e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 8.1525346e-01 2.19e-08 1.68e-04 -11.0 1.13e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 8.1525342e-01 4.43e-08 8.09e-05 -11.0 9.05e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 8.1525345e-01 2.23e-08 6.47e-05 -11.0 9.73e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 8.1525334e-01 1.01e-07 1.87e-04 -11.0 5.96e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 8.1525314e-01 4.31e-07 1.74e-04 -11.0 8.79e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 8.1525318e-01 1.15e-07 1.66e-04 -11.0 5.55e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 8.1525143e-01 1.84e-06 1.29e-02 -11.0 4.20e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 8.1525021e-01 2.28e-06 3.33e-03 -11.0 9.48e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 8.1525294e-01 9.32e-07 2.09e-03 -11.0 3.62e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 8.1524641e-01 3.15e-06 2.75e-03 -11.0 1.22e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 8.1525322e-01 1.20e-07 4.65e-05 -11.0 2.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 8.1525330e-01 7.97e-08 2.17e-04 -11.0 1.06e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 8.1525289e-01 3.20e-07 2.20e-05 -11.0 8.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 8.1525344e-01 1.59e-07 1.25e-05 -11.0 3.95e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 8.1525343e-01 8.75e-08 7.97e-05 -11.0 1.34e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 8.1525340e-01 7.39e-08 2.27e-05 -11.0 4.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 8.1525289e-01 2.33e-06 3.53e-03 -11.0 6.87e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 8.1525332e-01 5.35e-07 1.06e-04 -11.0 2.80e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 8.1525284e-01 7.96e-07 1.94e-03 -11.0 4.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.1525347e-01 1.86e-07 4.26e-05 -11.0 8.15e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 8.1525341e-01 1.52e-07 4.19e-05 -11.0 1.09e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 8.1525326e-01 1.21e-07 1.29e-04 -11.0 4.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 8.1525304e-01 4.27e-07 1.18e-04 -11.0 3.29e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 8.1525227e-01 1.09e-06 1.91e-03 -11.0 8.05e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 8.1525266e-01 6.79e-07 8.55e-05 -11.0 3.71e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 8.1525348e-01 1.40e-10 1.39e-04 -11.0 6.80e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 77 8.1525305e-01 4.99e-07 4.14e-05 -11.0 3.44e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 8.1525341e-01 3.76e-08 3.88e-05 -11.0 9.99e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 8.1525164e-01 1.78e-05 2.02e-03 -11.0 7.02e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 8.1521070e-01 5.03e-05 1.32e-02 -11.0 3.33e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 8.1516313e-01 1.44e-04 4.85e-03 -11.0 4.91e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 8.1524864e-01 3.03e-08 9.53e-05 -11.0 1.40e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 8.1524861e-01 1.59e-08 4.28e-04 -11.0 7.68e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 8.1524819e-01 2.09e-07 1.32e-05 -11.0 3.49e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 8.1524864e-01 1.10e-09 3.77e-06 -11.0 1.19e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 8.1524864e-01 2.48e-10 5.69e-05 -11.0 5.27e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 8.1524864e-01 9.77e-10 1.94e-05 -11.0 3.21e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 8.1524864e-01 1.26e-09 4.55e-04 -11.0 1.60e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 8.1524828e-01 2.81e-07 9.93e-06 -11.0 2.61e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 8.1524865e-01 3.69e-11 1.71e-04 -11.0 1.83e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 8.1524865e-01 7.22e-10 6.50e-05 -11.0 1.94e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 8.1524865e-01 1.90e-11 3.55e-05 -11.0 6.27e-06 - 1.00e+00 1.00e+00H 1\n",
|
||
" 93 8.1524865e-01 2.21e-09 6.49e-05 -11.0 1.32e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 8.1524864e-01 7.52e-09 3.93e-05 -11.0 2.01e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 8.1524864e-01 1.34e-08 1.48e-04 -11.0 7.71e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 8.1524861e-01 6.21e-08 3.54e-05 -11.0 1.52e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 8.1524864e-01 3.04e-09 8.45e-05 -11.0 3.83e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 8.1524865e-01 2.97e-10 1.52e-04 -11.0 6.11e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 8.1524864e-01 7.21e-09 7.26e-05 -11.0 3.82e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.1524864e-01 1.09e-08 1.35e-04 -11.0 6.43e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.1524864225738936e-01 8.1524864225738936e-01\n",
|
||
"Dual infeasibility......: 1.3534010113984548e-04 1.3534010113984548e-04\n",
|
||
"Constraint violation....: 1.0933330685247711e-08 1.0933330685247711e-08\n",
|
||
"Complementarity.........: 1.0000000000000001e-11 1.0000000000000001e-11\n",
|
||
"Overall NLP error.......: 1.3534010113984548e-04 1.3534010113984548e-04\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 153\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 153\n",
|
||
"Number of inequality constraint evaluations = 153\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.417\n",
|
||
"Total CPU secs in NLP function evaluations = 136.604\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 687.00us ( 4.49us) 687.14us ( 4.49us) 153\n",
|
||
" nlp_g | 6.88 s ( 44.98ms) 6.57 s ( 42.91ms) 153\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 436.00us ( 4.27us) 428.15us ( 4.20us) 102\n",
|
||
" nlp_jac_g | 132.50 s ( 1.30 s) 126.54 s ( 1.24 s) 102\n",
|
||
" total | 140.88 s (140.88 s) 134.53 s (134.53 s) 1\n",
|
||
"Timestamp 17700\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.17e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9635133e+01 1.46e+01 1.17e+04 -1.5 1.17e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.0440468e+01 5.09e+00 1.15e+01 0.6 1.46e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.4844494e+01 1.80e+00 6.29e-01 -1.5 1.12e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.6048917e+01 4.18e-04 7.83e-02 -3.5 2.21e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 5 1.6049131e+01 2.68e-06 1.48e-03 -5.3 5.80e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.6049134e+01 7.18e-07 7.16e-05 -7.4 2.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.6049130e+01 1.73e-06 4.21e-03 -11.0 1.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.6049133e+01 7.72e-07 8.75e-04 -11.0 5.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.6049134e+01 6.44e-07 1.80e-03 -11.0 6.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.6049134e+01 4.98e-07 3.65e-05 -11.0 4.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.6049135e+01 1.35e-07 3.86e-05 -11.0 1.47e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.6049106e+01 2.34e-05 1.67e-02 -11.0 2.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.6049086e+01 2.57e-05 1.25e-03 -11.0 1.52e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.6049128e+01 7.12e-06 2.77e-03 -11.0 5.97e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.6049115e+01 1.24e-05 2.03e-03 -11.0 6.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.6048771e+01 1.86e-04 1.47e-02 -11.0 3.23e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.6049093e+01 1.15e-05 7.71e-04 -11.0 9.40e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.6048900e+01 1.15e-04 2.34e-02 -11.0 1.12e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.6043659e+01 5.49e-03 1.60e-03 -11.0 1.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.6040205e+01 1.21e-02 4.96e-03 -11.0 2.81e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.6044901e+01 8.54e-03 2.79e-03 -11.0 2.86e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.6022550e+01 4.48e-02 2.34e-03 -11.0 2.94e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.6012694e+01 3.15e-02 1.55e-03 -11.0 1.99e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.5744261e+01 1.13e-01 9.70e-03 -11.0 5.84e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 25 1.6041420e+01 1.13e-02 8.45e-03 -11.0 2.12e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.5930923e+01 7.66e-02 4.85e-03 -11.0 1.35e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.6061470e+01 4.70e-04 2.13e-03 -11.0 2.15e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.6058957e+01 1.61e-03 3.57e-03 -11.0 9.50e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.5736441e+01 2.06e+00 6.72e-02 -11.0 2.22e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.5430956e+01 1.14e+00 2.19e-02 -11.0 4.81e+04 - 1.00e+00 2.15e-01h 1\n",
|
||
" 31 1.5865296e+01 1.15e-01 5.49e-02 -10.0 1.01e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.4977799e+01 3.91e+00 2.53e-01 -4.7 7.07e+04 - 1.26e-01 7.48e-01f 1\n",
|
||
" 33 1.4977787e+01 3.91e+00 2.53e-01 -4.7 2.63e+04 - 1.00e+00 3.66e-05h 1\n",
|
||
" 34 1.5872718e+01 1.08e-02 1.61e-01 -4.7 8.29e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.5863624e+01 1.08e-03 2.14e-03 -3.7 5.73e+01 - 9.60e-01 1.00e+00h 1\n",
|
||
" 36 1.5855249e+01 3.67e-02 5.11e-03 -9.7 4.83e+02 - 2.94e-02 1.00e+00h 1\n",
|
||
" 37 1.5115533e+01 1.95e+00 4.93e-02 -9.7 6.01e+06 - 5.15e-05 7.20e-03f 1\n",
|
||
" 38 1.3943351e+01 1.77e+00 8.70e-02 -3.8 4.29e+04 - 5.08e-02 1.00e+00h 1\n",
|
||
" 39 1.6276228e+01 2.38e-01 6.14e-02 -3.5 2.12e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.5511951e+01 1.65e+00 3.97e-02 -3.6 9.95e+04 - 2.94e-01 2.35e-01f 1\n",
|
||
" 41 1.5509480e+01 1.65e+00 3.90e-02 -3.6 9.04e+03 - 1.00e+00 5.62e-03h 1\n",
|
||
" 42 1.5865054e+01 6.02e-02 6.54e-02 -3.6 4.24e+02 - 5.04e-01 1.00e+00h 1\n",
|
||
" 43 1.5555499e+01 5.05e-01 1.39e-01 -3.6 3.53e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.4829842e+01 8.28e-01 1.97e-01 -2.5 2.16e+04 - 1.00e+00 7.79e-02f 1\n",
|
||
" 45 1.5562715e+01 1.28e-01 7.98e-02 -3.8 6.08e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.5196569e+01 2.41e-01 6.14e-02 -5.3 2.67e+03 - 9.22e-01 1.00e+00h 1\n",
|
||
" 47 1.2180874e+01 1.03e+01 1.18e+00 -10.8 4.20e+05 - 6.89e-04 1.08e-01f 1\n",
|
||
" 48 1.2134045e+01 1.03e+01 1.17e+00 -5.0 6.87e+04 - 8.87e-01 6.67e-03h 1\n",
|
||
" 49 1.1585127e+01 9.58e+00 1.05e+00 -5.0 4.07e+03 - 1.00e+00 7.40e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.3963991e+01 2.83e+00 4.84e-01 -5.0 1.26e+03 - 6.09e-01 1.00e+00h 1\n",
|
||
" 51 1.2945128e+01 4.82e+00 3.80e-01 -3.8 1.01e+04 - 1.00e+00 8.99e-01h 1\n",
|
||
" 52 1.4449990e+01 5.85e+00 6.82e-02 -1.9 1.82e+04 - 1.57e-01 1.00e+00H 1\n",
|
||
" 53 1.5136756e+01 2.15e+00 2.78e-01 -2.5 1.68e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.1616211e+01 5.47e+00 5.13e-01 -2.5 1.24e+05 - 1.57e-01 3.30e-01f 1\n",
|
||
" 55 1.2257339e+01 6.20e+00 5.00e-01 -0.7 5.77e+05 - 1.97e-01 3.37e-03f 6\n",
|
||
" 56 1.2386067e+01 1.93e+00 6.33e-01 -1.2 1.09e+05 - 4.41e-01 4.19e-01h 1\n",
|
||
" 57 1.5206256e+01 1.06e+01 1.02e+00 0.1 6.96e+05 - 3.15e-02 4.25e-02f 2\n",
|
||
" 58 1.4613195e+01 1.04e+00 1.73e+01 -0.3 1.85e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 59 1.5405044e+01 5.21e-04 1.61e-01 -2.2 1.40e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.5405302e+01 1.40e-06 2.84e-03 -4.1 5.44e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.5404871e+01 2.12e-04 4.06e-03 -6.2 8.72e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.5405266e+01 7.73e-06 1.55e-03 -8.3 1.29e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.5405290e+01 7.19e-09 8.89e-05 -11.0 1.64e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 64 1.5405037e+01 1.36e-04 3.38e-03 -11.0 9.19e-01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 65 1.5403697e+01 9.12e-04 8.69e-03 -11.0 2.33e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.5405377e+01 1.92e-07 1.09e-04 -11.0 1.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.5405377e+01 1.52e-07 8.86e-05 -11.0 7.66e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.5405377e+01 1.22e-07 1.90e-04 -11.0 5.41e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.5405377e+01 8.28e-08 9.78e-05 -11.0 2.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.5405377e+01 4.55e-08 3.15e-05 -11.0 2.31e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.5405377e+01 7.58e-09 8.15e-05 -11.0 5.00e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.5405377e+01 3.08e-08 1.58e-04 -11.0 1.11e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.5405377e+01 3.54e-08 1.65e-04 -11.0 9.81e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.5405376e+01 9.92e-07 7.40e-03 -11.0 2.13e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.5405299e+01 3.26e-05 1.75e-02 -11.0 1.45e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.5405375e+01 5.03e-06 2.83e-03 -11.0 2.33e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.5405372e+01 8.47e-06 2.98e-03 -11.0 5.90e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.5405376e+01 3.81e-07 8.62e-05 -11.0 1.29e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.5405376e+01 5.03e-08 2.87e-05 -11.0 2.28e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.5405369e+01 9.78e-06 1.82e-03 -11.0 7.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.5405333e+01 1.95e-05 1.36e-03 -11.0 6.54e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.5405373e+01 4.51e-06 1.23e-03 -11.0 1.90e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.5405368e+01 3.51e-06 1.49e-03 -11.0 1.00e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.5405374e+01 1.26e-06 1.38e-03 -11.0 9.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.5405382e+01 1.01e-08 9.91e-05 -11.0 4.32e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 86 1.5405313e+01 4.57e-05 1.65e-03 -11.0 3.20e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.5405329e+01 3.44e-05 1.39e-03 -11.0 1.49e-01 - 1.00e+00 5.00e-01h 2\n",
|
||
" 88 1.5405258e+01 1.55e-04 1.19e-02 -11.0 4.06e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.5405307e+01 3.21e-05 4.41e-03 -11.0 4.61e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.5405322e+01 2.35e-05 3.47e-03 -11.0 3.56e-01 - 1.00e+00 2.50e-01h 3\n",
|
||
" 91 1.5405328e+01 2.03e-05 3.10e-03 -11.0 7.68e-02 - 1.00e+00 1.25e-01h 4\n",
|
||
" 92 1.5405366e+01 1.13e-05 1.92e-03 -11.0 9.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.5405368e+01 9.98e-06 1.23e-03 -11.0 5.06e-02 - 1.00e+00 1.25e-01h 4\n",
|
||
" 94 1.5405368e+01 9.82e-06 1.85e-03 -11.0 3.33e-02 - 1.00e+00 1.56e-02h 7\n",
|
||
" 95 1.5405368e+01 9.78e-06 1.28e-03 -11.0 2.32e-02 - 1.00e+00 3.91e-03h 9\n",
|
||
" 96 1.5405368e+01 9.77e-06 3.80e-03 -11.0 2.44e-03 - 1.00e+00 9.77e-04h 11\n",
|
||
" 97 1.5405368e+01 9.77e-06 3.80e-03 -11.0 7.93e-04 - 1.00e+00 1.22e-04h 14\n",
|
||
" 98 1.5405387e+01 2.04e-07 1.48e-04 -11.0 2.44e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.5405387e+01 6.99e-07 1.24e-02 -11.0 1.48e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.5405387e+01 2.98e-07 1.49e-04 -11.0 2.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.5405386838249614e+01 1.5405386838249614e+01\n",
|
||
"Dual infeasibility......: 1.4881836839076543e-04 1.4881836839076543e-04\n",
|
||
"Constraint violation....: 2.9829703152017828e-07 2.9829703152017828e-07\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 1.4881836839076543e-04 1.4881836839076543e-04\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 164\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 164\n",
|
||
"Number of inequality constraint evaluations = 164\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.455\n",
|
||
"Total CPU secs in NLP function evaluations = 137.625\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 764.00us ( 4.66us) 756.12us ( 4.61us) 164\n",
|
||
" nlp_g | 7.38 s ( 44.99ms) 7.05 s ( 42.96ms) 164\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 382.00us ( 3.75us) 363.54us ( 3.56us) 102\n",
|
||
" nlp_jac_g | 132.90 s ( 1.30 s) 126.96 s ( 1.24 s) 102\n",
|
||
" total | 141.75 s (141.75 s) 135.41 s (135.41 s) 1\n",
|
||
"Timestamp 18000\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 7.77e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9723516e+01 1.27e+01 7.77e+03 -1.5 7.77e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.7410467e+00 4.31e+00 8.01e+00 0.6 4.40e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 5.1068642e+00 8.97e-01 8.74e-01 -1.5 1.11e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 5.7438518e+00 2.53e-03 1.03e-01 -3.3 1.34e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 5.7448152e+00 1.03e-07 7.34e-05 -5.1 2.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 5.7448147e+00 2.80e-07 8.04e-05 -11.0 2.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 5.7448141e+00 1.35e-06 1.18e-03 -11.0 5.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 5.7448136e+00 7.96e-07 1.27e-03 -11.0 3.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 5.7448149e+00 3.48e-07 5.00e-05 -11.0 2.58e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 5.7448152e+00 2.56e-07 8.79e-05 -11.0 1.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 5.7448145e+00 8.10e-07 1.46e-03 -11.0 5.69e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 5.7448142e+00 1.14e-06 2.24e-03 -11.0 6.20e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 5.7448146e+00 5.09e-07 2.22e-03 -11.0 8.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 5.7448147e+00 2.77e-07 3.59e-05 -11.0 5.11e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 5.7448151e+00 4.60e-07 1.02e-04 -11.0 3.88e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 5.7448146e+00 2.89e-06 7.23e-04 -11.0 1.29e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 5.7448142e+00 5.93e-07 1.32e-03 -11.0 1.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 5.7448034e+00 2.17e-05 2.37e-03 -11.0 6.35e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 5.7448083e+00 1.26e-05 1.84e-03 -11.0 3.14e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 5.7447984e+00 9.85e-06 2.08e-03 -11.0 7.60e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 5.7447745e+00 1.86e-05 3.63e-03 -11.0 2.58e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 5.7447528e+00 3.09e-05 3.63e-03 -11.0 3.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 5.7442533e+00 3.35e-04 1.32e-02 -11.0 9.86e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 5.7448017e+00 1.36e-08 1.23e-04 -11.0 5.11e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 5.7448013e+00 2.12e-07 1.16e-04 -11.0 1.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 5.7448015e+00 1.30e-07 1.05e-04 -11.0 6.73e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 5.7448000e+00 7.11e-07 4.57e-05 -11.0 5.56e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 5.7448015e+00 6.31e-08 3.26e-05 -11.0 6.65e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 5.7448015e+00 4.75e-08 3.81e-05 -11.0 8.41e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 5.7448015e+00 6.23e-08 9.78e-05 -11.0 6.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 5.7446596e+00 1.70e-04 5.52e-03 -11.0 1.00e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 5.7448233e+00 3.06e-06 1.93e-03 -11.0 5.91e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 5.7448211e+00 5.38e-06 8.61e-04 -11.0 4.25e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 5.6984514e+00 9.78e-02 3.87e-02 -11.0 1.35e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 35 5.6535611e+00 8.33e-02 8.96e-03 -11.0 1.06e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 4.4117545e+00 1.12e+00 3.69e-01 -11.0 4.51e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 37 4.9722678e+00 4.43e-01 4.48e-01 -11.0 5.95e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 38 4.3971862e+00 9.19e-01 1.89e-01 -11.0 1.47e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 5.3541225e+00 9.63e-02 1.68e-01 -11.0 1.87e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 4.7497663e+00 3.94e+00 4.76e-01 -10.7 2.79e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 41 4.6519407e+00 3.41e+00 3.10e-01 -10.9 6.74e+04 - 6.99e-01 1.38e-01h 1\n",
|
||
" 42 5.7298675e+00 3.98e-01 6.12e-01 -10.9 5.39e+03 - 6.96e-10 1.00e+00h 1\n",
|
||
" 43 5.6527978e+00 3.70e-01 5.95e-01 -10.9 4.34e+04 - 1.00e+00 3.05e-02h 1\n",
|
||
" 44 4.1533704e+00 1.05e+00 3.84e-01 -10.9 5.89e+03 - 1.79e-09 1.00e+00f 1\n",
|
||
" 45 4.0427597e+00 1.22e+00 3.45e-01 -11.0 3.97e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 46 3.7326277e+00 1.44e+00 2.61e-01 -8.1 1.30e+05 - 1.00e+00 3.27e-01h 1\n",
|
||
" 47 3.7335877e+00 1.43e+00 2.57e-01 -6.3 6.56e+04 - 1.00e+00 6.49e-03h 1\n",
|
||
" 48 3.7337014e+00 1.43e+00 2.55e-01 -4.3 1.47e+03 - 1.00e+00 2.89e-03h 1\n",
|
||
" 49 4.9471862e+00 1.89e-01 4.61e-01 -2.6 1.09e+03 - 7.25e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 4.8527037e+00 1.03e+00 5.13e-01 -1.3 4.28e+05 - 7.34e-01 1.45e-01f 1\n",
|
||
" 51 3.9339581e+00 1.11e+00 1.88e-01 -1.6 7.60e+03 - 7.41e-01 1.00e+00f 1\n",
|
||
" 52 6.1012435e+00 4.13e-01 2.06e-01 -1.6 1.33e+04 - 1.00e+00 7.15e-01H 1\n",
|
||
" 53 3.9480363e+00 1.17e+00 1.62e-01 -1.6 5.36e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 54 5.0671344e+00 2.87e-01 1.61e-01 -2.2 2.69e+03 - 9.54e-01 1.00e+00h 1\n",
|
||
" 55 4.8752353e+00 2.18e-01 1.48e-01 -2.4 2.17e+04 - 4.17e-01 8.04e-02f 1\n",
|
||
" 56 3.8765854e+00 3.22e+00 5.52e-01 -8.4 8.00e+03 - 1.61e-01 1.00e+00f 1\n",
|
||
" 57 4.8370506e+00 2.78e+00 3.86e-01 -2.6 2.44e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 4.3820276e+00 2.50e+00 3.23e-01 -2.6 4.36e+03 - 8.48e-01 1.00e+00h 1\n",
|
||
" 59 4.3425536e+00 2.56e+00 2.45e-01 -2.6 3.42e+05 - 1.00e-01 4.19e-02h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 3.9310960e+00 1.61e+00 1.50e-01 -2.6 1.12e+04 - 4.51e-01 1.00e+00h 1\n",
|
||
" 61 3.8387298e+00 1.44e+00 1.49e-01 -2.6 3.99e+05 - 1.01e-01 3.43e-02h 1\n",
|
||
" 62 3.2197733e+00 2.20e+00 8.44e-01 -2.6 2.26e+05 - 1.75e-02 1.31e-01f 1\n",
|
||
" 63 4.6589519e+00 2.70e+00 6.34e-01 -2.4 2.41e+04 - 6.11e-01 1.00e+00h 1\n",
|
||
" 64 4.0555759e+00 7.75e-01 2.62e-01 -2.1 1.53e+04 - 1.17e-01 6.85e-01h 1\n",
|
||
" 65 4.0351136e+00 1.44e+00 2.62e-01 -2.1 2.90e+04 - 1.00e+00 2.04e-01h 2\n",
|
||
" 66 5.2137432e+00 1.02e+00 2.47e-01 -2.1 1.12e+04 - 1.86e-01 1.00e+00H 1\n",
|
||
" 67 4.8635197e+00 2.77e-01 2.77e-01 -2.1 8.68e+03 - 1.00e+00 8.18e-01h 1\n",
|
||
" 68 4.5710069e+00 9.66e-01 9.23e-02 -1.9 1.02e+04 - 1.99e-01 1.00e+00h 1\n",
|
||
" 69 5.1718495e+00 1.06e+00 1.09e-01 -1.8 7.85e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 4.6728377e+00 1.40e+00 1.48e-01 -1.9 5.82e+03 - 8.38e-01 1.00e+00h 1\n",
|
||
" 71 4.5300111e+00 2.22e+00 2.37e-01 -1.9 1.41e+04 - 4.38e-02 5.00e-01h 2\n",
|
||
" 72 4.2824656e+00 2.25e+00 7.11e-01 -1.9 4.70e+05 - 4.06e-02 1.01e-01F 1\n",
|
||
" 73 4.2313324e+00 2.23e+00 6.89e-01 -1.9 7.73e+04 - 6.96e-01 5.77e-03h 1\n",
|
||
" 74 3.8560227e+00 1.64e+00 3.66e-01 -1.9 1.85e+03 - 1.00e+00 2.95e-01f 1\n",
|
||
" 75 4.2242355e+00 1.05e+00 1.71e-01 -1.9 1.40e+04 - 2.15e-01 5.00e-01h 2\n",
|
||
" 76 4.2582618e+00 1.00e+00 1.48e-01 -1.9 8.71e+03 - 8.32e-01 4.59e-02h 1\n",
|
||
" 77 5.6357470e+00 7.58e-02 2.87e-01 -1.9 2.53e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 78 5.5020070e+00 3.09e-01 4.19e-02 -7.2 1.36e+04 - 6.88e-01 1.00e+00h 1\n",
|
||
" 79 5.3843263e+00 5.10e-01 3.26e-02 -2.5 9.68e+03 - 3.83e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 5.2246274e+00 4.71e-01 6.45e-02 -2.5 2.67e+04 - 5.83e-01 1.00e+00h 1\n",
|
||
" 81 5.1178036e+00 5.36e-01 3.40e-02 -2.5 1.00e+04 - 1.00e+00 5.21e-01h 1\n",
|
||
" 82 4.7389247e+00 1.51e+00 1.55e-01 -2.5 4.25e+03 - 6.75e-02 5.86e-01f 1\n",
|
||
" 83 4.7315814e+00 1.50e+00 1.50e-01 -2.5 2.13e+04 - 1.50e-02 1.44e-02h 1\n",
|
||
" 84 5.0651380e+00 1.09e+00 7.99e-02 -2.5 2.99e+04 - 8.65e-01 2.50e-01h 3\n",
|
||
" 85 5.1641818e+00 9.90e-01 1.12e-01 -2.5 4.02e+03 - 9.89e-01 5.00e-01h 2\n",
|
||
" 86 5.6650363e+00 1.24e-01 3.75e-02 -2.5 2.84e+03 - 9.02e-01 1.00e+00h 1\n",
|
||
" 87 5.6371154e+00 6.72e-02 8.38e-02 -2.5 8.55e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 88 5.4266660e+00 3.81e-01 1.73e-02 -2.5 1.77e+05 - 1.67e-01 1.40e-02f 2\n",
|
||
" 89 5.4930430e+00 3.30e-01 5.57e-03 -2.5 2.85e+03 - 1.00e+00 4.98e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 5.5042844e+00 1.57e-01 3.39e-02 -2.5 6.19e+03 - 1.00e+00 3.53e-01h 1\n",
|
||
" 91 5.2206606e+00 5.72e-01 2.32e-02 -3.1 1.29e+04 - 9.30e-01 1.00e+00h 1\n",
|
||
" 92 5.0648895e+00 8.84e-01 5.07e-02 -3.1 3.58e+04 - 1.00e+00 2.47e-01h 1\n",
|
||
" 93 5.0653594e+00 8.86e-01 5.08e-02 -3.1 1.21e+04 - 1.00e+00 4.33e-03h 8\n",
|
||
" 94 5.6824018e+00 3.65e-02 1.25e-01 -3.1 1.24e+02 - 8.60e-01 1.00e+00h 1\n",
|
||
" 95 5.6881368e+00 1.34e-02 1.24e-02 -2.7 9.35e+01 - 1.00e+00 9.18e-01h 1\n",
|
||
" 96 5.6084783e+00 1.01e-01 4.66e-03 -8.6 5.41e+02 - 8.53e-01 1.00e+00h 1\n",
|
||
" 97 5.6579741e+00 2.12e-02 2.28e-02 -4.6 2.91e+02 - 1.68e-01 1.00e+00h 1\n",
|
||
" 98 5.6365453e+00 9.20e-02 4.38e-02 -9.7 2.67e+02 - 1.76e-01 1.00e+00h 1\n",
|
||
" 99 5.6624393e+00 4.75e-02 8.99e-03 -2.7 2.10e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 5.6692678e+00 4.30e-02 7.55e-03 -2.8 2.58e+02 - 8.88e-01 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 5.6692678240369192e+00 5.6692678240369192e+00\n",
|
||
"Dual infeasibility......: 7.5517271448209966e-03 7.5517271448209966e-03\n",
|
||
"Constraint violation....: 4.2956885750708551e-02 4.2956885750708551e-02\n",
|
||
"Complementarity.........: 1.5955702499493912e-03 1.5955702499493912e-03\n",
|
||
"Overall NLP error.......: 4.2956885750708551e-02 4.2956885750708551e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 136\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 136\n",
|
||
"Number of inequality constraint evaluations = 136\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.400\n",
|
||
"Total CPU secs in NLP function evaluations = 136.085\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 629.00us ( 4.63us) 626.61us ( 4.61us) 136\n",
|
||
" nlp_g | 6.07 s ( 44.60ms) 5.78 s ( 42.50ms) 136\n",
|
||
" nlp_grad | 1.38 s ( 1.38 s) 1.32 s ( 1.32 s) 1\n",
|
||
" nlp_grad_f | 358.00us ( 3.51us) 353.36us ( 3.46us) 102\n",
|
||
" nlp_jac_g | 132.75 s ( 1.30 s) 126.83 s ( 1.24 s) 102\n",
|
||
" total | 140.34 s (140.34 s) 134.06 s (134.06 s) 1\n",
|
||
"Timestamp 18300\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.15e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0549012e+01 1.21e+01 3.15e+04 -1.5 3.15e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.6784013e+00 4.11e+00 4.21e+00 1.0 4.04e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.5543074e+00 4.62e-01 1.82e-01 -1.1 1.36e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 9.3326609e-01 6.13e-03 5.04e-01 -7.0 1.23e+01 - 9.90e-01 1.00e+00h 1\n",
|
||
" 5 9.3928946e-01 2.63e-04 1.12e-02 -4.7 1.04e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 9.3806131e-01 1.78e-03 1.27e-02 -6.5 7.99e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 9.3432056e-01 4.90e-03 2.47e-02 -8.4 1.51e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 8.9197161e-01 3.58e-02 4.86e-02 -10.2 1.72e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 9.4097462e-01 2.22e-03 4.82e-02 -11.0 5.92e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 9.0799791e-01 1.01e-01 1.73e-01 -11.0 6.13e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 8.1169545e-01 1.55e-01 1.54e-01 -11.0 9.97e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 9.5749594e-01 1.72e-02 8.52e-02 -11.0 3.46e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 9.5529814e-01 2.55e-02 3.99e-02 -11.0 2.27e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 9.0022548e-01 5.68e-02 4.19e-02 -11.0 1.85e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 9.7384488e-01 2.82e-02 5.11e-02 -11.0 2.78e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 16 9.8092214e-01 1.54e-02 7.91e-02 -11.0 3.28e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 17 7.4718279e-01 2.71e-01 5.43e-01 -11.0 1.79e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 18 6.2113558e-01 2.19e-01 3.08e-01 -11.0 3.77e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 7.0536465e-01 4.26e-01 3.80e-01 -11.0 6.95e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 6.7212138e-01 4.04e-01 8.47e-02 -11.0 2.31e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 7.2388259e-01 3.39e-01 1.64e-01 -11.0 6.12e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 7.2341539e-01 2.62e-01 1.23e-01 -11.0 5.38e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 8.9352402e-01 2.26e-01 4.62e-01 -11.0 7.45e+05 - 3.47e-02 2.49e-02h 2\n",
|
||
" 24 8.6892960e-01 1.94e-01 4.06e-01 -11.0 5.43e+04 - 3.69e-01 5.11e-02h 5\n",
|
||
" 25 6.7088316e-01 4.25e-01 3.29e-01 -11.0 1.46e+05 - 8.68e-01 7.14e-02f 3\n",
|
||
" 26 6.0236107e-01 3.81e-01 6.56e-01 -11.0 4.85e+05 - 6.96e-02 3.47e-02h 2\n",
|
||
" 27 6.0215295e-01 2.76e-01 4.49e-01 -9.2 8.34e+05 - 1.00e+00 7.20e-04f 7\n",
|
||
" 28 6.1769340e-01 1.54e-01 2.04e-01 -9.8 1.41e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 29 6.1459131e-01 1.33e-01 1.75e-01 -8.8 4.04e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 7.3922419e-01 3.59e-01 2.16e-01 -9.6 6.79e+03 - 7.82e-01 9.94e-01H 1\n",
|
||
" 31 8.9871968e-01 2.34e-01 2.83e-01 -9.5 1.07e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 8.7522546e-01 1.54e-01 1.87e-01 -9.5 1.40e+04 - 1.00e+00 6.89e-01H 1\n",
|
||
" 33 8.6447815e-01 6.39e-01 3.83e-01 -7.6 1.98e+04 - 2.20e-07 1.00e+00f 1\n",
|
||
" 34 1.0639103e+00 3.02e-01 6.51e-01 -9.7 4.48e+04 - 4.12e-01 9.64e-01h 1\n",
|
||
" 35 7.7107984e-01 6.88e-01 8.61e-01 -9.7 2.62e+04 - 9.50e-01 1.00e+00F 1\n",
|
||
" 36 9.1177148e-01 2.22e-01 7.00e-01 -9.7 8.45e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 9.9671693e-01 2.01e-01 1.30e-01 -9.7 1.29e+03 - 8.80e-01 1.00e+00h 1\n",
|
||
" 38 8.8519445e-01 6.53e-01 1.86e-01 -9.7 4.34e+04 - 1.00e+00 2.48e-01h 1\n",
|
||
" 39 8.9544414e-01 5.46e-01 1.84e-01 -9.7 8.65e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 9.4027429e-01 1.39e-01 2.52e-01 -9.7 4.96e+03 - 1.00e+00 2.18e-01h 2\n",
|
||
" 41 9.1065876e-01 1.88e-01 2.79e-01 -9.7 5.46e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 9.0864955e-01 1.86e-01 1.75e-01 -9.7 1.36e+04 - 6.39e-02 3.12e-02h 6\n",
|
||
" 43 9.0136574e-01 2.67e-01 1.71e-01 -9.7 2.69e+03 - 1.00e+00 2.90e-01h 1\n",
|
||
" 44 9.0463248e-01 2.62e-01 1.82e-01 -9.7 7.73e+02 - 1.00e+00 1.25e-01h 4\n",
|
||
" 45 9.4280961e-01 4.56e-02 1.78e-01 -9.7 9.40e+02 - 4.30e-01 1.00e+00h 1\n",
|
||
" 46 9.3282799e-01 1.14e-01 1.66e-01 -9.7 9.19e+04 - 7.73e-01 2.61e-02h 5\n",
|
||
" 47 9.2836468e-01 1.35e-01 1.21e-01 -9.7 1.27e+04 - 1.00e+00 1.92e-01H 1\n",
|
||
" 48 9.3058709e-01 4.71e-02 7.71e-02 -9.7 5.54e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 9.6430995e-01 1.85e-02 7.88e-02 -9.7 2.17e+02 - 3.29e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 9.2953994e-01 3.20e-02 3.66e-01 -9.7 4.26e+03 - 1.00e+00 1.00e+00F 1\n",
|
||
" 51 9.7144772e-01 1.38e-02 1.15e-01 -10.7 3.44e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 52 9.4653487e-01 2.64e-01 2.53e-01 -10.3 1.04e+05 - 5.01e-01 7.12e-03f 2\n",
|
||
" 53 8.9506159e-01 1.32e-01 7.28e-02 -10.3 3.06e+04 - 6.44e-01 8.56e-02h 1\n",
|
||
" 54 8.9314888e-01 1.12e-01 1.31e-01 -10.3 1.17e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 55 9.4634345e-01 1.22e-01 1.97e-01 -10.3 8.31e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 9.1724686e-01 9.73e-02 7.96e-02 -10.3 5.44e+02 - 1.00e+00 3.37e-01h 1\n",
|
||
" 57 9.7792348e-01 2.00e-02 2.61e-02 -10.3 1.39e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 58 8.9995344e-01 7.91e-02 1.67e-01 -10.3 1.26e+03 - 4.45e-01 1.00e+00h 1\n",
|
||
"In iteration 58, 1 Slack too small, adjusting variable bound\n",
|
||
" 59 8.9995343e-01 7.91e-02 1.67e-01 -10.3 5.52e+03 - 1.00e+00 3.11e-08h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 7.5723875e-01 4.42e-01 1.07e+00 -10.3 2.23e+04 - 2.04e-01 1.00e+00F 1\n",
|
||
" 61r 7.5723875e-01 4.42e-01 9.99e+02 -0.4 0.00e+00 - 0.00e+00 1.37e-09R 3\n",
|
||
" 62r 6.6746104e-01 2.43e-01 6.74e+02 -6.4 3.47e+02 - 1.00e+00 1.23e-03f 1\n",
|
||
" 63 6.6739203e-01 2.42e-01 7.65e-01 -8.3 4.46e+02 - 1.00e+00 8.21e-04h 1\n",
|
||
" 64 6.6398148e-01 2.61e-02 4.40e-01 -6.3 5.52e+02 - 9.92e-01 1.00e+00h 1\n",
|
||
" 65 6.3911667e-01 9.24e-02 2.22e-01 -4.4 1.84e+03 - 9.69e-01 5.00e-01f 2\n",
|
||
" 66 6.3214533e-01 1.91e-01 1.73e-01 -3.0 1.85e+04 - 1.00e+00 3.47e-02f 4\n",
|
||
" 67 6.2316713e-01 1.98e-01 2.56e-01 -3.1 1.86e+04 - 7.43e-02 3.05e-02h 4\n",
|
||
" 68 6.1505410e-01 1.75e-01 1.74e-01 -3.1 3.28e+03 - 8.55e-01 1.11e-01h 1\n",
|
||
" 69 6.6670944e-01 7.93e-02 4.17e-01 -4.5 3.22e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 6.5718830e-01 3.09e-02 1.05e-01 -4.5 4.30e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 5.9481032e-01 6.93e-02 1.31e-01 -4.3 1.11e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 6.2442552e-01 8.01e-02 4.71e-02 -5.1 8.32e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 6.6054539e-01 2.50e-02 1.16e-01 -3.1 4.12e+02 - 2.69e-01 1.00e+00h 1\n",
|
||
" 74 6.5615784e-01 2.40e-02 8.49e-02 -3.8 3.80e+03 - 1.00e+00 4.83e-02h 1\n",
|
||
" 75 6.6318221e-01 4.19e-02 5.79e-02 -2.8 9.24e+02 - 9.87e-01 5.00e-01f 2\n",
|
||
" 76 6.6095021e-01 7.20e-02 7.14e-02 -3.0 7.43e+02 - 1.00e+00 5.00e-01h 2\n",
|
||
" 77 6.7318048e-01 3.77e-02 1.27e-01 -3.0 9.62e+02 - 6.88e-01 1.00e+00h 1\n",
|
||
" 78 6.5730402e-01 7.50e-02 7.42e-02 -3.0 1.18e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 79 6.5394275e-01 1.07e-01 6.09e-02 -3.0 2.94e+04 - 1.15e-01 1.95e-02h 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 6.7283761e-01 7.76e-02 4.21e-02 -3.6 4.38e+02 - 9.95e-01 1.00e+00h 1\n",
|
||
" 81 6.5530883e-01 2.23e-01 1.98e-01 -3.8 1.64e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 6.5874946e-01 2.35e-01 9.36e-02 -3.8 2.89e+03 - 6.60e-01 6.60e-01s 22\n",
|
||
" 83r 6.5874946e-01 2.35e-01 9.99e+02 -0.6 0.00e+00 - 0.00e+00 0.00e+00R 1\n",
|
||
" 84r 6.9483004e-01 1.03e-02 1.37e+02 -2.8 1.90e+02 - 1.00e+00 1.22e-03f 1\n",
|
||
" 85 6.8800908e-01 1.75e-02 1.07e-01 -3.5 3.52e+02 - 7.76e-01 1.00e+00h 1\n",
|
||
" 86 6.7373203e-01 6.70e-02 1.60e-01 -1.7 2.73e+04 - 1.00e+00 6.00e-02f 1\n",
|
||
" 87 7.0602208e-01 1.24e-01 5.73e-02 -1.7 1.45e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 88 6.9161978e-01 3.07e-03 7.02e-02 -2.5 5.00e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 6.8722469e-01 1.44e-02 7.07e-02 -2.5 2.43e+03 - 9.61e-01 3.10e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 6.7668310e-01 5.80e-02 9.31e-02 -2.6 6.34e+02 - 9.77e-01 1.00e+00h 1\n",
|
||
" 91 6.8828294e-01 1.16e-02 5.73e-02 -2.8 1.73e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 6.9159336e-01 6.80e-03 6.03e-02 -4.1 1.54e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 93 6.9093390e-01 6.44e-03 5.93e-02 -3.5 5.83e+02 - 1.00e+00 4.76e-02h 2\n",
|
||
" 94 6.9556732e-01 2.43e-04 2.35e-02 -3.6 3.21e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 95 6.9139516e-01 4.58e-03 1.48e-02 -5.3 1.69e+02 - 1.00e+00 1.52e-01h 1\n",
|
||
" 96 6.9458408e-01 2.80e-03 2.00e-02 -4.9 4.60e+01 - 8.97e-01 1.00e+00h 1\n",
|
||
" 97 6.8953985e-01 1.38e-02 4.05e-02 -4.9 1.09e+02 - 2.02e-01 1.00e+00h 1\n",
|
||
" 98 6.5522077e-01 1.08e-01 9.58e-02 -4.9 2.00e+03 - 1.00e+00 2.17e-01h 1\n",
|
||
" 99 7.0023882e-01 5.07e-05 4.83e-02 -4.9 1.10e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 7.0022146e-01 1.35e-08 1.66e-04 -6.8 9.56e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 7.0022145919768453e-01 7.0022145919768453e-01\n",
|
||
"Dual infeasibility......: 1.6569659886078562e-04 1.6569659886078562e-04\n",
|
||
"Constraint violation....: 1.3463743897546010e-08 1.3463743897546010e-08\n",
|
||
"Complementarity.........: 2.0810504229823627e-07 2.0810504229823627e-07\n",
|
||
"Overall NLP error.......: 1.6569659886078562e-04 1.6569659886078562e-04\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 240\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 240\n",
|
||
"Number of inequality constraint evaluations = 240\n",
|
||
"Number of equality constraint Jacobian evaluations = 103\n",
|
||
"Number of inequality constraint Jacobian evaluations = 103\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.457\n",
|
||
"Total CPU secs in NLP function evaluations = 157.497\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 1.25ms ( 5.20us) 1.23ms ( 5.14us) 240\n",
|
||
" nlp_g | 11.90 s ( 49.60ms) 11.46 s ( 47.77ms) 240\n",
|
||
" nlp_grad | 1.92 s ( 1.92 s) 1.87 s ( 1.87 s) 1\n",
|
||
" nlp_grad_f | 476.00us ( 4.67us) 392.78us ( 3.85us) 102\n",
|
||
" nlp_jac_g | 148.72 s ( 1.43 s) 143.25 s ( 1.38 s) 104\n",
|
||
" total | 162.71 s (162.71 s) 156.74 s (156.74 s) 1\n",
|
||
"Timestamp 18600\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.83e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9832027e+01 1.55e+01 1.83e+04 -1.5 1.83e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.2538103e+01 5.57e+00 1.42e+01 1.0 5.71e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.9552846e+01 2.18e+00 8.48e-01 -1.1 1.26e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 2.0956482e+01 2.26e-04 8.61e-02 -2.9 2.53e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 5 2.0956810e+01 7.03e-05 9.99e-03 -4.7 4.27e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.0956411e+01 2.35e-04 2.42e-02 -6.6 9.73e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.0956817e+01 5.41e-08 3.52e-04 -8.5 1.19e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 8 2.0955153e+01 5.77e-04 1.87e-03 -11.0 9.88e-01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 9 2.0956600e+01 1.18e-04 2.08e-03 -11.0 3.96e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.0956725e+01 4.28e-05 1.41e-03 -11.0 1.64e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.0956841e+01 3.56e-06 1.44e-03 -11.0 4.91e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.0955715e+01 8.41e-04 9.30e-03 -11.0 9.51e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.0954196e+01 1.46e-03 9.90e-04 -11.0 7.41e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.0955239e+01 7.85e-04 2.35e-03 -11.0 3.84e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.0956994e+01 8.98e-05 1.81e-03 -11.0 5.89e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.0956909e+01 2.92e-07 1.27e-05 -11.0 4.65e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 17 2.0956721e+01 2.58e-04 1.44e-03 -11.0 3.99e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.0928469e+01 1.44e-02 8.05e-03 -11.0 4.36e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 19 2.0951646e+01 1.85e-03 1.30e-03 -11.0 1.07e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.0857304e+01 8.58e-02 8.62e-03 -11.0 2.16e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 21 2.0932594e+01 3.04e-02 3.52e-03 -11.0 1.39e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.0375103e+01 3.16e-01 9.60e-03 -11.0 6.82e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 23 2.0964591e+01 1.95e-02 1.30e-02 -11.0 3.49e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.0936249e+01 1.25e-02 1.06e-02 -11.0 8.17e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.0905486e+01 3.17e-02 6.36e-03 -11.0 2.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.0804739e+01 2.70e-01 7.57e-03 -11.0 1.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.0785116e+01 1.56e-01 3.63e-03 -11.0 5.57e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.0756834e+01 1.48e-01 7.31e-03 -11.0 1.68e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.0560721e+01 2.23e-01 1.31e-02 -11.0 2.62e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.0571464e+01 1.37e-01 4.71e-03 -11.0 1.78e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.0655394e+01 2.85e-01 9.03e-03 -11.0 1.12e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.9413144e+01 8.53e-01 3.19e-02 -11.0 4.94e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 33 1.7010803e+01 1.23e+01 6.18e-01 -10.7 3.59e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 34 1.7110466e+01 1.03e+01 3.89e-01 -11.0 3.55e+04 - 1.00e+00 1.71e-01h 1\n",
|
||
" 35 1.9388394e+01 5.57e+00 2.67e-01 -9.1 6.19e+04 - 5.54e-09 1.00e+00h 1\n",
|
||
" 36 1.9137430e+01 2.80e+00 3.75e-01 -10.1 2.62e+05 - 1.00e+00 2.13e-01h 1\n",
|
||
" 37 1.8942211e+01 2.78e+00 3.72e-01 -7.5 2.61e+05 - 1.00e+00 2.68e-03f 1\n",
|
||
" 38 2.0893131e+01 1.27e-01 1.60e-01 -7.3 3.43e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.9948602e+01 1.60e+00 4.44e-02 -7.0 1.08e+04 - 1.06e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.4595735e+01 2.60e+00 3.00e-01 -7.0 9.86e+04 - 1.96e-01 4.37e-01f 1\n",
|
||
" 41 1.3420489e+01 3.12e+00 3.60e-01 -11.0 3.08e+05 - 2.41e-08 5.43e-02f 1\n",
|
||
" 42 1.3403865e+01 3.12e+00 3.60e-01 -5.3 2.42e+06 - 1.00e+00 1.20e-04f 1\n",
|
||
" 43 1.8406082e+01 6.37e-01 1.31e-01 -11.0 1.18e+04 - 5.86e-04 7.53e-01h 1\n",
|
||
" 44 1.8406879e+01 6.37e-01 1.31e-01 -3.4 5.74e+04 - 1.00e+00 1.55e-03h 1\n",
|
||
" 45 1.8621317e+01 5.57e-01 1.22e-01 -1.5 7.44e+03 - 2.74e-01 1.25e-01f 4\n",
|
||
" 46 1.9802658e+01 8.78e-01 9.51e-02 -3.0 7.81e+03 - 8.96e-01 1.00e+00h 1\n",
|
||
" 47 1.6813189e+01 3.05e+00 2.85e-01 -2.3 1.20e+04 - 7.36e-01 1.00e+00f 1\n",
|
||
" 48 1.9405766e+01 2.18e+00 1.67e-01 -2.4 2.17e+04 - 6.62e-01 8.79e-01h 1\n",
|
||
" 49 1.8740085e+01 2.08e+00 1.08e-01 -2.4 2.50e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.8597848e+01 1.88e+00 5.84e-02 -2.4 4.22e+04 - 7.92e-01 5.93e-01h 1\n",
|
||
" 51 2.0002303e+01 4.23e-01 5.61e-02 -2.4 4.87e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.7868353e+01 2.38e+00 1.75e-01 -1.3 2.49e+04 - 1.00e+00 8.93e-01f 1\n",
|
||
" 53 1.7322640e+01 2.64e+00 1.48e-01 -1.4 3.55e+04 - 9.36e-01 1.06e-01f 1\n",
|
||
" 54 2.0147518e+01 2.83e-01 3.81e-02 -2.1 2.04e+03 - 7.67e-01 1.00e+00h 1\n",
|
||
" 55 2.0165881e+01 2.77e-01 2.50e-02 -1.9 1.13e+03 - 6.92e-01 1.72e-01h 1\n",
|
||
" 56 2.0538041e+01 5.17e-02 2.16e-02 -2.9 4.84e+02 - 4.68e-01 1.00e+00h 1\n",
|
||
" 57 2.0316728e+01 2.12e-01 8.15e-02 -3.2 6.34e+02 - 4.60e-01 1.00e+00h 1\n",
|
||
" 58 1.8912611e+01 6.50e-01 4.81e-02 -8.4 1.03e+04 - 6.01e-02 1.00e+00f 1\n",
|
||
" 59 1.8558931e+01 5.81e-01 6.61e-02 -2.3 5.40e+04 - 1.00e+00 6.41e-02f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.0276566e+01 2.31e-02 2.56e-02 -2.6 2.18e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.0256712e+01 6.63e-02 2.83e-02 -3.6 5.85e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.0284140e+01 1.27e-02 6.02e-03 -5.4 9.84e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.0180691e+01 9.09e-02 1.15e-02 -6.2 6.86e+02 - 8.21e-01 1.00e+00h 1\n",
|
||
" 64 2.0091383e+01 1.35e-01 1.57e-02 -3.9 1.16e+03 - 5.19e-03 1.00e+00h 1\n",
|
||
" 65 2.0301843e+01 1.37e-03 5.69e-03 -4.9 2.94e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 66 2.0293628e+01 6.45e-02 4.99e-03 -4.2 3.68e+04 - 1.00e+00 8.77e-02h 1\n",
|
||
" 67 1.9686060e+01 6.83e-01 2.25e-02 -4.4 4.08e+03 - 3.97e-03 1.00e+00f 1\n",
|
||
" 68 1.9620632e+01 7.28e-01 2.49e-02 -2.7 6.58e+04 - 1.00e+00 7.69e-03f 1\n",
|
||
" 69 2.0256135e+01 6.57e-02 2.78e-02 -3.4 3.74e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.0293299e+01 2.67e-03 2.27e-03 -3.9 1.12e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.0269293e+01 2.53e-02 3.13e-03 -6.0 1.28e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 72 2.0290836e+01 3.58e-03 1.71e-03 -5.9 4.86e+01 - 9.11e-01 1.00e+00h 1\n",
|
||
" 73 2.0282844e+01 1.31e-02 1.19e-03 -3.9 1.41e+02 - 7.96e-02 1.00e+00f 1\n",
|
||
" 74 2.0257552e+01 2.50e-02 2.12e-03 -5.1 7.39e+05 - 1.90e-04 4.01e-04f 1\n",
|
||
" 75 2.0276768e+01 5.27e-03 8.05e-04 -5.4 8.68e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.0267711e+01 1.37e-02 1.45e-03 -6.0 6.70e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 2.0254809e+01 1.16e-02 2.08e-03 -4.6 6.40e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.0271658e+01 5.97e-03 1.86e-03 -6.6 3.58e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 2.0230748e+01 3.59e-02 5.98e-03 -8.4 2.22e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.0273310e+01 1.79e-02 1.82e-03 -6.3 1.54e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.0272764e+01 1.69e-02 1.77e-03 -6.4 9.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.0278932e+01 5.14e-03 1.20e-03 -6.4 6.53e+01 - 7.56e-01 1.00e+00h 1\n",
|
||
" 83 2.0234234e+01 6.56e-02 1.64e-03 -6.1 2.72e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.0095807e+01 2.10e-01 5.22e-03 -6.2 8.18e+02 - 3.93e-01 1.00e+00h 1\n",
|
||
" 85 2.0294402e+01 1.07e-02 5.88e-03 -6.2 5.73e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 2.0223279e+01 1.13e-01 7.25e-03 -6.2 4.83e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.9423724e+01 7.58e-01 4.81e-02 -6.2 1.51e+04 - 1.52e-01 1.00e+00f 1\n",
|
||
" 88 1.9221043e+01 1.59e+00 6.41e-02 -4.2 1.32e+05 - 1.00e+00 3.43e-02f 1\n",
|
||
" 89 1.9218914e+01 1.59e+00 6.42e-02 -4.3 1.58e+05 - 1.37e-01 2.90e-04h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.0295368e+01 2.84e-03 3.67e-02 -4.3 1.60e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 2.0251817e+01 3.30e-02 3.76e-02 -2.3 3.14e+02 - 4.68e-01 1.00e+00f 1\n",
|
||
" 92 2.0294284e+01 1.39e-03 2.49e-03 -4.2 4.99e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 93 2.0177594e+01 8.78e-02 7.40e-03 -5.2 6.48e+02 - 1.00e+00 6.26e-01f 1\n",
|
||
" 94 2.0229075e+01 1.82e-02 9.02e-03 -4.4 3.20e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.9998597e+01 2.13e-01 1.99e-02 -4.5 1.99e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 96 1.9137771e+01 1.12e+00 4.44e-02 -4.0 9.48e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 97 1.7798583e+01 2.72e+00 4.47e-02 -4.1 2.95e+04 - 3.51e-01 1.00e+00f 1\n",
|
||
" 98 1.7201932e+01 2.44e+00 1.31e-01 -4.2 1.88e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.9691784e+01 8.72e-01 6.52e-02 -4.8 9.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.8652658e+01 3.57e+00 5.99e-02 -4.9 2.15e+04 - 1.00e+00 3.68e-01f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.8652657682624014e+01 1.8652657682624014e+01\n",
|
||
"Dual infeasibility......: 5.9948947529471719e-02 5.9948947529471719e-02\n",
|
||
"Constraint violation....: 3.5685637181708714e+00 3.5685637181708714e+00\n",
|
||
"Complementarity.........: 1.3846085666771378e-05 1.3846085666771378e-05\n",
|
||
"Overall NLP error.......: 3.5685637181708714e+00 3.5685637181708714e+00\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 107\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 107\n",
|
||
"Number of inequality constraint evaluations = 107\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.924\n",
|
||
"Total CPU secs in NLP function evaluations = 155.465\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 639.00us ( 5.97us) 632.20us ( 5.91us) 107\n",
|
||
" nlp_g | 5.59 s ( 52.28ms) 5.41 s ( 50.54ms) 107\n",
|
||
" nlp_grad | 1.50 s ( 1.50 s) 1.45 s ( 1.45 s) 1\n",
|
||
" nlp_grad_f | 432.00us ( 4.24us) 417.80us ( 4.10us) 102\n",
|
||
" nlp_jac_g | 153.48 s ( 1.50 s) 148.47 s ( 1.46 s) 102\n",
|
||
" total | 160.74 s (160.74 s) 155.49 s (155.49 s) 1\n",
|
||
"Timestamp 18900\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.01e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0018910e+01 1.17e+01 2.01e+04 -1.5 2.01e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.9108666e+00 3.80e+00 7.50e+00 0.8 2.40e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.0625251e+00 5.18e-01 6.21e-01 -1.3 6.39e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 9.3588083e-01 2.51e-03 8.63e-01 -3.0 1.81e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 9.3678040e-01 4.66e-06 6.87e-04 -4.9 2.93e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 9.3676993e-01 1.91e-05 1.59e-03 -7.0 3.33e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 9.3677722e-01 6.06e-06 9.95e-04 -9.1 1.32e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 9.3676493e-01 2.46e-05 1.38e-03 -11.0 1.00e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 9.3677812e-01 8.33e-06 8.65e-04 -11.0 6.85e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 9.3677970e-01 5.35e-06 5.85e-04 -11.0 2.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 9.3678092e-01 1.74e-06 8.68e-04 -11.0 3.67e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 9.3677158e-01 1.88e-05 1.40e-03 -11.0 8.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 9.3678009e-01 5.07e-06 5.59e-04 -11.0 2.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 9.3677607e-01 5.71e-06 6.41e-04 -11.0 4.98e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 9.3678161e-01 7.87e-09 3.96e-05 -11.0 9.06e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 16 9.2598330e-01 4.51e-04 4.44e-02 -11.0 4.02e+03 - 1.00e+00 1.00e+00F 1\n",
|
||
" 17 9.1726293e-01 5.74e-02 5.25e-02 -11.0 8.79e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 8.9057696e-01 3.84e-01 4.61e-01 -11.0 6.55e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 19 9.4081528e-01 2.82e-02 5.69e-01 -11.0 3.45e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 9.3932176e-01 2.92e-01 1.48e-01 -11.0 3.16e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 8.9687957e-01 3.67e-01 2.03e-01 -11.0 1.90e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 9.2473646e-01 1.52e-01 3.60e-01 -11.0 1.80e+05 - 1.57e-01 1.25e-01H 1\n",
|
||
" 23 8.8323754e-01 6.59e-02 1.67e-01 -11.0 3.51e+04 - 1.75e-10 1.25e-01h 4\n",
|
||
" 24 8.6189945e-01 1.42e-01 1.71e-01 -9.1 1.20e+05 - 1.00e+00 8.53e-03f 1\n",
|
||
" 25 8.9419560e-01 1.48e-01 1.39e-01 -8.6 2.39e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 8.7864805e-01 2.08e-01 1.56e-01 -7.8 3.34e+04 - 1.00e+00 2.87e-01h 1\n",
|
||
" 27 9.3112043e-01 9.58e-02 1.68e-01 -7.8 1.50e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 9.0936260e-01 6.71e-02 5.89e-02 -7.8 1.75e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.8163351e-01 2.99e-01 2.60e-01 -7.2 2.47e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.5026705e-01 1.63e-01 2.55e-01 -5.8 1.84e+03 - 3.22e-01 1.00e+00h 1\n",
|
||
" 31 8.5000357e-01 1.63e-01 2.55e-01 -4.3 3.34e+04 - 1.00e+00 5.32e-04h 1\n",
|
||
" 32 8.4682130e-01 1.53e-01 2.49e-01 -4.0 3.95e+04 - 1.00e+00 3.12e-02h 6\n",
|
||
" 33 9.5387896e-01 1.09e-01 2.75e-01 -4.7 5.13e+03 - 9.48e-01 1.00e+00H 1\n",
|
||
" 34 8.9827039e-01 3.78e-01 2.26e-01 -4.8 5.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 9.1317234e-01 1.38e-01 2.70e-01 -4.8 1.01e+03 - 6.30e-01 1.00e+00h 1\n",
|
||
" 36 9.1263103e-01 1.41e-01 2.45e-01 -4.8 1.07e+04 - 1.00e+00 2.64e-02h 1\n",
|
||
" 37 8.8657272e-01 5.26e-01 2.34e-01 -4.8 1.46e+04 - 1.00e+00 2.50e-01f 3\n",
|
||
" 38 9.0428869e-01 2.86e-01 2.53e-01 -4.8 5.64e+03 - 8.27e-01 5.00e-01h 2\n",
|
||
" 39 8.8424711e-01 1.82e-01 3.91e-01 -4.8 1.67e+05 - 1.28e-01 1.61e-01F 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.9821017e-01 7.59e-02 1.42e-01 -4.8 1.78e+04 - 5.82e-04 1.00e+00H 1\n",
|
||
" 41 8.7764696e-01 1.91e-01 1.43e-01 -4.8 4.72e+05 - 2.58e-02 9.82e-03f 3\n",
|
||
" 42 8.5226732e-01 2.30e-01 2.26e-01 -4.8 1.02e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 9.2894703e-01 1.19e-01 4.16e-01 -4.8 4.37e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 44 9.2334058e-01 1.17e-01 4.09e-01 -4.8 2.33e+04 - 2.03e-01 1.56e-02h 7\n",
|
||
" 45 9.2287207e-01 1.39e-01 4.18e-01 -4.8 1.50e+05 - 2.20e-01 7.82e-04h 9\n",
|
||
" 46 9.1120735e-01 2.13e-01 4.14e-01 -4.8 4.99e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 47 1.0347895e+00 5.03e-02 2.70e-01 -4.8 2.00e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 48 9.9183678e-01 8.04e-02 2.16e-01 -4.8 1.12e+05 - 7.62e-02 1.53e-02f 4\n",
|
||
" 49 9.8905110e-01 1.27e-01 1.73e-01 -4.8 4.20e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 9.9136121e-01 1.18e-01 1.65e-01 -4.8 1.71e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 51 9.8850361e-01 1.29e-01 3.12e-02 -4.8 6.32e+02 - 9.34e-01 1.00e+00h 1\n",
|
||
" 52 9.4734534e-01 1.84e-01 1.11e-01 -4.8 1.66e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 9.4838077e-01 1.81e-01 1.07e-01 -4.8 1.06e+04 - 1.00e+00 1.56e-02h 7\n",
|
||
" 54 9.4836808e-01 1.81e-01 1.06e-01 -4.8 7.98e+04 - 7.42e-01 6.73e-04h 9\n",
|
||
" 55 1.0520111e+00 1.66e-02 2.36e-01 -4.8 1.54e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 56 9.7777260e-01 7.96e-02 8.99e-02 -4.8 9.02e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 9.8559239e-01 7.68e-02 9.81e-02 -4.8 9.74e+02 - 1.00e+00 2.50e-01h 3\n",
|
||
" 58 9.8126739e-01 7.51e-02 9.16e-02 -4.8 1.33e+04 - 8.64e-01 1.28e-02h 6\n",
|
||
" 59 8.9990111e-01 5.72e-01 3.59e-01 -4.8 9.78e+04 - 8.89e-01 2.93e-02f 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 9.4363523e-01 2.78e-01 1.21e-01 -4.8 1.44e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 61 9.3721027e-01 1.65e-01 1.97e-01 -4.8 2.33e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 62 9.4639320e-01 1.45e-01 2.08e-01 -4.8 5.70e+02 - 9.43e-01 1.25e-01h 4\n",
|
||
" 63 9.4278141e-01 8.65e-02 2.62e-01 -4.8 5.39e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 64 1.1145850e+00 9.60e-03 3.31e-01 -4.8 5.52e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 65 1.0770797e+00 8.57e-02 5.26e-02 -4.8 3.72e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 9.9287463e-01 9.58e-02 4.31e-02 -4.8 3.06e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 9.3738548e-01 3.80e-01 2.38e-01 -4.8 1.86e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 68 1.0402080e+00 4.43e-03 4.68e-01 -4.8 4.95e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.0410878e+00 9.41e-08 5.00e-05 -4.8 4.62e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.0410878e+00 1.40e-08 1.38e-04 -7.2 8.08e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.0410878e+00 1.79e-08 1.83e-04 -7.2 4.25e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.0373827e+00 4.07e-03 7.49e-02 -7.2 6.62e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 73 1.0408781e+00 2.07e-04 3.29e-03 -7.2 1.67e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.0404388e+00 9.94e-04 8.40e-04 -7.2 3.58e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.0412473e+00 5.82e-09 1.17e-04 -7.2 2.04e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 76 1.0336516e+00 1.07e-02 1.17e-02 -7.2 6.61e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 77 1.0366021e+00 6.11e-03 2.74e-03 -7.2 2.63e+01 - 1.00e+00 5.00e-01h 2\n",
|
||
" 78 1.0373299e+00 5.20e-03 4.08e-03 -7.2 3.52e+01 - 1.00e+00 5.00e-01h 2\n",
|
||
" 79 1.0412083e+00 4.65e-10 1.08e-04 -7.2 4.06e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.0412082e+00 4.59e-08 6.83e-05 -11.0 1.61e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.0412082e+00 3.62e-08 1.41e-05 -11.0 3.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.0412083e+00 2.68e-09 1.19e-04 -11.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.0412082e+00 1.93e-07 4.65e-05 -11.0 3.19e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.0412083e+00 3.78e-08 5.15e-05 -11.0 1.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.0412083e+00 1.49e-08 1.04e-04 -11.0 1.50e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.0412083e+00 3.06e-08 6.70e-05 -11.0 1.05e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.0412082e+00 9.58e-08 2.44e-05 -11.0 3.76e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.0412082e+00 1.17e-07 7.36e-05 -11.0 1.15e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.0412080e+00 3.10e-07 9.29e-05 -11.0 8.38e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.0412081e+00 2.33e-07 3.85e-05 -11.0 4.29e-04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 91 1.0412083e+00 4.01e-09 8.06e-05 -11.0 5.68e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.0412083e+00 1.71e-08 1.91e-05 -11.0 1.18e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.0412083e+00 3.15e-09 5.53e-05 -11.0 2.19e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.0412083e+00 4.19e-08 6.51e-05 -11.0 1.50e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.0412083e+00 7.61e-09 2.70e-05 -11.0 4.68e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.0412083e+00 7.13e-09 9.66e-05 -11.0 2.57e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.0412083e+00 4.64e-09 9.78e-05 -11.0 6.34e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.0412083e+00 1.04e-08 5.29e-05 -11.0 5.65e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.0412083e+00 6.35e-11 4.07e-05 -11.0 4.64e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.0412083e+00 3.75e-08 6.33e-06 -11.0 1.09e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.0412082837404029e+00 1.0412082837404029e+00\n",
|
||
"Dual infeasibility......: 6.3279061331312928e-06 6.3279061331312928e-06\n",
|
||
"Constraint violation....: 3.7507525973978773e-08 3.7507525973978773e-08\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 6.3279061331312928e-06 6.3279061331312928e-06\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 231\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 231\n",
|
||
"Number of inequality constraint evaluations = 231\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.605\n",
|
||
"Total CPU secs in NLP function evaluations = 143.969\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 1.07ms ( 4.65us) 1.06ms ( 4.58us) 231\n",
|
||
" nlp_g | 10.51 s ( 45.51ms) 10.05 s ( 43.50ms) 231\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 394.00us ( 3.86us) 383.43us ( 3.76us) 102\n",
|
||
" nlp_jac_g | 136.69 s ( 1.34 s) 130.95 s ( 1.28 s) 102\n",
|
||
" total | 148.69 s (148.69 s) 142.41 s (142.41 s) 1\n",
|
||
"Timestamp 19200\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.65e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9583104e+01 1.44e+01 1.65e+04 -1.5 1.65e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.0542207e+01 4.92e+00 1.15e+01 0.8 2.20e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.4990392e+01 1.76e+00 8.94e-01 -1.3 4.81e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.6157348e+01 3.82e-04 8.67e-02 -3.0 2.15e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.6157654e+01 1.13e-05 5.96e-03 -4.9 1.14e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.6123451e+01 1.67e-02 2.49e-02 -6.8 1.04e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 7 1.6099846e+01 4.28e-02 3.10e-03 -8.5 1.36e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.6141435e+01 1.04e-02 1.55e-03 -10.3 3.73e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.6145609e+01 1.95e-03 1.50e-03 -11.0 2.77e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.6143214e+01 5.55e-03 2.34e-03 -11.0 5.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.6124017e+01 1.20e-02 2.32e-03 -11.0 1.02e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.5944771e+01 9.51e-02 9.49e-03 -11.0 3.65e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 13 1.6129519e+01 4.24e-05 1.44e-01 -11.0 1.44e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.6129576e+01 1.31e-06 1.90e-03 -11.0 5.09e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.6129577e+01 5.78e-07 1.70e-04 -11.0 3.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.6129578e+01 6.90e-11 9.86e-05 -11.0 2.04e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 17 1.6129577e+01 4.48e-07 6.77e-05 -11.0 2.86e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.6129577e+01 1.39e-07 2.32e-04 -11.0 1.06e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.6129577e+01 4.55e-07 7.86e-05 -11.0 9.63e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.6129577e+01 4.67e-07 8.24e-05 -11.0 1.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.6129577e+01 4.10e-07 2.02e-04 -11.0 3.52e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.6129566e+01 2.24e-05 9.39e-03 -11.0 4.46e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.6129575e+01 2.92e-06 2.14e-03 -11.0 2.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.6129576e+01 1.18e-06 2.02e-03 -11.0 1.38e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.6129576e+01 6.44e-07 1.19e-03 -11.0 9.48e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.6129574e+01 2.71e-06 2.67e-03 -11.0 1.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.6129575e+01 7.48e-07 1.36e-03 -11.0 1.88e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.6129568e+01 1.09e-05 2.52e-03 -11.0 3.60e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.6129573e+01 2.08e-06 1.19e-03 -11.0 2.52e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.6129556e+01 9.00e-06 1.80e-03 -11.0 6.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.6129573e+01 3.58e-06 3.49e-03 -11.0 2.85e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.6129574e+01 2.46e-06 1.13e-03 -11.0 1.57e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.6129574e+01 1.45e-06 2.00e-03 -11.0 1.08e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.6129578e+01 3.30e-07 1.25e-04 -11.0 2.97e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.6129579e+01 1.15e-09 1.06e-04 -11.0 1.06e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 36 1.6129576e+01 1.63e-06 1.71e-03 -11.0 7.93e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.6129571e+01 8.28e-06 2.57e-03 -11.0 2.37e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.6129573e+01 3.15e-06 1.03e-03 -11.0 1.37e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.6129578e+01 9.40e-07 1.75e-03 -11.0 7.46e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.6129575e+01 2.12e-06 1.81e-03 -11.0 5.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.6129559e+01 1.76e-05 7.15e-03 -11.0 5.04e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.6129567e+01 9.29e-06 2.23e-03 -11.0 1.43e-02 - 1.00e+00 5.00e-01h 2\n",
|
||
" 43 1.6129564e+01 6.40e-06 3.07e-03 -11.0 3.82e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.6129577e+01 5.09e-06 2.07e-03 -11.0 1.14e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.6129539e+01 1.97e-05 3.58e-03 -11.0 6.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.6129573e+01 4.84e-06 2.01e-03 -11.0 1.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.6129573e+01 3.82e-06 2.18e-03 -11.0 3.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.6129572e+01 3.81e-06 1.66e-03 -11.0 1.95e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.6095741e+01 1.81e-02 3.22e-02 -11.0 1.11e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.6112808e+01 1.17e-02 4.54e-03 -11.0 1.54e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.6123411e+01 3.39e-03 2.50e-03 -11.0 1.85e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.6104251e+01 1.37e-02 8.98e-03 -11.0 1.86e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.5487058e+01 2.24e+00 9.18e-02 -11.0 6.02e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 54 1.4703020e+01 1.16e+00 4.70e-02 -11.0 8.43e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.2517225e+01 2.38e+00 1.91e-01 -11.0 1.40e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 56 1.6211295e+01 4.50e-02 5.08e+00 -11.0 5.35e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.6239852e+01 1.38e-06 1.78e-03 -11.0 5.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.6239853e+01 4.60e-07 2.24e-04 -11.0 2.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.6239853e+01 2.75e-07 1.92e-04 -11.0 1.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.6239833e+01 6.26e-06 5.69e-03 -11.0 3.52e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.6239841e+01 5.65e-06 2.03e-03 -11.0 1.69e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.6239849e+01 1.36e-06 2.31e-03 -11.0 7.33e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.6239849e+01 3.51e-06 4.38e-03 -11.0 1.13e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.6239464e+01 1.93e-04 1.18e-02 -11.0 1.28e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.6232739e+01 4.62e-03 1.54e-02 -11.0 2.52e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.6232064e+01 5.35e-03 2.53e-03 -11.0 1.53e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.6239275e+01 2.85e-04 3.66e-03 -11.0 2.38e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.6239288e+01 2.24e-04 1.57e-03 -11.0 1.30e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.6238766e+01 2.27e-04 1.33e-03 -11.0 1.98e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.6237100e+01 1.90e-03 1.75e-03 -11.0 1.52e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.6238186e+01 9.66e-04 9.01e-04 -11.0 3.00e+00 - 1.00e+00 5.00e-01h 2\n",
|
||
" 72 1.6239409e+01 5.42e-07 2.31e-05 -11.0 1.50e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 73 1.6239306e+01 1.80e-04 9.75e-04 -11.0 1.96e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.6239445e+01 1.14e-07 2.29e-05 -11.0 1.15e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 75 1.5824723e+01 7.42e-01 3.39e-02 -11.0 3.54e+06 - 1.25e-02 2.53e-03f 1\n",
|
||
" 76 1.6262358e+01 4.68e-02 3.20e-02 -10.7 2.69e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.6197136e+01 6.15e-02 1.17e-02 -2.4 2.98e+02 - 1.00e+00 8.99e-01h 1\n",
|
||
" 78 1.5736075e+01 5.65e-01 2.22e-02 -2.4 4.73e+03 - 2.58e-01 1.00e+00f 1\n",
|
||
" 79 1.5918239e+01 3.08e-01 4.57e-02 -2.6 3.25e+03 - 9.85e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.4941946e+01 1.60e+00 6.29e-02 -3.4 2.28e+04 - 9.98e-01 7.28e-01f 1\n",
|
||
" 81 1.4935546e+01 1.58e+00 6.05e-02 -3.5 2.24e+04 - 8.00e-01 7.48e-03h 1\n",
|
||
" 82 1.6612596e+01 8.34e-02 1.28e-01 -3.5 8.94e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.6691085e+01 1.68e-05 9.55e-02 -3.5 9.55e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.6691091e+01 8.44e-07 1.08e-03 -3.5 1.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.6691091e+01 9.50e-07 1.98e-03 -5.2 4.96e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.6691094e+01 9.50e-08 1.13e-04 -5.2 6.11e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.6691093e+01 1.11e-07 9.39e-05 -5.2 4.75e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.6691092e+01 5.41e-07 6.16e-05 -5.2 4.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.6691093e+01 1.37e-07 7.56e-05 -7.8 8.13e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.6691093e+01 8.79e-08 4.47e-05 -7.8 9.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.6691093e+01 1.06e-07 1.18e-04 -7.8 4.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.6691093e+01 1.24e-10 3.94e-04 -7.8 1.99e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 93 1.6691091e+01 9.50e-07 2.25e-03 -7.8 1.71e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.6691093e+01 1.57e-07 1.29e-04 -7.8 1.15e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.6691093e+01 1.66e-07 9.43e-05 -7.8 6.55e-04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 96 1.6691094e+01 6.48e-11 6.53e-05 -7.8 2.24e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 97 1.6689385e+01 4.68e-03 1.34e-02 -7.8 3.78e+01 - 1.00e+00 3.11e-01f 1\n",
|
||
" 98 1.6689799e+01 1.26e-03 2.52e-03 -7.8 7.85e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.6690641e+01 3.14e-04 3.49e-03 -7.8 1.85e+00 - 1.00e+00 8.01e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.6690879e+01 1.73e-04 1.86e-03 -7.8 4.63e-01 - 8.23e-01 5.00e-01h 2\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.6690879481873381e+01 1.6690879481873381e+01\n",
|
||
"Dual infeasibility......: 1.8636224404836588e-03 1.8636224404836588e-03\n",
|
||
"Constraint violation....: 1.7285710675096766e-04 1.7285710675096766e-04\n",
|
||
"Complementarity.........: 1.7178169560669866e-08 1.7178169560669866e-08\n",
|
||
"Overall NLP error.......: 1.8636224404836588e-03 1.8636224404836588e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 111\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 111\n",
|
||
"Number of inequality constraint evaluations = 111\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.418\n",
|
||
"Total CPU secs in NLP function evaluations = 134.308\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 491.00us ( 4.42us) 498.39us ( 4.49us) 111\n",
|
||
" nlp_g | 4.98 s ( 44.83ms) 4.75 s ( 42.77ms) 111\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 367.00us ( 3.60us) 362.03us ( 3.55us) 102\n",
|
||
" nlp_jac_g | 132.02 s ( 1.29 s) 126.05 s ( 1.24 s) 102\n",
|
||
" total | 138.49 s (138.49 s) 132.23 s (132.23 s) 1\n",
|
||
"Timestamp 19500\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.15e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9434891e+01 1.37e+01 3.15e+04 -1.5 3.15e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.1704001e+01 5.44e+00 1.25e+01 0.8 3.44e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.6981285e+01 1.86e+00 8.62e-01 -1.3 6.41e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 1.8067109e+01 6.87e-05 8.23e-02 -3.0 2.08e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.8067259e+01 1.80e-05 3.75e-03 -4.9 1.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.8067243e+01 3.39e-05 4.29e-03 -7.0 1.27e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.8067146e+01 9.63e-05 1.22e-03 -9.1 3.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.8067244e+01 1.02e-05 2.98e-03 -11.0 1.02e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.8067249e+01 1.91e-05 1.77e-03 -11.0 8.60e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.8067173e+01 4.97e-05 4.57e-03 -11.0 7.24e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.8067277e+01 5.18e-07 4.37e-05 -11.0 1.50e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.8067257e+01 1.17e-05 3.95e-03 -11.0 3.71e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.8067264e+01 1.63e-05 1.58e-03 -11.0 1.62e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.8067257e+01 1.69e-05 2.27e-03 -11.0 1.18e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.8067269e+01 3.05e-06 2.14e-03 -11.0 5.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.8067238e+01 2.76e-05 5.14e-03 -11.0 6.02e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.8066105e+01 1.10e-03 9.21e-03 -11.0 2.09e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.8063810e+01 1.78e-03 1.43e-02 -11.0 3.46e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.7986734e+01 4.14e-02 7.36e-03 -11.0 9.24e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.8051942e+01 2.51e-03 3.17e-03 -11.0 7.80e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.7996258e+01 3.34e-02 3.58e-03 -11.0 8.60e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.8017086e+01 3.91e-02 1.51e-03 -11.0 4.03e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.8029603e+01 7.55e-03 2.55e-03 -11.0 7.91e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.8015086e+01 1.60e-02 3.58e-03 -11.0 9.33e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.7860494e+01 7.33e-01 2.33e-02 -11.0 2.05e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 26 1.5893227e+01 2.72e+00 1.88e-01 -9.0 1.61e+06 - 1.00e+00 1.73e-02f 1\n",
|
||
" 27 1.7490143e+01 3.24e-01 1.04e-01 -9.6 4.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.7430270e+01 7.22e-01 7.83e-02 -7.7 5.83e+03 - 1.00e+00 7.76e-01h 1\n",
|
||
" 29 1.7431467e+01 7.12e-01 7.62e-02 -5.7 3.76e+03 - 1.00e+00 2.54e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.7438015e+01 6.79e-01 7.13e-02 -3.8 2.18e+03 - 1.00e+00 8.65e-02h 1\n",
|
||
" 31 1.7666625e+01 1.47e-03 2.20e-02 -5.9 1.75e+02 - 9.63e-01 1.00e+00h 1\n",
|
||
" 32 1.7642219e+01 3.28e-02 2.34e-02 -3.2 2.08e+03 - 1.19e-01 1.00e+00f 1\n",
|
||
" 33 1.7623603e+01 5.23e-02 2.15e-02 -2.6 2.66e+05 - 7.35e-01 6.76e-03f 1\n",
|
||
" 34 1.7684895e+01 6.69e-03 1.17e-02 -3.6 3.50e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 35 1.6702717e+01 1.50e+00 1.33e-01 -3.7 1.75e+04 - 7.03e-01 1.00e+00f 1\n",
|
||
" 36 1.7719950e+01 2.01e-04 1.84e+00 -5.6 1.93e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.7720082e+01 2.70e-07 1.02e-04 -7.5 2.64e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.7720082e+01 3.13e-07 1.55e-04 -11.0 9.89e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.7720082e+01 7.61e-07 1.65e-03 -11.0 3.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.7720082e+01 3.12e-07 1.19e-04 -11.0 4.39e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.7720080e+01 1.63e-06 2.70e-03 -11.0 8.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.7720082e+01 1.13e-07 6.15e-05 -11.0 1.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.7720082e+01 4.82e-07 8.90e-05 -11.0 3.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.7720082e+01 7.16e-07 1.47e-04 -11.0 4.34e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.7720082e+01 1.86e-07 7.95e-05 -11.0 2.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.7720075e+01 3.01e-06 7.25e-03 -11.0 2.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.7720070e+01 4.85e-06 1.21e-02 -11.0 3.65e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.7720077e+01 2.87e-06 1.59e-03 -11.0 1.37e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.7720079e+01 5.33e-07 2.54e-04 -11.0 6.39e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.7720037e+01 1.30e-05 2.35e-03 -11.0 3.84e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.7720021e+01 3.76e-05 3.21e-03 -11.0 9.03e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.7720060e+01 1.20e-05 2.05e-03 -11.0 3.54e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.7720075e+01 1.46e-06 2.43e-03 -11.0 1.31e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.7720066e+01 6.64e-06 1.55e-03 -11.0 6.70e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.7719901e+01 7.54e-05 4.96e-03 -11.0 9.72e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.7720017e+01 5.29e-09 6.28e-05 -11.0 8.63e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 57 1.7719980e+01 7.97e-05 2.59e-03 -11.0 5.78e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.7718311e+01 1.73e-03 1.12e-02 -11.0 2.62e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.7719397e+01 8.09e-04 3.76e-03 -11.0 2.27e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.7708137e+01 6.88e-03 4.47e-03 -9.0 3.08e+03 - 1.00e+00 1.57e-01h 1\n",
|
||
" 61 1.7716013e+01 4.11e-03 2.21e-03 -9.1 1.70e+02 - 1.67e-04 1.00e+00h 1\n",
|
||
" 62 1.6703197e+01 9.27e-01 6.09e-02 -9.1 7.40e+06 - 3.88e-03 4.63e-03f 1\n",
|
||
" 63 1.7863325e+01 2.85e-01 6.58e-02 -8.9 4.92e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.7298393e+01 2.33e-01 1.84e-02 -1.8 6.49e+03 - 4.42e-01 1.00e+00h 1\n",
|
||
" 65 1.7665682e+01 2.07e-04 4.13e-01 -4.2 4.27e-01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 66 1.7665797e+01 3.33e-07 3.89e-05 -6.0 3.51e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.7665792e+01 1.68e-06 3.02e-03 -11.0 2.47e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.7665772e+01 1.89e-05 7.48e-03 -11.0 5.62e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.7665781e+01 5.55e-06 1.79e-03 -11.0 3.93e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.7665629e+01 2.82e-04 7.25e-03 -11.0 6.49e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.7665734e+01 2.63e-05 2.15e-03 -11.0 2.94e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.7665588e+01 1.04e-04 1.01e-02 -11.0 1.22e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.7665096e+01 2.46e-04 1.93e-02 -11.0 2.24e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.7660254e+01 3.04e-03 4.65e-02 -11.0 6.08e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.7665788e+01 6.87e-07 4.40e-03 -11.0 4.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.7665786e+01 1.12e-06 1.81e-03 -11.0 7.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.7665788e+01 1.45e-07 5.39e-05 -11.0 1.71e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.7665787e+01 4.52e-07 4.74e-05 -11.0 5.93e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.7665769e+01 8.90e-06 6.84e-03 -11.0 1.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.7665767e+01 1.44e-05 1.94e-03 -11.0 8.75e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.7665784e+01 9.80e-06 6.40e-03 -11.0 4.10e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.7665776e+01 5.98e-06 4.32e-03 -11.0 2.92e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.7665782e+01 2.25e-06 7.79e-03 -11.0 3.02e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.7665767e+01 9.06e-06 1.52e-02 -11.0 5.62e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.7665780e+01 4.59e-06 3.81e-03 -11.0 3.90e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.7665779e+01 5.65e-06 1.54e-03 -11.0 3.90e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.7665737e+01 4.63e-05 2.23e-03 -11.0 3.02e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.7665667e+01 1.87e-04 1.18e-03 -11.0 1.11e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.7657143e+01 7.55e-03 5.58e-03 -11.0 1.12e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.7664072e+01 5.24e-04 2.29e-03 -11.0 4.92e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.7665538e+01 4.46e-05 9.35e-04 -11.0 3.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.7665491e+01 1.80e-04 2.33e-03 -11.0 1.57e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.7665327e+01 1.23e-04 1.42e-03 -11.0 3.84e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.7662853e+01 1.92e-03 1.77e-03 -11.0 9.08e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.7661848e+01 4.00e-03 1.59e-03 -11.0 5.17e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.7651844e+01 4.62e-02 1.71e-03 -11.0 1.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.7626245e+01 1.07e-01 2.36e-03 -11.0 1.75e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.7668569e+01 4.97e-07 8.96e-02 -11.0 9.37e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.7668569e+01 5.43e-07 1.24e-04 -11.0 3.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.7668540e+01 1.65e-05 4.20e-03 -11.0 7.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.7668540433240064e+01 1.7668540433240064e+01\n",
|
||
"Dual infeasibility......: 4.1985732799884393e-03 4.1985732799884393e-03\n",
|
||
"Constraint violation....: 1.6523876915641722e-05 1.6523876915641722e-05\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 4.1985732799884393e-03 4.1985732799884393e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 103\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 103\n",
|
||
"Number of inequality constraint evaluations = 103\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.388\n",
|
||
"Total CPU secs in NLP function evaluations = 142.797\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 500.00us ( 4.85us) 492.83us ( 4.78us) 103\n",
|
||
" nlp_g | 4.94 s ( 47.97ms) 4.74 s ( 46.03ms) 103\n",
|
||
" nlp_grad | 1.32 s ( 1.32 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 393.00us ( 3.85us) 388.18us ( 3.81us) 102\n",
|
||
" nlp_jac_g | 140.76 s ( 1.38 s) 135.18 s ( 1.33 s) 102\n",
|
||
" total | 147.16 s (147.16 s) 141.32 s (141.32 s) 1\n",
|
||
"Timestamp 19800\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 4.91e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9807678e+01 1.20e+01 4.91e+03 -1.5 4.91e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.8165761e+00 3.91e+00 8.20e+00 0.4 1.60e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 3.2351732e+00 7.11e-01 6.46e-01 -1.6 5.45e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 3.5970582e+00 2.14e-03 1.74e-01 -3.4 1.08e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 3.5978500e+00 1.03e-07 4.87e-05 -5.2 2.14e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 3.5978500e+00 1.25e-07 2.20e-05 -11.0 6.11e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 3.5978501e+00 1.25e-08 3.30e-05 -11.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 3.5978501e+00 5.94e-11 4.35e-05 -11.0 1.94e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 9 3.5978501e+00 2.44e-08 5.11e-05 -11.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 3.5978501e+00 8.35e-09 9.56e-05 -11.0 1.38e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 3.5978501e+00 4.62e-11 4.07e-05 -11.0 6.57e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 12 3.5978501e+00 2.98e-08 2.91e-04 -11.0 1.13e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 3.5978496e+00 4.14e-07 1.09e-04 -11.0 5.66e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 3.5978501e+00 4.01e-08 6.86e-05 -11.0 1.41e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 3.5978501e+00 3.05e-08 3.78e-05 -11.0 1.09e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 3.5978501e+00 1.28e-08 1.33e-05 -11.0 5.41e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 3.5978501e+00 1.36e-09 7.43e-05 -11.0 1.82e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 3.5978501e+00 1.08e-08 6.96e-05 -11.0 8.35e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 3.5978501e+00 5.47e-09 3.03e-05 -11.0 5.06e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 3.5978501e+00 5.54e-11 3.83e-05 -11.0 4.73e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 21 3.5978501e+00 3.23e-09 1.25e-04 -11.0 3.65e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 3.5978501e+00 1.25e-08 1.29e-04 -11.0 4.74e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 3.5978501e+00 3.05e-08 6.31e-05 -11.0 1.10e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 3.5978501e+00 2.05e-09 6.09e-05 -11.0 1.16e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 3.5978501e+00 6.85e-09 6.86e-05 -11.0 4.12e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 3.5978501e+00 4.04e-09 5.73e-05 -11.0 2.24e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 3.5978501e+00 3.17e-09 3.50e-05 -11.0 1.45e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 3.5978501e+00 1.60e-09 1.90e-04 -11.0 1.32e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 3.5978501e+00 6.12e-09 2.48e-05 -11.0 1.32e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 3.5978501e+00 2.14e-10 1.15e-04 -11.0 2.44e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 3.5978501e+00 4.42e-10 2.12e-05 -11.0 1.46e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 3.5978501e+00 1.51e-10 6.20e-05 -11.0 2.62e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 3.5978501e+00 3.95e-10 5.60e-05 -11.0 2.28e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 3.5978501e+00 3.36e-10 5.03e-05 -11.0 1.34e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 3.5978501e+00 6.36e-10 7.42e-05 -11.0 1.55e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 3.5978501e+00 1.40e-08 5.74e-05 -11.0 1.33e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 3.5978501e+00 6.41e-09 2.11e-05 -11.0 4.30e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 3.5978501e+00 1.72e-09 9.91e-05 -11.0 3.02e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 3.5978501e+00 7.13e-08 2.62e-05 -11.0 1.56e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 3.5978498e+00 7.00e-07 1.28e-04 -11.0 3.54e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 3.5978498e+00 1.81e-06 2.58e-03 -11.0 5.56e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 3.5978444e+00 4.76e-06 9.61e-03 -11.0 2.62e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 3.5978487e+00 1.40e-06 1.44e-03 -11.0 7.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 3.5978494e+00 1.08e-06 2.54e-03 -11.0 1.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 3.5976474e+00 5.22e-04 9.95e-03 -11.0 3.22e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 3.5974951e+00 1.46e-03 6.15e-03 -11.0 8.15e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 3.5847291e+00 2.33e-02 7.19e-03 -11.0 3.01e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 3.5884178e+00 1.24e-02 1.12e-03 -11.0 9.40e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 3.5951277e+00 2.68e-03 1.57e-03 -11.0 7.13e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 3.5877671e+00 1.18e-02 6.50e-03 -11.0 9.71e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 3.5744393e+00 3.49e-02 1.17e-02 -11.0 2.62e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 3.5678310e+00 4.55e-02 6.45e-03 -11.0 6.44e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 3.2326528e+00 4.04e-01 2.12e-01 -11.0 1.29e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 54 3.2030776e+00 1.22e+00 4.87e-01 -9.0 2.10e+04 - 1.00e+00 8.68e-01h 1\n",
|
||
" 55 3.1259057e+00 1.34e+00 3.06e-01 -9.2 1.55e+04 - 1.00e+00 7.00e-01h 1\n",
|
||
" 56 3.2905650e+00 5.50e-01 1.40e-01 -9.0 3.08e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 57 3.2895474e+00 6.99e-01 1.38e-01 -9.1 4.54e+04 - 1.39e-01 3.20e-02h 5\n",
|
||
" 58 3.1007482e+00 1.40e+00 3.46e-01 -9.1 5.15e+04 - 3.26e-01 6.21e-01h 1\n",
|
||
" 59 3.0861916e+00 1.33e+00 2.67e-01 -10.2 1.26e+05 - 8.20e-01 3.62e-02h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 3.7507697e+00 8.49e-01 3.76e-01 -3.8 4.16e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 3.0591803e+00 4.75e-01 1.95e-01 -4.0 1.50e+04 - 1.01e-01 1.00e+00h 1\n",
|
||
" 62 3.2307480e+00 3.03e-01 2.77e-01 -5.3 8.94e+04 - 1.59e-01 5.10e-01h 1\n",
|
||
" 63 3.1931841e+00 5.10e-01 1.08e-01 -4.2 1.97e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 3.1200875e+00 1.34e+00 3.79e-01 -4.4 1.94e+06 - 5.94e-03 2.82e-03f 3\n",
|
||
" 65 3.1064910e+00 1.22e+00 3.30e-01 -4.4 1.61e+06 - 3.00e-02 9.45e-03h 1\n",
|
||
" 66 3.0906397e+00 5.86e-01 1.09e-01 -4.4 7.70e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 3.2016304e+00 1.23e+00 2.29e-01 -4.4 3.29e+03 - 9.89e-01 9.89e-01s 22\n",
|
||
" 68 3.4076452e+00 3.40e-03 2.84e-01 -4.4 1.69e+00 - 1.00e+00 0.00e+00S 22\n",
|
||
" 69 3.4093476e+00 4.63e-07 1.09e-03 -6.3 5.09e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 3.4093484e+00 2.92e-08 8.68e-05 -8.4 3.10e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 3.4093483e+00 1.35e-07 4.48e-05 -11.0 5.42e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 3.4093485e+00 1.77e-08 4.57e-05 -11.0 1.82e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 3.4093484e+00 2.65e-07 7.03e-05 -11.0 1.06e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 3.4093479e+00 3.50e-07 2.96e-05 -11.0 1.97e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 3.4093484e+00 1.08e-07 2.82e-05 -11.0 6.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 3.4093484e+00 3.89e-08 6.65e-05 -11.0 3.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 3.4093480e+00 1.10e-06 1.11e-03 -11.0 5.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 3.4093467e+00 2.35e-06 3.11e-03 -11.0 9.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 3.4093392e+00 5.74e-06 5.03e-03 -11.0 2.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 3.4093475e+00 1.54e-08 4.36e-05 -11.0 9.98e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 3.4093475e+00 8.43e-09 4.77e-05 -11.0 3.59e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 3.4093475e+00 4.28e-08 6.67e-05 -11.0 3.76e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 3.4093475e+00 8.73e-09 4.56e-05 -11.0 8.19e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 3.4093475e+00 2.08e-08 9.22e-05 -11.0 7.04e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 3.4093473e+00 1.41e-07 3.48e-05 -11.0 6.15e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 3.4093475e+00 7.27e-08 4.83e-05 -11.0 4.55e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 3.4093475e+00 2.59e-08 4.68e-05 -11.0 2.59e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 3.4093470e+00 6.82e-07 3.69e-03 -11.0 3.80e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 3.4093460e+00 1.60e-06 8.15e-03 -11.0 6.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 3.4092043e+00 9.36e-05 6.90e-03 -11.0 7.95e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 3.4093315e+00 7.44e-06 1.24e-03 -11.0 1.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 3.4091780e+00 9.98e-05 2.87e-03 -11.0 8.97e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 3.3753842e+00 5.21e-02 1.52e-02 -11.0 2.11e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 94 3.4005532e+00 3.33e-03 2.09e-02 -11.0 1.21e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 3.3952034e+00 1.90e-02 6.56e-03 -11.0 1.44e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 3.3309511e+00 1.06e-01 2.67e-03 -11.0 3.64e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 3.4007074e+00 2.62e-04 1.19e-01 -11.0 1.28e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 3.4007675e+00 1.04e-07 7.61e-05 -11.0 4.03e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 3.4007676e+00 4.55e-11 1.01e-04 -11.0 3.20e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 3.4007675e+00 1.13e-07 7.53e-05 -11.0 3.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 3.4007675233460768e+00 3.4007675233460768e+00\n",
|
||
"Dual infeasibility......: 7.5320058245210959e-05 7.5320058245210959e-05\n",
|
||
"Constraint violation....: 1.1321691317789373e-07 1.1321691317789373e-07\n",
|
||
"Complementarity.........: 1.0000000000000001e-11 1.0000000000000001e-11\n",
|
||
"Overall NLP error.......: 7.5320058245210959e-05 7.5320058245210959e-05\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 143\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 143\n",
|
||
"Number of inequality constraint evaluations = 143\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.423\n",
|
||
"Total CPU secs in NLP function evaluations = 141.681\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 714.00us ( 4.99us) 710.14us ( 4.97us) 143\n",
|
||
" nlp_g | 6.80 s ( 47.57ms) 6.52 s ( 45.62ms) 143\n",
|
||
" nlp_grad | 1.32 s ( 1.32 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 419.00us ( 4.11us) 414.23us ( 4.06us) 102\n",
|
||
" nlp_jac_g | 137.75 s ( 1.35 s) 132.11 s ( 1.30 s) 102\n",
|
||
" total | 146.02 s (146.02 s) 140.04 s (140.04 s) 1\n",
|
||
"Timestamp 20100\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 5.10e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9587713e+01 1.31e+01 5.10e+03 -1.5 5.10e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.3034835e+00 4.44e+00 9.19e+00 0.4 1.70e+01 - 9.97e-01 1.00e+00f 1\n",
|
||
" 3 8.2873452e+00 1.17e+00 8.04e-01 -1.6 7.78e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 9.0898942e+00 1.63e-03 7.45e-02 -3.4 1.60e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 9.0906705e+00 6.70e-07 1.79e-03 -5.2 1.87e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 9.0906709e+00 4.75e-08 1.07e-04 -7.3 4.40e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 9.0906709e+00 6.33e-08 5.80e-05 -11.0 3.42e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 9.0906710e+00 1.25e-08 1.28e-04 -11.0 1.20e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 9.0906709e+00 9.50e-08 2.85e-05 -11.0 4.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 9.0906711e+00 1.32e-10 2.21e-04 -11.0 5.84e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 9.0906682e+00 1.73e-06 1.16e-02 -11.0 4.14e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 9.0906711e+00 4.63e-09 3.48e-05 -11.0 6.08e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 9.0906711e+00 5.99e-09 8.92e-05 -11.0 4.77e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 9.0906677e+00 4.07e-06 7.87e-03 -11.0 3.73e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 9.0906656e+00 1.28e-05 9.66e-03 -11.0 4.98e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 9.0906593e+00 1.39e-05 1.93e-03 -11.0 1.50e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 9.0906454e+00 1.29e-05 9.09e-04 -11.0 9.42e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 9.0906335e+00 3.89e-05 1.87e-03 -11.0 1.69e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 9.0906570e+00 1.45e-05 1.02e-03 -11.0 8.04e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 9.0906627e+00 5.58e-06 1.08e-03 -11.0 3.60e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 9.0906604e+00 1.64e-05 1.74e-03 -11.0 1.45e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 9.0905605e+00 1.33e-04 8.87e-04 -11.0 1.79e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 9.0906144e+00 8.33e-05 1.66e-03 -11.0 9.30e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 9.0905723e+00 8.04e-05 1.07e-03 -11.0 1.99e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 9.0905233e+00 1.74e-04 2.77e-03 -11.0 2.42e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 9.0906644e+00 2.97e-08 1.78e-05 -11.0 2.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 9.0906644e+00 1.28e-08 1.15e-04 -11.0 6.73e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 9.0906565e+00 1.59e-05 5.02e-03 -11.0 6.95e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 9.0906546e+00 7.67e-06 1.19e-03 -11.0 4.85e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 9.0906639e+00 7.09e-07 1.94e-03 -11.0 1.15e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 9.0906622e+00 1.07e-06 1.73e-03 -11.0 1.36e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 9.0906615e+00 1.09e-05 2.68e-03 -11.0 4.49e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 9.0906537e+00 2.97e-05 1.50e-03 -11.0 1.67e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 9.0891055e+00 1.86e-03 4.56e-03 -11.0 3.80e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 9.0885672e+00 1.25e-03 1.21e-03 -11.0 2.60e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 9.0890410e+00 9.16e-04 1.44e-03 -11.0 1.46e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 9.0826497e+00 4.33e-03 2.18e-03 -11.0 3.25e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 9.0908260e+00 5.70e-05 1.59e-03 -11.0 3.38e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 9.0892949e+00 7.77e-04 2.36e-03 -11.0 1.47e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 9.0910040e+00 6.82e-07 1.20e-04 -11.0 2.13e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 41 9.0909978e+00 3.15e-05 1.89e-03 -11.0 3.72e+01 - 1.00e+00 3.91e-03h 9\n",
|
||
" 42 9.0909972e+00 1.53e-09 2.39e-04 -11.0 2.16e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 43 9.0906527e+00 2.70e-04 1.72e-03 -11.0 1.78e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 44 9.0910934e+00 1.01e-07 7.37e-05 -11.0 1.22e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 45 9.0910933e+00 5.70e-07 1.34e-04 -11.0 2.74e+01 - 1.00e+00 1.22e-04h 14\n",
|
||
" 46 9.0910917e+00 4.93e-10 6.32e-05 -11.0 2.35e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 47 9.0841041e+00 6.18e-03 3.37e-03 -11.0 2.14e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 48 9.0894782e+00 1.03e-03 2.48e-03 -11.0 8.04e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 9.0815830e+00 5.21e-03 4.18e-03 -11.0 2.66e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.5226719e+00 4.81e-01 7.41e-02 -9.0 3.36e+05 - 1.00e+00 9.23e-02f 1\n",
|
||
" 51 8.5317333e+00 4.77e-01 7.15e-02 -7.0 8.07e+03 - 1.00e+00 1.99e-02h 1\n",
|
||
" 52 8.5318579e+00 4.77e-01 7.15e-02 -5.1 5.91e+03 - 1.00e+00 2.72e-04h 1\n",
|
||
" 53 9.0654052e+00 5.49e-03 6.97e-02 -7.0 4.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 9.0588858e+00 1.19e-02 3.53e-03 -5.0 5.08e+01 - 1.09e-01 1.00e+00h 1\n",
|
||
" 55 9.0498858e+00 1.17e-02 2.09e-02 -4.7 2.73e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 9.0185237e+00 5.90e-02 1.28e-02 -4.3 8.35e+02 - 1.00e+00 5.06e-01h 1\n",
|
||
" 57 9.0185215e+00 1.95e-02 7.74e-03 -3.3 7.57e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 9.0704273e+00 3.22e-03 1.37e-03 -5.2 9.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 9.0592037e+00 1.23e-02 1.72e-03 -6.4 9.12e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 9.0574164e+00 2.28e-02 1.62e-03 -5.1 5.70e+01 - 5.00e-01 1.00e+00h 1\n",
|
||
" 61 6.9468419e+00 3.53e+00 1.04e+00 -5.1 3.38e+06 - 8.84e-06 9.92e-03f 1\n",
|
||
" 62 8.7966860e+00 2.29e+00 4.20e-01 -4.0 4.05e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 9.2147465e+00 2.94e-02 1.64e-01 -2.1 5.73e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 9.2845290e+00 1.24e-02 2.58e-02 -2.1 4.98e+02 - 6.86e-01 1.00e+00h 1\n",
|
||
" 65 9.0756073e+00 4.66e-01 1.12e-01 -3.2 4.77e+03 - 8.58e-01 1.00e+00f 1\n",
|
||
" 66 9.0450369e+00 4.69e-01 9.92e-02 -3.2 1.46e+03 - 1.00e+00 8.08e-02h 1\n",
|
||
" 67 9.3242846e+00 6.47e-03 4.46e-02 -3.2 3.10e+02 - 3.11e-01 1.00e+00h 1\n",
|
||
" 68 9.2547266e+00 2.55e-01 6.30e-02 -3.2 2.12e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 9.3255617e+00 1.64e-02 1.29e-02 -3.2 3.38e+02 - 7.16e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 9.2243681e+00 2.12e-01 3.22e-03 -3.2 8.25e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 9.0428203e+00 1.83e-01 9.93e-03 -3.2 6.41e+04 - 3.56e-01 1.06e-01f 1\n",
|
||
" 72 9.2491341e+00 6.02e-02 1.61e-02 -3.2 2.37e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 9.1710368e+00 9.92e-02 1.23e-02 -3.2 4.58e+03 - 5.80e-01 1.00e+00h 1\n",
|
||
" 74 9.3375202e+00 1.97e-04 6.79e-03 -3.2 1.04e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 75 8.4984674e+00 7.40e+00 1.08e+00 -3.2 2.25e+05 - 5.10e-03 1.43e-01F 1\n",
|
||
" 76 8.4760310e+00 7.39e+00 1.08e+00 -3.2 5.25e+04 - 8.78e-01 4.12e-03h 1\n",
|
||
" 77 7.5112359e+00 3.73e+00 9.41e-02 -3.2 7.73e+02 - 3.78e-02 5.00e-01f 2\n",
|
||
" 78 7.7275779e+00 2.08e+00 9.79e-02 -2.8 4.89e+04 - 1.00e+00 6.51e-01h 1\n",
|
||
" 79 9.6877640e+00 1.46e+00 1.85e-01 -1.7 1.19e+04 - 7.13e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 9.9941067e+00 4.89e-01 1.20e-01 -1.9 2.05e+03 - 6.89e-01 1.00e+00h 1\n",
|
||
" 81 8.9932575e+00 8.59e-01 1.00e-01 -1.9 8.16e+03 - 1.48e-01 1.00e+00f 1\n",
|
||
" 82 6.8435542e+00 4.41e+00 7.93e-01 -1.9 1.22e+04 - 7.22e-01 1.00e+00f 1\n",
|
||
" 83 8.4445055e+00 2.77e+00 1.40e-01 -0.3 5.16e+03 - 1.00e+00 8.95e-01h 1\n",
|
||
" 84 9.1951635e+00 3.75e-01 2.05e-01 -0.4 1.20e+04 - 5.96e-01 1.00e+00f 1\n",
|
||
" 85 7.9877155e+00 1.61e+00 1.07e-01 -1.1 5.82e+03 - 8.85e-01 1.00e+00f 1\n",
|
||
" 86 9.4297912e+00 5.56e-01 5.25e-02 -7.0 7.67e+03 - 5.90e-01 1.00e+00h 1\n",
|
||
" 87 9.2272149e+00 1.40e+00 5.67e-02 -1.5 9.21e+04 - 5.00e-01 1.09e-01h 1\n",
|
||
" 88 6.9818059e+00 1.28e+00 3.28e-01 -1.5 2.13e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 89 6.9866232e+00 1.25e+00 3.13e-01 -1.5 3.07e+04 - 1.00e+00 2.69e-02h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 9.1659414e+00 1.04e+00 1.68e-01 -7.4 5.40e+03 - 5.15e-01 1.00e+00h 1\n",
|
||
" 91 9.0401031e+00 5.73e-01 9.50e-02 -2.0 1.92e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 9.8651208e+00 1.24e-01 1.06e-01 -2.0 1.13e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 9.2244714e+00 3.13e-01 7.13e-02 -2.0 1.31e+04 - 7.91e-01 1.00e+00f 1\n",
|
||
" 94 9.7074107e+00 1.28e-02 2.60e-02 -2.0 4.04e+04 - 1.00e+00 9.74e-01H 1\n",
|
||
" 95 9.6923578e+00 1.60e-02 2.43e-02 -3.0 1.02e+04 - 8.82e-01 3.84e-02h 1\n",
|
||
" 96 9.5025095e+00 1.18e-01 3.07e-02 -3.0 1.72e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 97 9.1713591e+00 2.51e-01 2.76e-02 -3.0 1.50e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 8.0917367e+00 6.84e-01 1.09e-01 -3.0 1.15e+05 - 1.42e-01 3.27e-02f 1\n",
|
||
" 99 9.3536069e+00 2.17e-01 8.43e-02 -9.0 1.28e+03 - 7.23e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 9.5536014e+00 8.38e-02 2.64e-02 -3.7 1.09e+03 - 2.93e-02 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 9.5536013918613989e+00 9.5536013918613989e+00\n",
|
||
"Dual infeasibility......: 2.6371688707022556e-02 2.6371688707022556e-02\n",
|
||
"Constraint violation....: 8.3835035220502618e-02 8.3835035220502618e-02\n",
|
||
"Complementarity.........: 6.6368300191372242e-04 6.6368300191372242e-04\n",
|
||
"Overall NLP error.......: 8.3835035220502618e-02 8.3835035220502618e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 145\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 145\n",
|
||
"Number of inequality constraint evaluations = 145\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.418\n",
|
||
"Total CPU secs in NLP function evaluations = 140.247\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 665.00us ( 4.59us) 662.59us ( 4.57us) 145\n",
|
||
" nlp_g | 6.71 s ( 46.30ms) 6.42 s ( 44.26ms) 145\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 362.00us ( 3.55us) 350.92us ( 3.44us) 102\n",
|
||
" nlp_jac_g | 136.40 s ( 1.34 s) 130.63 s ( 1.28 s) 102\n",
|
||
" total | 144.60 s (144.60 s) 138.46 s (138.46 s) 1\n",
|
||
"Timestamp 20400\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.02e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9802025e+01 1.46e+01 3.02e+04 -1.5 3.02e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.2609246e+01 5.64e+00 1.15e+01 0.8 3.54e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.8614184e+01 2.14e+00 8.37e-01 -1.3 7.07e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 1.9879456e+01 5.60e-05 8.62e-02 -3.0 2.40e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
" 5 1.9879383e+01 7.65e-06 3.64e-03 -4.9 1.06e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.9879193e+01 6.19e-05 1.53e-03 -7.0 8.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.9878067e+01 3.94e-04 4.29e-03 -9.1 9.78e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.9876832e+01 1.18e-03 2.80e-03 -11.0 6.96e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.9878347e+01 3.08e-04 1.08e-03 -11.0 3.25e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.9879051e+01 1.58e-04 1.83e-03 -11.0 4.93e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.9878240e+01 3.91e-04 2.69e-03 -11.0 3.21e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.9878225e+01 4.02e-04 1.46e-03 -11.0 2.00e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.9878860e+01 1.45e-04 2.15e-03 -11.0 1.29e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.9878672e+01 5.74e-04 2.32e-03 -11.0 3.16e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.9845190e+01 1.23e-02 5.18e-03 -11.0 2.02e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.9835319e+01 3.34e-02 4.16e-03 -11.0 2.38e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.9800142e+01 4.49e-02 2.32e-03 -11.0 1.71e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.9587294e+01 6.40e-02 5.70e-03 -11.0 3.17e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.9871908e+01 1.56e-02 3.57e-03 -11.0 4.25e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.9879128e+01 4.55e-05 1.84e-03 -11.0 2.82e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.9860826e+01 7.02e-03 6.07e-03 -11.0 7.53e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 22 1.9876718e+01 4.29e-03 2.79e-03 -11.0 1.30e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.9875184e+01 1.60e-03 1.74e-03 -11.0 1.29e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.9874326e+01 1.69e-03 1.40e-03 -11.0 7.18e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.9875991e+01 8.84e-04 2.68e-03 -11.0 4.08e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.9877710e+01 1.59e-04 1.61e-03 -11.0 1.38e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.9874095e+01 9.70e-03 2.18e-03 -11.0 3.39e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.9811438e+01 3.67e-02 7.66e-03 -11.0 1.41e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.9622709e+01 4.01e-01 8.93e-03 -11.0 1.06e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.9814959e+01 8.09e-02 8.46e-03 -11.0 3.51e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.9045024e+01 1.81e+00 8.84e-02 -11.0 6.87e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 32 1.9142105e+01 4.85e-01 1.75e-02 -11.0 3.87e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.9462728e+01 3.55e-01 3.68e-02 -11.0 2.03e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.6541809e+01 6.46e+00 3.21e-01 -9.0 2.79e+05 - 1.00e+00 9.73e-02f 1\n",
|
||
" 35 1.6509021e+01 6.49e+00 3.22e-01 -7.0 1.88e+05 - 1.00e+00 1.45e-03h 1\n",
|
||
" 36 1.6513011e+01 6.47e+00 3.20e-01 -5.1 1.06e+03 - 1.00e+00 2.56e-03h 1\n",
|
||
" 37 1.9701937e+01 7.62e-02 2.66e-01 -5.2 1.53e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.9413021e+01 3.24e-01 2.33e-02 -6.4 1.48e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.9353323e+01 1.84e-01 1.23e-02 -8.1 1.64e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.9805153e+01 2.45e-02 9.45e-03 -5.7 7.43e+02 - 3.87e-01 1.00e+00h 1\n",
|
||
" 41 1.8548458e+01 7.83e-01 1.01e-01 -4.2 3.95e+03 - 4.30e-03 1.00e+00f 1\n",
|
||
" 42 1.2678001e+01 3.69e+00 2.18e-01 -4.0 1.13e+05 - 1.08e-01 1.79e-01f 1\n",
|
||
" 43 1.2632734e+01 3.67e+00 2.22e-01 -3.0 2.25e+06 - 3.38e-02 2.87e-04f 1\n",
|
||
" 44 1.5959178e+01 2.42e+00 7.99e-02 -3.3 1.82e+04 - 1.00e+00 4.59e-01h 1\n",
|
||
" 45 1.9248939e+01 7.71e-01 1.85e-01 -2.9 1.10e+04 - 8.76e-01 1.00e+00h 1\n",
|
||
" 46 2.0142116e+01 1.17e-01 3.46e-02 -3.1 1.03e+03 - 7.86e-02 1.00e+00h 1\n",
|
||
" 47 1.3816809e+01 3.18e+00 1.99e-01 -3.1 3.29e+04 - 1.00e+00 6.41e-01f 1\n",
|
||
" 48 1.7176596e+01 1.35e+00 1.46e-01 -2.8 1.47e+04 - 3.71e-01 1.00e+00h 1\n",
|
||
" 49 1.9967128e+01 3.08e-01 2.49e-01 -1.9 1.28e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.8609162e+01 8.13e-01 2.01e-01 -1.6 1.75e+04 - 5.27e-01 1.03e-01f 1\n",
|
||
" 51 1.8835979e+01 6.79e-01 1.65e-01 -1.3 4.51e+03 - 1.00e+00 1.62e-01h 2\n",
|
||
" 52 1.8526235e+01 4.18e+00 4.83e-01 -1.8 1.78e+04 - 3.90e-01 9.82e-01h 1\n",
|
||
" 53 1.8294865e+01 3.24e+00 2.79e-01 -1.7 2.97e+04 - 3.19e-01 2.76e-01h 1\n",
|
||
" 54 1.9587512e+01 4.33e-01 8.19e-02 -1.7 5.85e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.0079072e+01 4.78e-02 9.03e-02 -1.7 1.68e+03 - 9.69e-01 1.00e+00H 1\n",
|
||
" 56 1.9505920e+01 8.39e-01 3.15e-02 -2.5 6.41e+03 - 6.00e-01 5.39e-01f 1\n",
|
||
" 57 1.9603432e+01 2.22e-01 3.85e-02 -2.5 8.19e+02 - 3.21e-01 1.00e+00h 1\n",
|
||
" 58 1.9846994e+01 1.22e-01 2.42e-03 -2.5 3.91e+02 - 3.19e-01 1.00e+00h 1\n",
|
||
" 59 1.9781906e+01 1.07e-01 2.25e-03 -2.5 3.27e+04 - 8.95e-01 5.31e-03f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.9990740e+01 1.49e-01 7.87e-03 -2.5 7.39e+02 - 5.78e-01 1.00e+00h 1\n",
|
||
" 61 1.9913657e+01 1.18e-01 4.19e-03 -2.5 3.80e+03 - 2.89e-01 2.50e-01h 3\n",
|
||
" 62 2.0021919e+01 4.96e-02 4.64e-03 -2.5 4.66e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.0074464e+01 9.93e-03 4.91e-03 -2.5 1.26e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.0056955e+01 1.72e-02 1.45e-03 -3.8 2.01e+02 - 1.00e+00 8.68e-01h 1\n",
|
||
" 65 1.9960239e+01 5.44e-02 3.40e-03 -3.8 8.35e+02 - 1.17e-01 1.00e+00h 1\n",
|
||
" 66 1.9501999e+01 7.83e-01 2.91e-02 -3.8 2.04e+03 - 2.53e-02 1.00e+00f 1\n",
|
||
" 67 1.0903381e+01 1.14e+01 1.25e+00 -3.8 9.86e+05 - 4.61e-03 6.16e-02f 1\n",
|
||
" 68 1.0875772e+01 1.13e+01 1.24e+00 -1.8 5.03e+04 - 1.00e+00 3.68e-03h 1\n",
|
||
" 69 1.5746141e+01 2.98e+00 5.81e-01 -2.8 5.26e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.9632301e+01 9.43e-02 2.69e-01 -2.7 8.29e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.9790457e+01 1.25e-02 1.34e-02 -2.7 2.28e+02 - 9.77e-01 1.00e+00h 1\n",
|
||
" 72 1.9614407e+01 2.44e-01 5.57e-02 -3.3 1.34e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 73 1.9608637e+01 2.06e-01 4.71e-02 -3.4 3.40e+03 - 2.17e-01 4.02e-01h 1\n",
|
||
" 74 1.9844288e+01 3.06e-01 4.32e-02 -3.4 1.37e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.9762588e+01 5.56e-02 3.48e-03 -3.4 4.14e+02 - 7.70e-01 1.00e+00h 1\n",
|
||
" 76 1.9799374e+01 1.82e-05 5.24e-02 -3.4 5.24e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.9799365e+01 1.72e-06 1.08e-03 -5.3 5.40e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.9799348e+01 1.81e-05 1.91e-03 -7.4 1.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.9799361e+01 1.22e-06 1.79e-03 -9.5 1.57e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.9799361e+01 2.11e-06 1.06e-03 -11.0 8.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.9799364e+01 1.03e-06 1.62e-03 -11.0 7.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.9799361e+01 1.67e-06 1.67e-03 -11.0 5.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.9799365e+01 6.38e-07 8.19e-05 -11.0 3.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.9799364e+01 5.11e-07 4.44e-05 -11.0 3.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.9799365e+01 3.98e-07 4.52e-05 -11.0 1.91e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.9799363e+01 4.48e-06 5.78e-03 -11.0 1.81e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.9799363e+01 1.25e-06 1.17e-03 -11.0 1.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.9799365e+01 3.88e-07 2.26e-05 -11.0 1.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.9799365e+01 1.54e-06 3.39e-03 -11.0 6.20e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.9799364e+01 9.56e-07 3.52e-03 -11.0 1.57e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.9799364e+01 8.63e-07 1.59e-03 -11.0 1.56e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.9799364e+01 7.24e-07 2.32e-04 -11.0 2.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.9799364e+01 5.06e-07 1.35e-03 -11.0 8.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.9799354e+01 6.72e-06 2.72e-03 -11.0 9.49e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.9799365e+01 2.73e-10 2.17e-04 -11.0 8.46e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 96 1.9799358e+01 4.96e-06 2.59e-03 -11.0 6.69e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.9799335e+01 3.34e-05 3.85e-03 -11.0 4.48e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.9799350e+01 1.48e-05 1.05e-03 -11.0 2.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.9799303e+01 6.25e-05 2.19e-03 -11.0 1.80e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.9799291e+01 6.52e-05 1.67e-03 -11.0 2.60e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.9799291454812550e+01 1.9799291454812550e+01\n",
|
||
"Dual infeasibility......: 1.6699819093981272e-03 1.6699819093981272e-03\n",
|
||
"Constraint violation....: 6.5221200024723203e-05 6.5221200024723203e-05\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 1.6699819093981272e-03 1.6699819093981272e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 110\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 110\n",
|
||
"Number of inequality constraint evaluations = 110\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.395\n",
|
||
"Total CPU secs in NLP function evaluations = 136.900\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 532.00us ( 4.84us) 528.91us ( 4.81us) 110\n",
|
||
" nlp_g | 5.07 s ( 46.06ms) 4.84 s ( 44.04ms) 110\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 350.00us ( 3.43us) 343.25us ( 3.37us) 102\n",
|
||
" nlp_jac_g | 134.85 s ( 1.32 s) 129.05 s ( 1.27 s) 102\n",
|
||
" total | 141.41 s (141.41 s) 135.34 s (135.34 s) 1\n",
|
||
"Timestamp 20700\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.79e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0612945e+01 1.10e+01 2.79e+04 -1.5 2.79e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.2940360e+00 3.39e+00 5.43e+00 1.0 5.73e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 3.6589337e+00 3.16e-01 1.87e-01 -1.1 1.72e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 3.2580681e+00 1.30e-03 1.20e-01 -7.0 7.31e+00 - 9.90e-01 1.00e+00h 1\n",
|
||
" 5 3.2578319e+00 1.53e-03 1.46e-02 -4.7 6.70e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 3.2593709e+00 1.08e-03 4.27e-03 -6.6 4.38e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 3.2590075e+00 5.92e-04 1.22e-03 -8.6 5.89e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 3.2598184e+00 1.46e-04 9.16e-04 -11.0 1.91e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 3.2568805e+00 1.43e-03 9.11e-03 -11.0 5.32e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 3.2599529e+00 5.96e-05 1.41e-03 -11.0 1.05e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 3.2504026e+00 1.68e-02 3.80e-02 -11.0 9.32e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 3.1249311e+00 1.01e-01 1.33e-01 -11.0 3.36e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 3.2577104e+00 9.38e-03 2.08e-02 -11.0 9.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 3.2470342e+00 1.89e-02 1.20e-02 -11.0 5.76e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.9259385e+00 2.04e-01 4.62e-02 -11.0 1.67e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 16 2.5193632e+00 4.30e-01 7.97e-02 -11.0 2.43e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 3.2227371e+00 5.80e-03 1.28e-01 -11.0 1.18e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 3.1272619e+00 9.47e-01 1.99e-01 -11.0 1.77e+04 - 1.00e+00 2.50e-01f 3\n",
|
||
" 19 3.0352872e+00 8.33e-01 3.60e-01 -11.0 3.45e+04 - 1.00e+00 1.12e-01h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.7372083e+00 1.16e+00 8.14e-01 -11.0 2.08e+04 - 7.64e-01 2.50e-01f 3\n",
|
||
" 21 3.6054651e+00 2.27e-01 9.89e-01 -11.0 3.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.6569558e+00 1.59e+00 2.36e-01 -11.0 8.70e+03 - 1.00e+00 1.00e+00F 1\n",
|
||
" 23 2.4498826e+00 1.50e+00 2.97e-01 -11.0 2.90e+06 - 1.12e-02 1.05e-02f 1\n",
|
||
" 24 3.5452781e+00 8.59e-01 2.65e-01 -10.5 1.15e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 25 3.3871993e+00 9.07e-02 2.41e-01 -2.2 3.01e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 3.2083601e+00 6.24e-01 1.61e-01 -2.2 2.96e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 3.0797666e+00 2.01e+00 3.37e-01 -2.2 8.92e+04 - 4.74e-01 5.53e-02f 2\n",
|
||
" 28 3.0245300e+00 7.22e-01 1.71e-01 -2.2 2.32e+03 - 8.80e-01 1.00e+00h 1\n",
|
||
" 29 2.8687309e+00 6.43e-01 2.17e-01 -2.2 6.23e+05 - 2.87e-02 4.70e-03f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.4313736e+00 4.74e-01 1.99e-01 -2.2 1.58e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 31 2.4189565e+00 2.09e+00 5.85e-01 -8.0 5.48e+04 - 1.76e-01 8.34e-02f 4\n",
|
||
" 32 4.0638848e+00 1.23e+00 5.29e-01 -0.9 2.64e+04 - 1.00e+00 9.65e-01h 1\n",
|
||
" 33 2.5114250e+00 2.20e+00 3.46e-01 -1.1 9.57e+03 - 7.89e-01 1.00e+00f 1\n",
|
||
" 34 2.4618266e+00 2.68e+00 4.50e-01 -1.1 8.12e+04 - 6.53e-01 9.00e-02f 4\n",
|
||
" 35 4.4383062e+00 2.36e+00 1.09e+00 -1.1 3.97e+04 - 1.33e-01 6.98e-01H 1\n",
|
||
" 36 3.7584140e+00 2.06e+00 9.83e-01 -1.1 8.26e+04 - 8.53e-01 1.28e-01f 1\n",
|
||
" 37 2.4698298e+00 1.36e+00 5.09e-01 -1.1 1.49e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 38 2.5070475e+00 3.99e-01 2.91e-01 -1.1 3.40e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.2384502e+00 4.01e-01 1.74e-01 -2.4 3.82e+03 - 9.40e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.1321003e+00 2.66e-01 1.65e-01 -2.5 2.82e+04 - 9.85e-01 1.31e-01h 1\n",
|
||
" 41 2.1259532e+00 4.96e-01 2.37e-01 -1.8 1.23e+04 - 1.00e+00 9.82e-02h 3\n",
|
||
" 42 2.2564908e+00 4.99e-01 1.25e-01 -3.1 1.64e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.4038596e+00 4.37e-01 4.17e-02 -3.1 2.44e+03 - 4.67e-01 1.00e+00h 1\n",
|
||
" 44 2.3536318e+00 9.83e-02 1.75e-01 -2.6 3.80e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.3479126e+00 2.63e-01 9.27e-02 -2.5 2.58e+03 - 4.30e-01 2.50e-01h 3\n",
|
||
" 46 2.2780806e+00 4.47e-01 2.47e-01 -8.6 5.04e+04 - 2.98e-02 9.06e-02h 1\n",
|
||
" 47 2.4369805e+00 2.89e-01 7.33e-02 -2.7 7.70e+03 - 4.98e-02 1.00e+00h 1\n",
|
||
" 48 2.3747705e+00 1.09e-01 4.71e-01 -2.7 1.56e+04 - 8.41e-01 1.00e+00h 1\n",
|
||
" 49 2.3145155e+00 3.11e-01 3.33e-01 -2.7 7.34e+04 - 1.04e-01 4.67e-02h 5\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.0023749e+00 7.12e-01 2.17e-01 -3.5 6.55e+03 - 9.99e-01 5.00e-01f 2\n",
|
||
" 51 1.7469010e+00 6.35e-01 2.28e-01 -2.6 6.09e+06 - 6.84e-01 1.34e-03f 1\n",
|
||
" 52 2.4142392e+00 3.94e-01 5.39e-02 -1.5 1.23e+04 - 5.98e-01 1.00e+00h 1\n",
|
||
" 53 2.3877557e+00 3.63e-01 9.15e-02 -1.8 9.47e+04 - 5.77e-01 1.47e-02h 2\n",
|
||
" 54 2.2669006e+00 5.91e-01 2.56e-01 -1.8 1.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.4176412e+00 2.53e-01 7.43e-02 -1.8 2.74e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.3877304e+00 3.36e-01 5.56e-02 -1.8 2.18e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 57 2.2802785e+00 2.18e-01 1.84e-01 -1.8 3.11e+03 - 1.00e+00 5.40e-01h 1\n",
|
||
" 58 2.2914923e+00 7.71e-01 3.12e-01 -1.9 1.30e+04 - 9.04e-01 2.50e-01h 3\n",
|
||
" 59 2.5105935e+00 4.03e-01 2.88e-01 -2.1 1.00e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.4325870e+00 2.59e-01 6.29e-02 -2.1 3.31e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.0638427e+00 4.34e-01 2.02e-01 -2.1 8.95e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.1822031e+00 6.23e-01 2.85e-01 -2.0 9.72e+03 - 7.52e-01 1.00e+00h 1\n",
|
||
" 63 2.6358332e+00 6.27e-01 4.60e-01 -2.2 1.51e+04 - 8.33e-01 4.39e-01H 1\n",
|
||
" 64 2.2877025e+00 6.82e-01 2.17e-01 -2.2 3.40e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.3741626e+00 2.03e-01 2.53e-01 -2.2 6.77e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.6585765e+00 1.54e+00 5.12e-01 -2.2 2.02e+04 - 3.11e-01 1.00e+00f 1\n",
|
||
" 67 1.6363084e+00 1.42e+00 3.90e-01 -1.2 3.00e+04 - 1.00e+00 6.16e-02h 3\n",
|
||
" 68 2.4724749e+00 6.42e-01 4.77e-01 -7.2 6.86e+03 - 6.28e-01 1.00e+00h 1\n",
|
||
" 69 2.2241962e+00 3.02e-01 3.76e-01 -1.8 1.64e+04 - 1.00e+00 8.44e-01H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.6849622e+00 1.18e+00 1.97e-01 -1.8 9.65e+03 - 8.96e-01 5.33e-01f 1\n",
|
||
" 71 1.4986490e+00 1.10e+00 5.85e-01 -1.9 2.62e+04 - 1.00e+00 2.88e-01F 1\n",
|
||
" 72 2.5840687e+00 4.36e-02 1.46e+00 -3.3 1.46e+00 - 9.96e-01 1.00e+00h 1\n",
|
||
" 73 2.5372593e+00 5.86e-05 1.81e-02 -4.9 8.70e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.5372411e+00 2.02e-08 2.09e-05 -6.8 1.97e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 2.5372411e+00 6.34e-11 4.61e-05 -11.0 3.63e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 76 2.5372411e+00 2.38e-08 7.05e-05 -11.0 1.89e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 2.5372409e+00 2.89e-07 1.10e-04 -11.0 8.95e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.5372409e+00 9.19e-08 2.11e-05 -11.0 4.51e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 2.5372411e+00 1.58e-08 1.86e-04 -11.0 1.73e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.5372410e+00 2.72e-07 8.55e-05 -11.0 8.84e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.5372409e+00 1.68e-07 5.49e-05 -11.0 7.27e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.5372411e+00 9.02e-09 8.76e-05 -11.0 1.16e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 2.5372411e+00 3.98e-08 5.64e-05 -11.0 2.13e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.5372408e+00 3.50e-07 8.02e-05 -11.0 1.05e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 2.5372410e+00 1.61e-07 2.37e-04 -11.0 5.77e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 2.5372410e+00 3.86e-08 7.71e-05 -11.0 1.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.5372411e+00 6.67e-09 6.47e-05 -11.0 1.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 2.5371812e+00 6.79e-05 8.44e-03 -11.0 3.87e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 2.5371547e+00 9.47e-05 9.93e-03 -11.0 4.66e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.5372412e+00 3.72e-06 1.10e-03 -11.0 5.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 2.5372261e+00 4.82e-05 7.20e-04 -11.0 1.06e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 2.5371028e+00 3.27e-04 2.96e-03 -11.0 3.01e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 2.5371658e+00 6.22e-05 2.49e-03 -11.0 5.82e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 2.5369860e+00 2.01e-04 1.00e-03 -11.0 5.07e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.5371676e+00 5.87e-05 7.29e-04 -9.0 1.64e+00 - 1.00e+00 7.75e-01H 1\n",
|
||
" 96 2.5372307e+00 1.66e-05 4.07e-04 -8.7 9.55e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.5371640e+00 2.71e-04 7.47e-04 -6.1 1.43e+00 - 4.26e-02 1.00e+00h 1\n",
|
||
" 98 2.5324685e+00 2.28e-02 7.43e-03 -7.6 7.19e+01 - 4.47e-02 1.00e+00h 1\n",
|
||
" 99 2.5291478e+00 2.26e-02 5.05e-03 -5.5 1.13e+03 - 1.00e+00 5.89e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.5366060e+00 6.56e-04 6.67e-03 -4.5 3.82e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.5366060154078975e+00 2.5366060154078975e+00\n",
|
||
"Dual infeasibility......: 6.6709162795278720e-03 6.6709162795278720e-03\n",
|
||
"Constraint violation....: 6.5591998216874003e-04 6.5591998216874003e-04\n",
|
||
"Complementarity.........: 6.2474158471770421e-04 6.2474158471770421e-04\n",
|
||
"Overall NLP error.......: 6.6709162795278720e-03 6.6709162795278720e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 168\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 168\n",
|
||
"Number of inequality constraint evaluations = 168\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.465\n",
|
||
"Total CPU secs in NLP function evaluations = 142.332\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 817.00us ( 4.86us) 814.98us ( 4.85us) 168\n",
|
||
" nlp_g | 7.86 s ( 46.76ms) 7.53 s ( 44.80ms) 168\n",
|
||
" nlp_grad | 1.52 s ( 1.52 s) 1.47 s ( 1.47 s) 1\n",
|
||
" nlp_grad_f | 374.00us ( 3.67us) 360.12us ( 3.53us) 102\n",
|
||
" nlp_jac_g | 137.44 s ( 1.35 s) 131.86 s ( 1.29 s) 102\n",
|
||
" total | 146.97 s (146.97 s) 141.00 s (141.00 s) 1\n",
|
||
"Timestamp 21000\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.78e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9998521e+01 1.56e+01 2.78e+04 -1.5 2.78e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.3651457e+01 5.99e+00 1.30e+01 1.0 5.41e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.1199808e+01 2.34e+00 8.20e-01 -1.1 1.13e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 2.2584427e+01 1.53e-04 8.58e-02 -2.9 2.58e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 5 2.2584210e+01 2.95e-05 7.65e-03 -4.7 3.07e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.2584179e+01 6.75e-05 9.39e-03 -6.8 3.86e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.2580223e+01 3.65e-03 9.41e-03 -8.7 2.20e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.2584183e+01 1.11e-06 1.24e-03 -10.6 1.32e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 9 2.2584166e+01 6.38e-05 1.43e-03 -11.0 9.82e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.2583894e+01 1.05e-04 1.29e-03 -11.0 5.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.2456861e+01 6.09e-02 7.41e-03 -11.0 2.88e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 12 2.2566607e+01 4.45e-03 5.70e-03 -11.0 7.56e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.2556205e+01 1.24e-02 2.09e-03 -11.0 6.16e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.2569930e+01 1.11e-03 1.95e-03 -11.0 2.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.2552982e+01 1.27e-02 3.79e-03 -11.0 4.74e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.2548322e+01 8.09e-03 1.59e-03 -11.0 3.26e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.2570769e+01 3.69e-04 1.81e-03 -11.0 5.95e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.2570608e+01 1.08e-03 1.42e-03 -11.0 4.66e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.2527370e+01 1.66e-02 7.92e-03 -11.0 2.39e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.2414342e+01 1.18e-01 3.88e-03 -11.0 1.89e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.2560166e+01 1.64e-02 3.04e-03 -11.0 7.92e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.2551179e+01 3.53e-02 1.55e-03 -11.0 1.17e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.2565475e+01 7.52e-03 1.11e-03 -11.0 4.04e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.7521402e+01 2.90e+00 2.23e-01 -9.0 2.40e+07 - 1.54e-03 1.27e-03f 1\n",
|
||
" 25 1.7433589e+01 2.89e+00 2.27e-01 -9.1 3.10e+07 - 4.07e-03 7.40e-06f 1\n",
|
||
" 26 2.2074270e+01 3.28e-01 1.86e-01 -8.8 1.17e+02 - 1.00e+00 8.86e-01h 1\n",
|
||
" 27 2.2074525e+01 3.28e-01 1.86e-01 -6.8 6.36e+01 - 1.00e+00 3.89e-04h 1\n",
|
||
" 28 2.2728166e+01 2.47e-02 5.02e-03 -7.4 8.49e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.2734600e+01 2.02e-02 7.73e-03 -7.5 1.69e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.2738343e+01 6.05e-03 7.74e-03 -7.5 2.73e+02 - 9.07e-01 1.00e+00h 1\n",
|
||
" 31 2.2365881e+01 9.75e-01 3.20e-02 -7.5 2.74e+06 - 5.74e-09 5.57e-03f 1\n",
|
||
" 32 2.2611751e+01 5.43e-01 1.49e-02 -7.5 1.01e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 33 2.2491119e+01 9.06e-02 1.18e-02 -7.5 8.23e+03 - 7.47e-01 6.13e-01h 1\n",
|
||
" 34 2.2718816e+01 3.07e-02 2.34e-03 -7.5 3.21e+02 - 7.71e-01 1.00e+00h 1\n",
|
||
" 35 2.2718816e+01 3.07e-02 2.36e-03 -7.5 6.62e+03 - 5.56e-01 8.40e-08h 2\n",
|
||
" 36 2.2741794e+01 3.71e-03 5.47e-03 -7.5 2.08e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.2740993e+01 1.31e-03 1.75e-03 -7.5 1.38e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.2743789e+01 4.84e-07 5.19e-03 -7.5 1.50e+01 - 6.53e-02 1.00e+00H 1\n",
|
||
" 39 1.7253139e+01 1.10e+01 6.34e-01 -7.7 5.10e+04 - 3.37e-01 8.47e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.7254165e+01 1.09e+01 6.27e-01 -3.0 4.45e+04 - 1.00e+00 5.01e-03h 1\n",
|
||
" 41 1.9116635e+01 2.94e+00 1.75e-01 -3.7 9.89e+03 - 8.58e-01 7.20e-01h 1\n",
|
||
" 42 2.2819032e+01 7.48e-03 1.27e-01 -2.4 5.37e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.2784639e+01 2.56e-02 8.65e-03 -2.5 1.76e+02 - 2.95e-01 1.00e+00h 1\n",
|
||
" 44 2.2791981e+01 1.18e-02 2.52e-03 -3.8 1.07e+02 - 1.00e+00 5.90e-01h 1\n",
|
||
" 45 2.2616298e+01 1.07e-01 7.43e-03 -3.8 7.65e+02 - 1.00e+00 3.94e-01f 1\n",
|
||
" 46 2.2766396e+01 1.72e-02 2.40e-03 -3.8 8.93e+01 - 3.12e-02 1.00e+00h 1\n",
|
||
" 47 2.2760248e+01 1.61e-02 1.87e-03 -3.8 6.92e+02 - 1.00e+00 8.05e-02h 1\n",
|
||
" 48 2.2809045e+01 4.71e-04 4.89e-03 -3.8 2.22e+02 - 1.16e-01 1.00e+00H 1\n",
|
||
" 49 2.2804141e+01 5.88e-05 8.95e-03 -3.8 1.48e+02 - 1.00e+00 1.00e+00F 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.2755331e+01 3.75e-02 1.79e-03 -3.8 6.87e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 51 2.2797768e+01 2.90e-03 2.33e-03 -3.8 4.03e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.2727369e+01 5.60e-02 4.38e-03 -3.8 7.78e+02 - 2.69e-01 3.00e-01f 1\n",
|
||
" 53 2.2791515e+01 6.09e-03 1.55e-03 -3.8 1.34e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.2758905e+01 2.90e-02 3.22e-03 -3.8 3.26e+02 - 1.00e+00 8.90e-01h 1\n",
|
||
" 55 2.2761644e+01 2.20e-02 5.01e-03 -3.8 3.18e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.2792741e+01 8.50e-03 2.87e-03 -3.8 1.26e+02 - 7.31e-01 1.00e+00h 1\n",
|
||
" 57 2.2788284e+01 1.43e-02 1.54e-03 -3.8 3.89e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.2781900e+01 1.62e-02 1.80e-03 -3.8 2.95e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 2.2807156e+01 2.09e-05 2.07e-03 -3.8 1.14e+03 - 4.75e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.2805124e+01 4.79e-03 2.29e-03 -3.8 2.30e+04 - 1.66e-02 1.75e-03f 6\n",
|
||
" 61 2.2728699e+01 5.62e-02 2.38e-03 -3.8 2.73e+04 - 1.00e+00 4.47e-02f 1\n",
|
||
" 62 2.2745397e+01 1.88e-02 2.01e-03 -3.8 4.26e+02 - 1.24e-01 1.00e+00h 1\n",
|
||
" 63 2.2635933e+01 9.06e-02 2.16e-03 -3.8 2.63e+03 - 1.00e+00 5.99e-01h 1\n",
|
||
" 64 2.2632894e+01 9.21e-02 2.19e-03 -3.8 1.91e+03 - 4.98e-01 1.22e-02h 1\n",
|
||
" 65 2.2656609e+01 7.20e-02 1.46e-02 -3.8 4.20e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.2714532e+01 5.10e-02 9.77e-03 -3.8 3.09e+02 - 5.50e-01 5.00e-01h 2\n",
|
||
" 67 2.2778371e+01 1.28e-02 7.64e-03 -3.8 3.17e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.2808587e+01 6.66e-05 1.63e-03 -3.8 7.69e+02 - 6.54e-01 1.00e+00H 1\n",
|
||
" 69 2.2615334e+01 7.26e-02 2.15e-03 -5.6 9.95e+02 - 2.28e-02 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.2237981e+01 4.86e-01 2.43e-02 -5.6 7.26e+03 - 2.94e-01 1.00e+00h 1\n",
|
||
" 71 2.1313441e+01 7.49e-01 2.25e-02 -5.6 5.38e+03 - 8.43e-01 1.00e+00h 1\n",
|
||
" 72 2.2300227e+01 4.40e-01 1.46e-02 -5.5 2.52e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.7750558e+01 6.63e+00 3.88e-01 -5.6 1.68e+06 - 2.44e-03 1.70e-02f 1\n",
|
||
" 74 1.7750589e+01 6.63e+00 3.88e-01 -5.6 8.04e+03 - 1.00e+00 1.16e-05h 1\n",
|
||
" 75 2.2758298e+01 2.61e-01 3.98e-01 -5.6 3.66e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.2756072e+01 2.63e-01 3.97e-01 -5.6 5.08e+03 - 1.00e+00 3.38e-03h 1\n",
|
||
" 77 2.2497766e+01 3.50e-02 2.32e-02 -5.6 1.63e+02 - 7.57e-01 1.00e+00h 1\n",
|
||
" 78 2.2413387e+01 1.05e-01 5.52e-02 -5.9 2.50e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 79 2.2473798e+01 5.32e-02 1.81e-03 -6.4 1.11e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.2532604e+01 4.50e-04 2.77e-03 -5.0 6.90e+02 - 7.14e-01 1.00e+00H 1\n",
|
||
" 81 2.2377971e+01 2.46e-01 1.45e-02 -6.5 1.22e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 82 2.2315547e+01 5.94e-01 3.20e-03 -5.9 6.71e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 2.2466234e+01 4.16e-02 2.63e-02 -6.0 1.59e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.7865293e+01 4.05e+00 2.00e-01 -6.0 2.91e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 85 1.7583987e+01 4.07e+00 2.22e-01 -6.0 1.42e+05 - 1.00e+00 2.98e-02h 1\n",
|
||
" 86 2.3316509e+01 5.67e-02 2.12e-01 -6.0 2.96e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.2804847e+01 2.14e-01 5.62e-02 -6.0 8.20e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 88 2.2148524e+01 1.81e+00 2.65e-02 -6.0 9.92e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 89 2.2280290e+01 1.14e+00 3.58e-02 -6.0 1.15e+04 - 5.36e-02 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.2174855e+01 1.38e+00 3.15e-02 -6.0 2.32e+04 - 2.00e-01 4.66e-02h 1\n",
|
||
" 91 2.2306179e+01 1.02e+00 2.90e-02 -6.0 2.23e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 92 2.3168370e+01 3.87e-02 3.10e-02 -6.0 2.04e+03 - 9.31e-01 1.00e+00h 1\n",
|
||
" 93 2.3144217e+01 3.62e-02 2.17e-02 -6.0 1.39e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 2.3027493e+01 2.84e-01 3.01e-02 -6.0 2.24e+04 - 1.00e+00 1.86e-01f 3\n",
|
||
" 95 2.1423548e+01 9.20e-01 2.42e-02 -6.0 3.17e+05 - 1.59e-01 6.54e-02f 1\n",
|
||
" 96 2.3207782e+01 6.26e-03 1.85e+00 -6.0 1.89e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.3204106e+01 2.46e-06 1.98e-03 -6.0 1.96e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 2.3204041e+01 2.75e-05 2.06e-03 -6.0 2.06e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 2.3204033e+01 2.80e-05 3.17e-03 -6.0 3.01e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.3204094e+01 2.99e-06 3.29e-03 -6.0 1.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.3204093765773191e+01 2.3204093765773191e+01\n",
|
||
"Dual infeasibility......: 3.2903258338223135e-03 3.2903258338223135e-03\n",
|
||
"Constraint violation....: 2.9850335039327547e-06 2.9850335039327547e-06\n",
|
||
"Complementarity.........: 8.9462849766876619e-07 8.9462849766876619e-07\n",
|
||
"Overall NLP error.......: 3.2903258338223135e-03 3.2903258338223135e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 126\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 126\n",
|
||
"Number of inequality constraint evaluations = 126\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.405\n",
|
||
"Total CPU secs in NLP function evaluations = 137.213\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 596.00us ( 4.73us) 584.47us ( 4.64us) 126\n",
|
||
" nlp_g | 5.74 s ( 45.54ms) 5.48 s ( 43.47ms) 126\n",
|
||
" nlp_grad | 1.37 s ( 1.37 s) 1.31 s ( 1.31 s) 1\n",
|
||
" nlp_grad_f | 344.00us ( 3.37us) 337.20us ( 3.31us) 102\n",
|
||
" nlp_jac_g | 134.19 s ( 1.32 s) 128.26 s ( 1.26 s) 102\n",
|
||
" total | 141.44 s (141.44 s) 135.19 s (135.19 s) 1\n",
|
||
"Timestamp 21300\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 7.25e+02 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9602585e+01 1.26e+01 7.25e+02 -1.5 7.25e+02 - 9.90e-01 1.00e+00f 1\n",
|
||
" 2 7.9233730e+00 4.14e+00 9.03e+00 0.4 1.26e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 6.1218417e+00 9.81e-01 6.87e-01 -1.6 6.76e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 6.7587263e+00 1.74e-03 7.90e-02 -3.4 1.37e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 6.7594949e+00 2.99e-07 2.04e-05 -5.3 1.74e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 6.7594953e+00 3.20e-08 8.43e-05 -11.0 3.16e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 6.7594950e+00 1.89e-07 4.52e-05 -11.0 1.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 6.7594952e+00 6.12e-08 8.45e-05 -11.0 4.36e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 6.7594952e+00 4.11e-08 1.34e-04 -11.0 2.71e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 6.7594951e+00 1.07e-07 1.07e-04 -11.0 4.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 6.7594950e+00 1.59e-07 1.72e-04 -11.0 7.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 6.7594950e+00 1.53e-07 7.88e-05 -11.0 4.38e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 6.7594947e+00 2.99e-07 9.94e-05 -11.0 1.27e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 6.7594947e+00 2.57e-07 1.57e-05 -11.0 1.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 6.7594954e+00 2.56e-08 1.34e-04 -11.0 2.13e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 6.7594949e+00 2.73e-07 2.03e-05 -11.0 1.15e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 6.7594954e+00 3.21e-08 1.16e-04 -11.0 2.80e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 6.7594953e+00 4.36e-08 9.92e-05 -11.0 2.33e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 6.7594942e+00 5.39e-07 6.41e-05 -11.0 4.59e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 6.7594950e+00 1.52e-07 1.34e-04 -11.0 1.73e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 6.7594953e+00 1.36e-10 6.72e-05 -11.0 6.62e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 22 6.7594952e+00 7.81e-08 1.16e-04 -11.0 9.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 6.7594951e+00 2.28e-07 9.31e-05 -11.0 7.81e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 6.7594952e+00 1.34e-07 5.27e-05 -11.0 3.61e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 6.7594953e+00 2.07e-08 4.35e-05 -11.0 2.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 6.7594953e+00 5.62e-09 1.23e-04 -11.0 1.22e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 6.7594952e+00 2.94e-08 1.46e-04 -11.0 8.07e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 6.7594951e+00 8.85e-08 7.88e-05 -11.0 4.15e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 6.7594953e+00 2.10e-08 6.74e-05 -11.0 3.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 6.7594951e+00 1.80e-07 1.43e-04 -11.0 1.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 6.7594949e+00 3.50e-07 2.50e-05 -11.0 3.33e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 6.7594953e+00 4.47e-08 3.60e-05 -11.0 5.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 6.7594953e+00 1.20e-07 1.18e-04 -11.0 4.73e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 6.7594952e+00 1.22e-07 6.29e-05 -11.0 5.81e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 6.7594952e+00 1.02e-07 7.91e-05 -11.0 5.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 6.7594953e+00 6.32e-08 2.45e-05 -11.0 2.36e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 6.7594953e+00 1.09e-08 2.28e-05 -11.0 1.27e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 6.7594953e+00 5.14e-09 9.77e-05 -11.0 1.49e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 6.7594953e+00 5.05e-08 1.96e-05 -11.0 1.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 6.7594953e+00 2.29e-09 4.90e-05 -11.0 3.35e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 6.7594953e+00 1.75e-08 1.39e-04 -11.0 9.74e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 6.7594950e+00 2.81e-07 2.00e-05 -11.0 8.60e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 6.7594953e+00 2.63e-09 4.77e-05 -11.0 5.86e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 6.7594953e+00 2.37e-08 9.47e-05 -11.0 7.47e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 6.7594952e+00 7.57e-08 3.34e-05 -11.0 2.49e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 6.7594951e+00 2.67e-07 9.13e-05 -11.0 1.30e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 6.7594952e+00 4.55e-08 1.18e-04 -11.0 2.52e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 6.7594952e+00 1.11e-07 1.24e-04 -11.0 5.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 6.7594949e+00 2.04e-07 9.47e-05 -11.0 1.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 6.7594952e+00 2.97e-08 4.64e-05 -11.0 1.42e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 6.7594953e+00 1.11e-08 2.84e-05 -11.0 1.45e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 6.7594953e+00 4.65e-09 1.84e-05 -11.0 5.49e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 6.7594953e+00 3.09e-09 1.01e-04 -11.0 2.81e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 6.7594953e+00 5.17e-09 2.33e-05 -11.0 3.15e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 6.7594953e+00 7.82e-10 3.34e-05 -11.0 2.45e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 6.7594953e+00 3.38e-09 1.01e-04 -11.0 2.27e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 6.7594952e+00 5.55e-08 4.48e-05 -11.0 7.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 6.7594864e+00 3.87e-06 5.88e-02 -11.0 1.17e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 6.7594924e+00 1.89e-06 1.59e-03 -11.0 7.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 6.7594856e+00 1.31e-05 1.32e-03 -11.0 5.42e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 6.7594865e+00 6.35e-06 2.04e-03 -11.0 1.73e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 6.7594922e+00 2.61e-06 3.35e-03 -11.0 1.27e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 6.6166159e+00 7.41e-02 4.61e-02 -11.0 2.69e+04 - 1.00e+00 1.00e+00F 1\n",
|
||
" 64 6.4116968e+00 2.69e-01 1.19e-01 -9.2 6.35e+04 - 1.00e+00 7.45e-02f 1\n",
|
||
" 65 6.4115885e+00 2.69e-01 1.18e-01 -7.2 1.23e+05 - 1.00e+00 3.85e-04h 1\n",
|
||
" 66 6.5395778e+00 1.20e-01 6.54e-02 -5.5 1.88e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 6.4292366e+00 3.64e-01 1.80e-02 -5.1 1.92e+03 - 1.00e+00 9.67e-01h 1\n",
|
||
" 68 6.5147410e+00 1.17e-01 9.71e-03 -5.1 6.02e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 6.3523666e+00 3.08e+00 3.54e-01 -11.0 1.70e+05 - 5.63e-03 8.93e-02f 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 5.0208104e+00 1.41e+00 2.10e-01 -5.3 1.24e+05 - 5.81e-02 4.52e-01f 1\n",
|
||
" 71 6.3642716e+00 9.67e-01 1.84e-01 -5.7 1.76e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 72 6.2416822e+00 5.09e-01 1.04e-01 -1.4 8.82e+03 - 1.00e+00 9.38e-01h 1\n",
|
||
" 73 6.1653745e+00 1.85e+00 7.36e-02 -3.1 8.98e+03 - 7.38e-01 1.00e+00h 1\n",
|
||
" 74 6.6998209e+00 3.97e-02 1.28e-01 -2.1 4.99e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 6.3905522e+00 5.77e-01 1.07e-01 -3.3 1.84e+03 - 9.99e-01 1.00e+00f 1\n",
|
||
" 76 6.4269017e+00 2.14e-01 3.02e-02 -4.4 1.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 6.5795685e+00 8.72e-02 3.31e-02 -10.1 5.99e+02 - 7.75e-01 1.00e+00h 1\n",
|
||
" 78 6.3253279e+00 5.62e-01 9.72e-02 -5.2 5.76e+03 - 8.04e-03 1.00e+00h 1\n",
|
||
" 79 4.9171804e+00 1.49e+00 7.17e-01 -5.2 1.94e+06 - 1.82e-02 3.68e-03f 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 7.0310383e+00 1.07e+00 6.74e-01 -6.2 1.74e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 5.1739665e+00 2.07e+00 1.17e-01 -6.1 1.55e+04 - 7.22e-01 1.00e+00f 1\n",
|
||
" 82 6.5200708e+00 3.37e-01 2.70e-01 -6.1 3.24e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 83 6.2147943e+00 2.87e-01 2.53e-01 -6.1 1.29e+05 - 4.46e-01 4.08e-02f 1\n",
|
||
" 84 3.9248622e+00 2.91e+00 5.90e-01 -5.9 3.44e+04 - 1.00e+00 5.40e-01F 1\n",
|
||
" 85 3.9432140e+00 2.89e+00 5.81e-01 -1.2 5.58e+03 - 1.00e+00 5.77e-02h 1\n",
|
||
" 86 6.7795510e+00 2.62e-01 3.73e+00 -3.2 3.73e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 7.0807286e+00 1.09e-03 6.68e-02 -3.3 4.43e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 7.0811902e+00 2.50e-07 1.60e-04 -3.3 1.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 7.0811896e+00 5.59e-07 4.57e-05 -5.0 3.72e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 7.0811904e+00 2.11e-10 1.04e-04 -7.4 3.70e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 91 7.0811882e+00 1.68e-06 2.23e-03 -7.4 8.44e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 7.0811890e+00 9.71e-07 1.31e-03 -7.4 2.11e-03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 93 7.0811900e+00 2.37e-07 8.59e-05 -7.4 7.53e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 7.0811896e+00 3.69e-07 3.49e-05 -7.4 3.76e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 7.0811900e+00 1.87e-07 3.55e-05 -7.4 1.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 7.0811900e+00 2.35e-07 8.53e-05 -7.4 1.75e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 7.0811896e+00 6.64e-07 1.59e-04 -7.4 3.62e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 7.0811888e+00 1.24e-06 3.49e-03 -7.4 6.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 7.0811899e+00 2.01e-09 8.22e-05 -7.4 3.00e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 7.0811899e+00 2.43e-08 2.51e-05 -7.4 1.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 7.0811898890737996e+00 7.0811898890737996e+00\n",
|
||
"Dual infeasibility......: 2.5064025448113929e-05 2.5064025448113929e-05\n",
|
||
"Constraint violation....: 2.4312747370913712e-08 2.4312747370913712e-08\n",
|
||
"Complementarity.........: 3.7176552526513131e-08 3.7176552526513131e-08\n",
|
||
"Overall NLP error.......: 2.5064025448113929e-05 2.5064025448113929e-05\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 117\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 117\n",
|
||
"Number of inequality constraint evaluations = 117\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.431\n",
|
||
"Total CPU secs in NLP function evaluations = 139.264\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 603.00us ( 5.15us) 597.44us ( 5.11us) 117\n",
|
||
" nlp_g | 5.45 s ( 46.62ms) 5.22 s ( 44.60ms) 117\n",
|
||
" nlp_grad | 1.38 s ( 1.38 s) 1.32 s ( 1.32 s) 1\n",
|
||
" nlp_grad_f | 461.00us ( 4.52us) 453.35us ( 4.44us) 102\n",
|
||
" nlp_jac_g | 136.74 s ( 1.34 s) 130.97 s ( 1.28 s) 102\n",
|
||
" total | 143.72 s (143.72 s) 137.66 s (137.66 s) 1\n",
|
||
"Timestamp 21600\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.15e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9843163e+01 1.47e+01 3.15e+04 -1.5 3.15e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.2654946e+01 5.49e+00 1.39e+01 1.0 6.53e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.8853771e+01 2.11e+00 8.37e-01 -1.1 1.33e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 2.0100405e+01 4.71e-05 8.54e-02 -2.8 2.36e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 5 2.0100255e+01 3.29e-05 7.04e-03 -4.6 3.15e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.0098807e+01 9.04e-04 2.18e-03 -6.7 9.19e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.0096947e+01 1.15e-03 6.15e-03 -8.8 1.45e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.0100783e+01 1.65e-04 1.41e-03 -11.0 1.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.0096845e+01 2.33e-03 2.98e-03 -11.0 8.16e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.0101051e+01 9.03e-07 3.44e-03 -11.0 1.05e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 2.0099236e+01 8.82e-04 1.59e-03 -11.0 5.08e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 12 2.0100779e+01 2.32e-04 1.51e-03 -11.0 1.34e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.0100748e+01 8.02e-05 1.86e-03 -11.0 8.94e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.0094072e+01 2.45e-03 4.47e-03 -11.0 1.42e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.0100259e+01 2.83e-04 2.04e-03 -11.0 2.19e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.0099767e+01 3.14e-04 1.54e-03 -11.0 2.23e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.0100471e+01 5.09e-08 1.92e-04 -11.0 1.57e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 18 2.0099971e+01 2.98e-04 2.46e-03 -11.0 2.02e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 19 2.0100312e+01 1.54e-04 2.39e-03 -11.0 1.75e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.0100116e+01 3.13e-04 2.75e-03 -11.0 1.99e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.0099830e+01 6.08e-04 1.81e-03 -11.0 2.94e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.0100261e+01 1.02e-04 1.89e-03 -11.0 1.25e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.0100220e+01 1.15e-04 1.26e-03 -11.0 1.09e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.0094388e+01 4.31e-03 5.36e-03 -11.0 3.13e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.0073665e+01 1.86e-02 1.14e-02 -11.0 6.59e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.9958526e+01 1.16e-01 6.70e-03 -11.0 4.27e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.0085337e+01 4.96e-03 2.91e-03 -11.0 7.92e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.0080537e+01 8.01e-03 2.46e-03 -11.0 5.49e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.0086082e+01 1.45e-03 1.72e-03 -11.0 1.12e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.0077547e+01 6.56e-03 1.35e-03 -11.0 1.42e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.9979481e+01 1.69e-01 7.23e-03 -11.0 4.32e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.9943669e+01 3.43e-01 8.68e-03 -11.0 1.45e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.0063316e+01 9.24e-03 1.06e-02 -11.0 6.13e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 2.0051355e+01 7.54e-02 3.09e-03 -11.0 4.76e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.0070484e+01 4.49e-02 2.52e-03 -11.0 2.65e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.9921187e+01 1.26e-01 4.18e-03 -11.0 6.22e+05 - 6.12e-02 4.02e-02f 1\n",
|
||
" 37 1.9681224e+01 4.73e-01 1.15e-02 -10.5 1.53e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.0037054e+01 2.19e-02 1.22e-02 -8.2 1.87e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.9055536e+01 1.02e+00 4.25e-02 -9.4 7.28e+03 - 7.08e-02 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.7606438e+01 5.56e+00 2.22e-01 -10.3 5.32e+05 - 1.44e-02 1.03e-01f 1\n",
|
||
" 41 1.7584925e+01 5.61e+00 2.24e-01 -3.4 6.50e+05 - 7.08e-01 8.40e-04h 1\n",
|
||
" 42 1.9980828e+01 3.53e-02 2.01e-01 -3.3 4.29e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.8871129e+01 6.92e-01 1.21e-01 -2.4 4.51e+03 - 3.10e-01 1.00e+00f 1\n",
|
||
" 44 1.6478538e+01 5.04e+00 4.40e-01 -2.1 1.66e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 45 1.3760983e+01 3.08e+00 1.23e-01 -1.3 6.35e+04 - 1.00e+00 3.13e-01f 1\n",
|
||
" 46 1.6316961e+01 1.50e+00 2.03e-01 -1.5 8.31e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.3936395e+01 2.75e+00 1.99e-01 -2.0 2.90e+05 - 3.08e-02 3.46e-02f 1\n",
|
||
" 48 1.9013388e+01 2.65e-01 2.25e-01 -7.6 3.36e+03 - 2.13e-01 1.00e+00h 1\n",
|
||
" 49 1.7678814e+01 2.03e+00 1.76e-01 -2.9 2.64e+04 - 6.93e-01 4.50e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.7531877e+01 1.73e+00 1.40e-01 -2.4 4.54e+04 - 6.57e-01 2.71e-01h 1\n",
|
||
" 51 1.8311844e+01 1.38e+00 8.05e-02 -2.4 8.09e+03 - 3.37e-01 1.00e+00h 1\n",
|
||
" 52 1.4985670e+01 1.02e+01 1.01e+00 -2.6 4.89e+04 - 4.45e-01 1.00e+00F 1\n",
|
||
" 53 1.9200844e+01 1.45e+00 1.32e+01 -2.5 1.40e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.9916425e+01 7.53e-07 8.05e-05 -2.5 1.45e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.9916426e+01 3.04e-10 2.72e-04 -8.5 3.12e-03 - 9.97e-01 1.00e+00H 1\n",
|
||
" 56 1.9916424e+01 5.22e-07 6.88e-05 -10.9 2.33e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.9916426e+01 5.94e-08 3.17e-04 -11.0 4.65e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.9916426e+01 1.86e-07 1.42e-04 -11.0 3.77e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.9916426e+01 9.67e-11 1.15e-04 -11.0 5.27e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.9916426e+01 4.37e-07 5.53e-05 -11.0 1.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.9916426e+01 1.39e-07 1.29e-04 -11.0 7.98e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.9916426e+01 7.95e-08 7.99e-05 -11.0 2.41e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.9916426e+01 5.89e-08 1.68e-04 -11.0 7.07e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.9916426e+01 1.28e-07 1.14e-04 -11.0 4.88e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.9916426e+01 3.81e-08 5.78e-05 -11.0 3.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.9916424e+01 9.30e-07 4.27e-03 -11.0 7.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.9916425e+01 2.62e-07 5.39e-05 -11.0 2.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.9916426e+01 1.42e-07 1.01e-04 -11.0 1.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.9916425e+01 1.62e-07 1.03e-04 -11.0 1.10e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.9916426e+01 8.98e-08 8.03e-05 -11.0 3.60e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.9916425e+01 7.20e-07 2.86e-03 -11.0 3.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.9916425e+01 2.16e-07 3.54e-05 -11.0 5.40e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.9916425e+01 9.16e-07 7.91e-04 -11.0 8.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.9916425e+01 4.83e-07 5.55e-05 -11.0 3.86e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.9916425e+01 5.78e-07 9.54e-05 -11.0 4.46e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.9916408e+01 4.41e-05 9.62e-03 -11.0 1.01e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.9916420e+01 8.06e-06 2.39e-03 -11.0 5.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.9916412e+01 7.92e-06 8.63e-04 -11.0 3.93e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.9915966e+01 3.86e-04 2.87e-03 -11.0 1.67e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.9916379e+01 9.33e-05 1.48e-03 -11.0 4.12e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.9915604e+01 5.88e-04 1.84e-03 -11.0 1.93e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.9915087e+01 8.81e-04 2.44e-03 -11.0 2.48e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.9916404e+01 7.15e-07 1.32e-04 -11.0 1.10e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.9916404e+01 1.26e-07 9.19e-05 -11.0 1.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.9916404e+01 1.06e-07 9.11e-05 -11.0 6.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.9916392e+01 6.77e-06 8.44e-03 -11.0 6.03e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.9916388e+01 3.81e-05 1.81e-03 -11.0 1.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.9916401e+01 2.70e-08 1.56e-04 -11.0 7.55e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 89 1.9916377e+01 1.54e-05 1.43e-03 -11.0 4.93e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.9916350e+01 2.13e-05 3.42e-03 -11.0 1.62e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.9916168e+01 3.38e-04 2.10e-03 -11.0 1.81e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.9916320e+01 3.10e-05 2.91e-03 -11.0 1.23e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.9916248e+01 2.85e-04 9.05e-04 -11.0 1.30e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.9916315e+01 1.04e-04 1.19e-03 -11.0 1.23e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.9911642e+01 5.91e-03 2.88e-03 -11.0 1.22e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.9915310e+01 3.50e-04 1.68e-03 -11.0 3.34e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.9916288e+01 5.90e-05 2.44e-03 -11.0 8.20e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.9913696e+01 9.31e-03 4.28e-03 -11.0 4.10e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.9906639e+01 7.92e-03 7.74e-03 -11.0 8.07e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.9914621e+01 3.14e-03 6.42e-03 -11.0 2.17e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.9914620852367108e+01 1.9914620852367108e+01\n",
|
||
"Dual infeasibility......: 6.4169231242100500e-03 6.4169231242100500e-03\n",
|
||
"Constraint violation....: 3.1401816560894247e-03 3.1401816560894247e-03\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 6.4169231242100500e-03 6.4169231242100500e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 108\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 108\n",
|
||
"Number of inequality constraint evaluations = 108\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.446\n",
|
||
"Total CPU secs in NLP function evaluations = 145.378\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 555.00us ( 5.14us) 542.13us ( 5.02us) 108\n",
|
||
" nlp_g | 5.20 s ( 48.19ms) 4.99 s ( 46.22ms) 108\n",
|
||
" nlp_grad | 1.50 s ( 1.50 s) 1.44 s ( 1.44 s) 1\n",
|
||
" nlp_grad_f | 396.00us ( 3.88us) 393.53us ( 3.86us) 102\n",
|
||
" nlp_jac_g | 143.23 s ( 1.40 s) 137.95 s ( 1.35 s) 102\n",
|
||
" total | 150.08 s (150.08 s) 144.53 s (144.53 s) 1\n",
|
||
"Timestamp 21900\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 7.34e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9932191e+01 1.21e+01 7.34e+03 -1.5 7.34e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.9852754e+00 4.02e+00 7.95e+00 0.6 4.84e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 3.0564104e+00 7.09e-01 6.62e-01 -1.5 1.23e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 3.3858622e+00 2.46e-03 2.35e-01 -3.3 1.10e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 3.3868277e+00 5.63e-07 9.15e-04 -5.1 4.15e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 3.3868289e+00 3.06e-07 1.86e-04 -7.2 1.47e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 3.3868220e+00 5.13e-06 1.57e-03 -9.3 1.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 3.3868287e+00 1.08e-07 1.08e-04 -11.0 1.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 3.3868284e+00 3.44e-07 5.75e-05 -11.0 1.10e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 3.3868288e+00 1.21e-07 2.98e-05 -11.0 6.38e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 3.3868288e+00 3.19e-08 1.82e-04 -11.0 4.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 3.3868287e+00 1.19e-07 5.83e-05 -11.0 8.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 3.3868287e+00 2.58e-07 2.13e-05 -11.0 9.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 3.3868288e+00 2.72e-08 5.74e-05 -11.0 7.13e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 3.3868285e+00 4.52e-07 7.33e-05 -11.0 1.88e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 3.3868281e+00 8.53e-07 2.24e-05 -11.0 2.88e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 3.3868288e+00 3.06e-09 2.52e-05 -11.0 3.35e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 3.3868288e+00 3.71e-09 5.18e-05 -11.0 1.58e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 3.3868288e+00 2.01e-08 8.93e-05 -11.0 4.33e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 3.3868288e+00 9.98e-09 2.35e-05 -11.0 4.81e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 3.3868288e+00 2.03e-09 2.84e-05 -11.0 2.61e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 3.3868288e+00 3.44e-11 8.53e-05 -11.0 1.53e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 23 3.3868288e+00 2.59e-09 2.25e-05 -11.0 1.08e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 3.3868288e+00 6.60e-10 5.15e-05 -11.0 3.95e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 3.3868288e+00 8.04e-10 8.47e-05 -11.0 4.34e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 3.3868288e+00 5.42e-10 9.59e-05 -11.0 3.29e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 3.3868288e+00 2.63e-09 3.02e-04 -11.0 1.01e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 3.3868288e+00 3.14e-08 3.89e-05 -11.0 4.02e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 3.3868288e+00 3.77e-10 5.47e-05 -11.0 4.69e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 3.3868288e+00 4.22e-11 1.83e-04 -11.0 3.50e-06 - 1.00e+00 1.00e+00H 1\n",
|
||
" 31 3.3868288e+00 1.42e-08 1.50e-04 -11.0 3.11e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 3.3868288e+00 5.56e-09 3.55e-05 -11.0 1.39e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 3.3868288e+00 3.08e-10 3.98e-05 -11.0 2.32e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 3.3868288e+00 1.74e-10 2.72e-05 -11.0 3.36e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 3.3868288e+00 1.89e-11 2.43e-05 -11.0 1.67e-06 - 1.00e+00 1.00e+00H 1\n",
|
||
" 36 3.3868288e+00 7.03e-11 8.72e-05 -11.0 6.33e-07 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 3.3868288e+00 7.93e-10 7.37e-05 -11.0 4.12e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 3.3868288e+00 6.48e-09 4.81e-05 -11.0 2.93e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 3.3868288e+00 2.57e-09 5.16e-05 -11.0 3.67e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 3.3868285e+00 2.98e-07 5.53e-05 -11.0 2.13e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 3.3868288e+00 6.23e-08 1.39e-05 -11.0 4.74e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 3.3868288e+00 1.80e-08 5.44e-05 -11.0 2.41e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 3.3868288e+00 4.12e-09 2.55e-05 -11.0 6.02e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 3.3868288e+00 1.05e-07 4.35e-05 -11.0 4.85e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 3.3868288e+00 6.77e-08 1.28e-05 -11.0 8.82e-05 - 1.00e+00 5.00e-01h 2\n",
|
||
" 46 3.3868289e+00 7.08e-09 2.73e-05 -11.0 1.36e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 3.3868289e+00 9.47e-11 2.91e-05 -11.0 1.12e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 48 3.3868284e+00 2.09e-07 1.49e-05 -11.0 4.97e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 3.3868286e+00 7.07e-08 2.92e-05 -11.0 3.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 3.3868275e+00 1.39e-06 2.35e-03 -11.0 1.08e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 3.3868229e+00 4.81e-06 9.03e-04 -11.0 9.12e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 3.3868285e+00 1.59e-07 6.76e-05 -11.0 8.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 3.3868282e+00 4.04e-07 1.12e-04 -11.0 4.75e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 3.3868203e+00 7.01e-06 8.86e-03 -11.0 2.51e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 3.3868267e+00 9.45e-07 1.67e-03 -11.0 1.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 3.3868276e+00 2.01e-10 2.23e-05 -11.0 1.09e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 57 3.3868271e+00 1.32e-06 8.10e-04 -11.0 6.51e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 3.3868168e+00 1.39e-05 2.82e-03 -11.0 4.27e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 3.3868279e+00 8.89e-07 1.10e-03 -11.0 7.44e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 3.3868278e+00 3.87e-07 1.18e-03 -11.0 4.59e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 3.3868230e+00 1.20e-05 1.22e-03 -11.0 5.60e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 3.3867660e+00 4.32e-05 2.32e-03 -11.0 2.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 3.3867516e+00 6.02e-05 2.42e-03 -11.0 1.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 3.3868063e+00 1.53e-05 1.84e-03 -11.0 5.73e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 3.3868249e+00 2.20e-06 1.00e-03 -11.0 2.22e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 3.3867534e+00 7.69e-05 2.16e-03 -11.0 3.36e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.9543028e+00 8.84e-01 2.75e-01 -11.0 4.52e+04 - 6.96e-01 6.96e-01f 1\n",
|
||
" 68 3.0499895e+00 2.23e-01 9.15e-02 -10.6 7.72e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.9786130e+00 3.86e-01 1.34e-01 -3.3 5.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.7835125e+00 1.16e+00 2.86e-01 -3.1 3.30e+04 - 1.00e+00 1.92e-01f 3\n",
|
||
" 71 2.7625409e+00 1.28e+00 2.98e-01 -4.1 9.26e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 72 2.7497763e+00 8.75e-01 1.25e-01 -1.9 8.99e+04 - 1.00e+00 8.60e-02h 1\n",
|
||
" 73 2.7886309e+00 8.39e-01 2.68e-01 -2.1 4.99e+03 - 8.85e-01 1.00e+00h 1\n",
|
||
" 74 2.6803726e+00 3.65e-01 1.74e-01 -2.3 8.35e+03 - 9.80e-01 1.00e+00h 1\n",
|
||
" 75 2.8781848e+00 7.27e-01 1.87e-01 -2.0 7.51e+03 - 4.30e-01 1.00e+00H 1\n",
|
||
" 76 2.7370077e+00 5.21e-01 9.07e-02 -1.5 4.00e+04 - 1.00e+00 6.12e-01h 1\n",
|
||
" 77 2.2984730e+00 8.85e-01 2.35e-01 -1.6 1.80e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 78 2.7664691e+00 7.03e-01 7.17e-02 -2.2 1.10e+04 - 9.86e-01 1.00e+00h 1\n",
|
||
" 79 2.5085699e+00 8.03e-01 6.49e-02 -2.2 1.16e+04 - 4.27e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 4.5113175e+00 5.72e-01 5.79e-01 -2.2 3.87e+04 - 6.53e-01 1.00e+00H 1\n",
|
||
" 81 3.4527898e+00 6.50e-01 5.04e-01 -2.2 4.49e+04 - 5.10e-01 6.30e-01F 1\n",
|
||
" 82 3.2034388e+00 6.41e-01 4.70e-01 -2.2 1.56e+05 - 1.00e-01 5.19e-03f 1\n",
|
||
" 83 3.6025637e+00 5.21e-01 6.69e-01 -2.2 3.62e+03 - 5.22e-01 1.00e+00H 1\n",
|
||
" 84 3.1785467e+00 6.14e-01 4.53e-01 -2.2 5.72e+05 - 1.81e-03 2.70e-02f 3\n",
|
||
" 85 3.0841417e+00 7.08e-01 1.53e-01 -2.2 7.26e+04 - 7.76e-01 3.20e-01h 2\n",
|
||
" 86 3.1170517e+00 5.62e-01 9.50e-02 -2.2 1.88e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 3.0695391e+00 4.18e-01 1.29e-01 -2.2 1.77e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 88 3.0572565e+00 4.42e-01 1.15e-01 -2.2 2.73e+05 - 5.80e-02 2.66e-03h 7\n",
|
||
" 89 3.0509142e+00 4.48e-01 1.09e-01 -2.2 2.33e+05 - 3.53e-01 1.53e-03h 8\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 3.7231995e+00 4.93e-01 2.21e-01 -2.2 5.21e+03 - 9.41e-01 8.99e-01H 1\n",
|
||
" 91 3.5892478e+00 9.20e-01 1.65e-01 -2.2 1.61e+04 - 3.79e-02 5.00e-01f 2\n",
|
||
" 92 3.8073679e+00 3.38e-01 2.30e-01 -2.2 2.73e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 93 3.7555360e+00 5.83e-01 1.16e-01 -2.2 2.35e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 94 3.6830049e+00 4.30e-01 5.25e-02 -2.2 4.20e+04 - 1.00e+00 6.04e-01H 1\n",
|
||
" 95 4.0074000e+00 1.51e-01 1.16e-01 -2.2 2.26e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 3.6993383e+00 4.23e-01 3.84e-02 -2.2 2.30e+03 - 9.96e-01 1.00e+00h 1\n",
|
||
" 97 3.9373765e+00 1.20e-04 3.86e-01 -2.2 4.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 3.9375297e+00 2.76e-07 2.75e-05 -2.2 5.58e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 3.9375298e+00 7.34e-08 2.36e-04 -3.3 3.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 3.9375270e+00 2.89e-06 3.99e-03 -5.0 1.67e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 3.9375270332174521e+00 3.9375270332174521e+00\n",
|
||
"Dual infeasibility......: 3.9921153700333556e-03 3.9921153700333556e-03\n",
|
||
"Constraint violation....: 2.8926434367804177e-06 2.8926434367804177e-06\n",
|
||
"Complementarity.........: 1.1108970771658056e-05 1.1108970771658056e-05\n",
|
||
"Overall NLP error.......: 3.9921153700333556e-03 3.9921153700333556e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 157\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 157\n",
|
||
"Number of inequality constraint evaluations = 157\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.554\n",
|
||
"Total CPU secs in NLP function evaluations = 149.552\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 879.00us ( 5.60us) 963.85us ( 6.14us) 157\n",
|
||
" nlp_g | 7.80 s ( 49.69ms) 7.54 s ( 48.04ms) 157\n",
|
||
" nlp_grad | 1.49 s ( 1.49 s) 1.46 s ( 1.46 s) 1\n",
|
||
" nlp_grad_f | 473.00us ( 4.64us) 467.64us ( 4.58us) 102\n",
|
||
" nlp_jac_g | 145.72 s ( 1.43 s) 141.01 s ( 1.38 s) 102\n",
|
||
" total | 155.17 s (155.17 s) 150.16 s (150.16 s) 1\n",
|
||
"Timestamp 22200\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.68e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0104650e+01 1.53e+01 1.68e+04 -1.5 1.68e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.1589583e+01 5.54e+00 1.15e+01 0.8 2.71e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.6206746e+01 2.14e+00 8.67e-01 -1.3 6.47e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.7592373e+01 2.63e-04 9.71e-02 -3.0 2.61e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.7592749e+01 5.71e-06 2.61e-03 -4.9 3.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.7592625e+01 4.61e-05 1.18e-02 -7.0 2.59e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.7592607e+01 8.06e-05 1.27e-02 -8.9 2.87e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.7592773e+01 2.13e-08 1.50e-04 -10.7 1.40e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.7592773e+01 1.90e-08 3.04e-05 -11.0 1.46e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.7592773e+01 1.16e-08 1.19e-04 -11.0 6.10e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.7592773e+01 1.01e-08 7.41e-05 -11.0 1.31e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.7592773e+01 2.59e-08 7.38e-05 -11.0 2.61e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.7592773e+01 1.75e-08 1.11e-05 -11.0 1.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.7592773e+01 1.67e-09 1.22e-04 -11.0 6.20e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.7592773e+01 3.88e-08 8.08e-05 -11.0 1.91e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.7592773e+01 1.19e-08 6.58e-05 -11.0 8.04e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.7592773e+01 3.09e-09 3.11e-05 -11.0 2.00e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.7592773e+01 1.44e-09 5.52e-05 -11.0 2.64e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.7592773e+01 6.08e-09 2.19e-04 -11.0 8.52e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.7592773e+01 1.33e-08 7.43e-05 -11.0 5.45e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.7592773e+01 6.56e-09 1.31e-04 -11.0 4.48e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.7592773e+01 1.76e-08 4.04e-05 -11.0 5.84e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.7592773e+01 7.41e-10 1.36e-04 -11.0 9.00e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.7592773e+01 1.03e-08 1.92e-05 -11.0 4.26e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.7592773e+01 9.99e-10 2.67e-05 -11.0 1.22e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.7592773e+01 6.87e-10 1.26e-04 -11.0 8.76e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.7592773e+01 1.65e-08 1.09e-04 -11.0 2.45e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.7592773e+01 7.91e-09 1.81e-05 -11.0 1.00e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.7592773e+01 3.57e-09 8.29e-05 -11.0 3.78e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.7592773e+01 1.61e-07 4.74e-05 -11.0 2.29e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.7592773e+01 1.32e-07 9.59e-05 -11.0 3.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.7592773e+01 3.72e-07 1.77e-04 -11.0 1.24e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.7592771e+01 3.97e-06 1.59e-02 -11.0 2.39e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.7592610e+01 6.12e-05 6.47e-03 -11.0 2.28e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.7592765e+01 4.01e-06 3.02e-03 -11.0 9.36e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.7592651e+01 2.79e-04 4.60e-03 -11.0 5.81e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.7582884e+01 3.24e-03 4.10e-03 -11.0 6.42e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.7591001e+01 4.34e-03 1.93e-03 -11.0 2.66e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.7540194e+01 3.16e-02 1.84e-03 -11.0 6.39e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.7446442e+01 3.32e-01 1.33e-02 -11.0 5.09e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.7561753e+01 1.87e-02 8.35e-03 -11.0 1.41e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.7484611e+01 1.03e-01 1.34e-02 -11.0 3.05e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.6942330e+01 1.13e+00 5.89e-02 -9.0 1.62e+06 - 1.00e+00 1.60e-02f 1\n",
|
||
" 44 1.4972805e+01 8.44e+00 4.99e-01 -7.8 2.33e+05 - 1.00e+00 2.66e-01f 1\n",
|
||
" 45 1.4973410e+01 8.20e+00 4.77e-01 -5.8 6.67e+04 - 1.00e+00 9.31e-03h 1\n",
|
||
" 46 1.4974572e+01 8.19e+00 4.75e-01 -3.9 3.73e+03 - 1.00e+00 1.66e-03h 1\n",
|
||
" 47 1.8115509e+01 4.77e-01 5.16e-01 -2.5 1.90e+03 - 9.98e-01 1.00e+00h 1\n",
|
||
" 48 1.7771903e+01 2.05e-01 5.08e-01 -2.6 7.17e+04 - 5.72e-01 3.95e-02f 1\n",
|
||
" 49 1.7496159e+01 2.43e-01 2.17e-02 -2.6 3.09e+03 - 1.85e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.5328866e+01 1.68e+00 1.03e-01 -2.6 3.03e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 51 1.0484892e+01 4.62e+00 6.87e-01 -1.6 5.16e+04 - 2.13e-01 6.28e-01f 1\n",
|
||
" 52 1.6887243e+01 9.52e-01 2.32e-01 -1.3 1.08e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.5855929e+01 1.31e+00 7.94e-02 -6.9 9.68e+03 - 5.40e-01 1.00e+00h 1\n",
|
||
" 54 1.6871995e+01 1.07e+00 3.40e-02 -1.6 1.84e+04 - 1.00e+00 7.05e-01H 1\n",
|
||
" 55 1.7924893e+01 4.50e-02 5.57e-02 -7.6 1.87e+03 - 4.64e-01 1.00e+00h 1\n",
|
||
" 56 1.6904993e+01 6.43e-01 6.72e-02 -2.0 6.45e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 57 1.7029612e+01 3.47e-01 3.10e-02 -2.6 4.99e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.7463058e+01 2.62e-01 3.74e-02 -7.9 8.96e+02 - 7.53e-01 1.00e+00h 1\n",
|
||
" 59 1.5352107e+01 5.09e+00 3.99e-01 -3.3 7.60e+04 - 1.23e-01 3.37e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.6879913e+01 1.24e+00 8.30e-02 -3.3 1.34e+04 - 1.00e+00 6.64e-01h 1\n",
|
||
" 61 1.5107107e+01 1.16e+01 5.13e-01 -3.3 5.51e+04 - 1.39e-01 9.82e-01f 1\n",
|
||
" 62 1.5102915e+01 1.15e+01 5.10e-01 -3.3 1.91e+04 - 1.00e+00 3.63e-03h 1\n",
|
||
" 63 1.4697479e+01 8.83e+00 3.03e-01 -3.3 1.44e+04 - 1.00e+00 2.48e-01h 1\n",
|
||
" 64 1.6795942e+01 1.22e+00 4.57e-01 -3.3 4.55e+03 - 2.27e-01 1.00e+00h 1\n",
|
||
" 65 1.7606081e+01 1.81e-01 1.01e-01 -3.0 1.27e+03 - 5.17e-01 1.00e+00h 1\n",
|
||
" 66 1.7466434e+01 1.40e-01 1.57e-01 -3.2 1.04e+03 - 6.98e-01 1.00e+00h 1\n",
|
||
" 67 1.7645581e+01 1.22e-01 1.60e-01 -1.2 2.30e+06 - 1.75e-02 3.39e-03f 4\n",
|
||
" 68 1.7624696e+01 1.20e-01 1.58e-01 -2.2 3.85e+04 - 9.09e-01 2.78e-03h 1\n",
|
||
" 69 1.6423248e+01 2.89e+00 8.11e-02 -2.2 3.06e+04 - 1.00e+00 4.30e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.6457544e+01 2.76e+00 8.05e-02 -2.2 3.13e+03 - 7.19e-01 4.34e-02h 1\n",
|
||
" 71 1.6482471e+01 2.71e+00 8.06e-02 -2.2 9.56e+02 - 1.00e+00 2.38e-02h 1\n",
|
||
" 72 1.7951726e+01 9.79e-03 5.09e-02 -2.2 8.01e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.7968252e+01 5.19e-03 1.71e-03 -3.3 1.27e+01 - 6.73e-01 1.00e+00h 1\n",
|
||
" 74 1.7964027e+01 5.43e-03 1.52e-03 -3.3 3.24e+01 - 3.61e-01 1.00e+00h 1\n",
|
||
" 75 1.7497932e+01 1.70e-01 8.23e-02 -3.3 2.50e+03 - 2.46e-02 1.00e+00f 1\n",
|
||
" 76 1.7941552e+01 6.11e-03 3.29e-03 -3.3 1.37e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 77 1.7770838e+01 1.22e-01 3.41e-02 -3.3 2.37e+03 - 1.00e+00 3.86e-01f 1\n",
|
||
" 78 1.7768467e+01 1.21e-01 3.33e-02 -3.3 2.24e+03 - 1.00e+00 1.79e-02h 1\n",
|
||
" 79 1.7893289e+01 3.45e-02 3.15e-02 -3.3 4.97e+02 - 1.00e+00 9.74e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80r 1.7893289e+01 3.45e-02 9.99e+02 -1.5 0.00e+00 - 0.00e+00 3.03e-07R 20\n",
|
||
" 81r 1.7964917e+01 5.83e-03 3.38e+02 -3.6 5.15e+01 - 1.00e+00 1.30e-03f 1\n",
|
||
" 82 1.7959037e+01 7.45e-03 2.20e-03 -3.3 1.53e+02 - 3.22e-01 1.00e+00h 1\n",
|
||
" 83 1.7876213e+01 6.78e-02 1.66e-03 -3.3 2.04e+03 - 3.92e-03 5.75e-02f 1\n",
|
||
" 84 1.7954534e+01 8.24e-03 1.44e-03 -3.3 4.29e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.7964001e+01 4.24e-03 1.12e-03 -3.3 2.64e+01 - 9.36e-01 1.00e+00h 1\n",
|
||
" 86 1.7954720e+01 9.21e-03 1.36e-03 -5.0 3.93e+03 - 1.49e-01 5.94e-02h 1\n",
|
||
" 87 1.7843342e+01 4.79e-02 7.29e-03 -5.0 1.19e+03 - 9.98e-01 1.00e+00h 1\n",
|
||
" 88 1.7718311e+01 1.46e-01 1.00e-02 -5.0 1.81e+03 - 6.33e-01 6.46e-01h 1\n",
|
||
" 89 1.7954231e+01 4.42e-05 2.19e-01 -5.0 2.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.7954262e+01 2.54e-06 1.47e-03 -5.0 1.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.7954261e+01 2.04e-05 1.26e-03 -5.0 3.90e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.7954266e+01 3.52e-06 1.77e-03 -5.0 1.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.7954246e+01 4.36e-05 1.75e-03 -5.0 1.62e-01 - 4.99e-01 1.00e+00h 1\n",
|
||
" 94 1.7954182e+01 1.86e-04 2.16e-03 -5.0 6.30e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.7953901e+01 2.02e-04 9.47e-04 -5.0 5.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.7954215e+01 2.64e-05 2.03e-03 -5.0 3.64e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.7954073e+01 1.28e-04 1.51e-03 -5.0 3.55e+00 - 1.00e+00 1.50e-01h 1\n",
|
||
" 98 1.7952591e+01 7.94e-04 7.73e-03 -5.0 2.34e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.7952630e+01 8.17e-04 2.08e-03 -5.0 1.42e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.7952427e+01 1.52e-03 1.01e-02 -5.0 7.75e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.7952427363540043e+01 1.7952427363540043e+01\n",
|
||
"Dual infeasibility......: 1.0055397771444846e-02 1.0055397771444846e-02\n",
|
||
"Constraint violation....: 1.5209840532151020e-03 1.5209840532151020e-03\n",
|
||
"Complementarity.........: 1.2959753589077388e-03 1.2959753589077388e-03\n",
|
||
"Overall NLP error.......: 1.0055397771444846e-02 1.0055397771444846e-02\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 130\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 130\n",
|
||
"Number of inequality constraint evaluations = 130\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.546\n",
|
||
"Total CPU secs in NLP function evaluations = 150.285\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 746.00us ( 5.74us) 740.12us ( 5.69us) 130\n",
|
||
" nlp_g | 6.53 s ( 50.19ms) 6.35 s ( 48.83ms) 130\n",
|
||
" nlp_grad | 1.51 s ( 1.51 s) 1.46 s ( 1.46 s) 1\n",
|
||
" nlp_grad_f | 444.00us ( 4.35us) 445.43us ( 4.37us) 102\n",
|
||
" nlp_jac_g | 148.43 s ( 1.44 s) 144.76 s ( 1.41 s) 103\n",
|
||
" total | 156.61 s (156.61 s) 152.72 s (152.72 s) 1\n",
|
||
"Timestamp 22500\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.37e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0091757e+01 1.25e+01 1.37e+04 -1.5 1.37e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.1776525e+00 4.30e+00 7.87e+00 0.8 2.15e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.9846542e+00 7.56e-01 6.94e-01 -1.3 6.20e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 3.3490522e+00 2.70e-03 2.75e-01 -3.0 2.13e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 3.3508752e+00 2.91e-06 1.59e-03 -4.9 3.51e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 3.3508434e+00 1.77e-05 5.40e-03 -7.0 1.37e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 3.3508562e+00 1.40e-05 6.73e-04 -9.1 2.04e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 3.3507340e+00 9.48e-05 1.06e-03 -11.0 4.82e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 3.3508273e+00 4.15e-05 8.11e-04 -11.0 2.86e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 3.3508581e+00 6.20e-07 6.38e-05 -11.0 2.42e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 3.3507943e+00 4.32e-05 9.10e-04 -11.0 2.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 3.3505868e+00 2.95e-04 2.20e-03 -11.0 8.83e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 3.3508609e+00 3.18e-08 1.28e-04 -11.0 3.84e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 3.3508609e+00 3.33e-08 1.46e-05 -11.0 9.95e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 3.3508609e+00 3.68e-09 4.36e-05 -11.0 2.08e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 3.3508609e+00 3.44e-09 8.87e-05 -11.0 1.06e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 3.3508608e+00 6.46e-08 5.72e-05 -11.0 3.20e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 3.3508604e+00 4.52e-07 6.04e-05 -11.0 2.68e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 3.3508603e+00 3.58e-07 1.40e-04 -11.0 4.55e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 3.3508555e+00 3.12e-06 8.45e-03 -11.0 1.47e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 3.3508601e+00 2.22e-07 4.44e-05 -11.0 3.71e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 3.3508602e+00 3.41e-07 5.87e-05 -11.0 2.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 3.3508605e+00 3.24e-07 3.56e-05 -11.0 1.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 3.3508605e+00 3.28e-07 9.83e-05 -11.0 4.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 3.3508585e+00 3.42e-06 3.59e-03 -11.0 2.03e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 3.3506874e+00 1.86e-04 9.04e-03 -11.0 1.67e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 3.3508510e+00 2.03e-05 1.07e-03 -11.0 5.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 3.3507367e+00 1.31e-04 2.28e-03 -11.0 5.93e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 3.3508131e+00 2.88e-05 1.27e-03 -11.0 2.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 3.3506772e+00 1.52e-04 2.16e-03 -11.0 6.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 3.3508735e+00 1.81e-08 3.76e-05 -11.0 1.36e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 32 3.3507928e+00 1.54e-04 9.71e-04 -11.0 6.45e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 3.3508634e+00 2.78e-06 1.58e-03 -11.0 6.82e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 3.3505183e+00 2.95e-04 1.60e-03 -11.0 4.80e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 3.3508079e+00 5.46e-05 6.53e-04 -11.0 2.38e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 3.3508094e+00 4.96e-05 5.68e-04 -11.0 3.15e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 3.3506703e+00 3.77e-04 8.46e-04 -11.0 2.52e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 3.3505824e+00 5.31e-04 1.51e-03 -11.0 3.74e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 3.0878347e+00 1.75e-01 7.85e-02 -11.0 5.87e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 3.2148100e+00 2.32e-01 4.52e-02 -11.0 6.44e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.9996980e+00 1.46e+00 4.41e-01 -11.0 1.87e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 3.3516706e+00 2.96e-01 2.66e-01 -11.0 5.40e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.8399736e+00 8.23e-01 9.14e-02 -11.0 9.87e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 3.1932854e+00 8.50e-01 1.37e-01 -11.0 9.41e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.8022414e+00 7.62e-01 3.82e-01 -11.0 3.06e+04 - 7.06e-01 1.00e+00h 1\n",
|
||
" 46 2.6872957e+00 1.27e+00 2.35e-01 -10.4 9.65e+04 - 1.00e+00 7.41e-02h 4\n",
|
||
" 47 3.0959152e+00 6.81e-01 2.15e-01 -11.0 1.17e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 3.0745315e+00 1.49e+00 1.04e-01 -11.0 1.78e+04 - 8.97e-02 5.00e-01h 2\n",
|
||
" 49 3.0310646e+00 1.14e+00 1.14e-01 -11.0 1.13e+07 - 4.83e-03 8.45e-04f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 3.0207241e+00 1.56e+00 8.65e-02 -11.0 2.61e+04 - 6.58e-10 2.34e-01h 3\n",
|
||
" 51 3.1221165e+00 5.75e-01 1.60e-01 -11.0 3.45e+04 - 5.80e-01 5.00e-01h 2\n",
|
||
" 52 2.9968093e+00 1.37e+00 1.55e-01 -11.0 2.05e+05 - 7.87e-02 2.22e-01h 1\n",
|
||
" 53 3.2878851e+00 3.28e-01 1.87e-01 -11.0 1.40e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 3.1350299e+00 2.07e-01 1.99e-01 -11.0 3.94e+04 - 1.00e+00 1.10e-01h 1\n",
|
||
" 55 3.1270960e+00 1.06e-01 1.08e-01 -11.0 4.77e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 56 3.0766389e+00 4.32e-01 8.01e-02 -9.1 4.55e+03 - 1.00e+00 7.25e-01h 1\n",
|
||
" 57 3.0739373e+00 4.08e-01 7.59e-02 -6.5 1.22e+03 - 5.46e-01 2.70e-02h 1\n",
|
||
" 58 3.1050180e+00 2.15e-02 6.93e-02 -4.9 1.30e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 3.0977504e+00 1.01e-02 1.16e-02 -4.2 6.26e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 3.0927002e+00 1.40e-02 8.42e-03 -3.9 2.31e+02 - 1.00e+00 4.25e-01h 1\n",
|
||
" 61 3.0848404e+00 2.74e-02 1.38e-02 -2.9 6.20e+01 - 3.66e-01 1.00e+00h 1\n",
|
||
" 62 3.1010856e+00 8.38e-04 1.01e-02 -3.5 8.16e+00 - 8.25e-01 1.00e+00h 1\n",
|
||
" 63 3.0922302e+00 1.09e-02 1.13e-02 -3.9 1.62e+02 - 1.00e+00 3.65e-01h 1\n",
|
||
" 64 3.0996958e+00 3.62e-03 1.43e-03 -3.5 2.62e+01 - 9.69e-01 1.00e+00h 1\n",
|
||
" 65 3.0955956e+00 6.05e-03 5.73e-03 -5.1 1.37e+02 - 1.00e+00 3.84e-01h 1\n",
|
||
" 66 3.1002645e+00 2.07e-03 8.47e-04 -3.5 2.61e+01 - 1.00e+00 7.25e-01h 1\n",
|
||
" 67 3.0951318e+00 1.68e-02 4.33e-03 -9.4 3.85e+01 - 3.65e-01 1.00e+00h 1\n",
|
||
" 68 3.0516996e+00 1.05e-01 3.02e-02 -3.9 9.20e+02 - 1.00e+00 5.70e-01f 1\n",
|
||
" 69 3.0534185e+00 9.24e-02 2.40e-02 -3.8 3.23e+02 - 7.90e-01 1.25e-01h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 3.1028089e+00 2.37e-02 1.33e-02 -4.7 1.06e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 3.1079274e+00 6.31e-03 1.35e-02 -4.8 4.97e+01 - 2.31e-01 1.00e+00h 1\n",
|
||
" 72 3.1088196e+00 3.55e-03 2.02e-03 -4.8 3.71e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 3.1027370e+00 9.85e-03 8.46e-03 -4.8 4.92e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 3.1057210e+00 4.72e-03 2.05e-03 -4.8 4.29e+01 - 1.00e+00 6.82e-01h 1\n",
|
||
" 75 3.1059680e+00 4.43e-03 2.03e-03 -4.8 7.28e+00 - 6.21e-01 6.25e-02h 5\n",
|
||
" 76 3.1097620e+00 1.35e-04 4.97e-03 -4.8 1.46e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 77 3.0969889e+00 1.60e-02 5.65e-03 -9.4 7.23e+01 - 9.22e-01 1.00e+00h 1\n",
|
||
" 78 3.0846564e+00 2.65e-02 7.95e-03 -5.6 2.34e+02 - 2.68e-01 1.00e+00h 1\n",
|
||
" 79 3.0381572e+00 1.05e+00 6.00e-01 -4.1 1.45e+04 - 4.26e-01 5.00e-01f 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 3.0081749e+00 5.42e-01 1.66e-01 -3.9 4.81e+03 - 6.73e-01 1.00e+00h 1\n",
|
||
" 81 2.8740025e+00 9.21e-01 8.88e-02 -3.9 5.88e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.8669382e+00 6.76e-01 1.13e-01 -2.0 1.47e+04 - 1.00e+00 4.48e-01h 1\n",
|
||
" 83 2.8790417e+00 3.51e-01 1.84e-01 -2.0 6.59e+03 - 6.71e-01 1.00e+00f 1\n",
|
||
" 84 2.8620758e+00 6.33e-01 1.55e-01 -2.0 3.91e+05 - 6.14e-01 1.14e-02h 4\n",
|
||
" 85 3.0383527e+00 3.42e-01 1.78e-01 -1.5 1.78e+04 - 1.00e+00 6.18e-01h 1\n",
|
||
" 86 3.0546099e+00 1.41e-01 2.48e-01 -1.7 5.14e+03 - 9.82e-01 1.00e+00f 1\n",
|
||
" 87 2.6096363e+00 9.26e-01 2.07e-01 -1.9 1.49e+05 - 8.89e-01 3.84e-01f 1\n",
|
||
" 88 2.5975631e+00 9.04e-01 2.04e-01 -2.1 1.45e+04 - 9.69e-01 8.41e-02H 1\n",
|
||
" 89r 2.5975631e+00 9.04e-01 9.99e+02 -0.0 0.00e+00 - 0.00e+00 4.77e-07R 22\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90r 2.8611803e+00 2.63e-01 9.64e+02 -0.5 1.46e+02 - 1.00e+00 4.66e-03f 1\n",
|
||
" 91 3.0676259e+00 9.16e-02 2.61e-01 -4.1 1.48e+03 - 8.92e-01 1.00e+00H 1\n",
|
||
" 92 3.0101712e+00 8.16e-01 8.15e-02 -3.5 1.24e+04 - 1.00e+00 1.95e-01h 3\n",
|
||
" 93 3.0552445e+00 4.84e-01 4.30e-02 -3.6 1.03e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 3.0178541e+00 8.04e-02 2.10e-01 -3.6 2.53e+03 - 1.53e-01 1.00e+00h 1\n",
|
||
" 95 2.9407521e+00 2.13e-01 1.43e-01 -4.3 2.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 3.0619828e+00 4.82e-02 6.32e-02 -4.2 6.72e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 3.0533778e+00 8.22e-02 2.40e-02 -5.7 8.10e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 2.8357663e+00 1.04e+00 3.11e-01 -5.4 9.46e+07 - 6.17e-04 4.94e-05f 1\n",
|
||
" 99 2.9545410e+00 3.33e-01 1.36e-01 -5.4 3.14e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.9341100e+00 3.31e-01 1.58e-01 -5.4 4.43e+03 - 8.64e-01 1.47e-01h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.9341099578021805e+00 2.9341099578021805e+00\n",
|
||
"Dual infeasibility......: 1.5768052059500889e-01 1.5768052059500889e-01\n",
|
||
"Constraint violation....: 3.3080252248404562e-01 3.3080252248404562e-01\n",
|
||
"Complementarity.........: 4.4341279656479820e-06 4.4341279656479820e-06\n",
|
||
"Overall NLP error.......: 3.3080252248404562e-01 3.3080252248404562e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 164\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 164\n",
|
||
"Number of inequality constraint evaluations = 164\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.576\n",
|
||
"Total CPU secs in NLP function evaluations = 156.452\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 951.00us ( 5.80us) 942.94us ( 5.75us) 164\n",
|
||
" nlp_g | 8.46 s ( 51.58ms) 8.26 s ( 50.36ms) 164\n",
|
||
" nlp_grad | 1.49 s ( 1.49 s) 1.45 s ( 1.45 s) 1\n",
|
||
" nlp_grad_f | 407.00us ( 3.99us) 406.06us ( 3.98us) 102\n",
|
||
" nlp_jac_g | 152.86 s ( 1.48 s) 149.24 s ( 1.45 s) 103\n",
|
||
" total | 162.96 s (162.96 s) 159.11 s (159.11 s) 1\n",
|
||
"Timestamp 22800\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 6.06e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9930645e+01 1.38e+01 6.06e+03 -1.5 6.06e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.4777971e+00 5.00e+00 9.06e+00 0.6 7.95e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 7.6962710e+00 1.21e+00 9.00e-01 -1.5 2.15e+01 - 9.97e-01 1.00e+00f 1\n",
|
||
" 4 8.5976801e+00 3.15e-03 1.01e-01 -3.2 1.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 8.5991087e+00 1.25e-06 2.06e-03 -5.1 1.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 8.5991113e+00 5.03e-10 4.89e-05 -7.2 5.83e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 7 8.5991108e+00 2.98e-07 5.63e-05 -11.0 1.90e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 8.5991027e+00 5.76e-06 1.25e-02 -11.0 3.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 8.5990842e+00 3.34e-05 4.82e-03 -11.0 1.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 8.5990814e+00 1.33e-05 1.59e-03 -11.0 7.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 8.5991055e+00 7.84e-06 1.67e-03 -11.0 5.35e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 8.5991135e+00 7.29e-07 1.88e-03 -11.0 8.95e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 8.5991165e+00 1.90e-08 8.91e-05 -11.0 4.49e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 14 8.5990880e+00 1.80e-05 3.24e-03 -11.0 1.16e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 8.5991200e+00 1.02e-08 1.40e-04 -11.0 7.10e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 8.5991200e+00 1.18e-08 1.00e-04 -11.0 1.02e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 8.5991200e+00 2.07e-08 4.62e-05 -11.0 1.97e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 8.5991200e+00 2.11e-09 7.22e-05 -11.0 3.82e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 8.5991200e+00 9.66e-09 1.47e-04 -11.0 5.35e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 8.5991200e+00 5.53e-09 1.09e-04 -11.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 8.5991200e+00 8.61e-11 1.50e-04 -11.0 1.76e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 22 8.5991200e+00 5.51e-11 1.24e-04 -11.0 7.33e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 23 8.5991200e+00 1.79e-08 3.25e-05 -11.0 1.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 8.5991200e+00 7.76e-08 3.15e-05 -11.0 3.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 8.5991200e+00 7.16e-08 6.75e-05 -11.0 1.51e-04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 26 8.5991200e+00 5.35e-09 9.52e-05 -11.0 2.84e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.5991200e+00 4.51e-09 1.63e-04 -11.0 4.41e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 8.5991199e+00 1.62e-07 4.28e-05 -11.0 5.25e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.5991199e+00 4.84e-08 6.12e-05 -11.0 2.82e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.5991199e+00 1.56e-07 1.35e-04 -11.0 7.24e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 8.5991198e+00 8.37e-08 7.20e-05 -11.0 8.28e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 8.5991199e+00 2.42e-08 2.86e-05 -11.0 1.08e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 8.5991200e+00 3.86e-09 6.38e-05 -11.0 4.71e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 8.5991200e+00 2.52e-09 2.65e-05 -11.0 2.46e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 8.5991200e+00 1.19e-10 1.80e-04 -11.0 4.01e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 36 8.5991196e+00 3.56e-07 2.41e-05 -11.0 6.27e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 8.5991199e+00 1.41e-08 4.54e-05 -11.0 1.60e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 8.5991199e+00 1.96e-08 1.39e-04 -11.0 9.98e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 8.5991199e+00 1.08e-08 6.86e-05 -11.0 5.02e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.5991199e+00 6.72e-09 1.38e-04 -11.0 3.20e-05 - 1.00e+00 5.00e-01h 2\n",
|
||
" 41 8.5991199e+00 1.86e-08 1.85e-04 -11.0 8.52e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 8.5991199e+00 1.16e-08 2.54e-05 -11.0 4.14e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 8.5991200e+00 5.63e-09 2.61e-05 -11.0 3.07e-05 - 1.00e+00 5.00e-01h 2\n",
|
||
" 44 8.5991200e+00 2.91e-09 1.35e-04 -11.0 6.36e-06 - 1.00e+00 5.00e-01h 2\n",
|
||
" 45 8.5991200e+00 1.92e-09 2.88e-05 -11.0 1.53e-05 - 1.00e+00 2.50e-01h 3\n",
|
||
" 46 8.5991200e+00 1.14e-09 1.47e-04 -11.0 2.94e-06 - 1.00e+00 5.00e-01h 2\n",
|
||
" 47 8.5991199e+00 8.25e-09 1.49e-04 -11.0 3.08e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 8.5991200e+00 4.52e-09 5.39e-05 -11.0 1.57e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 8.5991200e+00 3.91e-09 1.02e-04 -11.0 5.67e-06 - 1.00e+00 1.25e-01h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.5991200e+00 3.47e-09 7.96e-05 -11.0 9.96e-06 - 1.00e+00 2.50e-01h 3\n",
|
||
" 51 8.5991200e+00 3.36e-09 2.13e-05 -11.0 5.28e-06 - 1.00e+00 3.12e-02h 6\n",
|
||
" 52 8.5991200e+00 3.66e-11 8.95e-05 -11.0 7.11e-07 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 8.5991200e+00 4.57e-11 7.69e-05 -11.0 3.81e-06 - 1.00e+00 1.00e+00H 1\n",
|
||
" 54 8.5991200e+00 2.74e-09 8.14e-05 -11.0 2.04e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 8.5991200e+00 6.53e-10 1.69e-04 -11.0 4.56e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 8.5991200e+00 1.23e-09 9.96e-05 -11.0 4.28e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 8.5991200e+00 4.56e-10 1.78e-04 -11.0 6.23e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 8.5991200e+00 5.95e-10 9.93e-05 -11.0 3.47e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 8.5991200e+00 6.60e-10 8.47e-05 -11.0 3.25e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 8.5991200e+00 4.89e-11 2.29e-04 -11.0 5.42e-06 - 1.00e+00 1.00e+00H 1\n",
|
||
" 61 8.5991200e+00 7.80e-10 3.66e-04 -11.0 2.37e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 8.5991200e+00 5.08e-09 8.36e-05 -11.0 1.49e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 8.5991200e+00 2.61e-09 5.55e-05 -11.0 3.71e-06 - 1.00e+00 5.00e-01h 2\n",
|
||
" 64 8.5991200e+00 1.31e-09 2.82e-05 -11.0 6.98e-06 - 1.00e+00 5.00e-01h 2\n",
|
||
" 65 8.5991200e+00 9.84e-10 1.51e-04 -11.0 1.56e-05 - 1.00e+00 5.00e-01h 2\n",
|
||
" 66 8.5991200e+00 5.75e-11 9.36e-05 -11.0 3.34e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 8.5991200e+00 2.13e-11 6.78e-05 -11.0 2.09e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 68 8.5991200e+00 4.59e-11 6.62e-05 -11.0 3.15e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 69 8.5991199e+00 5.91e-08 5.61e-05 -11.0 3.51e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.5991200e+00 3.52e-09 1.55e-05 -11.0 9.70e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 8.5991199e+00 7.53e-08 4.26e-05 -11.0 8.80e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 8.5991199e+00 1.60e-07 3.14e-05 -11.0 6.17e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 8.5991199e+00 1.48e-08 9.10e-05 -11.0 5.53e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 8.5991198e+00 2.07e-07 2.81e-05 -11.0 1.19e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 8.5991199e+00 1.67e-08 1.71e-04 -11.0 5.84e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 8.5991197e+00 5.98e-08 7.80e-05 -11.0 5.71e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 8.5991196e+00 4.76e-07 5.16e-05 -11.0 1.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 8.5991188e+00 8.38e-07 4.71e-03 -11.0 5.50e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 8.5991195e+00 7.65e-07 9.08e-05 -11.0 2.88e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 8.5991172e+00 1.32e-06 1.46e-03 -11.0 3.33e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 8.5991153e+00 8.67e-06 1.35e-03 -11.0 4.34e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 8.5991168e+00 4.79e-06 8.37e-04 -11.0 1.66e-02 - 1.00e+00 5.00e-01h 2\n",
|
||
" 83 8.5990709e+00 4.14e-05 9.48e-03 -11.0 7.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 8.5990808e+00 7.08e-05 1.17e-03 -11.0 9.10e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 8.5990422e+00 8.52e-05 2.27e-03 -11.0 9.95e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 8.5991045e+00 2.50e-05 1.78e-03 -11.0 2.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 8.5990624e+00 5.17e-05 1.12e-03 -11.0 8.04e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 8.5712734e+00 1.47e-02 8.35e-03 -11.0 2.54e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 89 8.5946265e+00 5.67e-03 2.20e-03 -11.0 7.73e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 8.5701755e+00 9.69e-02 6.98e-03 -11.0 2.10e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 8.5988076e+00 1.20e-02 5.58e-03 -11.0 2.29e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 92 6.4073249e+00 1.52e+00 3.01e-01 -11.0 3.44e+04 - 1.00e+00 7.97e-01f 1\n",
|
||
" 93 8.5441655e+00 3.45e-01 1.82e-01 -10.8 1.27e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 8.5035861e+00 2.97e-01 5.79e-02 -2.4 1.32e+03 - 1.00e+00 9.51e-01h 1\n",
|
||
" 95 8.5971998e+00 2.28e-02 1.15e-02 -8.5 7.14e+02 - 8.81e-01 1.00e+00h 1\n",
|
||
" 96 8.5922384e+00 2.72e-02 1.17e-02 -3.5 1.02e+04 - 1.00e+00 5.71e-03h 1\n",
|
||
" 97 5.9483518e+00 1.14e+00 2.32e-01 -3.5 1.24e+04 - 4.21e-01 1.00e+00f 1\n",
|
||
" 98 8.6722114e+00 7.10e-02 2.44e+00 -5.4 2.69e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 8.7492457e+00 3.24e-05 1.03e-02 -5.5 1.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.7492616e+00 1.47e-07 3.03e-04 -5.5 9.90e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.7492615794286461e+00 8.7492615794286461e+00\n",
|
||
"Dual infeasibility......: 3.0250032656493637e-04 3.0250032656493637e-04\n",
|
||
"Constraint violation....: 1.4725348052024856e-07 1.4725348052024856e-07\n",
|
||
"Complementarity.........: 2.9800400226805676e-06 2.9800400226805676e-06\n",
|
||
"Overall NLP error.......: 3.0250032656493637e-04 3.0250032656493637e-04\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 138\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 138\n",
|
||
"Number of inequality constraint evaluations = 138\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.597\n",
|
||
"Total CPU secs in NLP function evaluations = 153.809\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 806.00us ( 5.84us) 816.84us ( 5.92us) 138\n",
|
||
" nlp_g | 6.93 s ( 50.22ms) 6.73 s ( 48.74ms) 138\n",
|
||
" nlp_grad | 1.61 s ( 1.61 s) 1.60 s ( 1.60 s) 1\n",
|
||
" nlp_grad_f | 566.00us ( 5.55us) 568.66us ( 5.58us) 102\n",
|
||
" nlp_jac_g | 150.67 s ( 1.48 s) 146.80 s ( 1.44 s) 102\n",
|
||
" total | 159.38 s (159.38 s) 155.40 s (155.40 s) 1\n",
|
||
"Timestamp 23100\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.34e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9671095e+01 1.41e+01 1.34e+04 -1.5 1.34e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.0051612e+01 4.85e+00 9.84e+00 0.6 1.43e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.3149461e+01 1.70e+00 9.03e-01 -1.4 3.25e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 1.4244008e+01 7.89e-04 9.03e-02 -3.2 2.10e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.4244328e+01 7.58e-07 1.39e-04 -5.1 1.21e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.4244304e+01 1.53e-05 3.78e-03 -11.0 6.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.4244319e+01 5.74e-06 2.74e-03 -11.0 2.85e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.4244318e+01 6.40e-06 1.61e-03 -11.0 1.80e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.4244330e+01 5.50e-07 8.24e-05 -11.0 3.99e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.4244328e+01 1.81e-06 1.35e-03 -11.0 8.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.4240317e+01 3.08e-03 6.08e-02 -11.0 1.54e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 12 1.4244140e+01 8.48e-05 1.77e-03 -11.0 1.58e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.4243716e+01 5.85e-04 1.26e-03 -11.0 1.67e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.4243859e+01 2.24e-04 3.00e-03 -11.0 3.93e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.4243920e+01 1.12e-04 1.97e-03 -11.0 6.77e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.4244198e+01 4.22e-09 2.13e-04 -11.0 2.31e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 17 1.4243843e+01 1.73e-04 1.74e-03 -11.0 1.04e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 18 1.4243351e+01 4.27e-04 1.81e-03 -11.0 3.23e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.4244107e+01 4.13e-05 2.75e-03 -11.0 2.83e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.4244181e+01 1.57e-05 1.20e-03 -11.0 1.58e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.4244129e+01 9.36e-05 7.73e-04 -11.0 5.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.4242841e+01 2.26e-03 4.51e-03 -11.0 2.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.4243015e+01 7.55e-04 2.88e-03 -11.0 1.36e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.4241976e+01 1.41e-03 2.51e-03 -11.0 4.05e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.4139487e+01 1.51e-01 7.93e-03 -11.0 2.05e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 26 1.4214540e+01 6.86e-02 2.07e-03 -11.0 8.03e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.4193617e+01 4.32e-02 4.80e-03 -11.0 1.38e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.4235558e+01 1.26e-02 3.17e-03 -11.0 5.40e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.4236893e+01 4.58e-06 1.55e-03 -11.0 5.86e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.4169403e+01 4.62e-02 3.47e-03 -11.0 5.49e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 31 1.1616151e+01 7.48e+00 5.87e-01 -9.0 2.00e+05 - 1.00e+00 1.54e-01f 1\n",
|
||
" 32 1.1603215e+01 7.40e+00 5.79e-01 -7.0 7.58e+04 - 1.00e+00 4.06e-03h 1\n",
|
||
" 33 1.1603086e+01 7.40e+00 5.79e-01 -5.1 2.87e+04 - 1.00e+00 1.07e-04h 1\n",
|
||
" 34 1.4461470e+01 6.23e-01 5.02e-01 -5.4 9.75e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.4674841e+01 2.21e-03 1.46e-02 -5.5 8.63e+01 - 8.36e-01 1.00e+00h 1\n",
|
||
" 36 1.4462530e+01 5.87e-02 8.76e-02 -5.5 6.77e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 37 1.3641956e+01 1.58e+00 9.20e-02 -5.5 2.51e+04 - 2.12e-01 1.00e+00f 1\n",
|
||
" 38 1.3519076e+01 9.66e-01 3.98e-02 -5.5 8.82e+04 - 6.34e-01 3.52e-01h 1\n",
|
||
" 39 1.4569972e+01 3.62e-02 8.44e-02 -5.5 1.31e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.2257210e+01 2.78e+00 2.40e-01 -5.5 4.78e+04 - 2.15e-01 1.00e+00f 1\n",
|
||
" 41 1.1879491e+01 5.68e+00 4.53e-01 -11.0 4.08e+06 - 5.11e-04 1.48e-02f 1\n",
|
||
" 42 1.1930194e+01 5.37e+00 4.05e-01 -3.7 1.78e+04 - 1.00e+00 4.80e-02h 1\n",
|
||
" 43 1.2072766e+01 4.83e+00 2.19e-01 -4.2 1.07e+04 - 1.00e+00 2.92e-01h 1\n",
|
||
" 44 1.4602270e+01 5.45e-01 1.93e-01 -3.2 2.76e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.3102027e+01 9.72e-01 1.80e-01 -3.2 3.07e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 46 1.4486490e+01 3.45e-01 1.04e-01 -2.7 2.10e+03 - 8.61e-01 1.00e+00h 1\n",
|
||
" 47 1.4476452e+01 3.15e-01 1.02e-01 -2.9 2.68e+03 - 1.00e+00 4.28e-02h 1\n",
|
||
" 48 1.2055902e+01 2.49e+00 1.09e-01 -2.9 9.72e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 49 1.4422658e+01 9.95e-02 1.03e-01 -4.3 1.65e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.3134978e+01 3.27e+00 1.40e-01 -4.0 3.28e+04 - 8.11e-02 1.00e+00f 1\n",
|
||
" 51 1.3059502e+01 3.33e+00 1.45e-01 -4.0 6.63e+06 - 5.76e-04 9.20e-05f 1\n",
|
||
" 52 1.5970277e+01 2.02e+00 9.42e-02 -4.0 5.93e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.3529436e+01 7.47e-01 1.44e-01 -4.0 5.82e+03 - 8.39e-01 1.00e+00f 1\n",
|
||
" 54 1.4330362e+01 3.92e-01 4.39e-02 -4.0 1.75e+03 - 1.00e+00 6.18e-01h 1\n",
|
||
" 55 1.5255839e+01 9.14e-02 2.74e-02 -4.0 9.86e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.5139269e+01 1.67e-01 4.67e-02 -4.0 2.01e+03 - 5.20e-01 1.00e+00h 1\n",
|
||
" 57 1.4837299e+01 4.52e-01 5.90e-02 -4.0 7.42e+03 - 7.74e-02 2.50e-01f 3\n",
|
||
" 58 1.0824421e+01 4.11e+00 4.33e-01 -4.0 3.27e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 59 1.3769284e+01 8.38e-01 4.02e-01 -1.7 2.68e+04 - 6.78e-01 9.32e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.1986426e+01 3.20e+00 2.03e-01 -1.9 2.61e+04 - 5.85e-02 1.00e+00f 1\n",
|
||
" 61 8.4221335e+00 2.94e+00 4.26e-01 -2.0 6.35e+04 - 1.12e-01 3.80e-01f 1\n",
|
||
" 62 1.4411697e+01 3.41e+00 4.33e-01 -2.5 8.24e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.3510748e+01 2.97e+00 4.36e-01 -2.6 2.80e+05 - 7.94e-02 4.63e-02f 1\n",
|
||
" 64 1.0130828e+01 2.78e+00 2.36e-01 -2.6 1.32e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 65 9.0028028e+00 5.48e+00 5.52e-01 -8.0 3.77e+06 - 7.87e-03 8.60e-03f 1\n",
|
||
" 66 8.8811926e+00 5.43e+00 5.59e-01 -2.2 4.34e+04 - 5.67e-01 2.46e-02h 1\n",
|
||
" 67 8.7677296e+00 3.71e+00 2.24e-01 -2.2 1.13e+04 - 1.00e+00 6.15e-01f 1\n",
|
||
" 68 1.3700675e+01 6.73e-01 7.63e-01 -4.1 3.15e+03 - 2.89e-01 1.00e+00h 1\n",
|
||
" 69 1.3419916e+01 3.14e+00 2.24e-01 -1.6 2.30e+04 - 1.66e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.1041114e+01 2.16e+00 7.65e-02 -1.8 1.08e+06 - 7.15e-02 3.60e-02f 1\n",
|
||
" 71 1.3368419e+01 2.45e+00 1.14e-01 -1.8 2.12e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.3351610e+01 1.71e+00 6.50e-02 -1.8 1.42e+04 - 4.78e-01 1.00e+00h 1\n",
|
||
" 73 1.2663032e+01 1.93e+00 9.54e-02 -1.8 4.10e+04 - 8.43e-01 1.06e-01f 1\n",
|
||
" 74 1.3651082e+01 4.18e-01 9.32e-02 -1.6 5.49e+03 - 6.03e-01 1.00e+00h 1\n",
|
||
" 75 1.3157659e+01 4.06e-01 5.06e-02 -1.9 1.30e+04 - 1.00e+00 2.33e-01f 1\n",
|
||
" 76 1.3839786e+01 7.74e-02 8.56e-02 -7.9 3.54e+03 - 6.84e-01 1.00e+00H 1\n",
|
||
" 77 1.3561598e+01 1.63e-01 3.03e-02 -8.5 1.13e+03 - 4.46e-01 1.00e+00h 1\n",
|
||
" 78 1.1582546e+01 1.72e+00 1.40e-01 -3.0 2.06e+04 - 7.64e-01 5.56e-01f 1\n",
|
||
" 79 1.3371471e+01 2.25e-01 1.59e-01 -2.1 3.88e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.3215911e+01 3.31e-01 4.58e-02 -2.5 2.14e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.3552057e+01 5.80e-02 9.36e-03 -4.1 5.49e+02 - 9.99e-01 1.00e+00h 1\n",
|
||
" 82 1.3027712e+01 5.22e-01 1.07e-01 -3.1 5.17e+03 - 1.00e+00 9.94e-01f 1\n",
|
||
" 83 9.6634661e+00 3.94e+00 1.11e-01 -2.7 7.07e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 84 1.1161415e+01 1.56e+00 2.91e-01 -1.1 1.59e+04 - 1.00e+00 6.58e-01h 1\n",
|
||
" 85 1.1756667e+01 8.40e-01 1.13e-01 -1.3 7.27e+03 - 1.82e-01 1.00e+00h 1\n",
|
||
" 86 1.2192260e+01 2.21e+00 1.85e-01 0.7 5.71e+06 - 3.90e-02 4.93e-03f 2\n",
|
||
" 87 1.1402000e+01 8.34e-01 5.83e-02 -0.6 4.48e+04 - 5.89e-01 7.57e-01f 1\n",
|
||
" 88 1.1399186e+01 1.17e+00 9.70e-02 -1.3 4.61e+03 - 8.65e-01 1.00e+00h 1\n",
|
||
" 89 1.3292981e+01 1.26e-01 8.51e-02 -7.3 1.37e+03 - 8.33e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.3086048e+01 2.39e-01 1.13e-01 -2.1 1.71e+03 - 1.00e+00 9.64e-01h 1\n",
|
||
" 91 1.0750160e+01 3.03e+00 2.41e-01 -8.2 5.96e+04 - 1.16e-01 2.29e-01f 1\n",
|
||
" 92 1.5588416e+01 1.05e+00 3.70e-01 -2.4 1.94e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.2833458e+01 8.93e-01 2.80e-01 -2.4 9.98e+03 - 1.00e+00 5.34e-01f 1\n",
|
||
" 94 1.2589982e+01 2.31e-01 8.57e-02 -2.3 8.60e+03 - 6.39e-01 7.50e-01h 1\n",
|
||
" 95 1.2439730e+01 2.34e-01 2.70e-02 -2.7 1.90e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.2978654e+01 1.89e-02 1.56e-02 -2.4 5.58e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.3021121e+01 7.09e-03 1.91e-02 -8.4 1.82e+03 - 3.74e-01 1.00e+00H 1\n",
|
||
" 98 1.2306492e+01 6.80e-01 8.84e-02 -2.6 1.73e+04 - 6.23e-02 1.00e+00f 1\n",
|
||
" 99 1.2597026e+01 7.07e-01 5.22e-02 -2.1 9.26e+03 - 1.00e+00 9.84e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.2233468e+01 5.64e-01 5.64e-02 -2.2 7.53e+04 - 1.27e-01 2.08e-01h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.2233467737806393e+01 1.2233467737806393e+01\n",
|
||
"Dual infeasibility......: 5.6369956286998812e-02 5.6369956286998812e-02\n",
|
||
"Constraint violation....: 5.6403398976443953e-01 5.6403398976443953e-01\n",
|
||
"Complementarity.........: 1.0841644476417524e-02 1.0841644476417524e-02\n",
|
||
"Overall NLP error.......: 5.6403398976443953e-01 5.6403398976443953e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 113\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 113\n",
|
||
"Number of inequality constraint evaluations = 113\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.617\n",
|
||
"Total CPU secs in NLP function evaluations = 140.807\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 580.00us ( 5.13us) 572.33us ( 5.06us) 113\n",
|
||
" nlp_g | 5.42 s ( 47.95ms) 5.20 s ( 46.05ms) 113\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 376.00us ( 3.69us) 373.93us ( 3.67us) 102\n",
|
||
" nlp_jac_g | 138.63 s ( 1.36 s) 133.21 s ( 1.31 s) 102\n",
|
||
" total | 145.56 s (145.56 s) 140.01 s (140.01 s) 1\n",
|
||
"Timestamp 23400\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.94e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0208026e+01 1.22e+01 1.94e+04 -1.5 1.94e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.1953208e+00 4.13e+00 6.48e+00 0.8 2.23e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.5910889e+00 5.80e-01 6.52e-01 -1.3 6.37e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 1.6266972e+00 3.33e-03 5.94e-01 -3.1 2.37e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.6270488e+00 2.91e-04 1.14e-02 -4.9 3.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.6272711e+00 4.26e-05 4.94e-04 -6.8 1.10e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.6272598e+00 6.72e-05 1.57e-03 -8.9 1.93e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.5272985e+00 2.13e-01 1.90e-01 -9.6 6.32e+03 - 9.84e-01 1.00e+00F 1\n",
|
||
" 9 1.4954789e+00 4.77e-01 2.96e-01 -11.0 2.36e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.4766271e+00 4.56e-01 3.91e-01 -11.0 1.74e+04 - 1.00e+00 6.25e-02h 5\n",
|
||
" 11 1.5157551e+00 5.61e-02 4.23e-01 -11.0 1.44e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.4717083e+00 2.30e-01 2.85e-01 -11.0 4.03e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 13 1.4805996e+00 2.28e-01 5.85e-02 -11.0 2.99e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.4755301e+00 1.02e-01 9.23e-02 -11.0 7.16e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.4728986e+00 2.08e-01 1.04e-01 -11.0 5.71e+02 - 1.00e+00 5.00e-01h 2\n",
|
||
" 16 1.4691146e+00 1.65e-01 6.06e-02 -11.0 1.06e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 17 1.4696610e+00 2.15e-01 6.78e-02 -11.0 1.44e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 18 1.4696870e+00 1.46e-01 3.67e-02 -9.0 2.06e+05 - 1.00e+00 8.88e-03h 5\n",
|
||
" 19r 1.4696870e+00 1.46e-01 9.99e+02 -0.8 0.00e+00 - 0.00e+00 4.77e-07R 22\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20r 1.4891337e+00 2.64e-02 3.76e+02 -6.9 1.35e+02 - 1.00e+00 1.50e-03f 1\n",
|
||
" 21 1.4982055e+00 4.97e-02 1.59e-01 -11.0 2.66e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 22 1.4778222e+00 1.19e-01 3.24e-02 -11.0 3.58e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.4775511e+00 1.76e-01 4.87e-02 -10.2 1.55e+05 - 1.00e+00 3.42e-03h 7\n",
|
||
" 24 1.4774479e+00 1.85e-01 5.09e-02 -10.4 2.75e+03 - 1.00e+00 3.12e-02h 6\n",
|
||
" 25 1.5872463e+00 1.29e-01 3.00e-01 -10.4 9.51e+04 - 2.87e-01 1.08e-02h 6\n",
|
||
" 26 1.4765614e+00 2.30e-01 2.45e-01 -10.4 3.64e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 27 1.4754451e+00 1.33e-01 1.92e-01 -10.4 7.19e+02 - 1.00e+00 5.00e-01h 2\n",
|
||
" 28 1.4819730e+00 1.24e-01 5.17e-02 -10.4 4.09e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.4807379e+00 8.10e-02 8.65e-02 -10.4 2.11e+06 - 1.00e-02 9.96e-03h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.4756249e+00 2.65e-01 7.95e-02 -11.0 5.41e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 31 1.4689177e+00 1.96e-01 1.13e-01 -11.0 3.86e+06 - 9.82e-03 1.71e-04f 6\n",
|
||
" 32 1.5100393e+00 2.22e-01 2.09e-01 -8.9 9.18e+05 - 1.00e+00 1.15e-02h 3\n",
|
||
" 33 1.4863615e+00 2.81e-01 1.40e-01 -9.1 1.90e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 1.4445097e+00 4.91e-01 1.36e-01 -9.1 4.38e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.5542334e+00 7.81e-01 2.69e-01 -9.7 3.39e+03 - 8.47e-01 1.00e+00H 1\n",
|
||
" 36 1.5486406e+00 4.62e-01 2.79e-01 -9.7 1.90e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.5282929e+00 4.81e-01 1.96e-01 -9.7 2.94e+04 - 7.06e-01 1.00e+00h 1\n",
|
||
" 38 1.4514496e+00 2.87e-01 1.02e-01 -9.7 4.43e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.4653898e+00 2.04e-01 5.20e-02 -9.9 8.15e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.4747610e+00 2.60e-01 6.98e-02 -11.0 3.66e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.4607220e+00 3.47e-01 1.49e-01 -11.0 6.41e+06 - 5.58e-03 1.06e-03f 3\n",
|
||
" 42 1.4347488e+00 8.39e-01 4.48e-01 -11.0 6.40e+04 - 3.36e-01 3.03e-01h 2\n",
|
||
" 43 1.5342613e+00 5.49e-01 2.38e-01 -10.9 2.84e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.5065271e+00 4.26e-01 2.07e-01 -11.0 1.13e+08 - 4.24e-04 1.35e-04f 1\n",
|
||
" 45 1.5174341e+00 2.77e-01 1.92e-01 -11.0 1.47e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.4654539e+00 2.78e-01 1.15e-01 -11.0 2.08e+03 - 8.94e-01 1.00e+00h 1\n",
|
||
" 47 1.4560910e+00 3.66e-01 6.23e-02 -11.0 7.16e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 48 1.4623140e+00 3.78e-01 5.60e-02 -11.0 1.41e+04 - 1.18e-01 1.25e-01h 4\n",
|
||
" 49 1.4511614e+00 5.69e-01 1.21e-01 -11.0 1.64e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.4544927e+00 5.39e-01 1.16e-01 -11.0 1.27e+04 - 6.79e-01 1.25e-01h 4\n",
|
||
" 51 1.5564406e+00 1.80e-01 2.66e-01 -11.0 1.45e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.5480071e+00 2.13e-01 2.43e-01 -11.0 5.47e+04 - 5.18e-01 6.20e-02h 4\n",
|
||
" 53 1.5431354e+00 2.31e-01 2.30e-01 -11.0 3.50e+04 - 1.00e+00 1.06e-02h 7\n",
|
||
" 54 1.5438701e+00 2.61e-01 1.70e-01 -11.0 5.32e+04 - 3.35e-01 7.45e-01h 1\n",
|
||
" 55 1.5079567e+00 2.68e-01 1.14e-01 -11.0 4.20e+03 - 1.69e-09 2.50e-01h 3\n",
|
||
" 56 1.5038952e+00 2.62e-01 9.26e-02 -11.0 2.58e+04 - 1.00e+00 4.06e-02h 1\n",
|
||
" 57 1.6781106e+00 7.99e-02 2.43e-01 -11.0 3.04e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 58 1.5614090e+00 3.00e-01 9.18e-02 -11.0 2.03e+03 - 3.94e-01 1.00e+00F 1\n",
|
||
" 59 1.5610025e+00 3.03e-01 8.77e-02 -11.0 4.53e+03 - 1.00e+00 5.85e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.6211858e+00 6.79e-02 7.18e-02 -11.0 1.36e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.6184483e+00 1.21e-01 5.93e-02 -11.0 1.10e+03 - 1.00e+00 5.08e-01h 1\n",
|
||
" 62 1.5943728e+00 1.65e-01 2.53e-01 -11.0 1.31e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 63 1.5968052e+00 2.89e-02 7.38e-02 -11.0 1.64e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"In iteration 63, 1 Slack too small, adjusting variable bound\n",
|
||
" 64 1.5696596e+00 9.72e-02 4.08e-02 -11.0 9.85e+03 - 1.00e+00 1.21e-01h 1\n",
|
||
" 65 1.6410722e+00 2.63e-03 1.41e-01 -11.0 1.49e+03 - 9.33e-01 1.00e+00H 1\n",
|
||
" 66 1.6399730e+00 6.21e-03 1.05e-02 -11.0 1.71e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 1.6512398e+00 2.73e-03 5.20e-02 -11.0 2.67e+03 - 4.45e-01 1.00e+00H 1\n",
|
||
" 68 1.6429954e+00 7.94e-04 3.83e-02 -11.0 3.88e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 69 1.6428591e+00 3.29e-03 4.03e-02 -11.0 9.34e+04 - 4.33e-01 2.35e-04h 8\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.6448319e+00 1.81e-04 2.18e-02 -11.0 7.39e+02 - 9.75e-01 1.00e+00H 1\n",
|
||
" 71 1.5949589e+00 4.62e-02 5.51e-02 -11.0 8.17e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 72 1.5929967e+00 7.20e-02 6.81e-02 -11.0 1.38e+04 - 1.00e+00 3.16e-03h 6\n",
|
||
" 73 1.6422555e+00 2.48e-03 5.44e-02 -11.0 6.42e+02 - 7.57e-01 1.00e+00H 1\n",
|
||
" 74 1.5545007e+00 3.00e-01 1.38e-01 -11.0 3.73e+08 - 1.73e-04 5.13e-06f 6\n",
|
||
" 75 1.5413609e+00 2.84e-01 1.44e-01 -11.0 9.74e+03 - 1.00e+00 6.25e-02h 5\n",
|
||
" 76 1.5409782e+00 2.84e-01 1.46e-01 -11.0 3.73e+04 - 1.15e-01 4.88e-04h 12\n",
|
||
" 77 1.5121845e+00 3.57e-01 3.39e-01 -11.0 7.74e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 78 1.6248689e+00 4.69e-03 4.86e-01 -11.0 7.16e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.6262989e+00 6.99e-08 5.77e-05 -11.0 5.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.6262989e+00 2.54e-08 1.61e-05 -11.0 1.53e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.6262989e+00 3.46e-09 4.82e-05 -11.0 3.28e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.6262989e+00 3.77e-09 3.56e-05 -11.0 4.24e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.6262989e+00 7.48e-09 4.67e-05 -11.0 3.88e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.6262989e+00 4.97e-09 4.54e-05 -11.0 9.35e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.6262989e+00 1.23e-08 3.68e-05 -11.0 6.40e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.6262989e+00 7.25e-08 3.07e-05 -11.0 4.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.6262989e+00 3.35e-08 6.51e-05 -11.0 1.10e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.6262989e+00 1.63e-07 6.96e-05 -11.0 8.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.5354076e+00 2.56e-02 4.54e-01 -11.0 1.36e+04 - 4.51e-01 1.00e+00F 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.5541206e+00 9.73e-05 2.92e-02 -11.0 4.52e-01 -4.0 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.5527356e+00 5.03e-03 1.93e-02 -11.0 1.87e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.5538452e+00 2.99e-04 2.21e-03 -11.0 1.57e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.5531948e+00 3.86e-03 1.15e-03 -11.0 3.56e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.5512112e+00 1.81e-02 9.84e-03 -11.0 2.63e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.5468995e+00 1.72e-02 9.14e-03 -11.0 2.17e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.5537483e+00 1.16e-03 1.27e-02 -11.0 4.69e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.5534890e+00 5.07e-03 5.16e-03 -11.0 3.67e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.5438776e+00 1.75e-01 6.36e-02 -11.0 8.15e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.5348564e+00 5.07e-01 3.00e-01 -11.0 4.72e+04 - 9.95e-01 1.69e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.3926150e+00 1.87e-01 1.79e-01 -11.0 1.44e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.3926150226958480e+00 1.3926150226958480e+00\n",
|
||
"Dual infeasibility......: 1.7863531850547662e-01 1.7863531850547662e-01\n",
|
||
"Constraint violation....: 1.8699545397062423e-01 1.8699545397062423e-01\n",
|
||
"Complementarity.........: 1.1098949964310718e-11 1.1098949964310718e-11\n",
|
||
"Overall NLP error.......: 1.8699545397062423e-01 1.8699545397062423e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 299\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 299\n",
|
||
"Number of inequality constraint evaluations = 299\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.436\n",
|
||
"Total CPU secs in NLP function evaluations = 151.063\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 1.50ms ( 5.02us) 1.51ms ( 5.06us) 299\n",
|
||
" nlp_g | 14.27 s ( 47.74ms) 14.56 s ( 48.70ms) 299\n",
|
||
" nlp_grad | 1.48 s ( 1.48 s) 1.46 s ( 1.46 s) 1\n",
|
||
" nlp_grad_f | 453.00us ( 4.44us) 404.01us ( 3.96us) 102\n",
|
||
" nlp_jac_g | 140.23 s ( 1.36 s) 140.45 s ( 1.36 s) 103\n",
|
||
" total | 156.17 s (156.17 s) 156.67 s (156.67 s) 1\n",
|
||
"Timestamp 23700\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.44e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9837970e+01 1.38e+01 3.44e+03 -1.5 3.44e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.5171973e+00 4.95e+00 9.20e+00 0.4 1.38e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 8.4907839e+00 1.26e+00 6.46e-01 -1.6 7.88e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 9.4246435e+00 2.61e-03 8.28e-02 -3.4 1.80e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 9.4258942e+00 2.37e-07 8.66e-05 -5.3 2.62e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 9.4258944e+00 1.25e-07 1.64e-05 -11.0 8.35e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 9.4258946e+00 1.18e-08 8.97e-05 -11.0 2.19e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 9.4258946e+00 5.65e-08 5.62e-05 -11.0 2.48e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 9.4258939e+00 3.03e-07 2.06e-05 -11.0 2.14e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 9.4258945e+00 4.82e-08 5.12e-05 -11.0 7.11e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 9.4258944e+00 1.05e-07 8.53e-05 -11.0 5.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 9.4258945e+00 4.01e-08 8.52e-05 -11.0 1.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 9.4258945e+00 5.28e-08 8.89e-05 -11.0 3.84e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 9.4258945e+00 2.45e-08 3.88e-05 -11.0 1.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 9.4258944e+00 9.56e-08 1.33e-04 -11.0 1.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 9.4258942e+00 1.20e-07 3.04e-05 -11.0 3.60e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 9.4258945e+00 9.48e-08 1.10e-04 -11.0 5.55e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 9.4258945e+00 3.15e-08 3.79e-05 -11.0 2.66e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 9.4258945e+00 2.23e-08 1.03e-04 -11.0 3.99e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 9.4258945e+00 6.84e-09 8.24e-05 -11.0 9.32e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 9.4258945e+00 5.79e-08 2.19e-05 -11.0 4.99e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 9.4258945e+00 2.67e-08 8.47e-05 -11.0 5.94e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 9.4258944e+00 2.88e-07 2.76e-05 -11.0 6.32e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 9.4258944e+00 5.37e-08 5.75e-05 -11.0 8.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 9.4258945e+00 3.48e-08 5.02e-05 -11.0 2.93e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 9.4258629e+00 4.17e-05 2.40e-02 -11.0 1.93e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 9.4258720e+00 1.14e-05 1.50e-03 -11.0 2.12e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 9.4256935e+00 1.17e-04 3.02e-03 -11.0 7.68e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 9.4258595e+00 1.30e-04 1.10e-03 -11.0 2.68e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 9.4258135e+00 1.00e-04 1.16e-03 -11.0 2.58e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 9.4240542e+00 1.43e-03 6.46e-03 -11.0 2.78e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 9.4073536e+00 2.58e-02 2.04e-03 -11.0 3.79e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 9.4036567e+00 7.88e-03 1.60e-03 -11.0 4.18e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 9.4238552e+00 5.49e-03 1.58e-03 -11.0 8.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 9.4040107e+00 1.19e-02 1.23e-03 -11.0 1.87e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 8.6556411e+00 9.42e-01 9.33e-02 -11.0 3.31e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 37 8.9242093e+00 9.94e-01 4.96e-02 -11.0 1.78e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 7.5606480e+00 4.62e+00 8.35e-01 -10.2 1.68e+05 - 1.00e+00 9.70e-02f 2\n",
|
||
" 39 6.9783568e+00 2.89e+00 6.84e-01 -8.3 4.57e+04 - 1.00e+00 3.59e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.5478323e+01 3.63e+00 1.12e+00 -8.0 2.67e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.0421374e+01 1.03e+00 1.00e+00 -7.5 7.12e+05 - 2.98e-02 5.08e-02f 1\n",
|
||
" 42 8.5636611e+00 4.10e+00 3.67e-01 -7.5 2.40e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 43 7.4426435e+00 4.64e+00 7.97e-01 -7.5 8.73e+04 - 2.28e-01 4.07e-01F 1\n",
|
||
" 44 7.4426433e+00 4.64e+00 7.97e-01 -7.5 2.97e+05 - 1.87e-01 3.42e-09h 2\n",
|
||
" 45 7.5992901e+00 1.16e+00 5.49e-01 -7.5 6.75e+03 - 1.00e+00 8.69e-01h 1\n",
|
||
" 46 5.5782805e+00 2.19e+00 4.61e-01 -6.9 6.76e+03 - 2.32e-01 1.00e+00f 1\n",
|
||
" 47 8.2350091e+00 8.60e-01 3.81e-01 -2.3 1.45e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 48 6.7906621e+00 1.07e+00 3.35e-01 -2.2 5.92e+03 - 7.47e-01 1.00e+00f 1\n",
|
||
" 49 6.0327136e+00 2.84e+00 2.05e-01 -2.3 2.51e+04 - 1.00e+00 2.29e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 6.0455831e+00 2.79e+00 1.95e-01 -2.5 1.75e+04 - 6.46e-01 2.45e-02h 1\n",
|
||
" 51 7.9991903e+00 1.18e+00 4.33e-01 -2.5 1.49e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 7.5597941e+00 2.00e+00 1.62e-01 -1.9 1.48e+04 - 4.38e-02 9.88e-01f 1\n",
|
||
" 53 7.5575942e+00 1.99e+00 1.62e-01 -2.6 1.42e+04 - 1.00e+00 8.77e-03h 1\n",
|
||
" 54 8.6642218e+00 1.52e-01 5.93e-02 -2.6 7.47e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 8.6984012e+00 1.55e-01 7.91e-02 -8.6 1.33e+03 - 2.66e-01 1.00e+00h 1\n",
|
||
" 56 7.9200424e+00 4.21e-01 8.17e-02 -2.2 2.68e+04 - 1.00e+00 9.87e-02f 1\n",
|
||
" 57 8.7303802e+00 2.42e-02 4.59e-02 -2.4 1.13e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 8.6350719e+00 1.79e-01 4.39e-02 -8.5 2.49e+03 - 6.95e-01 1.00e+00h 1\n",
|
||
" 59 8.4827813e+00 1.00e-01 1.86e-02 -9.0 1.51e+04 - 5.10e-03 4.42e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 8.5159480e+00 1.01e-01 1.69e-02 -1.9 7.03e+04 - 1.00e+00 3.64e-02f 3\n",
|
||
" 61 7.5295769e+00 5.14e-01 2.70e-02 -1.9 2.86e+03 - 1.51e-01 1.00e+00f 1\n",
|
||
" 62 8.6509663e+00 8.10e-02 3.53e-02 -7.9 8.51e+02 - 7.78e-01 1.00e+00h 1\n",
|
||
" 63 8.5355651e+00 1.37e-01 2.49e-02 -2.6 2.03e+04 - 7.09e-01 1.99e-02f 1\n",
|
||
" 64 8.7378302e+00 3.49e-02 1.21e-02 -8.7 1.86e+02 - 3.15e-01 1.00e+00h 1\n",
|
||
" 65 8.4345975e+00 8.65e-01 1.14e-01 -3.1 6.69e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 66 8.2792433e+00 7.75e-01 1.10e-01 -3.2 8.92e+03 - 5.64e-01 1.00e+00h 1\n",
|
||
" 67 8.3363188e+00 3.38e-01 3.49e-02 -3.2 6.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 8.5553237e+00 1.22e-01 4.60e-02 -4.0 1.62e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 8.5048577e+00 1.10e-01 3.77e-02 -4.1 8.62e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.1449210e+00 6.37e-01 1.08e-01 -4.2 2.86e+04 - 1.00e+00 4.00e-01f 1\n",
|
||
" 71 8.6433309e+00 1.46e-01 9.83e-02 -4.2 8.03e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 8.8010819e+00 2.76e-04 1.87e-01 -4.2 2.01e-01 - 9.91e-01 1.00e+00h 1\n",
|
||
" 73 8.8012139e+00 6.17e-07 7.95e-05 -5.3 2.95e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 8.8012055e+00 3.12e-06 7.64e-03 -11.0 2.88e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 8.8012141e+00 1.21e-09 8.17e-05 -11.0 1.07e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 76 8.8012115e+00 2.09e-06 7.64e-04 -11.0 6.75e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 8.8012139e+00 3.48e-10 1.61e-04 -11.0 7.15e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 78 8.8012046e+00 7.14e-06 7.60e-03 -11.0 3.96e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 8.8012111e+00 1.09e-06 1.73e-03 -11.0 1.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 8.8009363e+00 2.80e-04 5.32e-03 -11.0 2.35e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 8.8007518e+00 3.25e-04 1.79e-03 -11.0 3.92e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 8.7999043e+00 9.36e-04 9.14e-04 -11.0 3.28e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 8.8002980e+00 7.18e-04 1.54e-03 -11.0 2.05e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 8.8000422e+00 6.90e-04 3.01e-03 -11.0 2.55e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 8.8002711e+00 7.23e-04 1.89e-03 -11.0 4.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 8.8008595e+00 2.34e-04 2.42e-03 -11.0 7.95e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 8.8004835e+00 6.48e-04 1.20e-03 -11.0 5.86e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 8.6925850e+00 6.75e-02 1.30e-02 -11.0 1.13e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 89 8.5831219e+00 4.31e-01 3.92e-02 -11.0 8.84e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 8.7864051e+00 2.68e-02 3.59e-02 -11.0 1.27e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 8.7445591e+00 5.47e-02 2.26e-02 -11.0 4.06e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 7.7527381e+00 1.75e+00 1.53e-01 -11.0 1.53e+05 - 1.79e-02 7.76e-02f 1\n",
|
||
" 93 8.6299101e+00 7.04e-02 1.02e-01 -11.0 1.71e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 8.2780756e+00 2.41e-01 1.48e-01 -1.9 3.34e+03 - 7.07e-01 1.00e+00f 1\n",
|
||
" 95 8.7667294e+00 2.11e-02 3.20e-02 -2.5 2.45e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 96 8.5407110e+00 1.87e-01 9.53e-03 -3.9 1.62e+03 - 9.97e-01 1.00e+00f 1\n",
|
||
" 97 7.3318572e+00 1.78e+00 2.93e-01 -3.0 2.51e+04 - 7.58e-01 1.00e+00f 1\n",
|
||
" 98 7.1457586e+00 2.73e+00 2.97e-01 -3.3 7.14e+05 - 1.20e-01 3.78e-02f 1\n",
|
||
" 99 7.6527739e+00 1.80e+00 2.78e-01 -3.3 3.46e+04 - 6.17e-03 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 7.5916654e+00 1.70e+00 2.10e-01 -3.3 3.19e+04 - 6.51e-01 3.19e-01h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 7.5916654389371487e+00 7.5916654389371487e+00\n",
|
||
"Dual infeasibility......: 2.1029543365471748e-01 2.1029543365471748e-01\n",
|
||
"Constraint violation....: 1.6970756385990349e+00 1.6970756385990349e+00\n",
|
||
"Complementarity.........: 9.0269724144513433e-04 9.0269724144513433e-04\n",
|
||
"Overall NLP error.......: 1.6970756385990349e+00 1.6970756385990349e+00\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 115\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 115\n",
|
||
"Number of inequality constraint evaluations = 115\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.437\n",
|
||
"Total CPU secs in NLP function evaluations = 139.743\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 570.00us ( 4.96us) 558.10us ( 4.85us) 115\n",
|
||
" nlp_g | 5.37 s ( 46.66ms) 5.15 s ( 44.82ms) 115\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 410.00us ( 4.02us) 402.64us ( 3.95us) 102\n",
|
||
" nlp_jac_g | 137.34 s ( 1.35 s) 132.13 s ( 1.30 s) 102\n",
|
||
" total | 144.19 s (144.19 s) 138.71 s (138.71 s) 1\n",
|
||
"Timestamp 24000\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.15e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0518905e+01 1.13e+01 3.15e+04 -1.5 3.15e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 9.3539179e+00 4.11e+00 4.42e+00 1.3 1.50e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.9190119e+00 4.38e-01 2.08e-01 -0.8 5.26e+02 - 9.96e-01 1.00e+00f 1\n",
|
||
" 4 2.3405569e+00 6.76e-03 2.17e-01 -6.5 3.68e+01 - 9.89e-01 1.00e+00h 1\n",
|
||
" 5 2.3378370e+00 5.60e-03 4.70e-02 -4.0 2.41e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.3436282e+00 2.68e-04 1.14e-03 -5.9 6.19e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.3437253e+00 1.76e-04 6.05e-03 -8.0 5.68e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.3426666e+00 1.91e-03 3.17e-03 -10.0 4.86e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.3425208e+00 2.65e-03 1.47e-02 -11.0 2.03e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.3351259e+00 6.49e-03 3.24e-02 -11.0 4.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.3438107e+00 5.70e-04 5.58e-03 -11.0 3.75e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.3414538e+00 3.04e-03 1.63e-02 -11.0 2.74e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.3451207e+00 1.04e-05 2.08e-03 -11.0 3.62e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 14 2.3366032e+00 4.75e-03 1.42e-03 -11.0 2.26e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 15 2.3449449e+00 4.63e-05 2.16e-03 -11.0 4.44e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 16 2.3431067e+00 7.27e-04 2.08e-03 -11.0 1.07e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.3349046e+00 6.88e-03 1.53e-03 -11.0 2.01e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.3443246e+00 4.62e-04 2.09e-03 -11.0 7.01e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.3428880e+00 2.74e-03 1.17e-03 -11.0 2.01e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.2849422e+00 4.01e-02 1.70e-02 -11.0 1.16e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.2781858e+00 4.88e-02 8.47e-03 -11.0 7.61e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.3387984e+00 5.12e-03 1.16e-02 -11.0 3.76e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.4178423e+00 1.64e+00 7.65e-01 -11.0 1.41e+05 - 2.23e-01 5.64e-02f 3\n",
|
||
" 24 2.4107234e+00 7.30e-01 7.10e-01 -11.0 7.15e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.8916926e+00 1.56e+00 5.20e-01 -11.0 1.06e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 26 1.8405201e+00 1.83e+00 8.76e-01 -10.8 1.32e+04 - 1.00e+00 5.00e-01f 2\n",
|
||
" 27 2.2219880e+00 9.82e-01 3.32e-01 -10.9 8.42e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.8444467e+00 1.12e+00 5.00e-01 -11.0 1.27e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 29 1.8345181e+00 1.35e+00 2.79e-01 -9.1 9.35e+05 - 1.00e+00 1.76e-03f 6\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.9001357e+00 1.18e+00 5.30e-01 -9.3 2.07e+04 - 6.33e-01 1.00e+00h 1\n",
|
||
" 31 2.2648052e+00 1.80e+00 3.25e-01 -9.3 2.76e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 32 2.2318527e+00 1.93e+00 2.83e-01 -9.3 1.61e+05 - 1.00e+00 7.24e-02h 2\n",
|
||
" 33 2.9739855e+00 4.54e-01 4.67e-01 -9.3 8.32e+04 - 1.29e-01 6.18e-01h 1\n",
|
||
" 34 1.7974366e+00 8.90e-01 5.22e-01 -9.3 1.56e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 35 1.5545404e+00 5.39e-01 6.47e-01 -2.7 1.79e+04 - 9.08e-01 1.00e+00h 1\n",
|
||
" 36 1.6206676e+00 5.14e-01 3.88e-01 -3.2 4.92e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 37 1.6508422e+00 5.07e-01 1.91e-01 -4.3 2.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.1479784e+00 2.80e-01 5.89e-01 -2.3 2.26e+04 - 1.00e+00 5.60e-01H 1\n",
|
||
" 39 1.6895561e+00 2.22e-01 4.73e-01 -2.5 9.14e+03 - 9.55e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.6784868e+00 3.92e-01 1.20e-01 -2.1 2.68e+03 - 8.39e-01 1.00e+00h 1\n",
|
||
" 41 1.5514113e+00 7.76e-01 3.12e-01 -1.1 1.65e+06 - 2.99e-02 1.79e-03f 4\n",
|
||
" 42 1.6799830e+00 7.67e-01 1.51e-01 -2.9 4.17e+03 - 9.97e-01 1.00e+00h 1\n",
|
||
" 43 1.6446073e+00 4.13e-01 1.88e-01 -2.9 1.02e+05 - 3.66e-01 3.84e-02h 2\n",
|
||
" 44 1.5810223e+00 5.35e-01 3.23e-01 -2.9 4.06e+04 - 1.08e-01 3.14e-01h 2\n",
|
||
" 45 1.6081326e+00 3.01e-01 7.97e-02 -2.9 5.08e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 46 1.6326123e+00 2.10e-01 1.64e-01 -2.6 1.24e+04 - 1.00e+00 9.76e-01h 1\n",
|
||
" 47 1.5289034e+00 2.12e-01 2.07e-01 -3.6 4.64e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.4706005e+00 5.03e-01 9.51e-02 -1.9 1.57e+04 - 8.64e-01 1.00e+00h 1\n",
|
||
" 49 1.6514670e+00 4.63e-01 1.58e-01 -2.9 6.44e+03 - 9.91e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.6348999e+00 2.80e-01 1.66e-01 -3.0 1.46e+05 - 2.40e-01 1.56e-01h 1\n",
|
||
" 51 1.8296992e+00 1.51e-01 2.10e-01 -3.0 5.32e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.6304149e+00 5.91e-01 3.11e-01 -1.6 1.52e+04 - 4.30e-01 5.51e-01h 1\n",
|
||
" 53 1.5363797e+00 3.87e-01 1.26e-01 -2.2 7.10e+04 - 2.26e-02 1.67e-01h 1\n",
|
||
" 54 1.5515123e+00 3.62e-01 1.38e-01 -2.2 8.46e+04 - 8.77e-01 1.71e-02f 4\n",
|
||
" 55 1.5455867e+00 4.25e-01 1.08e-01 -2.2 4.48e+03 - 8.17e-01 1.47e-01h 2\n",
|
||
" 56 1.5177568e+00 3.84e-01 8.11e-02 -2.2 2.07e+04 - 2.48e-01 1.66e-01h 1\n",
|
||
" 57 1.7110979e+00 2.63e-03 3.54e-01 -4.1 3.54e-01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 58 1.7102230e+00 1.48e-06 1.22e-03 -6.0 3.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.7102228e+00 2.13e-07 1.79e-05 -8.1 5.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.7102231e+00 4.55e-09 8.72e-05 -11.0 7.99e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.7102229e+00 7.00e-08 1.78e-05 -11.0 3.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.7102231e+00 1.86e-08 5.97e-05 -11.0 1.12e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.7102230e+00 1.42e-07 1.17e-04 -11.0 2.52e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.7102230e+00 1.26e-07 4.07e-05 -11.0 4.40e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.7102230e+00 7.23e-08 5.75e-05 -11.0 1.97e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.7102230e+00 4.38e-08 4.05e-05 -11.0 2.94e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.7102215e+00 6.22e-06 6.11e-03 -11.0 1.97e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.7102229e+00 2.95e-07 7.19e-05 -11.0 3.65e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.7102120e+00 8.45e-06 3.51e-03 -11.0 5.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.7102226e+00 1.58e-07 4.69e-05 -11.0 5.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.7102228e+00 9.48e-11 3.02e-05 -11.0 3.74e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 72 1.7102228e+00 1.02e-07 2.84e-05 -11.0 3.54e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.7102224e+00 2.86e-06 1.41e-03 -11.0 3.70e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.7101961e+00 2.50e-05 6.98e-03 -11.0 2.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.7102248e+00 4.26e-08 3.53e-05 -11.0 1.31e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 76 1.7099504e+00 1.29e-03 1.19e-03 -11.0 5.64e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.7101507e+00 2.98e-04 1.40e-03 -11.0 2.30e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.7101756e+00 2.28e-04 2.05e-03 -11.0 8.83e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.7102041e+00 7.48e-05 1.33e-03 -11.0 1.23e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.7094210e+00 5.22e-03 6.27e-03 -11.0 1.22e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.7087800e+00 1.46e-02 5.28e-03 -9.0 6.60e+01 - 1.00e+00 5.14e-01h 1\n",
|
||
" 82 1.7087728e+00 1.46e-02 5.26e-03 -7.1 8.30e+01 - 1.00e+00 3.40e-03h 1\n",
|
||
" 83 1.6987601e+00 3.10e-01 1.97e-01 -6.7 1.42e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.6926585e+00 1.05e-01 6.05e-02 -5.9 1.21e+03 - 9.71e-01 1.00e+00h 1\n",
|
||
" 85 1.6519824e+00 2.54e-01 5.81e-02 -4.0 1.98e+03 - 5.38e-01 1.00e+00h 1\n",
|
||
" 86 1.6552332e+00 2.87e-01 2.91e-02 -2.8 8.21e+04 - 1.00e+00 9.45e-03h 7\n",
|
||
" 87 1.7040471e+00 3.01e-02 1.26e-01 -2.9 2.22e+02 - 9.42e-01 1.00e+00h 1\n",
|
||
" 88 1.6935263e+00 1.71e-01 2.78e-02 -4.2 1.76e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.6882314e+00 2.17e-01 2.23e-02 -4.1 2.60e+04 - 2.56e-01 3.07e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.5745908e+00 3.69e-01 2.69e-01 -2.7 1.31e+04 - 1.00e+00 3.96e-01f 1\n",
|
||
" 91 1.6355017e+00 3.37e-01 1.46e-01 -2.6 1.68e+03 - 1.00e+00 6.84e-01H 1\n",
|
||
" 92 1.5653537e+00 1.54e-01 8.62e-02 -2.3 1.99e+03 - 6.57e-01 1.00e+00h 1\n",
|
||
" 93 1.6054045e+00 9.77e-02 5.43e-02 -3.5 1.56e+03 - 4.30e-01 4.35e-01H 1\n",
|
||
" 94 1.6124975e+00 1.44e-01 5.94e-02 -3.3 1.79e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 95r 1.6124975e+00 1.44e-01 9.99e+02 -0.8 0.00e+00 - 0.00e+00 3.89e-07R 20\n",
|
||
" 96r 1.6653905e+00 4.05e-02 4.27e+02 -3.0 1.25e+02 - 1.00e+00 1.55e-03f 1\n",
|
||
" 97 1.6866339e+00 1.93e-02 2.95e-01 -2.8 1.73e+03 - 1.00e+00 5.64e-01H 1\n",
|
||
" 98 1.6797925e+00 1.03e-01 2.06e-01 -2.9 3.61e+03 - 1.00e+00 4.17e-01h 1\n",
|
||
" 99 1.7050945e+00 1.22e-02 4.69e-02 -3.2 1.18e+02 - 6.21e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.7036867e+00 1.99e-01 6.88e-02 -2.9 1.24e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.7036866659822316e+00 1.7036866659822316e+00\n",
|
||
"Dual infeasibility......: 6.8836724868660354e-02 6.8836724868660354e-02\n",
|
||
"Constraint violation....: 1.9858184959344172e-01 1.9858184959344172e-01\n",
|
||
"Complementarity.........: 5.8650487999462085e-03 5.8650487999462085e-03\n",
|
||
"Overall NLP error.......: 1.9858184959344172e-01 1.9858184959344172e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 182\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 182\n",
|
||
"Number of inequality constraint evaluations = 182\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.428\n",
|
||
"Total CPU secs in NLP function evaluations = 139.145\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 830.00us ( 4.56us) 824.94us ( 4.53us) 182\n",
|
||
" nlp_g | 8.26 s ( 45.38ms) 7.89 s ( 43.33ms) 182\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 367.00us ( 3.60us) 364.38us ( 3.57us) 102\n",
|
||
" nlp_jac_g | 133.79 s ( 1.30 s) 127.81 s ( 1.24 s) 103\n",
|
||
" total | 143.56 s (143.56 s) 137.15 s (137.15 s) 1\n",
|
||
"Timestamp 24300\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.78e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0428656e+01 1.67e+01 2.78e+04 -1.5 2.78e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.5467021e+01 6.70e+00 1.13e+01 0.8 1.70e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.4910146e+01 2.50e+00 7.82e-01 -1.3 3.60e+01 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 2.6312521e+01 1.30e-04 7.48e-02 -3.0 2.63e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 2.6312437e+01 4.48e-07 1.46e-03 -4.9 6.52e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.6312434e+01 1.39e-06 1.34e-03 -7.0 7.87e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.6312435e+01 7.21e-07 2.04e-03 -9.1 3.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.6312434e+01 1.34e-06 2.78e-03 -11.0 1.31e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.6312433e+01 1.90e-06 1.14e-03 -11.0 8.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.6312436e+01 2.44e-07 1.17e-04 -11.0 3.13e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.6312390e+01 1.05e-04 6.45e-03 -11.0 2.32e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.6312380e+01 3.79e-05 1.96e-03 -11.0 1.49e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.6312257e+01 6.06e-05 6.28e-03 -11.0 6.52e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.6312219e+01 7.79e-05 1.18e-03 -11.0 4.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.6312174e+01 5.48e-05 1.58e-03 -11.0 4.51e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.6312420e+01 6.62e-06 1.45e-03 -11.0 1.33e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.6312378e+01 2.58e-05 1.57e-03 -11.0 2.26e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.6310826e+01 1.22e-03 8.50e-03 -11.0 1.31e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.6312450e+01 4.93e-07 6.39e-05 -11.0 1.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.6312449e+01 2.56e-08 8.25e-05 -11.0 9.59e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.6312449e+01 8.36e-08 4.82e-05 -11.0 5.59e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.6312449e+01 2.72e-08 7.04e-05 -11.0 3.16e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.6312449e+01 1.42e-07 6.65e-05 -11.0 9.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.6312449e+01 2.52e-08 7.30e-05 -11.0 2.54e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.6312449e+01 2.63e-08 1.36e-04 -11.0 4.90e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.6312449e+01 2.42e-07 1.84e-04 -11.0 1.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.6312450e+01 1.85e-10 3.39e-05 -11.0 7.23e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 28 2.6312449e+01 3.64e-08 2.47e-04 -11.0 4.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.6312447e+01 1.32e-06 1.37e-02 -11.0 3.19e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.6312449e+01 2.45e-07 4.88e-05 -11.0 1.14e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.6312450e+01 6.81e-08 2.52e-04 -11.0 3.73e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.6312444e+01 3.40e-06 9.97e-03 -11.0 3.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.6312450e+01 1.85e-10 3.16e-04 -11.0 3.05e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 2.6312405e+01 1.78e-05 6.88e-03 -11.0 9.69e-02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 35 2.6312404e+01 2.27e-05 4.47e-03 -11.0 1.56e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.6312448e+01 8.90e-06 1.90e-03 -11.0 6.06e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.6312447e+01 4.59e-06 1.89e-03 -11.0 3.49e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.6311308e+01 3.14e-03 8.91e-03 -11.0 7.46e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.6308206e+01 2.24e-03 1.28e-03 -11.0 1.22e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.6306161e+01 2.92e-03 8.69e-04 -11.0 1.45e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.6311311e+01 1.66e-04 9.98e-04 -11.0 2.18e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.6306973e+01 1.55e-03 1.14e-03 -11.0 1.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.6311107e+01 6.34e-04 1.19e-03 -11.0 8.31e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.6311746e+01 5.92e-04 1.42e-03 -11.0 6.13e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.6309927e+01 3.12e-03 1.38e-03 -11.0 2.63e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.6307276e+01 1.52e-03 2.39e-03 -11.0 4.06e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.6294700e+01 1.21e-02 1.87e-03 -11.0 1.94e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.6262208e+01 3.75e-02 6.08e-03 -11.0 7.85e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.6302527e+01 5.57e-03 1.81e-03 -11.0 2.52e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.6271500e+01 1.35e-02 1.82e-03 -11.0 4.28e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.5823126e+01 3.47e-01 1.42e-02 -11.0 6.88e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 52 2.3282654e+01 7.67e-01 4.58e-02 -9.0 1.06e+05 - 1.00e+00 2.36e-01f 1\n",
|
||
" 53 2.3256064e+01 7.71e-01 4.61e-02 -7.0 3.41e+08 - 1.84e-04 7.35e-07f 1\n",
|
||
" 54 2.3255779e+01 7.71e-01 4.61e-02 -7.1 5.63e+05 - 1.00e+00 4.45e-06h 1\n",
|
||
" 55 2.5833415e+01 1.22e-01 6.21e-02 -7.0 6.35e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.5869428e+01 1.84e-03 7.17e-03 -7.5 1.65e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 2.5840688e+01 3.36e-02 3.99e-02 -9.0 2.40e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.5813570e+01 4.03e-02 1.33e-02 -6.9 3.79e+02 - 3.17e-01 1.00e+00h 1\n",
|
||
" 59 2.5874081e+01 9.46e-06 4.61e-02 -9.3 4.61e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.5874074e+01 7.33e-07 1.28e-03 -11.0 8.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.5874074e+01 8.28e-07 2.18e-03 -8.7 6.55e-02 - 1.00e+00 1.92e-01h 1\n",
|
||
" 62 2.5874072e+01 1.40e-06 2.11e-03 -8.9 6.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.5874003e+01 2.91e-05 5.75e-03 -8.4 9.14e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.5874074e+01 1.18e-06 3.33e-03 -8.9 7.87e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.5874075e+01 4.69e-07 1.72e-03 -9.1 7.99e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.5873948e+01 1.20e-04 3.90e-03 -8.7 1.59e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.5874062e+01 1.50e-05 2.21e-03 -11.0 4.22e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.5874053e+01 4.60e-05 3.61e-03 -11.0 7.66e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.5874072e+01 1.92e-06 1.61e-03 -10.6 4.10e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.5874038e+01 3.93e-05 1.96e-03 -11.0 3.07e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.5873837e+01 1.02e-04 3.08e-03 -11.0 6.69e-01 - 3.40e-01 1.00e+00h 1\n",
|
||
" 72 2.5872394e+01 5.70e-04 6.30e-03 -11.0 8.68e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 2.5873717e+01 6.60e-04 8.51e-04 -11.0 3.53e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.5872432e+01 2.15e-03 5.07e-03 -11.0 5.42e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 2.5870548e+01 3.00e-03 5.67e-03 -11.0 1.48e+01 - 8.11e-01 1.00e+00h 1\n",
|
||
" 76 2.5873936e+01 1.28e-03 2.23e-03 -11.0 3.94e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 2.5873608e+01 4.83e-04 2.88e-03 -11.0 2.66e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.5869721e+01 4.24e-03 5.09e-03 -9.0 3.01e+01 - 1.00e+00 3.99e-01h 1\n",
|
||
" 79 2.5869732e+01 4.22e-03 4.49e-03 -9.1 9.99e+00 - 1.00e+00 3.48e-03h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.5869803e+01 4.15e-03 5.91e-03 -9.1 3.20e-01 - 1.00e+00 1.56e-02h 7\n",
|
||
" 81 2.5874314e+01 6.33e-07 1.15e-03 -9.1 1.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"In iteration 81, 1 Slack too small, adjusting variable bound\n",
|
||
" 82 2.5874308e+01 5.73e-06 1.72e-03 -9.1 3.05e-02 - 1.00e+00 5.09e-01h 1\n",
|
||
" 83 2.5873741e+01 2.14e-04 2.44e-02 -9.1 1.42e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.5874154e+01 1.51e-04 1.93e-03 -11.0 5.99e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"In iteration 84, 1 Slack too small, adjusting variable bound\n",
|
||
" 85 2.5874295e+01 1.39e-05 3.04e-03 -10.9 1.69e-01 - 1.00e+00 9.59e-01h 1\n",
|
||
" 86 2.5874279e+01 4.03e-05 1.59e-03 -10.9 1.68e-01 - 7.94e-02 1.00e+00h 1\n",
|
||
"In iteration 86, 1 Slack too small, adjusting variable bound\n",
|
||
" 87 2.5874279e+01 4.04e-05 1.42e-03 -10.9 4.40e+00 - 1.00e+00 1.58e-04h 1\n",
|
||
" 88 2.5874322e+01 1.93e-06 6.89e-04 -10.9 2.00e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 2.5874349e+01 6.93e-08 5.24e-05 -10.9 3.12e+00 - 7.52e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.5874245e+01 2.88e-04 2.26e-03 -10.2 3.43e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 2.5872868e+01 5.89e-04 2.05e-03 -8.6 3.53e+01 - 1.00e+00 1.26e-01h 1\n",
|
||
" 92 2.5874415e+01 4.44e-07 1.20e-04 -8.9 1.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 2.5874415e+01 1.24e-07 4.22e-05 -9.1 8.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 2.5874416e+01 5.63e-08 7.33e-05 -9.1 2.81e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.5874416e+01 1.19e-07 1.93e-05 -9.2 4.07e-04 - 1.00e+00 6.02e-01h 1\n",
|
||
" 96 2.5874416e+01 9.12e-09 3.49e-04 -9.4 9.07e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.5874416e+01 6.62e-08 5.87e-05 -11.0 2.52e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"In iteration 97, 1 Slack too small, adjusting variable bound\n",
|
||
" 98 2.5874416e+01 3.48e-08 1.06e-04 -10.9 2.44e-04 - 1.00e+00 4.37e-01h 1\n",
|
||
" 99 2.5874416e+01 1.56e-07 2.95e-05 -10.9 5.11e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.5874416e+01 1.96e-09 6.38e-05 -10.9 9.61e-05 - 7.64e-01 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.5874415758061470e+01 2.5874415758061470e+01\n",
|
||
"Dual infeasibility......: 6.3815434825973471e-05 6.3815434825973471e-05\n",
|
||
"Constraint violation....: 1.9562449438126350e-09 1.9562449438126350e-09\n",
|
||
"Complementarity.........: 1.1577740221464178e-11 1.1577740221464178e-11\n",
|
||
"Overall NLP error.......: 6.3815434825973471e-05 6.3815434825973471e-05\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 110\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 110\n",
|
||
"Number of inequality constraint evaluations = 110\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.476\n",
|
||
"Total CPU secs in NLP function evaluations = 139.753\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 522.00us ( 4.75us) 512.68us ( 4.66us) 110\n",
|
||
" nlp_g | 5.17 s ( 46.99ms) 4.95 s ( 44.97ms) 110\n",
|
||
" nlp_grad | 1.37 s ( 1.37 s) 1.31 s ( 1.31 s) 1\n",
|
||
" nlp_grad_f | 455.00us ( 4.46us) 372.67us ( 3.65us) 102\n",
|
||
" nlp_jac_g | 137.53 s ( 1.35 s) 131.85 s ( 1.29 s) 102\n",
|
||
" total | 144.21 s (144.21 s) 138.25 s (138.25 s) 1\n",
|
||
"Timestamp 24600\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 4.14e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9630622e+01 1.29e+01 4.14e+03 -1.5 4.14e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.3678609e+00 4.33e+00 9.76e+00 0.4 1.29e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 7.7230399e+00 1.16e+00 7.22e-01 -1.6 7.50e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 8.4748433e+00 1.65e-03 7.20e-02 -3.4 1.57e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 8.4756262e+00 1.72e-07 8.05e-05 -5.3 1.68e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 8.4756261e+00 1.07e-07 5.94e-05 -11.0 4.34e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 8.4756260e+00 2.32e-07 1.41e-05 -11.0 1.21e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 8.4756263e+00 4.27e-09 3.19e-05 -11.0 1.89e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 8.4756241e+00 1.11e-06 6.54e-03 -11.0 5.25e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 8.4756185e+00 3.89e-06 1.13e-03 -11.0 3.65e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 8.4756131e+00 1.29e-05 1.60e-03 -11.0 6.34e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 8.4756146e+00 5.00e-06 1.25e-03 -11.0 3.87e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 8.4755799e+00 2.80e-05 4.11e-03 -11.0 1.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 8.4755881e+00 2.30e-05 1.99e-03 -11.0 1.04e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 8.4756098e+00 6.23e-06 1.93e-03 -11.0 3.97e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 8.4756150e+00 6.77e-06 1.40e-03 -11.0 4.29e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 8.4755357e+00 6.17e-05 4.08e-03 -11.0 1.40e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 8.4755879e+00 1.10e-05 1.44e-03 -11.0 6.18e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 8.4755926e+00 1.76e-05 3.69e-03 -11.0 8.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 8.4755130e+00 8.20e-05 3.20e-03 -11.0 4.29e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 8.4755722e+00 5.00e-05 9.72e-04 -11.0 1.84e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 8.4755212e+00 4.85e-05 2.14e-03 -11.0 5.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 8.4755597e+00 3.21e-05 1.19e-03 -11.0 2.72e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 8.4756122e+00 1.41e-05 8.70e-04 -11.0 1.15e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 8.4749777e+00 3.96e-04 3.38e-03 -11.0 8.80e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 8.4726318e+00 1.61e-03 7.42e-03 -11.0 1.91e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.4760537e+00 2.62e-04 1.44e-03 -11.0 6.36e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 8.4584843e+00 1.44e-02 3.12e-03 -11.0 8.72e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.4317796e+00 2.07e-02 3.48e-03 -11.0 1.32e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.4715655e+00 1.97e-05 3.49e-02 -11.0 3.71e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 8.4715821e+00 3.59e-07 6.53e-05 -11.0 1.51e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 8.4715816e+00 4.08e-07 3.89e-05 -11.0 2.55e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 8.4715821e+00 3.64e-07 3.33e-05 -11.0 1.40e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 8.4715823e+00 9.41e-08 9.54e-05 -11.0 9.02e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 8.4715822e+00 2.73e-07 1.12e-04 -11.0 1.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 8.4715820e+00 3.37e-07 3.89e-05 -11.0 8.37e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 8.4715823e+00 3.81e-08 2.10e-05 -11.0 7.88e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 8.4715824e+00 1.63e-08 1.79e-05 -11.0 2.07e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 8.4715824e+00 3.71e-09 6.48e-05 -11.0 1.65e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 8.4715824e+00 5.08e-08 1.07e-04 -11.0 1.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 8.4715798e+00 1.25e-06 4.50e-03 -11.0 8.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 8.4715823e+00 8.97e-10 2.27e-04 -11.0 1.06e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 8.4715822e+00 4.11e-08 2.50e-05 -11.0 1.11e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 8.4715822e+00 6.70e-09 1.21e-04 -11.0 9.93e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 8.4715820e+00 1.49e-07 7.56e-05 -11.0 6.00e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 8.4715822e+00 5.92e-09 1.66e-04 -11.0 1.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 8.4715821e+00 1.63e-07 2.68e-05 -11.0 5.15e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 8.4715822e+00 7.60e-08 5.40e-05 -11.0 4.21e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 8.4715822e+00 1.05e-08 5.95e-05 -11.0 1.76e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.4715717e+00 1.29e-05 1.03e-02 -11.0 1.50e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 8.4715727e+00 8.21e-06 1.10e-03 -11.0 8.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 8.4715499e+00 3.23e-05 2.29e-03 -11.0 1.78e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 8.4715205e+00 6.06e-05 2.99e-03 -11.0 3.17e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 8.4714447e+00 1.50e-04 2.34e-03 -11.0 6.02e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 8.4715146e+00 3.31e-05 1.31e-03 -11.0 2.97e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 8.4715828e+00 2.13e-06 1.06e-03 -11.0 1.20e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 8.4715521e+00 2.63e-05 2.07e-03 -11.0 2.62e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 8.4715199e+00 6.43e-05 2.06e-03 -11.0 1.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 8.4715824e+00 6.89e-06 2.79e-03 -11.0 4.86e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 8.4715752e+00 1.33e-05 1.26e-03 -11.0 1.59e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 8.4715608e+00 1.92e-05 1.07e-03 -11.0 8.31e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 8.4715403e+00 2.18e-05 1.08e-03 -11.0 2.32e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 8.4715761e+00 1.30e-05 1.27e-03 -11.0 1.42e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 8.4715537e+00 2.55e-05 1.10e-03 -11.0 7.92e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 8.4715469e+00 4.08e-05 4.80e-03 -11.0 1.55e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 8.4715601e+00 3.03e-05 1.44e-03 -11.0 1.59e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 8.4713674e+00 3.92e-04 2.35e-03 -11.0 3.14e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 8.4710493e+00 1.34e-03 2.07e-03 -11.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 8.4714053e+00 4.94e-05 1.25e-03 -11.0 2.66e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.4709697e+00 1.61e-03 1.55e-03 -11.0 7.73e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 8.4704989e+00 9.41e-04 4.17e-03 -11.0 2.06e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 8.3790859e+00 8.20e-02 3.23e-02 -11.0 2.25e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 73 8.4629996e+00 3.62e-03 6.83e-03 -11.0 1.03e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 8.4505288e+00 3.93e-02 4.87e-03 -11.0 1.87e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 8.4307798e+00 1.92e-02 3.57e-03 -11.0 8.61e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 7.7572200e+00 3.98e-01 7.72e-02 -11.0 3.95e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 77 8.6304009e+00 1.42e-01 1.39e-01 -11.0 1.45e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 78 8.2218271e+00 5.57e-01 8.24e-02 -11.0 3.51e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 79 6.9104055e+00 2.36e+00 4.05e-01 -10.2 4.45e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 6.9668134e+00 2.72e+00 3.50e-01 -8.2 2.08e+04 - 1.00e+00 2.21e-01h 2\n",
|
||
" 81 9.9383389e+00 6.50e-01 4.48e-01 -7.4 5.41e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 8.2402264e+00 4.14e-01 1.05e-01 -7.5 1.03e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 83 6.9021249e+00 3.15e+00 1.18e-01 -6.4 8.57e+04 - 1.00e+00 5.69e-01f 1\n",
|
||
" 84 7.9025403e+00 2.97e+00 1.50e-01 -6.5 2.84e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 7.6187675e+00 1.33e+00 2.58e-01 -6.7 6.75e+04 - 7.19e-01 5.05e-01h 1\n",
|
||
" 86 7.0379381e+00 2.58e+00 1.93e-01 -6.3 1.06e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 6.7654926e+00 2.24e+00 2.07e-01 -4.4 1.63e+04 - 9.71e-01 1.00e+00h 1\n",
|
||
" 88 6.8448141e+00 2.01e+00 5.42e-02 -10.0 2.85e+04 - 5.30e-02 1.74e-01h 1\n",
|
||
" 89 6.7829441e+00 1.94e+00 5.96e-02 -2.6 1.00e+05 - 1.00e+00 2.92e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 7.0441988e+00 1.42e+00 7.71e-01 -0.4 3.48e+05 - 1.51e-01 1.79e-01f 1\n",
|
||
" 91 7.5441737e+00 5.08e-01 4.14e-01 -1.9 3.93e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 8.5124887e+00 2.01e-01 3.45e-02 -3.4 2.81e+03 - 4.16e-01 1.00e+00h 1\n",
|
||
" 93 8.4595483e+00 4.20e-01 7.76e-02 -2.0 9.84e+03 - 8.23e-01 1.00e+00h 1\n",
|
||
" 94 8.0262235e+00 4.75e-01 6.25e-02 -2.0 5.30e+04 - 2.39e-01 1.88e-01f 1\n",
|
||
" 95 8.9033753e+00 2.27e-02 1.81e-01 -2.0 3.05e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 96 8.3729615e+00 7.69e-01 8.48e-02 -2.0 7.15e+04 - 3.06e-01 1.45e-01f 1\n",
|
||
" 97 6.5739089e+00 9.50e-01 1.29e-01 -2.0 3.34e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 98 8.5514431e+00 1.89e-01 9.05e-02 -8.0 4.65e+03 - 4.11e-01 1.00e+00h 1\n",
|
||
" 99 8.3206077e+00 2.62e-01 6.84e-02 -2.4 5.56e+03 - 1.00e+00 2.03e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.2004289e+00 5.98e-01 7.34e-02 -2.2 3.93e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.2004289262760715e+00 8.2004289262760715e+00\n",
|
||
"Dual infeasibility......: 7.3404030947156362e-02 7.3404030947156362e-02\n",
|
||
"Constraint violation....: 5.9821003068528000e-01 5.9821003068528000e-01\n",
|
||
"Complementarity.........: 6.8067724042866215e-03 6.8067724042866215e-03\n",
|
||
"Overall NLP error.......: 5.9821003068528000e-01 5.9821003068528000e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 106\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 106\n",
|
||
"Number of inequality constraint evaluations = 106\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.458\n",
|
||
"Total CPU secs in NLP function evaluations = 136.438\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 509.00us ( 4.80us) 499.34us ( 4.71us) 106\n",
|
||
" nlp_g | 4.80 s ( 45.31ms) 4.60 s ( 43.39ms) 106\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.31 s ( 1.31 s) 1\n",
|
||
" nlp_grad_f | 378.00us ( 3.71us) 372.10us ( 3.65us) 102\n",
|
||
" nlp_jac_g | 134.84 s ( 1.32 s) 129.39 s ( 1.27 s) 102\n",
|
||
" total | 141.14 s (141.14 s) 135.44 s (135.44 s) 1\n",
|
||
"Timestamp 24900\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 7.67e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9901425e+01 1.26e+01 7.67e+03 -1.5 7.67e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.8879432e+00 4.29e+00 8.56e+00 0.6 1.57e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 4.1903345e+00 8.17e-01 7.11e-01 -1.5 5.88e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 4.7171718e+00 2.74e-03 1.09e-01 -3.3 1.26e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 4.7183269e+00 2.64e-07 2.74e-05 -5.2 2.76e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 4.7183269e+00 3.35e-08 8.17e-05 -11.0 2.11e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 4.7182903e+00 1.56e-05 7.08e-03 -11.0 2.70e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 4.7088586e+00 7.31e-03 4.74e-03 -11.0 6.62e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 9 4.7200928e+00 2.56e-06 6.87e-03 -11.0 6.66e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 4.7175136e+00 2.86e-03 1.81e-03 -11.0 2.44e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 11 4.7195378e+00 1.12e-03 2.55e-03 -11.0 9.85e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 4.5544337e+00 1.14e-01 2.09e-02 -11.0 2.70e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 13 4.7052978e+00 2.11e-02 2.55e-02 -11.0 8.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 4.6731131e+00 2.96e-02 1.09e-02 -11.0 7.38e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 4.6082257e+00 7.72e-02 1.26e-02 -11.0 8.04e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 4.1998443e+00 1.95e+00 4.80e-01 -11.0 2.56e+06 - 1.25e-02 3.01e-03f 3\n",
|
||
" 17 4.8423794e+00 1.06e-01 3.55e-01 -11.0 1.91e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 3.9273063e+00 1.01e+00 1.47e-01 -11.0 5.05e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 19 4.8844776e+00 4.34e-01 6.80e-01 -9.9 3.24e+05 - 1.00e+00 1.12e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 3.9619628e+00 2.11e+00 2.96e-01 -10.1 1.47e+04 - 2.32e-01 1.00e+00f 1\n",
|
||
" 21 3.9377015e+00 1.44e+00 4.77e-01 -10.1 1.13e+06 - 6.75e-02 2.11e-02f 2\n",
|
||
" 22 3.8149901e+00 1.70e+00 3.22e-01 -10.1 5.53e+04 - 1.00e+00 4.33e-01h 1\n",
|
||
" 23 5.1490704e+00 9.93e-01 9.04e-01 -9.9 4.75e+04 - 2.63e-10 1.00e+00h 1\n",
|
||
" 24 4.9119336e+00 5.07e+00 4.13e-01 -10.0 6.93e+04 - 9.18e-01 2.43e-01F 1\n",
|
||
" 25 4.4997797e+00 4.25e+00 3.67e-01 -10.0 1.08e+04 - 1.00e+00 3.14e-01h 1\n",
|
||
" 26 3.9997337e+00 2.61e+00 5.41e-01 -10.0 7.25e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 3.8586269e+00 1.50e+00 1.68e-01 -10.0 1.15e+04 - 2.28e-01 4.68e-01h 1\n",
|
||
" 28 3.7507630e+00 1.37e+00 6.02e-01 -8.1 1.83e+04 - 1.00e+00 8.87e-01h 1\n",
|
||
" 29 3.7499593e+00 1.30e+00 5.84e-01 -6.2 6.27e+03 - 1.00e+00 2.59e-02h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 3.6825272e+00 1.14e+00 5.63e-01 -11.0 2.11e+04 - 3.15e-04 4.96e-02h 1\n",
|
||
" 31 3.6840087e+00 1.14e+00 5.61e-01 -5.6 1.71e+04 - 1.00e+00 3.76e-03H 1\n",
|
||
" 32 3.8797682e+00 9.27e-01 4.10e-01 -3.7 2.30e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33r 3.8797682e+00 9.27e-01 9.99e+02 -0.0 0.00e+00 - 0.00e+00 4.77e-07R 22\n",
|
||
" 34r 4.4482330e+00 1.90e-01 9.07e+02 -2.2 2.57e+02 - 1.00e+00 3.95e-03f 1\n",
|
||
" 35 3.8017723e+00 1.89e+00 9.06e-02 -2.4 5.86e+03 - 1.00e+00 9.47e-01f 1\n",
|
||
" 36 3.7027548e+00 1.99e+00 2.94e-01 -3.3 1.60e+04 - 4.10e-01 1.37e-01F 1\n",
|
||
" 37 3.4398609e+00 1.03e+00 1.22e-01 -2.8 3.72e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 4.5814755e+00 1.45e-01 3.53e-01 -3.1 3.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 4.4788264e+00 2.08e-01 1.76e-01 -3.3 8.38e+02 - 7.06e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 4.5213670e+00 1.11e-01 2.61e-02 -3.6 5.54e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 4.1868534e+00 3.36e-01 2.57e-01 -4.4 4.25e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 4.1801499e+00 7.61e-01 2.84e-01 -2.4 4.31e+03 - 1.57e-01 1.00e+00h 1\n",
|
||
" 43 4.4018834e+00 4.53e-01 6.04e-02 -2.7 1.41e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 4.5763355e+00 2.01e-01 1.60e-02 -2.0 1.27e+03 - 3.23e-01 1.00e+00h 1\n",
|
||
" 45 3.9043481e+00 1.37e+00 3.79e-01 -2.5 5.84e+03 - 1.00e+00 8.44e-01f 1\n",
|
||
" 46 4.1592234e+00 5.36e-01 2.44e-01 -2.5 4.57e+03 - 7.00e-01 1.00e+00h 1\n",
|
||
" 47 4.0059594e+00 1.35e+00 1.94e-01 -4.5 4.63e+03 - 6.71e-01 1.00e+00H 1\n",
|
||
" 48 3.9646278e+00 1.33e+00 1.64e-01 -3.1 2.05e+04 - 1.00e+00 2.30e-02h 1\n",
|
||
" 49 3.9108116e+00 2.03e+00 6.22e-01 -3.1 3.02e+05 - 5.42e-02 3.86e-02f 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 4.4485963e+00 3.60e-01 2.88e-01 -3.1 5.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 4.2153445e+00 5.02e-01 2.53e-01 -3.1 7.45e+03 - 1.00e+00 1.53e-01h 1\n",
|
||
" 52 4.1743753e+00 5.00e-01 6.76e-02 -3.1 1.68e+03 - 2.24e-02 1.00e+00h 1\n",
|
||
" 53 4.0435006e+00 1.63e+00 1.74e-01 -4.1 4.82e+03 - 2.60e-01 1.00e+00h 1\n",
|
||
" 54 5.5007266e+00 1.79e+00 6.71e-01 -3.4 2.28e+04 - 3.91e-02 1.00e+00h 1\n",
|
||
" 55 3.8700252e+00 1.53e+00 2.01e-01 -3.4 1.37e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 56 3.6983678e+00 1.37e+00 3.48e-01 -3.4 1.88e+05 - 1.15e-01 2.21e-01h 1\n",
|
||
" 57 3.6976836e+00 1.37e+00 3.47e-01 -3.4 1.15e+04 - 1.00e+00 1.41e-03h 1\n",
|
||
" 58 4.6968397e+00 2.83e-01 4.01e-01 -3.4 7.45e+03 - 7.11e-01 1.00e+00h 1\n",
|
||
" 59 3.8991023e+00 1.70e+00 3.60e-01 -3.4 9.08e+03 - 1.00e+00 7.61e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 3.4863507e+00 1.00e+00 6.23e-01 -3.4 6.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 4.1771409e+00 3.34e-01 8.00e-02 -0.8 4.05e+03 - 7.19e-01 8.48e-01h 1\n",
|
||
" 62 4.5987010e+00 5.81e-01 1.16e-01 -1.2 1.56e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 4.6848828e+00 3.94e-01 6.12e-02 -1.9 2.45e+03 - 4.60e-01 1.00e+00h 1\n",
|
||
" 64 3.9418808e+00 1.08e+00 1.93e-01 -1.9 1.45e+04 - 2.19e-01 1.00e+00f 1\n",
|
||
" 65 3.4211003e+00 1.15e+00 2.69e-01 -1.9 1.33e+06 - 3.33e-03 4.15e-03f 4\n",
|
||
" 66 4.7796687e+00 2.27e-01 4.07e-01 -1.7 3.61e+04 - 9.89e-01 1.00e+00H 1\n",
|
||
" 67 4.6694284e+00 5.95e-01 4.26e-01 -1.9 1.77e+04 - 1.00e+00 2.13e-01h 1\n",
|
||
" 68 4.0483438e+00 1.88e+00 6.44e-01 -1.9 2.89e+04 - 1.63e-01 1.00e+00f 1\n",
|
||
" 69 3.4781248e+00 2.09e+00 3.94e-01 -1.9 1.01e+06 - 3.83e-02 2.35e-02f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 3.8329984e+00 1.76e+00 2.30e-01 -1.9 1.75e+04 - 5.45e-01 3.80e-01h 1\n",
|
||
" 71 4.3030348e+00 1.19e+00 2.52e-01 -1.9 1.29e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 72 3.4393242e+00 2.42e+00 4.08e-01 -1.9 3.04e+04 - 8.85e-02 5.36e-01F 1\n",
|
||
" 73 3.4262938e+00 2.27e+00 5.94e-01 -1.9 1.64e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 3.4466677e+00 1.26e+00 3.20e-01 -1.9 5.49e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 3.4221955e+00 9.92e-01 2.17e-01 -1.9 1.73e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 76r 3.4221955e+00 9.92e-01 9.99e+02 -0.0 0.00e+00 - 0.00e+00 4.77e-07R 22\n",
|
||
" 77r 3.6441686e+00 7.92e-01 9.88e+02 0.5 3.83e+02 - 9.84e-01 2.42e-03f 1\n",
|
||
" 78 3.9328255e+00 1.03e+00 1.36e-01 -2.9 3.31e+03 - 1.00e+00 1.00e+00s 22\n",
|
||
" 79 3.2684814e+00 1.53e+00 5.82e-01 -3.0 4.14e+04 - 1.00e+00 4.54e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 3.3924515e+00 1.45e+00 1.43e-01 -2.6 7.34e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 3.7665190e+00 2.01e+00 3.63e-01 -1.9 7.05e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 3.6827728e+00 1.65e+00 3.74e-01 -2.1 8.92e+04 - 3.65e-01 2.90e-01h 1\n",
|
||
" 83 3.6877763e+00 1.04e+00 9.59e-02 -2.1 1.12e+04 - 8.48e-02 1.00e+00h 1\n",
|
||
" 84 3.6582733e+00 4.39e-01 2.28e-01 -2.1 2.14e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 3.6358511e+00 1.04e+00 5.65e-02 -1.8 1.91e+04 - 7.70e-01 1.00e+00h 1\n",
|
||
" 86 3.5569614e+00 1.06e+00 1.17e-01 -2.0 2.41e+04 - 6.62e-01 1.00e+00h 1\n",
|
||
" 87 3.7093331e+00 8.79e-01 1.01e-01 -2.1 2.26e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 3.8239267e+00 1.81e-01 3.78e-01 -2.1 2.74e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 3.7316701e+00 6.00e-01 1.81e-01 -1.8 2.41e+04 - 8.32e-01 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 3.7232540e+00 1.17e+00 1.28e-01 -7.9 5.00e+03 - 4.01e-01 5.00e-01h 2\n",
|
||
" 91 3.6910319e+00 1.60e+00 1.01e-01 -2.3 3.51e+05 - 2.37e-02 8.20e-02h 2\n",
|
||
" 92 3.5877227e+00 1.59e+00 2.75e-01 -2.3 4.07e+04 - 1.00e+00 7.11e-01h 1\n",
|
||
" 93 3.5528260e+00 2.24e+00 5.73e-01 -2.3 7.35e+04 - 2.46e-01 8.00e-02h 4\n",
|
||
" 94 3.7386039e+00 5.28e-01 2.42e-01 -2.3 1.62e+04 - 7.34e-02 5.00e-01h 2\n",
|
||
" 95 3.1332392e+00 5.86e-01 1.45e-01 -2.3 1.93e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 3.8817758e+00 1.21e+00 1.08e-01 -2.3 4.99e+04 - 1.56e-01 9.70e-01H 1\n",
|
||
" 97 3.2746641e+00 4.68e-01 1.57e-01 -2.5 6.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 3.7777037e+00 1.94e-01 2.75e-01 -2.9 8.23e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 99 3.7641209e+00 5.09e-01 1.18e-01 -2.5 4.01e+03 - 8.87e-01 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 3.9756982e+00 3.44e-01 1.75e-01 -2.7 2.55e+04 - 1.63e-01 1.00e+00H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 3.9756981696393172e+00 3.9756981696393172e+00\n",
|
||
"Dual infeasibility......: 1.7519306104507060e-01 1.7519306104507060e-01\n",
|
||
"Constraint violation....: 3.4420719857108750e-01 3.4420719857108750e-01\n",
|
||
"Complementarity.........: 4.6912769134192095e-03 4.6912769134192095e-03\n",
|
||
"Overall NLP error.......: 3.4420719857108750e-01 3.4420719857108750e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 223\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 223\n",
|
||
"Number of inequality constraint evaluations = 223\n",
|
||
"Number of equality constraint Jacobian evaluations = 103\n",
|
||
"Number of inequality constraint Jacobian evaluations = 103\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.426\n",
|
||
"Total CPU secs in NLP function evaluations = 141.807\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 997.00us ( 4.47us) 994.13us ( 4.46us) 223\n",
|
||
" nlp_g | 9.97 s ( 44.71ms) 9.51 s ( 42.66ms) 223\n",
|
||
" nlp_grad | 1.38 s ( 1.38 s) 1.31 s ( 1.31 s) 1\n",
|
||
" nlp_grad_f | 351.00us ( 3.44us) 346.21us ( 3.39us) 102\n",
|
||
" nlp_jac_g | 134.75 s ( 1.30 s) 128.83 s ( 1.24 s) 104\n",
|
||
" total | 146.25 s (146.25 s) 139.80 s (139.80 s) 1\n",
|
||
"Timestamp 25200\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.23e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0276220e+01 1.27e+01 2.23e+04 -1.5 2.23e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.1210113e+00 4.41e+00 8.00e+00 0.8 1.49e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.0534719e+00 6.53e-01 6.23e-01 -1.3 4.48e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 2.3672198e+00 3.61e-03 4.35e-01 -3.1 1.71e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 2.3686762e+00 1.07e-06 9.95e-04 -4.9 1.36e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.3686772e+00 3.11e-10 1.22e-04 -7.0 1.62e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 7 2.3686717e+00 3.73e-06 1.08e-03 -11.0 1.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.3686753e+00 3.06e-06 6.42e-04 -11.0 8.06e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.3685987e+00 6.50e-05 2.35e-03 -11.0 2.80e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.3686843e+00 1.17e-08 1.54e-04 -11.0 3.53e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 2.3686250e+00 6.65e-05 1.37e-03 -11.0 2.20e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.3686162e+00 9.64e-05 2.65e-03 -11.0 1.29e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.3686260e+00 7.01e-05 1.33e-03 -11.0 3.83e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.3683122e+00 4.15e-04 2.56e-03 -11.0 9.58e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.3686664e+00 1.41e-05 1.93e-03 -11.0 2.57e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.3686681e+00 3.04e-05 8.20e-04 -11.0 1.55e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.3684082e+00 1.72e-04 2.56e-03 -11.0 2.07e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.3663000e+00 3.61e-03 4.66e-03 -11.0 2.08e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.3680732e+00 1.98e-03 1.53e-03 -11.0 1.10e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.3670071e+00 1.12e-03 1.17e-03 -11.0 8.72e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.3686733e+00 3.40e-06 2.37e-03 -11.0 1.24e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 22 2.3674650e+00 8.80e-04 9.87e-04 -11.0 7.20e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.3430830e+00 7.69e-02 2.55e-02 -11.0 1.75e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.1810932e+00 2.60e-01 9.52e-02 -11.0 5.00e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.3440905e+00 1.63e-02 1.11e-01 -11.0 3.94e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.1714271e+00 3.41e-01 3.67e-01 -11.0 2.93e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 27 2.2287244e+00 7.61e-02 9.50e-02 -11.0 1.10e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.2898766e+00 2.28e-02 4.47e-02 -11.0 4.64e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.1229547e+00 2.71e-01 1.34e-01 -11.0 4.15e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.3072747e+00 1.13e-01 1.05e-01 -11.0 6.83e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.1123081e+00 4.29e-01 5.31e-02 -11.0 5.46e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.8500793e+00 4.45e-01 1.64e-01 -11.0 1.18e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.9671570e+00 6.03e-01 2.10e-01 -11.0 3.15e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 2.0125252e+00 3.49e-01 1.84e-01 -11.0 6.98e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.9001103e+00 8.42e-01 3.65e-01 -11.0 4.63e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.1034611e+00 4.71e-01 5.09e-01 -11.0 2.63e+04 - 1.10e-01 1.00e+00H 1\n",
|
||
" 37 2.2292126e+00 2.33e-01 1.83e-01 -11.0 7.56e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.0398083e+00 7.12e-01 2.03e-01 -11.0 1.01e+04 - 1.00e+00 1.00e+00F 1\n",
|
||
" 39 1.8705072e+00 8.06e-01 2.94e-01 -11.0 9.50e+04 - 2.57e-01 4.04e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.9547611e+00 1.51e-01 1.51e-01 -11.0 1.45e+04 - 4.39e-10 9.48e-01h 1\n",
|
||
" 41 1.9375364e+00 1.65e-01 8.90e-02 -9.2 5.83e+07 - 1.36e-04 8.57e-05f 1\n",
|
||
" 42 1.9280500e+00 1.63e-01 8.33e-02 -9.2 3.16e+05 - 1.00e+00 2.97e-02h 1\n",
|
||
" 43 1.9280060e+00 1.63e-01 8.33e-02 -6.8 2.69e+06 - 5.91e-04 3.49e-05h 1\n",
|
||
" 44 1.9280035e+00 1.63e-01 8.33e-02 -7.2 5.41e+04 - 1.00e+00 1.73e-05h 1\n",
|
||
" 45 1.9003060e+00 2.57e-01 1.49e-01 -5.8 1.50e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.0716314e+00 5.68e-02 1.54e-01 -4.0 4.77e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.0221611e+00 1.14e-01 6.09e-02 -2.0 8.26e+02 - 6.23e-01 1.00e+00f 1\n",
|
||
" 48 1.9376662e+00 3.43e-01 2.34e-01 -2.9 8.04e+03 - 5.22e-01 1.00e+00h 1\n",
|
||
" 49 1.9242923e+00 2.31e-01 8.91e-02 -2.6 5.07e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.9821798e+00 2.21e-01 6.86e-02 -2.6 1.30e+04 - 3.78e-01 1.00e+00h 1\n",
|
||
" 51 1.9122566e+00 1.81e-01 1.55e-01 -2.6 2.11e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52r 1.9122566e+00 1.81e-01 9.99e+02 -0.7 0.00e+00 - 0.00e+00 4.77e-07R 22\n",
|
||
" 53r 1.9784621e+00 7.02e-02 6.18e+02 -2.9 1.79e+02 - 1.00e+00 1.48e-03f 1\n",
|
||
" 54 1.9163189e+00 8.19e-02 2.64e-02 -3.9 7.56e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.8624471e+00 3.42e-01 2.16e-01 -3.4 6.55e+04 - 1.00e+00 7.23e-01h 1\n",
|
||
" 56 1.8633961e+00 3.14e-01 6.23e-02 -3.9 9.79e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 57 1.9776970e+00 7.55e-02 2.37e-01 -4.7 3.48e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.9752327e+00 4.79e-01 1.55e-01 -4.5 5.65e+03 - 1.45e-01 3.87e-01H 1\n",
|
||
" 59 1.9741969e+00 4.76e-01 1.53e-01 -4.9 9.65e+03 - 1.00e+00 3.18e-03h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.9530036e+00 4.59e-01 1.48e-01 -4.9 1.22e+04 - 1.00e+00 7.05e-01h 1\n",
|
||
" 61 1.9529882e+00 6.69e-01 2.31e-01 -4.9 9.86e+03 - 2.27e-01 5.00e-01h 2\n",
|
||
" 62 1.8663882e+00 4.68e-01 2.93e-01 -4.9 2.58e+03 - 5.67e-01 1.00e+00h 1\n",
|
||
" 63 1.8681768e+00 4.50e-01 2.60e-01 -4.9 7.04e+04 - 1.61e-01 4.11e-02h 5\n",
|
||
" 64 1.8952216e+00 2.88e-01 2.60e-01 -4.9 1.07e+04 - 1.00e+00 3.22e-01h 1\n",
|
||
" 65 1.9191114e+00 2.66e-01 1.19e-01 -4.9 7.68e+03 - 1.00e+00 6.88e-01H 1\n",
|
||
" 66 1.8998582e+00 1.30e-01 7.60e-02 -4.9 4.70e+03 - 8.88e-01 1.00e+00h 1\n",
|
||
" 67 1.8954737e+00 1.92e-01 2.86e-02 -3.4 6.41e+03 - 9.36e-01 1.00e+00h 1\n",
|
||
" 68 1.8953888e+00 1.76e-01 3.42e-02 -2.8 3.66e+04 - 2.00e-01 3.91e-03h 9\n",
|
||
" 69 1.8571191e+00 4.06e-01 1.79e-01 -3.0 2.34e+05 - 1.00e+00 5.83e-02h 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.9920830e+00 1.28e-01 4.20e-01 -3.0 2.50e+04 - 9.54e-01 1.00e+00h 1\n",
|
||
" 71 1.9240766e+00 1.31e-01 3.58e-01 -3.0 1.53e+04 - 1.00e+00 3.20e-01H 1\n",
|
||
" 72 1.8835291e+00 1.91e-01 9.85e-02 -3.0 3.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.9285025e+00 2.38e-01 1.64e-01 -2.0 2.91e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.9155697e+00 3.73e-01 6.09e-02 -2.2 1.37e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 75 1.9200010e+00 1.08e-01 7.61e-02 -2.2 1.30e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.9201350e+00 3.81e-01 9.26e-02 -2.4 1.95e+03 - 6.25e-01 1.00e+00H 1\n",
|
||
" 77 1.9092461e+00 7.60e-02 3.47e-02 -2.4 1.36e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.8912510e+00 1.38e-01 4.18e-02 -2.1 1.99e+04 - 8.08e-01 3.83e-01h 2\n",
|
||
" 79 1.8687098e+00 3.38e-01 1.33e-01 -1.6 2.86e+04 - 1.00e+00 3.35e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.9256627e+00 1.30e-01 1.61e-01 -1.7 4.41e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.9140507e+00 1.09e-01 6.80e-02 -2.6 1.07e+04 - 8.27e-01 1.00e+00h 1\n",
|
||
" 82 1.9078712e+00 2.72e-01 5.35e-02 -2.6 1.06e+04 - 8.42e-01 1.00e+00h 1\n",
|
||
" 83 1.9437905e+00 1.11e-01 1.99e-01 -2.6 7.29e+04 - 1.00e+00 5.14e-01H 1\n",
|
||
" 84 1.9232031e+00 1.01e-01 8.62e-02 -2.6 1.38e+04 - 1.00e+00 4.55e-01h 2\n",
|
||
" 85 1.9011270e+00 1.15e-01 1.65e-01 -2.6 2.54e+03 - 2.94e-01 1.00e+00h 1\n",
|
||
" 86 1.8943544e+00 1.63e-01 1.89e-01 -2.6 4.90e+03 - 1.00e+00 6.25e-02h 5\n",
|
||
" 87 1.9208647e+00 1.52e-01 3.37e-02 -2.3 4.67e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 88 1.9092443e+00 1.89e-01 9.23e-02 -2.5 3.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.9056542e+00 1.37e-01 6.88e-02 -2.5 2.04e+04 - 1.67e-01 6.25e-02h 5\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.9029848e+00 3.60e-01 8.82e-02 -2.5 1.62e+05 - 7.72e-01 1.80e-01h 2\n",
|
||
" 91 1.9047505e+00 2.88e-01 5.11e-02 -2.5 1.54e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 92 1.9047679e+00 2.91e-01 9.57e-02 -2.5 6.82e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 93 1.9091872e+00 9.44e-02 7.55e-02 -2.5 5.91e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.9075436e+00 7.30e-01 1.97e-01 -2.2 4.07e+04 - 2.67e-01 2.45e-01h 3\n",
|
||
" 95 2.0498886e+00 3.19e-01 2.14e-01 -2.5 1.26e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.9884844e+00 2.86e-01 1.37e-01 -2.5 5.62e+04 - 5.69e-01 3.55e-01h 1\n",
|
||
" 97 1.8942668e+00 9.98e-01 3.31e-01 -2.5 9.29e+03 - 5.26e-02 1.00e+00f 1\n",
|
||
" 98 1.9032528e+00 8.07e-01 3.19e-01 -2.5 2.15e+05 - 9.88e-01 1.25e-01h 2\n",
|
||
" 99 1.8507659e+00 4.97e-01 1.41e-01 -2.5 1.33e+06 - 1.99e-02 6.78e-03f 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.8341280e+00 2.57e-01 1.41e-01 -1.7 9.78e+04 - 6.43e-01 2.68e-01h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.8341280168442564e+00 1.8341280168442564e+00\n",
|
||
"Dual infeasibility......: 1.4109949563609012e-01 1.4109949563609012e-01\n",
|
||
"Constraint violation....: 2.5683362841593294e-01 2.5683362841593294e-01\n",
|
||
"Complementarity.........: 1.5975449732170934e-02 1.5975449732170934e-02\n",
|
||
"Overall NLP error.......: 2.5683362841593294e-01 2.5683362841593294e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 210\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 210\n",
|
||
"Number of inequality constraint evaluations = 210\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.419\n",
|
||
"Total CPU secs in NLP function evaluations = 139.772\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 950.00us ( 4.52us) 939.71us ( 4.47us) 210\n",
|
||
" nlp_g | 9.38 s ( 44.67ms) 8.95 s ( 42.60ms) 210\n",
|
||
" nlp_grad | 1.41 s ( 1.41 s) 1.35 s ( 1.35 s) 1\n",
|
||
" nlp_grad_f | 354.00us ( 3.47us) 338.16us ( 3.32us) 102\n",
|
||
" nlp_jac_g | 133.19 s ( 1.29 s) 127.14 s ( 1.23 s) 103\n",
|
||
" total | 144.14 s (144.14 s) 137.58 s (137.58 s) 1\n",
|
||
"Timestamp 25500\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 7.85e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9791056e+01 1.32e+01 7.85e+03 -1.5 7.85e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.8984351e+00 4.61e+00 8.40e+00 0.6 2.79e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 6.1285034e+00 9.98e-01 8.74e-01 -1.5 7.37e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 6.8745122e+00 2.51e-03 9.96e-02 -3.3 1.49e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 6.8757007e+00 2.12e-07 5.80e-05 -5.1 2.55e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 6.8757006e+00 2.81e-07 2.60e-05 -11.0 1.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 6.8757007e+00 2.99e-07 8.35e-05 -11.0 2.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 6.8756997e+00 5.23e-07 3.02e-03 -11.0 5.14e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 6.8756960e+00 6.43e-06 2.41e-03 -11.0 3.36e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 6.8756988e+00 1.72e-06 2.72e-03 -11.0 1.31e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 6.8756953e+00 2.07e-06 1.35e-03 -11.0 1.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 6.8756996e+00 1.04e-06 1.49e-03 -11.0 5.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 6.8756973e+00 1.67e-06 9.88e-04 -11.0 8.70e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 6.8756776e+00 1.71e-05 2.22e-03 -11.0 1.01e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 6.8756962e+00 2.84e-06 2.29e-03 -11.0 4.58e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 6.8756838e+00 7.28e-05 1.58e-03 -11.0 3.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 6.8752375e+00 5.92e-04 1.33e-02 -11.0 1.69e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 6.8754635e+00 3.22e-04 5.35e-03 -11.0 2.85e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 6.8701157e+00 7.10e-03 2.36e-02 -11.0 1.40e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 6.8739170e+00 1.69e-03 2.35e-03 -11.0 5.67e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 6.8754697e+00 2.00e-04 1.58e-03 -11.0 1.85e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 6.8754194e+00 2.05e-04 7.79e-04 -11.0 3.67e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 6.8705320e+00 3.44e-03 2.41e-03 -11.0 1.73e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 6.8745258e+00 5.51e-04 2.67e-03 -11.0 2.86e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 6.8744184e+00 4.67e-04 2.27e-03 -11.0 7.18e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 6.8723660e+00 4.35e-03 2.16e-03 -11.0 4.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 6.8721617e+00 4.97e-03 1.52e-03 -11.0 2.25e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 6.8706702e+00 2.86e-03 2.74e-03 -11.0 2.60e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 6.8733586e+00 1.31e-03 1.71e-03 -11.0 2.06e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 6.8722463e+00 2.13e-03 1.71e-03 -11.0 1.30e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 6.8722268e+00 2.65e-03 1.48e-03 -11.0 1.05e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 6.8737593e+00 1.27e-03 1.56e-03 -11.0 1.16e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 5.9701520e+00 6.98e-01 8.73e-02 -11.0 1.45e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 34 5.5389572e+00 1.93e+00 2.21e-01 -9.0 4.46e+04 - 1.00e+00 3.85e-01f 1\n",
|
||
" 35 5.4929858e+00 1.94e+00 2.54e-01 -7.1 7.68e+04 - 1.00e+00 2.24e-03h 1\n",
|
||
" 36 5.5123528e+00 1.88e+00 2.39e-01 -5.1 2.88e+02 - 1.00e+00 3.16e-02h 1\n",
|
||
" 37 6.4786267e+00 7.48e-02 2.57e-01 -5.6 4.34e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 6.5019295e+00 5.62e-03 3.13e-02 -5.4 8.53e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 6.5058016e+00 2.20e-03 5.25e-03 -4.5 4.94e+01 - 8.62e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 6.5048639e+00 5.88e-03 9.40e-03 -5.3 7.61e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 6.5043502e+00 1.82e-02 4.14e-03 -5.4 9.68e+02 - 1.53e-01 1.00e+00h 1\n",
|
||
" 42 6.4773635e+00 1.16e-01 8.98e-03 -5.4 2.13e+06 - 8.32e-05 1.31e-03f 1\n",
|
||
" 43 6.4882361e+00 1.40e-02 2.07e-02 -6.2 5.85e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 6.4987746e+00 3.92e-06 1.26e-02 -5.3 1.40e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 6.4987741e+00 2.96e-06 9.89e-04 -7.2 2.77e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 6.4987758e+00 1.26e-07 4.21e-05 -9.3 6.15e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 6.4987759e+00 2.33e-08 1.49e-04 -11.0 2.95e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 6.4987751e+00 3.40e-07 6.24e-05 -11.0 1.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 6.4987758e+00 3.10e-07 5.17e-05 -11.0 5.79e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 6.4987758e+00 5.99e-08 4.42e-05 -11.0 4.50e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 6.4987758e+00 1.12e-07 4.49e-05 -11.0 5.65e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 6.4987758e+00 3.58e-08 5.33e-05 -11.0 2.71e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 6.4987757e+00 7.27e-08 1.04e-04 -11.0 9.67e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 6.4987758e+00 8.57e-08 1.08e-04 -11.0 3.17e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 6.4987757e+00 1.60e-07 1.03e-04 -11.0 8.82e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 6.4987754e+00 6.20e-07 4.95e-05 -11.0 1.74e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 6.4987746e+00 2.29e-06 4.57e-03 -11.0 5.97e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 6.4987757e+00 3.72e-07 1.53e-04 -11.0 1.81e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 6.4987757e+00 1.86e-07 8.23e-05 -11.0 1.37e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 6.4987756e+00 1.65e-07 8.75e-05 -11.0 9.37e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 6.4972313e+00 1.15e-03 2.01e-02 -11.0 4.49e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 62 6.4975022e+00 1.29e-03 2.28e-03 -11.0 1.23e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 6.4908258e+00 3.88e-03 3.51e-03 -11.0 2.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 6.4980745e+00 3.03e-04 1.68e-03 -11.0 1.30e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 6.4986094e+00 2.90e-07 3.15e-05 -11.0 2.01e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 66 6.4972337e+00 3.48e-03 2.01e-03 -11.0 2.45e+02 - 1.00e+00 5.69e-02f 1\n",
|
||
"In iteration 66, 1 Slack too small, adjusting variable bound\n",
|
||
" 67 6.4972337e+00 3.48e-03 1.34e-03 -11.0 5.44e+02 - 1.00e+00 8.04e-07h 1\n",
|
||
" 68 6.4981007e+00 6.98e-04 1.49e-03 -11.0 2.46e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 6.4979761e+00 8.18e-04 2.30e-03 -11.0 2.66e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 6.4978075e+00 5.77e-04 2.33e-03 -9.0 2.93e+00 - 7.13e-01 1.00e+00h 1\n",
|
||
" 71 6.4985358e+00 2.88e-07 1.32e-04 -11.0 1.20e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 6.4985356e+00 1.85e-07 1.48e-04 -10.1 8.51e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 6.4985351e+00 4.69e-07 2.98e-04 -9.9 3.53e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 6.4985337e+00 1.46e-06 1.62e-03 -11.0 2.96e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 6.4985355e+00 2.49e-07 1.65e-04 -11.0 9.73e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 6.4985354e+00 1.06e-07 6.01e-05 -11.0 3.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 6.4985356e+00 6.03e-08 6.07e-05 -11.0 1.89e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 6.4985355e+00 1.10e-07 7.66e-05 -10.4 7.53e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 6.4985355e+00 3.88e-08 1.08e-04 -10.5 2.47e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 6.4985356e+00 2.57e-08 6.93e-05 -11.0 1.59e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 6.4985356e+00 1.27e-10 1.21e-04 -11.0 1.02e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 82 6.4985355e+00 8.03e-08 2.37e-05 -11.0 3.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 6.4985356e+00 9.11e-09 2.03e-04 -11.0 1.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 6.4985356e+00 3.63e-08 1.15e-04 -11.0 2.84e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 6.4985355e+00 9.53e-08 1.18e-04 -11.0 3.13e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 6.4985356e+00 2.81e-08 1.28e-04 -11.0 1.50e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 6.4985353e+00 1.05e-06 2.39e-03 -11.0 1.88e-03 - 3.36e-01 1.00e+00h 1\n",
|
||
" 88 6.4984404e+00 1.57e-04 5.34e-03 -11.0 5.45e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 6.4984512e+00 2.18e-04 2.55e-03 -8.4 1.05e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 6.4970229e+00 1.80e-03 1.80e-03 -8.5 1.93e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 6.4945493e+00 6.15e-03 3.05e-03 -9.6 3.16e+01 - 1.00e+00 9.75e-01h 1\n",
|
||
" 92 6.4945491e+00 6.14e-03 3.03e-03 -9.8 4.54e+01 - 1.00e+00 1.06e-03h 1\n",
|
||
" 93 6.4981777e+00 8.73e-07 4.27e-05 -9.8 1.04e-02 - 5.15e-03 1.00e+00h 1\n",
|
||
" 94 6.4981734e+00 1.17e-05 9.32e-04 -11.0 3.00e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 6.4981634e+00 9.66e-05 1.29e-03 -8.2 2.17e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 6.4968945e+00 1.17e-03 2.28e-03 -8.5 2.19e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 6.4955995e+00 2.15e-03 2.56e-03 -7.9 3.84e+03 - 1.00e+00 7.09e-03h 1\n",
|
||
" 98 6.4977008e+00 3.61e-04 1.53e-03 -6.1 1.96e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 6.4978256e+00 9.70e-05 1.59e-03 -4.1 5.97e+00 - 2.64e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 6.4982108e+00 1.04e-07 7.11e-05 -6.2 4.31e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 6.4982108333860689e+00 6.4982108333860689e+00\n",
|
||
"Dual infeasibility......: 7.1092061593135841e-05 7.1092061593135841e-05\n",
|
||
"Constraint violation....: 1.0393690530463573e-07 1.0393690530463573e-07\n",
|
||
"Complementarity.........: 8.3963265128649189e-07 8.3963265128649189e-07\n",
|
||
"Overall NLP error.......: 7.1092061593135841e-05 7.1092061593135841e-05\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 104\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 104\n",
|
||
"Number of inequality constraint evaluations = 104\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.428\n",
|
||
"Total CPU secs in NLP function evaluations = 134.122\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 461.00us ( 4.43us) 458.38us ( 4.41us) 104\n",
|
||
" nlp_g | 4.66 s ( 44.84ms) 4.44 s ( 42.74ms) 104\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 382.00us ( 3.75us) 374.81us ( 3.67us) 102\n",
|
||
" nlp_jac_g | 132.10 s ( 1.30 s) 126.06 s ( 1.24 s) 102\n",
|
||
" total | 138.25 s (138.25 s) 131.93 s (131.93 s) 1\n",
|
||
"Timestamp 25800\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.60e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9611285e+01 1.31e+01 3.60e+03 -1.5 3.60e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.9311303e+00 4.47e+00 9.17e+00 0.4 1.31e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 7.0831195e+00 1.07e+00 7.41e-01 -1.6 7.26e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 7.8539821e+00 2.22e-03 8.23e-02 -3.4 1.52e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 7.8550188e+00 7.99e-08 1.97e-04 -5.3 2.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 7.8550184e+00 3.38e-07 7.34e-05 -11.0 8.18e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 7.8550189e+00 4.73e-08 4.60e-05 -11.0 2.21e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 7.8550189e+00 5.26e-09 4.48e-05 -11.0 7.96e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 7.8550189e+00 1.26e-10 3.97e-05 -11.0 8.33e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 7.8550189e+00 5.85e-09 5.13e-05 -11.0 6.16e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 7.8550187e+00 1.89e-07 9.60e-05 -11.0 1.95e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 7.8550186e+00 2.21e-07 1.50e-04 -11.0 1.92e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 7.8550185e+00 1.44e-07 1.47e-04 -11.0 4.50e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 7.8550122e+00 6.49e-06 1.61e-02 -11.0 1.66e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 7.8550187e+00 3.33e-07 4.94e-05 -11.0 4.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 7.8550189e+00 3.25e-07 4.68e-05 -11.0 2.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 7.8550192e+00 1.31e-07 3.65e-05 -11.0 9.28e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 7.8549362e+00 4.81e-05 4.47e-03 -11.0 1.95e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 7.8546499e+00 4.06e-04 1.01e-03 -11.0 1.67e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 7.8549246e+00 3.94e-05 1.56e-03 -11.0 5.68e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 7.8549241e+00 1.03e-04 2.09e-03 -11.0 4.31e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 7.8550180e+00 2.85e-05 2.13e-03 -11.0 1.21e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 7.8549839e+00 3.41e-05 2.70e-03 -11.0 1.88e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 7.8516144e+00 5.47e-03 5.61e-03 -11.0 1.46e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 7.8541523e+00 5.74e-04 8.89e-04 -11.0 4.91e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 7.8539611e+00 5.62e-04 1.38e-03 -11.0 6.01e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 7.8548687e+00 2.22e-07 8.78e-05 -11.0 3.65e+00 - 1.00e+00 1.00e+00H 1\n",
|
||
" 28 7.8542148e+00 6.62e-04 1.74e-03 -11.0 2.83e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 7.8541069e+00 5.96e-04 1.14e-03 -11.0 2.40e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 7.8548138e+00 9.99e-05 2.25e-03 -11.0 1.04e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 7.8542805e+00 2.91e-04 2.25e-03 -11.0 2.19e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 7.8540144e+00 6.46e-04 2.77e-03 -11.0 1.63e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 7.8544601e+00 3.29e-04 1.06e-03 -11.0 1.38e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 7.8522537e+00 2.34e-03 2.73e-03 -11.0 1.31e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 7.8543889e+00 1.18e-03 1.94e-03 -11.0 7.83e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 7.8530928e+00 6.15e-03 2.17e-03 -11.0 2.40e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 7.8535518e+00 1.51e-03 1.46e-03 -11.0 8.72e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 7.5284473e+00 5.30e-01 6.93e-02 -11.0 3.60e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 39 7.0272888e+00 8.53e-01 7.64e-02 -11.0 5.54e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 6.5689923e+00 5.54e-01 8.79e-02 -11.0 3.83e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 7.6353964e+00 1.50e-01 1.29e-01 -11.0 8.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 7.7774293e+00 1.44e-02 2.30e-02 -11.0 3.73e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 7.0245760e+00 4.44e-01 4.90e-02 -9.0 5.33e+06 - 1.41e-02 5.85e-03f 1\n",
|
||
" 44 7.0166048e+00 4.45e-01 5.05e-02 -8.7 6.61e+05 - 1.00e+00 4.72e-04h 1\n",
|
||
" 45 7.0165909e+00 4.45e-01 5.05e-02 -6.8 1.04e+05 - 1.00e+00 3.00e-05h 1\n",
|
||
" 46 7.7372648e+00 6.98e-03 4.60e-02 -4.8 4.65e+01 - 1.04e-02 1.00e+00h 1\n",
|
||
" 47 7.7289307e+00 8.80e-03 5.99e-03 -8.4 3.87e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 7.7315628e+00 6.83e-03 5.11e-03 -6.2 4.66e+01 - 1.00e+00 2.25e-01h 1\n",
|
||
" 49 7.7404490e+00 2.51e-04 1.66e-02 -5.5 2.09e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 7.7387627e+00 3.86e-03 4.38e-03 -5.2 7.52e+01 - 1.00e+00 7.00e-01h 1\n",
|
||
" 51 7.7386936e+00 2.07e-03 1.42e-03 -7.3 4.40e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 7.7401648e+00 1.21e-03 1.53e-03 -5.2 2.98e+01 - 4.00e-01 1.00e+00h 1\n",
|
||
" 53 7.7304269e+00 3.29e-02 5.99e-03 -7.1 1.83e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 7.7296064e+00 3.23e-02 5.74e-03 -7.2 1.40e+02 - 1.00e+00 4.45e-02h 1\n",
|
||
" 55 7.7374459e+00 2.28e-04 2.70e-03 -7.2 1.75e+00 - 1.30e-01 1.00e+00h 1\n",
|
||
" 56 6.1205967e+00 6.12e-01 2.37e-01 -7.7 8.32e+03 - 9.48e-01 1.00e+00f 1\n",
|
||
" 57 8.0569858e+00 6.15e-02 9.43e-02 -3.1 5.91e+03 - 9.62e-01 1.00e+00H 1\n",
|
||
" 58 7.4345945e+00 1.97e-01 2.70e-02 -3.2 2.94e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 59 7.8879675e+00 5.19e-02 3.87e-02 -3.5 6.16e+03 - 8.65e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 7.7171513e+00 2.16e-01 2.20e-02 -3.6 1.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 7.8361326e+00 6.35e-02 1.02e-02 -3.6 5.76e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 7.5056038e+00 9.97e-01 4.11e-02 -3.6 3.53e+04 - 3.94e-01 1.00e+00F 1\n",
|
||
" 63 6.7530074e+00 1.33e+00 1.82e-01 -3.6 1.48e+06 - 9.93e-02 2.83e-03f 1\n",
|
||
" 64 7.5329493e+00 2.59e-01 2.56e-01 -3.6 2.17e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 7.5260225e+00 2.53e-01 2.39e-01 -3.6 2.20e+03 - 1.00e+00 8.74e-02h 1\n",
|
||
" 66 7.8786440e+00 2.66e-02 1.40e-01 -3.6 1.46e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 67 7.8172583e+00 2.23e-01 1.25e-01 -3.6 6.21e+03 - 1.00e+00 9.95e-02f 3\n",
|
||
" 68 6.1148030e+00 5.00e+00 1.04e+00 -3.6 2.73e+05 - 2.66e-02 2.06e-01f 1\n",
|
||
" 69 1.0971447e+01 4.81e+00 6.17e-01 -2.7 1.84e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 7.2598203e+00 9.78e-01 3.88e-01 -0.9 1.68e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 71 6.5668941e+00 2.00e+00 3.45e-01 -1.6 4.23e+04 - 3.88e-01 2.11e-01f 1\n",
|
||
" 72 7.8134257e+00 4.12e-02 1.63e-01 -1.6 1.54e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 7.3429396e+00 2.84e-01 1.74e-01 -2.5 4.03e+03 - 5.48e-01 1.00e+00f 1\n",
|
||
" 74 7.5626385e+00 1.77e-01 3.63e-02 -2.9 4.16e+03 - 9.95e-01 1.00e+00h 1\n",
|
||
" 75 5.3456484e+00 1.70e+00 5.51e-01 -2.9 4.58e+04 - 1.41e-01 3.45e-01f 1\n",
|
||
" 76 5.0253912e+00 1.66e+00 2.41e-01 -3.2 8.39e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 4.8259420e+00 1.26e+00 1.94e-01 -1.7 5.24e+04 - 1.00e+00 8.06e-02h 1\n",
|
||
" 78 7.9751640e+00 1.01e-01 3.33e-01 -2.0 2.82e+03 - 1.60e-01 1.00e+00h 1\n",
|
||
" 79 7.2535734e+00 5.50e-01 2.68e-01 -1.9 4.90e+03 - 1.00e+00 3.97e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 4.5930121e+00 1.62e+00 7.91e-01 -1.9 1.61e+04 - 3.94e-01 1.00e+00f 1\n",
|
||
" 81 7.4384944e+00 3.03e-01 2.10e-01 -1.5 7.71e+03 - 3.03e-01 1.00e+00h 1\n",
|
||
" 82 8.2114445e+00 2.87e-01 1.30e-01 -1.9 6.68e+03 - 8.41e-01 1.00e+00H 1\n",
|
||
" 83 7.0664655e+00 5.09e+00 5.32e-01 -1.9 1.21e+06 - 3.89e-02 1.76e-02f 1\n",
|
||
" 84 6.9968044e+00 5.00e+00 5.25e-01 -1.9 1.65e+04 - 1.00e+00 1.14e-02h 1\n",
|
||
" 85 8.2381933e+00 1.79e-01 4.90e-01 -1.9 4.28e+03 - 4.92e-01 1.00e+00h 1\n",
|
||
" 86 6.7885816e+00 1.12e+00 3.37e-01 -1.9 2.07e+04 - 2.17e-01 1.00e+00f 1\n",
|
||
" 87 5.8935260e+00 1.72e+00 3.26e-01 -1.9 1.11e+05 - 2.67e-01 2.04e-01f 1\n",
|
||
" 88 6.4130564e+00 1.13e+00 1.43e-01 -1.9 3.47e+04 - 4.07e-01 4.97e-01h 1\n",
|
||
" 89 6.9369481e+00 8.94e-01 1.25e-01 -1.9 5.91e+03 - 3.15e-01 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 7.1570790e+00 1.18e+00 2.48e-01 -1.9 7.16e+03 - 1.00e+00 9.07e-01h 1\n",
|
||
" 91 8.1448563e+00 2.47e-01 9.54e-02 -1.9 5.16e+03 - 3.58e-01 1.00e+00h 1\n",
|
||
" 92 8.1502758e+00 3.78e-01 7.90e-02 -1.9 9.03e+03 - 2.22e-01 1.80e-01h 3\n",
|
||
" 93 8.1014689e+00 5.41e-01 8.89e-02 -1.9 4.07e+04 - 1.06e-01 2.99e-02h 3\n",
|
||
" 94 7.8628949e+00 5.72e-01 1.05e-01 -1.9 1.07e+05 - 1.06e-01 1.72e-02f 2\n",
|
||
" 95 8.3573851e+00 4.15e-04 7.21e-01 -1.9 7.89e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 8.3576585e+00 3.24e-07 1.40e-04 -1.9 1.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 8.3576584e+00 1.98e-07 9.76e-05 -2.9 1.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 8.3576575e+00 1.25e-06 2.10e-03 -4.3 4.62e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 8.3576583e+00 4.83e-07 8.27e-05 -4.3 3.44e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.3576575e+00 7.95e-07 1.80e-03 -6.5 5.00e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.3576575357757434e+00 8.3576575357757434e+00\n",
|
||
"Dual infeasibility......: 1.7968993891387224e-03 1.7968993891387224e-03\n",
|
||
"Constraint violation....: 7.9480811976395671e-07 7.9480811976395671e-07\n",
|
||
"Complementarity.........: 3.0755576845560402e-07 3.0755576845560402e-07\n",
|
||
"Overall NLP error.......: 1.7968993891387224e-03 1.7968993891387224e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 130\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 130\n",
|
||
"Number of inequality constraint evaluations = 130\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.409\n",
|
||
"Total CPU secs in NLP function evaluations = 135.559\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 591.00us ( 4.55us) 573.58us ( 4.41us) 130\n",
|
||
" nlp_g | 5.88 s ( 45.20ms) 5.61 s ( 43.12ms) 130\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 354.00us ( 3.47us) 348.92us ( 3.42us) 102\n",
|
||
" nlp_jac_g | 132.39 s ( 1.30 s) 126.37 s ( 1.24 s) 102\n",
|
||
" total | 139.75 s (139.75 s) 133.40 s (133.40 s) 1\n",
|
||
"Timestamp 26100\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.05e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9891693e+01 1.50e+01 3.05e+04 -1.5 3.05e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.3228424e+01 5.90e+00 1.43e+01 0.8 2.15e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.0076971e+01 2.21e+00 8.24e-01 -1.3 4.30e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 2.1315008e+01 8.07e-05 8.34e-02 -3.0 2.39e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 2.1315143e+01 3.95e-05 1.16e-02 -4.9 1.66e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.1315092e+01 7.86e-05 1.35e-02 -6.8 2.04e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.1315261e+01 1.79e-05 2.34e-03 -8.7 9.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.1315171e+01 2.94e-05 3.58e-03 -11.0 8.37e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.1315044e+01 4.67e-04 4.07e-03 -11.0 1.33e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.1314844e+01 3.32e-04 6.94e-03 -11.0 1.74e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.1314464e+01 6.71e-04 7.68e-03 -11.0 1.37e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.1314979e+01 3.02e-04 5.50e-03 -11.0 1.31e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.1315050e+01 2.74e-04 1.97e-03 -11.0 1.81e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.1315147e+01 9.40e-05 3.06e-03 -11.0 2.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.1314960e+01 4.72e-04 3.66e-03 -11.0 3.10e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.1314296e+01 2.55e-04 1.53e-03 -11.0 4.79e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.1313902e+01 1.47e-03 3.16e-03 -11.0 4.75e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.8762636e+01 1.08e+00 1.00e-01 -11.0 8.92e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 19 2.0166252e+01 1.35e+00 3.27e-02 -11.0 4.29e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.5984560e+01 4.53e+00 2.34e-01 -10.7 2.67e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 21 1.5635566e+01 5.52e+00 2.38e-01 -8.8 4.53e+04 - 1.00e+00 1.81e-01h 1\n",
|
||
" 22 1.5654111e+01 5.47e+00 2.33e-01 -6.9 2.09e+04 - 1.00e+00 6.26e-03h 1\n",
|
||
" 23 1.5655223e+01 5.47e+00 2.32e-01 -4.9 2.30e+03 - 1.00e+00 3.48e-04h 1\n",
|
||
" 24 2.1120651e+01 3.57e-02 4.04e-01 -6.2 1.22e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.0814529e+01 1.16e-01 3.90e-01 -11.0 1.65e+04 - 7.49e-01 6.50e-02f 1\n",
|
||
" 26 2.0811799e+01 1.16e-01 3.89e-01 -4.8 8.91e+03 - 1.00e+00 1.20e-03h 1\n",
|
||
" 27 2.0819972e+01 1.13e-01 3.77e-01 -3.0 9.66e+01 - 1.00e+00 3.12e-02h 6\n",
|
||
" 28 2.1045019e+01 1.03e-02 2.26e-03 -4.4 2.72e+02 - 3.63e-01 1.00e+00h 1\n",
|
||
" 29 2.0936188e+01 2.49e-01 1.11e-02 -9.3 1.93e+03 - 2.98e-03 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.7071259e+01 4.77e+00 1.76e-01 -3.4 1.69e+06 - 1.31e-03 3.62e-02f 1\n",
|
||
" 31 1.6652406e+01 3.77e+00 3.21e-01 -3.4 4.14e+04 - 5.26e-02 3.77e-01h 1\n",
|
||
" 32 2.0817032e+01 5.32e-01 2.52e-01 -3.4 2.76e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.8914124e+01 2.71e+00 1.28e-01 -3.4 1.31e+04 - 2.48e-01 1.00e+00f 1\n",
|
||
" 34 1.5012050e+01 6.91e+00 3.64e-01 -3.4 4.93e+07 - 3.13e-04 6.38e-04f 1\n",
|
||
" 35 2.1428190e+01 7.33e-01 4.48e-01 -4.3 4.61e+04 - 1.34e-03 1.00e+00H 1\n",
|
||
" 36 2.0981715e+01 1.33e+00 4.53e-01 -3.6 2.56e+04 - 1.00e+00 9.51e-02f 1\n",
|
||
" 37 2.1051207e+01 5.38e-01 1.30e-01 -3.6 1.74e+04 - 1.00e+00 7.20e-01h 1\n",
|
||
" 38 2.1050543e+01 5.37e-01 1.30e-01 -3.6 2.17e+03 - 1.00e+00 1.32e-03h 1\n",
|
||
" 39 2.0139501e+01 9.30e-01 1.54e-01 -3.6 1.33e+04 - 1.00e+00 4.94e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.0863142e+01 7.42e-01 5.88e-03 -8.2 1.70e+03 - 8.87e-04 1.00e+00h 1\n",
|
||
" 41 1.9960800e+01 9.67e-01 8.99e-02 -2.3 7.07e+03 - 3.74e-01 1.00e+00f 1\n",
|
||
" 42 2.0843102e+01 2.66e-01 2.83e-02 -8.6 7.16e+02 - 3.53e-02 7.26e-01h 1\n",
|
||
" 43 2.0965552e+01 1.59e-01 4.83e-03 -2.8 1.42e+03 - 1.00e+00 3.83e-01h 1\n",
|
||
" 44 2.0969146e+01 1.93e-01 7.10e-03 -2.8 3.06e+03 - 1.00e+00 2.00e-01h 2\n",
|
||
" 45 2.0860273e+01 1.94e-01 8.20e-03 -2.9 4.26e+03 - 1.00e+00 1.77e-01h 1\n",
|
||
" 46 2.1233439e+01 1.47e-04 4.23e-01 -2.8 4.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.1233355e+01 2.59e-07 2.52e-04 -4.6 1.93e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.1233349e+01 1.57e-05 1.55e-03 -5.9 8.73e-02 - 9.97e-01 1.00e+00h 1\n",
|
||
" 49 2.1233340e+01 5.03e-06 1.79e-03 -8.0 4.27e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.1233352e+01 2.32e-06 1.11e-03 -10.1 1.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.1233346e+01 1.44e-05 1.65e-03 -11.0 6.32e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.1233346e+01 1.06e-05 3.23e-03 -11.0 5.61e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.1233352e+01 1.72e-06 1.52e-03 -11.0 2.72e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.1233344e+01 5.20e-06 2.10e-03 -11.0 7.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.1233353e+01 1.79e-06 1.82e-03 -11.0 6.99e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.1233201e+01 3.68e-04 3.92e-03 -11.0 5.26e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 2.1232469e+01 6.81e-04 7.05e-03 -11.0 1.17e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.1230250e+01 1.52e-03 1.02e-02 -9.0 2.77e+01 - 1.00e+00 5.91e-01h 1\n",
|
||
" 59 2.1232550e+01 8.59e-07 2.38e-03 -11.0 2.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.1232549e+01 1.12e-06 1.72e-03 -9.0 6.82e-03 - 1.00e+00 2.81e-01h 1\n",
|
||
" 61 2.1232549e+01 6.15e-07 7.62e-04 -8.8 2.85e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.1232550e+01 1.83e-07 9.80e-05 -11.0 9.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.1232543e+01 6.07e-06 3.77e-03 -6.5 3.09e-02 - 7.45e-01 1.00e+00h 1\n",
|
||
" 64 2.1232513e+01 3.81e-05 7.73e-03 -8.8 2.14e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.1232485e+01 3.61e-05 8.89e-03 -7.9 2.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.1232431e+01 6.28e-05 1.04e-02 -8.3 5.15e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.1232567e+01 5.10e-08 4.07e-05 -10.1 1.58e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.1232567e+01 2.34e-08 1.47e-04 -11.0 1.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.1232567e+01 1.65e-08 9.52e-05 -11.0 2.93e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.1232567e+01 4.43e-08 1.07e-04 -11.0 1.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.1232567e+01 1.09e-08 1.08e-04 -11.0 1.01e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 2.1232566e+01 1.41e-07 1.38e-04 -11.0 1.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 2.1232566e+01 1.55e-07 1.34e-04 -11.0 8.95e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.1232567e+01 7.22e-08 3.13e-04 -11.0 4.41e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 2.1232566e+01 4.32e-07 5.45e-05 -11.0 9.77e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.1232567e+01 6.64e-08 2.44e-04 -11.0 2.52e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 2.1232567e+01 7.15e-08 6.50e-05 -11.0 1.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.1232567e+01 2.54e-09 1.17e-04 -11.0 2.71e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 2.1232567e+01 5.71e-09 2.97e-04 -11.0 3.21e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.1232566e+01 1.24e-07 1.30e-04 -11.0 7.12e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.1232567e+01 3.31e-08 1.98e-04 -11.0 1.96e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.1232536e+01 1.89e-05 8.07e-03 -11.0 6.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 2.1232523e+01 1.71e-05 3.27e-03 -11.0 1.16e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.1232327e+01 1.53e-04 4.67e-03 -11.0 2.90e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 2.1179950e+01 1.92e-02 1.97e-02 -9.0 6.38e+01 - 2.10e-02 1.00e+00f 1\n",
|
||
" 86 2.1205405e+01 1.31e-02 1.87e-03 -10.6 3.32e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.1184077e+01 1.02e-02 1.30e-03 -8.6 2.74e+02 - 1.00e+00 1.14e-01h 1\n",
|
||
" 88 2.1184875e+01 1.00e-02 1.32e-03 -8.8 1.70e+01 - 1.00e+00 1.83e-02h 1\n",
|
||
" 89 2.1187847e+01 9.39e-03 1.78e-03 -8.8 3.84e+00 - 1.00e+00 6.25e-02h 5\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.1207042e+01 5.15e-03 1.44e-03 -8.8 1.02e+02 - 6.28e-01 4.72e-01h 1\n",
|
||
" 91 2.1232296e+01 3.40e-04 1.76e-03 -11.0 1.63e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 2.1229844e+01 1.86e-03 3.65e-03 -6.7 1.68e+01 - 1.00e+00 9.98e-01h 1\n",
|
||
" 93 2.1232739e+01 4.40e-07 7.14e-05 -8.5 1.00e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 94 2.1232115e+01 2.68e-04 5.02e-03 -7.3 6.36e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.1230848e+01 1.10e-03 1.50e-03 -6.5 4.97e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 2.1152181e+01 4.60e-02 4.26e-03 -5.4 2.85e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 97 2.1160742e+01 3.59e-02 2.46e-03 -5.5 1.73e+02 - 9.37e-01 1.00e+00h 1\n",
|
||
" 98 2.1221814e+01 6.89e-03 2.70e-03 -5.5 5.14e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 2.1169958e+01 3.92e-02 3.55e-03 -6.1 3.66e+02 - 1.00e+00 1.99e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.1231300e+01 2.46e-03 3.45e-03 -5.0 9.95e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.1231300017510573e+01 2.1231300017510573e+01\n",
|
||
"Dual infeasibility......: 3.4543849300957760e-03 3.4543849300957760e-03\n",
|
||
"Constraint violation....: 2.4555530190433217e-03 2.4555530190433217e-03\n",
|
||
"Complementarity.........: 1.5147019937326045e-04 1.5147019937326045e-04\n",
|
||
"Overall NLP error.......: 3.4543849300957760e-03 3.4543849300957760e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 115\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 115\n",
|
||
"Number of inequality constraint evaluations = 115\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.402\n",
|
||
"Total CPU secs in NLP function evaluations = 133.952\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 519.00us ( 4.51us) 513.45us ( 4.46us) 115\n",
|
||
" nlp_g | 5.18 s ( 45.04ms) 4.94 s ( 42.97ms) 115\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 370.00us ( 3.63us) 357.71us ( 3.51us) 102\n",
|
||
" nlp_jac_g | 131.63 s ( 1.29 s) 125.60 s ( 1.23 s) 102\n",
|
||
" total | 138.29 s (138.29 s) 131.97 s (131.97 s) 1\n",
|
||
"Timestamp 26400\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.08e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9442644e+01 1.25e+01 1.08e+04 -1.5 1.08e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.2845304e+00 4.06e+00 1.00e+01 0.8 1.80e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 7.7712092e+00 1.11e+00 9.37e-01 -1.3 3.94e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 8.4612117e+00 1.06e-03 8.13e-02 -3.0 1.46e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 8.4614489e+00 9.50e-06 1.79e-03 -4.9 4.47e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 8.4613785e+00 6.10e-05 2.45e-03 -7.0 6.73e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 8.4613466e+00 9.12e-05 1.05e-03 -9.1 4.17e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 8.4612052e+00 1.65e-04 1.14e-03 -11.0 3.01e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 8.4614559e+00 9.27e-06 9.41e-04 -11.0 9.93e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 8.4614479e+00 1.87e-05 2.25e-03 -11.0 1.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 8.4453315e+00 1.73e-02 2.84e-02 -11.0 8.56e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 12 8.4500631e+00 9.58e-03 6.55e-04 -11.0 4.88e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 8.4373194e+00 2.56e-02 3.00e-03 -11.0 6.93e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 8.4601597e+00 3.45e-05 3.84e-03 -11.0 6.12e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 15 8.4168795e+00 8.09e-02 8.42e-03 -11.0 1.65e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 16 8.4576347e+00 3.22e-03 6.41e-03 -11.0 1.93e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 8.3925420e+00 7.16e-02 8.37e-03 -11.0 1.53e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 8.4464978e+00 1.38e-02 9.19e-03 -11.0 8.68e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 8.4396213e+00 2.28e-02 6.69e-03 -11.0 1.14e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 8.4438432e+00 1.23e-02 3.09e-03 -11.0 6.70e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 8.4101392e+00 2.60e-02 6.15e-03 -11.0 3.15e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 8.4026272e+00 3.57e-02 3.61e-03 -11.0 2.09e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 8.4572204e+00 3.44e-03 5.10e-03 -11.0 5.74e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 8.4511301e+00 7.85e-03 7.24e-04 -11.0 1.12e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 8.4481326e+00 8.78e-03 1.42e-03 -11.0 7.16e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 8.3890348e+00 9.59e-02 4.63e-03 -11.0 2.70e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 8.4456789e+00 8.44e-03 7.16e-03 -11.0 1.03e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 8.4606306e+00 2.44e-03 3.38e-03 -11.0 3.52e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 8.4500013e+00 2.21e-02 5.26e-03 -11.0 2.17e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 8.3377018e+00 7.20e-02 7.62e-03 -11.0 5.40e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 8.4750303e+00 8.47e-03 1.44e-02 -11.0 1.25e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 6.8168501e+00 7.15e-01 1.51e-01 -11.0 1.23e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 33 7.8484287e+00 2.09e-01 8.46e-02 -11.0 2.60e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 8.4334089e+00 6.12e-02 3.24e-02 -11.0 3.76e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 35 6.6752863e+00 4.03e+00 4.88e-01 -11.0 3.22e+04 - 1.00e+00 8.35e-01f 1\n",
|
||
" 36 8.4711779e+00 4.55e-01 8.38e-02 -10.6 2.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 6.9700921e+00 1.35e+00 2.79e-01 -2.4 2.99e+03 - 7.16e-01 1.00e+00f 1\n",
|
||
" 38 7.3606802e+00 6.68e-01 2.33e-01 -2.4 5.37e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 6.4755212e+00 1.06e+00 1.12e-01 -2.3 5.68e+03 - 1.00e+00 9.06e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 7.3540906e+00 1.33e+00 1.90e-01 -2.1 1.12e+04 - 4.16e-01 9.29e-01H 1\n",
|
||
" 41 7.2433921e+00 3.97e-01 1.23e-01 -2.4 1.01e+04 - 4.72e-01 6.22e-01h 1\n",
|
||
" 42 7.2766329e+00 3.99e-01 1.16e-01 -1.9 1.22e+04 - 1.00e+00 6.25e-02f 5\n",
|
||
" 43 8.0670923e+00 2.46e-01 1.41e-01 -2.8 1.46e+04 - 1.86e-01 1.00e+00H 1\n",
|
||
" 44 7.0780215e+00 4.59e-01 7.75e-02 -2.0 3.84e+04 - 7.66e-01 5.33e-01f 1\n",
|
||
" 45 7.1750296e+00 3.24e-01 6.79e-02 -1.3 3.95e+03 - 1.00e+00 3.13e-01h 1\n",
|
||
" 46 6.3761792e+00 2.87e+00 9.88e-01 -7.4 1.51e+05 - 1.83e-01 9.45e-02f 2\n",
|
||
" 47 7.9024918e+00 2.24e-01 5.48e+00 -3.4 6.40e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
" 48 8.0937052e+00 2.29e-04 4.57e-02 -5.3 3.69e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 8.0937885e+00 4.68e-06 2.56e-03 -7.1 6.34e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 8.0937951e+00 2.19e-07 6.18e-05 -9.2 1.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 8.0937953e+00 9.07e-08 1.88e-04 -11.0 4.61e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 8.0937949e+00 4.01e-07 2.17e-04 -11.0 2.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 8.0936108e+00 1.83e-04 2.41e-02 -11.0 8.66e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 8.0931560e+00 3.30e-04 3.63e-03 -11.0 2.89e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 8.0938619e+00 9.76e-05 9.69e-04 -11.0 6.53e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 8.0919724e+00 9.14e-04 3.09e-03 -11.0 4.87e+00 - 1.00e+00 9.73e-01h 1\n",
|
||
" 57 8.0919730e+00 9.14e-04 2.80e-03 -10.5 1.78e+00 - 1.00e+00 4.88e-04h 12\n",
|
||
" 58 8.0932619e+00 2.54e-04 1.18e-03 -6.9 1.84e-02 - 1.00e+00 7.27e-01h 1\n",
|
||
" 59 8.0933225e+00 2.23e-04 1.85e-03 -5.3 3.08e-02 - 9.99e-01 1.25e-01h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 8.0937483e+00 9.13e-07 1.10e-03 -7.2 8.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 8.0937414e+00 1.10e-05 3.04e-03 -6.2 1.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 8.0936996e+00 5.84e-05 6.33e-03 -6.4 3.54e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 8.0937477e+00 9.35e-07 2.96e-03 -8.5 1.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 8.0936307e+00 1.49e-04 2.83e-03 -11.0 5.53e-01 - 1.00e+00 9.77e-01h 1\n",
|
||
" 65 8.0936313e+00 1.48e-04 2.90e-03 -9.0 2.01e-01 - 1.00e+00 6.46e-03h 1\n",
|
||
" 66 8.0937451e+00 1.06e-05 2.50e-03 -8.8 3.59e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 8.0937415e+00 1.55e-05 2.41e-03 -8.6 9.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 8.0937369e+00 1.22e-05 2.12e-03 -11.0 1.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 8.0937184e+00 7.04e-05 5.83e-03 -10.6 4.22e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.0935863e+00 8.72e-04 1.33e-02 -8.6 2.55e+00 - 1.38e-01 1.00e+00h 1\n",
|
||
" 71 8.0937635e+00 1.15e-05 3.58e-03 -9.6 9.76e-01 - 1.15e-02 1.00e+00h 1\n",
|
||
"In iteration 71, 1 Slack too small, adjusting variable bound\n",
|
||
" 72 8.0937633e+00 1.15e-05 1.87e-03 -9.6 4.59e+02 - 1.00e+00 2.82e-05h 1\n",
|
||
" 73 8.0937778e+00 4.37e-06 1.47e-03 -9.6 5.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 8.0937772e+00 1.90e-06 1.13e-03 -9.6 3.79e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"In iteration 74, 1 Slack too small, adjusting variable bound\n",
|
||
" 75 8.0937762e+00 1.24e-06 1.18e-03 -9.6 4.13e-01 - 1.00e+00 4.38e-01h 1\n",
|
||
" 76 8.0937792e+00 1.05e-10 2.33e-04 -9.6 9.06e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 77 8.0937738e+00 6.73e-06 1.79e-03 -7.6 2.18e-01 - 9.38e-01 1.00e+00h 1\n",
|
||
" 78 8.0935658e+00 5.38e-04 1.95e-03 -7.8 1.52e+01 - 3.46e-04 1.00e+00h 1\n",
|
||
" 79 8.0352726e+00 1.99e-01 1.29e-02 -7.8 2.87e+04 - 1.00e+00 5.08e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 8.0178545e+00 6.85e-01 3.83e-02 -7.3 1.15e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 81 7.9741907e+00 5.31e-01 2.97e-02 -3.1 1.70e+04 - 4.18e-01 1.76e-01h 1\n",
|
||
" 82 7.8600621e+00 1.58e-01 9.49e-02 -3.1 2.33e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 83 7.5595379e+00 3.82e-01 9.73e-02 -7.6 9.42e+04 - 2.84e-01 2.64e-01h 1\n",
|
||
" 84 7.5790981e+00 4.46e-01 8.93e-02 -1.8 1.98e+05 - 4.08e-01 4.90e-03h 7\n",
|
||
" 85 8.0325481e+00 5.32e-03 6.01e-01 -1.8 6.01e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 8.0353914e+00 4.45e-07 1.64e-04 -3.7 6.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 8.0353909e+00 7.15e-07 1.96e-04 -9.7 2.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 8.0353903e+00 1.17e-06 3.12e-03 -11.0 2.90e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 8.0353878e+00 2.46e-06 8.98e-03 -11.0 8.87e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 8.0353903e+00 5.91e-07 8.96e-05 -11.0 4.09e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 8.0353915e+00 1.99e-07 1.83e-04 -11.0 1.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 8.0353897e+00 1.42e-06 2.98e-03 -11.0 3.70e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 8.0353874e+00 1.98e-06 2.10e-03 -11.0 8.67e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 8.0353832e+00 4.91e-06 3.84e-03 -11.0 1.41e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 8.0353899e+00 1.34e-06 1.56e-03 -11.0 6.05e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 8.0353912e+00 2.89e-07 8.45e-05 -11.0 2.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 8.0353911e+00 3.62e-07 1.37e-04 -11.0 1.68e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 8.0353910e+00 2.96e-07 7.30e-05 -11.0 1.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 8.0353914e+00 6.41e-11 3.90e-04 -11.0 1.81e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.0353874e+00 3.02e-06 1.86e-03 -11.0 3.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.0353873892137084e+00 8.0353873892137084e+00\n",
|
||
"Dual infeasibility......: 1.8637684955299105e-03 1.8637684955299105e-03\n",
|
||
"Constraint violation....: 3.0178612959730344e-06 3.0178612959730344e-06\n",
|
||
"Complementarity.........: 1.0000000000000001e-11 1.0000000000000001e-11\n",
|
||
"Overall NLP error.......: 1.8637684955299105e-03 1.8637684955299105e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 138\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 138\n",
|
||
"Number of inequality constraint evaluations = 138\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.393\n",
|
||
"Total CPU secs in NLP function evaluations = 135.404\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 619.00us ( 4.49us) 618.71us ( 4.48us) 138\n",
|
||
" nlp_g | 6.19 s ( 44.86ms) 5.91 s ( 42.81ms) 138\n",
|
||
" nlp_grad | 1.32 s ( 1.32 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 386.00us ( 3.78us) 344.87us ( 3.38us) 102\n",
|
||
" nlp_jac_g | 132.02 s ( 1.29 s) 125.99 s ( 1.24 s) 102\n",
|
||
" total | 139.68 s (139.68 s) 133.30 s (133.30 s) 1\n",
|
||
"Timestamp 26700\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 6.92e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9687565e+01 1.31e+01 6.92e+03 -1.5 6.92e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.5589854e+00 4.45e+00 9.63e+00 0.6 2.68e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 8.2826841e+00 1.20e+00 8.34e-01 -1.5 7.74e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 9.0725641e+00 1.72e-03 7.41e-02 -3.3 1.63e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 9.0734286e+00 5.36e-07 7.31e-05 -5.1 2.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 9.0734288e+00 3.21e-07 4.68e-05 -11.0 1.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 9.0734291e+00 1.39e-07 6.14e-05 -11.0 6.48e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 9.0734289e+00 3.18e-07 8.88e-05 -11.0 1.83e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 9.0734290e+00 3.07e-07 6.31e-05 -11.0 1.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 9.0734293e+00 7.24e-11 8.56e-05 -11.0 7.90e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 9.0734291e+00 5.49e-08 1.34e-04 -11.0 4.49e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 9.0734289e+00 2.56e-07 2.71e-05 -11.0 6.89e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 9.0734293e+00 2.83e-08 2.26e-05 -11.0 3.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 9.0734292e+00 5.98e-08 4.14e-05 -11.0 9.44e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 9.0734230e+00 3.95e-06 4.78e-03 -11.0 5.96e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 9.0734220e+00 5.38e-06 4.39e-03 -11.0 1.31e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 9.0734139e+00 9.01e-06 1.69e-03 -11.0 9.99e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 9.0732728e+00 5.93e-05 4.24e-03 -11.0 2.87e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 9.0734056e+00 1.28e-05 2.39e-03 -11.0 1.01e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 9.0728239e+00 9.95e-04 5.25e-03 -11.0 4.04e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 9.0725756e+00 8.48e-04 8.26e-04 -11.0 2.61e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 9.0724691e+00 1.35e-03 2.42e-03 -11.0 3.36e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 9.0693163e+00 4.73e-03 4.59e-03 -11.0 2.13e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 9.0676950e+00 1.64e-02 4.18e-03 -11.0 4.69e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 9.0731536e+00 4.21e-04 1.28e-03 -11.0 5.17e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 9.0715219e+00 1.21e-03 9.36e-04 -11.0 4.98e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 9.0248157e+00 6.36e-02 4.63e-03 -11.0 2.44e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 9.0188431e+00 3.91e-02 3.98e-03 -11.0 2.68e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 9.0681483e+00 1.38e-05 6.82e-02 -11.0 7.39e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 9.0681526e+00 1.48e-06 1.13e-03 -11.0 4.20e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 9.0681539e+00 5.47e-07 1.89e-03 -11.0 3.86e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 9.0681534e+00 1.86e-06 2.52e-03 -11.0 1.06e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 9.0681504e+00 2.00e-06 1.52e-03 -11.0 8.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 9.0681551e+00 1.06e-09 9.88e-05 -11.0 7.12e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 35 9.0650661e+00 1.25e-03 2.49e-02 -11.0 8.05e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 36 9.0674583e+00 4.01e-04 1.26e-03 -11.0 2.38e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 9.0673818e+00 3.76e-04 2.79e-03 -11.0 1.62e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 9.0674442e+00 2.14e-04 1.50e-03 -11.0 9.30e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 9.0677024e+00 2.03e-04 2.04e-03 -11.0 5.55e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 9.0676836e+00 7.22e-05 1.40e-03 -11.0 3.94e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 9.0628231e+00 2.68e-03 4.58e-03 -11.0 9.64e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 9.0676223e+00 3.07e-04 3.58e-03 -11.0 2.81e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 9.0671710e+00 5.19e-04 9.43e-03 -11.0 8.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 9.0580297e+00 7.48e-03 2.20e-02 -11.0 3.12e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 9.0619373e+00 6.44e-03 8.77e-03 -11.0 3.33e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 9.0522743e+00 8.22e-03 2.15e-03 -11.0 2.60e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 8.9001992e+00 9.70e-02 1.44e-02 -11.0 4.94e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 48 9.0567104e+00 4.99e-03 1.24e-02 -11.0 1.29e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 9.0633735e+00 3.55e-05 7.08e-03 -11.0 7.23e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 7.4942045e+00 2.96e+00 3.97e-01 -11.0 2.04e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 51 7.6629719e+00 1.75e+00 8.60e-02 -9.1 2.95e+04 - 1.00e+00 3.76e-01h 1\n",
|
||
" 52 7.6528552e+00 1.73e+00 8.92e-02 -7.1 6.55e+04 - 1.00e+00 1.69e-03h 1\n",
|
||
" 53 7.6527181e+00 1.73e+00 8.92e-02 -5.2 2.60e+04 - 1.00e+00 4.27e-05h 1\n",
|
||
" 54 8.4157678e+00 2.45e-02 1.28e+00 -7.1 1.28e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 8.4378029e+00 1.95e-06 1.43e-03 -9.0 2.93e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 8.4378008e+00 1.91e-06 1.15e-03 -9.8 4.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 8.4378031e+00 4.99e-07 3.04e-05 -11.0 1.58e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 8.4378037e+00 2.32e-08 8.73e-05 -11.0 3.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 8.4377989e+00 3.95e-06 3.72e-03 -11.0 3.36e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 8.4377752e+00 2.40e-05 1.66e-02 -11.0 8.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 8.4377986e+00 2.20e-06 1.02e-03 -11.0 3.35e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 8.4377498e+00 1.32e-05 1.67e-03 -11.0 8.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 8.4377985e+00 9.25e-06 1.10e-03 -11.0 2.55e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 8.4377968e+00 4.31e-06 1.11e-03 -11.0 3.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 8.4377984e+00 3.31e-06 1.03e-03 -9.0 1.78e-02 - 1.00e+00 3.31e-01h 1\n",
|
||
" 66 8.4378027e+00 8.23e-07 1.26e-03 -10.3 8.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 8.4378019e+00 1.19e-06 9.49e-04 -10.5 1.72e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 8.4378016e+00 7.12e-07 2.14e-03 -11.0 1.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 8.4377337e+00 3.08e-05 9.83e-03 -11.0 1.81e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 8.4377882e+00 1.18e-05 1.46e-03 -9.0 5.11e-02 - 8.37e-01 1.00e+00h 1\n",
|
||
" 71 8.4377968e+00 3.27e-06 1.79e-03 -10.1 2.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 8.4377065e+00 6.34e-05 9.65e-03 -8.1 2.22e-01 - 2.16e-01 1.00e+00h 1\n",
|
||
" 73 8.4377987e+00 8.11e-06 1.12e-03 -8.0 4.25e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 8.4377892e+00 8.68e-06 1.41e-03 -8.1 6.34e-02 - 1.00e+00 9.25e-01h 1\n",
|
||
" 75 8.4377900e+00 8.22e-06 4.45e-04 -7.2 1.68e-02 - 1.00e+00 6.25e-02f 5\n",
|
||
" 76 8.4378021e+00 1.03e-06 1.71e-03 -9.3 8.85e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 8.4377986e+00 4.13e-06 2.79e-03 -9.3 2.66e-02 - 8.59e-01 1.00e+00h 1\n",
|
||
" 78 8.4378013e+00 1.22e-06 1.42e-03 -9.3 6.01e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 8.4378021e+00 7.98e-07 1.71e-03 -9.3 1.30e-02 - 8.67e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 8.4378039e+00 6.00e-10 9.08e-05 -9.3 6.99e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 81 8.4378000e+00 1.76e-06 1.07e-03 -11.0 1.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 8.4378019e+00 1.07e-06 3.40e-03 -11.0 1.06e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 8.4378028e+00 1.75e-06 1.61e-03 -11.0 7.08e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 8.4378038e+00 2.06e-07 1.29e-04 -11.0 2.82e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 8.4377962e+00 8.89e-06 4.56e-03 -11.0 5.65e-02 - 8.97e-01 1.00e+00h 1\n",
|
||
" 86 8.4377961e+00 3.64e-06 6.87e-04 -11.0 3.60e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 8.4377947e+00 5.86e-06 1.99e-03 -11.0 2.25e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 8.4378044e+00 5.39e-07 2.21e-03 -11.0 5.67e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 8.4378056e+00 5.51e-10 1.35e-04 -11.0 1.14e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 8.4377704e+00 2.40e-05 5.93e-03 -11.0 7.95e-02 - 6.24e-01 1.00e+00h 1\n",
|
||
" 91 8.4378028e+00 1.68e-06 1.02e-03 -11.0 2.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 8.4378047e+00 1.07e-06 1.31e-03 -11.0 1.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 8.4377962e+00 5.64e-06 9.71e-04 -11.0 1.47e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 8.4378041e+00 1.59e-06 2.19e-03 -11.0 1.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 8.4378082e+00 3.15e-09 2.48e-04 -11.0 5.64e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
"In iteration 95, 1 Slack too small, adjusting variable bound\n",
|
||
" 96 8.4377604e+00 3.05e-05 2.03e-03 -11.0 2.91e-01 - 1.00e+00 1.93e-01f 1\n",
|
||
" 97 8.4378078e+00 9.22e-07 1.02e-03 -11.0 9.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 8.4378098e+00 3.37e-10 9.22e-05 -7.8 1.34e-02 - 3.95e-01 1.00e+00H 1\n",
|
||
" 99 8.4378060e+00 3.21e-06 7.49e-04 -7.8 1.06e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 8.4377975e+00 3.46e-05 4.96e-03 -7.8 4.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 8.4377975380270147e+00 8.4377975380270147e+00\n",
|
||
"Dual infeasibility......: 4.9554420814188760e-03 4.9554420814188760e-03\n",
|
||
"Constraint violation....: 3.4572013213107766e-05 3.4572013213107766e-05\n",
|
||
"Complementarity.........: 1.5015713141616446e-08 1.5015713141616446e-08\n",
|
||
"Overall NLP error.......: 4.9554420814188760e-03 4.9554420814188760e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 112\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 112\n",
|
||
"Number of inequality constraint evaluations = 112\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.446\n",
|
||
"Total CPU secs in NLP function evaluations = 134.275\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 507.00us ( 4.53us) 499.34us ( 4.46us) 112\n",
|
||
" nlp_g | 5.00 s ( 44.66ms) 4.77 s ( 42.55ms) 112\n",
|
||
" nlp_grad | 1.32 s ( 1.32 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 351.00us ( 3.44us) 348.38us ( 3.42us) 102\n",
|
||
" nlp_jac_g | 132.04 s ( 1.29 s) 126.04 s ( 1.24 s) 102\n",
|
||
" total | 138.50 s (138.50 s) 132.20 s (132.20 s) 1\n",
|
||
"Timestamp 27000\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.18e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9930434e+01 1.22e+01 1.18e+04 -1.5 1.18e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.8268345e+00 4.03e+00 7.28e+00 0.6 6.60e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.5682548e+00 6.67e-01 6.82e-01 -1.5 1.74e+01 - 9.97e-01 1.00e+00f 1\n",
|
||
" 4 2.8961749e+00 2.44e-03 2.88e-01 -3.2 1.05e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 2.8972424e+00 1.35e-06 1.01e-03 -5.1 4.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.8972420e+00 2.15e-06 1.18e-03 -7.2 6.35e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.8972379e+00 4.66e-06 1.64e-03 -9.3 1.85e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.8972443e+00 3.90e-09 9.65e-05 -11.0 2.46e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 9 2.8972399e+00 4.36e-06 8.39e-04 -11.0 1.73e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.8972439e+00 1.87e-07 6.20e-05 -11.0 1.70e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.8972422e+00 3.77e-06 6.19e-04 -11.0 7.37e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.8972407e+00 2.42e-06 1.58e-03 -11.0 2.30e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.8972429e+00 4.02e-07 7.80e-05 -11.0 4.35e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.8972414e+00 1.47e-06 1.01e-03 -11.0 9.16e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.8972435e+00 3.18e-10 3.81e-05 -11.0 9.70e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 16 2.8972431e+00 4.70e-07 5.67e-05 -11.0 1.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.8972418e+00 1.28e-06 1.35e-03 -11.0 1.43e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.8972413e+00 1.68e-06 2.65e-03 -11.0 1.85e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.8972430e+00 1.24e-06 7.49e-04 -11.0 4.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.8972433e+00 9.76e-07 8.41e-04 -11.0 7.90e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.8972436e+00 1.96e-07 8.68e-05 -11.0 1.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.8972415e+00 3.25e-06 1.41e-03 -11.0 9.12e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.8972429e+00 9.64e-07 2.88e-03 -11.0 8.11e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.8972411e+00 1.63e-06 3.09e-03 -11.0 1.40e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.8972441e+00 2.45e-07 1.16e-04 -11.0 3.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.8972403e+00 7.20e-06 9.97e-04 -11.0 9.27e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.8972436e+00 3.36e-07 8.90e-05 -11.0 2.72e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.8972416e+00 1.85e-06 1.39e-03 -11.0 8.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.8972384e+00 5.49e-06 1.78e-03 -11.0 1.73e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.8972436e+00 1.25e-08 1.31e-04 -11.0 6.11e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.8972435e+00 6.98e-08 4.86e-05 -11.0 1.97e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.8972436e+00 1.97e-08 4.56e-05 -11.0 1.17e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.8972422e+00 7.64e-07 7.91e-03 -11.0 7.86e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 2.8972429e+00 5.60e-07 1.06e-03 -11.0 3.76e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.8972433e+00 9.19e-08 1.92e-05 -11.0 1.58e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.8972434e+00 6.18e-08 4.66e-05 -11.0 6.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.8972427e+00 7.27e-07 2.62e-05 -11.0 5.29e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.8972435e+00 2.15e-08 1.41e-04 -11.0 5.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.8972434e+00 1.00e-07 7.53e-05 -11.0 6.95e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.8972428e+00 9.49e-07 1.60e-03 -11.0 3.48e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.8972434e+00 1.57e-07 1.08e-04 -11.0 1.94e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.8972420e+00 8.37e-07 2.46e-03 -11.0 4.71e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.8972432e+00 3.14e-07 6.92e-05 -11.0 1.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.8972041e+00 2.86e-05 7.99e-03 -11.0 1.84e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.8972129e+00 3.16e-05 2.24e-03 -11.0 3.39e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.8972349e+00 4.52e-05 6.85e-04 -11.0 1.63e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.8972325e+00 5.51e-05 1.06e-03 -11.0 1.21e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.8972055e+00 7.23e-05 1.16e-03 -11.0 1.94e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.8971703e+00 1.85e-04 3.90e-03 -11.0 7.21e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.8972458e+00 1.57e-05 1.66e-03 -11.0 1.89e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.8959987e+00 1.08e-03 3.37e-03 -11.0 3.96e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.3305834e+00 5.03e-01 2.50e-01 -11.0 2.85e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 53 2.2602676e+00 9.65e-01 1.44e-01 -11.0 4.55e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.1358813e+00 8.48e-01 3.29e-01 -9.0 2.81e+06 - 1.00e+00 2.12e-03f 3\n",
|
||
" 55 2.4701296e+00 3.90e-01 5.25e-01 -7.0 4.60e+04 - 1.00e+00 7.43e-01H 1\n",
|
||
" 56 2.3426546e+00 7.23e-01 2.13e-01 -7.1 6.12e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 2.1855729e+00 3.73e-01 2.06e-01 -7.1 2.46e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.3524649e+00 1.49e-01 2.73e-01 -8.3 3.54e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 2.3100770e+00 2.52e-01 1.15e-01 -7.6 2.95e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.2916022e+00 4.66e-01 6.04e-02 -8.1 4.18e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.2893636e+00 4.41e-01 6.25e-02 -8.2 2.11e+04 - 1.00e+00 3.12e-02h 6\n",
|
||
" 62 2.5289712e+00 4.48e-01 2.87e-01 -8.2 1.72e+04 - 1.00e+00 5.87e-01h 1\n",
|
||
" 63 2.4717425e+00 3.38e-01 2.03e-01 -8.2 4.33e+03 - 1.91e-07 1.00e+00h 1\n",
|
||
" 64 2.3916907e+00 3.15e-01 7.03e-01 -8.2 9.45e+04 - 1.00e+00 1.00e+00F 1\n",
|
||
" 65 2.4321951e+00 3.39e-01 1.58e-01 -8.2 1.99e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.4289764e+00 4.36e-01 1.41e-01 -8.2 8.08e+04 - 1.96e-01 1.74e-02h 6\n",
|
||
" 67 2.4650119e+00 1.74e-01 1.43e-01 -8.2 8.97e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.5633372e+00 6.35e-04 1.78e-01 -8.2 1.89e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.5634898e+00 2.20e-08 5.05e-05 -10.0 6.44e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.5634898e+00 3.99e-08 4.58e-06 -11.0 2.97e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.5634898e+00 2.10e-10 3.04e-05 -11.0 3.36e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 2.5634898e+00 2.58e-09 2.27e-05 -11.0 2.66e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 2.5634898e+00 1.52e-08 4.69e-05 -11.0 5.04e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.5634898e+00 9.18e-09 6.45e-05 -11.0 8.09e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 2.5634898e+00 2.44e-08 4.58e-05 -11.0 7.22e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.5634898e+00 8.97e-09 5.04e-05 -11.0 5.75e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 2.5634860e+00 4.19e-06 1.65e-02 -11.0 1.63e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.5634887e+00 1.02e-06 9.84e-04 -11.0 1.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 2.5634808e+00 3.36e-05 3.91e-03 -11.0 9.09e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.5634709e+00 6.90e-05 3.03e-03 -11.0 1.44e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.5634315e+00 1.56e-04 3.58e-03 -11.0 6.79e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.5632084e+00 3.54e-04 3.86e-03 -11.0 1.51e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 2.5634090e+00 4.79e-05 1.13e-03 -11.0 5.84e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.5634764e+00 3.90e-08 2.61e-05 -11.0 3.43e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 85 2.5634574e+00 6.03e-05 1.04e-03 -11.0 2.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 2.5633932e+00 4.33e-05 1.33e-03 -11.0 6.23e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.5633453e+00 2.15e-04 1.40e-03 -11.0 6.61e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 2.5634702e+00 9.28e-06 1.20e-03 -11.0 2.14e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 2.5633710e+00 1.65e-04 5.16e-04 -11.0 1.19e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.5634226e+00 7.29e-05 7.87e-04 -11.0 1.18e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 2.5634203e+00 7.42e-05 9.01e-04 -11.0 1.12e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 2.5634699e+00 6.91e-05 8.56e-04 -11.0 1.38e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 2.5614896e+00 6.64e-03 3.78e-03 -11.0 1.82e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 2.5564284e+00 5.82e-03 4.63e-03 -11.0 1.32e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.5631440e+00 5.95e-05 5.48e-03 -11.0 3.60e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 96 2.5598883e+00 3.77e-03 1.85e-03 -11.0 3.44e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.5588026e+00 7.19e-03 2.07e-03 -11.0 4.65e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 2.4586786e+00 1.60e-01 6.86e-02 -11.0 2.34e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 2.4322626e+00 3.22e-01 2.28e-01 -11.0 4.08e+04 - 9.35e-01 7.66e-02h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.4186873e+00 4.66e-01 2.45e-01 -11.0 4.09e+04 - 1.00e+00 4.83e-01H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.4186872642860693e+00 2.4186872642860693e+00\n",
|
||
"Dual infeasibility......: 2.4513835381934262e-01 2.4513835381934262e-01\n",
|
||
"Constraint violation....: 4.6604725656796830e-01 4.6604725656796830e-01\n",
|
||
"Complementarity.........: 1.5318204563050927e-11 1.5318204563050927e-11\n",
|
||
"Overall NLP error.......: 4.6604725656796830e-01 4.6604725656796830e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 132\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 132\n",
|
||
"Number of inequality constraint evaluations = 132\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.427\n",
|
||
"Total CPU secs in NLP function evaluations = 134.944\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 598.00us ( 4.53us) 600.23us ( 4.55us) 132\n",
|
||
" nlp_g | 5.89 s ( 44.64ms) 5.62 s ( 42.58ms) 132\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 378.00us ( 3.71us) 365.67us ( 3.58us) 102\n",
|
||
" nlp_jac_g | 132.00 s ( 1.29 s) 126.02 s ( 1.24 s) 102\n",
|
||
" total | 139.39 s (139.39 s) 133.07 s (133.07 s) 1\n",
|
||
"Timestamp 27300\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.14e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0234725e+01 1.26e+01 2.14e+04 -1.5 2.14e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.1212933e+00 4.41e+00 7.51e+00 0.8 3.17e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.0992787e+00 6.48e-01 6.23e-01 -1.3 9.34e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 2.4278758e+00 4.79e-03 4.26e-01 -3.0 3.51e+00 - 9.99e-01 1.00e+00h 1\n",
|
||
" 5 2.4291580e+00 3.54e-05 5.56e-03 -4.9 1.82e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.4291487e+00 3.01e-05 1.47e-03 -7.0 2.42e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.4291598e+00 3.38e-05 8.89e-04 -9.1 1.94e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.4291445e+00 3.09e-05 1.71e-03 -9.2 1.17e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.4291152e+00 9.12e-05 2.96e-03 -11.0 2.41e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.4291571e+00 5.40e-05 1.61e-03 -11.0 1.85e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 2.4291714e+00 1.24e-05 1.10e-03 -11.0 1.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.4291696e+00 2.97e-05 5.39e-04 -11.0 6.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.4291667e+00 1.22e-05 1.60e-03 -11.0 1.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.4291761e+00 1.01e-05 1.84e-03 -11.0 4.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.4288791e+00 1.29e-04 3.80e-03 -11.0 4.91e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.4291738e+00 5.45e-06 1.23e-03 -11.0 8.63e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.4291751e+00 5.06e-06 1.50e-03 -11.0 5.62e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.4291728e+00 1.07e-05 1.40e-03 -11.0 7.97e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.4290929e+00 2.04e-04 5.46e-03 -11.0 6.41e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.4278788e+00 2.69e-03 6.84e-03 -11.0 1.15e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.4282675e+00 1.80e-03 2.64e-03 -11.0 1.14e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.4281298e+00 1.01e-03 1.30e-03 -11.0 7.65e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.4183319e+00 1.52e-02 5.36e-03 -11.0 1.09e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.3701710e+00 7.02e-02 2.63e-02 -11.0 2.59e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.4112309e+00 1.60e-02 1.57e-02 -11.0 1.12e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.4178039e+00 1.58e-02 1.48e-02 -11.0 1.27e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.4299638e+00 2.26e-03 9.73e-03 -11.0 8.88e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.3211202e+00 2.50e-01 9.37e-02 -11.0 8.40e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 29 2.4318651e+00 2.36e-03 8.24e-02 -11.0 2.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.4185450e+00 9.62e-03 5.83e-02 -11.0 9.83e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.3089327e+00 1.80e-01 4.30e-02 -11.0 3.56e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.2472503e+00 6.56e-01 2.05e-01 -11.0 2.69e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.0939336e+00 5.42e-01 2.82e-01 -11.0 8.25e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 2.3014259e+00 7.77e-02 3.90e-01 -11.0 1.08e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.1066577e+00 1.42e+00 5.86e-01 -10.8 2.77e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 36 1.8858248e+00 1.03e+00 1.72e-01 -10.9 5.17e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.8829871e+00 9.08e-01 1.23e-01 -11.0 5.49e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
" 38 1.9713202e+00 4.29e-01 3.22e-01 -11.0 9.03e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 39 1.9238208e+00 5.76e-01 4.97e-02 -11.0 4.27e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.8733884e+00 8.44e-01 1.83e-01 -10.4 1.29e+05 - 1.00e+00 2.31e-01h 1\n",
|
||
" 41 1.8727071e+00 8.39e-01 1.77e-01 -8.5 1.20e+04 - 1.00e+00 3.83e-02h 1\n",
|
||
" 42 1.8501705e+00 9.39e-01 1.63e-01 -11.0 1.28e+05 - 2.33e-05 1.52e-02h 6\n",
|
||
" 43 2.2311127e+00 1.98e-02 9.98e-01 -10.3 9.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.2423682e+00 5.83e-06 7.65e-03 -11.0 2.58e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.2423699e+00 4.69e-08 6.56e-05 -11.0 2.46e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.2423698e+00 1.49e-07 3.55e-05 -11.0 3.39e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.2423699e+00 2.17e-08 1.48e-05 -11.0 1.74e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.2423699e+00 1.07e-08 4.24e-05 -11.0 6.22e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.2423698e+00 1.60e-07 1.03e-04 -11.0 1.81e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.2423689e+00 1.00e-06 7.63e-03 -11.0 5.47e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.2423697e+00 2.46e-07 4.28e-05 -11.0 1.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.2423696e+00 2.16e-07 2.19e-05 -11.0 1.95e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.2423699e+00 1.01e-07 7.13e-05 -11.0 1.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.2423693e+00 5.33e-07 1.44e-03 -11.0 2.06e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.2423651e+00 2.56e-06 4.82e-03 -11.0 2.27e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.2423695e+00 9.34e-07 1.40e-03 -11.0 3.95e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 2.2423691e+00 3.76e-07 4.10e-05 -11.0 4.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.2423693e+00 4.11e-07 9.40e-05 -11.0 2.71e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 2.2423678e+00 2.01e-06 1.31e-03 -11.0 6.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.2423645e+00 3.99e-06 2.51e-03 -11.0 1.08e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.2423699e+00 1.40e-08 1.24e-04 -11.0 7.04e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.2423698e+00 7.53e-08 5.27e-05 -11.0 1.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.2423699e+00 6.12e-09 6.81e-05 -11.0 5.06e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.2423699e+00 1.35e-08 1.94e-04 -11.0 6.91e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.2423697e+00 1.67e-07 1.79e-05 -11.0 2.85e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.2423699e+00 1.12e-08 7.55e-05 -11.0 6.40e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.2423699e+00 8.95e-09 7.35e-05 -11.0 7.60e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.2423699e+00 9.86e-11 3.85e-05 -11.0 1.72e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 69 2.2423698e+00 2.31e-07 5.08e-05 -11.0 6.03e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.2423699e+00 2.73e-08 2.65e-05 -11.0 2.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.2423698e+00 1.17e-07 6.53e-05 -11.0 9.19e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 2.2423696e+00 3.62e-07 1.82e-05 -11.0 7.27e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 2.2423697e+00 2.79e-07 2.77e-05 -11.0 3.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.2423699e+00 7.71e-10 4.90e-05 -11.0 5.93e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 75 2.2423674e+00 1.08e-05 3.32e-03 -11.0 2.62e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.2423668e+00 3.73e-06 9.17e-04 -11.0 1.21e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 2.2423051e+00 4.67e-05 1.85e-03 -11.0 4.41e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.2179635e+00 4.59e-02 1.57e-02 -11.0 1.77e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 79 2.2064636e+00 6.10e-02 7.67e-03 -11.0 9.62e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.1556348e+00 7.20e-01 4.13e-01 -11.0 8.58e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.0359939e+00 3.02e-01 1.10e-01 -11.0 7.20e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.0407221e+00 2.65e-01 1.99e-01 -11.0 7.33e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 2.2181010e+00 8.04e-02 1.27e-01 -11.0 2.26e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.0365698e+00 4.37e-01 1.06e-01 -9.0 1.56e+04 - 1.00e+00 9.34e-01h 1\n",
|
||
" 85 2.0345983e+00 4.37e-01 1.03e-01 -9.2 3.10e+05 - 1.86e-01 4.73e-04h 1\n",
|
||
" 86 2.2053380e+00 2.28e-01 2.05e-01 -10.2 3.03e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.1714194e+00 9.64e-02 6.72e-02 -5.3 7.23e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 2.1896372e+00 1.24e-01 7.42e-02 -3.3 1.54e+03 - 6.07e-01 1.00e+00h 1\n",
|
||
" 89 2.0469924e+00 7.67e-01 3.04e-01 -9.5 1.00e+05 - 1.14e-03 5.18e-02f 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.3128948e+00 2.23e-01 6.06e-01 -3.6 5.55e+04 - 1.84e-01 6.55e-01H 1\n",
|
||
" 91 2.3124083e+00 2.20e-01 5.99e-01 -3.6 7.13e+03 - 1.00e+00 8.37e-03h 1\n",
|
||
" 92 2.3164723e+00 1.66e-01 4.18e-01 -3.6 6.71e+02 - 5.52e-01 2.50e-01h 3\n",
|
||
" 93 1.9544720e+00 1.08e+00 5.23e-01 -3.6 1.82e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 94 2.2750082e+00 8.27e-03 8.70e-01 -3.6 1.67e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.2853512e+00 1.64e-06 2.88e-03 -3.6 1.91e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 2.2853503e+00 1.24e-07 4.37e-05 -3.6 5.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.2853505e+00 1.01e-10 7.53e-05 -5.5 6.31e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 98 2.2853505e+00 3.29e-11 5.62e-05 -5.5 3.12e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 99 2.2853505e+00 2.36e-08 3.67e-05 -5.5 1.21e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.2853504e+00 4.29e-08 2.45e-05 -5.5 6.40e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.2853503958837309e+00 2.2853503958837309e+00\n",
|
||
"Dual infeasibility......: 2.4544855078754572e-05 2.4544855078754572e-05\n",
|
||
"Constraint violation....: 4.2898101071386918e-08 4.2898101071386918e-08\n",
|
||
"Complementarity.........: 3.3764972900734635e-06 3.3764972900734635e-06\n",
|
||
"Overall NLP error.......: 2.4544855078754572e-05 2.4544855078754572e-05\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 125\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 125\n",
|
||
"Number of inequality constraint evaluations = 125\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.407\n",
|
||
"Total CPU secs in NLP function evaluations = 134.678\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 563.00us ( 4.50us) 548.21us ( 4.39us) 125\n",
|
||
" nlp_g | 5.56 s ( 44.48ms) 5.30 s ( 42.38ms) 125\n",
|
||
" nlp_grad | 1.37 s ( 1.37 s) 1.31 s ( 1.31 s) 1\n",
|
||
" nlp_grad_f | 381.00us ( 3.74us) 370.34us ( 3.63us) 102\n",
|
||
" nlp_jac_g | 131.85 s ( 1.29 s) 125.81 s ( 1.23 s) 102\n",
|
||
" total | 138.92 s (138.92 s) 132.55 s (132.55 s) 1\n",
|
||
"Timestamp 27600\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 4.57e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9518165e+01 1.37e+01 4.57e+03 -1.5 4.57e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.7733855e+00 4.69e+00 1.04e+01 0.4 1.37e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 1.0494305e+01 1.38e+00 6.83e-01 -1.6 8.91e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.1459235e+01 1.39e-03 7.91e-02 -3.4 1.82e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.1459926e+01 8.47e-07 1.56e-03 -5.3 3.49e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.1459925e+01 2.50e-06 1.82e-03 -7.3 6.05e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.1459927e+01 8.72e-07 1.17e-03 -9.4 2.72e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.1459927e+01 7.90e-07 1.22e-03 -11.0 4.28e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.1459923e+01 3.87e-06 2.69e-03 -11.0 2.29e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.1459930e+01 2.46e-10 9.32e-05 -11.0 2.46e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 1.1459917e+01 9.74e-06 3.17e-03 -11.0 3.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.1459931e+01 2.80e-08 5.46e-05 -11.0 1.36e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.1459931e+01 1.48e-08 9.98e-05 -11.0 2.09e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.1459931e+01 9.95e-08 1.88e-04 -11.0 3.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.1459931e+01 6.98e-08 1.21e-04 -11.0 2.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.1459931e+01 1.86e-08 2.36e-05 -11.0 1.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.1459931e+01 5.64e-09 1.99e-04 -11.0 4.17e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.1459931e+01 1.33e-08 1.58e-04 -11.0 5.77e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.1459931e+01 3.23e-07 1.98e-04 -11.0 8.28e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.1459931e+01 1.16e-08 8.67e-05 -11.0 9.33e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.1459931e+01 5.33e-08 4.94e-05 -11.0 3.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.1459931e+01 3.91e-11 7.28e-05 -11.0 2.43e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 23 1.1459931e+01 3.10e-07 6.29e-05 -11.0 4.36e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.1459931e+01 2.43e-07 1.84e-04 -11.0 1.21e-04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 25 1.1459931e+01 3.75e-08 1.13e-04 -11.0 3.35e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.1459931e+01 8.25e-11 1.06e-04 -11.0 2.76e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 27 1.1459931e+01 4.35e-09 5.28e-05 -11.0 7.58e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.1459931e+01 3.10e-11 5.30e-05 -11.0 3.05e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 29 1.1459931e+01 1.33e-08 3.48e-05 -11.0 2.18e-03 - 1.00e+00 3.12e-02h 6\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.1459931e+01 4.79e-11 1.11e-04 -11.0 1.76e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 31 1.1459931e+01 2.96e-09 1.31e-04 -11.0 4.27e-05 - 1.00e+00 5.00e-01h 2\n",
|
||
" 32 1.1459931e+01 9.33e-09 7.38e-05 -11.0 5.91e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.1459931e+01 4.46e-11 9.53e-05 -11.0 2.26e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 1.1459931e+01 4.95e-11 1.59e-04 -11.0 1.90e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 35 1.1459928e+01 3.19e-06 2.08e-02 -11.0 4.08e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.1459892e+01 3.97e-05 5.75e-02 -11.0 1.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.1459929e+01 2.39e-06 1.82e-03 -11.0 2.62e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.1459930e+01 1.03e-06 3.55e-03 -11.0 4.05e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.1458008e+01 6.06e-04 3.72e-03 -11.0 9.64e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.1457393e+01 2.54e-03 2.33e-03 -11.0 1.54e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.1412041e+01 1.40e-01 2.01e-02 -11.0 8.00e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.1446404e+01 2.11e-02 4.36e-03 -11.0 2.98e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 9.7088786e+00 1.75e+00 1.48e-01 -11.0 1.98e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 44 8.9697493e+00 2.09e+00 2.11e-01 -9.0 2.58e+04 - 1.00e+00 4.56e-01h 1\n",
|
||
" 45 8.9853688e+00 2.07e+00 2.05e-01 -7.1 1.03e+04 - 1.00e+00 1.14e-02h 1\n",
|
||
" 46 8.9854586e+00 2.07e+00 2.05e-01 -5.1 1.15e+04 - 1.00e+00 1.02e-04h 1\n",
|
||
" 47 1.1594867e+01 6.44e-02 2.94e-01 -3.2 3.26e+02 - 1.79e-01 1.00e+00h 1\n",
|
||
" 48 1.1578155e+01 8.22e-02 2.52e-01 -4.0 2.31e+03 - 1.00e+00 1.47e-01h 1\n",
|
||
" 49 1.1583640e+01 7.19e-02 2.20e-01 -4.0 1.48e+03 - 1.00e+00 1.25e-01h 4\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.1631250e+01 3.25e-03 4.45e-03 -4.0 1.69e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.1628890e+01 2.99e-03 3.90e-03 -4.0 1.77e+03 - 1.00e+00 1.21e-01h 1\n",
|
||
" 52 1.1617378e+01 6.63e-04 1.80e-02 -4.0 3.48e+03 - 2.50e-01 1.00e+00F 1\n",
|
||
" 53 1.1150138e+01 3.07e-01 3.10e-02 -4.0 6.30e+04 - 5.50e-01 4.37e-01f 1\n",
|
||
" 54 1.1595289e+01 5.45e-02 2.22e-02 -3.7 1.79e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.1504684e+01 2.04e-05 1.12e-01 -2.0 1.18e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.1504694e+01 8.36e-07 2.07e-03 -2.0 3.59e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.1504695e+01 3.61e-08 1.13e-04 -3.0 4.27e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.1504695e+01 5.39e-08 4.42e-05 -4.5 3.15e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.1504695e+01 9.40e-09 9.33e-05 -6.7 1.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.1504695e+01 6.98e-08 8.05e-05 -6.7 2.13e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.1504695e+01 7.80e-11 1.85e-04 -6.7 3.01e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 62 1.1504694e+01 5.74e-07 1.11e-04 -6.7 1.76e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.1504692e+01 1.74e-06 4.23e-03 -6.7 1.65e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.1504693e+01 1.14e-06 1.57e-03 -6.7 8.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.1504690e+01 2.32e-06 2.89e-03 -6.7 1.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.1504685e+01 4.94e-06 2.19e-03 -6.7 1.58e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.1504695e+01 4.15e-08 8.67e-05 -6.7 9.55e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.1504695e+01 9.24e-09 1.34e-04 -6.7 6.89e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.1504695e+01 3.79e-08 9.51e-05 -6.7 1.94e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.1504694e+01 5.76e-08 1.52e-04 -6.7 2.90e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.1504694e+01 9.04e-08 4.37e-05 -6.7 3.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.1504695e+01 1.66e-08 7.35e-05 -6.7 6.56e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.1504694e+01 4.97e-07 1.30e-04 -6.7 1.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.1504157e+01 2.09e-04 2.93e-02 -6.7 1.87e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.1504513e+01 9.85e-05 1.41e-03 -6.7 8.19e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.1504639e+01 5.79e-05 1.45e-03 -6.7 3.25e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.1504654e+01 2.36e-05 1.76e-03 -6.7 3.87e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.1504493e+01 1.32e-04 1.21e-02 -6.7 9.09e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.1504317e+01 1.62e-04 1.28e-02 -6.7 1.30e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.1504418e+01 1.22e-04 9.07e-03 -6.7 3.12e-04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 81 1.1504570e+01 6.08e-05 4.97e-03 -6.7 2.34e-04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 82 1.1504646e+01 3.04e-05 2.11e-03 -6.7 1.17e-04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 83 1.1504684e+01 1.52e-05 2.17e-03 -6.7 5.87e-05 - 1.00e+00 5.00e-01h 2\n",
|
||
" 84 1.1504685e+01 1.47e-05 8.59e-04 -6.7 2.93e-05 - 1.00e+00 3.12e-02h 6\n",
|
||
" 85 1.1504722e+01 1.06e-08 1.31e-04 -6.7 2.84e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.1504722e+01 1.79e-09 3.84e-04 -6.7 1.04e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 1.1504722e+01 3.31e-08 1.38e-04 -6.7 1.97e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 1.1504722e+01 3.11e-08 9.84e-05 -6.7 2.18e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 1.1504722e+01 1.77e-08 2.10e-04 -6.7 1.56e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.1504722e+01 7.77e-08 9.98e-05 -6.7 4.28e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.1504722e+01 4.49e-08 1.37e-05 -6.7 5.00e-05 - 1.00e+00 5.00e-01h 2\n",
|
||
" 92 1.1504722e+01 9.57e-10 6.90e-05 -6.7 4.75e-06 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 1.1504722e+01 6.11e-11 1.06e-04 -6.7 1.01e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 94 1.1504722e+01 7.96e-11 7.31e-05 -6.7 6.55e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 95 1.1504722e+01 3.51e-11 7.88e-05 -6.7 2.06e-05 - 1.00e+00 2.44e-04h 13\n",
|
||
" 96 1.1504722e+01 4.60e-11 1.23e-04 -6.7 5.13e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 97 1.1504722e+01 3.26e-11 4.55e-05 -6.7 5.40e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 98 1.1504722e+01 1.46e-11 8.00e-05 -6.7 1.53e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 99 1.1504722e+01 2.89e-11 6.38e-05 -6.7 1.47e-04 - 1.00e+00 2.38e-07h 23\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.1504722e+01 4.68e-11 4.73e-05 -6.7 5.39e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.1504722054077973e+01 1.1504722054077973e+01\n",
|
||
"Dual infeasibility......: 4.7295367860465546e-05 4.7295367860465546e-05\n",
|
||
"Constraint violation....: 4.6807002718196600e-11 4.6807002718196600e-11\n",
|
||
"Complementarity.........: 1.8906669708292574e-07 1.8906669708292574e-07\n",
|
||
"Overall NLP error.......: 4.7295367860465546e-05 4.7295367860465546e-05\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 195\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 195\n",
|
||
"Number of inequality constraint evaluations = 195\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.412\n",
|
||
"Total CPU secs in NLP function evaluations = 137.507\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 881.00us ( 4.52us) 873.29us ( 4.48us) 195\n",
|
||
" nlp_g | 8.68 s ( 44.50ms) 8.27 s ( 42.41ms) 195\n",
|
||
" nlp_grad | 1.32 s ( 1.32 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 407.00us ( 3.99us) 397.68us ( 3.90us) 102\n",
|
||
" nlp_jac_g | 131.67 s ( 1.29 s) 125.65 s ( 1.23 s) 102\n",
|
||
" total | 141.81 s (141.81 s) 135.31 s (135.31 s) 1\n",
|
||
"Timestamp 27900\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.50e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9621580e+01 1.26e+01 3.50e+03 -1.5 3.50e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.7013213e+00 4.18e+00 8.57e+00 0.4 1.26e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 5.4647597e+00 9.05e-01 7.16e-01 -1.6 6.49e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 6.0822471e+00 1.97e-03 8.59e-02 -3.4 1.31e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 6.0831075e+00 1.13e-06 8.98e-04 -5.3 2.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 6.0831089e+00 2.23e-07 2.76e-04 -7.4 1.50e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 6.0831083e+00 6.36e-07 1.65e-04 -9.4 1.13e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 6.0831090e+00 1.14e-07 2.04e-04 -11.0 4.42e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 6.0831091e+00 7.53e-08 1.39e-04 -11.0 2.21e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 6.0831092e+00 2.63e-08 9.99e-05 -11.0 2.12e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 6.0831092e+00 1.03e-07 5.27e-05 -11.0 8.26e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 6.0831074e+00 3.04e-06 1.53e-03 -11.0 1.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 6.0831089e+00 1.23e-07 4.14e-05 -11.0 1.63e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 6.0831087e+00 1.01e-07 1.39e-05 -11.0 1.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 6.0831089e+00 1.39e-08 1.37e-04 -11.0 4.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 6.0831072e+00 1.61e-06 3.55e-03 -11.0 4.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 6.0831089e+00 9.62e-09 8.03e-05 -11.0 3.99e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 6.0831083e+00 3.83e-07 4.43e-05 -11.0 2.36e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 6.0831087e+00 2.19e-07 5.42e-05 -11.0 1.65e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 6.0831072e+00 9.45e-07 1.11e-03 -11.0 8.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 6.0831077e+00 1.41e-06 1.94e-03 -11.0 3.87e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 6.0831084e+00 3.35e-07 8.33e-05 -11.0 1.54e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 6.0830993e+00 4.11e-06 4.62e-03 -11.0 4.66e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 6.0831094e+00 2.28e-10 2.08e-04 -11.0 3.80e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 25 6.0830993e+00 9.64e-06 1.90e-03 -11.0 2.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 6.0831049e+00 5.52e-06 1.32e-03 -11.0 3.34e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 6.0831072e+00 1.17e-06 2.15e-03 -11.0 8.44e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 6.0831091e+00 8.29e-10 3.84e-05 -11.0 6.42e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 29 6.0831085e+00 8.93e-07 1.04e-03 -11.0 7.90e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 6.0831073e+00 1.65e-06 1.50e-03 -11.0 1.72e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 6.0831029e+00 1.85e-05 1.98e-03 -11.0 2.62e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 6.0831074e+00 1.03e-06 1.12e-03 -11.0 1.29e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 6.0831085e+00 3.40e-07 1.43e-04 -11.0 5.80e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 6.0831080e+00 6.45e-07 1.15e-03 -11.0 3.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 6.0831031e+00 4.99e-06 2.14e-03 -11.0 4.17e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 6.0830986e+00 3.14e-06 5.14e-03 -11.0 5.59e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 6.0831055e+00 4.35e-06 1.80e-03 -11.0 1.86e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 6.0831033e+00 1.55e-05 2.12e-03 -11.0 6.23e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 6.0830965e+00 1.20e-05 1.04e-03 -11.0 3.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 6.0829821e+00 1.51e-04 4.98e-03 -11.0 6.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 6.0831037e+00 8.85e-06 1.60e-03 -11.0 1.49e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 6.0830911e+00 2.12e-05 1.98e-03 -11.0 1.22e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 6.0830973e+00 2.14e-05 2.37e-03 -11.0 1.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 6.0830862e+00 1.80e-05 8.82e-04 -11.0 5.32e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 6.0830392e+00 9.10e-05 9.17e-04 -11.0 8.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 6.0829719e+00 1.39e-04 1.49e-03 -11.0 5.92e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 6.0828776e+00 1.23e-04 1.29e-03 -11.0 4.84e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 6.0830806e+00 7.56e-05 1.48e-03 -11.0 1.17e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 6.0829568e+00 8.52e-05 9.70e-04 -11.0 1.37e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 6.0788886e+00 3.86e-03 1.43e-03 -11.0 6.98e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 4.9512815e+00 4.43e-01 1.02e-01 -9.9 3.38e+04 - 1.00e+00 9.25e-01f 1\n",
|
||
" 52 4.9493558e+00 4.43e-01 1.02e-01 -7.9 1.63e+06 - 1.00e+00 7.39e-05h 1\n",
|
||
" 53 5.9700207e+00 1.28e-01 7.53e-02 -6.6 1.65e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 5.6159503e+00 2.47e-01 1.45e-01 -4.5 1.28e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 5.5792505e+00 1.85e+00 2.63e-01 -5.3 9.75e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 6.0868035e+00 5.54e-02 2.17e-01 -5.5 5.66e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 6.0123547e+00 1.65e-01 4.22e-02 -5.5 4.71e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 5.9289523e+00 4.39e-01 5.86e-02 -5.5 3.44e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 5.6184622e+00 1.10e+00 1.62e-01 -3.5 8.08e+04 - 1.00e+00 8.07e-02f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 5.5864989e+00 4.78e-01 5.02e-02 -3.6 3.15e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 5.5203002e+00 1.35e+00 8.25e-02 -4.5 3.89e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 5.5417517e+00 1.15e+00 1.11e-01 -4.1 1.02e+04 - 1.00e+00 1.25e-01h 1\n",
|
||
" 63 5.0741619e+00 1.67e+00 3.41e-01 -4.1 2.75e+04 - 1.12e-01 9.51e-01h 1\n",
|
||
" 64 5.7076388e+00 1.30e-01 1.18e-01 -4.1 6.43e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 6.6125267e+00 2.07e+00 2.11e-01 -0.1 4.35e+05 - 6.19e-02 7.03e-02f 2\n",
|
||
" 66 5.7523395e+00 1.33e-01 2.13e-01 -0.9 5.98e+03 - 9.12e-01 1.00e+00h 1\n",
|
||
" 67 5.1848586e+00 7.06e-01 1.10e-01 -1.6 7.39e+03 - 9.55e-01 1.00e+00f 1\n",
|
||
" 68 5.5625388e+00 5.59e-01 8.68e-02 -1.6 6.83e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 5.2252008e+00 2.92e-01 1.08e-01 -7.6 1.62e+05 - 1.94e-01 1.44e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 5.5845951e+00 1.24e-01 1.32e-01 -2.4 2.14e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 5.5075795e+00 1.15e-01 8.23e-02 -2.7 1.63e+03 - 5.79e-01 1.00e+00h 1\n",
|
||
" 72 4.9703853e+00 3.88e-01 4.29e-02 -3.1 1.29e+04 - 8.71e-01 9.61e-01f 1\n",
|
||
" 73 4.9153404e+00 6.32e-01 1.23e-01 -2.4 1.13e+04 - 1.00e+00 2.87e-01h 1\n",
|
||
" 74 5.3786406e+00 4.77e-01 1.52e-01 -1.9 4.21e+03 - 5.03e-01 1.00e+00h 1\n",
|
||
" 75 5.1944916e+00 1.95e+00 1.57e-01 -2.6 1.32e+06 - 3.12e-02 7.67e-03f 2\n",
|
||
" 76 5.8782457e+00 3.10e-01 3.90e-01 -2.2 4.62e+03 - 6.67e-01 1.00e+00h 1\n",
|
||
" 77 5.5458968e+00 8.69e-01 1.72e-01 -2.2 7.24e+03 - 7.99e-01 1.00e+00h 1\n",
|
||
" 78 5.1218448e+00 2.08e+00 9.39e-02 -2.2 5.05e+05 - 9.00e-01 9.43e-02f 1\n",
|
||
" 79 4.8736209e+00 2.50e+00 2.78e-01 -2.2 1.74e+04 - 1.00e+00 1.24e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 5.0792530e+00 3.38e-01 3.65e-01 -2.2 2.36e+03 - 2.73e-01 1.00e+00h 1\n",
|
||
" 81 5.0164007e+00 4.09e-01 2.53e-01 -3.0 1.63e+03 - 9.59e-01 1.00e+00h 1\n",
|
||
" 82 5.3137771e+00 1.36e-01 6.06e-02 -2.8 5.94e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 5.1174417e+00 5.62e-01 7.50e-02 -2.0 1.77e+04 - 3.60e-01 2.45e-01h 1\n",
|
||
" 84 5.2322404e+00 1.54e-01 1.92e-01 -2.5 4.66e+03 - 7.08e-01 1.00e+00h 1\n",
|
||
" 85 5.0054594e+00 2.51e-01 2.20e-01 -2.1 6.34e+04 - 9.26e-01 9.17e-02f 1\n",
|
||
" 86 4.9669666e+00 3.58e-01 2.40e-02 -1.9 3.85e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 87 4.8310659e+00 8.36e-01 1.90e-01 -7.8 2.83e+04 - 1.20e-01 1.74e-01h 1\n",
|
||
" 88 5.3219305e+00 3.01e-01 9.50e-02 -3.2 6.52e+03 - 9.93e-01 1.00e+00h 1\n",
|
||
" 89 4.2526850e+00 1.62e+00 3.76e-01 -3.1 2.63e+04 - 8.99e-01 6.20e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 4.2738219e+00 1.28e+00 2.56e-01 -2.5 3.62e+03 - 1.00e+00 3.72e-01h 1\n",
|
||
" 91 5.5256793e+00 4.64e-02 3.43e-01 -7.5 3.35e+03 - 5.31e-01 1.00e+00h 1\n",
|
||
" 92 5.3521779e+00 3.24e-01 1.41e-01 -3.8 1.88e+03 - 9.79e-01 6.07e-01h 1\n",
|
||
" 93 5.5433687e+00 7.62e-02 4.99e-02 -3.9 1.14e+03 - 1.03e-01 1.00e+00h 1\n",
|
||
" 94 5.4854641e+00 5.94e-01 6.80e-02 -2.4 4.69e+03 - 5.41e-01 1.00e+00h 1\n",
|
||
" 95 5.3770193e+00 5.16e-01 8.35e-02 -2.6 4.25e+03 - 8.78e-01 1.00e+00h 1\n",
|
||
" 96 5.3714896e+00 1.39e-01 7.34e-02 -2.6 6.66e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 4.3751969e+00 8.41e-01 2.77e-01 -2.3 1.17e+04 - 4.47e-01 1.00e+00f 1\n",
|
||
" 98 4.2307603e+00 1.72e+00 4.90e-01 -1.9 8.28e+04 - 1.00e+00 2.07e-01h 1\n",
|
||
" 99 8.3876673e+00 1.12e+00 3.51e-01 -1.4 3.28e+04 - 4.66e-01 9.80e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 6.6311414e+00 1.33e+00 2.51e-01 -1.8 2.15e+04 - 1.00e+00 7.66e-01f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 6.6311414009250349e+00 6.6311414009250349e+00\n",
|
||
"Dual infeasibility......: 2.5057553487554884e-01 2.5057553487554884e-01\n",
|
||
"Constraint violation....: 1.3314494211316337e+00 1.3314494211316337e+00\n",
|
||
"Complementarity.........: 2.1803551270338303e-01 2.1803551270338303e-01\n",
|
||
"Overall NLP error.......: 1.3314494211316337e+00 1.3314494211316337e+00\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 108\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 108\n",
|
||
"Number of inequality constraint evaluations = 108\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.429\n",
|
||
"Total CPU secs in NLP function evaluations = 133.957\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 481.00us ( 4.45us) 477.94us ( 4.43us) 108\n",
|
||
" nlp_g | 4.85 s ( 44.91ms) 4.62 s ( 42.80ms) 108\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 401.00us ( 3.93us) 350.82us ( 3.44us) 102\n",
|
||
" nlp_jac_g | 131.93 s ( 1.29 s) 125.94 s ( 1.23 s) 102\n",
|
||
" total | 138.26 s (138.26 s) 131.97 s (131.97 s) 1\n",
|
||
"Timestamp 28200\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 5.03e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9714196e+01 1.29e+01 5.03e+03 -1.5 5.03e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.8271230e+00 4.37e+00 9.24e+00 0.6 3.11e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 5.8060359e+00 9.51e-01 8.54e-01 -1.5 7.91e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 6.4785739e+00 2.40e-03 9.27e-02 -3.3 1.39e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 6.4796812e+00 6.74e-07 2.23e-03 -5.1 2.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 6.4796817e+00 3.28e-07 1.43e-04 -7.2 2.81e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 6.4796810e+00 8.38e-07 1.09e-03 -11.0 2.10e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 6.4796808e+00 9.91e-07 9.66e-04 -11.0 5.31e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 6.4796823e+00 8.42e-08 3.71e-05 -11.0 8.33e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 6.4796823e+00 6.49e-08 6.47e-05 -11.0 9.57e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 6.4796821e+00 1.26e-07 2.75e-05 -11.0 9.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 6.4796823e+00 6.54e-08 1.73e-04 -11.0 4.27e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 6.4796820e+00 4.57e-07 5.40e-05 -11.0 2.33e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 6.4796817e+00 1.40e-06 2.30e-03 -11.0 4.07e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 6.4796820e+00 2.33e-07 7.77e-05 -11.0 1.71e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 6.4796821e+00 1.85e-07 3.89e-05 -11.0 1.11e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 6.4796817e+00 3.97e-07 1.44e-04 -11.0 3.11e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 6.4796775e+00 2.79e-06 7.06e-03 -11.0 9.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 6.4796824e+00 2.65e-07 6.38e-05 -11.0 2.46e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 6.4796821e+00 5.10e-07 3.11e-05 -11.0 3.71e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 6.4796826e+00 2.31e-07 4.04e-05 -11.0 3.30e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 6.4796819e+00 8.73e-07 1.38e-03 -11.0 5.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 6.4796753e+00 1.06e-05 4.39e-03 -11.0 1.48e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 6.4796622e+00 8.76e-06 1.18e-02 -11.0 3.75e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 6.4796774e+00 8.56e-06 2.16e-03 -11.0 3.38e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 6.4796779e+00 3.43e-06 1.72e-03 -11.0 1.99e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 6.4796816e+00 7.91e-07 2.43e-03 -11.0 8.52e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 6.4796498e+00 2.30e-05 2.30e-03 -11.0 6.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 6.4791601e+00 5.44e-04 8.94e-03 -11.0 1.24e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 6.4796088e+00 1.00e-04 2.93e-03 -11.0 6.49e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 6.4795777e+00 1.38e-04 1.02e-03 -11.0 4.37e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 6.4796333e+00 5.95e-05 2.23e-03 -11.0 4.64e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 6.4793306e+00 1.95e-04 2.55e-03 -11.0 9.83e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 6.4794670e+00 1.46e-04 1.36e-03 -11.0 1.09e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 6.4796708e+00 1.82e-05 1.14e-03 -11.0 2.98e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 6.4792874e+00 2.97e-04 1.84e-03 -11.0 1.36e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 6.4796701e+00 1.79e-05 1.76e-03 -11.0 5.78e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 6.4793011e+00 2.24e-04 2.01e-03 -11.0 1.46e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 6.4472207e+00 3.43e-02 1.00e-02 -11.0 1.85e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 6.4651327e+00 1.74e-02 2.13e-03 -11.0 1.20e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 6.4738329e+00 4.50e-03 1.20e-03 -11.0 4.08e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 6.4649360e+00 7.51e-03 2.54e-03 -11.0 9.12e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 6.4802985e+00 6.32e-06 1.64e-03 -11.0 5.74e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 44 6.4753490e+00 2.95e-03 1.53e-03 -11.0 5.24e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 45 5.9768068e+00 1.42e+00 2.04e-01 -11.0 6.04e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 46 4.6979744e+00 2.25e+00 6.36e-01 -11.0 1.55e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 47 5.1921614e+00 1.61e+00 1.42e-01 -11.0 7.27e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 4.8573303e+00 1.85e+00 1.54e-01 -11.0 8.30e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 4.8011706e+00 2.27e+00 1.00e-01 -10.0 1.78e+05 - 1.00e+00 1.58e-02f 5\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 4.8003591e+00 2.46e+00 1.55e-01 -8.0 1.22e+07 - 4.70e-03 1.76e-04h 4\n",
|
||
" 51 4.5655773e+00 2.05e+00 9.93e-02 -11.0 5.18e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 4.5602199e+00 2.17e+00 1.12e-01 -9.6 9.28e+04 - 1.00e+00 3.04e-02h 5\n",
|
||
" 53 4.9457269e+00 1.45e+00 2.03e-01 -10.3 7.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 4.9195135e+00 2.23e+00 1.83e-01 -10.4 5.07e+04 - 1.00e+00 2.51e-01h 1\n",
|
||
" 55 5.1337777e+00 9.70e-01 4.57e-01 -10.4 1.96e+04 - 6.92e-10 1.00e+00H 1\n",
|
||
" 56 4.3987306e+00 2.06e+00 4.41e-01 -9.1 2.07e+05 - 1.00e+00 2.91e-01f 1\n",
|
||
" 57 4.3876191e+00 2.07e+00 4.42e-01 -6.6 8.84e+05 - 3.02e-01 6.83e-04h 1\n",
|
||
" 58 4.3874188e+00 2.07e+00 4.42e-01 -5.2 5.12e+04 - 1.00e+00 1.18e-04h 1\n",
|
||
" 59 5.6103382e+00 7.87e-02 2.07e+00 -6.9 2.07e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 5.6771585e+00 9.60e-05 1.25e-02 -8.8 1.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 5.6771942e+00 8.58e-08 1.05e-04 -10.6 6.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 5.6771942e+00 7.98e-08 7.45e-05 -11.0 3.07e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 5.6771943e+00 2.01e-08 9.50e-05 -11.0 1.99e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 5.6771943e+00 3.40e-08 2.97e-05 -11.0 1.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 5.6771943e+00 1.10e-08 4.84e-05 -11.0 1.40e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 5.6771943e+00 1.63e-08 5.23e-05 -11.0 1.24e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 5.6771935e+00 5.47e-07 3.60e-05 -11.0 6.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 5.6771941e+00 1.78e-07 9.20e-05 -11.0 2.13e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 5.6771879e+00 2.66e-06 3.94e-03 -11.0 2.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 5.6771929e+00 4.30e-07 1.78e-03 -11.0 5.67e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 5.6771911e+00 1.11e-06 8.67e-04 -11.0 4.85e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 5.6771937e+00 1.16e-10 2.11e-05 -11.0 3.46e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 73 5.6771935e+00 5.60e-07 1.80e-03 -11.0 2.42e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 5.6771932e+00 1.80e-06 1.89e-03 -11.0 6.84e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"In iteration 74, 1 Slack too small, adjusting variable bound\n",
|
||
" 75 5.6771564e+00 2.31e-05 1.03e-02 -11.0 1.04e-01 - 1.00e+00 4.59e-01h 1\n",
|
||
" 76 5.6771903e+00 9.61e-07 1.93e-03 -11.0 8.36e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 5.6771923e+00 2.52e-10 1.94e-05 -6.4 5.30e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 78 5.6771921e+00 3.67e-07 5.82e-05 -11.0 5.12e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 5.6771913e+00 1.82e-06 1.70e-03 -8.3 1.11e-02 - 1.00e+00 7.11e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 5.6771919e+00 2.71e-07 8.08e-05 -8.4 3.24e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 5.6771918e+00 1.53e-07 6.34e-05 -9.1 1.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 5.6771915e+00 2.70e-07 4.25e-05 -9.4 2.37e-02 - 1.00e+00 1.46e-01h 1\n",
|
||
" 83 5.6771873e+00 9.21e-06 1.97e-03 -10.7 4.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 5.6771843e+00 7.47e-06 1.87e-03 -11.0 2.97e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 5.6771785e+00 1.44e-05 2.23e-03 -9.0 2.16e-02 - 2.18e-01 1.00e+00h 1\n",
|
||
" 86 5.6771893e+00 1.15e-06 3.04e-03 -7.8 6.37e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 5.6771888e+00 6.31e-06 3.22e-03 -5.9 7.40e-02 - 8.35e-01 1.00e+00h 1\n",
|
||
" 88 5.6771765e+00 7.89e-06 3.54e-03 -6.1 1.24e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 5.6771839e+00 8.03e-06 2.17e-03 -6.8 2.67e-01 - 1.00e+00 7.40e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 5.6771891e+00 1.31e-06 2.43e-03 -7.5 7.72e-03 - 1.00e+00 9.99e-01h 1\n",
|
||
" 91 5.6771384e+00 1.53e-05 4.02e-03 -5.6 3.50e-01 - 4.03e-01 1.00e+00f 1\n",
|
||
" 92 5.6771269e+00 6.20e-05 1.07e-03 -6.2 2.20e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 5.6767237e+00 3.34e-04 2.03e-03 -7.2 6.33e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 5.6771784e+00 1.96e-05 5.96e-03 -5.2 1.19e-01 - 5.35e-01 1.00e+00h 1\n",
|
||
" 95 5.6369948e+00 3.76e-02 8.12e-03 -5.7 2.17e+02 - 6.11e-04 1.00e+00f 1\n",
|
||
" 96 5.5553525e+00 3.69e-01 4.66e-02 -5.9 5.03e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 5.2801145e+00 1.28e+00 1.67e-01 -4.3 9.50e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 5.7158845e+00 5.32e-02 2.15e-01 -5.1 1.94e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 5.2605428e+00 8.42e-01 1.12e-01 -5.3 1.96e+04 - 1.00e+00 8.49e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 5.0329525e+00 8.51e-01 6.00e-02 -4.4 5.13e+04 - 1.00e+00 3.49e-01f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 5.0329525007188671e+00 5.0329525007188671e+00\n",
|
||
"Dual infeasibility......: 6.0034601840248558e-02 6.0034601840248558e-02\n",
|
||
"Constraint violation....: 8.5061315355147116e-01 8.5061315355147116e-01\n",
|
||
"Complementarity.........: 1.1475602430191406e-01 1.1475602430191406e-01\n",
|
||
"Overall NLP error.......: 8.5061315355147116e-01 8.5061315355147116e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 123\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 123\n",
|
||
"Number of inequality constraint evaluations = 123\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.411\n",
|
||
"Total CPU secs in NLP function evaluations = 134.270\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 553.00us ( 4.50us) 545.71us ( 4.44us) 123\n",
|
||
" nlp_g | 5.47 s ( 44.49ms) 5.22 s ( 42.40ms) 123\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 365.00us ( 3.58us) 359.44us ( 3.52us) 102\n",
|
||
" nlp_jac_g | 131.61 s ( 1.29 s) 125.60 s ( 1.23 s) 102\n",
|
||
" total | 138.56 s (138.56 s) 132.23 s (132.23 s) 1\n",
|
||
"Timestamp 28500\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.83e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 2.0020854e+01 1.24e+01 1.83e+04 -1.5 1.83e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.8011680e+00 4.24e+00 6.48e+00 0.8 2.02e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.3989323e+00 6.63e-01 7.08e-01 -1.3 5.66e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 2.8104900e+00 2.95e-03 3.16e-01 -3.1 2.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 2.8117707e+00 2.93e-05 5.57e-03 -4.9 1.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.8118290e+00 9.39e-06 7.87e-04 -7.0 4.86e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.8118208e+00 1.40e-05 8.87e-04 -9.1 4.11e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.8118436e+00 3.79e-07 2.96e-05 -11.0 1.56e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.8118389e+00 5.62e-06 8.66e-04 -11.0 4.80e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.8118436e+00 4.49e-10 4.49e-05 -11.0 3.97e-02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 2.8117987e+00 7.18e-05 1.45e-03 -11.0 1.93e-01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 12 2.8117818e+00 6.91e-05 2.42e-03 -11.0 1.97e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.8118407e+00 1.20e-08 1.14e-04 -11.0 7.45e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.8118407e+00 1.99e-08 4.63e-05 -11.0 1.51e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.8118406e+00 1.71e-07 8.03e-05 -11.0 6.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.8118406e+00 1.23e-07 6.23e-05 -11.0 4.24e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.8118407e+00 4.70e-08 7.80e-05 -11.0 1.93e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.8118407e+00 8.94e-08 4.06e-05 -11.0 2.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.8118407e+00 5.27e-09 4.65e-05 -11.0 7.96e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.8118408e+00 2.65e-11 1.45e-04 -11.0 7.10e-05 - 1.00e+00 1.00e+00H 1\n",
|
||
" 21 2.8118407e+00 2.30e-08 4.93e-05 -11.0 1.58e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.8118407e+00 5.39e-11 9.81e-05 -11.0 1.33e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 23 2.8118407e+00 1.81e-08 2.12e-05 -11.0 1.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.8118407e+00 5.36e-09 1.74e-04 -11.0 3.89e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.8118407e+00 8.35e-09 6.25e-05 -11.0 4.63e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.8118407e+00 3.78e-08 1.94e-04 -11.0 1.46e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.8118407e+00 1.01e-08 1.03e-04 -11.0 3.09e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.8118407e+00 5.80e-09 8.44e-05 -11.0 2.92e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.8118407e+00 6.55e-09 1.45e-04 -11.0 2.78e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.8118407e+00 3.94e-08 2.89e-05 -11.0 2.37e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.8118407e+00 6.54e-09 5.53e-05 -11.0 2.92e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.8118407e+00 1.04e-09 6.09e-05 -11.0 2.58e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.8118407e+00 1.62e-08 1.48e-04 -11.0 8.85e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 2.8118406e+00 1.91e-07 5.09e-05 -11.0 4.08e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.8118407e+00 1.19e-08 1.39e-04 -11.0 1.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.8118407e+00 6.01e-08 5.91e-05 -11.0 2.80e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.8118407e+00 2.49e-08 7.33e-05 -11.0 1.34e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.8118407e+00 3.68e-08 9.50e-05 -11.0 1.95e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.8118407e+00 6.31e-08 4.13e-05 -11.0 1.21e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.8118407e+00 7.15e-09 3.29e-05 -11.0 1.08e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.8118407e+00 1.07e-08 9.49e-05 -11.0 1.18e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.8118406e+00 5.49e-08 5.17e-05 -11.0 3.38e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.8118407e+00 6.65e-09 7.56e-05 -11.0 7.75e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.8118407e+00 2.85e-08 1.42e-04 -11.0 1.13e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.8118407e+00 2.49e-08 1.71e-04 -11.0 7.30e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.8118407e+00 3.95e-08 1.25e-04 -11.0 1.04e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.8118389e+00 1.31e-06 7.77e-03 -11.0 4.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.8118394e+00 4.78e-07 8.54e-04 -11.0 2.60e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.8118407e+00 7.93e-08 7.58e-05 -11.0 4.20e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.8118407e+00 3.73e-08 1.02e-04 -11.0 3.22e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.8118407e+00 2.57e-08 6.02e-05 -11.0 7.85e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.8118406e+00 7.27e-07 5.89e-05 -11.0 1.79e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.8118401e+00 3.01e-07 2.59e-04 -11.0 3.96e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.8118269e+00 9.61e-06 1.41e-03 -11.0 1.79e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.8118403e+00 3.34e-08 7.51e-05 -11.0 4.91e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.8118358e+00 5.71e-06 2.47e-03 -11.0 7.22e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 2.8118253e+00 1.08e-05 1.10e-03 -11.0 5.87e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.8118395e+00 6.31e-07 1.46e-03 -11.0 1.71e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 2.8118390e+00 2.36e-06 1.91e-03 -11.0 1.91e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.8118373e+00 2.46e-06 9.00e-04 -11.0 1.40e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.8118352e+00 2.61e-06 2.44e-03 -11.0 9.74e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.8118391e+00 2.98e-06 1.14e-03 -11.0 1.62e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.8118397e+00 4.68e-07 3.04e-04 -11.0 3.93e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.8118239e+00 1.17e-05 4.77e-03 -11.0 2.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.8118356e+00 4.45e-06 9.57e-04 -11.0 3.30e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.8118343e+00 3.21e-06 7.49e-04 -11.0 1.21e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.8098748e+00 1.09e-03 2.54e-02 -11.0 4.96e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.8118571e+00 6.44e-05 1.10e-03 -11.0 1.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.8119181e+00 4.35e-05 9.69e-04 -11.0 2.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.8118409e+00 1.49e-04 1.92e-03 -11.0 8.91e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.8117955e+00 2.50e-04 2.00e-03 -11.0 6.31e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 2.8117599e+00 1.27e-04 1.43e-03 -11.0 1.79e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 2.8107999e+00 6.55e-04 1.58e-03 -11.0 1.57e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.8116407e+00 1.30e-04 1.46e-03 -11.0 9.22e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 2.8110510e+00 4.08e-04 1.26e-03 -11.0 2.42e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.8090823e+00 1.78e-03 5.56e-03 -11.0 1.05e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 2.8073860e+00 7.19e-03 6.67e-03 -11.0 2.20e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.7931426e+00 1.01e-02 9.13e-03 -11.0 4.67e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 2.7105791e+00 7.89e-02 2.70e-02 -11.0 5.01e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.7077543e+00 4.69e-02 1.16e-02 -11.0 3.03e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.7008660e+00 8.71e-02 7.19e-03 -11.0 2.01e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.7898254e+00 9.47e-03 2.40e-02 -11.0 5.36e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 2.7252344e+00 9.09e-02 2.59e-02 -11.0 1.51e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.7790084e+00 3.95e-02 2.83e-02 -11.0 1.41e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 2.7739637e+00 2.87e-02 6.89e-03 -11.0 2.07e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 2.7877508e+00 2.38e-02 6.14e-03 -11.0 1.19e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.4925977e+00 1.18e+00 3.44e-01 -9.0 3.39e+05 - 1.00e+00 1.15e-02f 4\n",
|
||
" 88 2.6634668e+00 2.15e-01 3.91e-01 -9.1 5.28e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 2.5095763e+00 8.54e-01 2.63e-01 -9.7 5.82e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.4798137e+00 9.04e-01 7.21e-01 -9.8 2.03e+05 - 1.59e-01 1.38e-01f 1\n",
|
||
" 91 2.4733651e+00 1.16e+00 7.17e-01 -9.8 2.73e+04 - 6.23e-01 1.25e-01h 4\n",
|
||
" 92 2.3014822e+00 1.87e-01 4.07e-01 -9.8 2.03e+03 - 6.24e-01 1.00e+00h 1\n",
|
||
" 93 2.1856195e+00 5.49e-01 1.08e-01 -10.6 4.11e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 2.1538788e+00 2.51e-01 2.14e-01 -10.9 1.59e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.1399770e+00 6.40e-01 1.75e-01 -9.9 5.53e+04 - 1.00e+00 2.46e-01h 3\n",
|
||
" 96 2.1345097e+00 7.90e-01 2.26e-01 -9.6 3.49e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 97 2.2755340e+00 2.75e-01 3.53e-01 -10.2 8.13e+03 - 9.46e-01 8.57e-01H 1\n",
|
||
" 98 2.2565040e+00 6.54e-01 2.50e-01 -9.3 1.56e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 99 2.4181195e+00 3.13e-01 2.46e-01 -9.4 3.89e+04 - 1.00e+00 7.47e-01H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.2991494e+00 2.80e-01 6.10e-02 -9.4 3.97e+03 - 6.44e-09 1.00e+00f 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.2991493577551703e+00 2.2991493577551703e+00\n",
|
||
"Dual infeasibility......: 6.1007977777345457e-02 6.1007977777345457e-02\n",
|
||
"Constraint violation....: 2.7984439096985270e-01 2.7984439096985270e-01\n",
|
||
"Complementarity.........: 4.7231307413761669e-10 4.7231307413761669e-10\n",
|
||
"Overall NLP error.......: 2.7984439096985270e-01 2.7984439096985270e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 128\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 128\n",
|
||
"Number of inequality constraint evaluations = 128\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.405\n",
|
||
"Total CPU secs in NLP function evaluations = 134.942\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 573.00us ( 4.48us) 567.71us ( 4.44us) 128\n",
|
||
" nlp_g | 5.73 s ( 44.78ms) 5.46 s ( 42.69ms) 128\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 394.00us ( 3.86us) 390.50us ( 3.83us) 102\n",
|
||
" nlp_jac_g | 132.02 s ( 1.29 s) 125.99 s ( 1.24 s) 102\n",
|
||
" total | 139.25 s (139.25 s) 132.90 s (132.90 s) 1\n",
|
||
"Timestamp 28800\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.25e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9772512e+01 1.28e+01 1.25e+04 -1.5 1.25e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.5916245e+00 4.41e+00 8.17e+00 0.6 6.47e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 4.6639390e+00 8.50e-01 8.96e-01 -1.5 1.72e+01 - 9.97e-01 1.00e+00f 1\n",
|
||
" 4 5.3125486e+00 2.85e-03 1.19e-01 -3.2 1.31e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 5.3139726e+00 2.24e-06 1.88e-03 -5.1 9.41e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 5.3139413e+00 1.27e-05 2.39e-03 -7.2 9.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 5.3138887e+00 5.76e-05 3.29e-03 -9.3 2.93e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 5.3090866e+00 3.42e-03 1.66e-02 -11.0 1.85e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 5.3039355e+00 5.56e-03 1.72e-02 -11.0 2.67e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 5.3085047e+00 3.59e-03 4.26e-03 -11.0 1.44e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 5.3136840e+00 6.76e-04 1.23e-03 -11.0 4.59e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 5.3144151e+00 3.05e-04 4.37e-03 -11.0 1.41e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 5.3118717e+00 4.75e-03 3.40e-03 -11.0 1.42e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 5.3146276e+00 2.21e-04 1.73e-03 -11.0 2.86e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 5.3122469e+00 3.53e-03 2.09e-03 -11.0 1.24e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 5.3092702e+00 2.95e-03 1.46e-03 -11.0 3.16e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 5.3047524e+00 1.25e-02 2.72e-03 -11.0 4.63e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 5.3073044e+00 1.03e-02 2.42e-03 -11.0 1.44e+01 - 1.00e+00 2.50e-01h 3\n",
|
||
" 19 5.3143018e+00 6.00e-04 1.93e-03 -11.0 4.03e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 5.3143677e+00 5.66e-04 1.84e-03 -11.0 4.05e+00 - 1.00e+00 6.25e-02h 5\n",
|
||
" 21 5.3157266e+00 2.45e-07 1.77e-04 -11.0 1.27e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 22 5.3077248e+00 2.77e-02 3.21e-03 -11.0 6.89e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 23 5.2903645e+00 2.47e-02 4.78e-03 -11.0 5.94e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 5.2283839e+00 6.91e-02 1.30e-02 -11.0 2.71e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 5.2877806e+00 1.96e-02 1.36e-02 -11.0 1.98e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 5.3031192e+00 6.67e-03 7.09e-03 -11.0 9.37e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 5.2914900e+00 1.54e-02 4.79e-03 -11.0 1.45e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 5.2518330e+00 7.14e-02 6.88e-03 -11.0 3.67e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 5.0903098e+00 9.17e-02 3.43e-02 -11.0 4.80e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 5.1471295e+00 1.15e-01 8.93e-03 -11.0 4.37e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 4.7551970e+00 4.74e-01 6.95e-02 -11.0 1.45e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 5.2838708e+00 3.40e-02 4.61e-02 -11.0 2.72e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 3.8354107e+00 1.83e+00 3.99e-01 -10.7 3.46e+04 - 1.00e+00 6.53e-01F 1\n",
|
||
" 34 3.9894564e+00 1.79e+00 3.19e-01 -9.9 1.30e+05 - 1.00e+00 7.23e-03H 1\n",
|
||
" 35 3.8315036e+00 4.21e-01 4.23e-01 -9.0 1.61e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.9732074e+00 2.05e+00 1.31e+00 -7.0 2.62e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 37 6.1008927e+00 1.81e+00 5.55e-01 -4.9 3.56e+04 - 1.00e+00 5.17e-01h 1\n",
|
||
" 38 5.8546435e+00 1.45e+00 5.65e-01 -5.0 7.47e+03 - 1.00e+00 1.28e-01h 1\n",
|
||
" 39 2.9304225e+00 1.94e+00 1.58e+00 -5.0 2.43e+04 - 1.00e+00 5.90e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.7567980e+00 1.74e+00 1.32e+00 -4.3 6.06e+04 - 1.00e+00 1.02e-01h 1\n",
|
||
" 41 5.4464155e+00 1.81e-01 3.05e-01 -2.0 1.64e+03 - 5.68e-01 1.00e+00h 1\n",
|
||
" 42 4.9727437e+00 2.63e-01 1.62e-01 -2.1 9.39e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 4.9858992e+00 1.77e-01 8.04e-02 -1.9 6.68e+02 - 9.92e-01 1.00e+00h 1\n",
|
||
" 44 5.0186377e+00 2.23e-01 8.36e-02 -2.4 2.83e+03 - 2.71e-01 2.71e-01s 21\n",
|
||
" 45 5.0406135e+00 3.38e-01 6.14e-02 -2.1 3.23e+03 - 4.81e-01 2.50e-01h 3\n",
|
||
" 46 5.1489881e+00 9.71e-02 1.86e-02 -2.1 9.36e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 3.7540822e+00 5.54e-01 1.70e-01 -2.6 3.71e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 48 4.5075979e+00 2.75e-01 1.61e-01 -1.7 3.48e+03 - 1.00e+00 9.44e-01h 1\n",
|
||
" 49 4.0732456e+00 3.01e+00 5.17e-01 -7.7 9.15e+04 - 7.16e-02 1.42e-01f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 4.0126515e+00 2.82e+00 5.08e-01 -2.0 2.54e+03 - 1.00e+00 6.17e-02h 1\n",
|
||
" 51 4.7248877e+00 5.65e-02 4.94e-01 -2.0 2.85e+02 - 1.48e-01 1.00e+00h 1\n",
|
||
" 52 3.8084287e+00 1.85e+00 2.83e-01 -2.2 7.71e+03 - 6.42e-01 1.00e+00f 1\n",
|
||
" 53 3.3002814e+00 1.57e+00 4.84e-01 -2.0 4.78e+04 - 7.93e-02 2.50e-01f 3\n",
|
||
" 54 5.7712885e+00 2.76e+00 4.18e-01 -3.2 2.71e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 4.2607325e+00 1.16e+00 1.70e-01 -2.8 3.90e+04 - 6.03e-01 7.85e-01f 1\n",
|
||
" 56 4.2177174e+00 9.83e-01 2.16e-01 -2.8 4.79e+04 - 1.00e+00 1.12e-01h 1\n",
|
||
" 57 5.1624152e+00 5.66e-01 1.81e-01 -2.8 8.34e+03 - 1.05e-01 1.00e+00h 1\n",
|
||
" 58 5.1081710e+00 6.28e-01 2.41e-01 -2.8 2.85e+04 - 1.00e+00 6.94e-01F 1\n",
|
||
" 59 5.1033301e+00 6.32e-01 2.42e-01 -2.8 1.13e+04 - 8.45e-01 2.31e-03h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 5.5439416e+00 5.69e-02 8.78e-02 -2.8 1.97e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 5.6279428e+00 4.49e-03 1.09e-02 -2.8 1.96e+02 - 3.53e-01 1.00e+00h 1\n",
|
||
" 62 5.5134071e+00 9.36e-02 5.11e-02 -4.2 6.94e+03 - 8.81e-01 1.00e+00f 1\n",
|
||
" 63 5.2550646e+00 1.85e-01 6.37e-02 -4.2 2.85e+05 - 1.89e-02 5.54e-02f 1\n",
|
||
" 64 4.3547956e+00 2.41e+00 5.74e-01 -4.2 1.60e+04 - 1.71e-03 1.00e+00f 1\n",
|
||
" 65 4.8199369e+00 1.33e+00 4.01e-02 -4.2 1.14e+04 - 8.04e-01 5.21e-01h 1\n",
|
||
" 66 5.0491330e+00 3.65e-01 4.12e-01 -4.2 1.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 4.9743166e+00 8.61e-01 2.57e-01 -4.2 5.16e+03 - 1.00e+00 7.61e-01h 1\n",
|
||
" 68 5.0101533e+00 5.85e-01 2.08e-01 -4.2 1.09e+04 - 1.00e+00 2.72e-01h 1\n",
|
||
" 69 4.1046667e+00 2.04e+00 3.57e-01 -4.2 1.37e+06 - 2.07e-02 1.53e-02f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 4.1025835e+00 2.04e+00 3.58e-01 -4.2 2.04e+05 - 8.85e-01 2.12e-04h 1\n",
|
||
" 71 4.5441352e+00 1.02e+00 1.10e-01 -4.2 5.44e+03 - 2.61e-03 5.00e-01h 2\n",
|
||
" 72 4.7068942e+00 8.30e-01 8.31e-02 -4.2 2.82e+03 - 3.35e-01 1.87e-01h 1\n",
|
||
" 73 5.4460186e+00 9.50e-02 2.12e-01 -4.2 1.35e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 5.5457387e+00 2.10e-02 1.55e-02 -4.2 1.07e+03 - 5.68e-01 1.00e+00h 1\n",
|
||
" 75 4.6547794e+00 8.97e-01 3.88e-01 -4.2 1.61e+04 - 8.85e-02 1.00e+00f 1\n",
|
||
" 76 5.3258432e+00 1.97e-01 1.45e-01 -4.2 9.55e+03 - 9.44e-01 1.00e+00h 1\n",
|
||
" 77 4.3933952e+00 5.76e-01 1.04e-01 -4.2 1.69e+04 - 9.94e-01 1.00e+00f 1\n",
|
||
" 78 4.0812607e+00 1.29e+00 2.76e-01 -4.2 1.55e+06 - 3.23e-02 7.90e-03f 1\n",
|
||
" 79 4.6166310e+00 7.20e-01 9.28e-02 -4.2 2.51e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 4.5733924e+00 6.29e-01 6.98e-02 -4.2 3.45e+03 - 1.00e+00 1.26e-01h 1\n",
|
||
" 81 4.6686304e+00 5.39e-01 6.45e-02 -4.2 2.50e+03 - 4.32e-01 1.81e-01h 1\n",
|
||
" 82 5.3686072e+00 4.47e-02 1.32e-01 -4.2 2.26e+02 - 2.32e-03 1.00e+00h 1\n",
|
||
" 83 5.3546636e+00 3.43e-02 3.71e-02 -4.2 7.80e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 5.3983570e+00 2.14e-02 3.88e-02 -4.2 1.59e+03 - 5.01e-01 1.00e+00H 1\n",
|
||
" 85 4.9979980e+00 4.28e-01 1.45e-01 -4.2 1.79e+04 - 7.54e-02 6.25e-02f 5\n",
|
||
" 86 5.3824549e+00 5.84e-04 5.56e-01 -4.2 6.45e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 5.3827749e+00 5.00e-07 7.77e-05 -4.2 1.84e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 5.3827750e+00 2.46e-07 9.87e-05 -10.2 2.66e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 5.3827671e+00 4.64e-06 2.87e-03 -11.0 2.93e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 5.3827696e+00 1.72e-06 2.12e-03 -11.0 1.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 5.3827741e+00 5.94e-07 2.44e-03 -11.0 5.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 5.3827619e+00 6.43e-06 4.00e-03 -11.0 2.70e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 5.3827610e+00 4.63e-06 1.08e-03 -11.0 1.79e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 5.3827733e+00 6.02e-07 1.03e-03 -11.0 5.76e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 5.3827741e+00 8.33e-07 8.67e-04 -11.0 2.57e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 5.3827371e+00 1.48e-05 3.79e-03 -11.0 8.07e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 5.3827028e+00 4.93e-05 2.39e-03 -11.0 2.60e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 5.3826319e+00 9.21e-05 2.94e-03 -11.0 6.44e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 5.3827341e+00 8.38e-06 1.52e-03 -11.0 1.91e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 5.3825065e+00 3.15e-04 3.42e-03 -11.0 3.34e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 5.3825064535268847e+00 5.3825064535268847e+00\n",
|
||
"Dual infeasibility......: 3.4243677886945378e-03 3.4243677886945378e-03\n",
|
||
"Constraint violation....: 3.1491322686250101e-04 3.1491322686250101e-04\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 3.4243677886945378e-03 3.4243677886945378e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 158\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 158\n",
|
||
"Number of inequality constraint evaluations = 158\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.411\n",
|
||
"Total CPU secs in NLP function evaluations = 135.754\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 705.00us ( 4.46us) 705.40us ( 4.46us) 158\n",
|
||
" nlp_g | 7.08 s ( 44.83ms) 6.76 s ( 42.78ms) 158\n",
|
||
" nlp_grad | 1.32 s ( 1.32 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 348.00us ( 3.41us) 338.11us ( 3.31us) 102\n",
|
||
" nlp_jac_g | 131.56 s ( 1.29 s) 125.58 s ( 1.23 s) 102\n",
|
||
" total | 140.10 s (140.10 s) 133.73 s (133.73 s) 1\n",
|
||
"Timestamp 29100\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.69e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9819378e+01 1.23e+01 1.69e+04 -1.5 1.69e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.6352685e+00 4.10e+00 7.29e+00 1.0 4.39e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 2.6611725e+00 7.13e-01 7.85e-01 -1.1 1.18e+02 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 3.1521902e+00 2.73e-03 2.58e-01 -2.9 3.36e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 5 3.1534189e+00 5.10e-05 5.60e-03 -4.7 3.52e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 3.1533988e+00 4.44e-05 1.78e-03 -6.8 3.66e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 3.1534600e+00 2.33e-05 2.59e-03 -8.9 2.05e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 3.1529248e+00 5.19e-04 5.41e-03 -11.0 8.99e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 3.1517880e+00 1.45e-03 5.41e-03 -11.0 2.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 3.1531717e+00 3.61e-04 1.02e-03 -11.0 9.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 3.1387786e+00 6.55e-03 7.60e-03 -11.0 5.12e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 3.1526566e+00 9.77e-04 2.60e-03 -11.0 9.37e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 3.1492290e+00 2.31e-03 1.94e-03 -11.0 2.37e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 3.1496601e+00 2.81e-03 1.10e-03 -11.0 1.45e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 3.1527143e+00 3.49e-04 1.06e-03 -11.0 4.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 3.1525824e+00 3.82e-04 1.27e-03 -11.0 3.41e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 3.1504252e+00 3.47e-03 2.49e-03 -11.0 2.93e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 3.1439713e+00 1.80e-02 3.24e-03 -11.0 5.16e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 3.1459909e+00 1.37e-02 2.11e-03 -11.0 2.34e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 3.1367451e+00 1.17e-02 4.85e-03 -11.0 1.12e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.7873549e+00 2.58e-01 8.55e-02 -11.0 5.63e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 22 2.9478565e+00 2.81e-01 9.00e-02 -11.0 5.88e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.9030969e+00 1.39e-01 7.10e-02 -11.0 1.31e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.7446703e+00 4.38e-01 1.48e-01 -11.0 1.85e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 3.0158441e+00 4.04e-02 1.47e-01 -11.0 1.19e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.9192657e+00 8.93e-01 2.54e-01 -11.0 1.14e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.8577726e+00 1.63e+00 6.88e-01 -11.0 4.78e+04 - 9.90e-01 3.73e-01F 1\n",
|
||
" 28 2.6276111e+00 1.39e+00 5.45e-01 -11.0 2.44e+03 - 1.00e+00 1.76e-01h 1\n",
|
||
" 29 3.2623500e+00 1.62e-01 1.13e+00 -9.4 3.03e+03 - 5.79e-02 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.6101997e+00 1.83e-01 1.46e-01 -2.3 1.04e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 31 2.8715125e+00 4.23e-02 4.57e-02 -4.1 2.15e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 32 2.8246901e+00 9.72e-03 4.47e-02 -4.7 1.29e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.6468144e+00 1.11e-01 7.90e-02 -6.2 6.61e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 34 2.4368236e+00 3.84e-01 2.25e-01 -4.3 7.38e+04 - 1.00e+00 2.08e-02f 1\n",
|
||
" 35 2.3470880e+00 4.35e-01 4.99e-02 -3.9 2.95e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.7334433e+00 1.48e-01 1.87e-01 -2.5 1.87e+03 - 1.00e+00 9.93e-01h 1\n",
|
||
" 37 2.4796114e+00 6.69e-01 1.18e-01 -2.7 1.34e+03 - 5.51e-02 1.00e+00f 1\n",
|
||
" 38 2.8314921e+00 2.60e-01 1.85e-01 -2.7 3.90e+03 - 9.12e-02 1.00e+00h 1\n",
|
||
" 39 2.6446106e+00 2.44e-01 1.26e-01 -2.7 6.71e+03 - 1.00e+00 6.28e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.6266159e+00 1.29e-01 2.88e-01 -2.7 1.23e+04 - 7.74e-01 1.00e+00F 1\n",
|
||
" 41 2.4394465e+00 1.21e+00 4.70e-01 -2.5 1.59e+04 - 6.45e-01 1.00e+00f 1\n",
|
||
" 42 2.6138537e+00 1.18e+00 3.69e-01 -2.7 6.21e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.5374647e+00 1.52e-01 3.54e-01 -2.7 2.04e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44r 2.5374647e+00 1.52e-01 9.99e+02 -0.8 0.00e+00 - 0.00e+00 3.03e-07R 22\n",
|
||
" 45r 2.6094827e+00 5.01e-02 3.78e+02 -3.0 1.65e+02 - 1.00e+00 1.18e-03f 1\n",
|
||
" 46 2.2922434e+00 7.60e-01 5.46e-01 -1.9 6.95e+03 - 9.36e-01 1.00e+00f 1\n",
|
||
" 47 2.3600662e+00 7.66e-01 1.27e-01 -2.4 6.22e+03 - 9.98e-01 1.00e+00h 1\n",
|
||
" 48 2.4825920e+00 5.96e-01 2.81e-01 -2.5 6.71e+04 - 1.46e-01 2.53e-01H 1\n",
|
||
" 49 2.1124748e+00 6.15e-01 2.52e-01 -2.5 2.92e+04 - 9.28e-01 8.71e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.1374912e+00 5.51e-01 2.37e-01 -2.2 1.06e+04 - 1.00e+00 2.32e-01h 1\n",
|
||
" 51 2.2276023e+00 4.86e-01 1.20e-01 -1.5 2.26e+03 - 9.56e-01 5.00e-01f 2\n",
|
||
" 52 2.4500592e+00 4.31e-01 2.83e-01 -1.7 1.19e+04 - 1.00e+00 9.64e-01h 1\n",
|
||
" 53 2.3015585e+00 3.17e-01 1.82e-01 -2.6 5.36e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.3624953e+00 4.52e-01 3.30e-01 -1.8 5.51e+03 - 1.01e-01 1.00e+00H 1\n",
|
||
" 55 2.3446649e+00 3.65e-01 2.63e-01 -2.4 1.12e+04 - 1.00e+00 2.72e-01H 1\n",
|
||
" 56 2.4485759e+00 1.27e-01 1.18e-01 -3.0 7.39e+02 - 9.43e-01 1.00e+00h 1\n",
|
||
" 57 2.4153221e+00 6.93e-02 9.64e-02 -3.9 4.87e+03 - 7.91e-01 4.17e-01H 1\n",
|
||
" 58 2.5392618e+00 2.98e-01 2.06e-02 -1.5 2.83e+05 - 6.50e-02 5.51e-02f 3\n",
|
||
" 59 2.5822816e+00 2.12e-01 4.37e-01 -2.0 1.50e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.4818561e+00 7.98e-04 2.07e-01 -2.0 2.07e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.4819905e+00 2.47e-08 7.77e-05 -3.9 7.98e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.4819904e+00 7.95e-08 3.87e-05 -9.9 4.86e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.4819905e+00 4.40e-08 8.72e-05 -11.0 3.50e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 2.4819905e+00 6.52e-08 2.57e-05 -11.0 2.37e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.4819906e+00 5.66e-09 2.53e-05 -11.0 5.25e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 2.4819906e+00 4.59e-09 7.41e-06 -11.0 3.68e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.4819906e+00 2.90e-09 3.58e-05 -11.0 2.13e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.4819906e+00 1.28e-09 8.83e-05 -11.0 2.02e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.4819905e+00 6.23e-09 5.17e-05 -11.0 4.22e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.4819906e+00 3.77e-09 8.82e-05 -11.0 1.94e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 2.4819905e+00 2.32e-08 5.27e-05 -11.0 4.29e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 2.4819905e+00 5.92e-08 3.40e-05 -11.0 1.54e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 2.4819906e+00 7.16e-09 3.61e-05 -11.0 4.70e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.4819906e+00 2.33e-09 4.31e-05 -11.0 2.32e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 2.4819906e+00 3.55e-09 2.51e-05 -11.0 2.03e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.4819906e+00 2.80e-08 3.79e-05 -11.0 7.41e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 2.4819904e+00 1.47e-07 5.67e-05 -11.0 2.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.4819906e+00 9.77e-09 1.22e-05 -11.0 4.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 2.4819906e+00 7.81e-08 6.09e-05 -11.0 3.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.4819904e+00 3.58e-07 5.89e-05 -11.0 3.12e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 2.4819903e+00 1.29e-06 2.48e-03 -11.0 4.97e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 2.4819426e+00 1.90e-04 5.56e-03 -11.0 7.09e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 2.4819706e+00 3.95e-05 1.50e-03 -11.0 4.68e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 2.4819468e+00 6.87e-05 1.40e-03 -11.0 7.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 2.4819620e+00 2.76e-05 7.02e-04 -11.0 2.94e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 2.4819710e+00 1.65e-05 7.09e-04 -11.0 2.31e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 2.4819746e+00 1.53e-05 8.51e-04 -11.0 1.42e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 2.4819853e+00 1.91e-07 3.76e-05 -11.0 3.85e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 89 2.4819542e+00 3.56e-05 1.10e-03 -11.0 6.30e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.4818779e+00 2.79e-04 5.96e-04 -11.0 3.94e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 2.4788168e+00 1.46e-02 6.47e-03 -11.0 7.13e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 2.4796821e+00 4.96e-03 4.72e-03 -11.0 1.03e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 93 2.4804844e+00 1.05e-03 1.40e-03 -11.0 6.25e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 2.4746357e+00 5.53e-02 1.43e-02 -11.0 1.92e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.4466776e+00 6.31e-02 2.19e-02 -11.0 2.02e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 2.4357873e+00 3.10e-01 1.04e-01 -11.0 1.56e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 2.4058887e+00 1.04e+00 5.68e-01 -11.0 4.95e+05 - 2.04e-02 4.36e-02f 1\n",
|
||
" 98r 2.4058887e+00 1.04e+00 9.99e+02 0.0 0.00e+00 - 0.00e+00 1.13e-10R 3\n",
|
||
" 99r 2.2128246e+00 5.61e-01 9.58e+02 -6.0 1.80e+02 - 1.00e+00 3.79e-03f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.4651686e+00 1.03e-02 1.41e-01 -11.0 2.32e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.4651686422500263e+00 2.4651686422500263e+00\n",
|
||
"Dual infeasibility......: 1.4111377446610385e-01 1.4111377446610385e-01\n",
|
||
"Constraint violation....: 1.0299406322253901e-02 1.0299406322253901e-02\n",
|
||
"Complementarity.........: 1.2124890396726157e-07 1.2124890396726157e-07\n",
|
||
"Overall NLP error.......: 1.4111377446610385e-01 1.4111377446610385e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 146\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 146\n",
|
||
"Number of inequality constraint evaluations = 146\n",
|
||
"Number of equality constraint Jacobian evaluations = 103\n",
|
||
"Number of inequality constraint Jacobian evaluations = 103\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.423\n",
|
||
"Total CPU secs in NLP function evaluations = 138.398\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 652.00us ( 4.47us) 636.65us ( 4.36us) 146\n",
|
||
" nlp_g | 6.50 s ( 44.50ms) 6.19 s ( 42.38ms) 146\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 371.00us ( 3.64us) 362.72us ( 3.56us) 102\n",
|
||
" nlp_jac_g | 134.69 s ( 1.30 s) 128.58 s ( 1.24 s) 104\n",
|
||
" total | 142.66 s (142.66 s) 136.17 s (136.17 s) 1\n",
|
||
"Timestamp 29400\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.49e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9740053e+01 1.24e+01 1.49e+04 -1.5 1.49e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.3799720e+00 4.11e+00 7.91e+00 0.6 8.31e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 3.2208080e+00 7.11e-01 8.47e-01 -1.5 2.15e+01 - 9.97e-01 1.00e+00f 1\n",
|
||
" 4 3.7541890e+00 2.56e-03 1.73e-01 -3.2 1.12e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 3.7551579e+00 2.29e-06 1.72e-03 -5.1 1.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 3.7551571e+00 2.12e-06 7.48e-04 -7.2 7.93e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 3.7551524e+00 4.81e-06 1.30e-03 -9.3 2.00e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 3.7551593e+00 1.13e-06 2.21e-03 -11.0 4.68e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 3.7551601e+00 5.85e-07 8.04e-05 -11.0 4.89e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 3.7551562e+00 2.28e-06 1.63e-03 -11.0 1.47e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 3.6679148e+00 3.76e-02 9.16e-02 -11.0 2.96e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 12 3.7400610e+00 2.06e-02 6.63e-03 -11.0 8.93e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 3.6704466e+00 5.24e-02 1.77e-02 -11.0 2.41e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 3.6926612e+00 2.50e-02 3.85e-03 -11.0 1.36e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 3.7449120e+00 5.31e-03 7.99e-03 -11.0 4.34e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 3.7237154e+00 3.14e-02 1.91e-02 -11.0 1.88e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 3.6724245e+00 7.51e-02 2.56e-02 -11.0 7.32e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 3.7067420e+00 6.48e-02 4.79e-03 -11.0 1.10e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 3.7239023e+00 4.07e-02 1.56e-02 -11.0 7.42e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.9355930e+00 6.34e-01 3.92e-01 -11.0 6.35e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 21 3.3975097e+00 3.69e-01 2.30e-01 -11.0 4.06e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.8321276e+00 1.11e+00 8.89e-01 -9.0 3.05e+04 - 1.00e+00 2.37e-01f 3\n",
|
||
" 23 3.1556214e+00 8.04e-01 5.18e-01 -7.0 1.92e+04 - 1.00e+00 8.86e-01h 1\n",
|
||
" 24 2.4421685e+00 9.50e-01 1.35e-01 -7.7 5.19e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 25 2.7045565e+00 9.07e-01 1.64e-01 -5.8 5.42e+03 - 1.00e+00 6.62e-01h 1\n",
|
||
" 26 2.7045571e+00 9.06e-01 1.64e-01 -3.8 4.00e+04 - 1.00e+00 8.98e-04h 1\n",
|
||
" 27 2.6760577e+00 1.21e+00 1.15e-01 -9.9 1.70e+05 - 2.83e-02 1.25e-02h 5\n",
|
||
" 28 2.5784339e+00 1.48e+00 5.23e-01 -4.0 1.07e+04 - 1.02e-03 1.00e+00h 1\n",
|
||
" 29 2.4753914e+00 1.58e+00 4.01e-01 -4.0 5.54e+03 - 1.00e+00 3.96e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.4454896e+00 1.25e+00 5.51e-01 -4.0 1.93e+04 - 1.00e+00 3.00e-01h 1\n",
|
||
" 31 2.4951641e+00 1.10e+00 5.81e-01 -3.7 1.87e+03 - 1.00e+00 1.32e-01h 1\n",
|
||
" 32 3.8833617e+00 2.16e-01 3.20e-01 -4.2 1.59e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 3.9981865e+00 5.37e-02 7.15e-02 -4.0 2.66e+02 - 7.29e-01 1.00e+00h 1\n",
|
||
" 34 3.6482242e+00 3.95e-01 1.81e-01 -4.0 1.17e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 35 4.1263054e+00 1.88e-02 5.17e-02 -4.0 3.71e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 3.4781440e+00 3.67e-01 8.79e-02 -4.0 1.76e+03 - 1.22e-01 1.00e+00f 1\n",
|
||
" 37 2.8249354e+00 1.21e+00 7.16e-01 -4.0 1.96e+04 - 3.89e-02 2.50e-01f 3\n",
|
||
" 38 2.6401370e+00 1.03e+00 7.59e-01 -4.0 1.54e+04 - 2.59e-01 1.25e-01h 4\n",
|
||
" 39 2.8570931e+00 5.73e-01 3.59e-01 -4.0 1.21e+04 - 1.00e+00 5.21e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.8464301e+00 1.33e+00 1.41e-01 -4.5 6.20e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 41 2.8175617e+00 1.08e+00 1.04e-01 -3.6 1.42e+04 - 1.00e+00 1.24e-01h 1\n",
|
||
" 42 3.4959774e+00 6.57e-01 9.93e-02 -3.4 1.88e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 3.2341616e+00 5.07e-01 2.96e-01 -3.5 4.96e+04 - 6.36e-01 8.92e-01F 1\n",
|
||
" 44 3.2165013e+00 4.01e-01 2.00e-01 -3.3 2.97e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 45 3.0772007e+00 6.24e-01 1.54e-01 -2.1 1.97e+04 - 1.00e+00 1.87e-01f 1\n",
|
||
" 46 3.5969467e+00 9.20e-02 1.07e-01 -3.0 3.07e+03 - 5.94e-01 1.00e+00H 1\n",
|
||
" 47 3.4363709e+00 1.10e-01 8.38e-02 -2.4 3.43e+03 - 1.00e+00 8.28e-02f 1\n",
|
||
" 48 3.5989878e+00 1.05e-02 4.30e-02 -3.3 1.67e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 3.5397781e+00 6.52e-02 1.62e-02 -5.1 1.59e+02 - 9.99e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 3.5984196e+00 1.20e-02 2.05e-02 -3.5 8.71e+01 - 4.90e-01 1.00e+00h 1\n",
|
||
" 51 3.5900059e+00 2.41e-02 1.40e-02 -3.6 1.41e+02 - 1.00e+00 7.80e-01h 1\n",
|
||
" 52 3.4654575e+00 1.00e-01 4.02e-02 -2.9 4.82e+02 - 1.00e+00 6.33e-01f 1\n",
|
||
" 53 3.5739587e+00 1.45e-02 6.66e-03 -3.0 3.00e+01 - 4.99e-01 1.00e+00h 1\n",
|
||
" 54 3.5959716e+00 2.83e-03 6.68e-03 -4.4 1.59e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 3.5685007e+00 3.93e-02 1.05e-02 -4.4 2.54e+02 - 1.00e+00 6.58e-01h 1\n",
|
||
" 56 3.5915013e+00 6.02e-03 8.69e-03 -4.2 1.34e+02 - 9.41e-01 1.00e+00h 1\n",
|
||
" 57 3.5089533e+00 5.78e-02 7.72e-03 -3.8 5.63e+02 - 1.00e+00 4.89e-01f 1\n",
|
||
" 58 3.6074493e+00 1.76e-03 1.08e-02 -5.7 4.94e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 3.5562335e+00 5.78e-02 4.32e-02 -5.5 1.14e+03 - 8.87e-02 4.52e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 3.5536472e+00 4.94e-02 8.26e-03 -4.8 2.93e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 61 3.5828304e+00 2.31e-02 1.97e-02 -3.1 2.47e+02 - 6.46e-01 1.00e+00h 1\n",
|
||
" 62 3.0922061e+00 3.73e-01 1.33e-01 -3.4 3.93e+03 - 2.40e-01 1.00e+00f 1\n",
|
||
" 63 2.0025530e+00 8.84e-01 3.25e-01 -2.7 7.50e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 64 4.0126366e+00 2.22e-01 4.94e-01 -2.7 6.99e+03 - 7.54e-01 1.00e+00H 1\n",
|
||
" 65 3.9815972e+00 4.23e-05 1.31e-01 -2.9 1.70e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 3.9816306e+00 2.44e-08 1.04e-04 -2.9 3.29e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 3.9816304e+00 6.21e-08 2.95e-05 -8.9 7.18e-04 - 9.99e-01 1.00e+00h 1\n",
|
||
" 68 3.9816306e+00 1.08e-08 3.25e-05 -11.0 1.73e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 3.9816306e+00 7.19e-08 8.45e-05 -11.0 1.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 3.9816305e+00 2.55e-07 2.43e-05 -11.0 6.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 3.9816306e+00 3.61e-08 4.89e-05 -11.0 6.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 3.9816302e+00 3.08e-07 4.74e-05 -11.0 2.53e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 3.9816278e+00 1.15e-06 9.20e-03 -11.0 1.64e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 3.9816306e+00 5.39e-10 5.81e-05 -11.0 9.37e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 75 3.9816162e+00 1.42e-05 3.44e-03 -11.0 1.01e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 3.9816106e+00 8.22e-06 1.44e-03 -11.0 5.02e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 3.9816308e+00 1.90e-09 1.14e-04 -11.0 1.02e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 78 3.9813500e+00 3.05e-04 3.53e-03 -11.0 1.70e+00 - 1.00e+00 1.00e+00f 1\n",
|
||
" 79 3.9815796e+00 4.45e-05 1.36e-03 -11.0 4.34e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 3.9797637e+00 1.96e-03 5.06e-03 -11.0 4.70e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 3.9812698e+00 2.94e-04 2.18e-03 -11.0 2.13e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 3.9570268e+00 2.35e-02 4.54e-03 -11.0 3.31e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 83 3.9738535e+00 6.03e-03 3.35e-03 -11.0 7.35e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 3.6386634e+00 2.36e-01 6.21e-02 -11.0 1.20e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 85 3.5652519e+00 7.06e-01 1.75e-01 -11.0 6.18e+03 - 5.43e-01 1.00e+00h 1\n",
|
||
" 86 3.9807550e+00 4.25e-04 2.40e-01 -11.0 4.25e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 87 3.6304724e+00 4.31e-01 4.80e-02 -11.0 2.04e+04 - 8.09e-01 8.01e-01f 1\n",
|
||
" 88 3.7731428e+00 1.88e-01 3.84e-02 -11.0 1.51e+03 - 1.00e+00 5.00e-01h 2\n",
|
||
" 89 3.7649788e+00 1.86e-01 3.39e-02 -11.0 8.59e+03 - 1.00e+00 8.43e-03h 7\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 3.7594270e+00 1.85e-01 3.08e-02 -11.0 1.90e+04 - 1.00e+00 4.97e-03h 4\n",
|
||
" 91 3.8359712e+00 1.83e-01 2.17e-02 -11.0 1.16e+03 - 1.00e+00 5.70e-01h 1\n",
|
||
" 92 3.9623032e+00 1.66e-02 5.78e-02 -11.0 2.51e+02 - 6.78e-01 1.00e+00h 1\n",
|
||
" 93 3.9467785e+00 1.54e-01 2.44e-02 -11.0 1.87e+03 - 1.00e+00 2.50e-01h 3\n",
|
||
" 94 3.9544774e+00 1.27e-01 2.02e-02 -11.0 3.44e+02 - 5.77e-01 2.50e-01h 3\n",
|
||
" 95 3.9569965e+00 1.17e-01 2.02e-02 -11.0 1.56e+02 - 1.00e+00 1.25e-01h 4\n",
|
||
" 96 3.9672421e+00 1.74e-02 1.03e-02 -11.0 5.08e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 3.9015390e+00 4.05e-02 1.92e-02 -11.0 6.67e+02 - 1.00e+00 7.34e-01h 1\n",
|
||
" 98 3.9772828e+00 4.20e-03 1.19e-02 -11.0 4.88e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 3.9649450e+00 1.10e-02 9.39e-03 -11.0 2.55e+02 - 1.00e+00 2.31e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 3.9790252e+00 6.72e-05 1.22e-03 -11.0 3.26e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 3.9790252198756066e+00 3.9790252198756066e+00\n",
|
||
"Dual infeasibility......: 1.2165440358422597e-03 1.2165440358422597e-03\n",
|
||
"Constraint violation....: 6.7238951245940370e-05 6.7238951245940370e-05\n",
|
||
"Complementarity.........: 9.9999999999999994e-12 9.9999999999999994e-12\n",
|
||
"Overall NLP error.......: 1.2165440358422597e-03 1.2165440358422597e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 169\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 169\n",
|
||
"Number of inequality constraint evaluations = 169\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.483\n",
|
||
"Total CPU secs in NLP function evaluations = 136.675\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 780.00us ( 4.62us) 760.43us ( 4.50us) 169\n",
|
||
" nlp_g | 7.57 s ( 44.77ms) 7.22 s ( 42.71ms) 169\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 343.00us ( 3.36us) 342.32us ( 3.36us) 102\n",
|
||
" nlp_jac_g | 131.96 s ( 1.29 s) 125.97 s ( 1.24 s) 102\n",
|
||
" total | 141.00 s (141.00 s) 134.60 s (134.60 s) 1\n",
|
||
"Timestamp 29700\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 2.11e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9278327e+01 1.35e+01 2.11e+04 -1.5 2.11e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.0187547e+01 4.38e+00 1.19e+01 0.8 2.17e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.4259752e+01 1.61e+00 9.04e-01 -1.3 4.31e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 1.5273928e+01 2.36e-04 8.46e-02 -3.0 1.91e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.5274086e+01 2.80e-06 2.47e-03 -4.9 2.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.5274081e+01 7.53e-06 2.37e-03 -7.0 4.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.5273264e+01 6.64e-04 2.32e-03 -9.1 5.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.5273057e+01 9.47e-04 3.84e-03 -11.0 7.06e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.5274087e+01 1.76e-04 1.38e-03 -11.0 3.41e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.5272730e+01 6.76e-04 1.78e-03 -11.0 7.06e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.5271165e+01 2.40e-03 4.08e-03 -11.0 9.37e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.5273859e+01 1.08e-07 7.82e-05 -11.0 3.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.5273855e+01 2.71e-06 1.60e-03 -11.0 1.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.5273839e+01 8.50e-06 1.87e-03 -11.0 2.55e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.5273859e+01 6.57e-07 1.52e-03 -11.0 4.52e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.5273856e+01 2.47e-06 5.81e-03 -11.0 9.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.5273859e+01 1.37e-06 3.27e-03 -11.0 6.75e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.5273858e+01 9.11e-07 1.14e-03 -11.0 4.82e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.5273853e+01 1.34e-05 3.82e-03 -11.0 3.06e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.5273846e+01 4.25e-06 1.10e-03 -11.0 2.08e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.5273845e+01 7.09e-06 2.26e-03 -11.0 2.12e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.5273857e+01 1.76e-06 1.66e-03 -11.0 6.84e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.5273857e+01 1.26e-06 2.45e-03 -11.0 3.48e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.5273859e+01 3.13e-07 4.00e-05 -11.0 2.27e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.5273859e+01 1.01e-07 1.80e-04 -11.0 1.03e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.5257086e+01 2.86e-02 7.23e-02 -11.0 8.70e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 27 1.5264558e+01 7.73e-03 1.14e-03 -11.0 4.83e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.5272839e+01 1.06e-03 1.27e-03 -11.0 1.08e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.5275631e+01 2.38e-06 2.02e-03 -11.0 2.75e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.5248074e+01 1.42e-02 8.85e-04 -11.0 2.57e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 31 1.5274516e+01 2.35e-04 2.05e-03 -11.0 5.42e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.5227410e+01 1.16e-01 4.19e-03 -11.0 5.44e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 33 1.5178813e+01 3.69e-01 1.14e-02 -11.0 8.19e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 1.5282406e+01 3.75e-02 1.74e-02 -11.0 4.98e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.5272780e+01 6.91e-02 7.77e-03 -11.0 3.87e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.5161636e+01 3.43e-01 5.06e-03 -11.0 7.26e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.5245170e+01 8.09e-02 6.00e-03 -11.0 3.00e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.5277732e+01 7.45e-03 1.02e-02 -11.0 6.83e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.5287343e+01 8.66e-06 1.33e-03 -11.0 1.68e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.5192234e+01 1.02e-01 5.05e-03 -11.0 5.96e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 41 1.5181712e+01 9.26e-02 6.03e-03 -11.0 5.01e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.5170886e+01 9.38e-02 1.09e-02 -11.0 5.83e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.5248324e+01 7.37e-02 2.88e-03 -11.0 3.41e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.5239320e+01 2.03e-01 1.30e-02 -11.0 4.45e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.5055057e+01 5.93e-02 4.18e-03 -11.0 4.18e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.5023071e+01 2.71e-01 1.21e-02 -11.0 7.07e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.5115762e+01 7.51e-02 1.25e-02 -11.0 3.78e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.2125399e+01 1.57e+00 9.45e-02 -11.0 6.31e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 49 1.4689349e+01 3.15e-01 6.47e-02 -11.0 1.44e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.4680030e+01 1.76e-01 4.56e-02 -11.0 6.30e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.4410005e+01 6.40e-01 1.08e-01 -11.0 2.99e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.3427090e+01 9.65e-01 1.07e-01 -11.0 1.15e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.4632042e+01 6.33e-01 3.30e-02 -11.0 5.91e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.1202727e+01 3.50e+00 2.76e-01 -11.0 1.10e+06 - 2.42e-02 3.30e-02f 1\n",
|
||
" 55 1.4541127e+01 1.99e-01 2.84e-01 -10.1 1.79e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.4364191e+01 4.04e-01 7.39e-02 -8.3 2.55e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.4059705e+01 3.14e-01 9.93e-02 -3.3 3.21e+03 - 1.00e+00 9.98e-01h 1\n",
|
||
" 58 1.4550423e+01 2.54e-01 1.42e-02 -4.2 1.68e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.4509890e+01 2.12e-01 5.67e-03 -5.7 8.14e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.4480684e+01 1.22e-01 8.01e-03 -6.8 6.69e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.4393716e+01 2.16e-01 1.49e-02 -6.2 7.48e+02 - 6.10e-01 1.00e+00h 1\n",
|
||
" 62 1.3500757e+01 7.68e-01 2.05e-02 -4.4 1.42e+03 - 6.32e-03 1.00e+00f 1\n",
|
||
" 63 1.4834733e+01 1.72e-01 6.55e-02 -4.9 1.56e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.4916787e+01 1.18e-01 1.92e-02 -6.5 9.95e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.4842800e+01 2.09e-01 2.17e-02 -4.2 2.61e+03 - 1.00e+00 2.38e-01h 1\n",
|
||
" 66 1.3851213e+01 7.20e-01 3.04e-02 -2.6 1.15e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 67 1.4945232e+01 5.65e-03 1.10e+00 -2.4 1.15e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.4948243e+01 2.51e-06 1.74e-03 -4.3 7.02e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.4948246e+01 8.15e-08 1.19e-04 -6.4 1.05e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.4948201e+01 5.82e-05 3.21e-03 -8.4 3.47e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.4947351e+01 3.14e-04 1.25e-02 -10.4 1.54e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.4948051e+01 7.88e-05 1.28e-03 -11.0 5.18e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.4948104e+01 6.95e-05 1.28e-03 -11.0 2.92e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.4946607e+01 7.03e-04 6.08e-03 -11.0 1.91e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.4947615e+01 1.92e-04 9.81e-04 -11.0 8.81e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.4948179e+01 2.06e-05 2.93e-03 -11.0 1.99e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.4943611e+01 4.65e-03 1.40e-02 -11.0 1.94e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.1357202e+01 1.17e+01 1.14e+00 -9.0 8.19e+05 - 1.82e-04 5.59e-02f 1\n",
|
||
" 79 1.1313401e+01 1.16e+01 1.12e+00 -10.9 1.74e+05 - 2.16e-01 2.66e-03h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.3337524e+01 2.48e+00 9.88e-01 -10.9 1.07e+04 - 9.83e-09 1.00e+00h 1\n",
|
||
" 81 1.3307113e+01 2.47e+00 9.88e-01 -10.9 2.46e+04 - 1.00e+00 2.32e-03h 1\n",
|
||
" 82 1.3840526e+01 1.93e+00 7.74e-01 -10.9 1.62e+03 - 6.95e-09 2.35e-01h 1\n",
|
||
" 83 1.3544808e+01 9.03e-01 3.58e-01 -10.9 2.10e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.5736909e+01 3.75e-01 6.07e-02 -10.9 1.33e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.4931222e+01 5.81e-01 3.20e-02 -10.9 1.16e+04 - 1.00e+00 4.94e-01f 1\n",
|
||
" 86 1.4767785e+01 1.09e+00 4.55e-02 -10.9 1.48e+04 - 9.42e-01 2.61e-01h 1\n",
|
||
" 87 1.0716451e+01 6.36e+00 4.99e-01 -10.9 1.26e+05 - 4.73e-09 9.20e-02f 1\n",
|
||
" 88 1.0715492e+01 6.36e+00 4.99e-01 -11.0 2.34e+05 - 4.35e-11 7.20e-05h 1\n",
|
||
" 89 1.0715485e+01 6.36e+00 4.99e-01 -9.1 9.23e+04 - 1.00e+00 1.83e-06h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.0715700e+01 6.36e+00 4.99e-01 -9.2 1.72e+03 - 2.02e-03 2.30e-04h 1\n",
|
||
" 91 1.5759192e+01 6.58e-01 3.61e-01 -11.0 7.42e+00 - 3.32e-03 9.71e-01h 1\n",
|
||
"In iteration 91, 1 Slack too small, adjusting variable bound\n",
|
||
" 92 1.5769642e+01 6.46e-01 3.54e-01 -9.2 1.46e+03 - 3.13e-04 1.96e-02h 1\n",
|
||
" 93 1.5991742e+01 4.00e-01 2.04e-02 -9.2 8.40e+03 - 4.06e-03 1.00e+00h 1\n",
|
||
"In iteration 93, 1 Slack too small, adjusting variable bound\n",
|
||
" 94 1.5983961e+01 3.96e-01 2.11e-02 -9.2 1.98e+04 - 4.00e-01 4.52e-03h 1\n",
|
||
" 95 1.6331948e+01 5.40e-04 3.66e-01 -9.2 3.66e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.6332615e+01 1.08e-05 1.70e-03 -9.2 2.58e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.6332618e+01 8.38e-06 4.16e-03 -9.2 3.73e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 98 1.6332635e+01 7.51e-07 1.49e-04 -9.2 7.37e-03 - 8.14e-01 1.00e+00h 1\n",
|
||
" 99 1.6332623e+01 6.47e-06 3.19e-03 -9.2 2.68e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.6332631e+01 2.18e-06 1.58e-03 -9.2 1.16e-02 - 9.91e-01 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.6332631428324397e+01 1.6332631428324397e+01\n",
|
||
"Dual infeasibility......: 1.5824434980724884e-03 1.5824434980724884e-03\n",
|
||
"Constraint violation....: 2.1846382338708281e-06 2.1846382338708281e-06\n",
|
||
"Complementarity.........: 6.7167837085255704e-10 6.7167837085255704e-10\n",
|
||
"Overall NLP error.......: 1.5824434980724884e-03 1.5824434980724884e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 103\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 103\n",
|
||
"Number of inequality constraint evaluations = 103\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.375\n",
|
||
"Total CPU secs in NLP function evaluations = 133.551\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 465.00us ( 4.51us) 456.96us ( 4.44us) 103\n",
|
||
" nlp_g | 4.62 s ( 44.84ms) 4.40 s ( 42.72ms) 103\n",
|
||
" nlp_grad | 1.34 s ( 1.34 s) 1.28 s ( 1.28 s) 1\n",
|
||
" nlp_grad_f | 412.00us ( 4.04us) 333.77us ( 3.27us) 102\n",
|
||
" nlp_jac_g | 131.67 s ( 1.29 s) 125.61 s ( 1.23 s) 102\n",
|
||
" total | 137.76 s (137.76 s) 131.42 s (131.42 s) 1\n",
|
||
"Timestamp 30000\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 3.07e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9185332e+01 1.27e+01 3.07e+04 -1.5 3.07e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 1.0179939e+01 4.40e+00 1.49e+01 1.3 1.60e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 1.3807014e+01 1.64e+00 9.05e-01 -0.8 3.10e+02 - 9.98e-01 1.00e+00h 1\n",
|
||
" 4 1.4758722e+01 3.58e-04 8.41e-02 -6.6 3.99e+00 - 9.90e-01 1.00e+00h 1\n",
|
||
" 5 1.4758876e+01 2.60e-04 2.44e-02 -4.3 2.37e+00 - 9.98e-01 1.00e+00h 1\n",
|
||
" 6 1.4758876e+01 9.55e-05 8.12e-04 -6.1 1.33e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.4757252e+01 6.93e-04 1.49e-03 -8.2 4.15e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.4756329e+01 9.03e-04 1.08e-03 -11.0 2.72e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.4758796e+01 2.16e-04 1.03e-03 -11.0 7.82e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.4751612e+01 3.98e-03 5.08e-03 -11.0 1.90e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.4759288e+01 3.44e-04 7.94e-04 -11.0 2.50e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 1.4759490e+01 7.44e-05 9.46e-04 -11.0 1.18e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.4755697e+01 8.74e-03 1.37e-03 -11.0 5.03e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.4746710e+01 5.74e-03 1.61e-03 -11.0 4.08e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.4738540e+01 8.01e-03 1.37e-03 -11.0 5.14e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.4645452e+01 4.51e-02 3.84e-03 -11.0 1.19e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 1.4757953e+01 4.15e-05 9.96e-02 -11.0 1.03e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.4757992e+01 7.63e-07 1.86e-04 -11.0 6.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.4757983e+01 4.19e-06 1.73e-03 -11.0 1.50e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.4757943e+01 2.42e-05 5.11e-03 -11.0 1.00e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.4757979e+01 4.44e-06 2.28e-03 -11.0 6.06e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.4757965e+01 1.39e-05 1.55e-03 -11.0 4.38e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.4757982e+01 5.42e-06 1.63e-03 -11.0 3.34e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.4757935e+01 3.37e-05 2.21e-03 -11.0 2.36e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 1.4757855e+01 3.04e-05 5.35e-03 -11.0 3.96e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 1.4757930e+01 6.17e-05 1.88e-03 -11.0 3.75e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 1.4757949e+01 3.47e-05 8.47e-04 -11.0 1.87e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 1.4757872e+01 1.95e-04 2.42e-03 -11.0 6.26e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.4755928e+01 7.96e-04 1.25e-02 -11.0 1.41e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.4757920e+01 3.65e-04 1.15e-03 -11.0 3.43e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.4757688e+01 3.46e-04 1.07e-03 -11.0 5.41e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 1.4744510e+01 1.54e-02 3.13e-03 -11.0 2.30e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.4480171e+01 2.90e-01 2.01e-02 -11.0 1.06e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 34 1.4633192e+01 6.90e-02 5.83e-03 -11.0 3.91e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 1.4465188e+01 2.94e-01 5.33e-03 -11.0 3.12e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 1.4708638e+01 9.43e-03 1.89e-02 -11.0 5.87e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 1.4731078e+01 1.03e-03 2.29e-03 -11.0 6.59e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 38 1.4315734e+01 7.87e-01 5.36e-02 -9.0 1.43e+05 - 1.00e+00 1.06e-01f 1\n",
|
||
" 39 1.4312907e+01 7.93e-01 5.39e-02 -7.0 2.12e+06 - 1.00e+00 1.63e-05f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.4718332e+01 1.16e-05 7.54e-01 -9.0 7.95e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.4718348e+01 3.61e-07 1.88e-04 -10.7 1.38e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.4718347e+01 2.46e-07 1.64e-04 -11.0 8.62e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.4718348e+01 1.57e-07 9.83e-05 -11.0 3.44e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.4718348e+01 8.28e-08 1.46e-04 -11.0 5.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.4718347e+01 1.37e-07 3.32e-05 -11.0 8.97e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.4718348e+01 3.38e-09 2.49e-05 -11.0 4.42e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.4718348e+01 7.28e-09 5.94e-05 -11.0 1.67e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.4718348e+01 3.33e-08 1.04e-04 -11.0 8.27e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.4718345e+01 6.97e-07 8.69e-03 -11.0 1.04e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.4718348e+01 5.08e-10 1.51e-04 -11.0 4.34e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 51 1.4718343e+01 7.01e-06 1.05e-02 -11.0 2.66e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.4718342e+01 6.19e-06 2.00e-03 -11.0 2.94e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.4718345e+01 3.07e-06 3.55e-03 -11.0 2.92e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.4718188e+01 3.92e-04 3.73e-03 -11.0 5.74e+02 - 1.00e+00 2.70e-03f 1\n",
|
||
" 55 1.4718188e+01 3.92e-04 3.40e-03 -9.0 5.18e+00 - 1.00e+00 5.99e-04h 1\n",
|
||
" 56 1.4718337e+01 1.31e-06 1.27e-03 -9.5 3.86e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.4718338e+01 2.06e-08 2.65e-04 -7.6 7.30e-04 - 9.87e-01 1.00e+00h 1\n",
|
||
" 58 1.4718338e+01 6.61e-08 7.43e-05 -8.7 5.79e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.4718337e+01 1.12e-06 2.32e-03 -6.9 1.89e-02 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.4718301e+01 4.36e-05 1.03e-03 -6.4 1.46e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 1.4718248e+01 9.90e-05 2.31e-03 -7.9 2.18e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.4717237e+01 1.22e-03 5.81e-03 -8.2 7.92e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.4718268e+01 7.24e-09 1.52e-04 -10.2 1.69e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 64 1.4718268e+01 1.18e-07 1.70e-04 -11.0 5.67e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.4718268e+01 1.05e-07 5.45e-05 -11.0 4.58e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 66 1.4718268e+01 1.28e-07 9.20e-05 -11.0 3.88e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.4718268e+01 7.48e-08 1.41e-04 -11.0 2.44e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.4718266e+01 9.28e-07 7.82e-03 -11.0 6.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.4718268e+01 4.65e-07 4.02e-05 -11.0 2.29e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.4718268e+01 1.62e-07 1.32e-05 -11.0 6.59e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.4718268e+01 3.53e-08 7.15e-05 -11.0 5.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.4718268e+01 4.02e-08 9.70e-05 -11.0 3.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.4718268e+01 8.56e-07 3.20e-03 -11.0 1.30e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.4718267e+01 6.08e-07 2.24e-04 -11.0 1.13e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.4718266e+01 5.68e-07 3.50e-03 -11.0 2.61e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.4718268e+01 1.49e-07 1.02e-04 -11.0 1.18e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.4718268e+01 1.20e-07 1.33e-04 -11.0 6.55e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.4718150e+01 7.09e-05 2.88e-02 -11.0 3.07e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.4718282e+01 3.71e-08 1.81e-04 -11.0 4.53e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.4718078e+01 1.21e-04 2.13e-03 -11.0 2.54e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.4713674e+01 1.33e-03 1.26e-02 -11.0 8.04e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.4718153e+01 8.93e-07 4.12e-03 -10.7 1.97e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 83 1.4707432e+01 1.31e-02 6.17e-03 -11.0 1.09e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 84 1.4673939e+01 1.76e-02 2.81e-03 -9.0 1.84e+02 - 1.00e+00 9.13e-01h 1\n",
|
||
" 85 1.4718137e+01 1.34e-04 1.94e-03 -9.9 8.16e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 86 1.4681537e+01 3.44e-02 2.66e-03 -8.0 1.22e+02 - 1.00e+00 8.03e-01f 1\n",
|
||
" 87 1.4707978e+01 4.11e-03 3.18e-03 -8.1 2.59e+01 - 2.09e-03 1.00e+00h 1\n",
|
||
" 88 1.4681869e+01 3.20e-02 1.87e-03 -6.0 9.79e+01 - 1.00e+00 6.13e-01h 1\n",
|
||
" 89 1.4703464e+01 1.56e-02 1.44e-03 -6.1 4.19e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.4700734e+01 2.04e-02 1.45e-03 -4.2 4.01e+01 - 9.85e-01 1.00e+00h 1\n",
|
||
" 91 1.4693937e+01 1.11e-02 1.91e-03 -5.7 1.09e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.4718381e+01 7.76e-06 1.56e-03 -7.1 2.91e+02 - 1.00e+00 1.00e+00H 1\n",
|
||
" 93 1.4128331e+01 4.69e-01 2.69e-02 -7.2 1.85e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 94 1.4650968e+01 8.86e-02 1.44e-02 -8.9 5.68e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.4694464e+01 3.68e-02 1.19e-02 -8.9 2.96e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 1.4444415e+01 3.89e-01 2.57e-02 -8.9 4.55e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 97 1.1997915e+01 3.56e+00 3.04e-01 -7.4 3.89e+05 - 1.00e+00 1.43e-01f 1\n",
|
||
" 98 1.2012599e+01 3.53e+00 2.96e-01 -5.5 5.12e+04 - 1.00e+00 1.09e-02h 1\n",
|
||
" 99 1.4892755e+01 1.24e-01 3.35e-01 -4.4 7.02e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.4904648e+01 4.69e-02 1.11e-01 -3.7 5.57e+02 - 1.00e+00 6.51e-01h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.4904648203551995e+01 1.4904648203551995e+01\n",
|
||
"Dual infeasibility......: 1.1105110857683237e-01 1.1105110857683237e-01\n",
|
||
"Constraint violation....: 4.6913698273513660e-02 4.6913698273513660e-02\n",
|
||
"Complementarity.........: 2.2201347773967000e-04 2.2201347773967000e-04\n",
|
||
"Overall NLP error.......: 1.1105110857683237e-01 1.1105110857683237e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 107\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 107\n",
|
||
"Number of inequality constraint evaluations = 107\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.420\n",
|
||
"Total CPU secs in NLP function evaluations = 134.318\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 481.00us ( 4.50us) 475.82us ( 4.45us) 107\n",
|
||
" nlp_g | 4.78 s ( 44.68ms) 4.56 s ( 42.58ms) 107\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 363.00us ( 3.56us) 366.32us ( 3.59us) 102\n",
|
||
" nlp_jac_g | 132.20 s ( 1.30 s) 126.16 s ( 1.24 s) 102\n",
|
||
" total | 138.44 s (138.44 s) 132.12 s (132.12 s) 1\n",
|
||
"Timestamp 30300\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 5.57e+02 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9527380e+01 1.12e+01 5.57e+02 -1.5 5.57e+02 - 9.90e-01 1.00e+00f 1\n",
|
||
" 2 7.4311910e+00 3.41e+00 8.11e+00 0.4 1.12e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 2.4416747e+00 5.95e-01 5.96e-01 -1.6 5.07e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 2.6816675e+00 1.26e-03 2.18e-01 -3.4 8.71e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 2.6821001e+00 3.15e-09 2.93e-05 -5.3 1.26e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 2.6821001e+00 3.25e-08 7.84e-05 -11.0 1.34e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 2.6820989e+00 7.09e-07 2.54e-03 -11.0 7.19e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 2.6820877e+00 8.73e-06 9.76e-03 -11.0 2.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 2.6820997e+00 9.61e-08 2.65e-05 -11.0 4.98e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 2.6820999e+00 5.48e-11 7.16e-05 -11.0 2.45e-03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 11 2.6820995e+00 4.21e-07 7.00e-04 -11.0 2.05e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 2.6820985e+00 1.37e-06 1.97e-03 -11.0 1.11e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 2.6820953e+00 2.92e-06 3.81e-03 -11.0 2.03e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 2.6820996e+00 1.95e-08 7.97e-05 -11.0 8.24e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 2.6820995e+00 1.49e-07 3.53e-05 -11.0 3.96e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 2.6820996e+00 8.60e-08 2.31e-05 -11.0 3.32e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 2.6820996e+00 1.42e-07 3.36e-05 -11.0 9.29e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 2.6820996e+00 4.17e-08 1.17e-05 -11.0 3.17e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 2.6820996e+00 9.02e-10 5.25e-05 -11.0 3.15e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 2.6820996e+00 1.45e-08 4.37e-05 -11.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 2.6820996e+00 1.71e-07 1.23e-04 -11.0 2.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 2.6820993e+00 4.15e-07 6.29e-05 -11.0 2.11e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 2.6820996e+00 2.17e-08 6.81e-05 -11.0 5.72e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 2.6820996e+00 1.12e-08 1.13e-05 -11.0 2.76e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 2.6820996e+00 6.56e-09 2.59e-05 -11.0 5.63e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 2.6820996e+00 6.43e-08 1.87e-05 -11.0 3.23e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 2.6820996e+00 9.10e-08 6.31e-05 -11.0 5.35e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 2.6820994e+00 1.26e-07 4.95e-05 -11.0 2.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 2.6820990e+00 4.42e-07 5.62e-05 -11.0 1.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 2.6820982e+00 1.66e-06 1.38e-03 -11.0 2.02e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 2.6820950e+00 4.34e-06 2.88e-03 -11.0 3.43e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 2.6820997e+00 6.68e-09 9.79e-05 -11.0 3.65e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 2.6820996e+00 7.00e-08 1.05e-04 -11.0 1.47e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 34 2.6820996e+00 2.92e-08 2.62e-05 -11.0 6.93e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 35 2.6820997e+00 9.04e-09 3.63e-05 -11.0 1.79e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 36 2.6820997e+00 6.83e-09 5.94e-05 -11.0 3.80e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 37 2.6820997e+00 3.42e-09 6.75e-05 -11.0 2.69e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 2.6820876e+00 4.47e-06 2.09e-02 -11.0 6.34e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 2.6820953e+00 2.06e-06 1.61e-03 -11.0 4.29e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 2.6820957e+00 2.64e-06 7.70e-04 -11.0 2.27e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 2.6820920e+00 4.77e-06 1.38e-03 -11.0 1.79e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 2.6820895e+00 7.70e-06 1.41e-03 -11.0 2.10e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 2.6820971e+00 3.68e-07 3.83e-05 -11.0 3.93e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 2.6820968e+00 1.37e-06 8.63e-04 -11.0 8.74e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 2.6813875e+00 1.11e-03 2.90e-03 -11.0 9.02e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 2.6822534e+00 2.56e-05 9.51e-04 -11.0 1.27e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 2.6816473e+00 1.00e-03 1.25e-03 -11.0 7.69e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 2.6806236e+00 1.40e-03 1.13e-03 -11.0 1.21e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 2.6818681e+00 7.44e-07 8.65e-05 -11.0 1.98e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 2.6818685e+00 1.18e-07 8.74e-05 -11.0 5.40e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 2.6818686e+00 7.92e-08 4.42e-05 -11.0 2.69e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 2.6818685e+00 8.27e-08 1.32e-04 -11.0 6.27e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 2.6818681e+00 5.31e-07 3.39e-05 -11.0 1.32e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 2.6818686e+00 2.09e-08 4.44e-05 -11.0 2.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 2.6818686e+00 1.10e-07 2.36e-05 -11.0 4.14e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 2.6818686e+00 1.61e-08 8.23e-06 -11.0 2.98e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 2.6818687e+00 3.06e-09 1.70e-05 -11.0 1.05e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 2.6818686e+00 3.98e-08 1.40e-05 -11.0 2.43e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 2.6818686e+00 8.80e-09 8.97e-05 -11.0 1.32e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 2.6818684e+00 1.36e-07 1.08e-04 -11.0 4.70e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 2.6818685e+00 9.44e-08 1.12e-04 -11.0 3.76e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 2.6818686e+00 3.12e-08 5.99e-05 -11.0 1.45e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 2.6818687e+00 5.13e-11 2.04e-05 -11.0 3.40e-04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 64 2.6818616e+00 9.93e-06 8.13e-03 -11.0 6.77e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 2.6818690e+00 5.89e-09 6.90e-05 -11.0 1.01e-01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 66 2.6818658e+00 6.48e-06 7.36e-04 -11.0 5.54e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 2.6818663e+00 6.31e-06 1.41e-03 -11.0 3.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 2.6818654e+00 3.56e-06 2.56e-03 -11.0 9.02e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 2.6818524e+00 1.15e-05 8.25e-04 -11.0 3.53e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 2.6343317e+00 4.65e-02 2.79e-02 -11.0 1.38e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 71 2.4554536e+00 5.11e-01 2.17e-01 -11.0 1.94e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 2.5406071e+00 3.22e-01 8.93e-02 -11.0 1.21e+04 - 1.00e+00 8.01e-01H 1\n",
|
||
" 73 2.6173928e+00 7.58e-02 1.02e-01 -11.0 1.01e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 2.5999300e+00 7.39e-02 7.59e-02 -2.7 4.24e+03 - 1.00e+00 2.36e-01h 1\n",
|
||
" 75 2.6799300e+00 2.66e-03 2.43e-02 -3.8 9.99e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 2.6768485e+00 9.14e-03 1.83e-02 -5.7 4.25e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 2.6800040e+00 4.67e-04 5.74e-03 -7.6 1.23e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 2.6143352e+00 6.85e-02 3.17e-02 -9.0 1.13e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 79 2.1853602e+00 9.16e-01 3.61e-01 -6.4 4.66e+03 - 3.30e-01 1.00e+00F 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 2.8717997e+00 3.34e-01 4.58e-01 -4.9 2.33e+03 - 7.18e-02 1.00e+00h 1\n",
|
||
" 81 2.5888297e+00 1.45e+00 3.43e-01 -6.1 7.23e+04 - 3.77e-04 4.64e-01F 1\n",
|
||
" 82 2.5888270e+00 1.45e+00 3.43e-01 -6.1 4.28e+04 - 5.75e-01 5.42e-07h 2\n",
|
||
" 83 2.1994926e+00 8.01e-01 3.97e-01 -6.1 1.71e+03 - 3.73e-01 1.00e+00h 1\n",
|
||
" 84 2.1841106e+00 8.28e-01 3.43e-02 -6.3 3.36e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 2.2041299e+00 6.53e-01 4.60e-02 -3.4 2.71e+03 - 5.96e-01 1.00e+00H 1\n",
|
||
" 86 2.2077150e+00 4.33e-01 1.02e-01 -2.9 3.48e+04 - 2.11e-02 1.25e-01h 4\n",
|
||
" 87 2.2019027e+00 5.45e-01 6.56e-02 -4.0 4.39e+04 - 1.00e+00 2.50e-01h 3\n",
|
||
" 88 2.1808790e+00 9.71e-01 1.13e-01 -9.9 8.03e+05 - 1.43e-02 1.58e-02h 3\n",
|
||
" 89 2.1764955e+00 1.22e+00 1.57e-01 -4.8 9.63e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 2.2917494e+00 8.19e-01 2.90e-01 -3.7 4.88e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 91r 2.2917494e+00 8.19e-01 9.99e+02 -0.1 0.00e+00 - 0.00e+00 4.77e-07R 22\n",
|
||
" 92r 2.3546198e+00 4.89e-01 8.96e+02 -2.2 2.30e+02 - 1.00e+00 1.92e-03f 1\n",
|
||
" 93 2.3578786e+00 4.25e-01 6.51e-01 -3.8 2.55e+04 - 1.00e+00 1.25e-01h 4\n",
|
||
" 94 2.2842427e+00 1.21e+00 1.69e-01 -4.4 1.88e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 2.2019955e+00 1.03e+00 1.49e-01 -4.4 7.80e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 96 2.1989287e+00 8.97e-01 2.72e-01 -4.4 3.19e+04 - 2.06e-01 1.06e-01h 4\n",
|
||
" 97 2.1908968e+00 6.89e-01 2.24e-01 -4.4 3.81e+05 - 8.12e-02 5.29e-03h 5\n",
|
||
" 98 2.8425445e+00 1.69e-01 4.86e-01 -4.4 1.65e+04 - 1.00e+00 1.00e+00H 1\n",
|
||
" 99 2.2760311e+00 3.26e-01 1.39e-01 -4.4 3.19e+04 - 3.06e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 2.2261172e+00 1.11e+00 3.99e-01 -10.5 1.81e+06 - 1.78e-03 5.67e-03f 2\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 2.2261171520212768e+00 2.2261171520212768e+00\n",
|
||
"Dual infeasibility......: 3.9942701271284364e-01 3.9942701271284364e-01\n",
|
||
"Constraint violation....: 1.1148591687454079e+00 1.1148591687454079e+00\n",
|
||
"Complementarity.........: 4.6263146912458321e-05 4.6263146912458321e-05\n",
|
||
"Overall NLP error.......: 1.1148591687454079e+00 1.1148591687454079e+00\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 167\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 167\n",
|
||
"Number of inequality constraint evaluations = 167\n",
|
||
"Number of equality constraint Jacobian evaluations = 102\n",
|
||
"Number of inequality constraint Jacobian evaluations = 102\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.405\n",
|
||
"Total CPU secs in NLP function evaluations = 138.084\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 755.00us ( 4.52us) 744.51us ( 4.46us) 167\n",
|
||
" nlp_g | 7.44 s ( 44.55ms) 7.09 s ( 42.44ms) 167\n",
|
||
" nlp_grad | 1.36 s ( 1.36 s) 1.30 s ( 1.30 s) 1\n",
|
||
" nlp_grad_f | 396.00us ( 3.88us) 394.29us ( 3.87us) 102\n",
|
||
" nlp_jac_g | 133.38 s ( 1.29 s) 127.30 s ( 1.24 s) 103\n",
|
||
" total | 142.33 s (142.33 s) 135.83 s (135.83 s) 1\n",
|
||
"Timestamp 30600\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 5.76e+03 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9718911e+01 1.24e+01 5.76e+03 -1.5 5.76e+03 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 7.6307185e+00 4.09e+00 8.60e+00 0.6 2.51e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 3 4.1600980e+00 7.82e-01 7.98e-01 -1.5 6.26e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 4.6705233e+00 2.22e-03 1.05e-01 -3.3 1.18e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 4.6714611e+00 1.55e-08 7.31e-05 -5.1 2.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 4.6714610e+00 2.84e-07 6.08e-05 -11.0 5.30e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 4.6714609e+00 1.33e-07 1.39e-04 -11.0 1.25e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 4.6714607e+00 3.13e-07 8.70e-05 -11.0 9.22e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 4.6714610e+00 3.76e-08 1.09e-04 -11.0 2.87e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 4.6714610e+00 1.07e-07 8.88e-05 -11.0 2.83e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 4.6714606e+00 4.51e-07 1.64e-04 -11.0 1.43e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 4.6713687e+00 5.40e-05 2.53e-02 -11.0 2.80e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 4.6714458e+00 9.00e-06 2.71e-03 -11.0 7.75e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 4.6714644e+00 7.44e-08 2.22e-05 -11.0 3.22e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 4.6714584e+00 5.80e-06 2.34e-03 -11.0 4.76e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 4.6714589e+00 3.04e-06 8.39e-04 -11.0 2.80e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 4.6714513e+00 1.31e-05 2.10e-03 -11.0 4.80e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 4.6695182e+00 2.64e-03 9.58e-03 -11.0 7.42e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 4.6713324e+00 1.48e-04 2.42e-03 -11.0 7.59e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 4.6703325e+00 9.46e-04 2.03e-03 -11.0 5.03e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 4.6710969e+00 5.21e-04 2.29e-03 -11.0 9.16e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 4.6681742e+00 2.14e-03 2.01e-03 -11.0 8.09e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 4.6712444e+00 3.47e-04 9.78e-04 -11.0 1.75e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 4.6701346e+00 2.00e-03 4.42e-03 -11.0 1.38e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 4.6708660e+00 1.20e-03 1.17e-03 -11.0 2.81e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 26 4.6681163e+00 2.34e-03 1.21e-03 -11.0 1.62e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 27 4.6187044e+00 2.42e-02 7.42e-03 -11.0 1.67e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 28 4.1449420e+00 2.47e-01 5.75e-02 -11.0 1.94e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 29 4.5777985e+00 3.82e-02 4.99e-02 -11.0 4.70e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 4.6265310e+00 9.09e-03 2.61e-02 -11.0 2.71e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 4.5340576e+00 4.64e-02 5.41e-02 -11.0 6.74e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 32 4.0661944e+00 9.86e-01 1.70e-01 -11.0 1.01e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 33 3.1496302e+00 1.64e+00 4.39e-01 -9.0 7.35e+04 - 1.00e+00 2.76e-01f 1\n",
|
||
" 34 3.1495654e+00 1.62e+00 4.34e-01 -7.0 6.59e+04 - 1.00e+00 3.08e-03h 1\n",
|
||
" 35 3.1496588e+00 1.62e+00 4.34e-01 -5.1 7.44e+03 - 1.00e+00 2.73e-04h 1\n",
|
||
" 36 4.4821316e+00 1.42e-01 3.56e-01 -3.1 4.86e+03 - 1.47e-02 1.00e+00h 1\n",
|
||
" 37 3.6945226e+00 5.53e-01 4.31e-01 -4.0 7.95e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 38 3.9470284e+00 2.84e-01 2.59e-01 -4.6 6.34e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 4.1507880e+00 2.01e-01 1.96e-01 -3.5 2.91e+04 - 4.00e-01 5.31e-01H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 3.8852563e+00 1.31e+00 1.48e-01 -3.7 3.19e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 4.1055284e+00 6.46e-01 8.74e-02 -3.3 4.81e+04 - 1.00e+00 6.69e-01h 1\n",
|
||
" 42 4.1106133e+00 6.34e-01 8.18e-02 -3.3 9.43e+03 - 4.37e-01 1.51e-02H 1\n",
|
||
" 43 4.0969558e+00 4.24e-01 9.52e-02 -3.3 1.88e+03 - 8.62e-01 1.00e+00f 1\n",
|
||
" 44 3.9105652e+00 4.72e-01 1.24e-01 -3.3 7.00e+04 - 1.17e-03 2.88e-01f 1\n",
|
||
" 45 4.6026701e+00 5.24e-02 1.32e-01 -3.7 9.57e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 4.4395905e+00 1.19e-01 5.26e-02 -3.5 1.58e+03 - 1.00e+00 8.45e-01h 1\n",
|
||
" 47 3.7691962e+00 6.07e-01 1.05e-01 -2.3 5.75e+05 - 1.00e+00 3.41e-02f 1\n",
|
||
" 48 4.3234719e+00 1.66e-01 2.07e-01 -1.4 1.22e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 4.2433337e+00 1.92e-01 1.84e-01 -2.3 1.23e+03 - 1.00e+00 6.55e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 4.3105702e+00 1.14e-01 2.59e-02 -8.3 6.11e+02 - 8.43e-01 1.00e+00h 1\n",
|
||
" 51 4.3251327e+00 1.04e-01 2.74e-02 -2.4 9.96e+02 - 3.59e-01 2.50e-01f 3\n",
|
||
" 52 4.2569227e+00 8.37e-01 1.59e-01 -3.5 3.51e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 4.2213555e+00 2.33e+00 8.56e-01 -3.1 1.93e+06 - 1.66e-03 1.14e-02f 1\n",
|
||
" 54 4.2155974e+00 2.33e+00 8.54e-01 -3.1 7.47e+03 - 1.00e+00 2.29e-03h 1\n",
|
||
" 55 4.2196994e+00 2.20e-01 1.73e-01 -3.1 9.93e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 56 4.3387461e+00 2.45e-02 3.00e-02 -9.0 1.15e+02 - 4.76e-01 1.00e+00h 1\n",
|
||
" 57 4.3407992e+00 4.26e-03 3.91e-02 -3.6 5.38e+01 - 4.92e-01 9.62e-01h 1\n",
|
||
" 58 4.3319503e+00 3.34e-02 2.37e-02 -4.2 3.31e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 4.3093436e+00 2.66e-02 2.91e-02 -10.0 8.87e+02 - 1.19e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 3.8720370e+00 1.44e+00 3.82e-01 -10.3 1.24e+05 - 5.46e-03 1.31e-01f 1\n",
|
||
" 61 4.1981481e+00 3.45e-01 2.16e-01 -4.4 2.24e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 3.7332037e+00 4.77e-01 1.93e-01 -4.4 3.68e+04 - 5.48e-02 1.00e+00h 1\n",
|
||
" 63 3.9069347e+00 6.49e-01 4.34e-02 -2.9 3.16e+04 - 1.00e+00 7.70e-01H 1\n",
|
||
" 64 3.8033755e+00 1.55e+00 7.17e-01 -8.8 2.20e+04 - 4.56e-03 3.70e-01f 1\n",
|
||
" 65 3.7348647e+00 1.47e+00 6.43e-01 -3.0 5.23e+04 - 6.63e-01 5.44e-02h 1\n",
|
||
" 66 4.5851143e+00 1.25e+00 7.65e-01 -3.0 3.27e+04 - 8.20e-01 1.00e+00h 1\n",
|
||
" 67 4.4415907e+00 9.31e-01 6.78e-01 -3.0 2.31e+04 - 1.33e-01 1.21e-01h 1\n",
|
||
" 68 3.6935569e+00 6.08e-01 9.17e-02 -3.0 5.54e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 69 3.6910383e+00 8.42e-01 7.22e-02 -8.0 4.45e+03 - 6.61e-01 2.50e-01h 3\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 3.9556131e+00 1.14e+00 1.90e-01 -2.5 1.93e+04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 3.8994383e+00 1.33e+00 1.30e-01 -2.6 5.17e+04 - 5.10e-01 2.93e-01h 1\n",
|
||
" 72 3.9366603e+00 1.33e+00 7.33e-02 -2.6 1.27e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 73 3.9299066e+00 1.47e+00 6.49e-02 -2.6 1.62e+04 - 1.00e+00 4.12e-01h 2\n",
|
||
" 74 3.9224376e+00 1.36e+00 4.38e-02 -2.6 3.47e+04 - 9.20e-01 1.93e-01h 1\n",
|
||
" 75 3.9650006e+00 8.01e-01 1.45e-01 -2.6 9.28e+02 - 1.00e+00 4.55e-01h 1\n",
|
||
" 76 4.1155030e+00 7.95e-02 2.37e-01 -2.6 1.88e+03 - 3.48e-01 1.00e+00h 1\n",
|
||
" 77 4.0648666e+00 2.50e-01 2.00e-01 -3.6 1.99e+04 - 1.00e+00 3.26e-01h 1\n",
|
||
" 78 3.7919737e+00 1.70e+00 1.88e-01 -2.2 1.23e+05 - 1.00e+00 4.37e-01f 1\n",
|
||
" 79 3.7421980e+00 2.00e+00 1.76e-01 -2.3 3.80e+04 - 6.07e-02 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 3.6579754e+00 1.23e+00 3.71e-02 -2.3 6.73e+05 - 2.70e-01 3.62e-02h 1\n",
|
||
" 81 3.9074812e+00 1.36e+00 8.20e-02 -3.2 3.70e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 3.9092036e+00 1.33e+00 6.91e-02 -3.2 4.55e+04 - 4.12e-01 8.39e-02h 1\n",
|
||
" 83 4.1902861e+00 7.32e-02 3.32e-01 -3.2 5.67e+02 - 3.12e-01 1.00e+00h 1\n",
|
||
" 84 4.1840434e+00 4.34e-02 3.27e-01 -3.2 1.93e+05 - 5.14e-02 2.95e-03h 1\n",
|
||
" 85 4.1691431e+00 2.69e+00 1.67e+00 -1.9 3.35e+05 - 1.00e+00 3.28e-01F 1\n",
|
||
" 86 4.1440924e+00 1.35e+00 4.35e-01 -0.9 2.94e+05 - 1.00e+00 2.03e-01F 1\n",
|
||
" 87 4.1139613e+00 6.21e-01 1.64e-01 -0.9 3.85e+03 - 9.09e-01 1.00e+00h 1\n",
|
||
" 88 4.1043409e+00 7.06e-01 1.21e-01 -1.6 6.57e+03 - 2.77e-01 2.77e-01s 21\n",
|
||
" 89 4.3629424e+00 8.78e-01 2.19e-01 -1.6 4.55e+03 - 1.00e+00 1.00e+00s 21\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 3.8524889e+00 7.47e-01 2.14e-01 -1.6 9.00e+03 - 9.81e-01 0.00e+00S 21\n",
|
||
" 91 3.8327841e+00 1.36e+00 3.77e-01 -1.6 2.69e+04 - 4.44e-01 2.29e-01h 2\n",
|
||
" 92 3.6860592e+00 1.65e+00 5.77e-01 -1.6 7.96e+04 - 6.80e-01 3.96e-02f 2\n",
|
||
" 93 3.4268577e+00 1.44e+00 3.17e-01 -1.6 6.70e+03 - 1.00e+00 2.57e-01h 2\n",
|
||
" 94 4.2256135e+00 1.65e-01 3.13e-01 -1.5 1.75e+03 - 1.00e+00 9.04e-01h 1\n",
|
||
" 95 4.4833515e+00 8.92e-02 3.04e-02 -1.6 6.75e+02 - 5.65e-01 1.00e+00h 1\n",
|
||
" 96 4.3530694e+00 1.47e+00 2.85e-01 -2.4 9.51e+03 - 4.10e-02 5.00e-01f 2\n",
|
||
" 97 4.3131225e+00 1.92e+00 4.40e-01 -2.4 3.38e+05 - 1.06e-01 5.31e-03f 6\n",
|
||
" 98 4.1757444e+00 1.08e+00 1.80e-01 -2.4 1.47e+04 - 9.11e-01 9.07e-01h 1\n",
|
||
" 99 4.4727688e+00 1.19e+00 9.97e-02 -2.4 5.19e+03 - 1.84e-01 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 4.4264714e+00 5.35e-01 1.22e-01 -2.4 7.91e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 4.4264714232526430e+00 4.4264714232526430e+00\n",
|
||
"Dual infeasibility......: 1.2232772597669378e-01 1.2232772597669378e-01\n",
|
||
"Constraint violation....: 5.3499198339064691e-01 5.3499198339064691e-01\n",
|
||
"Complementarity.........: 1.7591180153408374e-02 1.7591180153408374e-02\n",
|
||
"Overall NLP error.......: 5.3499198339064691e-01 5.3499198339064691e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 162\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 162\n",
|
||
"Number of inequality constraint evaluations = 162\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.416\n",
|
||
"Total CPU secs in NLP function evaluations = 136.037\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 723.00us ( 4.46us) 718.94us ( 4.44us) 162\n",
|
||
" nlp_g | 7.21 s ( 44.49ms) 6.87 s ( 42.43ms) 162\n",
|
||
" nlp_grad | 1.33 s ( 1.33 s) 1.27 s ( 1.27 s) 1\n",
|
||
" nlp_grad_f | 353.00us ( 3.46us) 346.04us ( 3.39us) 102\n",
|
||
" nlp_jac_g | 131.65 s ( 1.29 s) 125.63 s ( 1.23 s) 102\n",
|
||
" total | 140.34 s (140.34 s) 133.92 s (133.92 s) 1\n",
|
||
"Timestamp 30900\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 1.25e+04 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9245219e+01 1.27e+01 1.25e+04 -1.5 1.25e+04 - 9.90e-01 1.00e+00h 1\n",
|
||
" 2 8.3930375e+00 4.06e+00 1.03e+01 0.6 1.24e+02 - 9.99e-01 1.00e+00f 1\n",
|
||
" 3 9.5887960e+00 1.22e+00 9.61e-01 -1.5 2.63e+01 - 9.97e-01 1.00e+00h 1\n",
|
||
" 4 1.0369217e+01 8.37e-04 8.36e-02 -3.2 1.54e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 1.0369547e+01 7.70e-07 1.99e-03 -5.1 6.45e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 1.0369547e+01 9.17e-07 1.46e-03 -7.2 7.17e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 1.0369547e+01 1.14e-06 1.75e-03 -9.3 1.01e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 1.0369544e+01 3.33e-06 9.43e-04 -11.0 8.81e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 1.0369547e+01 1.07e-06 1.71e-03 -11.0 6.23e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 1.0369548e+01 2.27e-07 1.42e-04 -11.0 1.70e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 1.0363747e+01 2.24e-03 4.27e-02 -11.0 2.55e+01 - 1.00e+00 1.00e+00f 1\n",
|
||
" 12 1.0368000e+01 1.57e-03 1.30e-03 -11.0 1.16e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 1.0354235e+01 7.70e-03 1.76e-03 -11.0 3.09e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 1.0361043e+01 4.59e-03 1.25e-03 -11.0 1.57e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 1.0357109e+01 7.33e-03 2.89e-03 -11.0 1.80e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 1.0200734e+01 3.62e-01 2.54e-02 -11.0 4.29e+02 - 1.00e+00 1.00e+00f 1\n",
|
||
" 17 1.0371410e+01 4.17e-05 2.58e-02 -11.0 7.77e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 1.0366878e+01 8.61e-03 1.86e-02 -11.0 7.28e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 1.0325323e+01 5.07e-02 5.94e-03 -11.0 2.62e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 1.0287461e+01 3.43e-02 3.72e-03 -11.0 2.05e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 1.0369451e+01 2.40e-03 4.51e-03 -11.0 7.16e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 1.0337278e+01 3.63e-02 3.17e-03 -11.0 4.35e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 1.0262804e+01 9.94e-02 8.87e-03 -11.0 1.28e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 24 1.0347183e+01 5.21e-02 5.63e-03 -11.0 1.03e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 25 9.8630032e+00 7.04e-01 6.96e-02 -9.0 9.04e+05 - 1.00e+00 3.19e-02f 1\n",
|
||
" 26 9.8538791e+00 7.12e-01 7.08e-02 -7.0 1.65e+06 - 1.00e+00 1.75e-04f 1\n",
|
||
" 27 9.8538649e+00 7.12e-01 7.08e-02 -5.1 5.02e+04 - 1.00e+00 2.87e-05h 2\n",
|
||
" 28 1.0363599e+01 1.87e-03 6.92e-02 -3.6 2.45e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 1.0340255e+01 1.08e-02 1.63e-02 -2.6 2.14e+02 - 6.23e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 1.0358236e+01 1.92e-02 1.70e-02 -4.1 1.85e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 1.0342895e+01 1.43e-02 1.78e-02 -4.2 1.20e+03 - 1.00e+00 3.53e-02h 1\n",
|
||
" 32 1.0365499e+01 1.66e-03 2.91e-03 -4.2 1.50e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 33 1.0367963e+01 6.99e-05 2.28e-03 -6.3 1.87e+01 - 1.00e+00 1.00e+00H 1\n",
|
||
" 34 1.0358225e+01 1.30e-02 7.63e-03 -6.0 1.51e+02 - 1.00e+00 2.55e-01f 1\n",
|
||
" 35 1.0366307e+01 1.36e-03 3.20e-03 -6.1 1.37e+01 - 6.33e-01 1.00e+00h 1\n",
|
||
" 36 1.0366105e+01 2.01e-03 3.17e-03 -5.8 1.46e+03 - 1.00e+00 1.68e-03h 1\n",
|
||
" 37 1.0367866e+01 2.20e-04 2.18e-03 -6.0 1.66e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 38 1.0367331e+01 2.03e-04 1.98e-03 -7.9 4.93e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 39 1.0330613e+01 5.51e-02 1.45e-02 -5.9 7.20e+01 - 6.82e-01 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 1.0366831e+01 7.85e-07 6.06e-02 -8.0 6.52e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 41 1.0366831e+01 2.03e-07 8.16e-05 -9.9 1.21e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 1.0366831e+01 6.54e-08 1.55e-04 -11.0 5.64e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 43 1.0366830e+01 5.89e-07 3.70e-03 -11.0 2.37e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 44 1.0366831e+01 2.73e-07 3.66e-05 -11.0 9.89e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 45 1.0366831e+01 4.44e-08 6.96e-05 -11.0 1.90e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 1.0366828e+01 6.28e-06 4.56e-03 -11.0 2.57e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 1.0366828e+01 1.32e-06 1.58e-03 -11.0 3.11e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 48 1.0366775e+01 9.75e-05 4.14e-03 -11.0 1.69e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 49 1.0366829e+01 7.38e-07 1.18e-03 -11.0 1.21e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 1.0366824e+01 6.43e-06 1.67e-03 -11.0 3.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 51 1.0366829e+01 7.12e-07 1.72e-03 -11.0 7.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 52 1.0366830e+01 1.72e-07 7.74e-05 -11.0 7.64e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 53 1.0366827e+01 1.81e-06 2.11e-03 -11.0 2.25e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 1.0366827e+01 1.95e-06 1.89e-03 -11.0 1.26e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 55 1.0366820e+01 8.27e-06 3.81e-03 -11.0 2.71e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 56 1.0366822e+01 7.51e-06 2.56e-03 -11.0 4.47e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 57 1.0366829e+01 3.80e-06 1.37e-03 -11.0 4.89e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 58 1.0366824e+01 5.97e-06 1.23e-03 -11.0 1.08e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 59 1.0366695e+01 2.29e-04 3.79e-03 -11.0 2.34e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 1.0366702e+01 2.44e-04 2.24e-03 -9.0 1.45e+00 - 1.00e+00 5.07e-01h 1\n",
|
||
" 61 1.0366377e+01 8.65e-04 3.10e-03 -7.4 5.39e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 1.0366485e+01 3.02e-04 7.67e-04 -8.4 2.91e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 63 1.0366447e+01 3.09e-04 1.25e-03 -5.8 1.34e+01 - 1.00e+00 7.69e-02h 1\n",
|
||
" 64 1.0366660e+01 1.29e-04 1.28e-03 -7.9 8.16e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 65 1.0366753e+01 6.39e-05 1.19e-03 -5.9 3.87e-01 - 2.19e-01 1.00e+00h 1\n",
|
||
" 66 1.0366342e+01 2.32e-04 2.50e-03 -8.0 1.40e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 67 1.0366509e+01 1.85e-04 1.11e-03 -10.1 7.27e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 1.0366680e+01 1.15e-04 1.07e-03 -9.6 3.50e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 1.0365924e+01 4.52e-04 1.34e-03 -10.2 9.44e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 1.0365516e+01 6.30e-04 3.41e-03 -11.0 1.12e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 71 1.0366720e+01 3.09e-07 7.36e-05 -11.0 1.10e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 72 1.0366720e+01 2.13e-08 7.58e-05 -11.0 1.10e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 73 1.0366720e+01 2.99e-08 1.70e-04 -11.0 1.52e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 74 1.0366720e+01 5.36e-08 4.15e-05 -11.0 1.03e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 75 1.0366720e+01 2.89e-09 2.15e-04 -11.0 4.54e-05 - 1.00e+00 1.00e+00h 1\n",
|
||
" 76 1.0366720e+01 6.48e-08 1.84e-04 -11.0 2.17e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 77 1.0366695e+01 1.11e-05 1.65e-02 -11.0 3.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 78 1.0366717e+01 1.74e-06 1.23e-03 -11.0 5.78e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 79 1.0366716e+01 3.22e-06 1.64e-03 -11.0 9.81e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 1.0366718e+01 3.82e-07 4.79e-05 -11.0 3.90e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 81 1.0366718e+01 2.34e-06 2.38e-03 -11.0 9.04e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 82 1.0366718e+01 9.00e-07 2.94e-05 -11.0 2.91e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 83 1.0366719e+01 4.11e-07 1.63e-04 -11.0 1.92e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 84 1.0366717e+01 1.43e-06 2.81e-03 -11.0 1.03e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 85 1.0366697e+01 9.14e-06 8.13e-03 -11.0 1.93e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 86 1.0366539e+01 5.24e-05 6.75e-03 -11.0 2.75e+01 - 1.00e+00 4.91e-02h 1\n",
|
||
" 87 1.0366549e+01 4.90e-05 5.89e-03 -11.0 6.43e-02 - 1.00e+00 6.25e-02h 5\n",
|
||
" 88 1.0366687e+01 7.80e-06 1.29e-03 -5.8 4.32e-02 - 5.68e-01 1.00e+00h 1\n",
|
||
" 89 1.0366684e+01 1.01e-05 1.07e-03 -7.2 4.07e-01 - 1.00e+00 1.16e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 1.0366689e+01 2.31e-06 7.66e-04 -7.0 3.00e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 91 1.0366692e+01 3.26e-06 1.37e-03 -8.9 2.43e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 1.0366685e+01 4.75e-06 1.15e-03 -6.4 3.19e-02 - 1.00e+00 7.69e-01h 1\n",
|
||
" 93 1.0366685e+01 6.18e-06 7.77e-04 -8.6 1.44e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 94 1.0366692e+01 6.75e-07 1.22e-03 -11.0 6.47e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 95 1.0366685e+01 4.87e-06 2.13e-03 -8.4 2.24e-02 - 2.48e-01 1.00e+00h 1\n",
|
||
" 96 1.0366680e+01 6.44e-06 2.86e-03 -7.4 7.45e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 97 1.0366685e+01 1.09e-05 2.24e-03 -4.9 6.04e-02 - 6.94e-01 1.00e+00h 1\n",
|
||
" 98 1.0366667e+01 1.76e-05 1.64e-03 -5.2 9.53e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 99 1.0366687e+01 4.20e-06 1.92e-03 -6.0 3.19e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 1.0366687e+01 6.46e-06 1.88e-03 -6.1 4.16e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 1.0366686506612778e+01 1.0366686506612778e+01\n",
|
||
"Dual infeasibility......: 1.8777253844102226e-03 1.8777253844102226e-03\n",
|
||
"Constraint violation....: 6.4582653607203611e-06 6.4582653607203611e-06\n",
|
||
"Complementarity.........: 9.1837204213732223e-07 9.1837204213732223e-07\n",
|
||
"Overall NLP error.......: 1.8777253844102226e-03 1.8777253844102226e-03\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 108\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 108\n",
|
||
"Number of inequality constraint evaluations = 108\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.421\n",
|
||
"Total CPU secs in NLP function evaluations = 134.741\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 501.00us ( 4.64us) 489.28us ( 4.53us) 108\n",
|
||
" nlp_g | 4.85 s ( 44.91ms) 4.62 s ( 42.77ms) 108\n",
|
||
" nlp_grad | 1.35 s ( 1.35 s) 1.29 s ( 1.29 s) 1\n",
|
||
" nlp_grad_f | 358.00us ( 3.51us) 349.58us ( 3.43us) 102\n",
|
||
" nlp_jac_g | 132.57 s ( 1.30 s) 126.54 s ( 1.24 s) 102\n",
|
||
" total | 138.92 s (138.92 s) 132.59 s (132.59 s) 1\n",
|
||
"Timestamp 31200\n",
|
||
"This is Ipopt version 3.13.4, running with linear solver mumps.\n",
|
||
"NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
|
||
"\n",
|
||
"Number of nonzeros in equality constraint Jacobian...: 70\n",
|
||
"Number of nonzeros in inequality constraint Jacobian.: 5\n",
|
||
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
||
"\n",
|
||
"Total number of variables............................: 35\n",
|
||
" variables with only lower bounds: 0\n",
|
||
" variables with lower and upper bounds: 0\n",
|
||
" variables with only upper bounds: 0\n",
|
||
"Total number of equality constraints.................: 30\n",
|
||
"Total number of inequality constraints...............: 5\n",
|
||
" inequality constraints with only lower bounds: 0\n",
|
||
" inequality constraints with lower and upper bounds: 5\n",
|
||
" inequality constraints with only upper bounds: 0\n",
|
||
"\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 0 4.4721360e+01 5.59e+01 1.85e-05 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
||
" 1 1.9403926e+01 1.17e+01 5.59e+01 -1.5 5.59e+01 - 9.90e-01 1.00e+00f 1\n",
|
||
" 2 7.3008909e+00 3.65e+00 8.57e+00 0.4 1.17e+01 - 9.98e-01 1.00e+00f 1\n",
|
||
" 3 4.1852306e+00 7.45e-01 5.77e-01 -1.6 5.92e+00 - 9.98e-01 1.00e+00f 1\n",
|
||
" 4 4.6467365e+00 1.21e-03 8.02e-02 -3.4 1.05e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 5 4.6472508e+00 2.51e-07 3.12e-05 -5.3 1.21e-03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 6 4.6472510e+00 5.72e-08 1.31e-04 -11.0 3.98e-04 - 1.00e+00 1.00e+00h 1\n",
|
||
" 7 4.6472057e+00 3.60e-05 5.06e-03 -11.0 2.23e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 8 4.6472383e+00 1.59e-05 1.59e-03 -11.0 8.71e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 9 4.6471988e+00 6.10e-05 2.60e-03 -11.0 2.15e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 10 4.6472291e+00 2.95e-05 1.73e-03 -11.0 7.39e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 11 4.6471878e+00 4.43e-05 3.11e-03 -11.0 3.27e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 12 4.6470099e+00 1.29e-04 3.28e-03 -11.0 6.38e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 13 4.6472368e+00 3.07e-05 1.39e-03 -11.0 7.34e-02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 14 4.6472240e+00 1.73e-05 1.52e-03 -11.0 2.67e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 15 4.6472058e+00 7.37e-05 2.18e-03 -11.0 3.45e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 16 4.6471668e+00 3.98e-05 1.29e-03 -11.0 4.06e-01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 17 4.6470773e+00 4.02e-04 2.36e-03 -11.0 1.61e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 18 4.6430522e+00 2.93e-03 1.18e-02 -11.0 4.06e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 19 4.6457123e+00 8.68e-04 1.30e-03 -11.0 1.10e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 20 4.6456909e+00 1.39e-03 9.45e-04 -11.0 8.71e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 21 4.6457698e+00 5.52e-04 1.40e-03 -11.0 2.60e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 22 4.6383390e+00 6.27e-03 5.28e-03 -11.0 4.99e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 23 4.3492799e+00 2.25e+00 8.69e-01 -9.7 2.48e+05 - 1.00e+00 6.28e-02f 2\n",
|
||
" 24 5.5841381e+00 2.02e+00 2.33e-01 -9.8 1.59e+05 - 2.80e-01 4.98e-02h 2\n",
|
||
" 25 3.9174599e+00 1.82e+00 3.80e-01 -9.8 4.81e+04 - 1.54e-01 2.50e-01f 3\n",
|
||
" 26 3.5476742e+00 3.51e+00 6.41e-01 -9.4 3.32e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
" 27 3.4747137e+00 3.33e+00 6.52e-01 -7.5 2.54e+05 - 1.00e+00 9.39e-03h 1\n",
|
||
" 28 5.0247923e+00 7.92e-01 7.61e-01 -7.3 9.92e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 29 5.0177162e+00 7.71e-01 7.60e-01 -7.0 3.87e+04 - 1.00e+00 4.39e-03h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 30 5.1567135e+00 1.71e-01 1.19e-01 -7.0 2.00e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 31 4.2939901e+00 1.22e+00 4.24e-01 -7.0 4.28e+03 - 3.23e-01 1.00e+00f 1\n",
|
||
" 32 3.6301922e+00 2.00e+00 4.10e-01 -7.0 2.10e+04 - 1.00e+00 3.61e-01f 1\n",
|
||
" 33 6.2657583e+00 1.13e+00 4.31e-01 -6.5 5.74e+03 - 3.21e-01 1.00e+00h 1\n",
|
||
" 34 5.8657659e+00 1.25e+00 3.57e-01 -1.9 1.96e+04 - 7.68e-01 3.74e-01f 1\n",
|
||
" 35 4.9981730e+00 2.77e-01 4.97e-02 -1.9 3.18e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 36 5.1027780e+00 1.70e-01 8.37e-02 -1.9 2.67e+03 - 6.25e-01 1.00e+00h 1\n",
|
||
" 37 5.5768322e+00 2.48e-02 4.75e-02 -1.9 2.46e+03 - 1.00e+00 1.00e+00H 1\n",
|
||
" 38 5.0515206e+00 3.33e-01 9.20e-02 -2.8 1.24e+03 - 6.31e-01 1.00e+00f 1\n",
|
||
" 39 4.6806010e+00 5.08e-01 8.11e-02 -2.8 3.03e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 40 4.1457718e+00 6.78e-01 1.90e-01 -2.8 8.16e+04 - 4.13e-01 2.74e-02f 1\n",
|
||
" 41 5.4937581e+00 1.83e-02 1.66e-01 -2.5 2.60e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 42 5.3253421e+00 1.85e-01 4.89e-02 -2.6 1.55e+03 - 6.74e-01 1.00e+00f 1\n",
|
||
" 43 4.6985728e+00 5.81e-01 2.53e-02 -2.6 6.61e+03 - 6.67e-01 1.00e+00f 1\n",
|
||
" 44 4.2581931e+00 9.03e-01 2.19e-01 -2.6 8.18e+03 - 1.00e+00 6.16e-01h 1\n",
|
||
" 45 5.3054446e+00 5.20e-01 3.34e-01 -2.6 8.24e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 46 5.3256999e+00 4.41e-01 4.48e-01 -2.6 2.52e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 47 5.1434019e+00 5.78e-01 4.69e-02 -2.6 5.16e+03 - 1.00e+00 8.09e-01h 1\n",
|
||
" 48 5.3681720e+00 1.42e-01 2.47e-02 -2.6 5.31e+02 - 3.51e-01 1.00e+00h 1\n",
|
||
" 49 5.4595716e+00 1.28e-02 3.32e-02 -2.6 1.83e+04 - 2.17e-01 1.00e+00H 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 50 5.4142071e+00 8.56e-02 1.87e-02 -2.6 4.34e+04 - 3.43e-01 5.36e-02f 2\n",
|
||
" 51 5.2301958e+00 4.31e-01 5.77e-02 -2.6 1.77e+04 - 3.97e-02 2.10e-01F 1\n",
|
||
" 52 5.1559883e+00 6.88e-01 9.99e-02 -2.6 6.92e+04 - 1.00e+00 1.72e-02f 6\n",
|
||
" 53 5.4325768e+00 9.61e-03 1.16e-01 -2.6 1.46e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 54 5.4087991e+00 2.45e-02 4.24e-02 -2.6 8.13e+02 - 4.92e-01 1.00e+00h 1\n",
|
||
" 55 5.3157234e+00 2.66e-01 1.96e-02 -2.6 1.07e+05 - 6.05e-02 2.59e-02f 1\n",
|
||
" 56 5.4107732e+00 1.21e-02 2.34e-01 -2.6 9.58e+03 - 1.00e+00 9.48e-01H 1\n",
|
||
" 57 5.3262351e+00 2.59e-01 3.65e-02 -2.6 5.03e+03 - 1.00e+00 1.00e+00f 1\n",
|
||
" 58 5.2536455e+00 1.02e-01 4.93e-02 -2.6 9.45e+02 - 6.71e-01 1.00e+00h 1\n",
|
||
" 59 5.0311793e+00 2.48e-01 5.12e-02 -2.6 2.08e+04 - 4.49e-01 2.88e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 60 4.9835051e+00 1.91e-01 1.93e-01 -2.6 3.04e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 61 5.2024953e+00 1.38e-01 2.19e-02 -2.6 1.05e+03 - 1.00e+00 1.00e+00h 1\n",
|
||
" 62 5.2397975e+00 1.21e-01 1.96e-02 -2.6 4.75e+02 - 5.04e-01 5.00e-01h 2\n",
|
||
" 63 5.2286105e+00 1.22e-01 2.48e-02 -2.6 2.79e+04 - 1.00e+00 7.99e-03h 7\n",
|
||
" 64 4.8325418e+00 7.07e-01 1.42e-01 -2.6 1.07e+04 - 3.76e-02 2.03e-01f 3\n",
|
||
" 65 4.7995979e+00 8.84e-01 1.66e-01 -2.6 1.24e+05 - 1.00e+00 9.88e-03h 5\n",
|
||
" 66 4.7773270e+00 9.12e-01 1.75e-01 -2.6 6.98e+04 - 5.57e-02 1.46e-02h 5\n",
|
||
" 67 5.3290868e+00 2.61e-01 1.58e-01 -2.6 6.52e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 68 5.2724603e+00 3.57e-01 1.08e-01 -2.6 9.31e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 69 5.4249206e+00 3.68e-02 8.54e-02 -2.6 6.57e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 70 4.6537873e+00 1.36e+00 2.27e-01 -2.6 2.01e+04 - 2.33e-01 1.00e+00f 1\n",
|
||
" 71 4.7503974e+00 8.54e-01 1.13e-01 -2.6 2.31e+04 - 1.00e+00 1.72e-01h 3\n",
|
||
" 72 4.6968573e+00 8.16e-01 1.23e-01 -2.6 4.39e+05 - 7.67e-02 6.52e-02h 1\n",
|
||
" 73 5.0814574e+00 7.26e-01 2.13e-02 -2.6 1.04e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 74 4.6519415e+00 4.46e-01 6.29e-02 -2.6 9.23e+03 - 3.80e-01 1.00e+00h 1\n",
|
||
" 75 4.5070295e+00 5.60e-01 5.10e-02 -2.6 4.40e+05 - 7.67e-02 7.57e-02h 1\n",
|
||
" 76 5.0482118e+00 3.00e-01 1.01e-01 -2.6 3.11e+04 - 1.00e+00 5.00e-01h 2\n",
|
||
" 77 5.5400908e+00 1.02e-01 1.63e-01 -2.6 6.59e+03 - 7.57e-01 1.00e+00H 1\n",
|
||
" 78 5.4887663e+00 1.48e-01 1.49e-01 -2.6 2.27e+05 - 1.80e-01 2.57e-02h 2\n",
|
||
" 79 5.4723781e+00 1.01e-01 1.47e-01 -2.6 3.99e+04 - 1.00e+00 1.03e-01h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 80 5.6174030e+00 1.22e-01 1.13e-02 -2.6 2.60e+03 - 3.56e-01 1.00e+00h 1\n",
|
||
" 81 5.6156882e+00 1.26e-01 1.14e-02 -2.6 1.35e+06 - 3.58e-02 1.57e-05f 7\n",
|
||
" 82 5.6153099e+00 1.27e-01 1.13e-02 -2.6 2.18e+05 - 1.00e+00 2.74e-05h 9\n",
|
||
" 83 5.6149398e+00 1.27e-01 1.13e-02 -2.6 2.55e+04 - 1.00e+00 1.41e-04h 13\n",
|
||
" 84 5.6504675e+00 4.65e-02 5.96e-03 -2.6 2.87e+02 - 7.29e-01 1.00e+00h 1\n",
|
||
" 85 2.8041611e+00 1.14e+00 9.48e-01 -2.6 6.61e+04 - 4.07e-03 2.17e-01f 1\n",
|
||
" 86 5.2836306e+00 1.62e-01 3.42e-01 -2.1 1.46e+02 - 1.00e+00 1.00e+00h 1\n",
|
||
" 87 5.4830999e+00 2.92e-03 3.80e-02 -2.2 3.42e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
" 88 5.4737635e+00 6.43e-03 1.79e-02 -3.3 6.70e+01 - 1.00e+00 7.83e-01h 1\n",
|
||
" 89 5.4742346e+00 6.96e-03 3.50e-03 -3.3 3.33e+01 - 1.00e+00 1.00e+00h 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 90 5.4854428e+00 2.98e-04 1.80e-03 -3.3 8.64e+00 - 8.32e-01 1.00e+00h 1\n",
|
||
" 91 5.4851485e+00 6.40e-04 1.63e-03 -5.0 2.95e+00 - 1.00e+00 1.00e+00h 1\n",
|
||
" 92 5.4840183e+00 1.74e-03 2.26e-03 -5.0 3.47e+02 - 1.00e+00 1.93e-02h 1\n",
|
||
" 93 5.4837934e+00 6.22e-04 4.48e-03 -5.0 8.91e+00 - 2.88e-01 1.00e+00h 1\n",
|
||
" 94 4.9234758e+00 4.99e+00 6.81e-01 -5.0 4.32e+04 - 4.02e-05 3.55e-01F 1\n",
|
||
" 95 4.9227306e+00 4.99e+00 6.80e-01 -5.0 1.37e+04 - 6.71e-01 5.16e-04h 1\n",
|
||
" 96 5.1455026e+00 2.32e-01 4.95e-01 -5.0 1.62e+03 - 1.47e-03 1.00e+00h 1\n",
|
||
" 97 4.9910127e+00 1.21e+00 1.87e-01 -5.0 1.20e+04 - 1.95e-01 1.00e+00h 1\n",
|
||
" 98 5.0457091e+00 3.69e-01 5.26e-02 -5.0 8.41e+03 - 4.69e-01 5.00e-01h 2\n",
|
||
" 99 4.3236618e+00 1.46e+00 4.94e-01 -5.0 1.84e+04 - 1.00e+00 1.00e+00f 1\n",
|
||
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
||
" 100 4.9907946e+00 4.73e-01 2.02e-01 -5.0 9.12e+03 - 9.24e-01 1.00e+00h 1\n",
|
||
"\n",
|
||
"Number of Iterations....: 100\n",
|
||
"\n",
|
||
" (scaled) (unscaled)\n",
|
||
"Objective...............: 4.9907945997356000e+00 4.9907945997356000e+00\n",
|
||
"Dual infeasibility......: 2.0186139466316316e-01 2.0186139466316316e-01\n",
|
||
"Constraint violation....: 4.7274730179493574e-01 4.7274730179493574e-01\n",
|
||
"Complementarity.........: 9.8433840305800756e-06 9.8433840305800756e-06\n",
|
||
"Overall NLP error.......: 4.7274730179493574e-01 4.7274730179493574e-01\n",
|
||
"\n",
|
||
"\n",
|
||
"Number of objective function evaluations = 194\n",
|
||
"Number of objective gradient evaluations = 101\n",
|
||
"Number of equality constraint evaluations = 194\n",
|
||
"Number of inequality constraint evaluations = 194\n",
|
||
"Number of equality constraint Jacobian evaluations = 101\n",
|
||
"Number of inequality constraint Jacobian evaluations = 101\n",
|
||
"Number of Lagrangian Hessian evaluations = 0\n",
|
||
"Total CPU secs in IPOPT (w/o function evaluations) = 1.406\n",
|
||
"Total CPU secs in NLP function evaluations = 138.142\n",
|
||
"\n",
|
||
"EXIT: Maximum Number of Iterations Exceeded.\n",
|
||
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
||
" nlp_f | 888.00us ( 4.58us) 880.10us ( 4.54us) 194\n",
|
||
" nlp_g | 8.77 s ( 45.21ms) 8.37 s ( 43.16ms) 194\n",
|
||
" nlp_grad | 1.32 s ( 1.32 s) 1.26 s ( 1.26 s) 1\n",
|
||
" nlp_grad_f | 339.00us ( 3.32us) 334.79us ( 3.28us) 102\n",
|
||
" nlp_jac_g | 132.09 s ( 1.30 s) 126.05 s ( 1.24 s) 102\n",
|
||
" total | 142.33 s (142.33 s) 135.82 s (135.82 s) 1\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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7u3RJkiTVdytWwGmnwSuvQPPm8PTTMHYsHHtsnRraWK2HUIcQsoHJwP7A72KM74QQ9okxLgGIMS4JIXTe02J21MNVW2bPnk1WVhZ9+/YFoKCggJ49e+7WsXr16sXvf/97SkpKWLRoUdn9WuUVFhbSrl07mjdvzqxZs3j77bfL1uXk5LB161ZycnIYO3Ys48aN4wc/+AGdO3dm5cqVrF27lkMOOYTvfe97rFixgtatW/PYY48xdOjQndZ23HHHcc0113DWWWfRsmVLFi1aRE5Ozi5d3/jx47nqqqtYv349EyZM4KabbqJZs2bVOu6aNWto0aIFbdq04dNPP+Wf//wno0aNAqBVq1asXbuWjh07ArDPPvswc+ZM+vfvz5NPPkmrVq22OV7r1q3p3bs3jz32GKeffjoxRqZOnVqt74UkSZLqkVmz4MQTYeFC+MpX4IILPgtipfegTZxYf8JZjLEYyAshtAWeDCEMqu4JQggXABcA9OjRY3dqrFXr1q3ju9/9LqtXr6ZRo0bsv//+ZZN07KojjjiibIjgoEGDGDZs2DbbHH/88dx1110MGTKE/v37Vxj2d8EFFzBkyBCGDRvGI488wg033MCxxx5LSUkJOTk5/O53v+PQQw/luuuu47DDDqNLly4MGzasbKKQHTn22GOZOXMmhx12GJAM53z44YfJzs6u9vUdfPDBnHDCCSxYsIBrrrmG3NxccnNzq3XcoUOHctBBBzFw4ED69OnDEUccUeG6v/CFL9ClSxfy8/O56aabOPHEE+nevTuDBg1i3bp1VOWRRx7h4osv5oYbbmDr1q185StfMZxJkiQ1JC+9BF/+MjRpkszMmPqdtII6NKwxJKMWd2GHEK4F1gPnA6NSvWZdgAkxxv472nfEiBGxdPbDUjNnzuSAAw7Ytaq111133XW0bNmSH/3oR+kuZZf450uSJKmeuvNO+O534YAD4NlnYTdHv+1tIYTJMcYRVa3b6VT6IYROqR4zQgjNgM8Bs4CngXNSm50DjK+RaiVJkiRpe4qK4LLLksk/jj8e3nyzzgSznanOsMYuwAOp+86ygEdjjM+GEN4CHg0hfBtYAJy+o4OobrvuuuvSXYIkSZIausJCOPNMeP75ZDbGm2+GXbhNJ9NVZ7bGqcBBVSxfAYytjaIkSZIkqYKPPoKTToI5c+Cee+D889NdUY2r1oQgkiRJkpQ2r70Gp54KxcXwwgt1ZoKPXbXTe84kSZIkKW0eeCB5Zln79vDOO/U2mIHhTJIkSVImKimBq66Cc8+Fo46Ct9+G1LOJ6yvDGZCdnU1eXh6DBg3i9NNPZ8OGDbt9rHPPPZfHH38cgPPOO48ZM2Zsd9sJEybw5ptvln2+6667ePDBB3f73KXmzZvHoEEVH0V33XXX8atf/WqXjlNT9UiSJEm7ZP365PllN90EF14I//oXtGuX7qpqnfecAc2aNaOgoACAs846i7vuuovLL7+8bH1xcfEuPay51L333rvD9RMmTKBly5YcfvjhAFx00UW7fI7aUlRUlFH1SJIkqYFYuBBOPhmmTIFbb02mzQ8h3VXtFXWr5+zmm5Mnf5eXn58sryFHHXUUH3zwARMmTGD06NF87WtfY/DgwRQXF/PjH/+YkSNHMmTIEO6++24AYoxceumlHHjggZxwwgksXbq07FijRo2i9KHb//rXvxg2bBhDhw5l7NixzJs3j7vuuotbbrmFvLw8XnvttQq9WwUFBRx66KEMGTKEL33pS6xatarsmFdccQUHH3ww/fr147XXXtvla9zRsa+++mqOOeYYbrvttrJ6Fi9eTF5eXtkrOzub+fPnM3/+fMaOHcuQIUMYO3YsCxYsAJLew8suu4zDDz+cPn36lPUkSpIkSTs0cSIcfDB88AE88wx873sNJphBXQtnI0fCGWd8FtDy85PPI0fWyOGLior45z//yeDBgwF49913ufHGG5kxYwb33Xcfbdq0YeLEiUycOJE//OEPzJ07lyeffJLZs2czbdo0/vCHP1QYplhq2bJlnH/++fz9739nypQpPPbYY/Tq1YuLLrqIH/zgBxQUFHDUUUdV2Ocb3/gGv/jFL5g6dSqDBw/m+uuvr1Dnu+++y6233lpheXkffvhhhUB11113VevYq1ev5pVXXuGHP/xh2bLc3FwKCgooKCjg/PPP57TTTqNnz55ceumlfOMb32Dq1KmcddZZXHbZZWX7LFmyhNdff51nn32WK6+8chdbQpIkSQ3OY4/B0UdDkybJg6W/+MV0V7TXZdawxu9/H1LDC7crNxeOOw66dIElS+CAA+D665NXVfLyku7QHdi4cSN5eXlA0nP27W9/mzfffJODDz6Y3r17A/DCCy8wderUsl6gwsJC3n//fV599VW++tWvkp2dTW5uLmPGjNnm+G+//TZHH3102bHat2+/w3oKCwtZvXo1xxxzDADnnHMOp5/+2TO+Tz31VACGDx/OvHnzqjzGfvvtVzZUEz57iPTOjn3mmWdut6433niDe++9t6y37q233uKJJ54A4Oyzz+YnP/lJ2bannHIKWVlZHHjggXz66ac7vF5JkiQ1YDHCjTfCNdfAEUfAE09A587priotMiucVUe7dkkwW7AAevSokRsDy99zVl6LFi3K3scYueOOOzjuuOMqbPPcc88RdtLVGmPc6Ta7okmTJkAykUlRUVGNHRcqXnN5S5Ys4dvf/jZPP/00LVu2rHKb8tdYWiMk1y9JkiRtY9Mm+Pa34c9/hrPPhj/8Iek5a6AyK5ztpIcL+Gwo4zXXwJ13wrXX7pVnHRx33HHceeedjBkzhpycHObMmUPXrl05+uijufvuu/nGN77B0qVLyc/P52tf+1qFfQ877DAuueQS5s6dS+/evVm5ciXt27enVatWrFmzZptztWnThnbt2vHaa69x1FFH8dBDD5X1dO2p3Tn21q1bOeOMM/jFL35Bv379ypYffvjh/PWvf+Xss8/mkUce4cgjj6yRGiVJktQAfPopnHJKMkX+jTcm0+Y3oPvLqpJZ4WxnSoPZo48mgWz06Iqfa9F5553HvHnzGDZsGDFGOnXqxFNPPcWXvvQlXn75ZQYPHky/fv2qDDqdOnXinnvu4dRTT6WkpITOnTvz4osvctJJJ/HlL3+Z8ePHc8cdd1TY54EHHuCiiy5iw4YN9OnTh/vvv7/GrmVXj/3mm28yceJErr32Wq699log6TG8/fbb+da3vsUvf/lLOnXqVKM1SpIkqR6bNg1OPBGWLYPHH4fTTkt3RRkh7M0hZyNGjIilsxeWmjlzJgcccED1DnDzzcnkH+WDWH5+MqtLufudpFK79OdLkiRJte/ZZ+GrX4XWreHpp2H48HRXtFeFECbHGEdUta5u9ZxVFcBKe9AkSZIkZa4Y4ZZb4Ec/gmHDYPx46No13VVllLo1lb4kSZKkumfLFrjgAvjhD+HUU+HVVw1mVTCcSZIkSao9K1cmj8K691746U+T+SKaN093VRkpI4Y11vRU8xI4hb8kSVLazZ6dTPyxYAE89BB8/evpriijpb3nrGnTpqxYscJfpFWjYoysWLGCpk2bprsUSZKkhunf/4ZDD4XCwmQSP4PZTqW956xbt24sXLiQZcuWpbsU1TNNmzalW7du6S5DkiSp4bn7brjkEhgwIJmdsVevdFdUJ6Q9nOXk5NC7d+90lyFJkiRpTxUVJbMx3nYbfPGL8Je/JFPmq1rSPqxRkiRJUj1QWAgnn5wEs+9/P3mGmcFsl6S950ySJElSHTd3Lpx0UjIByN13J9Pma5cZziRJkiTtvjfegFNOSYY0Pv88jBmT7orqLIc1SpIkSdo9Dz2UhLF27eCddwxme8hwJkmSJGnXlJTA1VfDN74BRx4Jb78N/fqlu6o6z2GNkiRJkqpv/foklD3xRHJv2W9/Czk56a6qXjCcSZIkSaqeRYuSGRkLCuCWW+B734MQ0l1VvWE4kyRJkrRzkyYlwWzdumSa/BNOSHdF9Y73nEmSJEnasccfh6OPhsaNk9kZDWa1wnAmSZIkqWoxwo03wumnQ14evPsuDB6c7qrqLYc1SpIkSdrWpk1w3nnwyCNw1llw773QtGm6q6rX7DmTJEmSVNHSpTB2bBLMbrgheZ6ZwazW2XMmSZIk6TPTpsFJJyUB7bHH4MtfTndFDYY9Z5IkSZIS//gHHH44bN0Kr75qMNvLDGeSJElSQxdj8tyyk0+Gfv2SiT9GjEh3VQ2O4UySJElqyLZuhQsvhMsvh1NOSXrMunZNd1UNkuFMkiRJaqhWroTjj4c//AGuvjq5x6xFi3RX1WA5IYgkSZLUEM2ZAyeeCPPnw4MPwtlnp7uiBs+eM0mSJDVMN98M+fkVl+XnJ8vru5dfhkMPhVWrkvcGs4xgOJMkSVLDNHIknHHGZwEtPz/5PHJkeuuqaZVD6D33wOc/D40bJxN/HHFE+mpTBQ5rlCRJUsOyaRN89BGsW5eEsS9+EQYMgFmz4HOfS+67evxxyMpKXiF89n5nnzNxXZMmcOqp8L//Cx9+CLfdBjk5cO+90Lt3ultD5YQY41472YgRI+KkSZP22vkkSZLUQK1cmQSRql6LFlXctnFj2LIFWrZMJsMoKUleMVb9vqp1e/F36j3WrBk8/XQSRLXXhRAmxxirfE6BPWeSJEmqe0pKkpC1vQC2enXF7ffdF/bbD8aOTb6WvpYsgQsugIsvhjvvhL/8BUaP3vV6SgPazkJcba7b2Xb33w9//jP86EcGswxlOJMkSVJm2rwZ5s6tOnzNnZusL9WoEfTsmQSugw+uGMD69Kl6evj8/CSYPfpoEshGj06GOZZ+3hUhJC+A7Ozdv+bakp8PL7wA11yThNDS61VGMZxJkiQpfQoLk7D1wQfbBrCFCysOF2zRIglbBxyQTAFfPoD16JEEtF0xcWLFIDZ6dPJ54sT6FVxKJzqpiRCqWuU9Z5IkSao9MSZDB7c3/HDFiorbd+5cMXSVf3Xu/FnvlKrv5puTGSjLB7H8/CSE/uQn6aurgdrRPWeGM0mSJH1md36R37IleZBxVeHro49g48bPts3K+mz4YeVXnz7QqlXtXp+UZk4IIkmSpOopffZX6ZC30iFxf/oTFBRUHcAWLEgmnSjVrFkStvbfH447rmIA69kzmcZd0jbsOZMkSVJFL70Ep52WBKmZM5N7vQoLK27TseP2hx/uu6/DD6Xt2KOesxBCd+BBYF+gBLgnxnhbCCEPuAtoChQB34kxvltjVUuSJGnvKiyEP/4Rbr8d1qyBadOSgHbssdsOP2zTJt3VSvVOdYY1FgE/jDG+F0JoBUwOIbwI3AxcH2P8Zwjhi6nPo2qvVEmSJNWKDz+EO+5IgtnatTBoELRuDd/9Ltx9N3z1q87qJ+0FWTvbIMa4JMb4Xur9WmAm0BWIQOvUZm2AxbVVpCRJkmpYjPDqq/ClL0HfvvC738HJJyfPwPrkE3jqKbjhhuTeszPOSO49k1SrdhrOygsh9AIOAt4Bvg/8MoTwMfAr4KqaLk6SJEk1bMsWeOghGD4cjjkmCWhXXZXMtvjww8lwxu09+0tSrar2hCAhhJbAK8CNMcYnQgi3A6/EGP8eQjgDuCDG+Lkq9rsAuACgR48ew+fPn19z1UuSJKl6li+Hu+5Kesg++QQGDIDvfx/OPhuaN093dVKDscfPOQsh5ADPAs/HGH+TWlYItI0xxhBCAApjjK13dBxna5QkSdrLpk+H225Less2bUom9/jBD5KvWbs0iEpSDdjT2RoDcB8wszSYpSwGjgEmAGOA9/e8VEmSJO2xkhJ44QW45Zbka9OmSQ/Z974HAwemuzpJ21Gd2RqPAM4GpoUQClLLrgbOB24LITQCNpEauihJkqQ02bAh6SG79VaYNQu6dEkm9bjwwuS5ZJIy2k7DWYzxdWB7TxEcXrPlSJIkaZctWpTcS3b33bByJQwbloS0M86Axo3TXZ2kaqpOz5kkSZIy0aRJSS/Z3/4GxcVwyinJJB9HHQVhe/+3LilTGc4kSZLqkuLi5Blkt94Kr78OLVvCJZfAZZdBnz7prk7SHjCcSZIk1QWFhfDHP8Ltt8O8edCrF/zmN/Ctb0GbNumuTlINMJxJkiRlso8+SgLZH/8Ia9fCkUfCr38N48ZBdna6q5NUgwxnkiRJmSZGeO21ZCr88eOTEHbmmcn9ZCOqfDySpHrAcCZJkpQptmxJJve49VZ47z1o3x6uvDK5p6xr13RXJ6mWGc4kSZLSbflyuOuuZDr8Tz6BAQOSz2efDc2bp7s6SXuJ4UySJCldZsxIeskeegg2bYJjj4X770++ZmWluzpJe5nhTJIkaW+KEZ5/Prmf7IUXoGnTpIfse9+DgQPTXZ2kNDKcSZIk7Q0bNiQ9ZLfdBjNnQpcucMMNcMEF0KlTuquTlAEMZ5IkSbVp0aLkXrK774aVK+Ggg+DBB5PZFxs3Tnd1kjKI4UySJKk2TJqU3E/2t79BcTGcckoyFf5RR0EIaS5OUiYynEmSJNWU4uLkuWS33AKvvw4tWybT4F92GfTpk+7qJGU4w5kkSdKeWrMG7rsPbr8d5s2DXr3gN7+Bb30L2rRJd3WS6gjDmSRJUnXcfDOMHAmjR3+27JFH4M47YepUWLsWjjwSfv1rOPlkaOSvWZJ2jf9qSJIkVcfIkXDGGck9ZI0awdVXwxtvJM8j++pXk/vJRoxId5WS6jDDmSRJUnWMHg1//CMcdxwUFSWTenzta0mPWteu6a5OUj3go+clSZKq66ST4IADkvdXXJEMazSYSaohhjNJkqTqys+HJUvgmmvg3nuTz5JUQwxnkiRJ1ZGfn9xz9uij8L//m3w94wwDmqQaYziTJEmqjokTk0BWOlvj6NHJ54kT01uXpHojxBj32slGjBgRJ02atNfOJ0mSJEmZJIQwOcZY5dSu9pxJkiRJUgYwnEmSJElSBjCcSZIkSVIGMJxJkiRJUgYwnEmSJElSBjCcSZIkSVIGMJxJkiRJUgYwnEmSJElSBjCcSZIkSVIGMJxJkiRJUgYwnEmSJElSBjCcSZIkSVIGMJxJkiRJUgYwnEmSJElSBjCcSZIkSVIGMJxJkiRJUgYwnEmSJElSBjCcSZIkSVIGMJxJkiRJUgYwnEmSJElSBjCcSZIkSVIGMJxJkiRJUgYwnEmSJElSBjCcSZIkSVIGMJxJkiRJUgZolO4CJEmSGqKSkkhxjBSXREpKv5awzbIK62OkuISK62NMjlX2niqWVT5X1eevsD617MJj+tCkUXa6v11Sg2A4kyRJaRdTQaCo9FVcwtbiSFFJCUXFny1Lvka2li3/7OvW4uQYW4vLrSvdvrik7Pifrf/smKXrt1bYryR1rkhx6vhF5Y5TMTSxnSBVaX25ZXXFOYf3MpxJe8lOw1kIoTvwILAvUALcE2O8LbXuu8ClQBHwjxjjT2qxVkmSlAHWbtrKksJNLFq9kSWrN7F49UYWF25kxbot2wSj8oFma+WglQpZpaFob8oK0Cgri0bZgUZZgZzsLLJTX0uXla3PziInK5CdFWjeuBHZWcn6rKxAdkiWJ++pYlnqfQhkZ7HNsrLjpJaXHqf8/lUfk9QxqzpPpfVly8q9D4GsLCqdt3ItlB1X0t5RnZ6zIuCHMcb3QgitgMkhhBeBfYBxwJAY4+YQQufaLFSSJNW+LUUlfLrms8C1uDR8rd5YFsjWbiqqsE92VmDf1k3p2LJxWbhp3qhRKuRkkZMdPgs+WaFseaPscqEoKwlB5YNRTioYJfum9im3XU5WuXWVjl1hWWqfsjqyssgycEjKQDsNZzHGJcCS1Pu1IYSZQFfgfOCmGOPm1LqltVmoJEnaMzFGlq/bwpLCjanAtW0IW7ZuM7FSJ1a75jnktm1G9/bNOaR3e3LbNqNL22Z0bduU3LbN6NSyCY2ynWNMkvbULt1zFkLoBRwEvAP8EjgqhHAjsAn4UYxxYhX7XABcANCjR489rVeSJG3Hus1FLFm9MRluWLhpmwC2pHATW4pKKuzTNCeL3LbNyG3TjFH9O9GlTTO6tm2WCmBNyW3TjGaNvd9IkvaGaoezEEJL4O/A92OMa0IIjYB2wKHASODREEKfGCv+f1uM8R7gHoARI0bUnbtfJUnKIFuLS/ikcFNZ6EoCWMVhh2sqDTfMCrBP66R3a0i3thw/MHnfpU3ytWvbZrRtnkMIDvGTpExQrXAWQsghCWaPxBifSC1eCDyRCmPvhhBKgI7AslqpVJKkeirGyIr1W1iyelO50JUKXqn3S9duO9ywbfMccts0o1u7Zhzcuz1d2jQjt21TuqaGHe7TyuGGklSXVGe2xgDcB8yMMf6m3KqngDHAhBBCP6AxsLw2ipQkqS5bv7mIJYUbWbR6E0tSvVyLCytOtLG50nDDJo1Sww3bNuWovp1SPV1NUwEsWd68sU/EkaT6pDr/qh8BnA1MCyEUpJZdDfwR+GMI4b/AFuCcykMaJUlqyF6Y/gk/+8cMPl65scLyEGCfVk3JbduUgV3bcOzAfcuGGuamer/at2jscENJamCqM1vj68D2fjp8vWbLkSSp7lu6ZhPXPTOd56Z9woB9W/GT4/snQw1TwWuf1k3JcbihJKkSx0NIklRDYow8OuljbvzHTDYVlfDj4/pzwdF9DGKSpGoxnEmSVAPmLl/P1U9M462PVnBI7/b8/NTB9OnUMt1lSZLqEMOZJEl7YGtxCfe+NpdbX5pD40ZZ/PzUwZw5ojtZWd4vJknaNYYzSZJ207SFhVzx96nMWLKG4wfuy/XjBrJP66bpLkuSVEcZziRJ2kUbtxRzy0tzuPe1j+jYsgl3fX0Yxw/qku6yJEl1nOFMkqRd8Pr7y7n6yWksWLmBrx7cgyu/MIA2zXLSXZYkqR4wnEmSVA2rN2zhhn/M5PHJC+ndsQV/veBQDu3TId1lSZLqEcOZJEk7EGPk2alLuP6Z6azasJXvjNqPy8b2pWlOdrpLkyTVM4YzSZK2Y/HqjfzP+P/y0sylDOnWhge/dQgH5rZOd1mSpHrKcCZJUiUlJZFH3pnPL/41m6KSEv7fCQdw7uG9aOTDpCVJtchwJklSOe9/upYrn5jG5PmrOKpvR248ZTA9OjRPd1mSpAbAcCZJErClqIQ7J3zI7/I/oHmTbH59+lBOHdaVEHyYtCRp7zCcSZIavMnzV3HVE1OZ8+k6Th6ay/+cdCAdWzZJd1mSpAbGcCZJarDWbS7iV8/P5oG35tGldVP+eO4IxgzYJ91lSZIaKMOZJKlByp+1lJ8+OY0lazZxzmG9+NFx/WnZxB+LkqT08aeQJKlBWb5uM//7zAyenrKYvp1b8vhFhzO8Z7t0lyVJkuFMktQwxBh54r1F/OwfM1i/uYgffK4fF43qQ5NGPkxakpQZDGeSpHrv45UbuPrJabz2/nKG92zHTacOpu8+rdJdliRJFRjOJEn1VnFJ5P435vLrF+aQFeBn4wZy1iE9ycpyenxJUuYxnEmS6qUZi9dw1RNTmbKwkLEDOvOzUwaR27ZZusuSJGm7DGeSpHpl09Zi7nj5fe5+5SPaNMvhjq8exIlDuvgwaUlSxjOcSZLqjbc/WsHVT0zjo+Xr+fLwbvz0iwfQrkXjdJclSVK1GM4kSXVe4cat3PTPWfzl3QV0b9+Mh799CEf27ZjusiRJ2iWGM0lSnfav/37C/4z/L8vXbeaCo/vw/c/1pXljf7xJkuoef3pJkuqkT9ds4trx0/nX9E84oEtr7jtnJIO7tUl3WZIk7TbDmSSpTokx8teJH/N/z81kS1EJVxw/gPOO6k1Odla6S5MkaY8YziRJdcZHy9Zx1RPTeGfuSg7t056fnzqE3h1bpLssSZJqhOFMkpTxthaXcM+rH3Hbv9+nSaMsfnHaYM4Y0d3p8SVJ9YrhTJKU0aYuXM0Vf5/GzCVr+MKgfbn+5IF0bt003WVJklTjDGeSpIy0YUsRt7w4h/ten0unVk24++zhHDdw33SXJUlSrTGcSZIyzmvvL+PqJ6fx8cqNfO2QHlz5hQG0bpqT7rIkSapVhjNJUsZYtX4LN/xjJn9/byF9OrbgbxccyiF9OqS7LEmS9grDmSQp7WKMPDN1Cdc/PZ3CjVu5dPT+XDpmf5rmZKe7NEmS9hrDmSQprRat3sg1T/2Xl2ctZWi3Njx83iEc0KV1usuSJGmvM5xJktKipCTy0NvzuflfsyiJcM2JB3Lu4b3IznJ6fElSw2Q4kyTtdfOWr+fyRwt4b8Fqjurbkf/70mC6t2+e7rIkSUorw5kkaa8q3LCVc+5/l9UbtvKbM4bypYO6+jBpSZIwnEmS9qKSksj3//YfFq/eyF8vOJThPdunuyRJkjJGVroLkCQ1HLe+NIf82cv4n5MGGswkSarEcCZJ2itemP4Jt7/8AacP78bXD+mR7nIkSco4hjNJUq37cNk6Ln90CkO6teFnpwzyHjNJkqpgOJMk1ap1m4u48KHJNG6UxZ1fH+6DpSVJ2g4nBJEk1ZoYIz96dApzl6/noW8fTNe2zdJdkiRJGcueM0lSrfn9hA/51/RPuOoLAzh8v47pLkeSpIxmOJMk1YpX5izjVy/M5qShuXz7yN7pLkeSpIxnOJMk1bgFKzZw2V/+Q/99WvGL0wY7AYgkSdVgOJMk1aiNW4q58OHJxBi5++zhNG/s7c2SJFWHPzElSTUmxsiVT0xl1idr+OO5I+nZoUW6S5Ikqc7Yac9ZCKF7CCE/hDAzhDA9hPC9Sut/FEKIIQTv9JakBu7+N+YxvmAxl3+uH6P7d053OZIk1SnV6TkrAn4YY3wvhNAKmBxCeDHGOCOE0B34PLCgVquUJGW8tz9awY3PzeTzB+7DJaP3T3c5kiTVOTvtOYsxLokxvpd6vxaYCXRNrb4F+AkQa61CSVLGW1K4kUv//B49OzTnN2cMJSvLCUAkSdpVuzQhSAihF3AQ8E4I4WRgUYxxSm0UJkmqGzYXFXPRw++xcUsx95w9nFZNc9JdkiRJdVK1JwQJIbQE/g58n2So40+BY6ux3wXABQA9evTYrSIlSZnr2vHTmfLxau76+nD279wq3eVIklRnVavnLISQQxLMHokxPgHsB/QGpoQQ5gHdgPdCCPtW3jfGeE+McUSMcUSnTp1qrnJJUtr9+Z0F/HXix1wyej+OH7TNjwBJkrQLdtpzFpInh94HzIwx/gYgxjgN6Fxum3nAiBjj8lqqU5KUYd5bsIprn/4vR/frxOWf75/uciRJqvOq03N2BHA2MCaEUJB6fbGW65IkZbClazdx8cOT6dKmGbd/JY9sJwCRJGmP7bTnLMb4OrDDn7oxxl41VZAkKbNtLS7h0kf+Q+HGrTxx8cG0bd443SVJklQvVHtCEEmSAG78x0zenbeS276Sx4G5rdNdjiRJ9cYuTaUvSWrYnnhvIX96cx7fOqI34/K67nwHSZJUbYYzSVK1/HdRIVc9MY1Derfnqi8OSHc5kiTVO4YzSdJOrVy/hQsfmkz7Fo353VnDyMn2x4ckSTXNe84kSTtUVFzCZX/5D8vWbubRiw6jY8sm6S5JkqR6yXAmSdqhX70wh9c/WM4vThtMXve26S5HkqR6y3EpkqTtem7aEu565UO+dkgPzhzZI93lSJJUrxnOJElVmvPpWn702BQO6tGWa086MN3lSJJU7xnOJEnbKNy4lQsfmkzzxo246+vDadIoO90lSZJU7xnOJEkVlJRELv9bAR+v3MCdXx/GPq2bprskSZIaBMOZJKmC219+n3/PWso1Jx7IyF7t012OJEkNhuFMklTm3zM/5daX3ufUYV35xmE9012OJEkNiuFMkgTA3OXr+f7fChjUtTX/96XBhBDSXZIkSQ2K4UySxPrNRVzw4CQaZQXu+vpwmuY4AYgkSXubD6GWpAYuxsiPH5/Ch8vW8eC3DqFbu+bpLkmSpAbJnjNJauDufvUjnpv2CT85fgBH9u2Y7nIkSWqwDGeS1IC99v4ybv7XLE4Y3IULj+6T7nIkSWrQDGeS1EB9vHID3/3Lf9i/c0tu/vIQJwCRJCnNDGeS1ABt2lrMRQ9PprgkcvfZI2jRxFuQJUlKN38aS1IDE2Pk6ienMX3xGu47ZwS9O7ZId0mSJAl7ziSpwXnwrfk88d4ivv+5vow9YJ90lyNJklIMZ5LUgLw7dyU/e3YGnzugM5eN6ZvuciRJUjmGM0lqID4p3MR3HnmP7u2b85sz88jKcgIQSZIyifecSVIDsLmomIsfmcyGLUX8+fxDaN00J90lSZKkSgxnktQAXP/MDP6zYDW/P2sY/fZple5yJElSFRzWKEn13N8mLuDP7yzgomP244uDu6S7HEmStB2GM0mqxwo+Xs01T03nyP078uPj+qe7HEmStAOGM0mqp5av28zFD0+mU6sm3PHVg8h2AhBJkjKa95xJUj20tbiESx55j5Xrt/D3iw+nXYvG6S5JkiTthOFMkuqhnz83i3fmruQ3ZwxlUNc26S5HkiRVg8MaJameGV+wiD++MZdzD+/FqcO6pbscSZJUTYYzSapHpi8u5Iq/T+XgXu356QkHpLscSZK0CwxnklRPrN6whYsenkybZjn89qyDyMn2n3hJkuoS7zmTpHqguCRy2V8L+KRwE3+78DA6t2qa7pIkSdIuMpxJUj3wmxdn8+qcZfzflwYzrEe7dJcjSZJ2g+FMUsYqKi7hPx+vJn/WUtZtLuKovp04Yv8ONG/sP13l/eu/n/C7/A/5ysjufO2QHukuR5Ik7SZ/w5GUUVas28wrc5aRP3sZr85ZRuHGrWRnBZo0yuLBt+bTuFEWh/bpwJj+nRg9oDM9O7RId8lp9cHStfzw0QKGdm/L9eMGprscSZK0BwxnktKqpCQyffEaXp61lPzZS5mycDUxQseWTTj2wH0YPaAzR/btSNNG2Uyct7Jsu+uemcF1z8ygT6cWjOnfmdEDOjOyV3saN2o4k2Cs3bSVCx6aTLPG2dz19WE0aZSd7pIkSdIeCDHGvXayESNGxEmTJu2180nKTGs2beW1OcvJn72UCbOXsXzdZkKAod3aMmZAZ0b378zA3NZkZYXtHmPe8vXkz15K/uxlvP3hCrYUl9CySSOO3L8jowd0YnT/znRuXX8nxSgpiVz48GRenrWUR847hEP7dEh3SZIkqRpCCJNjjCOqWmfPmaRaF2Pk/aXrkl6vWUuZPH8VRSWRNs1yOLpfJ8YM6MTRfTvRoWWTah+zV8cWfLNjb755RG82bCnijQ9W8PKspUyYvZR/Tf8EgEFdWzM61as2tFtbsncQ9uqa3+V/wIszPuV/TjzQYCZJUj1hz5mkWrFhSxFvfbgi6d2atYxFqzcCcECX1ozu34kxAzqT170tjWr4WVwxRmZ9srYsqE2ev4qSCO1bNOaYfsl9asf07USb5jk1et69KX/2Ur71p4mcPDSXW8/MI4T6EzolSarvdtRzZjiTVGPmr1hP/qylvDx7GW9/tIItRSU0b5ydGmrYmVH9O9GlTbO9WtPqDVt4Zc4yJsxexoTZS1m1IZlgZHiPdowakITE/vu0qjMBZ97y9Zz829fp2q45T1x8OM0ae5+ZJEl1ieFMUq3YXFTMxLmrUr1jS/lo+XoA+nRqkQwn7N+Zkb3bZcxEFcUlkYLU1Pz5s5cyffEaAHLbNGXUgM6M6d+ZwzN4qv4NW4o49fdvsqRwE89ceiQ9OjRPd0mSJGkXGc4k1ZhPCjeVhbE3PljO+i3FNG6UxWF9OjC6fydG9e9Mr451Y3r7T9dsKgtqr7//2bWUTtU/ZsA+GROAYox89y//4R/TlvCnbx7MMf06pbskSZK0GwxnO/DH1+dSEiMnDc1ln3o8s5u0u8o/CDp/9jJmLkl6m7q2bcao1L1jh+2Xub1N1bWjXsAx/TszZkBnRqRxqv4/vPoRNz43kx8f159LRu+flhokSdKeM5ztwLf+NJGXZy0lBDisTwfG5eVy/MAudXqyAGlPbe9B0CN6tkumuh/Qmb6dW9aZ+7R2R+lU/S/PWso7H62sMFX/mNT9c3trqv43P1jO1+97h2MP3Jc7vz6sXn/fJUmq7wxnO/HhsnU8XbCY8QWLmLdiA42zsxjVvxPj8roy9oDONM3JjPtlpNqyowdBl/aOHbF/R9o0a5j/abF+cxFvfrii7FEAn6zZBCRT9Y/p35lRtThV/6LVGznpjtdp36IxT11yBC2b1O0eSkmSGjrDWTXFGJm2qJDxBYt5Zspilq7dTIvG2Rw3aF/G5XXliP061Pi031K6rNm0ldffX56acr7ig6BHp4bx7exB0A1R+an682ct5b0Fn03VP6pfJ0bV4FT9m7YWc8bdbzF32XqeuvQI9uvUsgauQJIkpdMehbMQQnfgQWBfoAS4J8Z4Wwjhl8BJwBbgQ+CbMcbVOzpWpoez8opLIu98tILxBYt57r9LWLupiA4tGnPikC6cnNeVYT3aOrRIdUrpg6DzZyVD9UofBN26aSOO6d+Z0f07cXS/TnTchQdB67Op+vNnLeWVOcsqTNU/ekBnRg/otFtT9ccY+cnjU3ls8kL+8I0RfP7AfWrpCiRJ0t60p+GsC9AlxvheCKEVMBk4BegGvBxjLAoh/AIgxnjFjo5Vl8JZeZuLipkwexlPFyzmpZmfsrmohG7tmjEuL5dxeV3pt0+rdJcoVWlnD4IePaAzB9XCg6AbqvJT9b88aykzlnw2Vf/oAcmjBao7Vf9Db8/nmqf+y2Vj9ufyY/vXdumSJGkvqdFhjSGE8cBvY4wvllv2JeDLMcazdrRvXQ1n5a3dtJUXpn/K+CmLef39ZZREGLBvK8bldeWkoV3o1i4zpt1Ww1X6IOj82ct4K0MeBN1QfVK4iQmpSUVe/2A5Gyo9dmB7U/VPnr+Sr9zzNkfu35H7zhnp0FJJkuqRGgtnIYRewKvAoBjjmnLLnwH+FmN8eEf714dwVt6ytZv5x9TFjJ+ymP8sWA3AyF7tODmvKycM7kL7Fo3TW6AahApTwM9eykfLUlPAd2xR1luTSQ+CbqhK26l00pW5qan690s9sLt0qv7VG7Zwwh2v07xxNk9fcqQzx0qSVM/USDgLIbQEXgFujDE+UW75T4ERwKmxioOFEC4ALgDo0aPH8Pnz5+/6FdQBC1Zs4Okpi3iqYDEfLF1Ho6zAUX07Mi6vK58/cB9aOMOaasjmomKmL17D5HmreHfeSt78YNuHJ9elB0E3VHOXry97AHb5qfrbNMth5fotPHnJ4QzYt3W6y5QkSTVsj8NZCCEHeBZ4Psb4m3LLzwEuAsbGGDfs7Dj1reesKjFGZi5Zy/gpi3imYDGLCzfRNCeLzx+4L+OG5nJ0v05pe4it6qYV6zYzef4qJi9YxeR5q5i6qJAtRSUA9GjfnKP6dqw3D4JuqNZvLuKND5aTP3sp785dyY+O7c8XBndJd1mSJKkW7OmEIAF4AFgZY/x+ueXHA78BjokxLqtOIQ0hnJVXUhKZNH8V4wsW8Y9pS1i9YSttmuXwxcFdGJeXy8G92nsviSooKYl8uGwdk+avSgLZ/FVlw99ysgODurZhRM92DO/ZjmE929G51d55CLIkSZJqxp6GsyOB14BpJFPpA1wN3A40AVaklr0dY7xoR8dqaOGsvC1FJbz+wTLGFyzmhemfsnFrMV3aNOWkobmcPDSXgbmtnZq/AdqwpYgpHxcyef5KJs9fxXsLVlO4cSuQPDdrWI92jOiVhLHBXdv4QHRJkqQ6zodQZ5gNW4p4ccanPF2wmFfmLKOoJLJfpxaMy+vKuLxcenbwXqH6aknhRibPX8Wkeat4b8Eqpi9eQ3FJ8newb+eWDE/1ig3v2Y7eHVsY2CVJkuoZw1kGW7V+C8/9dwnjCxbz7tyVAOR1b8u4vFxOGNLFYWt1WFFxCbM+WZuEsfmreG/+qrLnjDXNySKve1uG92zHiJ7tOahHW9o2d3ZPSZKk+s5wVkcsXr2RZ6YsZnzBYmYsWUNWgCP278jJQ3M5btC+tG7qlNqZrHDjVv6zIAlhk+avouDj1WzYUgzAvq2bMrxXu7L7xQ7o0pocH/wsSZLU4BjO6qD3P13L06mgtmDlBho3ymLsgM6My8tlVP/O3nuUZjFGFqzcwKR5n/WKzVm6lhghK8CBua0Z3qMdw3u1Z3jPdnRt60OfJUmSZDir02KMFHy8mvEFi3l26mKWr9tCqyaNOH7QvozL68ph+3Ug2xkfa93momL+u6iwwv1iy9dtAaBV00YM69EuNUSxHUO7t/W5dpIkSaqS4ayeKCou4a2PVvDUfxbz/PRPWLe5iE6tmnDikC6My+vK0G5tnECihixPPVusdIjitIWFbClOJivt2aF52aQdI3q2p2/nlj4SQZIkSdViOKuHNm0t5uVZSxlfsIj8WcvYUlxCzw7NGTc0l5PzurJ/55bpLrHOKCmJfLBsHZPmlT5bbCXzViTPVG+cncXgbm2S54qlesc6tWqS5oolSZJUVxnO6rnCjVt5/r+fMH7KIt78cAUxwsDc1ozLy+Wkobl0aeP9TuVt2FJEwcermTxvFZNTE3is2VQEQIcWjRnW87OJOwb5bDFJkiTVIMNZA7J0zSaembqEpwsWMWVhISHAwb3aMy6vK18cvG+DnK598eqNqR6x5DVjyWfPFuu3T0uG92xfdr9Yzw7NHRoqSZKkWmM4a6DmLl/P0wWLGT9lER8tWw9Ao6xAVlYgOwSyswJZAbKzSt9X/Fp+fcVlqfchkJVFFcvKHT8rkB2oYln5Y1HFskrrt3v+SutTy5as3lg2i+Liwk0ANMvJJq97W0b0asewnu0Y1r0dbZr7eAJJkiTtPYazBi7GyPTFa5gweykbtxZTXAIlMVJckrxK33+2rNz6GCmptF1xpGxZ2fpyX4tLqGJZpfXbLCtfR81cd26bpuWGKLbngC6taOSzxSRJkpRGOwpnzvfdAIQQGNS1DYO6tkl3KdUSYxLQKgbCWCkQUsWyz4Jeu+aNyfXZYpIkSapDDGfKOCEkQyF9fpskSZIaEsd4SZIkSVIGMJxJkiRJUgYwnEmSJElSBjCcSZIkSVIGMJxJkiRJUgYwnEmSJElSBjCcSZIkSVIGMJxJkiRJUgYwnEmSJElSBjCcSZIkSVIGCDHGvXeyEJYB8/faCauvI7A83UWoRtmm9Y9tWv/YpvWPbVr/2Kb1j22afj1jjJ2qWrFXw1mmCiFMijGOSHcdqjm2af1jm9Y/tmn9Y5vWP7Zp/WObZjaHNUqSJElSBjCcSZIkSVIGMJwl7kl3Aapxtmn9Y5vWP7Zp/WOb1j+2af1jm2Yw7zmTJEmSpAxgz5kkSZIkZYA6F85CCMeHEGaHED4IIVxZbvnfQggFqde8EEJBFfvmhRDeCiFMDyFMDSGcWW5d7xDCOyGE91PHaryd85+T2ub9EMI5u7q/tpUBbfqvEMLqEMKzlZbbprspnW1aE38mtK00t2nPEMLk1DmmhxAu2pX9VbV0/9ub2rZ1CGFRCOG3u7O/Kkp3m4YQisud5+ld3V/bqsU2vTR1zBhC6LiD8/t7794WY6wzLyAb+BDoAzQGpgAHVrHdr4H/qWJ5P6Bv6n0usARom/r8KPCV1Pu7gIur2L898FHqa7vU+3bV3d9X5rVpat1Y4CTg2UrLbdM62KY18WfCV8a1aWOgSep9S2AekGub1t02LXec24A/A78tt8w2raNtCqzbznLbNPPa9CCgV+rf047bOb+/96aj3dNdwC4VC4cBz5f7fBVwVaVtAvBx6R/GnRxvCtA3tc9yoFFV5ym3/VeBu8t9vju1rFr7+8q8Ni233yjKhTPbtO63aU3t7yuz2hToACwg+UXDNq3DbQoMB/4KnEsqnNmmdb5NtwlntmnmtWmlZfPYfjjz9940vOrasMauJH8ASy1MLSvvKODTGOP7OzpQCOFgkv+F+JDkh/3qGGNR5eOGEEaEEO7dyfm3u792Kt1tuj226e7LmDat7v7aqbS3aQihewhhaqqOX8QYF+9of+1UWts0hJBF8r/9P650ONt096X97ynQNIQwKYTwdgjhlNQy23T31Vab7mg7f+9Ns0bpLmAXhSqWVZ5u8qvAX3Z4kBC6AA8B58QYS0II2z1ujHEScN5Ozl+dulS1dLfpntSlqmVEm+7K/tqptLdpjPFjYEgIIRd4KoTwOFBSjbpUtXS36XeA52KMH1faxb+nuy/dbQrQI8a4OITQB3g5hDANWFONulS1WmnTHW3r773pV9fC2UKge7nP3YDFpR9CCI2AU0mGSlQphNAa+Afw/2KMb6cWLwfahhAapf4XoMJxK51/VKXzT9iF/bWtdLfp9timuy/tbVpLfyYasrS3aanUL37TSf63+O+7ur/KpLtNDwOOCiF8h+Q+wsYhhHUkw7Zs092T7jYl1aNNjPGjEMIEkvua/Hu6+2qrTXfl/KMqnX8C/jytVXVtWONEoG9qhpjGwFeAp8ut/xwwK8a4sKqdU/s8CTwYY3ysdHmMMQL5wJdTi84BxldxiOeBY0MI7UII7YBjScbYVnd/bSvdbVol23SPpLVNa+vPRAOX7jbtFkJolnrfDjgCmG2b7pG0tmmM8awYY48YYy/gR6njXGmb7pF0/z1tF0JoknrfkeTv6QzbdI/USpvuAn/vTYddvUkt3S/gi8AckjGzP6207k/ARTvY9+vAVqCg3Csvta4P8C7wAfAYn80MNgK4t9wxvpXa5gPgm+WWV7m/rzrRpq8By4CNJP9LdJxtWnfbdHf295Xxbfp5YCrJzexTgQvKHds2rYNtWulY51JxtkbbtA62KXA4MC3193Qa8G3bNKPb9DKS33mKSHq9StvR33vT/Aqpb7AkSZIkKY3q2rBGSZIkSaqXDGeSJEmSlAEMZ5IkSZKUAQxnkiRJkpQBDGeSJEmSlAEMZ5IkSZKUAQxnkiRJkpQBDGeSJEmSlAH+PxEnT0dO/LLRAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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zO2stw4cP59///vdhy6dOnXpKsxBaa3nkkUe4/fbbD1uenp5+6LWc6LXB0bMfGmNOeNxatWoder5hwwZeeOEFFi5cSFRUFCNGjDjuNPdlz3PkNgePWVpaSp06dQ71NIqI98kvKmHZ1r2HCrHk9F3k5Lv+pjSICKVni7rcOqAFiXF1ad8ogqDAP0bad20cyXX/m8et7yXz7s296NOy3vFOIyIi4jcqVJwZY4JxFWYTrbWfuRdvAT5zF2MLjDGlQH0gq+y+1tqxwFiAxMTEwwq3I52oh8tTVq1aRUBAAG3atAEgJSWF5s2bn9Kx4uLieP311yktLWXr1q2Hrtcqa+/evURFRVGzZk3S0tKYN2/eoXXBwcEUFRURHBzMOeecwyWXXML9999PgwYN2LVrF7m5ufTu3Zt7772X7OxsateuzSeffEJ8fHy52QYPHszjjz/ODTfcQHh4OFu3biU4OPikXt8XX3zBI488Ql5eHjNnzuTZZ5+lRo0aFTpuTk4OtWrVIjIyku3bt/Ptt98ycOBAACIiIsjNzT00rLFhw4akpqbSrl07Pv/8cyIiIo46Xu3atWnRogWffPIJV111FdZali5dWqG2EBHP2LO/kEUbd7PQXYgt3bKXwhLXB1atG4QzpGsMic3r0jOuLk3r1jjhB01RtUL4YFRvrhs7j5HvLuS9kb1IjKtbVS9FRETEERWZrdEA7wCp1tqXyqyaCpwNzDTGtAVCgBPPFe+F9u3bx5/+9Cf27NlDUFAQrVu3PjRJx8nq37//oSGCnTt3pnv37kdtc8EFF/Dmm2/StWtX2rVrd9iwv9tuu42uXbvSvXt3Jk6cyNNPP835559PaWkpwcHBvPbaa/Tp04cnn3ySvn37EhMTQ/fu3Q9NFHIi559/PqmpqfTt2xdwDef84IMPCAwMrPDr69WrF0OGDGHTpk08/vjjxMbGEhsbW6HjxsfH061bNzp16kTLli3p37//Ya/7wgsvJCYmhhkzZvDss89y8cUX07RpUzp37sy+ffuOmWfixInccccdPP300xQVFXHttdeqOBOpItZatuw+QPLGXYeKsdXbXf9WgwMNXRpHcnP/OBLj6tKjeRR1a4Wc9Dnqh4cy8dbeXPvWPEaMX8gHo3qT0LROJb8SERER72GOGIV49AbGnAH8BizDNZU+wN+AH4FxQAJQiOuasxPOxpCYmGgPzn54UGpqKh06dDiV7FKFnnzyScLDw3nwwQedjnJS9PslUjlKSi1p23IOTdyRnL6bbTmuYccRoUH0iIuiZ1xdEptHEd+0DmHBFf/gpzzb9uZz9Vtz2b2/kMm39qFz48hKO7aIiEhVM8YsstYmHmtdRWZrnAUcb+zJjacTTEREvNOBwhJSNu8hOX0XCzfuZvHG3ewrcF0vFhMZRq8WdekZF0ViXF3aNowgMODkr4WtqEaRYUy6tTfXvDWPG9+Zz+Rb+9AhprbHziciIuKUcnvOKpN6zqSq6fdLpGKy9xWQvNE1PHFh+m6Wb91LcanFGGjXMILEgz1jcXVpXKeGIxk3Ze/n6rfmUlRSyoe39aFNw6OvRxUREfF2p9VzJiIi/sVay6Zd+w9dK7YwfRfrsvIACAkMIL5pJLee2ZKecVH0aFaXyJonN3mQpzSrV5PJt/Xh6rfmcv3b8/notj60jA4vf0cREREfoeJMRMTPFZeUkpqZ67pWzD2BR1au676AkTWCSWwexZU9mtIzLorOjSMr9Xqxytaifi0mjerNtWPncf3/5vPR7X1oXq9W+TuKiIj4ABVnIiJ+bPzsDbzw/SryCl2zujaJqsEZresfGqbYOjqcAA9eL+YJbRpGuKbZ/98fBVqTqJpOxxIRETltKs5ERPxQaanl6a9TGTd7AwPbRXNF9yYkxkURE+nM9WKVrUNMbT64pTfXlynQ/OW1iYh4jf/8B3r2hEGD/lg2YwYsXAh//atzufxYgNMBvEFgYCAJCQl07tyZq666iv3795/ysUaMGMGUKVMAGDVqFCtXrjzutjNnzmTOnDmHnr/55pu89957p3zug9LT0+ncufNhy5588kleeOGFkzpOZeURkaqVX1TC3ZMXM272Bkb2b8E7w3syND7W74qXzo0jee+W3uzKK+SG/81nh3tqfxERr/Sf/7gKm7JmzHAt91Y9e8LVV/+Re8YM1/OePZ3N5cfUcwbUqFGDlJQUAG644QbefPNNHnjggUPrS0pKTupmzQe9/fbbJ1w/c+ZMwsPD6devHwCjR48+6XN4SnFxsVflEZGK2Z1XyK3vJbNo024eG9KBUQNaOh3JoxKa1mHCyJ4Me2cB1789nw9v60P98FCnY4mIHO1gofPxx66eqIOFzscfg7VQXAyFha5HUdHR31fFsmOtDwmBc8+FRo1g7154//3De9KkUvlWcVYFXasDBgxg6dKlzJw5k6eeeoqYmBhSUlJYtmwZDz/8MDNnzqSgoIC77rqL22+/HWstf/rTn/j5559p0aIFZW9NMHDgQF544QUSExP57rvv+Nvf/kZJSQn169fnnXfe4c033yQwMJAPPviAMWPG8NNPPx260XNKSgqjR49m//79tGrVinHjxhEVFcXAgQPp3bs3M2bMYM+ePbzzzjsMGDDgpF7jiY7dr18/Zs+eTVJSErm5uYSHh3P99ddz0UUXHdp/2bJlrF+/HoCRI0eSlZVFdHQ048ePp1mzZowYMYLatWuTnJzMtm3b+M9//sOVV15ZKT8fETm+zbv2M3z8ArbsPsBr13fnoi4xTkeqEj2a12XciJ6MGL+AG9923QctqlaI07FEfIuGr3mOtbBuHWzfDmefDYMHuwqe/ftdXwcPdhVCnhQc7HqEhPzx9Xjfh4ZCRMThy1asgKVLXce6+mq46CIYPhwuvti1XiqNbxVnJ/rEoRIUFxfz7bffcsEFFwCwYMECli9fTosWLRg7diyRkZEsXLiQgoIC+vfvz/nnn8/vv//OqlWrWLZsGdu3b6djx46MHDnysONmZWVx66238uuvv9KiRQt27dpF3bp1GT169KFiDOCnn346tM9NN93EmDFjOOuss/j73//OU089xcsvv3wo54IFC/jmm2946qmn+PHHH496LevWrSMhIeHQ823bth06z4mOvWfPHn755RfANRQSIDY29lDP4muvvcYvv/xC8+bNGTp0KDfddBPDhw9n3Lhx3HPPPUydOhWAzMxMZs2aRVpaGklJSSrORDxs6ZY9jHx3IcWllomjetMzrq7TkapUn5b1ePumnoycsJAb35nPpFF9vOYWACI+4eB7rGefhUsugWXLKvU9VrVhLWzYAMnJsGiR6+vixbBnj2t9SAhER0NGhqvNzzzz2AXS6Sw7cn1wMJjTmPjp4Pvtxx+HV1+F88+HX36BadOgXj247jpXodajx+mdRwBvK87uuw/cRcBxxca6PmGIiYHMTOjQAZ56yvU4loQEcBcex3PgwIFDhcyAAQO45ZZbmDNnDr169aJFixYATJ8+naVLlx66nmzv3r2sWbOGX3/9leuuu47AwEBiY2M5++yzjzr+vHnzOPPMMw8dq27dE79p2rt3L3v27OGss84CYPjw4Vx11VWH1l9++eUA9OjRg/T09GMeo1WrVocKKvij0Crv2Ndcc81xc82ePZu3336b3377DYC5c+fy2WefATBs2DD+WuaTtUsvvZSAgAA6duzI9u3bT/h6ReT0/JS6nbsn/U698BAmjOxFq2p6768z2tTnrWE9uP29Rdw0fgEf3NKLiDAVaCIVMmiQqxA791wYNcr1JrtLF9eyZcugY0fXIyZGb8APshY2bjy8EFu0CHbvdq0PDob4eLjmGkhMdBUvWVlwww2uQueNN+C557x7iGDZjpBBg1yPq6+GyZNdvX0TJsD//ucq2jp2dBVpN97oer8up8S7irOKiIpy/WHYtAmaNXM9P01lrzkrq1atP+6dY61lzJgxDB48+LBtvvnmG0w5f6SsteVuczJCQ13XUwQGBlJcXFxpx4XDX3NZmZmZ3HLLLUybNo3w8GO/8Sv7Gg9mBA4b6ikilWvi/I08PnU5nWIjeWdEIg0iwpyO5KhB7Rrw2g3dueODRYwYv5D3RvaiVqjv/Vcn4ohBg+Daa2HSJNeH2zVrwkcf/VFsANSu/Ueh1qHDH983awYBfjzPnLWwefPRhVh2tmt9UBB07QpXXukqxBIToXPnw4f8zZjhKsyOLHQOPvdGCxcenu9gEX9wuOuFF7p6BT/6yFWoPfQQPPKIq3dt+HBXL2wN/5qMytO863+scnq4gMO7Vt94A554okp+oQcPHswbb7zB2WefTXBwMKtXr6Zx48aceeaZvPXWW9x0003s2LGDGTNmcP311x+2b9++fbnrrrvYsGHDYcMaIyIiyMnJOepckZGRREVF8dtvvzFgwADef//9Qz1dp+tUjl1UVMTVV1/Nc889R9u2bQ8t79evHx9++CHDhg1j4sSJnHHGGZWSUUTKZ63l+e9X8frMdQxqF82r13dXEeJ2XseGjLmuG3dP/p1bJixk/Ihe1Ajx3htri3iNGTNg+vQ/3mN9/DEMHAg7dsDKla5Haqrr69dfw7hxf+xbsya0b3900daypatw8SXWwtathxdiycmwc6drfVCQq/C69NI/CrEuXVzXap3IiQodby3OjnW94cHC8qA6deD2212P1avhvfdcj+uug8hIV8/h8OHQt696XSvAt/61HK9rtQo+cRg1ahTp6el0794day3R0dFMnTqVyy67jJ9//pkuXbrQtm3bYxY60dHRjB07lssvv5zS0lIaNGjADz/8wNChQ7nyyiv54osvGDNmzGH7TJgw4dCkHS1btmT8+PGV9lpO9thz5sxh4cKFPPHEEzzxxBOAq8fwlVdeYeTIkTz//POHJgQREc8rLC7lr1OWMDUlg+t6NeOfl3QiKNCPP7E+BRd2ieGlklLu/yiFW99L5u3hiYQFq0ATOa7y3mM1bHj0e61du/4o1g5+/eUX+OCDP7YJCYG2bY8u2tq0Kb+YqSoZGX8UYAeLsR07XOsCA6FTJ0hKcg1LTEx09ZCFncIohYoUOr6ubVt4+mn4xz9cv1MTJrh+H8aOdf3Mb7rJ9WjWzOmkXstU5ZCzxMREm5ycfNiy1NRUOnToULEDaCYhOUkn9fsl4gNy8osY/f4i5qzL5i+D23HnwFaVOmza30xZtIW/TFnCWW2jeWtYD0KDVKCJHFNlvsfKzYW0tMOLtpUrYf16V68UuIqeVq0OL9g6dHD1vh3nEotKkZl5+LDE5GTYts21LiDAlePg9WGJia5rxjQs7/Tk5sKnn8K777qKd2Ncv2fDh8MVV3j25+2ljDGLrLWJx1znU8WZyEnS75f4k4w9B7h5/ELWZe3jP1d25fLuTZyO5BMmL9jEI58t47yODXn9hu4Eq5dRxBkHDriGvZUt2FJTXcvKXkMfF3d00dahg2v4XFnlFZTbtx9diGVkuLYzxnXMIwuxalgoVKkNG1z3SZswwVWs16oFV13lKtTOPNO/r1ssQ8WZVFv6/RJ/kZqZw83jF5JXUMybw3rQv3V9pyP5lPfmpvP3L1ZwUZdGvHJtNw0DFfEmRUWwdu3RRVtaGuTn/7FdbOzhRVt+vmsI3SefuK4BGzfONZyuWzfXLIpbtrj2MwbatTu8EEtIgONMcCZVwFqYNctVpH38sat3LS4Ohg1zFWqtWjmd0KNUnEm1pd8v8Qez1uxk9AeLCA8N4t2RPWnfqLbTkXzS27+t5+mvU7kkIZaXrk4gMEDDQX3J9BXbePbbNB67uANnt2/odBypCiUlkJ5+dNG2ciXs2/fHdsb8MVwSXIXYwSKsRw9XsRYRUeXxpYL274epU13DHn/80fWzPOMMV5F21VWuSUX8jNcXZ+3bt9c1E1LprLWkpaWpOBOf9umiLTz06VJaNwhn/M09iYnUtQ+n4/WZa/nPd6u4skcT/nNFVwJUoPmEvIJizn5xJjtyC7AWbj+zJQ8ObqchqtXVwdkUDxZskya5hjIOGwZjxvjlm/lqY8sW1wQiEya4ek7DwuDyy12F2jnnuK5V9AMnKs4c/6sWFhZGdna27oUllcpaS3Z2NmGnMpuSiBew1jLmpzX8+ZMl9G5Zl49H91VhVgnuHNia+89ty5RFW3h06nL93+MjXp2xlu05BUwa1YcbejfjrV/Xc/Vbc9mye7/T0cQJxkCTJq57acXHu65jevxx+PZbWLzY6XRyOpo0gYcfdhXd8+bBzTe7fq6DB0Pz5q51qalOp/Qox3vOioqK2LJlC/llxxSLVIKwsDCaNGlCcHCw01FETkpxSSmPTV3Ohws3c3m3xjx7RVdCghz/LM1vWGt5YfoqXpuxjuF9m/NkUieN3vBiG3bmMfj/fmVofCwvXh0PwFdLM3j402UEBhiev7Ir53dq5HBKccSR0/8f+Vz8Q0EBfPmla9jjd9+5hrv26uXqTbv2Wqhb1+mEJ82rhzWKiMgf8gqKuWvSYmauyuLuQa358/ltVTh4gLWWZ75J5X+/bWDUGS14dEgHtbOXGvnuQhZs2MXPD55Fg4g/RkOk78zj7smLWb41h5H9W/Dwhe31IUZ1o1ssVT/btrmGsU6YAEuXuu6jl5TkKtQGDwYf+UBexZmIiA/YkZvPyHcXkpqZyz8v6cz1vXWTTk+y1vLUlyt5d046dw5sxV8Gt1OB5mV+TtvOyHeTefSiDtx6Zsuj1hcUl/Dvb9J4d0468U0iefX67jStW9OBpCJS5VJSXL1pkyZBVhY0aAA33OC6JjEpyauLdhVnIiJebu2OfYwYv4DsfYW8fkN3BrVv4HSkasFay6NTlzNp/ibuO7cN953b1ulI4lZQXMLg//uVwADDt/eeecJese+WZ/KXKUsBeP7KrlzQOaaqYoqI04qKXNelTZjgGv5YVOSaOOT22+GJJ2DFCq8b7nqi4iyoqsOIiMjhFmzYxa3vJRMcaPjo9j50bVLH6UjVhjGGpy/pTFFxKS//uIaQoADuHNja6VgCvDNrA+nZ+3lvZK9yhyte0DmGTrGR3D1pMaM/WMzwvs155KIOhAX7x8xuInICwcGunrKkJMjOhsmTXbN2vv46TJ8Oe/Z4VWFWHg3OFhFx0NdLM7nxnfnUCw/h8zv7qzBzQECA4dkrunJpQiz/+W4Vb/+23ulI1d62vfm8+vNazu/YkDPbRldon6Z1a/LJ6H7cckYLJszdyBVvzCF9Z56Hk4qIV6lXD+6+G1atgtGjXTc3v+MOnynMQMWZiIgjrLW8/dt67pq0mK6NI/l0dD9dK+OgwADDC1fFM6RLDE9/ncqEOelOR6rW/v1tKsWllseGdDyp/UKCAnj84o7876ZEtuw+wMVjZvHlkgwPpRQRrzVjBkyZ4rrFwhtvuJ77CBVnIiJVrKTUNRHF01+nclGXRnwwqjdRtUKcjlXtBQUG8PK1CZzXsSFPTFvBpPmbnI5ULS1M38UXKRmMPrMlzeqd2gcW53VsyDf3DqBtw3D+NPl3/vb5MvKLSio5qYh4pbK3VPjHP1xfr77aZwq0coszY0xTY8wMY0yqMWaFMeZe9/InjTFbjTEp7sdFno8rIuLb8otKuHPiIt6dk84tZ7Tg1eu667oYLxIcGMCr13djULtoHp26jCmLtjgdqVopKbU88cUKYiPDuOM0r/1rXKcGH93el9vPasmk+Zu49LXZrMvaV0lJRcRrLVx4+DVmgwa5ni9c6GyuCip3tkZjTAwQY61dbIyJABYBlwJXA/ustS9U9GSarVFEqrNdeYWMmrCQ3zfv4fEhHRl5RgunI8lx5BeVcOt7ycxau5OXr0ngkoTGTkeqFt6ft5HHpy7nteu7M6Rr5c24OCNtBw98nEJBcSnPXNaFS7vp5ykizjnRbI3l9pxZazOttYvd3+cCqYD+qomInISN2Xlc8cYcVmTk8Pr13VWYebmw4EDGDkukd4u6PPDxEr5Zlul0JL+3O6+QF6evom/LelzUpVGlHntQ+wZ8c+8AOsXW5r6PUnhoylIOFGqYo4h4n5O65swYEwd0A+a7F91tjFlqjBlnjImq7HAiIv4gZfMeLn99Drv3FzLp1t5c2EX3YPIFNUICeWd4T7o1rcM9k39n+optTkfyay/+sIrc/GKeTOrkkZuBx0TWYPKtfbh7UGs+XrSZS16bxZrtuZV+HhGR01Hh4swYEw58Ctxnrc0B3gBaAQlAJvDicfa7zRiTbIxJzsrKOv3EIiI+5MeV27l27Fxqhgby6R396NG8rtOR5CTUCg1i/M096dw4krsmLWbGqh1OR/JLKzL2Mmn+Job1aU67RhEeO09QYAAPDm7HeyN7kb2vkKRXZ/NJ8maPnU9E5GRVqDgzxgTjKswmWms/A7DWbrfWllhrS4H/Ab2Ota+1dqy1NtFamxgdXbF7lYiI+IP3523ktveTadswgs/u6E+r6HCnI8kpiAgLZsLIXrRrFMHt7y9i1pqdTkfyK9Zanpq2kjo1Q7j/3LZVcs4BbaL59t4BxDeN5C9TlvLAxynkFRRXyblFRE6kIrM1GuAdINVa+1KZ5WXH5VwGLK/8eCIivqe01PLcd2k8PnU5g9o14MPb+hAdEep0LDkNkTWCeX9kb1rWr8Wo9xYyb32205H8xrQlGSxI38VfB7cjsmZwlZ23Qe0wJo7qw73ntOHz37eS9Oos0rblVNn5RUSOpSI9Z/2BYcDZR0yb/x9jzDJjzFJgEHC/J4OKiPiCguIS7v84hTdmruOG3s14a1gPaoYEOR1LKkFUrRAmjupN06iajHx3Icnpu5yO5PPyCop55ptUujSO5KrEplV+/sAAw/3ntWXiLb3JyS/mkldn8+GCTZQ3k7WIiKeUO5V+ZdJU+iLiz/YeKOL295OZt34Xf72gHXec1cojExuIs3bk5nPtW/PYkVvAB6N6k9C0jtORfNZz36Xxxsx1fHZnP7o3c3ZesazcAu7/KIVZa3dySUIs/7qsC+Gh+mBFRCrfaU2lLyIi5du65wBXvTmHRRt38/I1Cdw5sLUKMz/VICKMSbf2oW6tEIa9M5/lW/c6HcknbdiZxzu/beCK7k0cL8wAoiNCmTCyFw+e35Yvl2QwdMwsVmToZysiVUvFmYjIaVqRsZfLX59N5p58JtzcSze4rQYaRYYx6dbe1A4L5sZ35pOaqWuVTtY/v1pJSFAAD13YzukohwQGGO4+uw2Tb+3D/sJiLnt9Du/P26hhjiJSZVSciYicht/WZHHNW/MIMIYpd/SjX+v6TkeSKtIkqiaTb+1DjeBAbnx7Pmt36J5ZFfVz2nZ+TtvBvee0oUFEmNNxjtK7ZT2+uWcAfVvW4/Gpy7l70u/k5Bc5HUtEqgEVZyIip+iT5M3cPH4hTaJq8Pmd/T16fybxTs3q1WTSrX0ICDCMGL+QnfsKnI7k9QqKS/jHlytpGV2L4f3inI5zXPXCQxk/oicPXdCe71Zs4+JXZrFsi4Y5iohnqTgTETlJ1lr+++Ma/jJlKX1a1uOT0X1pFOl9n/5L1WhRvxbvDE8kK7eA0e8voqC4xOlIXm3crHTSs/fz5NBOhAR599uQgADDHQNb8dFtfSgqKeWKN+bw7uwNGuYoIh7j3X8VRUS8TFFJKQ9/uoz/+3E1l3dvzLgRPYkIq7p7M4l36tqkDi9eHU/yxt088tkyvXk/jm178xnz8xrO69iQM9tGOx2nwhLj6vLNPQMY0KY+T365ktEfLGLvfg1zFJHKp+JMRKSCCopLGDUhmY+SN3PP2a158ap4r//kX6rOxV1juf/ctny2eCtv/bre6The6d/fplJcanl8SEeno5y0qFohvD08kceGdOCn1B0MGfMbKZv3OB1LRPyM3lWIiFTQ1N+38svqLP55SSceOL+dpsqXo9xzTmuGxsfy3Hdp/LByu9NxvMrC9F18kZLB6DNb0qxeTafjnBJjDKMGtOST0X2xFq58Yw5v/7ZePaUiUmlUnImIVIC1lvGz02nfKIIb+zR3Oo54KWMMz1/Zla6NI7n3w981xb5bSanliS9WEBsZxh0DWzsd57R1axbFN/cM4Oz2DXj661RufS+ZPfsLnY4lIn5AxZmISAXMXZdN2rZcRvZvoR4zOaGw4EDG3pRI7bBgRk1IJitXMzhOXrCJlZk5PDqkIzVCAp2OUykiawbz1rAePDG0I7+szuKi//7Goo27nI4lIj5OxZmISAWMm51O3VohJCXEOh1FfEDD2mG8PTyR7LwCbn8/mfyi6juD4+68Ql6Yvoq+LetxUZdGTsepVMYYbu7fgk/v6EdQYABXvzWPN39ZR2mphjmKyKlRcSYiUo6N2Xn8lLadG3o3IyzYPz71F8/r3DiS/7s6gcWb9lTrGRxf+mE1ufnFPJHU0W97nbs2qcNX95zB4E4NefbbNEZOWEi27nknIqdAxZmISDkmzNlIoDG61kxO2oVdYnjw/LZ8/vtWXp+5zuk4VW5lRg4T529kWJ/mtG9U2+k4HlU7LJjXru/OPy/tzJx12Vz0ym8s2KBhjiJyclSciYicwL6CYj5J3syQrjE0rK0bTcvJu2tQay5JiOX571fx3fJtTsepMtZanpy2gjo1Q7j/3LZOx6kSxhiG9WnO53f2o2ZIENeOncv42RucjiUiPkTFmYjICUxJ3kxuQTE392/hdBTxUcYYnruiKwlN63D/Ryks37rX6UhVYtqSDBak7+Ivg9sRWbN63ai9U2wkX/7pDAa0iebf36aRm68bVotIxag4ExE5jtJSy7tz0unWrA4JTes4HUd8mGsGxx5E1Qzm1veS2ZGb73Qkj8orKOaZb1Lp0jiSqxObOh3HEeGhQdxzThsKi0uZvkL3vBORilFxJiJyHDNW7SA9e796zaRSNIgI43/DE9mzv4jb3lvk1zM4vjZjLdtzCngyqROBAf45CUhFdG9WhyZRNZi2JMPpKCLiI1SciYgcx/jZ6TSqHcaFnf1r+m9xTqfYSF6+NoGUzXv465SlfjmDY/rOPN7+bQOXd29Mj+ZRTsdxlDGGofGxzFq7U7M3ikiFqDgTETmG1dtzmbV2J8P6Nic4UH8qpfIM7tSIv17QjmlLMnj157VOx6l0//xqJSFBATx8QXuno3iFpPhYSkot31SjyWBE5NTpHYeIyDGMn51OaFAA1/dq5nQU8UN3nNWKy7s15sUfVvPNskyn41SaGWk7+CltB/ec05oGmt0UgPaNImjTIJwvUzS0UUTKp+JMROQIu/MK+fz3LVzWrTFRtUKcjiN+yBjDv6/oQo/mUTzwcQrLtvj+DI4FxSU89eUKWkbXYkQ/Xad5kDGGpPhYFqTvYuueA07HEREvp+JMROQIkxduIr+olBH945yOIn4sNCiQt4b1oF6tUG59L5ntOb49g+O4WemkZ+/niaGdCAnS24uyhsbHAvCVJgYRkXLor6eISBlFJaW8P3cj/VrVo32j2k7HET9XPzyUt4cnkpNfxK3vJXOg0DdncNy2N58xP6/hvI4NOatttNNxvE5c/VrEN62jWRtFpFwqzkREyvh+xTYy9+YzUtPnSxXpEFOb/17bjWVb9/LglCU+OYPjs9+mUlxqeXxIR6ejeK2k+FhWZOSwLmuf01FExIupOBMRKWP87HSa16vJ2e0bOB1FqpHzOjbk4Qva8/XSTP770xqn45yUhem7mJqSwe1ntqRZvZpOx/FaF3eNwRiYpolBROQEVJyJiLgt3bKHRRt3M7xvHAHV+Ma54ozbzmzJlT2a8PKPa/jSR4a/lZRanvhiBbGRYdw5sLXTcbxaw9ph9GlRjy+XZPhk76iIVI1yizNjTFNjzAxjTKoxZoUx5t4j1j9ojLHGmPqeiyki4nnjZ6cTHhrEVYlNnI4i1ZAxhn9d1pmecVE8+MkSlmze43Skck1esImVmTn8bUgHaoQEOh3H6yUlxLJ+Zx4rMnKcjiIiXqoiPWfFwJ+ttR2APsBdxpiO4CrcgPOATZ6LKCLieTty8vlqaQZX9mhCRFiw03GkmgoNCuTNG3sQHeGawTFzr/dOvb47r5AXpq+iT8u6DOkS43Qcn3Bh50YEBxpNDCIix1VucWatzbTWLnZ/nwukAo3dq/8P+Cug/nkR8WkfzNtIcallRL84p6NINVcvPJR3hvckr6CYW99LZn9hsdORjumlH1aTm1/Mk0mdMEbDgCuiTs0QzmwTzZdLMigt1VsnETnaSV1zZoyJA7oB840xScBWa+0STwQTEakq+UUlTJy/ibPbNSCufi2n44jQrlEEY67vxoqMHB78ZInXvZFfmZHDxPkbGdanuW45cZKSEmLJ3JvPwvRdTkcRES9U4eLMGBMOfArch2uo46PA3yuw323GmGRjTHJWVtap5hQR8Zgvl2SQnVfIyDM0fb54j7PbN+TRizrwzbJtvPzjaqfjHGKt5clpK6hTM4T7z23rdByfc17HhtQIDtTQRhE5pgoVZ8aYYFyF2URr7WdAK6AFsMQYkw40ARYbYxodua+1dqy1NtFamxgdrRtTioh3sdYyfnY67RpG0K9VPafjiBzmljNacE1iU175eS1fpGx1Og4A05ZksCB9F38Z3I7Imro+82TVDAni3I4N+WZZJkUlpU7HEREvU5HZGg3wDpBqrX0JwFq7zFrbwFobZ62NA7YA3a212zyaVkSkki3YsIuVmTmM6B+n62bE6xhj+OelnenVoi5/mbKU3zftdjRPXkEx//4mjS6NI7k6samjWXxZUnwsu/cXMWvtTqejiIiXqUjPWX9gGHC2MSbF/bjIw7lERKrE+Nnp1KkZzKUJjcvfWMQBIUEBvHljDxrVDuPW9xaRsce5GRxfm7GWbTn5PJnUiUDdC/CUndm2PrXDgvhSN6QWkSNUZLbGWdZaY63taq1NcD++OWKbOGutPv4REZ+yedd+pq/cxnW9mukeTeLV6tYK4Z3hiRQUlXDLhGTyCqp+Bsf0nXm8/dsGLu/emB7No6r8/P4kNCiQCzvH8P2KbeQXlTgdR0S8yEnN1igi4k/em5uOMYZhfZo7HUWkXG0aumZwXLUth/s/SqnyGRz/+dVKQoICePiC9lV6Xn+VlBBLXmEJP6XucDqKiHgRFWciUi3lFRTz4cLNXNi5EbF1ajgdR6RCBrZrwGNDOjJ95XZe/GFVlZ13RtoOfkrbwT3ntKZB7bAqO68/69OyHtERoUxb4h0TvYiId1BxJiLV0meLt5CbX8zN/TV9vviWm/vHcV2vZrw2Yx2f/77F4+crKC7hH1+tpGV0LUb007+XyhIYYLi4awwzVmWRk1/kdBwR8RIqzkSk2ikttYyfk058k0i6N6vjdByRk2KM4R+XdKJPy7o8NGUZizZ6dgbHcbPS2bAzjyeGdiIkSG8bKlNSfCyFxaV8v1yTXYuIi/7Kiki18+uaLNZn5XFz/xaaPl98UnBgAG/c0IPYOmHc/n4yW3bv98h5tufkM+bnNZzXsSFntdW9SitbQtM6NKtbUzekFpFDVJyJSLUzbnY6DSJCuahLjNNRRE5ZVK0Q3h7ek4LiUkZNSGafB2Zw/Pc3qRSXWh4f0rHSjy2uXtCh8THMWZfNzn0FTscRES+g4kxEqpW1O/bx6+osbuzTXEO0xOe1bhDO6zd0Z82Ofdz3YQollTiD48L0XUxNyeD2M1vSrF7NSjuuHC4pvjElpZZvlmU6HUVEvIDemYhItfLunA2EBAZwfe9mTkcRqRQD2kTz94s78mPqdv7zfVqlHLOk1PLEFyuIjQzjjoGtKuWYcmztGkXQrmEE03RDahFBxZmIVCN79xfx6aKtXJIQS/3wUKfjiFSam/o258Y+zXjrl/VMWXT6MzhOXrCJlZk5/G1IB2qGBFVCQjmRpIRYkjfu9ti1gyLiO1SciUi18VHyJg4UlWj6fPE7xhieGNqJ/q3r8chnS1mYvuuUj7VnfyEvTF9Fn5Z1GaLrMqvE0K6xAHy5REMbRao7FWciUi0Ul5QyYc5GereoS8fY2k7HEal0wYEBvH59D5pG1eT29xexedep9cK8OH01ufnFPJnUSbOZVpFm9WrSrVkdzdooIirORKR6+DF1O1v3HFCvmfi1yJrBvD08keIS1wyOuSd5c+OVGTlMnL+RYX2a076RPsSoSknxsaRm5rB2R67TUUTEQSrORKRaGDcrnSZRNTivY0Ono4h4VMvocN64sQdrs/Zx70nM4Git5clpK6hTM4T7z23r4ZRypCFdYwgwaGIQkWpOxZmI+L3lW/eyIH0Xw/vGERigYVri//q3rs9TSZ34OW0Hz36bWqF9pi3JYEH6Lv4yuB2RNYM9nFCO1CAijL6t6jFtSQbWVt4tEUTEt6g4ExG/N352OjVDArm6Z1Ono4hUmRv7NGd43+b877cNfLRw0wm3zSso5t/fpNG5cW2uTtS/E6ckxceSnr2fZVv3Oh1FRByi4kxE/FpWbgFfLsngyh5NiKyh3gCpXh6/uCMD2tTnsanLmb8++7jbvTZjLdty8nkqqZN6lx10QacYggONhjaKVGMqzkTEr02av4nCklKG94tzOopIlQsKDODV67vTtG5NRn+wiE3ZR8/gmL4zj7d/28Dl3RrTo3ldB1LKQZE1gzmrbQO+XJpR4WsFRcS/qDgTEb9VUFzCB/M3MrBdNK2iw52OI+KIyBrBjBvek1ILt0xYSM4RMzj+86uVBAcaHr6wvUMJpaxLEmLZnlPAgg2nfq86EfFdKs5ExG99vTSTrNwCTZ8v1V5c/Vq8cWN3NuzM40+Tfqe4pBSAGWk7+CltB/ec04YGtcMcTikA53ZoSM2QQN3zTKSaUnEmIn7JWsv42em0iq7FmW3qOx1HxHH9WtXnn5d25pfVWTzzTRoFxSX846uVtIyupQ8wvEiNkEDO69iQb5dnUlhc6nQcEaliKs5ExC8t2ribZVv3MqJ/C4zRBAciANf1asbI/i0YN3sDw8ctYMPOPP5+cUdCgvR2wJskxceyZ38Rs9ZmOR1FRKqY/hqLiF8aPzud2mFBXNG9sdNRRLzK3y5qz1lto5m3fhfndmjIwHYNnI4kRxjQJprIGsGatVGkGgpyOoCISGXL2HOA71ZsY9QZLagZoj9zImUFBQYw5vpuvPXLOob1iXM6jhxDSFAAF3VpxBcpGRwoLKFGSKDTkUSkiqjnTET8zntzN2KtZVjf5k5HEfFKtcOC+cvg9jSK1CQg3mpofCz7C0v4MXW701FEpAqpOBMRv7K/sJjJCzYxuFMjmkTVdDqOiMgp6d2iHg0iQjVro0g1o+JMRPzK579vZe+BIs0+JyI+LTDAMDQ+ll9WZbH3QFH5O4iIXyi3ODPGNDXGzDDGpBpjVhhj7nUv/6cxZqkxJsUYM90YE+v5uCIix2et5d3Z6XSKrU3PuCin44iInJak+FgKS0r5fvk2p6OISBWpSM9ZMfBna20HoA9wlzGmI/C8tbartTYB+Ar4u+diioiUb9banazZsY+Rmj5fRPxA1yaRNK9XU0MbRaqRcosza22mtXax+/tcIBVobK3NKbNZLcB6JqKISMWMn51O/fBQLo6PcTqKiMhpM8aQFB/LnHU72ZGb73QcEakCJ3XNmTEmDugGzHc//5cxZjNwA+o5ExEHbdiZx89pO7ihdzNCgzTttIj4h6T4WEotfLM00+koIlIFKlycGWPCgU+B+w72mllrH7XWNgUmAncfZ7/bjDHJxpjkrCzd6V5EPOPd2RsIDjTc0KeZ01FERCpNm4YRtG8UoaGNItVEhYozY0wwrsJsorX2s2NsMgm44lj7WmvHWmsTrbWJ0dHRp55UROQ4cvKLmLJoC0O7xtIgQvdtEhH/kpQQy+JNe9i8a7/TUUTEwyoyW6MB3gFSrbUvlVnepsxmSUBa5ccTESnfxws3k1dYounzRcQvDe3qmhBbvWci/q8iPWf9gWHA2e5p81OMMRcBzxpjlhtjlgLnA/d6MqiIyLGUlFomzE0nsXkUXZpEOh1HRKTSNa1bkx7No/hSxZmI3wsqbwNr7SzgWHNSf1P5cURETs5PqdvZvOsAj1zYwekoIiIekxQfyxPTVrB6ey5tG0Y4HUdEPOSkZmsUEfE242en07hODc7v2NDpKCIiHnNRlxgCDExLUe+ZiD9TcSYiPis1M4e567MZ1rc5QYH6cyYi/is6IpT+reszbUkG1urWsiL+Su9mRMRnjZ+9gbDgAK7t2dTpKCIiHjc0PpZNu/azZMtep6OIiIeoOBMRn5S9r4CpKRlc3r0JdWqGOB1HRMTjBndqREhggIY2ivgxFWci4pMmL9hEYXEpN/eLczqKiEiViKwRzMB20Xy5NIOSUg1tFPFHKs5ExOcUlZTy/ryNDGhTnzaatUxEqpFLEhqTlVvA/PXZTkcREQ9QcSYiPuebZZlszylgpG46LSLVzDkdGlArJFA3pBbxUyrORMTnjJ+dTsv6tTirbbTTUUREqlRYcCDnd2rEt8u3UVhc6nQcEalkKs5ExKcs3rSblM17GN4vjoAA43QcEZEqlxQfy94DRfy6OsvpKCJSyVSciYhPGT87nYjQIK7o0cTpKCIijjijTX2iagZraKOIH1JxJiI+Y9vefL5dlsnVPZsSHhrkdBwREUcEBwZwYZcYfli5nf2FxU7HEZFKpOJMRHzG+/PSKbGW4X3jnI4iIuKopPhYDhSV8MPK7U5HEZFKpOJMRHxCflEJk+Zv4rwODWlWr6bTcUREHNUrri6NaofxpYY2ivgVFWci4hO+SNnK7v1F3Kzp80VECAgwDI2P4ZfVWezZX+h0HBGpJCrORMTrWWsZNyud9o0i6NOyrtNxRES8QlJ8Y4pKLN8t3+Z0FBGpJCrORMTrzV2XzartuYzs3wJjNH2+iAhA58a1aVG/lmZtFPEjKs5ExOuNm51O3VohJCXEOh1FRMRrGGMYGh/L3PXZ7MjJdzqOiFQCFWci4tU2ZufxU9p2ru/VjLDgQKfjiIh4laT4WKyFr5ZmOh1FRCqBijMR8WoT5mwk0BiG9W3udBQREa/TukE4HWNqa2ijiJ9QcSYiXmtfQTGfJG9mSNcYGtYOczqOiIhXSkqIJWXzHjZm5zkdRUROU7UvztZl7WPvgSKnY4jIMUxJ3kxuQbGmzxcROYGh8a7rcXXPMxHfV62LM2st932YwqAXZjJp/iZKSq3TkUTErbTU8u6cdLo1q0NC0zpOxxER8VqN69SgZ1yUhjaK+IFqXZwZY3j2ii60bhDO3z5fxsVjZjFvfbbTsUQEmLFqB+nZ+9VrJiJSAUnxsazevo+0bTlORxGR01CtizOATrGRfHRbH167vjs5B4q4duw87pq4mC279zsdTaRaGz87nUa1w7iwcyOno4iIeL2LusQQGGCYlqLeMxFfVu2LM3D1oA3pGsNPfz6LB85ry09p2znnxV94afoq9hcWOx1PpNpZvT2XWWt3Mqxvc4ID9WdKRKQ89cJD6d+6Pl8uzcBaXaYh4qv0rqeMsOBA7jmnDT//eSAXdG7EKz+v5ewXfuGLlK36QydShcbPTic0KIDrejVzOoqIiM9Iio9l864D/L55j9NRROQUlVucGWOaGmNmGGNSjTErjDH3upc/b4xJM8YsNcZ8boyp4/G0VSS2Tg3+e203pozuS3REKPd+mMKVb85l6ZY9TkeTKpKamcMH8zYyd102e/YXOh2nWtmdV8jnv2/hsm6NqVsrxOk4IiI+Y3CnhoQEBWhoo4gPC6rANsXAn621i40xEcAiY8wPwA/AI9baYmPMc8AjwEMezFrlEuPq8sVd/ZmyeAv/+W4VSa/O5qoeTfjLBe1oEKF7Lvkbay0L03fzxsy1zFiVddi6mMgw2jeKoENMbdrH1KZjTARx9WoRpCF3lW7ywk3kF5Uyon+c01FERHxKRFgwZ7drwFdLM3lsSAf9HyXig8otzqy1mUCm+/tcY0wq0NhaO73MZvOAKz0T0VkBAYarE5tyYedGvDpjLeNmbeDb5du4++zW3Nw/jtCgQKcjymkqLbX8lLaDN2auZfGmPdSrFcKD57dlSNdYNu3aT1pmDqmZOaRty+W3NTspdt9yITQogLYNI2jfKIL2MbXpEBNBh0a1iVJvzykrKinl/bkb6deqHu0b1XY6joiIz7kkIZbvVmxj3vpdnNGmvtNxROQkVaTn7BBjTBzQDZh/xKqRwEeVlMkrRYQF88iFHbi2ZzP+9XUqz36bxuQFm3hsSEfO7dAAY4zTEeUkFZWUMi0lgzd/WceaHftoElWDf1zSiat6NKVGiKvoblG/Fme1jT60T0FxCet25LmLNVfBNmPVDj5ZtOXQNo1qh9E+xt3L5u5ta1lfvWwV8f2KbWTuzecfl3R2OoqIiE8a1L4B4aFBTFuyVcWZiA8yFZ3owhgTDvwC/Mta+1mZ5Y8CicDl9hgHM8bcBtwG0KxZsx4bN26sjNyO+3V1Fv/4aiVrd+xjQJv6PH5xR9o2jHA6llTA/sJiPlywmbd/W0/G3nzaN4rgjoGtGNIl5pQLqKzcgkMFW2pmLqmZOazL2kdRieufREhQAG0ahB8q2Dq6h0fqmqrDXfHGHLJyC5jx4EACA/SBh4jIqXjg4xR+WLmd5MfO1QgfES9kjFlkrU085rqKFGfGmGDgK+B7a+1LZZYPB0YD51hry70xWGJiok1OTq5wcG9XVFLKxHkbeemH1eQVljCsT3PuO7cNdWrqDbc32p1XyIS56bw7J509+4voFVeXOwa2YmC7aI/0fBYWl7Iua99hBVvatlyycgsObdMgItR9HZu7YGtUm5bRtarl9PFLt+wh6dXZ/P3ijow8QzeeFhE5VTNX7WDE+IWMHdaD8zvpXpEi3uZExVm5wxqN613rO0DqEYXZBbgmADmrIoWZPwoODGBE/xYkJTTm/35YzXtz05maspU/n9eW63o10zA2L7F1zwHe/m09Hy7YzIGiEs7t0JA7BrakR/O6Hj1vSFAAHWJq0yGmNpd1+2P5zn0FpLmLtdRtOaRl5jJ3XTaFJaWu/QIDaN0g/LCCrUNMBPXCQz2a12njZ6cTHhrEVYlNnI4iIuLT+reuT91aIUxbkqHiTMTHlNtzZow5A/gNWAaUuhf/DXgFCAWy3cvmWWtHn+hY/tZzdqS0bTk8NW0lc9dn065hBE8M7Ui/1hrv7ZQ123N585f1fJGyFYCkhFhGn9XKK4efFpWUsj4r71DBlpqZS1pmDjvK9LJFu3vZOjSKOHRNW8v64YQE+f6HADty8un/3M/c0Ls5TyZ1cjqOiIjPe2zqMqYs2sKix86jVuhJTTEgIh522sMaK4u/F2fgmo79+xXb+dc3K9m86wCDOzXk0Ys60qxeTaejVRuLNu7mjZnr+DF1OzWCA7m2V1NGDWhJ4zo1nI520rL3FZC2zd3LlplL2rYc1mzfd6iXLTjQ0LpBBB0OTjwSXYuYyBrERIZRp2awz0xU89L0VYyZsZYZfx5IXP1aTscREfF5Czbs4uq35vLfaxO4JKGx03FEpAwVZw7ILyrhnVkbeG3GWopLLKMGtOCuQa316ZWHWGuZuTqLN2auY8GGXdSpGczwvnEM7xfnd5NuFJWUsmFn3mEFW2pmDttzCg7bLjQogJjIMBpFhhETWcP9NYxGtf94Xq9WCAEOT7yRX1RC/2d/JqFpHd4Z0dPRLCIi/qK01NL/uZ/pEFObcfrbKuJVTuuaMzk1YcGB3DWoNVf2aMJz36Xx+sx1TFm0hYcuaM9l3Ro7/obYXxSXlPL1skzemLmOtG25xESG8feLO3JNz6Z+WwgHB7rur9a2YQSXJPyxfFdeIRuz89i2N5/Mvflsy3F/3XuAhem72J6Tf2j2yD+OZWhY2120uXvcGh167irioiNCPTpz4pdLMsjOK+Tm/poERESksgQEGJLiY3ln1gZ25xXqHpwiPkI9Z1Xk9027efLLlSzZvIf4pnV4cmhHujWLcjqWz8ovKuGT5M2M/W09m3cdoHWDcEaf1Yqk+Fi/uAbLE0pLLdl5he7i7UCZ4s393F3UFRSXHrZfYIChQURomZ63GmWKN9fXhrXDTmmGSWstQ16ZRXFpKd/fd6bPDMMUEfEFy7fu5eIxs3jmsi5c37uZ03FExE3DGr1EaallaspWnv02jR25BVzerTEPXdiehrXDnI7mM/buL+L9eemMn51Odl4h3ZrV4c6BrTmnfQP1RlYCay179he5e94OlCne/ijiMvfms7+w5LD9jIH64aFH9LwdXsQ1rB1GWPDh99uZvz6ba8bO49+Xd+G6XnrjICJSmay1nPPSLzSICOXD2/o6HUdE3DSs0UsEBBgu796E8zs14vUZa3n7tw18t2Ibdw1qzS1ntDjqjav8YXtOPu/M2sDEeRvJKyxhYLto7jirFb1a1FVvSyUyxhBVK4SoWiF0jK19zG2steQWFJcp2g4v4jZm72fe+mxy8ouP2rdurZDDhk0u37qXOjWDuVQXq4uIVDpjXEMb//vTGrbtzadRpD4MFvF26jlz0Kbs/TzzTSrfrdhGk6gaPDakA4M7NVKxUca6rH2M/WU9n/++leLSUobGx3L7ma2OWziI98grKGZbzh9FW+aeA2Tm5B9W1O3eX8QD57XlnnPaOB1XRMQvrc/ax9kv/sJjQzowakBLp+OICBrW6PXmrN3JU1+uZNX2XPq1qsffh3akfaPqXXws2byHN39Zx3crthESGMDViU25dUBL3ZLAzxQWl+oaQRERD7t4zG8EGsMXd5/hdBQRQcMavV6/1vX5+p4zmLxgEy/+sJqL/vsb1/duxgPntfO7aeBPxFrL7LXZvPHLWmavzSYiLIg7B7ZiRL8WREeEOh1PPECFmYiI5yXFx/LMN2ls2JlHC91LUsSrqefMy+zZX8jLP67h/XkbqRUSyP3nteXGPs1PaSY8X1FSavlu+Tbe+GUty7fm0CAilFEDWnBdr2ZEhAU7HU9ERMSnZew5QL9nf9YwchEvoWGNPmj19lz++dVKfluzk9YNwvn7xR05s22007EqVUFxCZ8t3spbv6wjPXs/LevX4vazWnJpt8aEBmlyFBERkcpy9Vtz2ZVXyA/367YlIk7TsEYf1LZhBO+N7MWPqTt4+uuV3DRuAed2aMhjQzoQ5+NDEnLzi5g4fxPjZm1gR24BXZtE8sYN3Tm/UyOP3uxYRESkukqKj+WxqctJzczVpFoiXkw9Zz6goLiE8bPTGfPTGgpLShnZvwVnto0mMMAQFGAICgwgKMAQGGAIDjQEBgS4l7uXBQQQGOje1r3OiXuCZeUWMH72Bt6ft5Hc/GIGtKnP6LNa0a9VPX2KJyIi4kG78grp9a8fGTWgJQ9f2N7pOCLVmoY1+okdufk8/90qPlm05bSPZQyuou1QgXd4UfdHsRdwWBF4ooIw2P3ctf/h22bvK+CLJRkUlZRyUecYRp/Vii5NIiuhVURERKQiRoxfwJrt+5j10CB9KCriIA1r9BMNIsJ4/qp47j67NdtzCiguLaW4xFJSaikutRSXlFJc6npeVFJ6zOWu55aS0lKKDi4rsa5jlVpKSixFpYfvW3a/4tJSikpK2V9Ycpxtjp0hMMBwRffG3HZmK80UJSIi4oCk+Fge+HgJizftpkfzuk7HEZFjUHHmg5rXq0XzeipwREREpOLO79SI0KBlTEvJUHEm4qX8d352ERERETkkPDSIczo04OtlmRSXlDodR0SOQcWZiIiISDWRFN+YnfsKmbMu2+koInIMKs5EREREqomB7aKJCA1i2pIMp6OIyDGoOBMRERGpJsKCAxncuRHfL99GflGJ03FE5AgqzkRERESqkaT4WHILipm5KsvpKCJyBBVnIiIiItVIv1b1qB8ewpca2ijidVSciYiIiFQjQYEBXNQlhh9Tt7OvoNjpOCJShoozERERkWomKT6WguJSfli5zekoIlKGijMRERGRaqZ7syga16nBFyka2ijiTVSciYiIiFQzAQGGofGxzFqzk115hU7HERE3FWciIiIi1VBSfCzFpZZvlmU6HUVE3FSciYiIiFRDHWIiaN0gXDekFvEi5RZnxpimxpgZxphUY8wKY8y97uVXuZ+XGmMSPR9VRERERCqLMYak+FgWpu8ic+8Bp+OICBXrOSsG/myt7QD0Ae4yxnQElgOXA796MJ+IiIiIeEhSfCzWwldLNLRRxBuUW5xZazOttYvd3+cCqUBja22qtXaVpwOKiIiIiGfE1a9F1yaRGtoo4iVO6pozY0wc0A2Y75E0IiIiIlKlkuJjWbZ1L+uz9jkdRaTaq3BxZowJBz4F7rPW5pzEfrcZY5KNMclZWVmnklFEREREPOTirrEYg3rPRLxAhYozY0wwrsJsorX2s5M5gbV2rLU20VqbGB0dfSoZRURERMRDGkWG0btFXaYtycBa63QckWqtIrM1GuAdINVa+5LnI4mIiIhIVUqKb8z6rDxWZFR4cJSIeEBFes76A8OAs40xKe7HRcaYy4wxW4C+wNfGmO89mlREREREPOLCzo0ICjB8qaGNIo4KKm8Da+0swBxn9eeVG0dEREREqlpUrRDObBvNl0syeOiC9gQEHO+tn4h40knN1igiIiIi/ikpPpaMvfks2rTb6Sgi1ZaKMxERERHhvI4NCQsO4N056U5HEam2VJyJiIiICLVCgxh9Viu+XprJ579vcTqOSLWk4kxEREREALh7UGt6xkXx2OfL2Zid53QckWpHxZmIiIiIABAUGMDL13YjMMBwz+TfKSwudTqSSLWi4kxEREREDmlcpwbPXdGVJVv28tIPq52OI1KtqDgTERERkcNc2CWG63o1481f1jFrzU6n44hUGyrOREREROQof7+4I60bhHP/xylk7ytwOo5ItaDiTERERESOUiMkkDHXdWPvgSIe/GQJ1lqnI4n4PRVnIiIiInJMHWJq8+hFHZixKovxs9OdjiPi91SciYiIiMhx3dS3Oed2aMCz36axImOv03FE/JqKMxERERE5LmMM/7kynqhawfxp8u/sLyx2OpKI31JxJiIiIiInVLdWCP93TQIbdubx1LSVTscR8VsqzkRERESkXP1a1efOga34KHkzXy3NcDqOiF9ScSYiIiIiFXLfuW1JaFqHRz5bxuZd+52OI+J3VJyJiIiISIUEBwYw5rpuYOHeD3+nuKTU6UgifkXFmYiIiIhUWNO6NXn6ss4s3rSHV35a43QcEb+i4kxERERETsolCY25skcTxsxYy9x12U7HEfEbKs5ERERE5KQ9ldSJuHq1uP+jFHbnFTodR8QvqDgTERERkZNWKzSIMdd1IzuvgIc+XYq11ulIIj5PxZmIiIiInJLOjSN56IL2TF+5nQ/mb3I6jojPU3EmIiIiIqdsZP8WnNU2mqe/WsmqbblOxxHxaSrOREREROSUBQQYXrgqnoiwYP40eTH5RSVORxLxWSrOREREROS0REeE8tLV8azevo+nv17pdBwRn6XiTERERERO25lto7ntzJZ8MG8T36/Y5nQcEZ+k4kxEREREKsWD57ejS+NI/jplKRl7DjgdR8TnlFucGWOaGmNmGGNSjTErjDH3upfXNcb8YIxZ4/4a5fm4IiIiIuKtQoICeOW6bhSVlHL/RymUlGp6fZGTUZGes2Lgz9baDkAf4C5jTEfgYeAna20b4Cf3cxERERGpxlrUr8U/L+nM/A27eH3GWqfjiPiUcosza22mtXax+/tcIBVoDFwCTHBvNgG41EMZRURERMSHXN69MZckxPLyT2tITt/ldBwRn3FS15wZY+KAbsB8oKG1NhNcBRzQoNLTiYiIiIjPMcbw9KWdia0Txr0fprD3QJHTkUR8QoWLM2NMOPApcJ+1Nuck9rvNGJNsjEnOyso6lYwiIiIi4mMiwoJ55dpubM/J52+fLcNaXX8mUp4KFWfGmGBchdlEa+1n7sXbjTEx7vUxwI5j7WutHWutTbTWJkZHR1dGZhERERHxAd2aRfHA+W35elkmHydvdjqOiNeryGyNBngHSLXWvlRm1TRguPv74cAXlR9PRERERHzZ6DNb0b91PZ6ctpK1O/Y5HUfEq1Wk56w/MAw42xiT4n5cBDwLnGeMWQOc534uIiIiInJIQIDhpasTqBESyJ8m/05+UYnTkUS8VkVma5xlrTXW2q7W2gT34xtrbba19hxrbRv3V03FIyIiIiJHaVg7jOev7EpqZg7PfZfmdBwRr3VSszWKiIiIiJyKczo0ZES/OMbPTufntO1OxxHxSirORERERKRKPHxhezrE1ObBT5ayIyff6TgiXkfFmYiIiIhUibDgQMZcl8D+wmLu/ziF0lJNry9SloozEREREakyrRtE8OTQTsxem81bv653Oo6IV1FxJiIiIiJV6pqeTRnSJYYXp68iZfMep+OIeA0VZyIiIiJSpYwxPHN5FxrWDuOeyb+Tm1/kdCQRr6DiTERERESqXGSNYP57bQJbdu/n71+scDqOiFdQcSYiIiIijkiMq8t957bl89+38tniLU7HEXGcijMRERERccxdg1rTq0VdHp+6nPSdeU7HEXGUijMRERERcUxggOHlaxIICgzgng9/p7C41OlIIo5RcSYiIiIijoqtU4PnrujK0i17eXH6KqfjiDhGxZmIiIiIOO6Czo24oXcz3vp1Pb+uznI6jogjVJyJiIiIiFd4/OKOtG0YzgMfL2HnvgKn44hUORVnIiIiIuIVwoIDeeW6buTkF/Hnj5dQWmqdjiRSpVSciYiIiIjXaN+oNo8P6cAvq7MYN3uD03FEqpSKMxERERHxKjf2ac55HRvy3HdpLN+61+k4IlVGxZmIiIiIeBVjDP+5oiv1aoVyz+TfySsodjqSSJVQcSYiIiIiXieqVgj/d00CG7LzeHLaCqfjiFQJFWciIiIi4pX6tqrH3YNa88miLUxbkuF0HBGPU3EmIiIiIl7r3nPa0L1ZHR79bBmbd+13Oo6IR6k4ExERERGvFRQYwH+v7QbAPR/+TlFJqcOJRDxHxZmIiIiIeLWmdWvyzOVd+H3THv774xqn44h4jIozEREREfF6Q+NjuTqxCa/NXMucdTudjiPiESrORERERMQnPJnUiRb1a3H/Rynsyit0Oo5IpVNxJiIiIiI+oWZIEK9c243deUX8dcpSrLVORxKpVCrORERERMRndG4cyUMXtufH1O28P2+j03FEKlW5xZkxZpwxZocxZnmZZfHGmLnGmGXGmC+NMbU9G1NERERExGVk/zgGtYvm6a9TSduW43QckUpTkZ6zd4ELjlj2NvCwtbYL8Dnwl0rOJSIiIiJyTMYYnr8qntphwfxp0u8cKCxxOpJIpSi3OLPW/grsOmJxO+BX9/c/AFdUci4RERERkeOqHx7K/10Tz5od+5i0YJPTcUQqRdAp7rccSAK+AK4CmlZaIhERERGRChjQJprJt/ahd4u6TkcRqRSnOiHISOAuY8wiIAI47lymxpjbjDHJxpjkrKysUzydiIiIiMjR+raqR0CAcTqGSKU4peLMWptmrT3fWtsDmAysO8G2Y621idbaxOjo6FPNKSIiIiIi4tdOqTgzxjRwfw0AHgPerMxQIiIiIiIi1U1FptKfDMwF2hljthhjbgGuM8asBtKADGC8Z2OKiIiIiIj4t3InBLHWXnecVf+t5CwiIiIiIiLV1qlOCCIiIiIiIiKVSMWZiIiIiIiIF1BxJiIiIiIi4gVUnImIiIiIiHgBFWciIiIiIiJeQMWZiIiIiIiIFzDW2qo7mTFZwMYqO2HF1Qd2Oh2iGlA7e57auGqonT1PbVw11M6epzauGmpnz1MbV57m1troY62o0uLMWxljkq21iU7n8HdqZ89TG1cNtbPnqY2rhtrZ89TGVUPt7Hlq46qhYY0iIiIiIiJeQMWZiIiIiIiIF1Bx5jLW6QDVhNrZ89TGVUPt7Hlq46qhdvY8tXHVUDt7ntq4CuiaMxERERERES+gnjMREREREREv4HPFmTHmAmPMKmPMWmPMw2WWf2SMSXE/0o0xKcfYN8EYM9cYs8IYs9QYc02ZdS2MMfONMWvcxwo5zvmHu7dZY4wZfrL7+wIvaOPvjDF7jDFfHbHcb9oYnG3nyvg5+QKH27i5MWaR+xwrjDGjT2Z/X+L03wz3trWNMVuNMa+eyv7ezuk2NsaUlDnPtJPd31d4sJ3vdh/TGmPqn+D8eo/h+TbWewwPt3Nl/V2vtqy1PvMAAoF1QEsgBFgCdDzGdi8Cfz/G8rZAG/f3sUAmUMf9/GPgWvf3bwJ3HGP/usB699co9/dRFd3fFx5Ot7F73TnAUOCrI5b7RRt7QztXxs/J2x9e0MYhQKj7+3AgHYj1pzb2hnYuc5z/ApOAV8ss84t29oY2BvYdZ7lftHEVtHM3IM79d6D+cc6v9xgebmP3dnqP4fnfZb9/j+HRn5/TAU4qLPQFvi/z/BHgkSO2McDmg78U5RxvCdDGvc9OIOhY5ymz/XXAW2Wev+VeVqH9feHhdBuX2W9g2T+c/tTG3tTOlbW/Nz68qY2BesAm939SftPG3tLOQA/gQ2AE7uLMn9rZS9r4qOLMn9rYk+18xLJ0jv+GVu8xPNzGZbYZiN5jeLydj9zf39rZUw9fG9bYGNcv0kFb3MvKGgBst9auOdGBjDG9cH2asA7XG6c91triI49rjEk0xrxdzvmPu78PcrqNj8ef2hi8qJ0rur8PcryNjTFNjTFL3Tmes9ZmnGh/H+VoOxtjAnB9+vuXIw7nT+3s+O8yEGaMSTbGzDPGXOpe5k9tDJ5r5xNtp/cYVdvGx+NPbQxe1M5+/B7DY4KcDnCSzDGW2SOeXwdMPuFBjIkB3geGW2tLjTHHPa61NhkYVc75K5LLVzjdxqeTy5d4RTufzP4+yPE2ttZuBroaY2KBqcaYKUBpBXL5Eqfb+U7gG2vt5iN20e/ykQc5vb8Xzay1GcaYlsDPxphlQE4FcvkSj7TzibbVewygatv4dHL5Eq9oZz9/j+ExvlacbQGalnneBMg4+MQYEwRcjmuIyzEZY2oDXwOPWWvnuRfvBOoYY4Lc1fxhxz3i/AOPOP/Mk9jfFzjdxsfjT20MXtDOHvo5eRPH2/gg95vaFbg+qfz0ZPf3ck63c19ggDHmTlzX9oUYY/bhGsbjL+3sdBvj7vXFWrveGDMT13Un+l0+wnHa+WTOP/CI889Ef5cPc5ptfDz+1MbgBe1cDd5jeIyvDWtcCLRxz/QSAlwLTCuz/lwgzVq75Vg7u/f5HHjPWvvJweXWWgvMAK50LxoOfHGMQ3wPnG+MiTLGRAHn4xorW9H9fYHTbXxMftbG4HA7e+rn5GWcbuMmxpga7u+jgP7AKj9rY3C4na21N1hrm1lr44AH3cd52M/a2enf5ShjTKj7+/q4fpdX+lkbg4fa+SToPYbn2/iY/KyNweF2ribvMTzHesGFbyfzAC4CVuMau/roEeveBUafYN8bgSIgpcwjwb2uJbAAWAt8wh+zrCUCb5c5xkj3NmuBm8ssP+b+vvjwgjb+DcgCDuD69Gewv7Wx0+18Kvv74sPhNj4PWIrrQuilwG1lju03bex0Ox9xrBEcPluj37Szw7/L/YBl7t/lZcAt/tjGHm7ne3D9f1aMq6fgYNvqPUbVt7HeY3i4ncvZ36/a2RMP424oERERERERcZCvDWsUERERERHxSyrOREREREREvICKMxERERERES+g4kxERERERMQLqDgTERERERHxAirOREREREREvICKMxERERERES+g4kxERERERMQL/D9QaGv8wnpt+gAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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jxp3QddBay4MPPsiNN954yPLVq1cffC7Hem7w14qUxphj7jc8PPzg/VWrVvHCCy8wa9YsYmJiGDly5FGnLyh7nMPXObDP0tJSatWqRUZGxvGeuiPKneAZYyKAMcCd1tpdxpgAIAboCXQDvjLGNHM3O5bd7gbgBoDGjRtXWuAiIieisLiU35duYczc9UxcuoXiUkvbhCj+cXYbzk1JIC4i+JD1o8MCuWNQMk/8bwm/ZW7h1DZ1HYpcRESq0rFa2qrKsmXL8PPzIzk5GYCMjAyaNGlyQvtKTEzkjTfeoLS0lJycnIPj18rauXMnMTExhIWFsXTpUqZPn37wscDAQIqKiggMDGTQoEEMHTqUu+66izp16rBt2zZ2795Njx49uOOOO9i6dStRUVGMHj2ajh07Hje2wYMH8+ijjzJ8+HAiIiLIyckhMDCwQs9v/PjxPPjgg+zdu5dJkybxzDPPEBoaWq797tq1i/DwcKKjo9m8eTM//vgjAwYMACAyMpLdu3cfLARTt25dMjMzadmyJWPHjiUyMvIv+4uKiqJp06aMHj2aiy66CGstCxYsKNdrUR3KleAZYwJxJXefWmu/cS9eD3zjTuhmGmNKgTggt+y21tpRwCiArl27HpL8iYhUB2stC9bv5Ju56/l2/ga25xcRHxnM1amJXNClIa3qHbvr5RU9m/Dx9DU8/UMm/VrEExSgGWZEROTk7dmzh7/97W/s2LGDgIAAkpKSDhY+qajU1NSD3R3btWtH586d/7LOGWecwVtvvUWHDh1o2bLlIV0Yb7jhBjp06EDnzp359NNPefLJJzn99NMpLS0lMDCQ119/nZ49e/L444/Tq1cv6tevT+fOnQ8WXzmW008/nczMTHr16gW4uqZ+8skn+Pv7l/v5de/enSFDhrB27VoeffRREhISSEhIKNd+O3bsSKdOnWjbti3NmjUjNTX1kOd95plnUr9+fSZOnMgzzzzD2WefTaNGjWjXrh179uw5YjyffvopN998M08++SRFRUVceumlHpPgmcMa3P66gqud8kNgm7X2zjLLbwISrLX/MMa0AH4DGh/egldW165d7YFqQCIiVW3Tzv2MnZfDmLnryd6yh6AAP05vU5cLujSkb1IcAf7lT9R+y9zMtR/O5h9nt+GaPk2rMGoREakumZmZtG7d2ukw5Dgef/xxIiIi+Pvf/+50KBV2pL8xY8wca23XqjpmeVrwUoErgYXGmAz3soeA/wL/NcYsAgqBEcdK7kREqsO+whJ+XryJMXPXMzU7j1ILXZvE8PR57RnSoT7RoRXrEnLAKa3q0Ccpjpd/y+L8zg2oFRZUyZGLiIiInLzyVNFMB442mvKKyg1HRKTiSksts1ZvY8zc9fywcBN7CoppUCuU2wYmcX7nhiTGhR9/J8dhjOGRs1tz1stpvPxbliNjNURERGqixx9/3OkQvIrnTsEuInIca7buZczcHMbOW8+6bfsID/LnrPb1uaBLQ7on1sbPr3IrXraqF8Ul3Rrz8bQ1XNGzCc3jI46/kYiIiEg1UoInIl5l1/4ifliwkTFz1zNr9XaMgdTmcdx9WgsGt61HWFDV/lu7+7QW/G/+Bv71QybvjuhWpccSERERqSgleCLi8UpKLWlZuYyZm8MvizdRUFxK8/hw7jujJed1akD96NBqiyU+MphbBjbnuZ+WMTU7j9SkuGo7toiIiMjxKMETEY+VtXk3X89Zz9h5OWzZXUCtsEAu6daI8zs3pGPDaMcmHb8mtSmfzVjLP79bwve398W/kruCioiIiJwoTeYkIh7px4UbOf2lKbyXvooODWvx1hWdmfHQIP5vaDtSGtVyLLkDCAn054EzW7F0025Gz17nWBwiIuL9/P39SUlJoV27dlx00UXk5+ef8L5GjhzJ119/DcB1113HkiVLjrrupEmT+OOPPw7ef+utt/joo49O+NgHrF69mnbt2h2y7PHHH+eFF16o0H4qK56aSC14IuJxFuXs5K6vMkhpVIt3rupKXESw0yH9xZD29Xm/yWpe+GU5Z3dMICJY/05FRKTiQkNDycjIAGD48OG89dZb3H333QcfLykpqdCE4Ae8++67x3x80qRJRERE0Lt3bwBuuummCh+jqhQXF3tUPN5GLXgi4lG27NrPdR/OpnZYEKOu9MzkDlzTJjx6dhvy9hTwxsRsp8MREZGq9txzMHHiocsmTnQtryR9+/YlOzubSZMmMXDgQC6//HLat29PSUkJ9957L926daNDhw68/fbbAFhrue2222jTpg1Dhgxhy5YtB/c1YMAAZs+eDcBPP/1E586d6dixI4MGDWL16tW89dZb/Oc//yElJYW0tLRDWtkyMjLo2bMnHTp04LzzzmP79u0H93n//ffTvXt3WrRoQVpaWoWf47H2/dBDD9G/f39efvnlg/Fs2LCBlJSUgzd/f3/WrFnDmjVrGDRoEB06dGDQoEGsXbsWcLVi3n777fTu3ZtmzZodbNGsSZTgiYjH2F9UwvUfzWbX/iLeHdGN+EjPTO4OSGlUi2EpCbybvop12068S42IiHiBbt3g4ov/TPImTnTd71Y5FZWLi4v58ccfad++PQAzZ87kqaeeYsmSJbz33ntER0cza9YsZs2axTvvvMOqVasYO3Ysy5YtY+HChbzzzjuHdLk8IDc3l+uvv54xY8Ywf/58Ro8eTWJiIjfddBN33XUXGRkZ9O3b95BtrrrqKp599lkWLFhA+/bteeKJJw6Jc+bMmbz00kuHLC9rxYoVhyRlb731Vrn2vWPHDiZPnsw999xzcFlCQgIZGRlkZGRw/fXXc8EFF9CkSRNuu+02rrrqKhYsWMDw4cO5/fbbD26zceNG0tPT+e6773jggQcqeCa8n/oUiYhHsNby99HzWZCzk1FXdqVNQpTTIZXLfWe04qfFm3ju52W8elknp8MREZETdeed4O4qeVQJCTB4MNSvDxs3QuvW8MQTrtuRpKTASy8dc5f79u0jJSUFcLXgXXvttfzxxx90796dpk2bAvDLL7+wYMGCg61RO3fuJCsriylTpnDZZZfh7+9PQkICp5xyyl/2P336dPr163dwX7Vr1z5mPDt37mTHjh30798fgBEjRnDRRRcdfPz8888HoEuXLqxevfqI+2jevPnBbqfw50Tlx9v3JZdcctS4pk6dyrvvvnuw1XDatGl88803AFx55ZXcd999B9cdNmwYfn5+tGnThs2bNx/z+foiJXgi4hFe+S2b7xZs5IEzW3Fam7pOh1NuCbVCuaFvM175PZuRvRPp0iTG6ZBERKSqxMS4kru1a6FxY9f9k1R2DF5Z4eHhB3+31vLqq68yePDgQ9b54Ycfjlt0zFpbqYXJgoNdvWv8/f0pLi6utP3Coc+5rI0bN3Lttdfy7bffEhERccR1yj7HAzGC6/nXNOqiKSKO+37BRv7z63LO79yAG/s1czqcCruxf3PqRAbzz++WUFpa8y4kIiI+4aWXYNKkY98eewzy8+HRR10/H3vs2Osfp/WuvAYPHsybb75JUVERAMuXL2fv3r3069ePL774gpKSEjZu3MjEw8cIAr169WLy5MmsWrUKgG3btgEQGRnJ7t27/7J+dHQ0MTExB1vKPv7444MtbifrRPZdVFTExRdfzLPPPkuLFi0OLu/duzdffPEFAJ9++il9+vSplBh9gVrwRMRRC9bv4J7RGXRtEsO/zm/v6PQHJyo8OIB7B7fk3q8X8L8FGxia0sDpkEREpLIdGHP31VcwcKDrVvZ+FbruuutYvXo1nTt3xlpLfHw848aN47zzzuP333+nffv2tGjR4ojJUnx8PKNGjeL888+ntLSUOnXqMGHCBM455xwuvPBCxo8fz6uvvnrINh9++CE33XQT+fn5NGvWjPfff7/SnktF9/3HH38wa9YsHnvsMR577DHA1XL5yiuvcM011/D8888THx9fqTF6O1OdzZZdu3a1B6r5iIhs2rmfoa+nE+Dnx/jbUj22YmZ5lJZaznktne17C/ntngGEBlW8pLWIiFSvzMxMWrduXb6Vn3vOVVClbDI3cSLMmgVlxn+JlHWkvzFjzBxrbdeqOqa6aIqII/YVuipm7tlfzHsjPXc6hPLy83NNm7Bh537eS1/pdDgiIlLZ7rvvry11AwcquROPowRPRKpdaanlntEZLNqwk1cu60Sret5RMfN4ejaLZXDburwxaQVbdu13OhwRERGpgZTgiUi1e+m3LH5YuIkHz2zFoNbeUzGzPB48szVFJaW88Msyp0MRERGRGkgJnohUq2/nb+CV37K4qEtDru/rfRUzjycxLpyRvRMZPWc9i3J2Oh2OiIgcR00soy/Vw6m/LSV4IlJtMtbt4N7R8+meWJsnz2vnlRUzy+O2U5KpFRrIk98v0QeHSnTTx3N4ZNxCvaYiUmlCQkLYunWr/q9IpbPWsnXrVkJCQqr92JomQUSqxcad+7j+o9nUiQrmzSs6Exzgu1Umo0MDueu0Fvxj/GImLNnM6W3rOR2S18vespufFm8CoG1CNJd1b+xwRCLiCxo2bMj69evJzc11OhTxQSEhITRs2LDaj6sET0SqXH5hMdd9OJt9hSV8el0PYr28YmZ5XN69MR9NW8PTP2QyoGUdggLUYeJkjJu3AT8DnRvH8Pi3i0lpVIvW9X2jOI+IOCcwMJCmTZs6HYZIpdInDhGpUqWllru/nE/mxl28elknWtSNdDqkahHg78fDQ1qzems+H01b7XQ4Xq201DIuI4fUpDjeurIL0aGB3PrZXPYWFDsdmoiIiMdRgiciVerFCcv5afEmHjqrNQNb1XE6nGo1sGUd+rWI55Xfsti+t9DpcLzWnLXbWb99H+d1akBcRDAvX9qJ1Xl7eXTcIo2bEREROYwSPBGpMuMzcnhtYjaXdmvEtX1qZheYR4a0Zk9BMS/9utzpULzWuHk5hAb6M9g9lrFX81juGNSCb+blMHrOeoejq5nmr9vB6NnrnA5DRESOQAmeiFSJuWu3c+/XC+jRtDb/N9R3K2YeT4u6kVzWvTGfzFhL9pY9TofjdQqLS/luwUZOa1OX8OA/h43fdkoSvZvH8o/xi1i+ebeDEdY81lruH7OAe79ewNJNu5wOR0REDqMET0QqXc6Ofdzw0RzqRYXw1hVdanyBkbtPa0FYoD9P/5DpdCheZ9KyLezcV8R5nRocstzfz/DSpSlEBAdw66dzyS/UeLzqMiUrj6WbXEn1fyaoZVpExNPU7E9dIlLp9ha4KmYWFJXw3oiuxIQHOR2S42IjgrntlCR+X7qFtCyV4q6IcRk5xIYH0Sc57i+P1YkM4aVLOpGdu4fHxi92ILqaadSUFdSNCubWgc35efFmFuXsdDokEREpQwmeiFSa0lLLXV9msGzTLl69vBPJNaRiZnmMTE2kce0wnvwuk+KSUqfD8Qq79hfxa+YWzumYQKD/kS9XfZLjuG1gEqPnrOebuRqPV9UW5exkavZWrkltyo39mxMdGsiLasUTEfEoSvBEpNK88MsyflmymUfPbsOAljWrYubxBAf48+CZrVi2eTdfzVYiUh4/LdxEYXEpww7rnnm4OwYl071pbR4Zt0jjHKvYqCkriQgO4LIejYkKCeSGfs34fekW5q7d7nRoIiLipgRPRCrFN3PX88akFVzeozEjeyc6HY5HOqNdPbon1ubFCcvYvb/I6XA83th5OTSNC6djw+hjrhfg78crl3YiJNCf2z6by/6ikmqKsGZZty2f7xdu5HJ3cgcwsncitcODNBZPRMSDHDfBM8Y0MsZMNMZkGmMWG2PucC9/3BiTY4zJcN/OqvpwRcQTzVmzjQfGLKRXs1ieOLdtja2YeTzGGB45uzV5ewp5feIKp8PxaBt27GP6qq0MTUko199TvegQXry4I0s37eaJ/2k8XlV4L30VBrg6NfHgsvDgAG7q34y0rDxmrd7mWGwiAjz3HEyceOiyiRNdy6VGKU8LXjFwj7W2NdATuNUY08b92H+stSnu2w9VFqWIeKz12/O54aM5JNQK4Y3hnY86VkpcOjSsxfmdG/Df9FWs25bvdDge69v5G7AWhqUcu3tmWQNa1uHmAc35fOY6xmfkVGF0Nc+O/EK+nLWOc1MSqB8deshjV/ZMJD4ymH//ssyh6EQEgG7d4OKL/0zyJk503e/Wzdm4pNod95OYtXajtXau+/fdQCZQ/iuuiPisPe6KmYUlpbw7opsqZpbTfYNb4e9neObHpU6H4rHGzcuhU+NaJMaFV2i7e05rQdcmMTz0zUJW5mo8XmX5ZPoa9hWVcEO/Zn95LDTIn1sGNGf6ym38kZ3nQHQiAsDAgfD55zB0KFx4IVxwAXzxhWu51CgV+qrdGJMIdAJmuBfdZoxZYIz5rzEmprKDExHPVVJqufOLeWRt2cMbwzuTVCfC6ZC8Rr3oEG7s34zvF25ktrq1/UXmxl0s3bT7L3PflUeAvx+vXNaJwAA/bv1snsbjVYL9RSV88Mca+reIp1W9qCOuc1n3xtSPDuHfE5Zjra3mCEUEgPnz4cEHYfduGDMGtm+H886Dfv3gjjvggw9c6xQWOh2pVLFyJ3jGmAhgDHCntXYX8CbQHEgBNgL/Psp2NxhjZhtjZufmav4nEV/x3M9L+TVzC4+d04a+yfFOh+N1bujXjHpRIfzzuyWUluoDcVnjMnII8DMMaV//hLZPqBXKixd3JHPjLp78fkklR1fzjJ2XQ96eAm48QuvdASGB/tw6MIk5a7Yzebmu9SLVav9+ePhh6NoVsrIgMhKuuw4iIuCUU6C4GN59F66+GlJSXI937gzXXguvvQZTp8Ie9XjwJeVK8IwxgbiSu0+ttd8AWGs3W2tLrLWlwDtA9yNta60dZa3taq3tGh+vD4EivmD07HW8PXklV/RszFW9Ep0OxyuFBQVw3xktmb9+J+Pna7zYAaWllvHzNtC/RTyxEcEnvJ9TWtXlhn7N+GT6Wr5bsKESI6xZSkst76StpF2DKHo1jz3muhd3bUTDmFBeVCueSPVJT3clbU8/7UrmAgJg/Hh45x349ltX8vbUU7BrF2Rmurpw3nknxMW5Hv/b36BPH4iKghYt4JJL4Jln4OefYcsWp5+dnKDyVNE0wHtAprX2xTLLy361eh6wqPLDExFPM2v1Nh4au5DUpFgeO6et0+F4tWEpDejQMJrnflrGvkJ1JQSYvmorm3btZ+gJdM883L2DW9KpcS0eGLOQNVv3VkJ0Nc+vmZtZmbuXG/s1P24106AAP24/JZkF63fya6Y+GIpUqV274NZboW9fVwveTz/BoEEwevSfY+4GDoSvvoJZs8DfH1q1gksvhWefhV9+cSVw69a5Er0nnoB27WDmTFc3zzPOgLp1oUEDGDIEHnnE1e1z5UrQFzgezxzvWzZjTB8gDVgIlLoXPwRchqt7pgVWAzdaazcea19du3a1s2fPPrmIRcQx67blM/T1qdQKDWTsLalEhwU6HZLXm7lqGxe/PY27Tm3BHacmOx2O4+77ej7fL9jI7EdOIzTI/6T3t25bPkNeSaNJbDhf39yL4ICT32dNcsGbf7B5134m/X0AAeWokFtcUsqpL04mNCiA7//WBz8/354yxVrL7oLig/MCilSL77+Hm26CnBy4/XZ48klXd8zKsn07ZGTAvHl//szMhBL3F5HR0a5Ww5QU6NTJdWvdGgIDXVMydOt2aGGXiRNdSeZ991VejF7OGDPHWtu1qvYfcLwVrLXpwJH+Q2taBJEaZPf+Iq79cBbFJaW8O6KrkrtK0r1pbc5qX4+3Jq/g0u6NqBsV4nRIjtlfVMKPCzdxRrv6lZLcATSqHcbzF3Xkxo/n8K8flvL4uWp1Lq85a7YxZ812Hj+nTbmSO3AVubnj1GTu+nI+Py3exFknOI7SW7wxaQXP/7yMxrXD6JoYQ7fE2nRtEkPz+AifT27FAbm5ru6Vn30Gbdq4Wud69ar848TEuBK0sknavn2waNGhSd+oUa7lAMHBrhbAunXhn/90dRm9/nqYNs01VcNXX1V+nHJUx03wRERKSi13fJHBity9fHRNd5rFq2JmZXrgjNb8umQLz/+8jBcu6uh0OI75fekWdhcUn1D1zGMZ3LYeV6cm8v7U1fRsFssZ7epV6v591duTV1IrLJCLuzWq0HbndmzA6xNX8J8Jyxncth7+PproLN20i5d+XU73prWJCQtkyvJcvpnrGk9bKyyQLo1j6JpYm26JMbRrEE1IoFqP5QRZ60rq7rjD1TXzscdc3SiDT3yccoWFhrpa5srOqVdSAsuXH5r0zZjhKthy++1w993g5wc33ODq6mktHKert1QOJXgiclzP/JjJ70u38M9h7UhNinM6HJ/TODaMq/skMmrKSkb2TqRdg2inQ3LE2Hk51IkMPm4xjxPx4JmtmbNmO/d9PZ+2CVE0qh1W6cfwJStz9zAhczO3DUwiLKhiHxX8/Qx3nprMbZ/N47sFGxhagcnqvUVxSSn3jl5AVEggbw7vTGxEMNZaVm/NZ9bqbcxZvZ1Za7bx21LXWMQgfz86NIymq7uFr0uTGM0bWpl8uVvg2rWu7pg//gg9eriqYbZr53RULv7+rq6ZrVvD5Ze7llkL69fDPfe4xgPWru2q1Pnaa9C0qWts3xlnuM5VZKSz8fuwCs2DJyI1z5ez1vJO2ipG9GrClT2bOB2Oz7p1YBK1w4L4v++W1MgKhNv3FjJp2RaGpiRUSYtPUIAfr13WGWvhts/nUVhcevyNarB30lYR6O93wlVyz2pXn1b1Inn51yyKS3zvtX4nbRULc3byxNC2B6u9GmNoGhfOxV0b8eyFHfj9ngHMeeRURl3ZhZGpiZRay3vpK7nuo9l0+ucETn1xMg9+s4Axc9azZuveGvm+rzTdurm6AT77rGuOt4kTXffLtjZ5m9JSV1LUti1Mngz/+Y+rIqanJHdHYwxkZ7vOwaOPulrwPvkE3ngD2reHjz5yTcQeG+uq+vnss665+fT3X6mOW2SlMqnIioh3mb5yK1e+N4OezWJ5f2S3co/DkRPzyfQ1PDJuEW9d0Zkz2vn22KXDHXju3/2tT5W2YP64cCM3fzqX6/o05ZGz21TZcbxZ7u4CUp/9nQs6N+Rf57c/4f38tGgTN30yhxcu6siFXRpWYoTOyt6yh7NeSeOUlnV484rOx60uWtb+ohIWrN/JrNXbmL3aNcZx1/5iAOIjg+na5M9una3rRxGo/7nl99prrpL/oaGuZOHFF10tX97YJTAz0zWP3R9/wGmnwdtvu1q/vMGB5Pqrr1ytdIffLyx0Jao//eS6LVjg2q5+fRg82NW6d+qprgTQh1V1kRUleCJyRGu35jP09XRiwoNcFTNDVVSlqhWXlHLWK2kUFJfyy139alTFxwvf/IOd+4r45a5+FfrAfCL+MX4RH01bw7tXdeXUNnWr9Fje6N+/LOO1idn8dnf/kxpva63lnNfS2bWvmN/u6e8TyUpJqeXCt/5gVd5efrmrH3UiT64oUmmpJWvLHle3zjXbmbV6G+u3u4pWhAb606lxrYNJX6fGtYhUtc6jKyqCq66CL75wdR0sKXFNC3DllXDFFdC4sdMRHl9hoau76T//CeHhrla7q67yriS1ot1lN2xwTdnw00+un9u3u1r9unX7sztnt26uc+pDlOCJSLXbW1DMsNensmV3AeNvTSUxLtzpkGqMKctzueq/M3n4rNZc36+Z0+FUi3Xb8un73ETuHdySWwcmVfnx9heVcMGbf7B++z5+uKMvDWqFVvkxvcXegmJ6P/M7PZvV5u0rT/6zx+9LN3PNB7P51/ntuay7F3zAPo5301by5PeZ/OeSjpzXqWpaJTft3M/sNduYvXo7s9dsY8mGXZRa8DPQql4U3RJj6OJu5asfrb/dgw60FN18s6s74MiRrjnd0tJcjw8c6Er2LrjANam3p5k1C669FhYudD2PV15xVaSsSUpKXK/Dzz+7Er4ZM1ytsTExcPrprha+wYMhIcHpSE+aEjwRqXbfzF3P3V/N5/2R3RjYqo7T4dQ4V78/k9lrtjPp7wMOju/xZa/9nsULvywn/f6BNIypnuInq/P2cvar6bSoG8GXN/byidalyvD+1FU88b8ljLm5N12axJz0/qy1nPfGH2zZtZ+J9w7w6lbp1Xl7OePlKaQ2j+PdEV2rvKX5gD0Fxcxbu/1gwjdv7Q7yC13zkTWoFUrXxBh6N4/lgs4Na243+mN1C2zSxDUG7OOPXWPDQkPhvPNcLWODBkGAw/UG9+6Ff/wDXnoJ6tWDN9+Ec891NiZPsXUr/Prrn905N21yLe/Q4c/WvdRUCPK+gkVVneDV0P8EInIsaVl51A4Pon+LeKdDqZEeHtKa/MISXvo1y+lQqpy1lrHzcujetHa1JXcAiXHh/Ov89sxdu4MXfllWbcf1ZMUlpbyXvupglcfKYIzhntNbsGHnfr6cta5S9umE0lLLfWMWEOjvx1Pnta+25A4gIjiAvsnx3HVaCz69ricLHjud/93Wh3+c3YaURrWYtmIr949ZyNUfzGJnflG1xeVRZs36M7kD18+vvnItb9bMlUAtX+4a0zZiBPzwgys5aNQI/v73P8eBVbfffnMVHnnxRdeccUuWKLkrKzYWLrkE3n/f1ZVz/nxXUZbYWFf31VNOcVXpPPdcV6vtypWu7Z57zpXklzVxomt5DaEET0QOYa0lPTuP1KQ4TdTrkKQ6kQzv0ZjPZq4la/Nup8OpUotydrEidy/DHCilf07HBC7v0Zi3J69korucfU32w6JNrN++jxsquWtwn6Q4uifW5rXfs9lfVFKp+64un8xYw8xV23h0SBvqRZ/cuLuTFeDvR/uG0VzTpymvD+/MjIcG8ewF7Zm+citDX08ne4tv/884ovvuO3TMF7julx3zZYxrUvA333S1BI0Z45p24OWXoWNHSEmBf/8bNm6s+ni3b4drrnEVE/H3h0mT4K23ILpmTpFTLsa4Wu7uuw9+/93Vujd+vCthX7QIbr0VmjeHFi1g+nQYNsyVyINvVFWtICV4InKIZZt3k7u7gL7Jmu/OSXee2oKwIH+e+iHT6VCq1Nh5OQT5+zGkvTNVQ/9xdhta1Yvk7q8y2LhznyMxeAJrLaOmrKBZfDintq7ccT/GGO4+vQVbdhfwyfQ1lbrv6rBuWz7P/LiUvslxXNTV86qBGmO4pFtjPr++J3sKihn2+h/8vnSz02F5tuBgOP98GDfOldC9+qpr2d//Dg0bwplnuiYWz8+v/GOPGQNt2rimC7j/flfrYf/+lX8cXxcZ6Wq5e/11WLHC1UL7yiuQnOzqzrlrFwwZAt27H9p9t4ZQgicih0hbngegBM9htcODuGNQMpOW5TJ5ea7T4VSJ4pJSvp2/gYGt4okOc6Y6YEigP68P70xBcSm3fz7PJ+dsK49pK7ayKGcX1/dtViUt9z2bxZKaFMtbk1eQX1hc6fuvKtZaHvhmAQZ45oIO1do1s6K6JtZm/G19aBIbxrUfzubNSSs0t155xMXBbbe5CnpkZsIDD7i6Sg4f7hoTd801rha20pP837BxoyupvPBC15QAM2fCM8+4xgTKyTHGldj97W/w/fewbRtMmAA9e7q66d58c41K7kAJnogcJi07j6Q6EarO5gGu6pVIYmwYT363xCcTjz9WbCVvTwHndar+7pllNY+P4Knz2jFr9Xb+8+tyR2NxyttTVhIXEVyl5+Lu01qSt6eQj6Z5TyveF7PWMTV7Kw+e1dorqq02qBXK1zf15qz29Xn2p6Xc+WWG13aLdUSrVvDUU7Bqlatb34UXwujRruSgaVN4+GFYurRi+7QW3n0XWreGH390JXUzZkDnzlXzHARCQlxdX7OzXZOtv/nmX8fk+TgleCJy0P6iEmas3EqfJLXeeYKgAD8ePKs1WVv28LkXF6g4mnHzcogKCWBAS+crtZ7XqSEXd23IG5NWMMVHW0yPJnPjLiYvz2Vk7yaEBFZdlcsuTWIY0DKetyevYE+B57fibdixj6e+z6Rns9pc7kVTPIQG+fPaZZ24d3BLxmds4OK3p9Xo7scnxM8PBgyA//4XNm92ddds08aVnLVu7Rq799prkJd37P1kZ7sqdV5/vWuM34IFrm6ZgZrPsEqVraL6f//n+nnxxTUqyVOCJyIHzVmznYLiUnXP9CCnt6lLj6a1eWnCcnbv950KefmFxfy0eBNDOtSv0qSiIp44tx3JdSK468sMNu/a73Q41eadKSsJC/Lnip5NqvxYd5/Wgu35RbyfvqrKj3UyrLU8NHYhJaWWZy/o4HUFp4wx3DowiXeu6sqKLXs497WpzF273emwvFNYGFx2mav1bf16eOEFKChwdQesX99VzGPMGHj66T8TiOJi13pt27oqd771lqswSHKyo0+lxjhWVdUaQgmeiBw0JSuXQH9Dz2axTocibsYYHhnShq17C3lj0gqnw6k0E5ZsJr+whKEOVM88mtAgf16/vDP5hSXc8cU8Skp9f/zShh37+Hb+Bi7p1ohaYVU/l1SHhrU4tXVd3klbyc59nvuFxZi5OUxalsu9g1vSJDbc6XBO2Glt6jL21lRCA/259O3pjJ7tez0BqlX9+nDPPZCR4brdcYeru+WFF8K//gVnnQVPPOEa+3Xvva5tPvoIbrzR1Soo1aM8VVV9nP7aROSg9Kw8OjWOITzY4Ylf5RDtG0ZzfqcGvJe+ivXbq6CqmwPGzsshITqE7om1nQ7lEMl1I/m/oW2ZvnIbL//m+/MQvj91FRa4tk/Tajvm3ae1YNf+Yt5LW1ltx6yILbv283//W0zXJjGM7J3odDgnrUXdSMbfmkrXxBju/XoB//TRMb3VrmNHVyvdunWuqo3nnusqxPL4465iLZGRrla/iy92OlKpgZTgiQgAeXsKWLxhF/3UPdMj/X1wS/wMPPeT90/KnbengLSsPIZ2auCRXd8u6tqI8zs34NXfs/gj+zhjbLzYrv1FfD5zHUPa16/WSebbJERxVvt6/HfqarbvLay245aHtZaHxy2ioLiU5y70vq6ZRxMTHsSH13RnZO9E3ktfVbMnRa9sAQEweDB8+ink5sKll7qmV7jzTtdE3CIOUIInIgBMdX+Q7ZMc73AkciQJtUK5vm8zvp2/gYx1O5wO56R8N38DJaXW8eqZx/LPoe1oFhfOHV9mkLu7wOlwqsRnM9ayp6C40ic2L487T23B3sJi3p7iWa14/1uwkQlLNnP3aS1oFh/hdDiVKtDfj8fPbcsz57smRR/2xtSaOSl6VZozB379tcZWbhTPoQRPRABX98zo0EDaN4h2OhQ5ihv7NycuIpgnv1vi1fNbjc3YQJv6UbSoG+l0KEcVHhzA68M7s2tfEXd9meFz4/EKi0t5f+oqUpNiaefAe75F3UjO7ZjAh3+s9pgEOm9PAY+NX0THRrW4rm/1J73V5dLujfns+p7s3l/Eea//wcSlW5wOyTeocqN4ECV4IoK1lrSsPFKTYvH3kS5JvigiOIB7Tm/B7DXb+WnRJqfDOSErc/cwf90Oj269O6BVvSieOLct6dl5vDEx2+lwKtX4jBw27yrghn7NHYvhjkHJFBSX8NZkzyge9Nj4xewtKOH5Czv4/P/Bbu5J0RvHhnHNh7M0KXplUOVG8SBK8ESEFbl72LRrP33VPdPjXdy1ES3rRvLMT0spLPa+QgnjMjZgDJzTMcHpUMrlkm6NGJqSwH9+Xc70lVudDqdSlJZa3klbSat6kY6OuW0WH8H5nRvyyfQ1jk9L8ePCjXy/cCO3D0ry6JblyqRJ0SuZKjeKB1GCJyJMWe4ef6cJzj2ev5/hoSGtWbM1n4+mrXY6nAqx1jJuXg69m8dSLzrE6XDKxRjDU+e1JzE2nDu+mMfWPZ7RnfBkTFq+heWb93BDv2YY42xL1R2DkikptY62kG7fW8ij4xfRNiGKG/s716LphCNNir5pZ82ZA1LEVynBExHSs/NIjA2jUe3qq6QnJ65/i3j6tYjnld+yPK4K4bHMW7eDtdvyGeZBc9+VR0RwAK9e3ont+UXc9dV8Sr18PN7bk1eSEB3iEa2ojWqHcVHXRnw+cx05O/Y5EsP/fbeEHflFPH9hRwL9a97HogOToo+6sgsrtuzhnNfSNSm6iJeref/JROQQhcWlTF+5Vd0zvczDZ7VmT0Exr/zuPXO1jZuXQ3CAH2e0q+d0KBXWNiGaR89uw5TlubzmxePx5q/bwYxV27imT1OPSWb+dkoSAK/9Xv2v62+Zmxk7L4dbBibRJiGq2o/vSU5vW49vbvlzUvSv56x3OiQROUGe8d9dRBwzd+128gtL6KP577xKy3qRXNKtMR9PW8OqvL1Oh3NcRSWl/G/+Bk5rU5fIkECnwzkhV/RozLCUBF6csJz/pq9yOpwTMmrKSiJDAri0e2OnQzkooVYol3VvxOjZ61i7Nb/ajrtzXxEPjV1Iy7qR3DYwqdqO68la1vtzUvS/j57Pk5oUXcQrKcETqeHSsnLx9zP0ah7rdChSQXef1oLgAD+e+THT6VCOa8ryXLbnF3lF9cyjMcbw/EUdGdy2Lv/33RI+mOpdSd7arfn8uGgjw3s0ISI4wOlwDnHLwCT8/Uy1tkg/9f0S8vYU8vxFHQgK0MehA8pOiv6uJkUX8Ur6jyZSw6Vn5ZHSqBZRXtqqUpPFRwZz84Dm/Lx4MzM8vMLj2Hk5xIQF0q+Fd3cFDvT349XLOnNam7o8/r8lXlXo5t30lfj7Ga5OTXQ6lL+oGxXCFT2b8M3c9azM3VPlx5u8PJevZq/nhn7N6NCwVpUfz9sceVL0qj8vIlI5lOCJ1GA78gtZkLOTvuqe6bWu7dOM+tEhPPVDpscW/9i9v4gJSzZzdocEjxn3dTKCAvx4/fLOnNq6Dv8Yv5iPp69xOqTj2ra3kK9mr2NYSgPqRnlmBdObBzQnOMCfl3+r2la83fuLeHDMAprHh3PHoOQqPZa3O3RS9KmaFF3ESxz3SmuMaWSMmWiMyTTGLDbG3HHY4383xlhjjD4hiniZqdlbsRYleF4sNMifewe3ZMH6nYyfn+N0OEf08+LNFBSXMsyLu2ceLijAj9eHd2ZQqzo8Om4Rn87w7CTv42lr2F9Uyg39mjkdylHFRQQzonci387fwPLNu6vsOM/8uJSNu/bz3IUdCQn0r7Lj+IrDJ0V/a7ImRRfxdOX5KrUYuMda2xroCdxqjGkDruQPOA1YW3UhikhVSc/OJTI4gI7qouTVhqU0oH2DaJ7/aZlHTlQ8bl4OjWuH0blxLadDqVTBAf68cUVnBraM5+Gxi/h8pmdeCvcVlvDhtNWc0qoOyR4+ifeN/ZoRHhTAS78ur5L9/5Gdx6cz1nJtalO6NImpkmP4orKToj/z41Lu0qToIh7tuAmetXajtXau+/fdQCZw4GvY/wD3AfoqR8TLWGuZsjyPXs1jCfCBbnM1mZ+f4ZEhrdmwcz/veVh1x8279jN1RR7DOjVwfFLtqhAc4M+bV3RhQMt4HvxmIV/O8rwk7+u569m2t5AbPbj17oCY8CCuSU3kh4WbWLxhZ6XuO7+wmPu/WUBibBj3nN6yUvddExyYFP3vp7dgnCZFF/FoFfpUZ4xJBDoBM4wx5wI51tr5VRGYiFSt1Vvzydmxj75eXvRCXHo0i+X0NnV5Y2I2ubsLnA7noG8zNmAtDEtxflLtqhIS6M9bV3ShX4t4HvhmIV/NXud0SAeVlFreTVtJx0a16N60ttPhlMu1fZsRFRLAS79W7li8535axrpt+3j2gg6EBqlr5okwxnDbKcmHTIo+T5Oii3iccid4xpgIYAxwJ65umw8D/yjHdjcYY2YbY2bn5uaeaJwiUsnSslzvx75JGn/nKx44sxUFxaX8p4q6t52IsfNy6NgwmmbxEU6HUqVCAv0ZdWUX+iTFcf+YBR4zSfQvizexZms+N/Zr5jUtqNGhgVzftxkTlmxmwfodlbLPWau38eG01Yzo1YQezTQlzMkqOyn6JaOmM8ZD/t5FxKVcCZ4xJhBXcveptfYboDnQFJhvjFkNNATmGmPqHb6ttXaUtbartbZrfLxaCkQ8RVpWHg1jQmkSG+Z0KFJJmsVHcEXPJnwxc22VFqkor+Wbd7Nk4y6fKq5yLCGB/rxzVVdSm8dx79fz+Wausx96rbW8PWUlTWLDGNz2L5dnj3Z1n6bEhAXy4oST/7Jif1EJ9329gAa1QrnvjFaVEJ1AmUnRm8Rwj3tSdE+t5CtS05SniqYB3gMyrbUvAlhrF1pr61hrE621icB6oLO1dlOVRisilaKopJRpK7bSNznea77Vl/K5Y1AyEcEBPPW985Ofj5uXg7+f4ewOvts983AHkrxezWK5Z/R8xs5zLsmbtXo7Get2cF2fpvj7edf7PCI4gBv7N2fSslzmrDm5LoAvTljOqry9PHtBB8I9bIJ3b3dgUvQrejbm3fRVTMlSTy0RT1CeFrxU4ErgFGNMhvt2VhXHJSJVaP66HewpKNb0CD4oJjyI2wclM3l5LlOWO/dhq7TUMj5jA32T44iPDHYsDieEBvnz3ohu9Gwayz1fzWd8hjPTV4yasoLa4UFc2KWRI8c/WVf1akJcRBAvTlh2wvuYt3Y776at5LLujUhVd/QqEejvxyND2hAc4MeU5XlOhyMilK+KZrq11lhrO1hrU9y3Hw5bJ9Faq3e1iJdIy8rDz0Dv5hqL4ouu7NWExrXDePqHTEoc6jI1a/U2cnbs47wa0j3zcKFB/rw3sivdEmtz15cZfDt/Q7UeP3vLbn7N3MKVPZt4bUGRsKAAbh6QxNTsrUxfubXC2xcUu7pm1o0K4cGzWldBhHJASKA/3RJrk56tFjwRT6Da6CI1UFpWLu0b1qJWWJDToUgVCA7w54EzW7F0025GO1TRcVxGDmFB/pzWpq4jx/cEYUEBvH91N7q6k7zvFlRfkjdqykqCA/y4qleTajtmVRjeozF1o4J58ZflFZ5c+5Xfssjasoenz29PVEhgFUUoB/RJjmP55j1s3qWpE0ScpgRPpIbZua+I+et30k/dM33ame3q0bVJDP+esJy9BcXVeuyC4hK+X7CRwW3rERZUs8c8hQUF8P7IbnRuXIs7vsjgh4Ubq/yYW3btZ9y8DVzUtSGxEd7dPTYk0J9bByYxc/U20rPL31FoUc5O3pq8kgs6N2RgyzpVGKEc0MfdBXZqBc6TiFQNJXgiNcy0FVspKbUHL8bim4wxPDykNbm7C3h78opqPfbEpbns2l9cY6pnHk94cADvX92dTo1q8bfP5/FjFSd57/+xmuLSUq7r4/kTm5fHJd0akRAdwr/L2YpXWFzK30fPJzY8iH+c3aYaIhSANvWjiA0PIj1LCZ6I05TgidQw6dm5hAf506lxjNOhSBXr1DiGczomMCptJRt37qu2446bl0NcRDCpGuN5UESwq7tmx4bR/O3zefy0qGqKTu8pKOaT6Ws4o109EuPCq+QY1S04wJ+/DUomY90OJi7bctz135y0gqWbdvPUee2JDlPXzOri52fonRRHenZehbvTikjlUoInUsOkZeXRs1ksQQF6+9cE9w1uSamF538+8UqEFbEzv4jfl27h3I4JBPjrb6ysyJBAPrymO+0bRnPbZ3P5ZXHlJ3lfzFzL7v3F3NCveaXv20kXdmlI49phvDjh2K14Szft4rWJWZzbMaFGj/90Sp+kWLbsLmD55j1OhyJSo+nqK1KDrNuWz5qt+fTR+Lsao1HtMK5Jbco3c3NYlLOzyo/3w6KNFJaU1tjqmcdzIMlr2yCaWz+by69LNlfavotKSvlv+iq6N61NSqNalbZfTxDo78ftg5JZlLOLX47ymhWXlHLv6AVEhQTy+LltqzlCAeiTHA+4CnmJiHOU4InUIGnusRF93RdhqRluGdic2uFBPPn9kirvOjV2Xg7N4sNp1yCqSo/jzaJCAvnomu60qR/FzZ/O4bfMyknyvl+wkQ0793NjP98Ye3e4YSkJNIsL5z8TllN6hOk/RqWtZGHOTv5vaDtqh6tCsBMa1AqlWVy4Cq2IOEwJnkgNkpaVS/3oEJrH+8bYHCmfqJBA7jo1mekrt/Fr5vHHMJ2onB37mLlqG+elNMAYU2XH8QXRoYF8dG0PWteP4uZP5jJx6cmdF2stb09ZSVKdCJ+tGhng78cdpyazdNNuflh0aKGa7C27eWlCFme2q8eQDvUdilDANV3CjFXbKCwudToUkRpLCZ5IDVFSapmanUff5Dh9+K6BLuvemObx4fzrh0yKSqrmg9f4jBwAhqaoe2Z5RIcG8vE1PWhRL4IbP57DpHIUEDmatKw8Mjfu4oa+zfDz89339zkdEmhRN4KXfs2ixN2KV1JquffrBYQF+/N/Q9s5HKGkJsWRX1jC3LXbnQ5FpMZSgidSQyxYv4Nd+4sPjpGQmiXA34+HzmrNyry9fDp9TaXv31rL2Lk5dG0SQ+PYsErfv6+KDgvkk2t7kFQnghs+nsPk5Sc2dmnUlJXUiQxmaKeESo7Qs/j5Ge46tQXZW/bw7XzXFwrvT13FvLU7ePyctsRHeve8f76gV/NY/P2MpksQcZASPJEaIj0rD2PQ/Hc12Cmt6pCaFMvLv2Wxc19Rpe57ycZdZG3Zo7nvTkCtsCA+va4HzeMjuP6j2RUuULEoZyfp2XlcndqU4AD/KorScwxuW4829aN4+dcssrfs5vmfl3Fq6zoMTfHt5NZbRIUE0rFhNGkahyfiGCV4IjVEWlYebROiVHygBjPG8PBZbdixr4jXJ2ZX6r7Hzcsh0N8wpL3GP52ImHBXktcsLpzrPpxdoSIV76StJDzIn8t7NK7CCD2Hn5/h7tNasHprPhe/PZ2gAD+eHNZeXc89SJ/keBau38HO/Mr9IklEykcJnkgNsKegmLlrt6t6ptAmIYoLOzfkg6mrWbctv1L2WVJq+Xb+Bvq3qEOMvkA4YbXDg/js+p40jQvn2g9n8Uc5krz12/P5bsFGLuvemOjQmjOp96DWdejYMJptewt59Ow21IsOcTokKaNvchylFqatVCueiBOU4InUANNXbKW41NJX3TMF+Pvglvj7GZ75aWml7G/6yq1s3lWgue8qQW13S16T2uFc8+Espq3Yesz1/5u+GgNc06dp9QToIYwx/Pvijjx2Thsu6tLQ6XDkMCmNahEe5H9wah4RqV5K8ERqgPTsPEIC/eiSGON0KOIB6kaFcEO/Zny/YCNz1px8pbux83KIDA5gUGvfLM9f3WIjgvn0+h40ignjmg9mMX3lkZO8nflFfDFrLed0TCChVmg1R+m8pDqRXJ3aVF0zPVCgvx89m8WSrnF4Io5QgidSA6Rl5dKjaWyNKMAg5XNj/2bUiQw+6cnP9xWW8NOiTZzZvh4hgfr7qixxEcF8dn1PGsSEcvX7s5i5attf1vlkxhryC0u4vq9vTmwu3q1PchxrtuZXWldwESk/JXgiPm7Djn2syN1L32R1z5Q/hQUF8PfBLZm3dgffLdh4/A2O4tfMzewpKFb1zCoQHxnMZ9f3IKFWCCPfn8ms1X8mefuLSnh/6mr6JsfRJiHKwShFjuzANUeteCLVTwmeiI87MBeRCqzI4S7o3JDW9aN49qel7C8qOaF9jM/IoV5UCD2bxlZydAJQJzKEz6/vSb2oEEb+dyZz1riSvHHzcsjbU8CN/Zo7HKHIkTWPj6BeVIjmwxNxgBI8ER83JSuXOpHBtKgb4XQo4mH8/QyPDGnN+u37+PCP1RXeftveQiYty2VoSgJ+fhoHVVXqRIXw+Q09qRMVwoj/zmL26m2MSltJ24QoUpOUWItnMsaQmhTH1BV5lJSeeDdwEak4JXgiPqy01DI1O48+yXEqRCBHlJoUxymt6vDaxGy27S2s0LbfL9hAcalV98xqUDfK1ZIXFxHEZe9MZ2XuXm7o10zva/FofZPj2JFfxOINO50ORaRGUYIn4sMWb9jF9vwijb+TY3rorFbkF5bw8q/LK7Td2Hk5tKoXSev6GgNWHepFu1ryEmqF0iQ2TJPKi8dLdU/No+kSRKqXEjwRH5aWnQv8eZEVOZKkOpFc1r0Rn8xYS/aWPeXaZs3Wvcxdu0Otd9WsfnQoP93Rj3G3pBLgr0u4eLb4yGBa1YtkqgqtiFQrXR1EfFja8jxa1YukTmSI06GIh7vz1BaEBfrzzI+Z5Vp/3LwNGAPndkyo4sjkcKFB/sSEBzkdhki59E2OY/bq7ewrPLFCTiJScUrwRHzUvsIS5qzZru6ZUi5xEcHcMjCJXzO38MeKY3/bbq1lfEYOPZrWrpETbItI+aUmxVFYUsrM1X+dy1FEqoYSPBEfNWPVVgpLSjU9gpTb1amJNKgVylPfZ1J6jKp3C9bvZGXeXs5T90wROY4eTWMJ8vcjPSvX6VBEagwleCI+Ki0rj6AAP7o3re10KOIlQgL9ue+MlizesItv5uUcdb2x83IICvDjjHYq8iEixxYa5E+XJjGkZ291OhSRGkMJnoiPSs/Ko3tibUIC/Z0ORbzIuR0T6NioFi/8vIz8wuK/PF5UUsr/5m/g1NZ1iA4NdCBCEfE2fZLjyNy4i9zdBU6HIlIjKMET8UGbd+1n2ebd9NH4O6kgYwyPDmnNpl37eWfKqr88np6dx9a9hQxLUfdMESmfA2PBjze+V0QqhxI8ER+U7p5zSAVW5ER0TazNWe3r8faUFWzZtf+Qx8bPy6FWWCADWtZxKDoR8TZtE6KJDg3UfHgi1eS4CZ4xppExZqIxJtMYs9gYc4d7+T+NMQuMMRnGmF+MMaqVLeIh0rJyiQ0PonU9TUAtJ+b+M1pRVFLKv3/5c/LzvQXF/Lx4M2e1r09QgL4fFJHy8fczpCbFkp6Vh7VHL+AkIpWjPFfoYuAea21roCdwqzGmDfC8tbaDtTYF+A74R9WFKSLlVVpqSc/eSp/kOPz8jNPhiJdqEhvOiF6JfDVnHZkbdwHwy5JN7CsqUfVMEamwPknxbNq1nxW5e50ORcTnHTfBs9ZutNbOdf++G8gEGlhrd5VZLRzQVzIiHmDppt3k7SmgT5K6Z8rJ+dspyUSHBvL0D5lYaxk7bwMNY0Lp0jjG6dBExMscGDKg6RJEql6F+tgYYxKBTsAM9/2njDHrgOGoBU/EI6Rnuy6emv9OTlZ0WCC3n5JMWlYeo+esJz0rl2EpDdQyLCIV1qh2GE1iw0jP1jg8kapW7gTPGBMBjAHuPNB6Z6192FrbCPgUuO0o291gjJltjJmdm6tvbUSqWlpWHsl1IqgXHeJ0KOIDrujZhMTYMB78ZiGlFoZ10nBrETkxqUlxTF+5jaKSUqdDEfFp5UrwjDGBuJK7T6213xxhlc+AC460rbV2lLW2q7W2a3y8WhREqtL+ohJmrtqm6RGk0gQF+PHAma0pKbW0bxBNUp1Ip0MSES/VNymOPQXFZKzb4XQoIj6tPFU0DfAekGmtfbHM8uQyq50LLK388ESkImav3k5BcSn91D1TKtHgtnW5vm9T7j6thdOhiIgX6908Dj/z51Q+IlI1AsqxTipwJbDQGJPhXvYQcK0xpiVQCqwBbqqSCEWk3NKycgn0N/RoVtvpUMSHGGN4eEgbp8MQES8XHRZI+4a1SM/O4y59YSRSZY6b4Flr04Ejjaj/ofLDEZGTkZaVR5cmMYQFlee7GxERkerVNymONyevYNf+IqJCAp0OR8QnaaZaER+Ru7uAJRt3qXqmiIh4rNSkOEpKLdNXbHU6FBGfpQRPxEf8scI1pqGvCqyIiIiH6tykFqGB/pouQaQKKcET8RFpWXnUCgukbUK006GIiIgcUXCAPz2a1VaCJ1KFlOCJ+ABrLWlZuaQmxeGvSahFRMSD9UmKY2XuXjbs2Od0KCI+SQmeiA/I3rKHzbsK6Juk7pkiIuLZDowV13QJIlVDCZ6ID5jivkhqgnMREfF0LepGEB8ZTJq6aYpUCSV4Ij4gPSuXZnHhNIwJczoUERGRYzLG0CcpjqnZeZSWWqfDEfE5SvBEvFxBcQnTV25T652IiHiNPklxbNtbSOamXU6HIuJzlOCJeLm5a3awr6hE89+JiIjXOPClpMbhiVQ+JXgiXi4tKxd/P0PPZrWdDkVERKRc6kaF0KJuhKZLEKkCSvBEvFx6dh6dG9ciMiTQ6VBERETKLTUpjpmrtrG/qMTpUER8ihI8ES+2fW8hC3N20idJ3TNFRMS79E2Oo6C4lNmrtzsdiohPUYIn4sWmrsjDWujbQgVWRETEu/RoGkugv1E3TZFKpgRPxIulZ+URGRJAhwbRTociIiJSIeHBAXRqHEN6dq7ToYj4FCV4Il7KWktaVh69m8cS4K+3soiIeJ++SXEs3rCLbXsLnQ5FxGfoU6GIl1qVt5ecHfs0PYKIiHit1OQ4rIWp6qYpUmmU4Im4lZZaJi7bwluTV1BUUup0OMeV5p47qK8mOBcRES/VoUE0kSEBmg9PpBIFOB2AiNN25BcyevZ6PpmxhjVb8wFYvmk3L1zUET8/43B0R5eWlUfj2mE0iQ13OhQREZETEuDvR+/msaRn52GtxRjPve6KeAsleFJjLcrZycfT1jB+fg77i0rplhjDPae3ZGXuHl76NYuY8CAeGdLaIy82RSWlTF+5lXNTEpwORURE5KT0SY7n58WbWb01n6Zx+tJS5GQpwZMapbC4lB8XbeSjaWuYs2Y7oYH+nNepIVf2bEKbhCjAVbxk574i3ktfRe3wIG4dmORw1H+VsW4HewqK6afumSIi4uX6JrmuZelZuUrwRCqBEjypETbs2MdnM9byxay15O0ppGlcOI+e3YYLuzQkOjTwkHWNMTw6pA078ot4/udlxIQFcXmPxg5FfmRpWXn4GejVXAmeiIh4tyaxYTSoFUpaVh5X9kp0OhwRr1fjE7yiklIC/IxHdsOTk2OtZdqKrXw0bQ0TMjdTai2DWtXhyl6J9E2KO+b4Oj8/w3MXdmBHfiGPjFtITFggZ7avX43RH1taVi4dG9X6S3IqIiLibYwx9E2O4/uFGykuKdXUPyInqUYneNZa7vt6AQDPXNCe4AB/hyOSyrCnoJhv5q7no2lryN6yh5iwQK7v24zhPRrTqHZYufcT6O/HG8O7cOV7M7jjiwyiQgNJTXK+xWznviLmr9vBbR7YdVRERORE9EmO44tZ61iQs5POjWOcDkfEq9XoBA8gqU4Ez/+8jPXb83n7yq7UDg9yOiQ5QVmbd/Px9DWMmbOevYUldGgYzQsXdeTsDvUJCTyx5D00yJ/3RnTjklHTuOGj2Xx+Q086NKxVuYFX0LQVeZRa16B0ERERX5DaPA5jID0rTwmeyEmq0W3gxhhuHZjEa5d3YsH6nQx7fSrZW/Y4HZZUQHFJKT8u3Mhlo6Zz2n+m8MWsdQxuV49xt6by7W19uLBLwxNO7g6IDgvkw2u6ExMexMj3Zzn+N5KWlUd4kD+dGtdyNA4REZHKEhMeRLuEaM2HJ1IJanSCd8DZHRL44oae5BcWc94bU5marX8uni53dwGv/pZF3+cmcvOnc1m7LZ/7zmjJtAdO4cWLU0hpVKtSj1c3KoRPru2Bn4Gr3pvBhh37KnX/FZGWlUev5rEEaoyCiIj4kNSkOOau3c6egmKnQxHxavqE6NapcQxjb0mlfnQII/47k89nrnU6JDmMtZY5a7Zx++fz6P3Mb/x7wnKS6kTwzlVdmXLfQG4ZkERsRHCVHT8xLpwPr+nO7v3FXPXfmWzfW1hlxzqaNVv3snZbPn3VPVNERHxM3+Q4ikstM1dtdToUEa+mBK+MRrXDGHNzb1KT4njwm4U8/UMmJaXW6bBqvH2FJXw5ay1DXknngjenMXHpFq7o2YTf7+nPx9f24LQ2dfE/RkXMytQ2IZp3RnRl7bZ8Rn4wi73V/C1jmrvrSh/NfyciIj6mS5MYggP8Dl7rROTE1PgiK4eLDAnkvRFd+b/vljBqykpW5e3l5UtTCAvSS1XdVuft5ZPpaxg9Zz079xXRql4kT53XjmEpDQgPdu589GwWy2uXdeKmT+Zw0ydzeHdE12qrwJqelUeDWqE000SwIiLiY0IC/enetLbG4YmcpOO24BljGhljJhpjMo0xi40xd7iXP2+MWWqMWWCMGWuMqVXl0VaTAH8//m9oOx4/pw2/ZW7m4rensWnnfqfDqhFKSi2/L93MyPdnMuCFSXzwx2r6JMfx1Y29+PGOvgzv0cTR5O6A09vW45kLOpCWlcfdX82vlpbe4pJS/liRR5+kOM3bKCIiPqlvchxZW/boc5fISSjPJ+Vi4B5r7VxjTCQwxxgzAZgAPGitLTbGPAs8CNxfhbFWu5GpTWkcG8bfPpvHsNen8u6IrrRrEO10WD7JWss3c3N46bflrNu2jzqRwdx5ajKXdW9M3agQp8M7oou7NmJHfiFP/7CUmLBA/jm0XZUmXgtydrJrfzF9W6h7poiI+KYD882mZ+dxYZeGDkcj4p2O24Jnrd1orZ3r/n03kAk0sNb+Yq09MABpOuCT78JTWtXl65t742fg4renMWHJZqdD8jmr8vYy/N0Z3DN6PrXDgnjt8k5MfeAU7jy1hccmdwfc0K85N/ZvxifT1/KfX7Oq9FjpWXkY45orSERExBe1rhdFbHiQKpqLnIQK9XUzxiQCnYAZhz10DfBlJcXkcVrXj2Lcralc/9Fsbvh4Ng+f1Zpr+zRVN7mTVFhcyjtpK3n5tyyCA/x46rx2XNatMX7VVDClsjxwRit27C3ild+yqB0WyMjUplVynLSsXNo3iCYmPKhK9i8iIuI0Pz9DalIc6dl5WGv1WUvkBJS7iqYxJgIYA9xprd1VZvnDuLpxfnqU7W4wxsw2xszOzc092XgdUycqhC9u6MUZbevx5PeZPDxuEUUlpU6H5bXmrNnG2a+m8fzPyzitdV1+u7s/w3s08brkDsAYw1PnteP0NnV5/H9LGJ+RU+nH2L2/iHlrd9AnSa13IiLi2/okx5G7u4Blm3c7HYqIVypXgmeMCcSV3H1qrf2mzPIRwNnAcGvtEatMWGtHWWu7Wmu7xsd799xdoUH+vH55Z24e0JzPZqzlmg9msXNfkdNheZWd+4p4eOxCLnxrGnv2F/PeiK68PrwzdTy8K+bxBPj78cplnejZrDb3fDWficu2VOr+p6/cRnGp1fQIIiLi8w58malqmiInpjxVNA3wHpBprX2xzPIzcBVVOddam191IXoWPz/D/We04rkLOzBtxVYuePMP1m6tMU//hFlr+WHhRk57cTKfz1zLNalNmXB3fwa1rut0aJUmJNCfd67qSqv6kdz8yRzmrNlWaftOz8olNNCfLk1iKm2fIiIiniihVijN4sM1H57ICSpPC14qcCVwijEmw307C3gNiAQmuJe9VZWBepqLuzbi42t7kLu7gGFvTK3UD/O+JmfHPq77cDa3fDqX+Mhgxt/ah0fPbuMR0x1UtsiQQD64ujv1o0O5+v1ZLNtUOd1L0rLz6NGsdrXNtyciIuKkvklxzFy1jYLiEqdDEfE65amimW6tNdbaDtbaFPftB2ttkrW2UZllN1VHwJ6kV/NYxt7Sm6iQAC57Z0aVjL3yZiWllvfSV3Hai5P5Y8VWHj6rNeNvTaV9Q9+eaiIuIpiPrulOaJA/V/13Buu2nVwLb86OfazM3UvfZO/u4iwiIlJefZLj2VdUwtw1O5wORcTrlLvIihxZs/gIxt6SSkqjWtzxRQYv/bqcowxHrFEW5ezkvDem8s/vltC9aW1+uasf1/drRoB/zfiTa1Q7jI+u6cH+olKufG8GubsLTnhf6Vmu4kR9Nf5ORERqiJ7NauPvZ0jP9t4CfSJOqRmftqtYTHgQH1/bnQs6N+SlX7O488sM9hfVzC4F+YXFPPX9Eoa+PpUNO/bz6mWdeH9kNxrVDnM6tGrXsl4k/x3Zjc27Chj5/kx27T+xgjxTsvKoGxVMcp2ISo5QRETEM0WGBNKpUS0VWhE5AUrwKklwgD8vXNSBewe3ZHzGBoa/O4Ote0681cYbTVy2hdNenMI7aau4uGsjfru7P+d0TKjRc9h0aRLDm1d0Ztmm3Vz/4ewKJ/4lpZap2Xn0SYqv0a+jiIjUPKlJcSzI2cmO/EKnQxHxKkrwKpExhlsHJvH65Z1ZlLOTYW9MJXuL78/hsmX3fm77bC5Xvz+L0CB/Rt/Ui3+d357osECnQ/MIA1rW4d8Xd2TGqm3c/vk8iiswf+LiDTvZkV9EvxbqnikiIjVL3+Q4rIVpK7Y6HYqIV1GCVwWGdKjPFzf0ZF9hKee98YfPdi8oLbV8PnMtp/57Mr8s3szdp7Xg+9v70C2xttOheZyhKQ14/Jw2/LJkMw+NXVjucZoHSkSnaoJzERGpYTo2qkVEcABp2b75OUqkqijBqyKdGscw7tbeJESHMuL9mXw2Y63TIVWq7C27uWTUNB78ZiGt60fx4519uX1Qssr4H8PI1KbcPiiZr2av55mflpZrm7SsXFrXjyIuIriKoxMREfEsgf5+9GwW67NflItUFSV4VahhTBhf39yLvslxPDR2IU99v4SSUu+usLm/qIQXJyznzJfTWL55D89d0IEvbuhJ83gVACmPu05N5sqeTXh78kpGTVlxzHXzC4uZs2Y7/VQ9U0REaqi+yXGs3ZbP2q0nN+WQSE3iezNNe5jIkEDevaor//xuCe+krWL11nxevjSFsCDve+mnr9zKQ2MXsjJ3L8NSEnjk7DZqWaogYwyPn9uW7fmFPP3DUmqFBXFx10ZHXHfGqm0UlVj6KMETEZEa6sAQhbTsXIbHNnE4GhHvoBa8ahDg78cTQ9vx+Dlt+C1zMxe9NY1NO/c7HVa57cgv5L6v53PpqOkUlZTy4TXdeenSTkruTpC/n+HFi1PomxzHA2MW8MviTUdcL215HsEBfhrTKCIiNVbz+HDqR4cwVePwRMpNCV41GpnalPdGdGN13l6Gvp7OopydTod0TNZaxmfkMOjfkxkzN4cb+zfjlzv7079FvNOheb2gAD/euqILHRrW4rbP5zF95V8rhKVn59K9aW1CAjWuUUREaiZjDH2S4piavdXrh7mIVBcleNVsYKs6fH1zb/yN4aK3ph219cZpa7fmc9V/Z3LHFxk0rB3G/27rw4NntiY0SMlGZQkPDuD9kd1oXDuM6z+cfUjCv2nnfpZv3kNfdc8UEZEark9yHDv3FXn8F+MinkIJngNa149i3G2ptKgbwY2fzOGdKSvLXTa/qhWVlPLW5BWc/tJk5q7ZzuPntOGbm3vTJiHK6dB8Ukx4EB9f252o0EBGvj+TVXl7AUh3d0Xpk6TWUhERqdkOjMNLVzdNkXLxvkofPqJOZAhf3NCLe0Zn8NQPmfy8eBMx4UEEBfgR5O++Bfx5C/T3Izjgz+WBZR93P3b4sqAAQ5C//2H7MQT5+2GM+UtMGet28MCYBSzdtJvT2tTliXPbklAr1IFXp2apHx3KR9d256K3pnHlezMYc3Nv0rJyiYsIplW9SKfDExERcVRcRDCt60eRlpXLrQOTnA5HxOMpwXNQaJA/r13WmdfrZfNr5mbWbcunqKSUwpJSCov/vBWVWApLSiv12H8miuZg8rd++z7qRAbz1hVdOKNdvUo9nhxb8/gIPri6G5eNms5V780kb08BfZPj8PP7ayIuIiJS0/RNjuODqavJLyz2ykrkItVJ7xCH+fkZ/jYomb8NSj7metbag4lfUYn9MwEsKaGw2B6aFB5hWdGB30tKKSh7v/jP5ed2DOGm/s2JDAmspmcvZXVoWItRV3Xl6vdnUVhSSp9kdc8UEREB6JMUx6gpK5m5ahsDWtZxOhwRj6YEz0sYYwgO8Cc4QEVOfFlqUhyvXNaJUVNWcEorXcBEREQAujetTVCAH+lZeUrwRI5DCZ6IhzmjXT11kRURESkjJNCfbokxKrQiUg6qoikiIiIiHi81KY6lm3azZfd+p0MR8WhK8ERERETE4/V1Tx30R/ZWhyMR8WxK8ERERETE47VNiCImLJC0LHXTFDkWJXgiIiIi4vH8/Ay9k+JIz87FWut0OCIeSwmeiIiIiHiFvklxbN5VQPaWPU6HIuKxlOCJiIiIiFdITYoDUDVNkWNQgiciIiIiXqFR7TASY8NI1zg8kaNSgiciIiIiXqNPchzTV26lqKTU6VBEPJISPBERERHxGn2S4tlbWMK8tTucDkXEIynBExERERGv0at5LH4G0rNynQ5FxCMpwRMRERERrxEdGkiHhrVUaEXkKJTgiYiIiIhX6Zscx/z1O9m1v8jpUEQ8znETPGNMI2PMRGNMpjFmsTHmDvfyi9z3S40xXas+VBERERER6JMUR0mpZdqKrU6HIuJxytOCVwzcY61tDfQEbjXGtAEWAecDU6owPhERERGRQ3RqHENYkL+mSxA5goDjrWCt3QhsdP++2xiTCTSw1k4AMMZUbYQiIiIiImUEBfjRo2ltjcMTOYIKjcEzxiQCnYAZFdjmBmPMbGPM7NxcVTsSERERkZPXJzmeVXl7Wb893+lQRDxKuRM8Y0wEMAa401q7q7zbWWtHWWu7Wmu7xsfHn0iMIiIiIiKH6JscB8BUteKJHKJcCZ4xJhBXcveptfabqg1JREREROTYkutEUDcqmDSNwxM5RHmqaBrgPSDTWvti1YckIiIiInJsxhhSk+L4Y8VWSkut0+GIeIzytOClAlcCpxhjMty3s4wx5xlj1gO9gO+NMT9XaaQiIiIiImX0SYpj295Clmws9+ghEZ9Xniqa6cDRSmWOrdxwRERERETKp0+SaxxeenYe7RpEOxyNiGeoUBVNERERERFPUScqhJZ1IzUfnkgZSvBERERExGv1SY5j5upt7C8qcToUEY+gBE9EREREvFaf5DgKi0uZtXqb06GIeAQleCIiIiLitXo0rU2gv1E3TRE3JXgiIiIi4rXCggLo3DiGdE14LgIowRMRERERL9c3OY7FG3axdU+B06GIOE4JnoiIiIh4tf4t6gDw4R+rnQ1ExAMowRMRERERr9auQRTnd27AqxOzmaqumlLDKcETEREREa9mjOHJYe1Iio/gji/msWXXfqdDEnGMEjwRERER8XphQQG8MbwzewtKuP2LeRSXlDodkogjlOCJiIiIiE9IrhvJk8PaMX3lNl7+LcvpcEQcoQRPRERERHzGBV0aclGXhrw2MZspy3OdDkek2inBExERERGf8n9D29GiTiR3fpnBpp0ajyc1ixI8EREREfEpoUH+vD68M/uLSrj9c43Hk5pFCZ6IiIiI+JykOhE8fV57Zq7exosTljsdjki1UYInIiIiIj5pWKcGXNa9EW9MWsHEZVucDkekWijBExERERGf9dg5bWlVL5K7v8xgw459TocjUuWU4ImIiIiIzwoJ9OeN4Z0pLC7lb5/Po0jj8cTHKcETEREREZ/WLD6Cf13QgTlrtvPCL8ucDkekSinBExERERGfd27HBIb3aMzbk1fyW+Zmp8MRqTJK8ERERESkRnj07Da0qR/FPaPnk6PxeOKjlOCJiIiISI1wYDxecYnlts/mUlis8Xjie5TgiYiIiEiNkRgXzrMXdGDe2h08//NSp8MRqXRK8ERERESkRhnSoT5X9WrCO2mrmLBE4/HEtyjBExEREZEa5+EhrWnXIIp7vspg3bZ8p8MRqTRK8ERERESkxgkO8Of1yztjLdz2+TyNxxOfoQRPRERERGqkJrHhPHdhB+av28G/fsx0OhyRSqEET0RERERqrDPb12dk70Ten7qanxZtcjockZN23ATPGNPIGDPRGJNpjFlsjLnDvby2MWaCMSbL/TOm6sMVEREREalcD53Vmo4No7n36/ms3arxeOLdytOCVwzcY61tDfQEbjXGtAEeAH6z1iYDv7nvi4iIiIh4laAAP167vDMGuPWzuRQUlzgdksgJO26CZ63daK2d6/59N5AJNACGAh+6V/sQGFZFMYqIiIiIVKlGtcN4/qKOLMzZydPfazyeeK8KjcEzxiQCnYAZQF1r7UZwJYFAnUqPTkRERESkmgxuW49r+zTlw2lr+H7BRqfDETkh5U7wjDERwBjgTmvtrgpsd4MxZrYxZnZubu6JxCgiIiIiUi3uP6MVKY1qcf+YBazO2+t0OCIVVq4EzxgTiCu5+9Ra+4178WZjTH334/WBLUfa1lo7ylrb1VrbNT4+vjJiFhERERGpEq7xeJ3w9zPc+tlc9hdpPJ54l/JU0TTAe0CmtfbFMg99C4xw/z4CGF/54YmIiIiIVK+GMWH8+6KOLN6wiye/X+J0OCIVUp4WvFTgSuAUY0yG+3YW8AxwmjEmCzjNfV9ERERExOud2qYuN/RrxifT1/K/+RucDkek3AKOt4K1Nh0wR3l4UOWGIyIiIiLiGe4d3JI5a7bzwJgFtE2Ioll8hNMhiRxXhapoioiIiIjUFIH+frx6WSeCAvy49bN5Go8nXkEJnoiIiIjIUSTUCuXFi1PI3LiLJ/6n8Xji+ZTgiYiIiIgcw8BWdbipf3M+n7mW8Rk5TocjckxK8EREREREjuPvp7egW2IMD36zkOwte5wOR+SolOCJiIiIiBxHgL8fr17WmZBAf279dC77CjUeTzyTEjwRERERkXKoFx3Cfy5JYfmW3Tz+7WKnwxE5IiV4IiIiIiLl1L9FPLcOSOLL2ev4Zu56p8MR+QsleCIiIiIiFXDnqcn0aFqbh8cuImvzbqfDETmEEjwRERERkQoI8Pfjlcs6ERbkzy2fziW/sNjpkEQOUoInIiIiIlJBdaNCePnSTmTn7uHRcRqPJ55DCZ6IiIiIyAnokxzH305JZszc9Yyevc7pcEQAJXgiIiIiIifsjkHJ9GoWy6PjF7Fsk8bjifOU4ImIiIiInCB/P8PLl6UQERzILZ/OYW+BxuOJs5TgiYiIiIichDqRIbxyaQqr8vby0bQ1TocjNVyA0wGIiIiIiHi73klxfHZ9T7ol1nY6FKnhlOCJiIiIiFSCns1inQ5BRF00RUREREREfIUSPBERERERER+hBE9ERERERMRHKMETERERERHxEUrwREREREREfIQSPBERERERER+hBE9ERERERMRHKMETERERERHxEUrwREREREREfIQSPBERERERER9hrLXVdzBjcoE11XbA8osD8pwOQgCdC0+ic+FZdD48h86F59C58Bw6F55F58NzHOlcNLHWxlfVAas1wfNUxpjZ1tquTschOheeROfCs+h8eA6dC8+hc+E5dC48i86H53DiXKiLpoiIiIiIiI9QgiciIiIiIuIjlOC5jHI6ADlI58Jz6Fx4Fp0Pz6Fz4Tl0LjyHzoVn0fnwHNV+LjQGT0RERERExEeoBU9ERERERMRHeF2CZ4w5wxizzBiTbYx5oMzyL40xGe7bamNMxhG2TTHGTDPGLDbGLDDGXFLmsabGmBnGmCz3voKOcvwR7nWyjDEjKrq9L/GAc/GTMWaHMea7w5Z/YIxZVSaGlMp71p7JyXNhjGlijJnjPsZiY8xNFdne1zj9vnCvG2WMyTHGvFZmWY17X0CVno/b3Pu0xpi4Yxxf1ww3DzgXuma4OXkudM04lNPvC/e6uma4VeH5+NS930XGmP8aYwKPcvzKuWZYa73mBvgDK4BmQBAwH2hzhPX+DfzjCMtbAMnu3xOAjUAt9/2vgEvdv78F3HyE7WsDK90/Y9y/x5R3e1+6OX0u3I8NAs4Bvjts+QfAhU6/RjXlXLiPGez+PQJYDSRU5Fz6ys3pc1FmPy8DnwGvlVlWo94X1XA+OgGJ7r/3uKMcX9cMDzkX7vV0zfCAc4GuGR5zLsrsR9eMqj8fZwHGffv8SH/bVOI1w9ta8LoD2dbaldbaQuALYGjZFYwxBrgY14t3CGvtcmttlvv3DcAWIN69zSnA1+5VPwSGHeH4g4EJ1tpt1trtwATgjAps70ucPhdYa38DdlfKs/Fujp4La22htbbAfTcYd88AvS+ceV8YY7oAdYFfKuMJebkqOR/u+/OstauPc3xdM/7k9LnQNeNPjp4LXTMO4fj7QteMQ1Tl+fjBugEzgYZHOH6lXTO8LcFrAKwrc3+9e1lZfYHNB17gozHGdMeVna8AYoEd1triw/drjOlqjHn3OMc/6vY+zOlzcTxPuZvH/2OMCS7nNt7K8XNhjGlkjFngjuNZ9z82vS+q+VwYY/xwfbN471F2W5PeF1B15+NY6+macWROn4vjqUnvDcfPha4ZBzl6LnTN+IsqPx/urplXAj+571fJNcPbEjxzhGWHlwG9jCNk1YfsxJj6wMfA1dba0mPt11o721p73XGOX564fI3T5+JYHgRaAd1wNXPfX45tvJnj58Jau85a2wFIAkYYY+qWMy5f4/S5uAX4wVq77gjr17T3BVTd+TgqXTOOyulzcSw17b3h+LnQNeMgp8+FrhmHqo7z8QYwxVqbBlV3zfC2BG890KjM/YbAhgN3jDEBwPnAl0fbgTEmCvgeeMRaO929OA+o5d7+L/stx/HLu70vcfpcHJW1dqO7FbwAeB9Xk7sv85hz4f4WdjGub7j0vqj+c9ELuM0Ysxp4AbjKGPMM1Mj3BVTd+TjZ4+u9Uf3n4qhq4HvDY86FrhmOnwtdMw5VpefDGPMYri6bd1fw+BV+b3hbgjcLSDauSjJBwKXAt2UePxVYaq1df6SN3duMBT6y1o4+sNzdH3YicKF70Qhg/BF28TNwujEmxhgTA5wO/FyB7X2J0+fiqNzfnBzoJz0MWFSR7b2Qo+fCGNPQGBPq/j0GSAWW6X1R/efCWjvcWtvYWpsI/N29nwfc+65p7wuoovNRAbpm/Mnpc3FUNfC94ei50DXjEI6eC10z/qLKzocx5jpcY+wuO0Yra+VdM6wHVK2pyA1XFZrluPq0PnzYYx8ANx1j2yuAIiCjzC3F/VgzXIMes4HR/FnhqSvwbpl9XONeJxtX0yvH2t6Xbx5wLtKAXGAfrm89BruX/w4sxPXP6BMgwunXypfPBXAasABXtakFwA1l9q33RTWei8P2NZJDK6LVuPdFFZ+P293/d4pxfZN64P2ga4bnngtdMzzgXKBrhseci8P2NRJdM6ryfBS793lg+T+OdD6opGuGcW8kIiIiIiIiXs7bumiKiIiIiIjIUSjBExERERER8RFK8ERERERERHyEEjwREREREREfoQRPRERERETERyjBExERERER8RFK8ERERERERHyEEjwREREREREf8f9qES3wRWxSTwAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
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"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
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"data": {
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"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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hbrnllhOW79y58/hzKe25wanVD40xpR43NDT0+OMdO3bw3HPPsXTpUho1asT1119fYpn7ouc5eZvCYxYUFNCwYUNWrlxZ1lMXERERkbpi5kx4/XXo2BEmTYLZs2tU18YykzNn01uG86G/82attdOKbPYHcElVgymthctdNm3ahI+PD+3atQNg5cqVtGzZslLHiouL44033qCgoIDdu3cfH69VVGpqKo0aNSIkJISNGzfyxx9/HF/n7+9Pbm4u/v7+jBw5kgsuuID77ruPyMhIDh06RHp6Ov379+eee+7h4MGD1K9fnylTptCjR48yYxs9ejSPPvooV111FWFhYezevRt/f/8KPb/vvvuOhx9+mKNHjzJ79myeeuopgoODy3XctLQ0QkNDadCgAfv27eOXX35h2LBhANSrV4/09HSaNGkCQFRUFBs2bKBDhw5888031KtX75Tj1a9fn1atWjFlyhTGjRuHtZbVq1eX67UQERERkVrq008hPx/++U8w5n9j0JYurR3JGYAxxhdYDrQFXrfWLj5pkxuBL0vYdwIwASA2NrbykbpJRkYGd911F0eOHMHPz4+2bdseL9JRUYMGDTreRbBr16706tXrlG3OOussJk6cSPfu3enQocMJ3f4mTJhA9+7d6dWrF59++ilPPPEEZ555JgUFBfj7+/P6668zYMAAHn/8cU477TSaNWtGr169jhcKKc2ZZ57Jhg0bOO200wBHd85PPvkEX1/fcj+/fv36MWbMGHbt2sWjjz5K8+bNad68ebmO26NHD3r27EmXLl1o3bo1gwYNOuF5n3322TRr1oxZs2bx1FNPce655xITE0PXrl3JyMigOJ9++im33XYbTzzxBLm5uVx++eVKzkRERETqsqQkaN4cLinSblSDujUaR8NYOTc2piHwDXCXtXatc9kjQB9grC3jYH369LGF1Q8LbdiwgU6dOlUwbKlujz/+OGFhYTzwwAOeDqVC9PslIiIiUkesWwddu8KTT8Lf/+7paEpkjFlure1T3LoKldJ3FvyYDZzlPPB1wLnAVWUlZiIiIiIiIm7zyisQFAQTJng6kkorT7XGCCDXWnvEGBMMjAKeNsachaMAyFBrbcVmEJYa5/HHH/d0CCIiIiIixTt4ED76CK65Bpx1DGqi8ow5awZ86Bx35gNMttb+aIzZiqO8/nRndb0/rLW3ui9UERERERGRYrz9NmRlwd13ezqSKilPtcbVQM9ilrd1S0QiIiIiIiLllZvrKJ8/apRjzFkNVq5qjSIiIiIiIl7p669h92546y1PR1JlFSoIIiIiIiIi4lVeegnatYOzz/Z0JFWm5Azw9fUlPj6erl27Mm7cODIzK1/f5Prrr+err74CYPz48axfv77EbWfPns3ChQuPP544cSIfffRRpc9daOfOnXQ9qUn38ccf57nnnqvQcVwVj4iIiIiIW/zxByxe7Bhr5lPzUxt1awSCg4NZuXIlAFdddRUTJ07kL3/5y/H1+fn5FZqsudC7775b6vrZs2cTFhbGwIEDAbj1Vu+pp5KXl+dV8YiIiIiInOLll6FBA7j+ek9H4hI1K7185hmYNevEZbNmOZa7yODBg9m6dSuzZ89m+PDhXHnllXTr1o38/Hz++te/0rdvX7p3785bzj6t1lruvPNOOnfuzJgxY9i/f//xYw0bNozCSbd//fVXevXqRY8ePRg5ciQ7d+5k4sSJvPjii8THxzNv3rwTWrdWrlzJgAED6N69OxdddBGHDx8+fswHH3yQfv360b59e+bNm1fh51jasf/+978zdOhQXn755ePxJCcnEx8ff/zm6+tLQkICCQkJjBw5ku7duzNy5Eh27doFOFoP7777bgYOHEjr1q2PtySKiIiIiLhMUhJMmQLjx0NYmKejcYmalZz17QuXXvq/BG3WLMfjvn1dcvi8vDx++eUXunXrBsCSJUt48sknWb9+PZMmTaJBgwYsXbqUpUuX8s4777Bjxw6++eYbNm3axJo1a3jnnXdO6KZYKCUlhZtvvpmvv/6aVatWMWXKFOLi4rj11lu57777WLlyJYMHDz5hn2uvvZann36a1atX061bN/71r3+dEOeSJUt46aWXTlhe1LZt205IqCZOnFiuYx85coQ5c+Zw//33H1/WvHlzVq5cycqVK7n55pu5+OKLadmyJXfeeSfXXnstq1ev5qqrruLuIqVL9+zZw/z58/nxxx956KGHKnglRERERETK8MYbYC3ceaenI3EZ7+rWeO+94OxeWKLmzWH0aGjWDPbsgU6d4F//ctyKEx/vGCRYimPHjhEfHw84Ws5uuukmFi5cSL9+/WjVqhUA06ZNY/Xq1cdbgVJTU9myZQtz587liiuuwNfXl+bNmzNixIhTjv/HH38wZMiQ48cKDw8vNZ7U1FSOHDnC0KFDAbjuuusYN27c8fVjx44FoHfv3uzcubPYY7Rp0+Z4V0343yTSZR37sssuKzGuBQsW8O677x5vrVu0aBFTp04F4JprruFvf/vb8W0vvPBCfHx86Ny5M/v27Sv1+YqIiIiIVEhmpqM644UXQlycp6NxGe9KzsqjUSNHYrZrF8TGOh5XUdExZ0WFhoYev2+t5dVXX2X06NEnbPPzzz/jnIS7RNbaMrepiMDAQMBRyCQvL89lx4UTn3NRe/bs4aabbuL7778nrIRm46LPsTBGcDx/ERERERGX+eQTOHTI0bhTi3hXclZGCxfwv66Mjz4Kb74Jjz0Gw4e7PbTRo0fz5ptvMmLECPz9/dm8eTPR0dEMGTKEt956i2uvvZb9+/cza9YsrrzyyhP2Pe2007jjjjvYsWMHrVq14tChQ4SHh1OvXj3S0tJOOVeDBg1o1KgR8+bNY/DgwXz88cfHW7qqqjLHzs3N5dJLL+Xpp5+mffv2x5cPHDiQL774gmuuuYZPP/2U008/3SUxioiIiIiUyFpHIZBevaCWff70ruSsLIWJ2eTJjoRs+PATH7vR+PHj2blzJ7169cJaS0REBN9++y0XXXQRv//+O926daN9+/bFJjoRERG8/fbbjB07loKCAiIjI5k+fTrnnXcel1xyCd999x2vvvrqCft8+OGH3HrrrWRmZtK6dWvef/99lz2Xih574cKFLF26lMcee4zHHnsMcLQYvvLKK9x44408++yzREREuDRGEREREZFizZgB69fDhx+CC3uneQNTnV3O+vTpYwurFxbasGEDnTp1Kt8BnnnGUfyjaCI2axYsXQpFxjuJFKrQ75eIiIiIeL8xY2D5ckhIgCJDaWoKY8xya22f4tbVrJaz4hKwwhY0ERERERGp3TZtgp9/dhQDrIGJWVlqVil9ERERERGpW4rOdfzqqxAQAF26uHSuY2+h5ExERERERLxX4VzH338P77/v6DV3660um+vYm3hFt0ZXl5oXAZXwFxEREakVhg93FAA891zH/GaLF8PUqbVyaJPHW86CgoI4ePCgPkiLS1lrOXjwIEFBQZ4ORURERESqKj4e8vMd9++6q1YmZuAFLWctWrQgKSmJlJQUT4citUxQUBAtWrTwdBgiIiIiUlV33gnZ2TBhgmOu41paFNDjyZm/vz+tWrXydBgiIiIiIuKNvv4aPvsMRo6Et96Cyy+vtrmOq5vHuzWKiIiIiIiU6IUXwMcHJk50PC4cg7Z0qWfjcgOPt5yJiIiIiIgUa+dORxJ2883Qtu3/ltfSbo1qORMREREREe/0r385Ws0efdTTkVQLJWciIiIiIuJ91q+Hjz5yFAOJjvZ0NNVCyZmIiIiIiHiff/4TQkPhoYc8HUm1UXImIiIiIiLeZdkyR5XG+++HJk08HU21KTM5M8YEGWOWGGNWGWPWGWP+5VweboyZbozZ4vzZyP3hioiIiIhIrffII9C4Mdx3n6cjqVblaTnLBkZYa3sA8cBZxpgBwEPATGttO2Cm87GIiIiIiEjlzZ4N06bB3/8O9et7OppqVWZyZh0ynA/9nTcLXAB86Fz+IXChOwIUEREREZE6wlpHUhYdDbfd5uloql25xpwZY3yNMSuB/cB0a+1iIMpauwfA+TPSbVGKiIiIiEjt99NPsGgRPPYYBAd7OppqV67kzFqbb62NB1oA/YwxXct7AmPMBGPMMmPMspSUlEqGKSIiIiIitVpBgWOsWdu2cP31no7GIypUrdFaewSYDZwF7DPGNANw/txfwj5vW2v7WGv7REREVC1aERERERGpnb78Elavhn//G/z9PR2NR5SnWmOEMaah834wMArYCHwPXOfc7DrgOzfFKCIiIiIitVlurmNes+7d4bLLPB2Nx/iVY5tmwIfGGF8cydxka+2PxphFwGRjzE3ALmCcG+MUEREREZHa6v33YetW+OEH8Km7UzGXmZxZa1cDPYtZfhAY6Y6gRERERESklnvmGejbFwYMcHRlPO00CAlxLP/b3zwdnUfU3bRUREREREQ8p29fuPRSuP9+2L3bcf+yyxzL66jydGsUERERERFxreHDYdIkuPBCaN0annwSJk92LK+j1HImIiIiIiKeMX26Y+Lp7dsdk07X4cQMlJyJiIiIiIgnrF4Nr78OQUHw6KPw5pswa5ano/IoJWciIiIiIlK9rIVrrnHc/+ILR0GQyZMd487qcIKm5ExERERERKpX4YTT990HF1zgWDZ8uCNBW7rUs7F5kLHWVtvJ+vTpY5ctW1Zt5xMRERERES+TkQEdOkDTprBkCfj6ejqiamWMWW6t7VPcOlVrFBERERGR6vPkk5CcDF99VecSs7KoW6OIiIiIiFSPLVvg+efh2msdk07LCZSciYiIiIhI9bj3Xkd1xqef9nQkXkndGkVERERExP1+/BF+/hmee84x3kxOoZYzERERERFxr6wsuOce6NgR7rrL09F4LbWciYiIiIiIez3/PGzfDtOmQUCAp6PxWmo5ExERERER90lMhP/8B8aOhTPO8HQ0Xk3JmYiIiIiIuM8DD0BBgaP1TEql5ExERERERNxj1iyYPBkeegji4jwdjddTciYiIiIiIq6Xm+so/hEXB3/7m6ejqRGUnImIiIiIiGs884yjtQzgjTdg3Tq44QZ49VXPxlVDKDkTERERERHX6NsXLr0Upk6Fxx6D3r0diVnfvp6OrEZQKX0REREREXGN4cMdrWeXXuooArJ9O3z9tWO5lEktZyIiIiIiUnUFBfDii3DbbY65zKyFO+9UYlYBSs5ERERERKRqdu2CUaPgL3+BXr0gOBgefRTefPN/Y9CkTErORERERESkcqyFjz+Gbt1g6VLHnGZbtsBXX8G//+0oo3/ppUrQyknJmYiIiIiIVNyBAzBuHFx7rSM5W7UKIiIcCVlhV8bhwx2Ply71bKw1hLHWlr6BMTHAR0BToAB421r7sjEmHpgIBAF5wO3W2iWlHatPnz522bJlrohbRERERESqyzPPOCouFiZdP/8MV18Nqanwn/84Wsx8fT0bYw1hjFlure1T3LryVGvMA+631q4wxtQDlhtjpgPPAP+y1v5ijDnH+XiYq4IWEREREREvUVgi/8MP4fvv4a23HMnYW2/B+PGejq7WKDM5s9buAfY476cbYzYA0YAF6js3awAkuytIERERERHxoOHD4YUX4LzzHFUZg4Phm29g9GhPR1arVGjMmTEmDugJLAbuBZ41xiQCzwEPuzo4ERERERHxEhdfDHFxjvsPPKDEzA3KnZwZY8KAr4F7rbVpwG3AfdbaGOA+YFIJ+00wxiwzxixLSUlxRcwiIiIiIlLdFi+GtDSVyHejMguCABhj/IEfgd+stS84l6UCDa211hhjgFRrbf3SjqOCICIiIiIiNdCsWY4xZ4WVGE9+LOVWWkGQMlvOnInXJGBDYWLmlAwMdd4fAWypaqAiIiIiIuKFli5VifxqUJ5S+qcD84A1OErpA/wdSANexlFUJAtHKf3lpR1LLWciIiIiIlKXVamUvrV2PmBKWN27KoGJiIiIiIiIQ4WqNYqIiIiIiIh7KDkTERERERHxAkrOREREREREvICSMxERERERES+g5ExERERERMQLKDkTERERERHxAkrOREREREREvICSMxERERERES+g5ExERERERMQLKDkTERERERHxAkrOREREREREvICSMxERERERES+g5ExERERERMQLKDkTERERERHxAkrOREREREREvICSMxERERERES+g5ExERERERMQLKDkTERERERHxAkrOREREREREvICSMxERERERES+g5ExERERERMQLKDkTERERERHxAkrOREREREREvICSMxERERERES9QZnJmjIkxxswyxmwwxqwzxtxTZN1dxphNzuXPuDdUERERERGR2suvHNvkAfdba1cYY+oBy40x04Eo4AKgu7U22xgT6c5ARUREREREarMyW86stXustSuc99OBDUA0cBvwlLU227luvzsDFe9nrWXB1gP849s17E/P8nQ4IiIiIiI1Snlazo4zxsQBPYHFwLPAYGPMk0AW8IC1dqnLIxSvV1BgmbFhH6/P3saqxCOOZRb+c1E3zwYmIiIiIlKDlDs5M8aEAV8D91pr04wxfkAjYADQF5hsjGltrbUn7TcBmAAQGxvrssDF8/LyC/hhdTJvzt7G5n0ZxIQH8+RFXVmTlMqUZYncNrQNMeEhng5TRERERKRGKFdyZozxx5GYfWqtnepcnARMdSZjS4wxBUATIKXovtbat4G3Afr06XNC4iY1U1ZuPlOWJ/H23G0kHjpGh6h6vHx5PGO6NcPP14c9HY8xdcVuXp+1lacu7u7pcEVEREREaoQykzNjjAEmARustS8UWfUtMAKYbYxpDwQAB9wRpHiH9KxcPl28i0nzd5CSnk3P2IY8dm4XRnSMxMfHHN+uWYNgrugXw6eLd3HH8LZqPRMRERERKYfytJwNAq4B1hhjVjqX/R14D3jPGLMWyAGuO7lLo9QeHy/aybO/bSItK4/B7ZrwyuU9GdA6HEfufqrbh7fl86WJvPr7Fp65pEc1RysiIiIiUvOUmZxZa+cDxX8Ch6tdG454oxnr9/Hod+s4vW0T/nZWB7q3aFjmPlH1g7iqfywfLUrg9mFtiWsS6v5ARURERERqsDJL6UvdlnDwKPdNXkmX5vV597o+5UrMCt02tA1+PoZXf9/qvgBFRERERGoJJWdSoqzcfG79ZAUGmHh1b4L8fSu0f2T9IK4Z0JJv/kxix4Gj7glSvIa1loMZ2Z4OQ0RERKTGUnImxbLW8o9v17JhTxovXR5f6aIetwxtQ4CfD6/M3OLiCMXbfLk0kdP++zuJhzI9HUqtsWDrATbuTfN0GCIiIlJNlJxJsb5YmshXy5O4e0RbRnSMqvRxIuoFcu1pcXy3cjfbUjJcGKF4m4//SCAnv4DvVyV7OpRaYX96Fjd+sJR/frfO06GIVNmug5lcM2mxvmwQESmDkjM5xeqkIzz23ToGt2vCPaPaV/l4E4a0JtDPt1paz9Ylp/L6rK2ocGj1Wrs7lXXJafj7Gr79c7defxd4e852svMKWJ5wmNTMXE+HI1Jp+QWW+6esZN6WAzz76yZPhyMi4tWUnMkJDh/N4bZPVhBRL5CXL++Jr09JhTrLr0lYINcNjOP7Vcls2ZfugihL9srMLTz72yYWbjvo1vPIiaYsSyTAz4d7R7Vny/4MNu5173Wu7VLSs/lkcQIdm9Yjv8Ayb2uKp0MSqbRJ87ezdOdh+rRsxMyN+1m7O9XTIYmIeC0lZ3JcfoHl3i9XkpKezRtX9SI8NMBlx54wpDUh/r687MbWs4zsPGZtcnyIfX2WKkRWl6zcfL5dmcxZXZpyRb9Y/HwM361U18aqeGfednLyCnjtyp40DPFn1sa6lZxZa/llzR7++8sG8gvUCluTbdqbznO/bWZ0lyjeu6Ev9YP8ePV3jUEWESmJkjM57tXftzBncwr/PK8zPWIauvTY4aEBXDcwjp/W7GGTm1pVZqzfR05eAWd3bcrCbQdZseuwW84jJ/pt3V5Sj+VyaZ8YwkMDGNyuCT+sSqZAH6or5UBGNh8vSuD8Hs1pG1mPoe0jmLN5f515PRMOHuX695dy26creGvOdpYn6O+4psrJK+Avk1dSL8iP/1zUjfpB/twwqBW/rdvHhj0aeyYiUhwlZwLA7E37eXnmFsb2iuaq/rFuOcfNg1sTGuDHyzM3u+X4P67eQ7MGQTxzSXcaBPvzxqxtbjmPnGjyskRaNApmYJvGAFwQH83uI8dYruS4Ut6dt4OsvHzuHNEOgOEdIjmQkcOaWt4VLCs3n5dnbOGMF+eyPOEwD53dkQBfH35bt9fToUklvfb7FtYlp/Gfsd1oHBYIwI2DWhEW6Mdr6t0gIlIsJWdC4qFM7v1yJR2i6vHkhd0wpurjzIrTKDSAGwbF8fOavS7/1jQtK5e5m1M4p1sz6gX5c8OgOGZs2KfKYG6WeCiTBVsPMq53DD7O8YlndI4iyN+H71bu9nB0Nc+hozl8tGgn53VvTtvIMACGto/AGPh9434PR+c+87akcPbL83hxxmbO7BzFzPuHcuvQNgxq25jf1u1VgZkaaGXiEV6fvY2Le7VgdJemx5c3CPHnuoEt+XnNHrbu19hUEZGTKTmr47Jy87n90xXkF1gmXt2b4ICKTTRdUeNPb029QD9enuHaMQcz1u8jJ7+AMd2bAXD9wDhCAnx5c3b1tp69MXsrny/ZVa3n9KQpy5MwBi7p0+L4stBAP87o3JSfVu8hN7/Ag9FVTeqxXN6cvY0Xprunpbc4787bzrHcfO4e2fb4skahAfSMacjsTbUvOduXlsWdn63gmklLAPj4pn68dmUvouoHAXBW16YkHT7GenWBq1GO5eTzl8kriaoXyGPndz5l/U2ntybY35fXflfrmYjIyZSc1XH/+mEda3an8vy4HsQ1CXX7+RqE+HPj6a34dd1e1iW7rpvWT6v3EN0wmJ7OsXINQwK4ekBLfliVTMLBoy47T2kSD2Xy3G+beOqXjWTl5lfLOT0pv8Dy1bJEBreLILph8AnrLujRnMOZuczfcsBD0VVe4qFM/v3Degb+dyZP/7qRV2ZuYbObq4yCo1Lqhwt3MqZbM9pG1jth3YiOkaxKSiUlPdvtcVSHvPwC3pu/g5HPz2Ha+n3cN6o9v9wzmMHtIk7YblSnKHwM/LZun4cilcp4+teNbE85yrPjelA/yP+U9eGhAVwzoCXfr0pmx4Hq+f8sIlJTKDmrw+ZvOcDnSxK5bVgbzizS7cTdbjy9FfWC/HjJRa1nqZm5zN2SwpjuzU7okjn+9Fb4+fgwcc52l5ynLJPm76DAOlpcflm7p1rO6Unztx4gOTWLS4u0mhUa0j6CBsH+Napr4+qkI9z1+Z8Me242Hy3ayeguTfnkpv74+himrnD/85g0fweZufncPbLdKeuGdYgEYM7mml+1cWXiEc5/bQH//nE9vVs2Yvp9Q7hnVDuC/E9ttW8cFkifuHCmadxZjbFw6wE+WLiT6wfGMahtkxK3Gz+4NQF+PqqsK4KjQu2va/fw2Hdryc6r/V/uSumUnNVhi3ccxMfA3SNO/TDoTg2C/Rl/emumr9/HmqSqt55NW7+X3HzLmG7NTlgeWT+IcX1a8PXyJPamZlX5PKU5fDSHL5cmMrZnNC0bh/D5kkSXHXvKskTu+GwFU1ckcSQzx2XHrarJSxNpFOLPGZ2jTlkX4OfDOd2aMW39PjJz8jwQXfkUFFh+37iPy99exPmvLWD2xv2MP70V8x4czguXxXN6uyYMax/Bt3/udmtJ9yOZOXywcCfndG1G+6h6p6zv0rw+kfUCmVXDx53tTc3isrcWcehoDm9e1YsPbuhLy8alt9iP7tKUjXvT2akWFq+XlpXLX79aTesmoTx4VsdSt42oF8iV/VryzZ+72XUws5oirF0mztnG+A+XaUxmDbfzgKNC7a2frODDRQk1/v+8VJ2SszpsfXIabSLC3D7OrDg3nB5Hg2B/XppR9fE8P63ZQ4tGwXRv0eCUdbcMaUO+tbw7z72tZ58uTuBYbj4Thrbm8r6xLNlxiK37M6p83MycPJ74aQO/rd3LXyavovcTM7jynT/4YMEOko8cc0HklXPoaA7T1u/lwp7RBPoV//tzQXxzMnPymbHB+95osnLz+XLpLs58aS43frCMXQcz+ceYTix8eAQPn9OJZg3+101zbK8W7E3LYpEbJzZ/b/4OMrLzuKvIWLOijDEM7xDJ3C0pNXoc36T528krsEy+5TTO7tasXMWHznQm/6ra6P3+/cN69qQe47lLe5TrfeWWoa3x9TG8OUetZ5Xx7Z+7mbFhH7+s1d9GTZSVm8+L0zdz5kuOCrX/GNOJiHqBfPNnzelxIu6h5KwOW5ecRpfm9T1y7vpB/ow/vRUzN+5nbRVKhB/JzGH+lgOndGksFNs4hPN7NOfTxbs4fNQ9rU5Zufl8sDCBoe0j6Ni0Ppf0boGfj+HLpVUvDPL18iRSj+Xy+YQBfHfHIG4d2pqU9Gwe/2E9A5/6nfNenc+rM7ewaW96tX57+u2fu8nNt1zWN6bEbfrFhdO0fhDfe7BrY0Z2Hmt3p/Lj6mRe+30LD0xZxSVvLqTfkzN48Os1BPj68PLl8cz523DGD25NvWLGx4zsFEm9ID+mrkhyS4ypmbm8v2AnZ3VpSsemJf89Du8YQXpWHitq6LxfRzJz+HTxLs7r3ozYxiHl3i8mPIQuzesrOfNy09fv46vlSdw2rA29YhuVa5+o+kFc3jeGr5YnsduDXzad7Ld1e9lSDeNMqyItK5dNzhif+21Tjf7Spi6atXE/Z744l5dnbuGsLk2Zef9Qxg9uzfk9mvP7xv1e1UtGqp+SszrqYEY2e9Oy6NL81Nam6nLdoDjqB/nxyszKjz2btm4feQWW87o3L3Gb24e14VhuPu8v3Fnp85Tmmz93cyAjm1uGtAYc3XXO6BzF1yt2V6nveH6BZdL8HcTHNKRPy0b0iGnIX0d3ZPpfhvL7/UN56OyO+Psanp++mdEvzWXYc7P5z88b3JaEFrLWMnlZIt1bNCg1mfDxMZwf35w5m1Pc/kZzJDOHmRv28fqsrTwwZRXjJi6kzxMz6PrYb5z76nzu/OxPnpu2mXlbUvD1MZzTrRmfju/PT3efzgXx0fj7lvyvMMjfl3O7N+eXtXs5mu36LprvLdhBenZesWPNihrUtgn+vobfa2jVxg8XJpCZk89tw4pvHSzN6C5NWbHrCPvT3Ns9WSrnYEY2D09dTadm9blnZPsK7Xvr0DYATKzmyrolSUnP5vZPV3D7pyvI8+KEZ+WuI1jrqEy8/cBRJi9zXVd6cZ/dR45xy8fLuOGDpfj7Gj4b359Xruh5vELtRT2jyc23/LTGNePW96ZmccvHy9za80Ncz8/TAYhnrEt2lKb2VMsZOFrPbhjUipdnbmHDnjQ6Nat4LD+sTqZl45BSn0e7qHqM7hLFBwt2MGFIa8ICXfdrX1BgeWfedro0r89pzkmYAS7vF8sva/cybd0+zutRcuJYmpkb9rHzYCavje5wSqtg64gwbh0axq1D27A/LYsZG/Yzbf1e3pu/g4170/ng+r7H5x1ztdVJqWzcm84TF3Ytc9vzezTn7bnb+WXtXq7o55rJza217DhwlGUJh1m+8zDLdx0+oQtpZL1A4pqEMqJjBHFNQmnVOJS4JqG0bBxCSEDlrv3FvaL5fMkufl27l4t7n1oApbJSj+Xy3oIdnNk5is5l/C3WC/Knb1w4szem8PDZnVwWQ3XIzMnjg4U7GNUpkg5NTx1TV5bRXZrywvTNTFu/j6sHtHRDhFJZ1loe+WYtacfy+GR8DwL8Kvadb/OGwVzSO4YvlyZyx/C2NG0Q5KZIy+e7lY7xpVv2Z/DF0kSv/X1bnnAYHwP3n9metbtTeXnGFsb2bOGRYQpStpy8At6dv51XZzq68P7trA6MP731KX8vXZrXp11kGN/+uZur+lf9d++9BTv4bd0+pq3fx13D23L3yHb4lfJlpHgHXaE6qnDeoLI+ELrbjYNaERboV6n5bg4dzWHhtoOMKcfYlduHtSUtK49P/0iobKjFmrlxP9tTjjJhSOsTYhjctgnRDYP5ogpdG9+dv4PohsGcVUYlzcj6QVzZP5YPbujHY+d3Ye7mFD5ctLPS5y3L5GWJBPn7cH582Ulnl+b1aRMRWqWqjVm5+Szbeej44PfeT8xgxPNz+NtXq/l13V5iw0P46+gOfDFhAGv/NZolj4xi8i2n8cwlPbh9WFvO7taMTs3qVzoxA+jdshGx4SFM/dO1XRs/WLCT9KyyW80KjegYyaZ96V7VBaw8vliSyOHMXG4b1qZS+7ePCiOucYi6NnqhqSt28+u6vdx3RvtSW9JLc/swx9jgt+Z6vvXs6xW76d6iAf1ahfPi9M2kZ+V6OqRirdh1mA5N61MvyJ8Hz+7I/vRs3luww9NhSTGW7DjE2S/P5ZlfNzG4XROm/2UItw9rW+wXGcYYLuwZzdKdh0k8VLVCOVm5+UxelsioTpFc3KsFr/y+lcvf/qPGvX/URUrO6qh1yWlENwymYUiAR+NoEOLP9QPj+HntngrPJfXbur3kF9jjE0+XpkdMQwa3a8I783a4dA6yt+duI7ph8CmVIn18DJf3jWHB1oOVmmdtddIRluw4xA2D4ir0LdfV/WMZ2TGS//6ykU17XT9m4lhOPt+vTOacrs2Knb/oZMYYLoiPZvGOQ+xJrfgbwrwtKfT6v+lcMnERT/2ykW0pGYzoGMlTY7sx/b4h/PnoGbx3fV/uGN6WAa0bu7RVtChjDGN7RbNw20GXFWJJy8pl0vztjOoURdfo8nUvLiypX5OqeeXkFfDOvO30axVO75bhlTqGMYbRXZqyaNtBUo9V7cNygRurbtY1XyzZxd++Xk3fuEZMcHbrroyY8BDG9ozms8W72J/uua6r65JT2bAnjXG9W/CPMZ04eDSHN7yku2VR+QWWP3cdoU9Lx9i+vnHhjOwYycQ52zRWycscy8nn+veXkJNfwPvX9+Xta/vQolHpY24v7BkNOMZ2V8UPq5I5kpnL+MGteW5cD16+PJ6Ne9M5+6W5/FoHpvupyZSc1VHrklM93mpW6KbTWxHs71vh1rOfVu+hVZNQOpezO+Ttw9pyICObKctd0/qxYtdhlu48zE2ntyo2gRrXJwZfH8MXSys+FuDdeTuoF+hXasGN4hhjePqS7tQP8uOeL/50+WTYv6zdQ3p2HuP6lD+u83s0x1r4cVXF3gx2HDjKHZ+uoEWjYN6+pjfL/jGKWQ8M47lxPbi8Xyztouq5retmccb2bIG18K2LCpx8tHAnaVl53FPOVjOANhGhxIaH1Kjk7NuVu9mTmsXtlWw1K3Rml6bkFdgqPfc5m1OI//c0tcBVUUGB5ZlfN/LQ1DUMatuE967vi28V/xbvGN6W3PwC3p3nudafr5YnEeDrw3k9mtO9RUPG9oxm0vwdJB32rlL/m/elk5GdR++W/yu88tezOpCRneeVyWRd9sf2g2Tm5PPkhd0Y3jGyXPtENwymf6twvlm5u0qFvj7+I4H2UWH0b+X4UuyC+Gh+uvt0WjUJ5dZPVvDIN2tc/hlBXEPJWR10NDuPHQeOenS8WVGNQgO49rQ4flidXO7y8wcyslm47QDnllClsTgDWofTK7Yhb83Z5pLKVu/M3U79oJITqKYNghjeIZIpy5IqdL7kI8f4ac0eLu8XU2z1wLI0CQvk2Ut6sHFvOs/+tqnC+5fmy6WJtGwcwoDW5W8BiWsSSo8WDfhuVfmTmrSsXMZ/uBRfH8Ok6/pyZpemNAkLrEzILhPbOIS+cY2YuqJqb5jgqCL57vwdjOwYSbdipoAoiaOkfgQLth2o9Jvq0ew8Drm5aEyh/ALLxDnb6NysPkPbR1TpWD1jGhJZL7DSiVV+geXJn9aTlpXHXZ//yeLtGiBfGVm5+dz9xZ+8MXsbV/SLZdJ1fSr1f+pkcU1CuSA+mo8XJXAwI9sFkVZMTl4B361M5ozOUcd7lDwwugMGeOZX1/4frarlzoqtRZOzjk3rc1HPaD5YuNOj06zIieZsTiHI34d+rSrWa+CintFsTznKmkpWs16VeITVSalcM6DlCZ+RWjYOZcqtA7llSGs+XbyLC15bUOFeS+J+Ss7qoI1707CWcrc4VYfxg1sR5OfLG7PK13r269q9FFjK1aWxkDGGO4a3JenwMX5YlVzZUAHHpJG/rtvL1QNaElpKV7or+sVwICObmRv2lfvYHzirSl43MK7S8Q3vGMm1p7Vk0vwdzNuSUunjFLXzwFEW7zjEpX1iyp0QFzo/Ppq1u9PKlXznF1ju+fxPEg5m8sZVvYkJL3/ZdXcb26sFW/dnVPoNs9CHC3dyJDOXe0ZVfAL4YR0jycotYPGOQxXeN7/AcuU7f9D7iemMm7iQd+dtr/K4htJMX7+X7SlHuW1Ymwr/zpzMx8dwRucoZm9KqVRiOnVFEpv3ZfDkRV1p0SiY8R8tY4Nz7K2Uz6GjOVz97mJ+XL2Hh87uyH8u6lpqpdOKumN4W7Ly8pk0v/pbz2Zt2s+hozlcUqTgT/OGwUwY0prvVyXz5y7vmcJiRcJhIuoF0qJR8AnL/3JGe7C4ZP5QcY3Zm/ZzWuvGBPlXrFDL2d2aEeDnw9QVleup8dGiBEIDfI93kSwqwM+Hh8/pxIc39uPg0WzOf20+ny3epcnMvYiSszpofWGlxnKOc6kOTcICuXpALN+u3M3OA2WP0fpp9R7aRITSIapild9GdIykY9N6vDF7W5XGnkyavwN/Hx+uLyOBGto+gmYNgvhsSfm6NmZk5/H54l2c3bVpmf3Sy/L3czrRNjKM+yevcklLyZTlifgYuLhXxasVnte9GcbA9+VIip/5dSOzNqXw+PldTqiA6Q3Ocb5hfl2FrrFHs/N4d952hneIoHuLhhXe3/FG71Op7n0fL9rJqqRULuoZTUZ2Pk/8tIHBz8zinJfn8YqL58uz1vLG7G3ENQ7hnG7l/xKlNGd1bcqx3HzmbTlQof0KJ3vt0aIBV/aL5eOb+hMa4Me17y1xa3Jam+w4cJSxbyxg9e5UXruyJ7cOrXrCfbK2kWGc062Z88uL6h079dXyJCLqBTK4XZMTlt8ytA1NwgJ54qcNXvPhdfmuw/SObXTK69+iUQhXD2jJV8uTvH6etrpg54Gj7DyYeXyscEU0CPZnVKdIfliVXOGePoeP5vDD6mTG9mpRaqv20PYR/HzPYPrGhfP3b9Zwx2crqjymV1yjzOTMGBNjjJlljNlgjFlnjLnnpPUPGGOsMaZJSccQ77IuOY2GIf4093DJ4pPdPKQ1/r4+vF5G69n+9CwW7zjImO7NK/zhwBjD7cPbsnV/BtPWl781q6hDR3OYsjyRC3s2J7J+6a+hn68P4/rEMG9LSrk+BE5emkh6dh7jB1d+cH2hIH9fXr48nsOZOTw8dXWVPljk5Rfw1fIkhraPqFSp68j6QQxs05jvy+hDP3VFEm/N3c7VA2K9soR1g2B/zugcxferksnJq1zX2NdmbeVwZm65KzSeLMjfl4FtmvD7xv0VuqZ7U7N4btpmBrdrwvPjevDLPYOZ+9fhPHJOJ4IDfHlxhmO+vOHPzea/v2xgxa7DVfoCY8HWg6xOSuWWoW2qPB6p0IDWjakf5Ffhro2f/JFAcmoWD57VEWMM0Q2D+eimfuTkFXDNpMUc8EA3utLk5BXw8R8JLm/Zyy+w5Ffimi7beYixbywg9Vgun43vz7mlzCtZVXeNaMvRnHzeW7DTbec42YGMbGZt3M/YntGnjB8OC/TjgTPbszzhMD+v8fxYxZT0bBIOZp7QpbGoO0e0JSTAz+Vd2qXi5mx29FqpbJfuC+OjOXg0h/lbK/Zl1ORliY7/baeV/R4aWS+ID2/ox8Nnd2Taun3c+dmKSsUqrlWelrM84H5rbSdgAHCHMaYzOBI34Ayg8vXCpdqtS06jS/P6Lv/Ws6oi6wVxRb9Yvvlzd6mJzG/OLo3nVqBLY1FjujUjrnEIb8zeWqmE5aNFO8nKLSh3dbLCMWlTypgkNC+/gPcW7KBvXCPiYxpWOK7idGnegL+O7sBv6/ZVaZLSuVtS2JeWXeECJUVd0COanQczWZ1UfJfAP3cd5qGpaxjQOpzHzutS6fO428W9ojmcmcvsSkwGvWznId6as41L+7SgZ2zxH67KY3jHSHYdymR7OVqZC/37x3Xk5hfwxIVdj//txzYO4eYhrfn6toEsfngkT1zYlZjwECbN28HYNxYy8KnfmVXJSa/fmL2VyHqBjO11areayvL39WFkpyhmbthX7gmC07JyeW3WVga3a8LAtv/7DrF9VD3eu74Pe9OyuOH9pWS4YYLxykg4eJRxExfy6LdrOffV+fz35w1k5lQttvwCy6eLE+jzxHR6/nsat368nI8X7WR7SkaZ/wN/XJ3Mle8upmFIAN/cPog+cZWruFleHZvW5+yuTZk4Z9vxD7fu9t3KZPIKbIlzGI7rE0PHpvV46tcNZOd5toDCCmf3yl4lJGfhoQFMGNKaaev3HR+bJp4xZ3MKcY1DiGsSWqn9h3WIpGGIf4WqNhYUWD5ZnED/VuG0L2fPIh8fwy1D23BZ35gS35+lepWZnFlr91hrVzjvpwMbgMJ32xeBvwHe0dYvZcrNL2DT3nSvGm9W1K1D2+BjTKkVp35YvYf2UWHl/sdzMl8fw61D27A6KZWZGyr2wTMrN5+PFiUwsmMkbSPLd/7ohsEMbR/Bl8sSS/1AOW39PpIOH+Om06vealbU+NNbM7BNYx7/fj07KvBhvqjJS5NoHBrAiI5RlY5jdNemBPj68N3KU7s27k3N4paPlxNVP5A3rurt0nEsrja4XQRNwgIqPBYgIzuPv0xeRXSjYP5ZxeRzeAfHN7Hl7do4a+N+fl6zl7tGtKVl4+I/KETWD+LqAS35+Kb+LP/HGbx4WQ8ahvgz4aNl/LS6YpU2VyYeYeG2g4wf3IpAP9dOiju6SxSHM3NZsrN8Y+7enrOdI5m5PHhWx1PW9W4ZzhtX9WL9njRu+XiZxz94f/vnbsa8Mp8dB47ywqU9GNe7BW/N3c6ZL86t1JcBAEt3HuK8V+fzyDdraR9Vj7O6NmXN7lQe/W4dI56fw8CnfueBKav49s/dJ5Sxt9by5uxt3PnZn/Ro0YCptw2s9IfMivrv2G60jQhjwkfLmF/BLqyV8dXyJLq3aFDie4qvj+GRMZ1IPHSMD51jgj1lRcJhAnx96Bpd8nv4Tae3oklYIE//utFrumLWNVm5+SzcdqBKhZAC/Hw4t3szflu3t9xfHs3ZnELioWPlajU7WVzjUFKP5ZKaqa6NnlahT0DGmDigJ7DYGHM+sNtau8odgYl7bEvJICe/gC7NvWe8WVFNGwRxWd8YvlqeWOxEifvSsli68xBjulWtW81FvaKJaxzCLZ8s59nfNpa7wMBXy5M4dDSnwnP6XNEvln1p2czeVPI3we/O207LxiGc0bnyCVBxfHwMz1/agwA/H+794s8K918/kJHNjA37GNsruthJM8urQbA/wztG8MPq5BO6VmXl5nPLx8scY7Gu7Ut4qGfn3iuLv68P5/eIZubGfRUaF/PEj+tJPJzJC5fGV3k+thaNQmgfFVauVq1jOfk8+t1a2kaGMWFI+crZNwjx56KeLZh862n0aNGQuz5fUaGW1zdnb6VBsD9X9nd919Qh7SMI9PNh2rqyuyXvT8ti0vwdnNejeYlzyY3oGMUzF3dnwdaD/GXyqkp1+6uqo9l53D95Ffd+uZJOzerxy71DGNurBU9d3J0vJwwg0M+H699fyl2f/0lKevm6YO5NzeLeL/5k3MRFHM7M4bUre/LFhAE8c0kP5j84nNkPDOOJC7vSM7YhMzbs494vV9LvyZmc+eIc/vXDOu6fsoqnf93IeT2a8/FN/WlUjX+XDUMC+HR8f1o1CeWmD5eysILduiqicG6zS0poNSs0uF0EwztE8OrvW6ut2mlxliccpluLBqV+6REa6MfdI9uyZMehUt9zxH2W7jxEVm5BpcabFXVRz2iycgv4bW35utR+tGgnEfUCGd2laYXPVVh8a5fG4XpcuT9pGWPCgK+Be3F0dXwE+Gc59ptgjFlmjFmWkqJ/Ep62brezGIiXlNEvzq3O+ZAmFtN69suaPVgLY7pX/B9PUYF+vnx7xyAujI/m9VnbGPPKvDK7gOQXWN6dt50eLRpUuCzuiI6RRNQL5PMlxfcAXp5wmBW7jnDjoFYuG59TVLMGwfznom6sSkrllZlbKrTvNyt2k1dgubQCc5uV5IL4aFLSs/nDWcbcWstDX69mVVIqL14WT4emlWsNrW5je0WTm2/5oZwtSjPW7+OLpYncOrQNfV3ULWx4h0iW7DhU5jeqr/y+haTDx3jiwq4VTq7rB/nz0U39GNS2CX/7ajUfLCi7it7W/en8tm4f153W0i2TgocE+DGkfQTT1u0ts1Xgld+3kJtfwP1ntC91u4t7t+Dv53Tkp9V7+NcP66q1tWHt7lTOfXU+3/yZxN0j2/H5zQOIbvi/Knz9Wzfm53sGc++odvy2di8jn5/N50t2lTgeMDsvnzdmb2XE87P5ea2jtXTm/UM5t8gYXWMMcU1CuXpAS964qjcr/nEGP951Og+d3ZGo+kF8tngXU1fs5o7hbXj5svgKV5pzhUahjgQtrnEoN364lEXb3DP1wdfLdzvmNivHOLq/n9OJzJx8XvZQNcTsvHxW704tcbxZUZf3jSU2PISnf92oidc9YPamFAL8fBjQumpFrXrFNiImPLhc82vuOpjJ7M0pXNEvtlK9T2KVnHmNcl09Y4w/jsTsU2vtVKAN0ApYZYzZCbQAVhhjTvnEbK1921rbx1rbJyKiavPcSNWtS04jyN+H1hFhng6lRNENg7mkdwxfLk1kb2rWCet+WrOHjk3rlbtLYWkahgTw/KU9+OCGvmTlFnDJxIX864d1JY7vmL5+HzsPZjJhSMWrlPn7+nBpnxbM2rSfPamntghOmu+YM62sb2+rYkz3ZlzSuwWvz9rK0jK6hO1Ly+K3dXt5+teNvD1vOz1jG9Kukt1IixrRMZKwQD++c77RvDV3O9+uTOaBM9tzZiW+6fOULs3r0yGqHlNXlF218WBGNg9NXU2nZvW5b1TpSUJFDO8YSW6+LbXb16a96bwzdzuX9G5R6Q8JIQF+vHtdH87sHMXjP6zn9Vmlj9WcOGc7Qf4+XD+oVaXOVx6juzQlOTWr1CkNdh44yhdLErmiX2y5uuNNGNKGCUNa89GiBF79vXxTelSFtY4vey56YwHHcvL57OYB/OWM9sVOaB/o58u9o9rz8z2D6dSsPg9PXcNlby9i6/4TK/L9vnEfo1+cyzO/bmJQ2ybMuG8o95/ZgZCA0pNkHx9D1+gG3Dq0DR/f1J9Vj53JoodH8NfRHat1oveTNQ4L5NOb+xPTKIQbP1jKkkpMH1GanLwCvl25m1GdI8vVMtguqh5X9Ivhk8W7yj0npyut3Z1GTl4BvcoxXjXAz4f7z2zPxr3pFZpjUlxjzuYU+rcKJzigal9sGGO4KD6aBVsPsC8tq9RtP12cgI8xXNkvtlLnim2s5MxblKdaowEmARustS8AWGvXWGsjrbVx1to4IAnoZa31fCkjKdW65FQ6Nq3vltYZV7p9WBsKrGMC20J7U7NYuvMwY1xUlrvQsA6R/HbfEK4Z0JL3F+zkzBfnFvuB9+2524gND+GsrpVLIi7rE0uBdYzfKirxUCa/rt3LVWXMmeYKj5/fhRaNQrj3i5WkZTn6lWdk57Fw2wHenL2NWz5exoD/zKT/f2Zyy8fLeWfudprWD+Lv53RyyfmD/H0Z3aUpv6zdy69r9/D0rxs5t3sz7hje1iXHry7GGMb2iubPXUfYnlLyhzRrLQ9PXUPasTxeuiy+St1CT9a7ZSPqBfqVOBapoMDyyDdrqBfkV+XrF+jnyxtX9eKintE8+9smnv51U7EJ2u4jx/j2z91c3jfWrd1TR3WKxNfH8GspXX2em7YJf18f7hpZ/t+th87qyNhe0bwwfTOfLk5wRajFOpCRzY0fLOWJnzYwrEMkv9wzuFzJc9vIMEfXxIu7s3lfBme/PI8Xpm1i0950bnh/CTd+sAwfH8OHN/bjnWv7HP+wVVFB/r40axBc9obVoElYIJ/dPIDoRsFc//4SlpVzrGF5zC5mbrOy3DuqPcH+vjz1ywaXxVFeKxIKi4E0LNf253VvTpfm9Xl+2maPj6esS5IOZ7J1f0aVxpsVdWHPaAosfF/MeO1CWbn5fLkskTM7R1WqojI4KpM2Dg1QcuYFyvNJYRBwDTDCGLPSeTvHzXGJG1hrWb8njc5e3KWxUEx4CGN7RfP5kl3HB6n/tMbRhawiE0+XV1igH/++oCuTbzkNf18frp60mAe/Wn18zo9lOw+xYtcRxg+ufLfD2MYhDG7XhMnLEk8Y1/Legh34GMN1p8W54qmUKizQjxcvi2dvWhZXvP0HZ744h26P/8aV7yzm6V83smlvOv1bh/PPczsz9faBrP3XaH6463SXdcUDuCC+OelZedz+6Qo6N6vPs5f08LrKoeVxYc9ofAx8U0olrSnLk5i2fh9/Hd3B5V02/X19GNy+CbM2FV9Sf8ryRJYlHObhczq5JFHy8/Xh+XE9uKp/LBPnbOPR79ae0l3qnbnbAce0GO7UMCSA/q3CSyypvyYplR9X72H84FZE1iv/BxUfH8PTF3dnRMdI/vHtWn5dW7FCKOWxYOsBzn55Hgu2HeTfF3Th7Wt6V2g8lzGGS/vGMPP+oYzp1oxXft/K6JfmsnTnYR45pxO/3jPEZR8KvUVEvUA+u7k/TRsEcd17S1xWhfCr5Uk0CQtkSLvyv15NwgK5Y3hbZmzY79axcMVZnnCY2PCQcv9O+/gY/nZWR5IOH+OzxSqqXV0Kq4xWdbxZodYRYfSIaVjqe82Pq/dwJDO3UoVAiooJD2HXocoVDhPXKU+1xvnWWmOt7W6tjXfefj5pmzhrbfX+l5IKSzp8jPSsPK8eb1bUHcPbkldgj3/g+2l1Mp2b1Xdrl8x+rcL55Z7B3DK0NVOWJ3Lmi3OYvn4fb8/dTsMQ/yp3O7y8byy7jxxj7hbHP+/UY7lMXprIeT2aV/rbrorq3bIRfx3dgZT0bKIbBnPvyPZ8cENf/nz0DGb/dTgvX96TG09vRa/YRm4ZazKwTWOahAUSHhrAO9f2qXK3D0+Jqh/EoLZNmLpid7FjOhIPZfLvH9YzoHU4N53uni5+wztEsi8tm/UnzYd1MCOb//6ykX5x4YxzYVdZHx/DExd25ZYhrfnkj108MGXV8QqkBzOy+WLpLi7sGX3CmCl3Gd2lKdtSjhbbveyZ3zbSKMS/woV7wJH0vn5lL+JjGnLfl6tO6TpYFe8v2MHVkxbTINif7+4YxLWnxVX6i4kmYYG8dHlPPrqxH7cMbc3v9w/l5iGtXdo6600i6wXx+c0DiKzvSND+3FW1BO1gRja/b9zP2F6nzm1WlhsGxRHdMJgnftpQbQVkrLWOyafLMd6sqCHtmnBa68a89vtWr5kuojRHMnNIz6rZ1QJnb0ohumEwbSJcV930ovjmrN+Txqa9xf8/+viPBNpEhHJaFce4xYaHqOXMC9TO/+JSrHXJjvEZ3lqp8WQtG4dyQY/mfPLHLtYkpbJi1xG3tJqdLMjfl4fP7sS3dwyiUUgAN3+0jGnr93HtgJZljt0oyxmdo2gcGsAXzsIgXy7dxdGcfLd9eC/JrUPbsOSRUbx/Qz/uGdWOYR3KN+bCFfx8ffj85v58e8cgmlfDh3h3urhXC3YfOXZKWff8Asv9k1dhgOfG9XDbuJ2hzpL6J1dke/LnDRzNzuPJi7q6vFXSGMNDZ3fkgTPbM/XP3dzx2Qqy8/L5cOFOsvMKuHWoe1vNCp3ZxVHV9OTWswVbDzBvywHuGN6WekH+lTp2cIAvE6/uTUiAL7d/uqLK84wBzNuSwv/9uJ4zOkXx/Z2D6OSi6UyGtI/g4bM7EVm/er7c8aSo+o4ErXFYANdOWsKqxCOVPtbxuc16VfzLiyB/Xx48uyPr96TxdTnGnbpC0uFjpKRnlzi/WUmMMTx4dkcOHs3hMzd21XWF/ALLxW8u5PZPa+5EyDl5BSzceoBhHSJc+r/3vB7N8fUxxbaerU46wqrEI1wzoGWVzxkbHkLykawKV3UW11JyVoesS07Dx0DHGlIRD+COEW3JystnwsfLAFw+3qw03Vs05Ps7T+e+Ue3p0aIB1w6Mq/IxA/x8uKR3C2Zs2E/ykWO8v2Anp7VuXGKZ79qqXVQ9WjSq3HgYb3JmlyhCA3xPKQzy7rztLNl56PgYP3eJrBdE9xYN+L3IfGcLtx1g6ordTBjS2iVFXIpjjOHOEe3457md+W3dPsZ/uIwPFyVwZucolxTrKY9mDYLp0aIB04okZ9Zanv51I9ENg7l6QNW690TVD+Kly+PZsj+Df363rkrHSjyUyd2f/0m7yHq8dHl8lb/kqcuaNnAkaI1CA7hm0mLWVHLS3MK5zSrb3fi87s3oGduQ537b5JLkvSyFXTl7V2Ly+viYhvRu2Ygpy5K8et6z6ev3si3lKPO2HGBnJefk9LRlCYc4mpPv8q7FjcMCGdo+gu9WntpT4+NFCYQE+DLWBb0kYsNDyC+w7DlSevERcS8lZ3XI+uQ02kSEeaQscmW1iQjjvO7N2ZOaRdfo+tU2CWqhAD8f7hnVju/uPJ0mYYEuOeZlfWPIL7Dc9ukK9qRmMX5w9baaieuEBPhxdrdm/LxmL8dyHAPuN+xJ4/lpmzm7a1PG9op2ewzDOkTy567DHD6aQ3ZePv/4di2x4SHcNaKd28994+mtnHOEHSD1WC63D6vewi5ndmnKqqTU4xVQf16zl9VJqdw7qp1L/s8NbhfBXcPb8tXyJKZUYJ63orJy87nt0+XkFVgmXtNbiZkLNG8YzOcTBlA/2J+rJy2ucBfH9clprN+TVqlWs0LGGP5+Tif2p2dXy3iu5QmHCQ3wrXQyeUnvFmzZn8HqSiaz7mat5S1nASpfH8OXlfx787Q5m1Pw9zUMbNvE5ce+sGc0e1KzWFykaunhozl8vyqZC3tGU7+SPQWKKiwilKBxZx6l5KwOWZecVmPGmxV114i2+PoYLox3/wfd6tA6IowBrcNZlXiE1hGhDHfRoGHxjLG9osnIzmPa+r1k5+Vz35craRDiz5MXdauWQicjOkZSYGHulhTemrOd7SlH+fcFXartS5hL+8bwzrV9ePjsjvSIaVgt5yxUONHqtHX7yM0v4Llpm2gfFcbYKnzoPtk9o9pzWuvGPPrd2hLHe5TEWssj36xl7e40XrosnlbV/OVSbRbdMJjPbx5A/WA/LnvrjxLnkCzO1yuS8Pc1nN+j7LnNStM3LpxBbRszcc52snLdWw1xecJhesY2qnRBqjHdmxHo58OU5d6Z9CxLOMyfu45w+/A2DO8QyZRlSTWya92cTSn0aRnuljkez+gURVigH98W6dr41fIksvMKuKaKPQUKaa4z76DkrI44mJHN3rSsGjPerKh2UfWY/cAwrndBt0JvcYVzHpKbTm/l0XmEpOoGtGpM8wZBTF2xmxembWbj3nSeuaS7W0vJF9U9ugGNQwP45I8EXpu1lTHdm7msSlh5jewUxS1D21TrOcFRWr5tZBi/rdvL5GWJ7DhwlL+O7ujSqUJ8fQwvXxFPWKA/t3+6nKMVKKrwyR8JfL0iiXtHtWNkpyiXxSQOMeEhfH/H6Qxo05iHp67hwa9Wl5kk5eYX8O2fuxnVKcol42zvGtGOAxnZx8cRu0NGdh4b96ZVuBhIUfWD/Dmra1O+X5ns9kSyMt6as41GIf6M6x3D5X1jOOAs2FKT7E3NYuPedIZ1cE+11OAAX87q2pSf1+whKzefggLLJ4sT6BvXyGVjWKPqBxHg66PkzMOUnNUR65Id1dxqYssZON6EK1pRy5ud1705713fh8v7Vm6ySPEePj6Gi3pFM29LCm/P285V/WOrtTXUx8cwtEMES3ceJtDXh8fO7Vxt5/YGo7tEsXjHIV6cvoU+LRsxqpPrX/vIekG8ckU8Ow4c5ZFv1pRr3M7yhEP864f1jOwYyd3V0MW0rmoUGsD71/flrhFt+XJZIpe+tYjdR46VuP3sTSkcrODcZqUZ0Lox/VqFM3HOdrfNJbYq8QgFliolZwDjeseQlpXHjA37XBSZa2zdn86MDfu59rQ4ggN8GdYhgqj6gW5NeN1hzmZHMjnUTckZwEU9o0nPzmPmhv3M2ZJCwsFMrnHhNDy+PoYWjYJJVHLmUbXn066UqjA5qwlznNUFPj6GER2jvH4ycCmfsb1aUGChZXgIj4xxzYTdFTGyo6NV5q9ndagTVfuKGt2lKfkFlgMZ2Tx4dke3dSUd2KYJ945qz7crk/liaeldw/anZXHbJyuIbhTMC5fFq3XczXx9DPef2YG3r+nNjpSjnPvKPOZvKX52n6+WJzrmNnNhwYa7R7Rjb1oWU5a5p3Lj8oTDGAPxsQ2rdJzT2jha+d0VZ2W9M3cHgX4+XOuco8vP14dxvWOYszmF5FISbVdLSc/mjBfm8MOqkid7Ls2czSk0rR9EBzcVYgLHlwFR9QP55s/dfLIogSZhgZzl7N7tKjEqp+9xSs7qiPV70ohuGEzDkOrpaiVSl7SJCOM/F3XjnWv7eKTgw9ldm/LZzf25ur9rxh3UJN2iGxDXOIQzO0e5dLL04twxvC2D2zXhse/XsT45rdhtcvIKuP3TFaRn5fHWNb1pEFz1QfpSPmd2acp3dw6iSVgg1763mDdnbzuhlfNgRjYzN+znop7N8XdhT4xBbRvTK7Yhb87eRk6e68dJLU84TIeoelUu+ODrYxjbqwXztqSwN9U7qvHtT8vimz93M65PCxoXKbp1Wd8YCizVmkhOmr/DWZ11LYeO5lRo37z8AuZtOcDQ9q4toX+ywvH3szft5/dN+7miX4zL5zZs2TiEhIOZXl3Zs7ZTclZHrEtOVauZiBtd2T/WbaXry+LjYxjYpkmdbKExxvDdnafzyhU93X4uXx/Di5fF0yjEnzs+W1HsZLlP/rSeZQmHefqS7nRsqv+51a11RBjf3jGIs7s14+lfN3LbJ/+7Tt+vcs5t5sKJ2cHxO3jXyHbsPnLslGk1qqqgwLJi1+EKz29Wkkt6O1r5i5svyxM+WLiT3IICxp9+4vyIMeEhnN62CZOXJVbLRN+px3L55I8EerdsRHpWHk/+tKFC+/+ZeIT0rDy3jTcr6sKe0eQVWAz/G7/uSrHhIaRn5ZF6rGZPBl6TKTmrA45m57HjwNEaO95MRKQ0DYL9q606ZZOwQF65vCcJB4/y8NQTx599vTyJDxclMP70VlWuBCiVFxrox2tX9OSRczoxfcM+Lnx9AVv3Z/DV8iS6RTdwS9I8rH0E3Vs04PXZW11aZXDL/gzSs/IqNb9ZceKahNI3rhFfLU/0eMtIRnYen/yRwNldmxY7Tc7l/WLYfeQY87cW30XVlT75I4GM7Dz+fUEXbh7Smq9XJLFo28Fy7z970358fdxTQv9knZrVp2dsQ87r0ZzmDYNdfvwYVWz0OCVndcDGvWlYC51dVM1HRKQu69+6Mfef2YEfV+/hE+ccV2t3p/L3b9YwoHU4D53d0cMRijGGm4e05uOb+nEkM5fzXp3PuuQ0lxUCKe58d49oR+KhY3y3snJjlopzfPJpF7WcgaP1bFvKUf5MPOKyY1bGF0t2kZaVx4QhxVd6PaNzFI1C/PlyqXsLgxzLyee9+TsY1iGCLs0bcPeIdsSEB/PIt2vKXeRl9qYUesc2qrZuzJNvOY3nx/Vwy7FVTt/zlJzVAYVjI7pE17wy+iIi3ui2oW0Y1iGC//thPfO2pHDrJ8sJDw3gtSt71arKsjXdwDZN+PHu02nftB6hAb5ubdEc2SmSzs3q8/qsrS7rirc84TCNQwNo6Zwc2BXO6daMIH8fvlruucIgufkFvDd/B/1ahRNfwvyIgX6+XNyrBdPX7+NARrbbYvly6S4OHs3h9mFtAUfJ+n9f0JXtKUd5a872Mvffn57FuuQ0t1ZpPJm/r4/b/s8oOfM8vYPUAeuS02gY4k/zBnWripuIiLv4+BheuDSexmEBXDNpCfvTsnnz6t40KVLUQLxDswbBfH3racz923CXzG1WEmMMd49sy44DR/lxtWtazwrHm7myyES9IH/O6dqMH1Z5bs6zn1bvITk1i1uGtC51u8v7xZCbb10+lq9Qbn4B78zbQZ+WjejX6n8FhYZ3iGRMt2a8NmsrOw4cLfUYczc7ul0OdWEFUE8KDfSjSVgAuw4qOfMUJWd1wLrkNLo0r+/WCkIiInWNo6WsJw1D/Hnioq4ltgCI5/n5+pxQDdBdzuzclA5R9Xj1960UVLH17GBGNjsOHHVpl8ZCl/RuQXpWHtPWV/+cZ9Za3pq7nXaRYWXOCdk2sh59Wjbii6XuGSP33cpkdh85xu3DT+1a+c/zOhPo68Oj364t9dxzNqfQJCywVg0dUTl9z1JyVsvl5hewaW96rfqnISLiLXq3DGfFP87g0j4xng5FvICPj+HOEW3Zuj+DX9burdKxVuw6Arh2vFmhAa0bE90wmCnLSp+zzx3mbz3Ahj1p3DykdbkqzF7eL5btKUdZuvOwS+MoKLBMnLONjk3rFZskRtUP4oHRHZi/9QDflzD3WX6BZd6WFIa2j6hV1XJjlZx5lJKzWm5bSgY5+QV0aa7xZiIi7lCbPpRJ1Z3TrRmtI0J59fctVWo9W55wGH9fQzc3jBf38TFc3LsF87ceYE9q9U30DPD23O1E1gvkgvjyjf87p1tT6gX68cUS1xYGmbZ+H1v3Z3D78LYl9iy6ekBLerRowP/9uJ7UzFNLy69KOsKRzNxqHW9WHWLDQ0g+csyllUel/JSc1XLrdjuLgaiMvoiIiNv5+hjuGtGWjXvTmb6h8t0GVyQcpkvzBm6bJuLiXtFYC1NXVN+cZ2t3pzJvywFuGNSKQL/yPa+QAD8u6Nmcn9bscdncW9Za3py9lZaNQzina9MSt/P1MTx5UTcOHc3h6d82nrJ+9qYUfAwMroYS+tUpNjyEAgvJR6o3cRcHJWe13LrkNIL8fWgdEebpUEREROqE87o3J65xCK/+vqVSY6Vy8gpYlXTELV0aC7VsHEq/VuF8tTyp2uY8e2fedkIDfLmyf8UmT768byzZeQV8t9I1ieTCbQdZlZTKLUPalFn1sGt0A24Y1IrPFu86PrVBoTmbU+gR09CthWY8obBiY4KKgniEkrMawlpLZk5ehfdbl5xKh6b18VW3GxERkWrh5+vD7cPbsnZ3GrM27a/w/uv3pJGdV+DW5AwchUF2HDjKil0VH8+VcPAoh4/mlHv7pMOZ/Lh6D1f2j63wfGBdoxvQNbo+ny9xTWGQ12dtJbJeIBf3ji7X9n85oz3NGgTx96lrjnf1O5iRzeqkIwxrX3pRk5ootrHK6XuSkrMaIPnIMS5/+w/6PzmTnWWUdC3KWsv6PWnq0igiIlLNLuoZTYtGwbwyc2uFEwp3TD5dnDHdmhES4FvhOc++Xp7EqBfm0P8/M7nzsxXM33KgzPF1783fiQFuGNSqUrFe1jeWDXvSWLM7tVL7F1qZeISF2w4yfnD5u1aGBvrx+Pld2LQvnUnzdwCOwibWUuvGmwFE1QsiwNeHRCVnHqHkzMv9uDqZs16ay1rnP6MHpqwq9+SWSYePkZ6Vp+RMRESkmvn7+nD7sLasTDzCvC0HKrTv8oRDRDcMJqq+e+cnDQ304+yuzfhx1R6O5ZQ955m1lpdmbOb+KavoGxfOlf1jmbflAFdPWsyQZ2fx6swtxRYYSc3M5Yuluzi/R3OaNwyuVKwXxDcnyN+Hz5dUrcLkG7O20iDYnyv7t6zQfqO7NGVUpyhemrGZxEOZzN6UQnhoAN3dULDF03x8DC3Cg9Vy5iFKzrxURnYeD0xZxZ2f/UmriDB+unswj5/fhWUJh3nP+a1NWdYlOxI6VWoUERGpfhf3jqZ5gyBemVn+sWfWWpYnHKZPnHtbzQpd0rsF6dl5/Lau9NL/OXkF3D9lFS/N2MIlvVvwwQ39ePz8Liz++0hevjyelo1DeH76ZgY99Ts3frCU39btPd4F8JPFCWTm5HNzGZNOl6Z+kD9jujXn+5W7OZpd8WEeAFv2pTNt/T6uO60lYYF+Fd7/Xxd0wccY/vndWuZuTmFIuya1tlpry/AQjTnzECVnXujPXYcZ88o8pq5I4q4Rbfnq1tOIaxLK2F7RnNE5imenbWLLvvQyj7MuOQ0fAx2i6lVD1CIiIlJUoJ8vtw5rw7KEwyzafrBc++w+cox9adlu79JYqH+rcFo0Ci61a2NqZi7XvbeEqSt285cz2vPsJd0J8HN8hAzy9+WC+Gg+HT+AuX8dzu3D2rIuOZVbPl7Oaf/9nad+2cj7C3YytH0Enao45+oV/WI4mpPPT6v3VGr/N+dsI9jfl+sr2bUyumEwfzmjPbM2pXDwaE6t7NJYKDY8hMRDmdVWLEb+p04nZ9ZaXp6xhVdmbvF0KIBjMsPXft/CJRMXkZdv+WLCadx/Zgf8nZWEjDH856JuhAb4cv+UVeSVMf/EuuQ02kSEERzgnjK8IiIiUrpL+8QQVT+Qmz9cxv2TVzFvS0qpwxMKx5v1iq2e5MzHx3BJ7xYs2HaA3cWUTk88lMnFExeyLOEQL17Wg7tHtitxXrDYxiE8MLoDCx4cwaTr+tAztiHvzNvOgYxsbqlCq1mh3i0b0TYyjC+WVnzOs6TDmXy/Mpkr+sUSXoXqitcPjKNzs/oYA4Pb1d7kLCY8hPTsPI4UM7+buFfF23RrmYRDR5m6YjctG4dwQXz5qva4Q9LhTP7y5SqW7DzEeT2a88SFXYutZhRRL5AnLuzGHZ+t4M3Z27hrZLsSj7k+OY0BrcPdGbaIiIiUIsjfl49v6s+keTv4ee0evl6RRES9QM7v0ZwL46PpGl3/hGRnRcJhQgJ86di0+nq9XNyrBS/N2MI3K5K4c8T/PlesTDzC+A+XkpNXwMc39WdA68blOp6frw8jO0UxslMU+9Oy2LI/g4EumAvMGMPlfWN44qcNbN6XTvsK9Ax6Z+52jIGbh1Su1ayQn68Pb17dizW7U2kSFlilY3mzwnL6uw5l1rqpArxdmS1nxpgYY8wsY8wGY8w6Y8w9zuXPGmM2GmNWG2O+McY0dHu0LmaM4b9ju9GvVTh/nbKapTsPeSSO71clc/bL81i/J40XLu3BK5fHl1pmdkz3ZpzbvRmv/L7l+Liykx3MyGZvWpbGm4mIiHhY+6h6PH1Jd5Y+Moo3r+pFr9iGfLwogfNem8/IF+bwyswt7HKO71m+6zDxMQ3LnH/LlWLCQxjQ+sQ5z35bt5fL315EkL8vU28fWO7E7GSR9YMY5MJJmi/qGY2/r+GLChQGOZCRzRdLE7moZzTNGlSuIElRLRuHcm735lU+jjdTOX3PKc9ffh5wv7W2EzAAuMMY0xmYDnS11nYHNgMPuy9M9wn08+Wtq3sT3SiYCR8tq1Cp+qrKys3ngSmruPvzP2kbGcbPdw9mbK8WJXYXKOr/LuhKg+AA7p+8ipy8U7s3rktOA1ClRhERES8R5O/L2d2a8dY1fVj6yCj+O7YbEWGBvDB9M0OencXYNxawYU96tY03K2pc7xh2Hsw8Xnjs1k+W06Fpfb65fRBtI71n7HrjsEDO7NKUqX8msXlferGfgU723vwd5OQXcMvQNtUQYe1QtOVMqleZ3RqttXuAPc776caYDUC0tXZakc3+AC5xT4ju1yg0gPeu78tFbyzgxg+WMvX2gTQMcW8TbnpWLhM+Ws6i7Qe5e0Rb7h7ZrkLfkjUKDeCpsd0Y/9EyXpm5hQdGdzhhfWFy1lnJmYiIiNdpEOLPFf1iuaJfLLuPHOP7lcl8t3I3+QXWI2OZzu7WlH9+t5a7PvuTvWlZjO4SxUuX9fTKcevXDGjJL2v2cOaLc/H1McSGh9AmIpTWEWG0iQilTUQYbSLCaBQaQFpWLh8vSuDsrk1pExHm6dBrjJAAP5qEBR5v0ZXqU6ExZ8aYOKAnsPikVTcCX5awzwRgAkBsbGzFI6wmrZqE8vY1fbj63cXc+slyPrqx//FKRK52ICOb699fwoY96bx4WQ8u6tmiUscZ1TmKS3q34M052xjVOYr4mIbH163fk0Z0w2C3J5kiIiJSNdENg7ltWBtuG9aG1MxcGoSUPLTBXUIC/BjTvRmTlyVx0+mt+Ps5nfD10jLxA1o3Ztp9Q1iXnMa2/RlsSznKtpQM5m45cEJLWnhoAA2D/UnPzuP2YW09GHHNFKu5zjyi3MmZMSYM+Bq411qbVmT5Izi6Pn5a3H7W2reBtwH69Onj1fU4+7UK5+lLunHfl6t4eOoanhvXvVxdDCsi8VAm1763hD2px3jn2t6M6BhVpeP987zOLNh6gPsnr+SnuwcT5O/4hmtdcqpazURERGoYTyRmhR4Z05kLe0YzsI3rxoi5S9vIeqd0t8wvsCQfOcbWlIz/JW37Mxjcrglda+Fk0e4WGx7C0p2HPR1GnVOu5MwY448jMfvUWju1yPLrgHOBkbaWTIRwUc8W7DyQycsztxDXOKTUaogVtWlvOte+t5hjOfl8clN/+sRVvZJi/SB/nr64O9e+t4Tnp23ikTGdOZqdx44DRzm/R+0erCoiIiKu0yDYv0YkZiXx9THEhIcQEx7C8A6Rng6nxosND+H7Vcnk5BW4rTeZnKrM5Mw4mo4mARustS8UWX4W8CAw1Fpbq9o87x3VjoSDR3l++mZiXVRif3nCIW78YBmBfj5MvvU0OjZ1XavWkPYRXNU/lnfn7+CMzk3x9QFroXMVJ3sUERERkboptnEoBdYxMXqrJqGeDqfOKE8aPAi4BhhhjFnpvJ0DvAbUA6Y7l010Z6DVyRjD05d0p29cI/761WqWJ1StxP6sTfu56t3FNArx5+vbBro0MSv093M60aJRMH/9ahXLnE3QXdSELyIiIiKVoIqNnlFmcmatnW+tNdba7tbaeOftZ2ttW2ttTJFlt1ZHwNUl0M+Xt67pQ7MGQdz80XISDlauxP63f+7m5g+X0SYijCm3DiTG+YvuaqGBfjx3SQ92HcrkhembaRjiT/MGQW45l4iIiIjUbkrOPEMdSEsRHhrA+9f3Jb/AcsMHS0nNzK3Q/u8v2MG9X66kd8tGfD5hABH13DuTfP/WjblxUCuy8wro0ry+y4uZiIiIiEjdEFkvkAA/HxKVnFWrCpXSr4taR4Tx1jW9uWbSYm75ZBkThrQm2N+P4ABfgv2dtwDf4499fQzWWl6YvplXf9/KmZ2jeOWKnserKLrbX0d3YHXSEc7s3LRaziciIiIitY+PjyGmUbDmOqtmSs7KYUDrxjw1tjv3T1nFH9tLH38W4OdDoK8P6dl5XNYnhicv6lqhyaWrKsjflym3Dqy284mIiIhI7dSycSgJajmrVkrOyuni3i3o3zqcAxk5ZObkkZWbz7GcAo7l5nMsJ8/50/E4Kzef1hGhXDOgpboWioiIiEiNFBsewpIdh7DW6jNtNVFyVgEtGoXQopF7CnqIiIiIiHiTmPAQMrLzOJyZS3hogKfDqRNUEERERERERE6hio3VT8mZiIiIiIicomVjJWfVTcmZiIiIiIicIsY5nGdXJef7lYpTciYiIiIiIqcIDvAlol6gWs6qkZIzEREREREpVmx4iJKzaqTkTEREREREihUbHkLioWOeDqPOUHImIiIiIiLFig0PITn1GNl5+Z4OpU5QciYiIiIiIsWKDQ/BWth9WK1n1UHJmYiIiIiIFCtW5fSrlZIzEREREREpVuFE1IlKzqqFkjMRERERESlWRFgggX4+ajmrJkrORERERESkWD4+htjwEBIOKjmrDkrORERERESkRJrrrPooORMRERERkRLFhIeQeCgTa62nQ6n1lJyJiIiIiEiJYsNDOJqTz6GjOZ4OpdZTciYiIiIiIiUqrNioro3up+RMRERERERK1FJznVUbJWciIiIiIlKiFo2cyZkqNrqdkjMRERERESlRcIAvkfUC1XJWDcpMzowxMcaYWcaYDcaYdcaYe5zLw40x040xW5w/G7k/XBERERERqW4qp189ytNylgfcb63tBAwA7jDGdAYeAmZaa9sBM52PRURERESklol1ltMX9yozObPW7rHWrnDeTwc2ANHABcCHzs0+BC50U4wiIiIiIuJBsY1D2JOWRXZevqdDqdUqNObMGBMH9AQWA1HW2j3gSOCASJdHJyIiIiIiHhcbHoK1kHT4mKdDqdXKnZwZY8KAr4F7rbVpFdhvgjFmmTFmWUpKSmViFBERERERD9JcZ9WjXMmZMcYfR2L2qbV2qnPxPmNMM+f6ZsD+4va11r5tre1jre0TERHhiphFRERERKQaFSZnGnfmXuWp1miAScAGa+0LRVZ9D1znvH8d8J3rwxMREREREU+LqBdIkL8PCZrrzK38yrHNIOAaYI0xZqVz2d+Bp4DJxpibgF3AOLdEKCIiIiIiHmWMUTn9alBmcmatnQ+YElaPdG04IiIiIiLijVRO3/0qVK1RRERERETqphhny5m11tOh1FpKzkREREREpEyx4SFk5uRz8GiOp0OptZSciYiIiIhImVo2dlRsVFEQ91FyJiIiIiIiZSosp7/jwFEPR1J7KTkTEREREZEytWoSRnhoAPO2pHg6lFpLyZmIiIiIiJTJ18cwomMkszbuJze/wNPh1EpKzkREREREpFxGdYoiLSuPpTsPeTqUWknJmYiIiIiIlMvgdk0I8PNhxvr9ng6lVlJyJiIiIiIi5RIa6MegNo2ZvmGv5jtzAyVnIiIiIiJSbqM6R5F46Bhb9md4OpRaR8mZiIiIiIiU28iOUQBMX7/Pw5HUPkrORERERESk3Jo2CKJ7iwbM2KDkzNWUnImIiIiISIWM6hTFysQj7E/P8nQotYqSMxERERERqZBRnaKwFmZtVNVGV1JyJiIiIiIiFdKpWT2iGwYzXSX1XUrJmYiIiIiIVIgxhlGdIpm/NYVjOfmeDqfWUHImIiIiIiIVNqpzFFm5BSzYesDTodQaSs5ERERERKTC+rdqTFign6o2upCSMxERERERqbAAPx+Gdohgxob9FBRYT4dTKyg5ExERERGRSjmjUxQHMrJZlXTE06HUCkrORERERESkUoZ1iMDXxzBzg6o2uoKSMxERERERqZSGIQH0jWukcWcuouRMREREREQqbVSnKDbuTSfxUKanQ6nxlJyJiIiIiEiljeoUBaDWMxcoMzkzxrxnjNlvjFlbZFm8MeYPY8xKY8wyY0w/94YpIiIiIiLeKK5JKG0jw5ScuUB5Ws4+AM46adkzwL+stfHAP52PRURERESkDhrVKYrF2w+ReizX06HUaGUmZ9baucChkxcD9Z33GwDJLo5LRERERERqiDM6R5JXYJmzOcXTodRolR1zdi/wrDEmEXgOeNhlEYmIiIiISI0SH9OIxqEBzFivro1VUdnk7DbgPmttDHAfMKmkDY0xE5zj0palpCiTFhERERGpbXx9DCM6RjJr035y8ws8HU6NVdnk7DpgqvP+FKDEgiDW2rettX2stX0iIiIqeToREREREfFmozpHkZ6Vx9IdJ4+IkvKqbHKWDAx13h8BbHFNOCIiIiIiUhMNbteEAD8fpqtqY6WVp5T+58AioIMxJskYcxNwM/C8MWYV8B9ggnvDFBERERERbxYS4MfpbZswY8M+rLWeDqdG8itrA2vtFSWs6u3iWEREREREpAYb1SmK3zfuZ/O+DDo0refpcGqcynZrFBEREREROcHITpEAmpC6kpSciYiIiIiIS0TVD6JHiwZMV0n9SlFyJiIiIiIiLjOqUxQrE4+wPz3L06HUOErORERERETEZUZ1jgLg9w37PRxJzaPkTEREREREXKZj03pENwzWuLNKUHImIiIiIiIuY4zhjM5RzNtygKzcfE+HU6OUWUpfRERERESkIm46vRXXD4wjyN/X06HUKErORERERETEpWLCQzwdQo2kbo0iIiIiIiJeQMmZiIiIiIiIF1ByJiIiIiIi4gWUnImIiIiIiHgBJWciIiIiIiJeQMmZiIiIiIiIF1ByJiIiIiIi4gWUnImIiIiIiHgBJWciIiIiIiJeQMmZiIiIiIiIFzDW2uo7mTEpQEK1nbD8mgAHPB2EeISufd2la1836brXXbr2dZeufd3lrde+pbU2orgV1ZqceStjzDJrbR9PxyHVT9e+7tK1r5t03esuXfu6S9e+7qqJ117dGkVERERERLyAkjMREREREREvoOTM4W1PByAeo2tfd+na10267nWXrn3dpWtfd9W4a68xZyIiIiIiIl5ALWciIiIiIiJeoEYlZ8aYs4wxm4wxW40xDxVZ/qUxZqXzttMYs7KE/cONMdONMVucPxs5l19VZP+VxpgCY0x8Mft/6jz/WmPMe8YYf+dyY4x5xRnXamNML/e8AnWXF1/7jsaYRcaYbGPMA+559nWbF1/7q5x/76uNMQuNMT3c8wrUXV587S9wXveVxphlxpjT3fMK1F1uvPb+xpgPjTFrjDEbjDEPl7B/K2PMYuf+XxpjApzL9X7vZl587fV+70ZefN2r/73eWlsjboAvsA1oDQQAq4DOxWz3PPDPEo7xDPCQ8/5DwNPFbNMN2F7C/ucAxnn7HLityPJfnMsHAIs9/XrVppuXX/tIoC/wJPCAp1+r2nbz8ms/EGjkvH+2/u7r1LUP43/DAroDGz39etWmmzuvPXAl8IXzfgiwE4grZv/JwOXO+xP1fq9rj97v6+p1r/b3+prUctYP2Gqt3W6tzQG+AC4ouoExxgCX4ngTLc4FwIfO+x8CFxazzRUl7W+t/dk6AUuAFkWO+5Fz1R9AQ2NMs3I/MymL1157a+1+a+1SILdCz0jKy5uv/UJr7WHnZn/wv/8H4hrefO0znMsAQgEN3nYtd157C4QaY/yAYCAHSCvm2COAr4rZX+/37uW1117v927lzde92t/ra1JyFg0kFnmc5FxW1GBgn7V2SwnHiLLW7gFw/owsZpvLKPnCA44mUuAa4NcKxCaV583XXtyrplz7m3B8my6u49XX3hhzkTFmI/ATcGNp+0uFufPafwUcBfYAu4DnrLWHTtq3MXDEWptXzPn1fu9e3nztxX1qynWvlvd6P3efwIVMMctO/rayxG9Ay3UCY/oDmdbatWVs+gYw11o7rwKxSeV587UX9/L6a2+MGY7jH7bGHbmWV197a+03wDfGmCHA/wGjKhuHnMKd174fkA80BxoB84wxM6y128t5fr3fu5c3X3txH6+/7tX5Xl+TWs6SgJgij1sAyYUPnM2VY4Eviyx73zmA8Gfnon2F3Q+cP/efdI7LKfsb1MeACOAv5Y1Nqsybr724l1dfe2NMd+Bd4AJr7cEKPC8pm1df+0LW2rlAG2NMk/I8KSkXd177K4FfrbW51tr9wAKgz0nnP4Cju2LhF9hFz6/3e/fy5msv7uPV17263+trUnK2FGjnrKYSgONN9fsi60fhGJSdVLjAWnuDtTbeWnuOc9H3wHXO+9cB3xVua4zxAcbh6OdaLGPMeGA0cIW1tqDIqu+Ba43DACC1sGlVXMKbr724l9dee2NMLDAVuMZau7kKz1GK583Xvq1zjALGUa0vAFBy7jruvPa7gBHO9+tQHEU9NhY9uXM84SzgkmL21/u9e3nztRf38drr7pH3eusFVVrKe8NRJWkzjoouj5y07gPg1jL2bwzMBLY4f4YXWTcM+KOM/fOc517pvP3TudwArzvXrQH6ePq1qm03L772TXF845MGHHHer+/p16s23bz42r8LHC6yfJmnX6vadvPia/8gsM65bBFwuqdfq9p2c9e1x1Fpc4rz+q0H/lrC/q1xFIHZ6tw+0Llc7/d199rr/b5uXvdqf68vLAUsIiIiIiIiHlSTujWKiIiIiIjUWkrOREREREREvICSMxERERERES+g5ExERERERMQLKDkTERERERHxAkrOREREREREvICSMxERERERES+g5ExERERERMQL/D/frwJloZ5IaQAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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+flXjnwICAk4ZW1VTGfeFCxfy9ddfs3z5csLCwpg4cWKN21lrmTlzJn/5y19OWf7hhx82qAqhtZaHHnqIu+6665TlqampVa/lbK8Nzqx+aIw563HDw8Ornu/bt4+//e1vrF69mrZt23LzzTfXWua++nlO36bymBUVFbRp04b169fX9dJFREREpLV46y2YMwcuuQSefRamT29WqY11BmfGmDig1BmYhQLn4UhtjK+2TSqQYq09eznCOpyth8tTduzYgZ+fHz179gRg/fr1dO7cuUHH6tKlC88++ywVFRUcPny4arxWdTk5ObRt25awsDC2b9/OihUrqtYFBgZSWlpKYGAgU6ZM4fLLL+dnP/sZ8fHxZGdnk5eXx6hRo/jJT35CVlYWUVFRvPvuuwwePLjOtl144YX87ne/Y8aMGURERHD48GECAwPr9fo++ugjHnroIQoKCli4cCGPPfYYoaGhLh03NzeX8PBwoqOjOXbsGF988QUTJ04EIDIykry8PGJjHQU/27dvz7Zt2+jduzdz5swhMjLyjONFRUXRtWtX3n33XaZPn461lo0bN7r0XoiIiIhIC/XEExAYCM66DlVj0FavbhnBGdAReM0Y44+jgMhsa+2nnm1W08nPz+dHP/oRJ06cICAggB49elQV6aivcePGVaUIDhgwgGHDhp2xzUUXXcTzzz/PoEGD6N279ylpf3feeSeDBg1i2LBhvPnmm/zpT3/iggsuoKKigsDAQJ555hlGjx7NI488wpgxY+jYsSPDhg2rKhRyNhdccAHbtm1jzJgxgCOd84033sDf39/l1zdy5EguvfRSDhw4wO9+9zs6depEp06dXDru4MGDGTp0KP3796dbt26MGzfulNd98cUX07FjRxYsWMBjjz3G1KlTSUpKYsCAAeTn51OTN998k3vuuYc//elPlJaWct111yk4ExEREWmt1q93PB58EBISvl/ejNIajWNMWtNISUmxldUPK23bto2+ffs2WRukYR555BEiIiL45S9/6e2m1It+v0RERERaiYsvhpUrYe9eaNPG262plTFmrbU2paZ19RpzJiIiIiIi4nMWLIAvv3SkNfpwYFYXBWfikkceecTbTRAREREROZO18MADkJgI1eYcbo4UnImIiIiISPP1/vuOgh8vvwwhId5uTaP4ebsBIiIiIiIiDVJaCr/9LfTvDzfd5O3WNJp6zkREREREpHl6+WXYuRM++gjqUYXcV6nnTEREREREmp+CAnjkERg3Di67zNutcQsFZ4C/vz9DhgxhwIABTJ8+ncLCwgYf6+abb+a9994D4Pbbb2fr1q21brtw4UKWLVtW9fz555/nv//9b4PPXSk1NZUBAwacsuyRRx7hb3/7W72O4672iIiIiIi43ZNPwtGj8Ne/gjHebo1bKK0RCA0NZf369QDMmDGD559/np///OdV68vLy+s1WXOlWbNmnXX9woULiYiIYOzYsQDcfffd9T6Hp5SVlflUe0REREREqmRlOYKyH/zA0XPWQjSvnrPHH3fMYVDdggWO5W4yfvx4du/ezcKFC5k0aRI33HADAwcOpLy8nF/96leMGDGCQYMG8Z///AcAay33338//fr149JLLyU9Pb3qWBMnTqRy0u0vv/ySYcOGMXjwYKZMmUJqairPP/88//znPxkyZAiLFy8+pXdr/fr1jB49mkGDBnHllVdy/PjxqmM+8MADjBw5kl69erF48eJ6v8azHfs3v/kNEyZM4Mknn6xqz5EjRxgyZEjVw9/fn/3797N//36mTJnCoEGDmDJlCgcOHAAcvYc//vGPGTt2LN26davqSRQRERERcYs//xny8x3/tiDNKzgbMQKuueb7AG3BAsfzESPccviysjK++OILBg4cCMCqVat49NFH2bp1Ky+99BLR0dGsXr2a1atX8+KLL7Jv3z7mzJnDjh072LRpEy+++OIpaYqVMjIyuOOOO3j//ffZsGED7777Ll26dOHuu+/mZz/7GevXr2f8+PGn7HPTTTfx17/+lY0bNzJw4ED+8Ic/nNLOVatW8a9//euU5dXt2bPnlIDq+eefd+nYJ06c4Ntvv+UXv/hF1bJOnTqxfv161q9fzx133MHVV19N586duf/++7npppvYuHEjM2bM4Mc//nHVPmlpaSxZsoRPP/2UBx98sJ5XQkRERESkFvv3w7//DTNnOqo0tiC+ldb405+CM72wVp06wYUXQseOkJYGffvCH/7geNRkyBD417/OesiTJ08yZMgQwNFzdtttt7Fs2TJGjhxJ165dAZg3bx4bN26s6gXKyclh165dLFq0iOuvvx5/f386derE5MmTzzj+ihUrOPfcc6uOFRMTc9b25OTkcOLECSZMmADAzJkzmT59etX6q666CoDhw4eTmppa4zG6d+9elaoJ308iXdexr7322lrbtXTpUmbNmlXVW7d8+XI++OADAG688UZ+/etfV217xRVX4OfnR79+/Th27NhZX6+IiIiIyFk9/rijQ2bSJPj97x1jzC680LG82nfQ5s63gjNXtG3rCMwOHIDkZMfzRqo+5qy68PDwqp+ttTz99NNceOGFp2zz+eefY+oYgGitrXOb+ggODgYchUzKysrcdlw49TVXl5aWxm233cbHH39MREREjdtUf42VbQTH6xcRERERabDKDLrHHoPXX4fp0+H++2H2bG+3zK18Kziro4cL+D6V8Xe/g+eeg4cfdkTQHnbhhRfy3HPPMXnyZAIDA9m5cycJCQmce+65/Oc//+Gmm24iPT2dBQsWcMMNN5yy75gxY7jvvvvYt28fXbt2JTs7m5iYGCIjI8nNzT3jXNHR0bRt25bFixczfvx4Xn/99aqersZqyLFLS0u55ppr+Otf/0qvXr2qlo8dO5b//e9/3Hjjjbz55pucc845bmmjiIiIiMgpJk1yBGIXXQRBQTB/Prz7bpPEAU3Jt4KzulQGZrNnOy7EpEmnPveg22+/ndTUVIYNG4a1lri4OD788EOuvPJKvvnmGwYOHEivXr1qDHTi4uJ44YUXuOqqq6ioqCA+Pp6vvvqKyy67jGnTpvHRRx/x9NNPn7LPa6+9xt13301hYSHdunXjlVdecdtrqe+xly1bxurVq3n44Yd5+OGHAUeP4VNPPcWtt97KE088QVxcnFvbKCIiIiJyivh4KClx/PzrX7e4wAzANGXKWUpKiq2sXlhp27Zt9O3b17UDVM81rbRgAaxe3aJyTcV96vX7JSIiIiK+6+ab4bXX4Gc/c6Q2NkEHjScYY9Zaa1NqWte8es5qCsAqe9BERERERKRlWrAA3ngDBgyAf/wDLrusyTLomlLzKqUvIiIiIiKtzxdfQHk53Hab43nlGLTVq73bLjdrXj1nIiIiIiLS+rRr5/jXOaUU0CIz6Hyi50yl1sUT9HslIiIi0kK8/76j9kRysrdb4lFeD85CQkLIysrSF2lxK2stWVlZhISEeLspIiIiItIYBw440her95q1UF5Pa0xMTOTQoUNkZGR4uynSwoSEhJCYmOjtZoiIiIhIY8yZ4/j36qu9244m4PXgLDAwkK5du3q7GSIiIiIi4ovefx8GDoSePb3dEo/zelqjiIiIiIhIjY4ehSVLWkVKIyg4ExERERERX/Xhh2Btq0hpBAVnIiIiIiLiqz74wJHOOGCAt1vSJBSciYiIiIiI78nOhgULHL1mxni7NU1CwZmIiIiIiPiejz+GsrJWM94MFJyJiIiIiIgvev99x6TTKSnebkmTUXAmIiIiIiK+JS8P5s1z9Jq1kpRGUHAmIiIiIiK+5rPPoKSk1VRprKTgTEREREREfMv770P79jBmjLdb0qTqDM6MMSHGmFXGmA3GmC3GmD84lz9hjNlujNlojJljjGnj8daKiIiIiEjLVlgIn38OV14J/v7ebk2TcqXnrBiYbK0dDAwBLjLGjAa+AgZYawcBO4GHPNZKERERERFpHebNcwRorSylEVwIzqxDvvNpoPNhrbXzrLVlzuUrgEQPtVFERERERFqL99+HmBiYMMHbLWlyLo05M8b4G2PWA+nAV9baladtcivwRS373mmMWWOMWZORkdGoxoqIiIiISAtWUgKffAI/+AEEBnq7NU3OpeDMWlturR2Co3dspDFmQOU6Y8xvgTLgzVr2fcFam2KtTYmLi3NDk0VEREREpEWaPx9yclplSiPUs1qjtfYEsBC4CMAYMxOYCsyw1lp3N05ERERERFqRDz6AyEg47zxvt8QrXKnWGFdZidEYEwqcB2w3xlwEPAD8wFpb6NFWioiIiIhIy1ZWBh9+CJdeCiEh3m6NVwS4sE1H4DVjjD+OYG62tfZTY8xuIBj4yjhm7V5hrb3bc00VEREREZEWa/FiyMxstSmN4EJwZq3dCAytYXkPj7RIRERERERan/ffh9BQuPhib7fEa+o15kxERERERMTtKipgzhy46CIID/d2a7xGwZmIiIiIiHjXypVw5AhcdZW3W+JVCs5ERERERKTpPf44LFjg+Pn99x3zmkVHO5a3UgrORERERESk6Y0YAddcA9984wjOhg6FW291LG+lXKnWKCIiIiIi4l6TJsHs2Y5UxhMnHJUaP/7YsbyVUs+ZiIiIiIh4x6RJ0KeP4+c772zVgRkoOBMREREREW+ZO9dRDKRPH/jvf78fg9ZKKTgTEREREZGmt2ABTJ8O1sKTTzpSHK+5plUHaBpzJiIiIiIiTW/1aujRA44fh/POAz8/R4C2enWrTW9Uz5mIiIiIiDS9K6+E776D2293BGbgCMp+/WvvtsuLFJyJiIiIiEjTmzUL/P3hllu83RKfoeBMRERERESaVkkJvPoqTJ0KnTp5uzU+Q8GZiIiIiIg0rY8/hvR0R/l8qaLgTEREREREmtYLL0BSElx4obdb4lMUnImIiIiISNPZtw+++spRCMTf39ut8SkKzkREREREpOnMmuWoznjrrd5uic9RcCYiIiIiIk2jtBRefhkuuQQSE73dGp+j4ExERERERJrGZ5/B0aMqBFILBWciIiIiItI0XnjBUTr/4ou93RKfpOBMREREREQ8b/9++PJLuO02CAjwdmt8koIzERERERHxvJdfdvx7223ebYcPU3AmIiIiIiKeVVYGL73kmNesc2dvt8ZnKTgTERERERHP+uILOHxYhUDqoOBMREREREQ868UXoUMHmDrV2y3xaQrORERERETEcw4dcpTQv+UWCAz0dmt8moIzERERERHxnFdegYoKuP12b7fE5yk4ExERERERzygvh1mz4PzzoVs3b7fG5yk4ExERERERz5g3Dw4cgDvu8HZLmgUFZyIiIiIi4j6PPw4LFjh+fvFFiIuD6GjHcjkrBWciIiIiIuI+I0bANdfAe+/Bxx/D5MkwY4ZjuZxVncGZMSbEGLPKGLPBGLPFGPMH5/IYY8xXxphdzn/ber65IiIiIiLi0yZNgtmzYeZMx5izefMczydN8nbLfJ4rPWfFwGRr7WBgCHCRMWY08CAw31rbE5jvfC4iIiIiIq3dhAkQFOT4+f77FZi5qM7gzDrkO58GOh8WuBx4zbn8NeAKTzRQRERERESamX/+E06cgCuvhOee+34MmpyVS2POjDH+xpj1QDrwlbV2JdDeWpsG4Pw33mOtFBERERGR5mHBAvjtbyEiAt56y5HSeM01CtBc4FJwZq0tt9YOARKBkcaYAa6ewBhzpzFmjTFmTUZGRgObKSIiIiIizcLChY6xZrfeCiEh349BW73a2y3zefWq1mitPQEsBC4CjhljOgI4/02vZZ8XrLUp1tqUuLi4xrVWRERERER8W9u2UFYGt9/+/bJJk+DXv/Zem5oJV6o1xhlj2jh/DgXOA7YDHwMznZvNBD7yUBtFRERERKQ5sNYxt9moUTBwoLdb0+wEuLBNR+A1Y4w/jmButrX2U2PMcmC2MeY24AAw3YPtFBERERERX7diBWzd6gjQpN7qDM6stRuBoTUszwKmeKJRIiIiIiLSDL34oqMQyHXXebslzVK9xpyJiIiIiIjUKDcX3nnHEZhFRHi7Nc2SgjMREREREWm8t9+GwkK44w5vt6TZUnAmIiIiIiKNN2uWowjIiBHebkmzpeBMREREREQaZ/16WLPG0WtmjLdb02wpOBMRERERkcaZNQuCg2HGDG+3pFlTcCYiIiIiIg138iS88QZcfTXExHi7Nc2agjMREREREWm4996DnBwVAnEDBWciIiIiItJws2ZBjx4wYYK3W9LsKTgTEREREZGG2bEDFi2C229XIRA3UHAmIiIiIiIN89JL4O8PM2d6uyUtgoIzERERERGpv5ISePVVuOwy6NDB261pERSciYiIiIhI/X3yCWRkqBCIGyk4ExERERGR+ps1CxIT4cILvd2SFkPBmYiIiIiI1M/+/TB3Ltx6q2PMmbiFgjMREREREamfV15x/HvLLd5tRwuj4ExEREREROr2+OOwYAGUl8PLL8P558O+fY7l4hYKzkREREREpG4jRsA118ATT8DBgzBmjOP5iBHeblmLEeDtBoiIiIiISDMwaRLMng0XXQRhYfDMM47nkyZ5u2UthnrORERERETENUOHQlkZFBbCPfcoMHMzBWciIiIiIuKa776DNm3gpz+F555zjEETt1FwJiIiIiIidVuwwDHG7L334J//dKQ0XnONAjQ3UnAmIiIiIiJ1W7361DFmlWPQVq/2brtaEGOtbbKTpaSk2DVr1jTZ+URERERERHyJMWattTalpnXqORMREREREfEBCs5ERERERER8gIIzERERERERH6DgTERERERExAcoOBMREREREfEBCs5ERERERER8gIIzERERERERH1BncGaMSTLGLDDGbDPGbDHG/MS5fIgxZoUxZr0xZo0xZqTnmysiIiIiItIyBbiwTRnwC2vtOmNMJLDWGPMV8DjwB2vtF8aYS5zPJ3quqSIiIiIiIi1XncGZtTYNSHP+nGeM2QYkABaIcm4WDRzxVCNFRERERERaOld6zqoYY7oAQ4GVwE+BucaYv+FIjxzr7saJiIiIiIi0Fi4XBDHGRADvAz+11uYC9wA/s9YmAT8DXqplvzudY9LWZGRkuKPNIiIiIiIiLY6x1ta9kTGBwKfAXGvtP5zLcoA21lprjDFAjrU26mzHSUlJsWvWrHFDs0VERERERJofY8xaa21KTetcqdZocPSKbasMzJyOABOcP08GdjW2oSIiIiIiIq2VK2POxgE3ApuMMeudy34D3AE8aYwJAIqAOz3SQhERERERkVbAlWqNSwBTy+rh7m2OiIiIiIhI6+RyQRARERERERHxHAVnIiIiIiIiPkDBmYiIiIiIiA9QcCYiIiIiIuIDFJyJiIiIiIj4AAVnIiIiIiIiPkDBmYiIiIiIiA9QcCYiIiIiIuIDFJyJiIiIiIj4AAVnIiIiIiIiPkDBmYiIiIiIiA9QcCYiIiIiIuIDFJyJiIiIiIj4AAVnIiIiIiIiPkDBmYiIiIiIiA9QcCYiIiIiIuIDFJyJiIiIiIj4AAVnIiIiIiIiPkDBmYiIiIiIiA9QcCYiIiIiIuIDFJyJiIiIiIj4AAVnIiIiIiIiPkDBmYiIiIiIiA9QcCYiIiIiIuIDFJyJiIiIiIj4AAVnIiIiIiIiPkDBmYiIiIiIiA9QcCYiIiIiIuIDFJyJiIiIiIj4AAVnIiIiIiIiPkDBmYiIiIiIiA+oMzgzxiQZYxYYY7YZY7YYY35Sbd2PjDE7nMsf92xTRUREREREWq4AF7YpA35hrV1njIkE1hpjvgLaA5cDg6y1xcaYeE82VHxbXlEpWfkldIkN93ZTRERERESapTqDM2ttGpDm/DnPGLMNSADuAB6z1hY716V7sqHim8orLO+tPcgTc3eQe7KMxQ9Mon1UiLebJSIiIiLS7NRrzJkxpgswFFgJ9ALGG2NWGmO+NcaM8ED7xIetTs3m8meW8MD7m0hoE0pJeQX/W3XQ280SEREREWmWXA7OjDERwPvAT621uTh63doCo4FfAbONMaaG/e40xqwxxqzJyMhwU7PFm46cOMmP3/6O6c8vJzOvhCevG8KH943j3F5xvLVqP6XlFd5uooiIiIhIs+NScGaMCcQRmL1prf3AufgQ8IF1WAVUALGn72utfcFam2KtTYmLi3NXu8ULikrLeWr+Lqb8/Vu+3HKUH03uwTe/nMDlQxIwxnDj6M4cyy1m/rZj3m6qiIiIiEizU+eYM2dv2EvANmvtP6qt+hCYDCw0xvQCgoBMTzRSvMtayxebj/LoZ9s4fOIklwzswEMX9yUpJuyU7Sb3iSehTSivr9jPRQM6eqm1IiIiIiLNkyvVGscBNwKbjDHrnct+A7wMvGyM2QyUADOttdYjrRSv2XUsj999tJkVe7Pp0yGSt+8YzZju7Wrc1t/PcMOoZJ6Yu4Pd6fn0iI9o4taKiIiIiDRfrlRrXAKcMZbM6YfubY74kpzCUm6YtZKy8gr+dMUArhuRRID/2TNhr0lJ4l9f7+TNlft5+LL+TdRSEREREZHmr17VGqV1eezLbWQXlPD6baP44ejOdQZmAHGRwVw8oCPvrT1EYUlZE7RSRERERKRlUHAmNVqxN4u3Vx3ktnO6MiAhul773jimM3lFZXy8/oiHWiciIiIi0vIoOJMzFJWW85s5m0iKCeWn5/Ws9/4pndvSp0Mk/12+Hw1DbJnSc4t0bUVasUPHCzVtioiIByg4kzM8u2A3ezMKePSKgYQFuVIz5lTGGH44ujNb03L57uAJ9zdQvOpgdiHj/voN76495O2mNHvbj+Zy+MRJbzdDpF62H81l4hMLeXbBHm83RUSkxVFwJqfYeSyP577dw5VDEzi3V8PnpbtiaAIRwQG8sXy/G1snvmDulqOUllvmbj7q7aY0a0Wl5Vz3wgp+9+FmbzdFxGXWWh7+aAtlFZbZaw5SUaEedBERd1JwJlUqKiwPvr+RiOAA/t+lfRt1rIjgAK4alsCnG9PILihxUwtrVlRaTs7JUo+eQ743d4sjKFu6J5Oi0nIvt6b5+mxjGicKS1m9L5tyfcGVZuLTjWms3JfNhF5xHD5xkmV7srzdJBGRFkXBmVR5c+V+1h04wf+7tB/tIoIbfbwfju5MSXkFs9ccdEPravfbOZu54pml+oLbBDLyilmz/ziDk9pQVFrBqn3Z3m5Ss/XWqgMYA3nFZWw/muvt5ojUqaC4jEc/28aAhCie/+FwokICeHetZ/++i4i0NgrOBIC0nJP89csdjO8Zy1XDEtxyzF7tIxnVNYY3V+73WOBUXmH5etsx9mUWsHR3pkfOId/7etsxrIXfT+1HcIAfC3ake7tJzdK2tFzW7j/OzDFdAFitIFeagX8v2M3R3CL+8IP+hAb5c/mQBL7cfFSZCyIibqTgTACcYwgqePSKgRhT25zj9XfjmM4czD7Jop0ZbjtmdRsPnaj6YvDOat3B9bS5W46S2DaUYcltGNO9HQt3eOa6tnRvrTxAUIAfPz2vJx2jQ1idetzbTWoyy/dkcc3zy9Vb2Mzszchn1uK9XD0skeGdYwCYnpJIcVkFn27UtCkiIu6i4Ez4cnMa87Ye42fn9SK5XZhbj31Bvw7ERQbz+grPFAZZvCsTY+CqoQnM23qUrPxij5xHIK+olGW7s7iwfweMMUzsFce+zAJSMwu83bRmpaC4jDnfHWbqoI60CQtiRJcYVqVmt/ipCcorLE9+vYsZs1awKjWbOd8d9naTxEXWWv7wyVaCA/x54OLeVcsHJkTTu30k765R5VYREXdRcNbK5Zws5fcfbaFfxyhuO6er248fFODH9SOSWLAjnYPZhW4//qKdGQxMiObuid0pLbf6wudBC3dkUFJewYX9OwAwsXe8c7lSG+vj4w1HyC8uY8aozgCM6BpDRl4x+7Pc///DV6TnFXHjSyv559c7uXxIAoOT2rBstwpJNBdfb0vn250Z/PS8nsRHhlQtN8YwPSWR9QdPsDs9z4stFGlZdqfnaxx9K6bgrJV7/MvtZOYX89erBxHg75lfh+tHJeNnDG+uPODW4+YWlfLdwROM7xlLr/aRDEtuw/9WH2zxPRDeMnfLUdqFBzG8c1sAusSG0y02nAVKbXSZtZY3VuynTwfH7yvAyC6OFLFVqS1z3NnS3Zlc8uQS1h04zuNXD+If1wxmUu84Nh/JIadQY5V8XVFpOX/8dAs94yOYObbLGeuvGJpAgJ9R75mImxzIKuSCf37Lv7/Z7e2miJcoOGvFVqdm8+bKA9w6risDE6M9dp6O0aGc1zee2WsOurX0+rLdWZRXWM7t6ZiP7boRyexOz2fdgaYbv5NzspT84rImO5+3FJeVs3BHBuf3a4+/3/djEif0jmPF3ixOljTfkvql5RVNNiXAxkM5bDmSy4zRnavGdvaMjyA6NLDFFQUpr7D8Y94OfvjSStqEBfLRfedwzYgkjDGM7R6LtbB8r3rPfN0Li/ZyMPskf/hBfwJruIEXGxHMpD7xfPDdYcrKK7zQQpGW5dtdGVRYmLV4LycKPTsVkfgmBWetVHFZOQ++v5GENqH8/IJeHj/fjaO7kF1Qwheb09x2zEW7MggP8mdosqMn59JBHQkP8ud/q5qmMIi1luteWMFP3v6uSc7nTct2Z5FfXFaV0lhpUu94issqWNEMv2SXV1j+t+oA4x77hhtfWtkk53xz5X7Cgvy5YkinqmV+foYRXdqyugX1nB3LLeKGF1fw1De7uWpoIh/fP47eHSKr1g9JakNooD/L96jCqi87mF3IMwt2c+nAjoztEVvrdtOHJ5KRV8y3Hir8JNKaLNmVQXRoIHnFZby4eK+3myNeoOCslXpu4R72ZBTw6JUDCAsK8Pj5xnZvR7fYcF5f7p7CINZaFu3MYEz3WIICHL/G4cEB/GBIJz7dmEZekefTpTYcymFbWi7f7sxo8Xe35m45SkRwAGN7tDtl+ciuMYQG+jerkvrWWhbuSOeSJxfz4AebKKuwrE49zpETJz163pyTpXy84QiXD0kgMiTwlHUjusSQmlVIel6RR9vQFL7dmcElTy5m46Ec/jZ9MH+/ZvAZf2OCAvwY0TVGExj7uEc/24afMfzm0r5n3W5Sn3hiI4KU2ijSSGXlFSzbncUlAzswdVBHXlmaqkJnrZCCs1Yov7iMZxfs4QeDO1UVdfA0Pz/DjNGdWXfgBJsP5zT6eKlZhRw6fpIJvU69m3vtiGROlpbzyQb39dDV5v21h/AzUFZh+WrrMY+fz1vKna9vYu84ggP8T1kXEujPWGdJ/eYw1m/LkRxufGkVN7+ymqKycp6dMYzZd40GYP52zwaYc9Ydoqi0ghmjks9YN6KrY9zZ6n3Nt6R+WXkFT8zdzsyXV9EuIoiP7x/HtOGJtW4/rns7dqXnk57b/APSlmjxrgy+3HKU+yZ1J6FN6Fm3DfT344ohCczffozsgpZ9o0rEkzYcyiGvuIxzesTx0/N6UVRazvPf7vF2s6SJKThrhXan51NSXsHUQR2b9LzThiUSEujHmysb33tWOW/aeOd4s0qDE6Pp0yGSd1a7t/jI6YrLyvl4wxGmDupEQptQvtx81C3HzSsq5Q+fbOHzTWkUl/nGOK61+4+TVVByRkpjpYl94jmQXcheHy6pn5Zzkl++u4GpTy9h85Ecfj+1H1/9bAKXDOxI97gIurQLY/42zwXY1lreXHmAwYnRDEg4c3zngE7RhAT6NevUxleWpvLMgj1cm5LER/edQ8/2kWfdfmx3x40VjTvzPSVlFTzy8RY6twvj9vHdXNpnekoSpeWWD1UxV6TBljinBxrXox094iO4YkgC/12+v8luYhWVljNr8d4mG4ctNVNw1grtSc8HoHt8RJOeNzoskMsHJ/Dhd0eqJo5uqMW7MkiOCaNLbPgpy40xXDsiiQ2Hcth6xHOT3M7flk7OyVKmDU/kogEdWLwr0y2plO+uOcQrS1O59811jP7zfB75eItHX4cr5m45SpC/HxN7x9W4fmIvx/IFHu55aoj84jL+NncHk/62kI/XH+GO8d349peTuPWcrlXpsMYYpvRtz7I9WRSWeKa4y+rU4+xKz68qn3+6oAA/hiW3ZVUzLQpireXtVQcY2SWGv04bRGiQf5379OsURXRoIEt3a9yZr3l12T72ZBTw8GX9CAms+1oC9O4QyaDEaN5d6zupja8u3cdKBf/iYV9uTuPjDe6ZiH3xrgwGJUTTJiwIgB9P6UlZheXZhU3Te/bZxjT+9Nk2t9YHkPpTcNYK7cnIJ8DPkBzj3gmnXXHjmM6cLC1nzrqGf4CXlFWwfE8W5/aqeYD6FUMSCPL3Y/YazxUGeW/tITpEhTCuRywXD+hASXkF37ghOJnz3WH6d4riv7eOZFyPWN5aeYBLnlrM1KcX89/lqU0+ts1ay9wtRxnXo90Z46QqJcWE0TM+wmeKAeQUlrJybxbPf7uHiU8s4N8LdnNBvw7M/8UEfnNJX6LDznwdU/rGU1JWweJdngkU3ly5n8iQAKYOrr23ekSXGLYdzSW3CcZLutvq1OPszSzgmhFJLu/j72cY3U3jznzNsdwinvx6F5P7xDO5T/t67Tt9eCLb0nLdkrreWIUlZfzfZ9t49PNt3m7KWT30wSaufm4Zy/X/oNmx1vL3eTu4+411PPDexkZXLc5zTg90Ts/vv9t0iQ1n+vBE3lp5gMMeHhcNjkJr4LgBLd6j4KwV2pORT+d2YTWWRfa0AQnRDEqM5q1VBxo8Rmnt/uMUlJSfkdJYqW14EBcO6MAH6w55pGs+Pa+Ib3dmcNWwBPz9DMOS2xIfGcwXmxqX2rjrWB6bDudw1bBEzu0Vx79vGMaq307hDz/oj7Xw+4+2MPLP87n/rXUs2pnRJBNUbkvL49Dxk7WmNFaa2DuOlXuzKWjCaQWKSsvZfDiH99ce4s+fb+Oml1cx6s9fM/iP87j2hRU89sV2usVG8OF943jq+qEkneVmxIguMUSGBHgktTErv5gvNh3l6mGJZy2+M7JrDNY6fr+bm3dWHyQiOIBLBp799+R0Y7vHcuj4SQ604Am4m5vHvthOabnl91P71XvfHwxOICjAj/d8oPds46EcyissGw/lsOOob06Qba3l801prN1/nOtfXMFtr65m1zHfbKucqqi0nJ++s56nv9nNyC4xnCwt59udjQtoVuzNprzCnvHd5v7JPbBYj897VlFhWeK8QbloZwalmhrDaxSctUJ7MgroHte0KY3V3TAymZ3H8hv8JXTxrgwC/Axju7erdZvrRiSRW1TG3C3uGQtW3UffHaG8wnK1s9iBn5/hwv4dWLgzvVFpcR98dxh/P8MPBn9fZr1NWBAzx3bhsx+P59MfncMNI5NZvCuTm15exfi/fuPx3qq5W45iDEzpe/Y76JN6x1NSXuHRXpCD2YW8sWI/9721jsl/X0i/33/J1KeX8It3N/DqslQy84oZ1z2Why7uwyu3jGD5Q5N5567RDElqU+exA/39mNg7nm+2Z1Dh5qD3vbWHKCmv4IYaCoFUNzS5DQF+ptnNd5ZXVMrnm9K4bHCneld+Hees/rlMJfV9wtLdmcz57jB3ntvtjJRxV0SHBXJh/w58uP6w18fMVn6+BPgZ3lvbNNOr1NeRnCJyTpby/y7tywMX9WHVvmwu/NciHvpgowrl+LDsghJufGklH60/wq8u7M1bd4wiJjyIzxt5g3bxrgzCgvwZ5pweqFJi2zCuG5HMu2sOevRG1ta0XLIKSrigX3tyi8qa5Y3ClkLBWStTWl7B/qyCJh9vVt1lgzsRERzAWysbVrRj0a4MhiW3rTXNDmBMt3YkxYS6fc4zay3vrT3E0OQ2pwS4Fw/sQFFpBd/uaFiwVF7hGEg/oVcccZHBNW4zICGaR37Qn5W/mcK/bxhKSJA/v35vg0d7q+ZuOUpK57a1tqlSSpcYwoPcW1L/ZEk5C3ak88jHW5j8t4WMf3wB/+/Dzaw/cIKe8RHcP6kHz9wwjK9/PoGtf7iQz38ynn9cO4S7JnRnUu94OkaHVk307Irz+saTmV/MhkMn3PYaKiosbznHYvWqo0BGWFAA/ROim11RkE82pHGytJxrUmqvzFib7nERxEcGK7XRBxzNKeIn//uO7nHh3Dupe4OPM314IicKS/l6q3fTor47cJxuceFM6RvPnO8O+2QvQOV44qHJbblnYne+/fUkZo7twntrDzHhiYX846ud5DdhNoLUbV9mAVc9u5QNh3J4+vqh3DepBwH+flzYvz3ztx1rVLbOkl2ZjOoaUzUeurr7JvXAz8/w1De7GtP8s6pM63/okr4E+hufHEfeWig4a2UOZhdSWm692nMWHhzAFUM78emmNI7Xs+xyVn4xmw/nMr5n7ROigqM369qUJJbvzSLVjVUENx/OZcexPK4eduoX0ZFdYogJD+KLBlZtXLE3i7ScIq4cmlDntiGB/kwd1Iknpg3mWG4xzy70TKrDgaxCth/NqzOlERwFLcb1iOXbRpTUt9ay81gesxbv5caXVjL4j/O45ZXVvL3qAMntwnj4sn7M/8UEljwwif/cmMLPL+jNpYM60iM+ggA3pOhO7BWPv59xa6790j2Z7M8qZMbos/eaVRrZpS0bDuY0q0pZ76w5SK/2ES71UJ7OGEcP+LI9Wc1iKoaWqrS8gvvfWkdhSTnP/3B4o+a+HNcjlo7RIbzrxd4qay3rDpxgWHJbpg1PIjO/pME3zjxpW1ouxkAf5wTtMeFBPHxZf77++QQm943nqfm7mPjEAl5fsd8ng8vWZtW+bK58dim5RWW8fccoLquW5XLxgI4UlJQ3eNzyoeOOise1DdfoEB3CD0d15oN1h9ibkd+gc9Rl0c4M+naMomtsOCO7xnh8ehmpnYKzVmZPhiNQ6R5X/5QVd7phZGdKyip4v56FQZY4K7ud26vmP2DVTRuehJ/BrYVB3l93iKAAPy4b1OmU5QH+flzQrz3fbE9v0BfrD9YdJjI4gPP7uT4Af3jntlw5NIEXF+/zSKpDZUqoK8EZOCaiPXziJLvS6//BMW/LUcY+9g0X/HMRf/psG0dzirhpdGf+e+tINjx8Aa/eMpJbxnWle1xEvXrD6iM6LJCUzm352o3jzt5aeYCY8CAuGuDaeziiSwwl5RVs8oGCCq7YcTSPDQdPcE1KUoOvy9jusWTmFzfo90bc47EvtrNm/3Eeu3pQnVMg1MXfz3D1sEQW7czgaI53UvNSswrJLihheOe2TOwdR2xEkE+Mgzvd1iO5dGkXTnjwqcFw53bhPHPDMObcO5ZusRH87sPNXPivRSx0Y2aC1M9H6w/zw1kriQkLYs69YxneOeaU9WO6tyM6NJAvNjWsymHlWK+z3Xi+Z2J3ggP8+dfX7u89KywpY83+bM51nn9yn/bsTs/nYLbGA3uDgrNWpvKOSzcv9pyBo4z20OQ29S4M8u3ODNqGBdY4V9TpOkSHMKl3PO+uPUSZG+46lpRV8NH6w1zQr32NFf8uGtCB/OKyqj+yriosKeOLzWlcOqijy2WrKz1wUR8C/AyPfr61Xvu5Yu6Wo/TtGHXWQhrVVZbar28qRFrOSX7x7gaiQwP5y1UDWfrgZL76+QT+39R+nNsrrt7vSWOc17c924/mceh44z+QjuUWMW/rMaYPTzxj8u7ajOji+MBvLiX131l9kEB/w1XD6p/SWGmsc9xZY0rqr9ibxZNf73L7eMHW4LONaby0ZB83j+1yynjXxpg2PJEKCx98552AaJ1zrMyw5LY+PUH21rRc+nWMqnX90OS2vHPXaF68KQUs3PnftU1adEkcvbBPz9/FT/63niHJbfjg3rF0bnfmze1A5w3ar7Yda9B4y8W7M2kfFUyPsww5iYsMZubYLnyy8Yjbi9ys3JtNafn3xUgm94kHcEsVaqk/BWetzJ6MfOIig4kOrX28VlO5YWQyezMKWOniF1FrLYt3ZTKuRyz+fq7dpb92RBIZecUscENKyzfb0zleWFpVCOR0Y7vHEhkSUO/UxrlbjlJYUu5SSuPpOkSHcN+kHszdcsyt80Vl5BWz9sBxLuzvek9ex+hQ+nSIZGE93mtrLQ+8v4mycst/bhzO9SOTSWgT2pAmu8WUvo4PJHekNs5efZDyCsv1I11LaQRHpdGe8RHNYtxZcVk5c747xPn92hMTHtTg4yS2DSM5JqzB484qKiy/mbOJf369k7984dtl033Nnox8fv3eBoYmt+E3l/R123G7xIYzsksM76055JV01XUHjhMZHEBP5xfdaSmJlJZbPlrvOxNk5xWVciC7kL4dz95TaYzh/H7teeiSvpSUV7D9qHfnvWxNSsoq+NV7G/n7Vzu5amgCr982smr+sZpcMrAjeUVlLNtdv79l5RWWpbszGd8zrs4MhLvO7UZ4UAD/+npnvc5Rl0W7MggJ9COli6MYSdfYcLrGhiu10UsUnLUyjkqN3k1prDR1UCciQ1wvDLL9aB4ZecUupTRWmtQnnrjIYN5Z3bDiI9W9t/YQ8ZHBjO9Rc9pBUIAf5/dtz9fbjtVrfMAH6w6T2Da0qtekvm47pytJMaH84ZMtbukhBPhq6zGsdT2lsdLE3vGsTs12eULud1YfZNHODB66pE+NdyObWre4CLrFhjc6tbG8wjEp8/iesfWuejeiawxrU483aKqEotJyZq8+2CSpKF9vddysuCbF9bnNajO2eztW7M1q0Gv+dlcGezMKGJAQxYuL9/Hf5amNbk9rUFhSxj1vrCU40J9nbhhWYxGCxpiWksjezALWHWj6im9r9x9nSHIb/Jw38fp0iGJgQrRPpTZud/Z89OtUe89Zdf2d2205ouCsqdzzxlreW3uIn57Xk79fM7jODIixPdoRGRLA5/VMbdxyJIcThaV1jqUHxw28W8d14YvNR9lyxH3p74t3ZTKqa7tTMlUm94lnxZ4s9dZ6gYKzVsRay+70fK8WA6kuNMifq4cl8uXmoy6lmyxylo135Q9YpUB/P6YNT+Sb7emNGv+QmV/Mwh3pXDks4azFJy4a0IGck6UuTyh6NKeIpbszuWpoQtUXifoKCfTnt5f0Y+exfN5sYAXM083dcpTkmLCqgequmtQ7jjLnXcC6HDpeyJ8+28aYbu344ajODW2q203pG8/KvdmNqpK2cEc6R3KKmFFH+fyajOwSQ15xWYPukL+0ZB+/fn8j5z6xgB/OWsknG454rKT57DUH6RQdUusA9voY2yOWvKKyBk1e/PKSfbSPCua9u8dyXt94Hvl4C19vdf98dS2JtZaHPtjErvR8nrpuKJ080Ft96cCOhAX58+6apg2I8opK2Xks74xy5NOGJ7LlSG5VhURvq2xHv451p+gDdIwOoW1YIFsO+0b7W7q0nJPM357Ojyb34Kfn9XJpTG1wgD/n923PvK31u0FbWURkXC03fk932/huRIUE8M+v3NN7duTESXan55/x3WpyH8cUOe7MyhHXKDhrRbILSsg5WeozwRnADaOSKSmvcGkemsW7MunVPoKO0fX7InFNShIVlnoXH6nuo/VHKKuwTKtjbM25veIIC/J3ObXxo/WHqbBwZSPG7ABc2L8943q04x9f7ax3BczT5RaVsmxPJhf2b1/vIg/DOrclMjigztRGRzrjRqy1PD5tUIMDU0+Y0rc9JeUVLG7EHHJvrjxAXGRwnfPD1WREV0cPan3nO8stKuWFRXs5p0csP53Si32ZBfzo7e8Y9ef5PPLxFremQx05cZJFuzKYNjzR5RTjsxnTrXK+s/qlA+08lueY929MF0IC/Xnq+qH07xTNj97+jk2HmkdRlcYoLa9o0Di7N1bs56P1R/j5eb04px43u+ojPDiASwZ25NONaY2a/7G+NhzMocI6/hZV94PBnQjy940JssERnMWEB9E+6uzTlFQyxjAgIZrNbuwtkdpVjvu9oF/9skcuHtixXjdowVEMpF/HKGIjXPtdiA4N5M5zu/H1tnS+c0PP9OJdjs+607OSRnSJISI4wK1T5IhrFJy1IlWVGr04x9nperWPJKVzW95edfCsYxNOlpSzKjWbcxtwl75rbDiju8XwzuqDDS4Y8N7aQwxOjK6zkllIoD+T+8Qzb8vROlO0rLV8sO4wQ5Pb0LUBE75WZ4zh91P7k19cxj8aeTdt4Y4MSsttvVMawdFTOb5XLAt2pJ/1er6x8gBLd2fx20v7uVxwpKmkdG5LdGggXzdw3Nnu9HwW7Ejn2pQkAhtQ4j+hTSgJbUJZnVq/D91XlqSSc7KUBy/uw0/O68niX0/i9dtGMq5HLG+u3M9F/1rM5c8s5a2VB1xOO63Ne2sPYS1Md0NKIzgGuvduH1nvyahfWbqP4AC/qnF9YUEBvHRzCjHhQdz62mq3FHbxVamZBUx4fAGT/r6QV5buc7mnd/3BE/zx061M6h3HfZN6eLSN04cnkl9cxpcNnGKkIdYdOI4xnDG1Q9vwIM7rF8+H6w9TUub9svTbjubSt2NkvW6A9esUxc5jeT7R/pZudWo2EcEBdY4JPN34nrGEB/nzxWbXUhsrqyTWJyMI4OZxXWkbFtjoz3uARbsy6RAVUjVGs1JQgB/je8byzfazf56L+9X5zcEYk2SMWWCM2WaM2WKM+clp639pjLHGGM/cfhO32eOs1OgrY84q3TAqmX2ZBWe907RyXxYlZRWMr8d4s+quG5HMgexCVuytf9GBLUdy2JaWW2shkNNdPKAjWQUldVbc25rmmDOtMZXuquvdIZIfjkrmzZX72ZbW8F6SuVuOEhsRfEZakKsm9o7nWG4x29JqriZ1IKuQv3y+jfE9Y7l+pHu+3LtTgL8fE3vHsWBHeoPGQP35821EBAVw87guDW7DiC5tWZWa7fIHYk5hKbOW7OXC/u2rKpn6+RnG94zjmRuGsfI35/G7qf04WVLGb+ZsYuSj8/nluxsaFLxUVFhmrznIuB7t3BpYj+nejtWp2S6nYWYXlPDBusNcNSzhlIIk8ZEhvHrLCIpKy7nlldXknGxcIOoO+cVlfLzhCK8tS3XLF+sDWYVc/+IKTpaW0y48iD98spUxf57Pnz7detbxhtkFJdz7xlraR4Xwz2uHeLzHemTXGDq3C+O5hXvIbeQNAVetO3CcnvERNRa9mjY8keyCEq/3BJSVV7D9aN5ZKzXWpH+naErLLbvS3VupT860al82wzu3rfccmiGB/kzp2565W465NAZ85T5HlcT69mBHBAdw94TuLN6V2agCUt8XI4mt8UbB5D6Oz3ONdWxarvzWlQG/sNb2BUYD9xlj+oEjcAPOB9wz0EU8ak96PiGBfnSqZ1qgp10ysCPRoYFnHS+1aGcmwQF+jOrasKIZFw3oQFRIAP9bXf85z95fe5gg/zPnNqvNxN5xBAf48WUdd84+WHeYQH/D1IEd692m2vzs/F5EhQbyx0+2NuhOV1FpOQu3p3N+v/YN/uI20RlAL9x55hegigrLr97bgL8x/PXqQR6bs6yxpvRtT3ZBCesP1q/3atHODL7Zns79k3u4nKJSkxFdY8jIK2a/i/PXzVqyl7yiMn56Xq8a18eEB3HbOV2Z+9NzmXPvWK4Y2onPN6Xxw1kr611efPneLA4dP+mWQiDVjesRS1FpBesPnHBp+7dXHaC4rIJbx3U9Y13P9pH858bhpGYVcPfra73S05BzspQP1h3i9tfWMOz/vuLHb3/Hwx9vYebLq8gpbHigcjD7+8DsjdtH8cG94/jwvnFM7BPPK8tSmfDEAu55Yy1rTgvuyyssP/nfd2Tml/DcjOFnrTrnLsYYHr1iIPsyC7jrv2s9Nv6xUkWFZd3+4wzvXPONpXN7xhEXGez11Ma9mQWUlFW4XAyk0oDKoiAad+ZR2QUl7DyWz8gGft+4ZGAHsl24QQuw2PndpiEFwW4a04XYiGCemt/wec82HXYWI6nlxvfE3o4KxvWdIkcap87gzFqbZq1d5/w5D9gGVNb8/ifwa0D9nc3Anox8usVG+NT4HnDcabp6WCJztxwlI6+4xm0W7cpgZNeYBs95FRLoz5VDE/hy81HWHzzh8n6l5Y65zc7rF09bF8uFhwcHMKFXHF9uOVprGmVZeQUfrT/C5D6uH9cVbcKC+MX5vVi+N6tBqUTL9mRSUFJerxL6p4uPCqF/pygWbj9zzNZ/l6eycl82v5vazyNFCNxlQq84AvxMvVIby8or+NNnW0mOCWtUrxk4ioIArHLhjujxghJeXrKPSwd2pG8dd+KNMQxNbstfrhrE67eN5EhOEXf8d029Jk5/Z/VBokICGpT2ejYju8bgZ2CpC2M1SsoqeG1ZKuN7xtaaajy2eyx/vXoQy/dm8eAHG5skLSe7oIR3Vh9g5surSPnTV/x89ga2HMlhxqhk3r17DP+4ZjBr9mdz9fPLGlRR89BxR2CWV1TKG7eNon8nRy/pkKQ2PH39UJY8MIk7z+3Osj1ZTHt+OZc/s5SPnGl8T83fxeJdmfzh8v4MTHStCIU7nNMzlsenOa7Dr97d6NG56PZm5pNbVMbQWnr9A/z9uGpoAgu2p5OZX/NnTVOobzGQSl3ahRMe5O/WKn1ypsqeqIYGZxN6xRMa6M/nLqQ2Ltnd8O82oUH+3DKuC4t3ZTZ4TPHinRkYA+fUUowkLjKYwYnRKqnfxOrVX2uM6QIMBVYaY34AHLbWbvBEw8T99mQU0M3HUhor3TAqmbIKy7s1FAaprCTUkPFm1d0+vhvxUcFc8/xyl0vrL9yRQVZBCVfXM/Xw4oEdOJZbzHe1BIKLd2eSmV/stpTG6q4fmUyfDpE8+vm2en3pBpi7+RgRwQGM6d6uUW2Y1DuetQeOn5JSlppZwGNfbmdi7zimp7j/dbtTdGggI7rEML8eJfX/t/ogO4/l85tL+rg86XRtesRH0DYs0KWiIC8s3kthaTk/Oa9nvc4xvHMM/7xmCGv3H+cX725w6UtzTmEpX245yhVDE9w+OXh0aCADE6JZ7sK4s883pZGeV8yt55zZa1bdVcMS+dl5vfhg3WH+9XXD7y6fTU5hKa+v2M+MWSsY8ejXPPD+JvZm5nPruK7MuXcsSx+YzMOX9WdElxiuGpbI67eNIiOvmCufXVqvG0VHTpzkhhdXknOylDduH1WVvlpdx+hQHry4D8sfmsz/XTGA/OIyfvK/9Zzz12946ptdXD0sketGNH0q8VXDEnngoj58vOEIj3253WPnWbf/BMBZU7KnDU+krMLy4Xfem/NsW1ouQf5+9f489vMz9O0YpRQzD1u1L5ugAD8GNfAmRmiQY+z5l5uPnTU1/lhuETuP5dcaGLlixqhkQgP9eWnxvgbtv3hXJgM6RZ91rsrJfdqz4dAJr97QaG1cDs6MMRHA+8BPcaQ6/hb4vQv73WmMWWOMWZOR0fiJgKVhikrLOXi80KcqNVbXIz6CUV1j+N+qM4t21FZJqL6SYsL45P5zGNk1hgfe38Rv5myqM83mvbUHiY0Irve5J/dpT6C/qTW18YN1h2kTFsgkZ8qAOwX4+/H7y/px6PhJXly01+X9yissX287xqQ+8Y0OLib1iaO8wrLEWSK4vMLyy3c3EOTvx2NX+W46Y3VT+saz81i+Sz0cOSdL+cdXOxnVNcYtPUrGGFK6xNQ5liAzv5jXlqVy2aBO9KqjWE1NLh3UkYcu7sNnG9N4fO6OOrevLKbg7pTGSmN7xPLdgRNnnVfHWsvLS/fRLS6cCS7csPnxlB5MG57Ik/N3uT2dzVrLTa+s4ncfbiYtp4h7JnTn0x+dw6JfTeKhS/oyNLntGZkKo7u14/17xhIa5M91Lyxn7pa6e7iP5hRx/YsrOF5Qwuu3jWJQYpuzbh8WFMCNozvz9c8m8MrNI+jdIZJRXWP40xUDvPZ/7+4J3bhpTGdeWLSXl5c07ItkXdbuP06bsEC6naXAUs/2kQxOauMsauOdpJ+tabn06hDRoIJBAxKi2ZqW69EeyNZudWo2Q5PaNOpz8OKBHcjML2bNWf6GV5bQb8x0JG3Cgrh6eAIfrT9Sa+ZRbfKKSll34HidxUgm94nHWuqswizu49JfBmNMII7A7E1r7QdAd6ArsMEYkwokAuuMMWd8K7HWvmCtTbHWpsTFNX4+HGmY1KwCrPWtSo2nu2GUo2jHktPm1Fi0M5P2UcH0at/4trcND+K1W0dy94TuvLXyANe/sIJjuTXPf5ZdUMI329O5cminen+IRocGMq5HLF9sPnrGF4C8olLmbTnKZYM6uX3i10pju8dy8YAOPLtwD2k5J8+6bXmFZcPBEzwxdwdZBSWNSmmsNCTJUfGwcuD9K0v3sWb/cR75QX86RIc0+vhN4TxnGXxXJqR+ZsFujheW8Lup/dz25XdklxhSswpJz6t9fr4XFu2lqLScH0+pX69ZdXee240bRiXz/Ld76pwQ/p3VB+nfKarGXht3GNu9HWUV9qxB6dr9x9l4KIdbx3V1KUXbGMOfrxzIuB7tePD9jW6ds+eb7elsOHiC/7tiAPN/PoFfXtibAQnRdf4O9IiPYM694+jTIYq731h71mDlWK4jMMvKL+G120aeUYXwbPz8DJP6xPP6baP4351jCA1yb29nfRhjePiy/lzYvz3/99lWPttYv4l6XbHuwHGGJrWp8/di2vBEth/N80oPlLWWrUdy610MpFK/TlEUlpSzL6vAzS0TcBTv2Xw4p8Hj2ytN6h1PcIDfWafVWbIrg9iIoHrPJ3q6W8d1paS8gtdX7K/Xfsv3ZFFWYeu8+dy/UxTxkcEad9aEXKnWaICXgG3W2n8AWGs3WWvjrbVdrLVdgEPAMGtt09XLlXrZk+4so++jaY3gKNoREx50yhfE8grLkt2ZjO8Z57Yvvf5+hgcv7sO/bxjK9qN5TH16SY13tz5ef5jScutylcbTXTygA4eOn2TzaYO3v9h0lOKyCq4allDLnu7xm0v6Um4tj31xahqRtZYdR/N4eck+bn9tDUP+OI/Ln1nK89/uYWSXGCb3aXxvnr+f4dxecSzckcHu9DyemLuD8/q258qhnn3N7tQlNpzuceHMr2PcWWpmAa8s3cf04YluDVq+n++s5qIk6XlF/Hd5KlcMSaBHI266GGP44w/6M6FXHL/7aDMLa6lkt/lwDlvTcrnWg2lxKZ1jCPL3O2vl1peW7CM6NLBe/3+CAvx47ofD6RYXzt2vr23QeK/TWWt5av4ukmJCuW5EUr3/PsVGBPP2HaO5sF8H/vjpVh75eMsZKVDpeY7ALD23iNduHdHgCqq+wt/P8OR1Qxme3JafvbOelQ2onlubnMJSdqXn11oMpLofOG+MeaMwSHpeMVkFJQ0OzvpXFgVRaqNHrN1/nAoLI7s2LrU/PDiAib3j+GJzWo29nBUVliW7sxjXI7bRdQC6xUVwXt943lixv15DGRbvyiQsyL/Ovyt+foZJveNZtDOjXpNrS8O5ctt+HHAjMNkYs975uMTD7RI3qyyj3y3Wd3vOggP8mTY8ka+2HSPd2Zu18dAJck6WNjqlsSZTB3Vizr3jCAvy5/oXV/D6iv2n9HK9t+4QAxKi6NOhYR+i5/frgL+fOWO+k/fXHaJbbHi97oA3RFJMGHed242P1h/hs41pvLXyAPe/tY4Rj37Nhf9axB8/3cqu9DymDurI09cPZfVvz2P23WMICwpwy/kn9Y4jM7+YmS+vJjTInz9f5b2UqoY6r297Vu7LOuu8YH/5YhuB/n788oLebj13/05RhAb619qL9PzCvZSWW37UiF6zSgH+fjwzYxi92kdy35vrqgoWVPfO6oMEBfhx+WDPBdihQf4MTW7D0lrGnR3MLmTulqNcPzK53r+nUSGBvDRzBKUVFTzhQgpnXb7dmcGGQzncN7FHg9LTwPF6n50xjDvGd+XVZanc9fqaqgmbM/KKueHFlRzNKeKVW0YyvHPj7uT7ipBAf2bNTCEpJpQ7/ruGncfcUxb+O2dlVVcC2OiwQC7o154P1x/2eAXJ0211TnNSV/Ge2vSMjyTQ36goiIes2peFv59haHKbRh/rkoEdnWPPz7zBtv1oHpn5xY0ab1bdbed0I7ughDn1GEu5eFcGY7q1cymDZ3LfePKKyxpVtl9c50q1xiXWWmOtHWStHeJ8fH7aNl2ste7LFRG325ORT0KbUK+mtbji+pHJlDvnUQLHnZ2zVRJqrN4dIvn4vnM4p0csv/twMw+8v5Gi0nK2H81l8+HcehcCqS4mPIjR3WL4slpq46Hjhazcl82VQxOaJFC5Z2J3OkSFcN9b6/jNnE2sTs1mfM84npg2iCUPTOLbX03iL1cN4rLBnYiLbHjp95pUBtSHT5zkDz/oT3xk80hnrG5K3/aUllsW7az5z9uyPZnM3XKM+yb1ID7Kva8v0N+PYZ3b1FiO+VhuEW+s3M9VQxMaPYF5pYjgAF65eQSRIYHc+upqjuZ8n05ZVFrOh+sPc/GADkSHnTl/lDuN6xHLliO5nCg8s8T/f5enYozhpjGdG3TspJgwbj+nGx9vOFKvYhyns9by5PxdJLQJbXRRHz8/w28v7ccfL+/PN9vTufY/K9iWlsuMWSs4fPwkL988osFV43xVmzBHenlIoD8zX15VZ+q1K9YdOIGfgcEu3vSaNjyRE4WlfNPAyeYbqvLGR996ltGvFBTgR+8OkSqn7yGr9x1nQEI04cGNv0k5uU88Qf5+fL7pzKSyJbsd47caM96sutHdYujXMYqXluxzaSzlgaxCUrMKXZ78+pwesQT5+ym1sYl4ZsCL+Jw9Gfk+Pd6sUtfYcMZ2b8fbqw5SXmFZtDODgQlnryTUWNFhjjvqP57cg9lrDnHtf5bzn2/3EuhvuHxI43oJLhrQkb2ZBew85ui5rKwQdkUTpfeFBQXw4k0pPHrlAOb/YgIrHprCP68dwvSUJBLbum8C4ZrERgQzuU88VwzpxA8GuzZHnK8ZltyGNmGBNVZtLK+w/OnTbSS0CeW2OqoGNtSILjFsO5p7xgS+zy7YTUWF5UeTG99rVl2H6BBevnkEeUWl3PLqavKdhTm+3HyUvKIyrvVQIZDqxnZvh7WcMWF8fnEZ/1t9kEsGdmzUNAx3T+xObEQQj37WsLkAAZbszuS7Aye4d1J3t40bvWlMF168KYXd6flc/ORiDmQX8tLNKYzu1rj0Kl+V2DaMV28ZSV5RGTe/3PjJwtftP06fDlEuf6ke3zOO9lFNP+fZ1rRckmJCiQpp+E2O/h2j2XIkx2sFTWpTVFrerL+8F5WWs/7giUaPN6sUGRLIub1i+WJT2hnXavGuTHrGR7htDLYxhtvHd2V3ej7f7qy7cMfiyuDQxayk8OAARnWLUUn9JqLgrBWw1rI3o8Cnx5tVd8OoZA6fOMmnG4/w3cETjS6h7wo/P8PPL+jNf24czp6MAuZ8d5jJfeIbHRRe2L89xsAXmx1/nD9Yd5hRXWNIivFsYFTdwMRoZozqTPe4iCZPK3z55hH889ohzS6dsVKAvx+Te8ezYEf6GeOB3lt7kK1puTx4cR+3l5WvNLJLDNY6xkFUOnLiJG+vOsj0lESS27n/96hfpyiemTGMncfyuO/NdZSVV/DO6oMkxYQ2SaAwKLENYUH+LDtt3Nn7aw+RV1TGrY2cQy4iOICfnd+L1anHmbvF9akSKllrefLrXXSMDmFaA8ej1mZK3/a8e/cYxnZvx0szRzC2u2cyBnxFv05R/OfG4ezNzOeu19c0OMWwvMKy/uAJhnVu4/I+/n6Gq4YlsnBnxlmL7rjbtiO59G1gqnyl/glRHC8sJS2n6drtileXpXLLq6sb1SvtTRsOnqCkvKJqnkl3uHhAR47kFLHh0PdpqEWl5azal805LvZauWrqoE7ERwbzkgvVUBftzCChTehZK5uebnKfePZmFJCaqWI0nqbgrBU4mltEYUm5z5bRP90F/ToQGxFUNUDe1W53d7iwfwc+vG8c5/WN596JPRp9vPjIEFI6t+XLzUfZcCiHvZkFHi8E4muaa2BWaUrf9hwvdJQcrpRfXMYTc3cyvHNbpg7q6LFzD01uS4CfOWW+s2cW7MZiud/NvWbVTewdz/9dPoBvd2Zw/1vfsXxvFtcMT2qSCeyDAvwY2TXmlKqKFRWWV5buY2hym1onGK6Pa1OS6BkfwWNfbKOkrH4D3JfvyWLN/uPcO7F7o6ecqMmAhGjeumM04zyUyu1rxvWI5Ylpg1mxN5tfvduwycJ3Hssjv7jMpWIg1U0bnkh5E855VlhSxr6sAvo1MKWxUuXk45sP+9a4s8oKnN/UY35IX1KZQp7SxX2Fd87r65hW54tN3489X5N6nOKyCrffeA4K8GPm2LonpS4rr2DZ7izO7RVbr8/nymJh36j3zOMUnLUC31dqbB7BWVCAH9NTkjheWEpEcADD6vmB21g94iOYNXOEy2MX6nLRgI5sP5rHP77aSXCAHxcP9NyXeXG/c3vFEuhvTimp/+yC3WTmF/N7N5bOr0lokD8DEqKrBmEfzC5k9pqDXDcimYRGpPa54oZRydw9oTtfbjmKn4FpTThx+Nju7diTUVA1zcU329NJzSrk1nHuSR8N8PfjN5f0JTWrkDdX1q/89JPzd9E+KpjpTZDi2VpcMTSBn53Xi483HGHF3voXHKi8cVLfapbd4yIYltyGd9c0zZxn24/mYS0NrtRYqW/HSIzxrYqNB7IK2XQ4B2PwSurbqn3ZjUpVBliVmk2fDpG0CXPfMIroMMe0Op9v/j61cfHuDAL9DaO6uX8s6YxRyYQE+p11eo4Nh06QV1xW7/Funds5KhgvqKWir7iPgrNWoLJSY/f45pHWCHD9iGQAxnRv1+BKaL7iogGO6f8W7czggv4dGjXWQJpeZEggo7q2qyqpfzC7kFlL9nHV0AS3BfBnM7JrDBsO5lBUWs6/v9mNMYZ7J3X3+HkBfn1hb24a05mZY7vQMdqzwWB1lel8lSX1X166j07RIVw8oPETfFea2DuOcT3a8eT8XS6Pd1qxN4uV+7K5e0J3j6WytlZ3TehGbEQQzy7cXe991+0/QbvwIJIbkC4+bXgSu9Lz2XjI871QlcVAGttzFhYUQLfYcJ8Kzj5z9gzNGJXMliO5pxQUagrPLdzNi4v38V0DUyrLyitYu/+4R4rvXDKgIwezv59WZ/HOTIYlt3VbZeTq2oQFMW14Ih9+V/uk1It2ZuJnHDfB6mtyn3hW7M2qGo8sntG8v/WKS/Zk5BMZEkBchHur8XlScrswnpg2iJ+d18vbTWm0hDahDE50pKFc1Yzm+ZLvTekbz+70fPZnFfDYl9vxM/Cri9xbOr82I7rEUFJewScbjvDeukPcMDK5yQIlPz/DHy8fwMOX9W+S81Xq1zGKNmGBLN2dyba0XJbtyeKmsV0IcOONGmMMv7mkLzknS3l2gWsBwVPzdxEXGcz1I5Pd1g5xCAn059ZzurJ4V2a90/XWHTjOsM5tG9SLPXVwR4KbaM6zrWm5RIUEuKXXe0BCNFt9qJz+55vSGJzUhpljugBNm/qWX1zG0t2OGzmzVx9s0DG2HMmlsKTcI8HZ+f3a4+9n+HxzGpn5xWxNy/XI9ECV6pqUetGuDAYltmlQD+HkPo4Kxkt2qUC7Jyk4awX2ZOR7pRhEY01PSWr0HUZfcdOYLgzv3LZJx8+J+5zXtz0Aj3+5g882pnH3hO5NFiClONN6H/54CwF+hnsnNk2vmTf5+RnGdGvHsj1ZvLxkH6GB/lzngcmv+3eK5uphibyyNLXOialXp2azbE8Wd53bTb1mHvLD0Z2JDA7guYV7XN4nu6CEfZkFDZ6gOyokkIsGdOCj9YfrNYFvQ2xLy6Vvxyi3fBb37xTFkZwisgvOnHKiqVWmNF46sAM94iNIigmtscKtpyzckU5JeQW920fyyYYjFDSgV6dyvJk7i4FUahsexNju7fhiU1rVWFpPTQ8EZ5+UOqewlA0HT3BuA7+LpHRpS2RIQLOuytkcKDhrBfakFzSb8WYt1dXDE3n/nrFuvfMvTScpJoxe7SP4bFMaHaJCuPPcbk127rbhQfRqH0FhSTk3ju7s9vnUfNXY7u04fOIkH3x3mKuHJ7h1HEh1v7ygN35+8HgdE1M/NX8XsRFBzBjVsDnWpG5RIYHcOKYzn29OY68zHb8u3znHm9W3GEh104cnkVtUdsq4Uncrr7BsT8tz2w3HyqIgvjAZdWVK4yUDO2KMYUqf9izZncnJkqaZ4HvelmO0Cw/i/64YQEFJeVVhkvpYuS+bLu3CPPb39eIBHUnNKmTW4n1EhwYyICHaI+epdOs5XWuclHrZnkwqLA3uuQv09+PcnnF8syOdigrfmsqhJdE3xRYuv7iMo7lFzWq8mYgvmuLsPXvg4t4eGStwNmO7xxIW5M9dE1p+r1mlMc5xZ+UVllvcVAikJh2iQ7hzfDc+2XCk6ov+6dbuP87iXZncMb4boUHqNfOkW8/pSpC/H//5dq9L26/df5wAP8OgxIZ/2R3TvR2dokM8mtqYmlXAydLyRhcDqdTfGeT5wrizzzYdYXBSm6q5M6f0jae4rILlez2f+lZSVsGC7emc17c9I7q0pVtcOO+sqV9qY0WFZc3+bI9O9n5B//b4Gdh0OIdzesTi7+HKt2O6tatxUupFuzKJDA5o1HjpyX3iycgr9onfvZZKwVkLV3n3UT1nIo1z67iuPHrlAC4f3PTjBn95YW/m/vRc4iKbz7jRxuoeF05i21Cm9In3+N+vOyd0JzYimD9/vq3Gam9Pf7OLmPAgfjhavWaeFhsRzLUjkvjgu0Ok5Zysc/t1B47Tr1NUo1JNK+c8W7Qzw2OFLCqLgfR1U3DWJiyIhDahXi+nvz+rgM2Hc7l04PfFekZ2jSE8yL+qiJInrdibRV5xGRf0b48xhmtTkli7/zi70/NcPsau9HxOFJYysqvn5nGMjQhmlPP47p7frCY1TUptrWXRzoxGF1qb2DvOWZWzeU6Z0BwoOGvh9ig4E3GLuMhgZozq3CRzfZ0uIjigSScu9wXGGN67eyz/vG6Ix88VERzALy6onJj66Cnr1h88wcIdGdw+vivhwU3bY9pa3TG+GxUWZi0++2S6ZeUVbDiY0+DxZtVNG55IheWMNDB32ZaWS4CfoWd7930W9+8UVRX0eUv1lMZKwQH+jO8Zxzfb0z0+RcHcLUcJC/KvmhfwqmGJBPgZZq9xvRd01T5HMZFRHuw5A7hyaAKB/sajxUCqO31S6n2ZBRw+cZLxjTx/u4hghiS10bgzD1Jw1sLtSS8gwM/QuV3r+mInIs1fh+iQJpt6YvrwRHq1j+CxL7afMjH10/N30SYskJucVejE85Jiwrh8cCfeXnWA42cpeLH9aB4nS8vdMhdml9hwRnRpy7trD3okoNialkuP+Ai3Tlzev1M0+7IKvFrWvLJKY2VKY6XJfeNJyyliW5rrPVj1VVFh+WrrMSb2jqvqOY2LDGZK33g+WHeI0nLXJphfuS+bjtEhJLb1bJGn6SmJLH1wssfnqKx0+qTUi50VFhtaDKS6yb3j2XAoh/S8pp0yobVQcNbC7cnIJzkmrNnPFSYi4kkB/n48dNrE1JsP5zB/ezq3n9OVCPWaNam7J3ansKScV5el1rrNOjcUA6lu+vAk9mYUNHiurLPZeiTX7dWHByREYa2jV84bKlMap1brNas0qXc8AN94MPVtw6ETpOcVc0G/U+c/vHZEEpn5JS6lVVprWbUvmxFdYjxe0doYQ3xk0xZ0umHk95NSL96VQed2YXRu1/gaBJP7Oq7vwu0ZjT6WnEnf2Fu4PRn5dFNKo4hInSb2imN8z1jHxNSFpTw5fxdRIQHcNLaLt5vW6vRqH8n5/drz6rLUWkujr91/nPZRwXSKds8X3ksGdSQ00N/thUEy84tJzyt2WzGQSlUVG7007qwypfHigWdODh8XGczgpDbM92Dq29wtxwjwM1WBYKVze8bRPiqYd1YfqPMYB7ILSc8r9mgxEG9qG/79pNRLd2e5bTqffh2jSIoJ5YPvPD8/YGuk4KwFKyuvIDWzUJUaRURcYIzhoYsdE1P/fPZ6vtp6jFvP6dpkqZVyqnsmdifnZClvr6r5S/a6A8cZltywyadrEhEcwMUDOvDJhiNunfOssmfL3cFZ+6hg2oUHea1q3ueb0hhSQ0pjpSl94ll/8ASZ+cUeOf+8rUcZ3a0d0WGn/v8M8Pdj2vBEvnWhwMtK5/xmnh5v5k2Vk1KfLC1nfE/3jHczxnDdiGRW7M1md7pr016I6xSctWCHjp+kpLxCxUBERFzUr1MU04YlMn97OpHBAdwy1nNl/OXshiW3ZXS3GF5cvJfislODpfS8Ig5mn3RbSmOlaSmJ5BWVnVEYpjHcXamxkjGG/gnRXgnOvq/SeGZKY6XJfeKxFhbucH/q2+70fPZmFHBh//Y1rr8mJYkKC++tPXtZ/VX7sokJD6JHfMv9ntQtLoIpfeIJ8DOM6e6+ipTXpCQR6G94a2XdPZRSPwrOWjBVahQRqb9fXtibyJAA7ji32xl35aVp3TuxB8dyi5mz7tQqiuv2nwBgqBsqNVY3ums7EtqEujW1cWtaLh2jQ2gb7v6J1Pt3imLnsbwzgldPO1tKY6X+naLoEBXCfA9M7j1vqyN4Pq9fzcFZ53bhjO4Ww+w1h846WbJjvJn7el991Z+vGsh/bx3p1iyAuMhgLuzfgffWHnRrT7MoOGvRvg/OlNYoIuKq9lEhrHhoCvdP6uHtprR643vGMiAhiv8s2kt5tS/Z3x04TpC/HwMS3Nsb5ednuHp4Ikt2Z3LkRN3zrLliW1qu21MaK/XvFEVZhWXXsaZNLasrpREcPXuT+8azaGfGKRVQ3WHulmMMToymY3TtlQ+vG5HMgexCVjhL5Z/uaE4RB7ILGdGl5aY0VmofFcLYHu6fX23GqM7kFpXxyYYjbj92a6bgrAXbk15AbEQQbcLcf7dORKQlCw8O8MqcdnIqYwz3TuzBvswCvticVrV87f7jDEiIcmtp+krThiVi3TTnWVFpOXsyCtxeqbHSgMqiIEeariiIKymNlab0iaegpJxVzrFd7nA0p4gNB09wQf/ae+0ALhrQgciQAGavrjm1cVVq5Xgzz00+3dKN7hZD97hw3lRqo1spOGvBVKlRRESauwv7d6BbbDjPLtiDtZaSsgo2HnbP5NM1SW4XxqiuMby7pvFznu08lkd5hfVYz1lyTBgRwQFsPtx0485cSWmsNLZ7LMEBfsx3Y0n9r5xpkrWNN6sUEujPFUMS+GLzUXJOlp6xftW+LCKCA+jbMdJtbWttjDHMGNWZ9QdPsNlLVUNbIgVnLdjezAKNNxMRkWbN389w94TubE3L5dudGWxNy6WkrMLtxUCqm56SRGpWIWv3H2/UcTxVDKSSn5+hX8eoJu05+2xj3SmNlUKD/BnXI5b529LdNrn3vC1H6RYb7tL3m2tHJFFcVsHH68/sBV21L5vhndsSoHlgG+XqYYkEB/jxVi1VVaX+9BvZQmUXlJBdUKLxZiIi0uxdMTSBjtEhPLtwT1XANMyDwdnFAzoQFtT4Oc+2peUSHuRPckzdgUxD9esUxba0vFPG5HnK/qwCthzJZeqgulMaK03uE8+B7EL2ZBQ0+vw5J0tZvieL8/u3d6mIx4CEaPp1jOJ/p6U2Hi8oYeex/BY7v1lTig4L5LLBnfjwu8PkFZ3ZQyn1p+CshdpbWQykBZeHFRGR1iEowI/bx3dj1b5s3lyxn4Q2obSPcs/k0zUJDw7gkoEd+XRjGoUlNU+C7Yqtabn07Rjl0fGLAxKiOVlazr7M+hUF+XJzGtuP1i8d8vuUxvoFZwDfuCG1ceGOdMoqLBfWMd6sumtHJLHlSO4paXernePNFJy5x4xRyRSWlPPhehUGcQcFZz7OWsvLS/bx1y+312u/ykqNPZTWKCIiLcD1I5NoGxbI3swCj/aaVZo+PJH84obPeVZRYdmWluexYiCV+juPX5/5zpbuzuTuN9Zx5TPL+Gqr60HTZxvTGJrchoQ2tVdJPF2nNqH07RjF/G3pLu9Tm3lbjhEXGcyQxDYu73PFkASCAvyYveb73rNV+7IJCvBjUGJ0o9skMCSpDf07RfHmiv1uS19tzRSc+bDS8goe+mATf/x0K88t3MOina5P5Lgno4DgAD861eMPqIiIiK8KCwrgZuek4MOS23j8fCO7xpAcE8a7axqW2njweCH5xWUeG29WqUd8BEEBfi4HZwXFZTzw/ka6xobTq30Ed76+hleX7qtzv9RMR0qjK1UaTzelTzxr9h8np7DhaW9FpeUs3JHO+f3a16snMjoskIsHdODD7w5Xzce1KjWboUltPFLtszWqLAyy/Wge6w6c8HZzmj0FZz4qv7iM215bw/9WH+Seid1Jignlz59vczmnfE96Pl1jw/FXKWgREWkhbh7XhetHJjUoQKgvYwzThieybE8Wh44X1nv/bWmOYMlTlRorBfr70adDpMtFQR7/cjuHT5zk8WmD+N+dYzi/b3se+WQrf/xk61m/YzQkpbHS5L7xlFdYvt3l+k3m0y3bk0lBSTkX1DLx9Nlcm5JEbpGjFzS/uIzNh3MYpZRGt/rBkE5EBAfw5or93m5Ks6fgzAcdzSli+vPLWbo7k8euGsgDF/Xh1xf2YfvRPN5f59odvD0Z+RpvJiIiLUp0aCB/uWoQ8R4cb1bdVcMSAPhgXf3nPNt6JBc/A707eL5Ue/9OUWw+nFtnStnKvVm8tnw/M8d0YUSXGEKD/Hnuh8O5dVxXXl66j3veWMvJkvIa9/18U/1TGisNTmxDu/AgvtnW8HFn87YcIzI4gLHd6z+Z8uhu7UiKCeWd1QdZu/84FRZGan4zt4oIDuCKoZ34dFMaxwtKvN2cZk3BmY/ZlpbLlc8u5UBWAS/fPILrRiYDMHVQR4YkteHv83bUOTi5uKycA9mFKqMvIiLSCIltwxjbvR3vrT1ERT2rIW5Ny6V7XAQhgZ5PnevXKZqck6UcPnGy1m1OlpTz6/c3khwTxq8v6l213N/P8PvL+vHIZf34etsxrnthORl5xafs25iUxspzTOoTz4IdGZSVV9R7//IKy9fbjjGxTzxBAfX/6urnZ7hmeBLL9mTx/tpD+PsZhjZBamxrM2NUZ0rKKlzuSJCatfrg7Outx/h0o29Ul1m0M4Ppzy/HWnj37rFM6BVXtc4Yw/+7tC/HcouZtfjsueH7swqpsKiMvoiISCNNT0nkQHZhVYU/V209kuvx8WaVBrhQFORv83awP6uQv149iLCggDPW3zyuK/+5MYWdx/K58tml7E7Pq1rXmJTGSlP6xJNzsrRBY5LWHThOZn5Jg1IaK01LScTPwMcbjjAgIZrw4DPfA2mcvh2jGN65LW+uPKDCII3QqoMzay0vLN7Lg+9v4mB2/fPJ3Wn26oPc+upqEtuGMue+sTVWd0rpEsPFAzrw/Ld7SM8tqvVYe9KdZfTVcyYiItIoF/XvSERwQL3mPDtRWMKRnCKPV2qs1KdDFH4GthyuedzZ2v3ZvLx0Hz8cncyY7rWn853frz3v3DWaotIKrnp2Gcv2ZAKNS2msdE7PWAL9DfMbUFJ/3pajBPn7MbF3XN0b16JjdCjnOm96a7yZ58wYlcy+zAKW78nydlOarVYdnBlj+Pv0wQD8fPb6JpnA8XTWWv4+bwe/fn8jY7q34927x9AxuvY/fg9c1IeSsgr++fXOWrepLKPfTT1nIiIijRIa5M/UQR35bFMaBcWuzXm2tYmKgVQKDfKne1xEjT1nRaXl/Oq9jXSKDuXBi/vWeaxBiW2Yc+9Y2keFMPPlVTw1f1ejUhorRYYEMqprO76pZ0l9ay3zth5jbI92RIYENqoN141wDBUZ003jzTzlkoEdaRMWyBsrVRikoeoMzowxScaYBcaYbcaYLcaYnziXP2GM2W6M2WiMmWOMaePx1npAUkwYf7y8P6tTj/OfRXua9NwlZRX8fPYGnv5mN9ekJPLyzSPq/MPTJTacG8d05p3VB9lxNK/GbfZkFNApOqTGtAURERGpn2nDEyksKeeLza7NebbVGSQ1VVojOIqC1BSc/fPrnezNKOCxqwcS4WIqX1JMGO/dM5YRXWL4x1eOm8GXuKFC5uQ+8exKz+dAluvZSjuP5bM/q5AL+rk+8XRtLuzfng/uHduoHjg5u5BAf6YPT2TelmNnzfKS2rnSc1YG/MJa2xcYDdxnjOkHfAUMsNYOAnYCD3mumZ515dAELh3YkX/M23nKDPKeVFRazq2vrmbOd4f5xfm9+OvVgwj0d60j88eTexIRHMBfvthW43pVahQREXGf4Z3b0jU2nHerTWR8NlvTcomLDCYuMtjDLfvegIRojuYWkZn/fTGP9QdP8OKivVw/MonxPesXkESHBvLqLSO5aUxnrhuR5JZ5U6f0jQfgm3qkNs7dchRj4Lx+8Y0+vzGGYcltMUbTDHnS9SOTKauwp0z8La6rMxqw1qZZa9c5f84DtgEJ1tp51trK/v0VQKLnmulZxhgevXIA7SKC+Mn/vqu1jKy7VFRYfjF7A0t2Z/L4tEH8aErPev2haBsexI8m92ThjgwWnzZniLWWPen5Gm8mIiLiJpVznq3cl82w//uKqU8v5s7/ruEPn2xh1uK9fL4pjfUHT5CeV0RFhWVbWl6TpTRW6ndaUZDisnJ+9e4G2keF8NAldacz1iQowI8/Xj6Ax64e5JY2dm4XTve4cOZvdz21cd7WowxLbkt8ZNNMnyCN1y0ugnE92vH2qoNeGTLU3NUr780Y0wUYCqw8bdWtwDu17HMncCdAcnJy/VvYRNqEBfH36UP44UsreeyLbfzh8gEeOY+1lj9+upXPNqXx20v6ck1KUoOOc9PYzry2PJVHP9vGZz+OrZps+lhuMQUl5arUKCIi4kYzx3bB389wMLuQwydOkppVwNLdjomRqwvy96OkvIKJvbs3afv6d4wGYMuRHCb0iuPp+bvZlZ7Pq7eMIKqRY7XcaUrf9ry6NJX84rI60ywPnzjJ5sO5PHRxnyZqnbjLjFGduffNdSzckc6Uvg2vstkauRycGWMigPeBn1prc6st/y2O1Mc3a9rPWvsC8AJASkqKT4fP5/SMrZqIcVKfeCb2bnwX+uleWLSXV5elcts5Xbnj3G4NPk5wgD8PXNSHH739HR+sO8R0Z5BXWQxEPWciIiLuExEcwN0TTg24rLXkFpVx+PhJjpw4yZGckxw+cZLMvBKudk5g3VSiwwJJiglly5FcNh/O4blv9zBteKJHvss0xuQ+8bywaC/Xv7CCpJhQ2oUH0y4iiHYRwcSGO/5tFxFEbHgwc51j/C7o3/jxZtK0zu/XnrjIYN5ceUDBWT25FJwZYwJxBGZvWms/qLZ8JjAVmGJbyIQGv76oN0t2Z/Cr9zYy96fnEhMe5LZjz/nuEH/5YjtTB3Xktw1MMahu6qCOvLRkH3+bt4OpgzoRGuT/fXCmMWciIiIeZYwhOjSQ6NDAJiubfzb9O0az8dAJfvnuBtqFB/G7S/t5u0lnSOnclhtGJbM7PZ8dR/PIKsjiRGFprdv3jI+ga6yygZqbQH8/rhuRxL8X7ObQ8UIS24Z5u0nNRp3BmXEMhnoJ2Gat/Ue15RcBDwATrLXenSTMjUIC/fnXtUO54pml/OaDTTz3w2FuGTi6eFcGv3p3I2O6tePv1wzGz6/xx6ycmHra88t5cfFefjylJ3szCogIDiC+CQchi4iIiPf17xTFl1scvU2zbkohOsx30hkrBfj78ecrB56yrLS8guOFJWTlOx8FxVX/1reQifiO60Ym88yC3fxv1UF+eWFvbzen2XCl52wccCOwyRiz3rnsN8BTQDDwlTN4WWGtvdsTjWxq/TpF8YsLevGXL7bz3trvUwYbavPhHO5+fS094iP4z03DCQ7wd1NLT52Y+rqRSY5KjXHhqkQkIiLSygxIcIw7u2JIJ87r13xSyQL9/YiPDFHRjxYmoU0ok3rH8/mmNH5xQS99N3VRncGZtXYJUNO7+bn7m+M7bh/fjQU70nnk4y2M6tqO5HYN6449mF3Iza+spk1YEK/eMtIjg3IfuKgPX209xj+/2sWe9HxGa3JFERGRVmdcj1gevLgP14/03QJs0rr88YoBtAkNVGBWD65NrNUK+fsZ/n7NEPz8DD+fvZ6y8op6HyO7oISbXl5FaXkFr906gg7Rnrkj9P3E1Ac4klOk8WYiIiKtUFCAH3dP6E50qO+lM0rrlNAmlHAXJz8XBwVnZ5HQJpQ/XTGANfuP8/y3e+q178kSxyTTR06cZNbMFHrER3qolQ4/ntyz6pdfZfRFRERERJofBWd1uHxIApcN7sS/vt7FxkMnXNqnrLyC+99ax8ZDJ3jyuqGM6BLj2UbimJj6J1N6AtCng/crRomIiIiISP2YpqyAn5KSYtesWdNk53OXnMJSLnpyEcVlFfSMjyA8OMDxCPI/5d+w4AAigv1ZtDOTOd8d5v+uGMCNozs3WTuttaRmFarkrIiIiIiIjzLGrLXWptS0TkmgLogOC+T5Hw7n6W92k1tUSnpeEQWZ5RQUlzkeJeVn7HPfpO5NGpiBo7S+AjMRERERkeZJwZmLBie1YdbMGgNcKiosJ0vLKSgpo6C4HIOjSIeIiIiIiIirFJy5gZ+fqUp1xLN1P0REREREpIVSQRAREREREREfoOBMRERERETEByg4ExERERER8QEKzkRERERERHyAgjMREREREREfoOBMRERERETEByg4ExERERER8QEKzkRERERERHyAgjMREREREREfoOBMRERERETEBxhrbdOdzJgMYH+TndB1sUCmtxshTULXunXR9W49dK1bF13v1kPXuvVoTde6s7U2rqYVTRqc+SpjzBprbYq32yGep2vduuh6tx661q2LrnfroWvdeuhaOyitUURERERExAcoOBMREREREfEBCs4cXvB2A6TJ6Fq3LrrerYeudeui69166Fq3HrrWaMyZiIiIiIiIT1DPmYiIiIiIiA9oVsGZMeYiY8wOY8xuY8yD1Za/Y4xZ73ykGmPW17J/jDHmK2PMLue/bZ3LZ1Tbf70xpsIYM6SG/d90nn+zMeZlY0ygc7kxxjzlbNdGY8wwz7wDrYsPX+8+xpjlxphiY8wvPfPqWxcfvtYznP+nNxpjlhljBnvmHWhdfPh6X+681uuNMWuMMed45h1oPTx4rQONMa8ZYzYZY7YZYx6qZf+uxpiVzv3fMcYEOZfrc9vNfPha6zPbA3z4ejf/z21rbbN4AP7AHqAbEARsAPrVsN3fgd/XcozHgQedPz8I/LWGbQYCe2vZ/xLAOB9vA/dUW/6Fc/loYKW336/m/vDx6x0PjAAeBX7p7fequT98/FqPBdo6f75Y/7db/PWO4Pt0/0HAdm+/X8354clrDdwA/M/5cxiQCnSpYf/ZwHXOn5/X53arvNb6zG5d17vZf243p56zkcBua+1ea20J8D/g8uobGGMMcA2OD9uaXA685vz5NeCKGra5vrb9rbWfWydgFZBY7bj/da5aAbQxxnR0+ZVJTXz2eltr0621q4HSer0iqY0vX+tl1trjzs1W8P3/eWk4X77e+c5lAOGABmU3jievtQXCjTEBQChQAuTWcOzJwHs17K/Pbffy2Wutz2yP8OXr3ew/t5tTcJYAHKz2/JBzWXXjgWPW2l21HKO9tTYNwPlvfA3bXEvtv0iAo8sVuBH4sh5tk/rx5est7tVcrvVtOO60S+P49PU2xlxpjNkOfAbcerb9pU6evNbvAQVAGnAA+Ju1Nvu0fdsBJ6y1ZTWcX5/b7uXL11rcr7lc72b5uR3g7QbUg6lh2el3NWu9U+rSCYwZBRRaazfXsemzwCJr7eJ6tE3qx5evt7iXz19rY8wkHH/kNQap8Xz6eltr5wBzjDHnAv8HnNfQdohHr/VIoBzoBLQFFhtjvrbW7nXx/Prcdi9fvtbifj5/vZvz53Zz6jk7BCRVe54IHKl84uz+vAp4p9qyV5wDEj93LjpWmbbg/Df9tHNcR913Wh8G4oCfu9o2aRBfvt7iXj59rY0xg4BZwOXW2qx6vC6pmU9f70rW2kVAd2NMrCsvSmrkyWt9A/CltbbUWpsOLAVSTjt/Jo50xcob0dXPr89t9/Llay3u59PXu7l/bjen4Gw10NNZnSUIx4fvx9XWn4dj8PahygXW2lustUOstZc4F30MzHT+PBP4qHJbY4wfMB1H3myNjDG3AxcC11trK6qt+hi4yTiMBnIqu2qlwXz5eot7+ey1NsYkAx8AN1prdzbiNcr3fPl693COZcA4qvcFAc3ug92HePJaHwAmOz93w3EU9dhe/eTO8YMLgGk17K/Pbffy5Wst7uez17tFfG5bH6hK4uoDR3WlnTgqxPz2tHWvAnfXsX87YD6wy/lvTLV1E4EVdexf5jz3eufj987lBnjGuW4TkOLt96olPHz4enfAcdcoFzjh/DnK2+9Xc3748LWeBRyvtnyNt9+rlvDw4ev9ALDFuWw5cI6336vm/vDUtcZRWfNd5/XaCvyqlv274Sj6stu5fbBzuT63W8+11md267rezf5zu7JksIiIiIiIiHhRc0prFBERERERabEUnImIiIiIiPgABWciIiIiIiI+QMGZiIiIiIiID1BwJiIiIiIi4gMUnImIiIiIiPgABWciIiIiIiI+QMGZiIiIiIiID/j/3pqrNru/ADYAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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+8pOfYNeuXcjPz8eNN94Ycsx98OtMv4/4nD6fD3l5eWhvb5/rUyeEEEIIISnQPmDA8nI1FLJFX6wXtrQKzmbLcCXKkSNHIJFIsHTpUgBAe3s7ampqonqu2tpa/O53v4PP58PQ0FCgXyuY0WhEfn4+srOz0dXVhXfffTfwMblcDrfbDblcji1btuCyyy7DF7/4RWg0GkxMTMBsNuOMM87A7bffDr1eD7VajccffxyrVq2a82wXXnghvvWtb+G6666DUqnE0NAQ5HJ5RJ/fM888g69//euwWq147bXXAqWY4TyvyWRCTk4OcnNzodVq8eKLL2Ljxo0AAJVKBbPZHBhaUlJSgs7OTjQ1NeGpp56CSqU65fnUajWWLFmCxx9/HFdffTU45+jo6Ajra0EIIYQQQhLL7fXhwJARHz69OtVHmVfSKjhLBYvFgs997nMwGAyQyWRoaGgIDOmI1Pr16wMlgitWrMCaNWtOuc9FF12E+++/H62trWhqappS9nfLLbegtbUVa9aswaOPPorvf//72Lp1K3w+H+RyOX7729/izDPPxN13342zzjoLZWVlWLNmTWBQyGy2bt2Kzs5OnHXWWQCEcs6//e1vkEqlYX9+p59+Oi6++GL09/fjW9/6FsrLy1FeXh7W865atQqrV6/G8uXLUVdXh/Xr10/5vN/3vvehrKwM27dvx7333otLLrkEVVVVWLFiBSwWy4znefTRR/HpT38a3//+9+F2u/GhD32IgjNCCCGEkDRwZNQMh9uHtqq8VB9lXmGc86S92Nq1a7k4/VDU2dmJZcuWJe0MJDp33303lEolvvSlL6X6KBGhv1+EEEIIIcn36M4T+OZTB/HGlzehujD8dUmLAWNsD+d87UwfowJQQgghhBBCSFy19xtQkKNAVUFWqo8yryz6skYSnrvvvjvVRyCEEEIIIfNE+4ABqypzoxpkt5jNmTljjFUxxrYzxjoZY4cYY7f7b29jjL3LGGtnjO1mjJ2e+OMSQgghhBBC0pnZ4cbRcQvaqvJTfZR5J5zMmQfAnZzzvYwxFYA9jLFtAO4D8B3O+YuMsff7f78xcUclhBBCCCGEpLuOQSM4B9qq81J9lHlnzuCMcz4CYMT/azNjrBNABQAOQNwolwtgOFGHJIQQQgghhMwP7QMGAMCqytzUHmQeiqjnjDFWC2A1gJ0AvgDgf4yxn0Aojzw7xGNuAXALAFRX054DQgghhBBCFrL2AQOWFOUgL1uR6qPMO2FPa2SMKQE8AeALnHMTgE8D+CLnvArAFwE8MtPjOOcPcs7Xcs7XFhcXx+PMcSeVStHW1oYVK1bg6quvhs1mi/q5brzxRvz73/8GANx00004fPhwyPu+9tprePvttwO/v//++/GXv/wl6tcW9fX1YcWKFVNuu/vuu/GTn/wkoueJ13kIIYQQQsjiwDlH+4CB9ptFKazMGWNMDiEwe5Rz/qT/5hsA3O7/9eMAHo7/8ZIjKysL7e3tAIDrrrsO999/P+64447Ax71eb0TLmkUPPzz7l+S1116DUqnE2WcLScdbb7014tdIFI/Hk1bnIYQQQggh6W/Y6MC42UnBWZTCmdbIIGTFOjnnPwv60DCA8/y/3gygJ/7Hm+a++4Dt26fetn27cHucbNiwAUePHsVrr72GTZs24SMf+QhWrlwJr9eLL3/5y1i3bh1aW1vxwAMPABCuDtx2221oaWnBxRdfjLGxscBzbdy4EeLS7f/+979Ys2YNVq1ahS1btqCvrw/3338/fv7zn6OtrQ07duyYkt1qb2/HmWeeidbWVlxxxRWYnJwMPOdXv/pVnH766WhsbMSOHTsi/hxne+5vfOMbOO+88/DLX/4ycJ7h4WG0tbUF/pNKpThx4gROnDiBLVu2oLW1FVu2bEF/fz8AIXv4+c9/HmeffTbq6uoCmURCCCGEELKw7ff3m1FwFp1wyhrXA7gewGb/2Px2/3TGmwH8lDG2H8AP4O8rS6h164BrrjkZoG3fLvx+3bq4PL3H48GLL76IlStXAgDee+893HPPPTh8+DAeeeQR5ObmYteuXdi1axceeugh9Pb24qmnnsKRI0dw4MABPPTQQ1PKFEXj4+O4+eab8cQTT2D//v14/PHHUVtbi1tvvRVf/OIX0d7ejg0bNkx5zMc+9jH86Ec/QkdHB1auXInvfOc7U8753nvv4Re/+MWU24MdO3ZsSkB1//33h/XcBoMBr7/+Ou68887AbeXl5Whvb0d7eztuvvlmfPCDH0RNTQ1uu+02fOxjH0NHRweuu+46fP7znw88ZmRkBG+++Saef/55fO1rX4vwT4IQQgghhMxH7QMGKKQSNJepUn2UeSmcaY1vAgi1Pe60uJ7mC18A/OWFIZWXAxdeCJSVASMjwLJlwHe+I/w3k7Y24Be/mPUp7XY72traAAiZs09+8pN4++23cfrpp2PJkiUAgJdeegkdHR2BLJDRaERPTw/eeOMNfPjDH4ZUKkV5eTk2b958yvO/++67OPfccwPPVVBQMOt5jEYjDAYDzjtPSEzecMMNuPrqqwMfv/LKKwEAp512Gvr6+mZ8jvr6+kCpJnByifRcz33ttdeGPNdbb72Fhx9+OJCte+edd/Dkk0KV6/XXX4+vfOUrgftefvnlkEgkaGlpgVarnfXzJYQQQgghC0N7vwEt5WpkyCJvCSIRTmtMC/n5QmDW3w9UVwu/j1Fwz1mwnJycwK855/j1r3+NCy+8cMp9XnjhhTk3n3PO47odPSMjA4AwyMTj8cTteYGpn3OwkZERfPKTn8Szzz4LpVI5432CP0fxjIDw+RNCCCGEkIXN4/XhwJAR166rSvVR5q30Cs7myHABOFnK+K1vAb//PXDXXcCmTQk/2oUXXojf//732Lx5M+RyObq7u1FRUYFzzz0XDzzwAD72sY9hbGwM27dvx0c+8pEpjz3rrLPw2c9+Fr29vViyZAkmJiZQUFAAlUoFk8l0ymvl5uYiPz8fO3bswIYNG/DXv/41kOmKVTTP7Xa7cc011+BHP/oRGhsbA7efffbZ+Oc//4nrr78ejz76KM4555y4nJEQQgghhMw/R7Rm2N1erKbl01FLr+BsLmJg9thjQkC2adPU3yfQTTfdhL6+PqxZswaccxQXF+Ppp5/GFVdcgVdffRUrV65EY2PjjIFOcXExHnzwQVx55ZXw+XzQaDTYtm0bLr30Ulx11VV45pln8Otf/3rKY/785z/j1ltvhc1mQ11dHf74xz/G7XOJ9Lnffvtt7Nq1C3fddRfuuusuAELG8Fe/+hU+8YlP4Mc//jGKi4vjekZCCCGEEDK/7B8wAqBhILFgySw5W7t2LRenF4o6OzuxbNmy8J7gvvuE4R/Bgdj27cCuXUBQvxMhooj+fhFCCCGEkKh95d/7se2wFnu/dUFcW3oWGsbYHs752pk+Nr8yZzMFYGIGjRBCCCGEEJIy7QMGrKrKo8AsBuGM0ieEEEIIIYSQkMwON3rGLFTSGCMKzgghhBBCCCExOTBkBOfUbxartAjOaNQ6SQT6e0UIIYQQkhztAwYAwKrKvJSeY75LeXCWmZkJvV5Pb6RJXHHOodfrkZmZmeqjEEIIIYQseO39BtQWZiM/R5Hqo8xrKR8IUllZicHBQYyPj6f6KGSByczMRGVlZaqPQQghhBCyoHHO0T5gwNn1hak+yryX8uBMLpdjyZIlqT4GIYQQQgghJAojRgfGzE7qN4uDlJc1EkIIIYQQQuav/f5+s7bq/NQeZAGg4IwQQgghhBAStfYBAxRSCZaVqVJ9lHmPgjNCCCGEEEJI1PYNGLCsXI0MmTTVR5n3KDgjhBBCCCGERMXj9eHAoBGrqd8sLig4I4QQQgghhESlZ8wCu9tLw0DihIIzQgghhBBCSFQCy6cpOIsLCs4IIYQQQgghUWnvNyAvW47awuxUH2VBoOCMEEIIIYQQEpX2AQNWVeaBMZbqoywIFJwRQgghhBBCImZxetA9ZqZ+szii4IwQQgghhBASsQODRnAOtFXnpfooCwYFZ4QQQgghhJCIBYaBVOal9BwLCQVnhBBCCCGEkIi1D0yipjAbBTmKVB9lwaDgjBBCCCGEEBKx9gED9ZvFGQVnhBBCCCGEkIiMGh3QmpwUnMUZBWeEEEIIIYSQiLQPTAKg5dPxRsEZIYQQQgghJCL7BgyQSxlaytSpPsqCIpvrDoyxKgB/AVAKwAfgQc75Lxlj/wLQ5L9bHgAD57wtQeckhBBCCCGEpIn2fgNaytTIlEtTfZQFZc7gDIAHwJ2c872MMRWAPYyxbZzza8U7MMZ+CsCYqEMSQgghhBBC0oPXx3FgyIirT6tM9VEWnDmDM875CIAR/6/NjLFOABUADgMAY4wBuAbA5gSekxBCCCGEEJIGesbMsLm8tHw6ASLqOWOM1QJYDWBn0M0bAGg55z1xPBchhBBCCCEkDXUMCAVztHw6/sIOzhhjSgBPAPgC59wU9KEPA/jHLI+7hTG2mzG2e3x8PPqTEkIIIYQQQlJu0GAHAFQVZKf4JAtPWMEZY0wOITB7lHP+ZNDtMgBXAvhXqMdyzh/knK/lnK8tLi6O9byEEEIIIYSQFNJZnCjIUUAupcHv8TbnV9TfU/YIgE7O+c+mffh8AF2c88FEHI4QQgghhBCSXnRmJ4qUilQfY0EKJ9xdD+B6AJsZY+3+/97v/9iHMEtJIyGEEEIIIWRh0VmcKFJmpPoYC1I40xrfBMBCfOzGeB+IEEIIIYQQkr50FhfaqvJSfYwFiQpFCSGEEEIIIWGjzFniUHBGCCGEEEIICYvN5YHN5UWRinrOEoGCM0IIIYQQQkhYdGYXAFDmLEEoOCOEEEIIIYSEZdziAAAUU3CWEBScEUIIIYQQQsIy7s+cFasoOEsECs4IIYQQQgghYdFZnACorDFRKDgjhBBCCCGEhEUMzgppCXVCUHBGCCGEEEIICYvO4kRethxyKYURiUBfVUIIIYQQQkhYdGYXlTQmEAVnhBBCCCGEkLAIC6ippDFRKDgjhBBCCCGEhEUIzihzligUnBFCCCGEEELCorNQWWMiUXBGCCGEEEIImZPd5YXF6aEdZwlEwRkhhBBCCCFkTuIY/WLKnCUMBWeEEEIIIYSQOY2LC6hVNBAkUSg4I4QQQgghhMxJZ/YHZ5Q5SxgKzgghhBBCCCFz0llcACg4SyQKzgghhBBCCCFzEnvOCmnPWcJQcEYIIYQQQgiZk87ihDpThgyZNNVHWbAoOCOEEEIIIYTMSWdxoojG6CcUBWeEEEIIIYSQOY2bndRvlmAUnBFCCCGEEELmpLO4aAF1glFwRgghhBBCCJmTzuykBdQJRsEZIYQQQgghZFYOtxdmpwdFNKkxoSg4I4QQQgghhMxKHKNPPWeJRcEZIYQQQgghZFa0gDo5KDgjhBBCCCGEzEpn9mfOaCBIQs0ZnDHGqhhj2xljnYyxQ4yx24M+9jnG2BH/7fcl9qiEEEIIIYSQVDhZ1kg9Z4kkC+M+HgB3cs73MsZUAPYwxrYBKAFwGYBWzrmTMaZJ5EFJ+nrkzV50j5rxo6taU30UQgghhBCSAONm6jlLhjkzZ5zzEc75Xv+vzQA6AVQA+DSAeznnTv/HxhJ5UJKe/vruCXzv+cP41+4BGG3uVB+HEEIIIYQkgM7ihCpThky5NNVHWdAi6jljjNUCWA1gJ4BGABsYYzsZY68zxtYl4HwkjT3TPoRvP3MQ9cU5AIB9A5MpPhEhhBBCCEkEncVFO86SIOzgjDGmBPAEgC9wzk0QSiLzAZwJ4MsAHmOMsRkedwtjbDdjbPf4+Hicjk1SbfuRMdz52H6sqy3AY586CxIG7Os3pPpYhBBCCCEkAcYtTippTIKwgjPGmBxCYPYo5/xJ/82DAJ7kgvcA+AAUTX8s5/xBzvlazvna4uLieJ2bpNDuvgl8+m970FSqwsM3rEWhMgONJSrs7afMGSGEEELIQqSzOFGkomEgiRbOtEYG4BEAnZzznwV96GkAm/33aQSgAKBLwBlJGukcMeETf9qF8tws/PkTp0OdKQcArKnJR/uAAT4fT/EJCSGEEEJIvOnMlDlLhnAyZ+sBXA9gM2Os3f/f+wH8AUAdY+wggH8CuIFzTu/MF7ATeiuuf+Q95GTI8JdPnj7lH+ia6nyYHR4cHbek8ISEEEIIISTenB4vTA4PBWdJMOcofc75mwBO6SXz+2h8j0PSldbkwEcf2Qmvz4d/3nIWKvOzp3x8TXUeAGDviUk0lqhScEJCCCGEEJIIeosLAI3RT4aIpjWSxclgc+Fjj7yHCYsLf/r46WjQnBp8LSnKQX62nPrOCCGEEEIWmJM7zqjnLNHCWUJNFjGby4NP/GkXenVW/PHj67CqKm/G+zHGsLo6H3tpYiMhhBBCyIKis/iDMxVlzhKNMmckJJfHh1v/thftAwb86sOrsb7hlGGcU6yuysPRMQuMdlpGTQghhBCyUIjBGe05SzwKzkhI33v+MN7oHse9V7biohWlc95/TU0+AKB9wJDgkxFCCCGEkGTR+XvOiilzlnAUnJEZ2Vwe/HvPIK5ZW4lr1lWF9ZhVVXmQMGEoCCGEEEIIWRjGzU4oM2TIlEtTfZQFj4IzMqNXu8Zgd3txxerKsB+jzJDRMmpCCCGEkAVGZ3HSMJAkoeCMzOj5/SMoVmXg9CUFET2OllETQgghhCwsQnBGJY3JQMEZOYXZ4cb2I2O4eGUZpJJQK+5mtroqj5ZRE0IIIYQsIDqLi4KzJKHgjJzi5U4tnB4fLl1VFvFjxaEg1HdGCCGEELIw6CxOFKmorDEZKDgjp3h+/wjKczOxuio/4sfWFeUgL1uOfbTvjBBCCCFk3nN5fDDY3JQ5SxIKzsgURpsbb/SM4+LWMkgiLGkE/Muoq/JoKMgCNGp0wGijHXaEEELIYqK3+nec0Rj9pKDgjEzxv8OjcHs5Lmktj/o51lTno4eWUS841z+yE9957lCqj0EISRKfj+OpfYNweXypPgohJIV0ZmHHGWXOkoOCMzLFc/uHUV2QjdbK3Kifg5ZRLzw2lwc9YxYcHjGl+ijz2r/3DOL3rx1L9TEICctr3WP44r/248WDI6k+CiEkhXQWIXNGwVlyUHBGAvQWJ94+psclrWVgLPKSRlFrZS4YLaNeUHq0wvTN4zorvLQmIWoP7ziOP73dm+pjEBKWN3v0AID9A8YUn4QQkkrj/uCsmIKzpKDgjAT899AovL7YShoBQJUpRxMto15QurVmAEJT8LDBnuLTzE8WpwfdWjO0JiecHm+qj0PInN4+pgMAHBgypPYghJCUCmTOaFpjUlBwRgKe3z+CuuIcLCtTxfxcq6tpGfVC0jN2cm8d7bCLTsegAeI/h6FJCnBJetNZnOgaNSNTLsHBIRM8Xuo7I2Sx0pldyFZIka2QpfooiwIFZwQAMGZy4N1ePS5pLY+ppFG0plpYRn0sgW/kHW4v7nxsPwYnbQl7DSI4MmpGWW4mAOD4uDXFp5mfgtdLDFBwRtLc28eEksYPrauG3e3FMfp3T8iipbM4qd8siSg4IwCAFw6MgHPg0tbIF0/PJLCMOoGljR2DRjyxdxDP7adm9UTr0ZpxxpIC5GXLExpwL2TtAwaoM4WrjnRBgaS7t4/qoMqU4SNnVAMQMr+EkMVp3OxEkZJKGpOFgjMCAHi+YwTNpSosLYm9pBEAlhTmIDdLjr0nDHF5vpn06oQggd40JJbZ4caw0YGlJSrUFytxnIKziHHOsa/fgC3LSiCXMgxMUOaMpLe3julwZl0hGoqVUGbI0DFIQ0EIWax0FiftOEsiCs4Ihg127D4xiUvilDUDAImEYXV1YpdR9+qE7MN+GtmfUN3+SY2NJSrUFeVQeVMUBift0FmcWFOTj4q8LAxQ5oyksYEJGwYm7FhfXwiJhGFFhRodQxScEbJYUVljclFwRvCfDqEsMNYpjdMlehm1mDkbNjowZnYk5DWIUNIIAE0lKtRrlBg3O2Fy0ILxSOzzX0BYXZWHyvxsDC6inrO3j+lwaJje2M8nbx0VpjSubygCALRW5qFz2ETLqAlZhNxeHyZtbgrOkoiCM4LnO4axsiIXtUU5cX3eNdWJXUbdq7MGvll00B6ehDmiNSNLLkVlfhbq/H9HaChIZNr7DciUS9BcqkJVQRYGJxZH5uzQsBE3/mEX7n2xK9VHIRF465geGlUGGjRKAMLuSpfXF1ipQQhZPCasLgBAEZU1Jg0FZ4tcv96G/YPGuJY0ilZVCcuo9yWgtNHr4+jT2/D+laWQShj2U99ZwvRoLWjQKCGRMNT736xR31lk9g1MorUiDzKpBJX52dBbXbA6Pak+VkLZXV58/h/74PL60KenYH6+8Pk43j6qw9n1hYHJva0VeQBAfWeELELjZnEBNQ0ESRYKzha55w8MAwAuTkBwdnIZtSHuzz1ssMPl8aGlTI3GEhX205uGhOnWmtHoHxRTXZANmYTRxMYIOD1eHBoyYXV1HgCgqiAbABZ8aeP3/nMYx8atOGNJAYYm7VQSN08c0Zqht7pwtr+kEQCqCrKQly2n4UuELEKBBdRU1pg0FJwtcs/tH8HqaqEPJhFWV+dhX/9k3JdR9+qEK/FLinLQVpWL/QMGcE4Lr+PNYHNhzOxEY4mQMZNLJaguzMaxMcqEhOvwsAkury8QnFXmZwFY2OP0/3doFH/f2Y9PnVuHa9ZWwccX9ue7kEzvNwMAxhhWVuRS5oyQRUhn8Zc1UnCWNBScLWLHxi3oHDHh0jgPAgm2ujo/IcuoA8FZcQ5aK/NgtLtxQk9v/uItMKmx9OSKhfpiJY7rKHMWLnH59Gp/D2aV/0LIwALtOxs1OvDVJzqwokKNO7c2oaZQ+HxPLNDPd6F5+5geS4pyUJGXNeX21spcHNGa4XB7U3QyQkgqiGWN1HOWPBScLWLP7x8BY4kpaRSJQ0HiPVK/V2eFMkOGYmUGVlXmAQD1nSWAOACgMWj/XV1xDvp0NnjjnA1dqNoHDCjLzUSJOhMAUKRUIFMuwcACLGv0+ji++K92ON0+/OpDq6GQSVBTKAyROaGjbGu6c3t92Hlcj7PrC0/5WGtlHrw+jsMjphScjJCFQezFnU8X53QWJzLlEuQopKk+yqIxZ3DGGKtijG1njHUyxg4xxm733343Y2yIMdbu/+/9iT8uiRfOOZ7rGMa62oLAm8ZEqCtKzDLqXp0VS4pywBhDY4kSmXIJ9tPExrjr1pqhzJChPPfk35H6YiVcXh+VqYVp38BkoKQREErEhHH6C+/r9+Abx/HOcT2+84HlqCsWSmGLlArkKKToo8x22usYNMDq8k4paRS1VuYCAA5QaSMhUesYNODZ/cN4rmM41UcJm7iAWhwQRBIvnMyZB8CdnPNlAM4E8FnGWIv/Yz/nnLf5/3shYackcXdEa8bRMQsuTWDWDEjcMupenTUw+l8mlWBFeS5lzhKgW2vG0hLllG/K9cXC152Ggsxt3OzEwIQdq6vyp9xelZ+FgYmFlTnbP2DAT186gotXluHqtZWB2xljqC7MQf88ulK8WL11VA/GgLPqTs2claozUazKoO+zhMRAbMnYn6AVQ4lAC6iTb87gjHM+wjnf6/+1GUAngIpEH4wk1vP7RyBhwPtWJjY4A4DVVfFdRu30eDE4acOSoL1sq6rycGjYCLc3ORPhfvlyD37wQmdSXiuVurUWNGpUU26rKxLH6c/PMrVRowO3/nUPjowmfmeTuOMvOHMGCBMbBxZQ5szi9OD2f+6DRpWBH1yx8pQrrLWF2TROfx5466gOLWVq5OecOjKbMYbWilzKnBESg169GJzNn39HOrOLgrMki6jnjDFWC2A1gJ3+m25jjHUwxv7AGMsP/UiSTjjneL5jGGfXFyXlH9yamjwA8btSNDBhg48jsBAZEEpuHO7kLUn9994BPLl3KCmvlSo6ixMTVteUYSAAkJ+jQEGOYl5mzsbNTnzk4Xfx30OjeLlTm/DXax+YhEzCsKIid8rtVfnZMDs8MNric8Ei1e5+9hD6J2z4xYdWIzdbfsrHqwuzMThhpz7FNGZ3ebGv3zBjSaNoZWUujo5bYFngO/oISZRe/0XNUZMDo0ZHik8THsqcJV/YwRljTAngCQBf4JybAPweQD2ANgAjAH4a4nG3MMZ2M8Z2j4+Px35iErODQyb06W0JWTw9k7aqPDAWv6EgYsYmOHPWVpUHIDlLUietLgxM2KGzOKH37/9YiE4OA1Ge8rH64hwcm2eZswmrCx99eCdGDA6oMmU4Opb44HJfvwHLytTIlE9tpBbH6S+E7Nlz+4fx7z2DuG1TA05fUjDjfWoLc+Dy+jBiXFilnAvJrr4JuLy+GYeBiFZV5oFz4NDQ/LnqT0g66dNbofFPPWyfB6WNHq8PEzYXLaBOsrCCM8aYHEJg9ijn/EkA4JxrOedezrkPwEMATp/psZzzBznnaznna4uLi+N1bhKD5zuGIZMwXLSiNCmvp8qUo1ETv2XUYs12bVBwVl2QjbxseVLquA8On3xjciRJmbpU6B49dVKjqK5IiePzKHNmtLnx0Yd3ok9vxSM3rEVbVV7CgzOvj2P/gOGUkkYgeBH1/A7OBidt+MZTB7CmOg+f37I05P0C4/RpKEjaeuuYDnIpCxlgA0LmDAAOUHBGSMR8Po4+vQ3vW1EKuZTNi/7NCZsLnNMY/WQLZ1ojA/AIgE7O+c+Cbg9Ou1wB4GD8j0cSYVunFusbipCXnbwrIWtq4reMuldnRZFSgdysk+VTjDG0VuYl5UpU8BuTZPQtpUr3mAW5WfLAVb5g9Zoc6CyueVGWZ3a48bE/7MTRMQseuP40nN1QhAaNEkfHLHFfjh6sZ8wMq8s7c3AW2HU2fzNJHq8PX/hnOzgHfvmh1ZBJQ/84CYzTp+Asbb19VI/VVfnIVshC3qdImYGKvCzsp74zQiI2bLTD5fGhqVSNZWXqeTEURGemBdSpEE7mbD2A6wFsnjY2/z7G2AHGWAeATQC+mMiDkvgZNtixVHNqqVoixXMZ9XH/GP3p2ipz0TNmgc2V2H6Ig0NGVBdkIz9bnrQet1ToHjWjcdqkRpE4FORYmi+jtjo9+Pgfd+HQsAm/vW4NNjZpAABLNSrY3V4MJ7DMrt2fKW6rOrUdV50lgypDNq8zZ7/dfgy7T0zinitWBDKBoZSpM6GQSXCChoKkJYPNhYPDRpzdELqkUbSyIhcH5sEVf0LSzcmqn2ysqsxDx6AxoRcI42Hc37pBwVlyhTOt8U3OOeOctwaPzeecX885X+m//QOc85FkHJjExubywOH2oTDJ/9DW+LMH++JQ2tgbIjhbVSUsST00nNglqR2DRqysyEVTqQpdccqcOdxe7DyuB+fp8Y2ac45urXnGkkYAqNek/8RGu8uLT/55F/YNGPCrD6/GBS0lgY81+M+fyNLGff0G5GXLUVt4auDCGENlQfa8XUTt8vjwu9eO4uLWMlzWNvfwXomEobogmzJnaeqdY3pwjlmHgYhWVuaiT2+bF1lzQtJJnz84qytSYlVVHixOD44n8QLn4KQtcIZw6cxCcFZMZY1JFdG0RjL/6S1CirpwhlHJiVRXpIQ6UxbzUBCL04NxsxNLik7N/LVW5gFI7P6QSasLg5N2rKzMRXOpGt2j5rhc+frrOydw7YPv4ua/7E6LISNjZidMDk/I4KwqPwtyKUvbiY0Otxe3/HU3dvZO4GfXrML7p62MSEpwNjCJ1VV5IRd3CrvO5mew0jVqgtPjw8URrOKoKaBx+unqrWM6ZCukWOX/Hjob8T7Ud0ZIZHp1NmTJpShRZ6CtSujfbE/iSP2vP3kAN/1ld0SP0QUyZzQQJJkoOFtk9FZ/cJbkf2jCMur8mIMz8arPkqJTsxHFqsT3Q4jDQFZW5KKxRAWry4shQ+zZj/YBA5QZMrzRrcOFv9iB17tTO9lU7KVbOsOkRkBY/F1TmINjSZh4GCmXx4fb/r4XO3p0+NEHW2fM7BT41wEkKjgzO9zoGbNgdXXoDSOV+dkYnLSnTbY0EuK/sdbK3DnueVKNfxH1fPx8F7q3j+px+pICKGRzvyVY6V8L0TFkSPCpCFlYenUW1BblgDGGuiIllBkytA/EZ4p1OI6NWXB0zBJRebnO4kSGTAJlRuheVBJ/FJwtMhNW4SpIQZIzZwCwplpYRm1yRF8OczwQnM0cNLRW5iY0cyaO6l9RLpQ1AvEZCnJw2IgNS4vwzG3rUZAjxw1/eA/ffe4wHG5vzM8dDbGXrilE5gwQ9swdj7BEItE8Xh9u/+c+vNw5hu9fvgLXrK0KeV9xKEgidAwawfnJFQ8zqSrIgt3tDVwwmU86BgwozFGgIi8r7MfUFGbD5vIGehhIehgx2nFcZ8U5YZQ0AkCuv1S3I02W6A4Z7LQYmyTM33f2Y/29r8blZ3Gf3ha4sCyRMP/7leT83XW4vRj271V7tWss7MfpLMIC6lAVICQxKDhbZHSW1E3eWV0t7MiJ5Yd677gVjJ0czT3dqqo89E/YMJmgN7wHh4yoKcxGbrY8sP8r1nH6RrsbJ/Q2rKjIxbIyNZ697RzceHYt/vBWLy7/7VspGTrSo7WgMEcxa29ivUaJE3orPF5fEk8WmtHuxh2P7ceLB0fxrUta8NEza2a9f4NGiZ4xS0IyOfv8GeJVswVngYmN86+0sWPQiNbK3Ih+YNM4/fT01lE9AODs+vCCMwBYWZmXNmWNP/nfEdz4x/fSNiPLOcd9/+2K255PkjzDBjvu+c9hDBns6NHGdiHP7fWhf8J2yn7WzhFTUi7CBv+ciSw4c9IY/RSg4GyRmfAHLanInIlvVGNJ4/fqLCjPzTplqW/gNcS+swRNEzswZMQKf1mPKlOOirysmDNnh/0DTJaXqwEAmXIp7v7AcvzxxnXQWZy49Ndv4s9v9yX1zccRrTlkSaOorigHbi9P6VCLCasL/9rVjxv/+B7Wfn8bnt0/jK9c1IRPnrNkzsc2FCthtLsDFyziaV+/AQ0a5ZR1D9OJEw7n21AQq9ODnjFzoMczXLX+cfqRNqSTxHr7qA4FOQo0l4bOkk/XWpGLIYM90I+SSif0Vuj9vcDpaNzixO9eO4brH96JPScoQJtP7n72EBwe4eJj52hsg8YGJ+3w+njg+yAgvCfy+DgOjyR2iBkgZO0AYF1tPnYen4DVGd5U63GzkxZQpwAFZ4uM3l8/nK2YObhJpNwsOeqKc2Ka2Nirs6Ku+NRJjaKVlblgDAkpFQgMA6k42WfTVKqKOTg76L8CvaJiav/OpmYNXrz9XJxdX4i7nj2ET/xpV1LeDHHO0aM1z1rSCJyc2JjsvrMxswN/e/cErnv4Xay752V89YkDODpmwcfXL8Ezn12Pz2xsCOt5xOAz3qWNnHPsGzBg9SxZMwCozBdKAufbOP2DQ0b4OLCqKvx+MwCoyM+CVMLQPw8zhQsV5xxvHdPhrPpCSCThZ0HFXsN0KCccNgilWgfTJJM33ZhJ+J7t9nHc+If30EFrCOaFlw6N4qXDWtxxQSMy5ZKYf873+qcyBr9/Ecvek7HvTOwzu/HsJXB5fXjrqC6sx4lljSS5KDhbZPTW1NYPr67KR/uAIaosEOc85I4zkTJDhoZiZUJ+AIplPNODs2PjFrg80Zf2HRw2oiw3c8ZvgMWqDPzhxnX4zgeW461jelz0izew/Uj4JQnRGDLYYXV5sXSu4Mzf95eMUcAjRjv++FYvrnngHZzxg1fwf08fxIjBgVvPq8PznzsHO76yCd94/7JZywinOzmxMb5lowMTdkxYXWibYfl0sJwMGQpyFPNuEXVHYBhIXkSPk0slqMjLClzBJal3bNwKrcmJ9RGUNALA8grhIlhHioMzl8cHrVkIztKlzHI6rUk43y+vbUNejhwffXhn2gaSRGB1enD3s4fQXKrCLefWYakm9ouwvTrh+15w5qxEnYlSdWaSgjMb1JkyXNBSAmWGDNuPzD10zOvjmLA6KThLAQrOFpkJqyslJY2ituq8qEtQ9FYXzA7PrMEZILxp3D8YXQA4G/GH/4ryk8FZc6kKHh8PLJeMxsEhI5aXh85CMMZww9m1eO62c1CkzMDH/7groT/cxdr6pjnKnHKz5ShSKnBsLHFlakfHzPjMo3tw1g9fxXeeOwyjzY3btyzF/75wLl658zx8+cJmrKiIrPdJVKrOhDJDFvfM2T5/2e7qGZZPT1eVnzXvMmf7Bw2oyMuK6gd2TWE2+mmcftp4+5hw9Xx9GMungyXyIlgktCYHxG/z6RucCZmztuo8/P2mM6HMkOGjj+xEZxJK2Uh0fr6tG8NGB+65YiXkUklcdpr26ixQZ8pOef+1qio3oROmRX16K2oKc6CQSbBhaRFeOzI253ukCasLPk47zlKBgrNFRm9xJX2MfjCx1Cua5ujewKTG2YOztqpc6CyuuIy4DxY8DEQk7gGLdiiI1enBcZ0VKyrUc963qVSFf91yFmQShv8cSNzOd3EASaNm7h6UuiJlQjJnAxM23PnYfmz9+Rt4/cg4btvUgFfuPA//++K5+ML5jWgqVcWc/WWMoV6jxNE472rb129AtkIaGBgzG3Gc/nwiDgOJRk1hNmXO0shbR3WoyMtCdcHMA5Zms7IyFx1DxpQO4hD/7VQVZOFAis8SitbkAGPCEK6qgmz845YzkSGT4KMP70RPCoY9kdkdHDLiD2/14iNnVOO0GuECW3OpCjqLM6YdpH06YRjI9J9bq6ry0KuzwmBL7NTeE3pbYCjTpmYNRowOdI7M/vfv5I4zCs6SjYKzRSbVmbOmUhUy5RK0R5HGDzc4E0vb4l1y0zFoPKUvrL5YCZmE4UiUzcKHR0zgfGqp5Gxys+U4o64A2w5ro3q9cBzRmqFRZUwJQkOp1+Tg2Hj8MiFjJge+/cxBbP7pa3i+YxifPGcJdnx1M750YRPqi+cOdiLVUKyMeQrXdPv6J9FamQuZdO5vr5UFWRiatMdlkXkyTFpd6J+wRVzSKKotzIHR7k74GxEyN6+P451jeqxvKIzqQseqyjyMm50Y9ZftpcKw/wLcRctLYbC50/JCx5jZgcKcDMj93w9qCnPwj5vPhETC8OGHduJYnC8Okeh5fRzffOoACnIy8NULmwO3N5cKF09jKW3sDdGS0RYYYpa47Jnb68OQwR4oqdzYVAwAc7ZI0ALq1KHgbBHhnAtjUVN4FUQulWBlRW7UwZlcyubcrdRcqoZCKolrHfekVcjEtU4LohQyCZYU5UT9TTvUMJDZbG0pxdExS8J+qPdoLXOWNIrqipSYsLpiXl0waXXhhy924twfb8ffd/bjmrVVeP3Lm/DNi1sSejGhQaPEmNkZ0+69YA63F4dHTGgLo6QREMbpu7wn+2bSXYf/7+uqKDNnYoaGxumn3qFhI0wOD9aHud9supX+vwOp7DsTqyO2Li8FkJ6ljVqTEyXqqT9z64qV+MfNZ4Bzjo889C5NME0Tf3v3BPYPGvGtS5ZNuTgp/jyMtrRR2DFmR+0MwdnJIWaGqJ47HEP+SZFi5kyjykRrZe6cI/UDwRmVNSYdBWeLiM3lhdPjS2nmDBAmFB0aMsHpiWy3R++4FdUF2XNmJBQyCZaVq+M6Tn+mYSCiplJV1GWNB4dMKFJmQBPBN78LWkoAICHZM5+Po2fMjKVhlDQCQuYMiH4oiMXpwS9f7sG5923Hg28cx/tWlOGVO8/DPVesRGluZlTPGYmlmvhObDw0bILby7F6jmEgosA4/XkyFKTD/wZiRZTBmfjmpI/6zlLuTf+0trPqI+s3E7WUqSGTsJRObBw22FGkzEBrZS7kUpbyASUz0ZocKFGf+r2sQaPCozefAZfHh4889O683He4kGhNDvz4f0ewYWkRPrCqfMrHilUZKMxRRH0Rtn/CBs5nrvpRZcrRUKxMaHAmfr8NDg43Nmmwr39y1gurOnPq9uIudhScLSJ6S+p2nAVbXZ0Pl9c3Z73zdEJZQHilbW2VuTgwaIQ3TuViYnC2fIbgrLlUhYEJOyxh7g0JdmjYiBUV6ojKisrzsrCiQp2Q4Gxg0gaH24em0vC+znX+P49oShvfOqrDufdtx89f7sbZDYX47+3n4ufXtqGmcPay1XgKTGyMU2mjuHx6rjH6ovk2Tn//oBF1xTlQZ85d8joTypylj7eP6tFYooRGFd1FkEy5FI0lqoTtlAzHkMGOivwsZMikaCpVpeUUxJkyZ6LmUjX+dtMZsLq8+PBD78a9T5qE7zvPHYLb68P3L18x489jYShIdO0Lx8dnb8lYVZWYIWYi8fttTVBv6eZmDXwceKMn9NRGncUJhVQCdaYsIecioVFwtojorelRPyzu9miPYCiIz8fRq599x1mw1so8WF3euJX+HRj0DwOZYamwOBQk0uZuh9uLnjFL2P1mwba2lGJv/yTG4lwO1+0PUuYaoy+qzM+CQiqJ6uv805eOIFshxTOfXY8Hrl8bdillPFUVZEMhk8RtKMi+AWGSoWaGK+UzEUt0503mbNAQ6JGIRqZcilJ1JgVnKeZwe7GrbwJnRzhCf7rWytyUDuIYMthRkSf8W1tZkdqzzMTt9UFvdc4aAC8vz8VfP3k6jDY3PvLQuxg1zo8S54Xk1S4tXjgwis9tbgh5cbCpVIVurSWq/uCZMlfBVlXlJWSImeiE3oYsuXTK1MXWilwUKRWzljaOW5woUipStnppMaPgbBGZsIqZs9SmqMtyM6FRZUTUdzZstMPl8c05DES0Ks7LHQ8MGUMGUdE2C3eOmOD18VnH6IeydXkJOAde6YzvzjNxUqNY7jcXmVSCmsLswJXBcJ3QW7G334DrzqiJaDdZvEklDHVFOXEra2zvN8y53yxYplyKEnUGBuZB5mzU6MCY2Rn1pEZRTWF2YCFqNDpHTHjojeNp9SZ8vtndNwmnxxd1v5motTIPBps7JRcXOOcYNtgDFzhWVuTBaE/NWULRWZzgHDOWNQZrrczDnz95OkaNDvx2+9EknY4AgM3lwbeePoQGjRK3nFsf8n7NpSrY3V70R1F+2jtuRZFSEbLiIDAUZCAxmd8TeitqCrOnBFkSCcN5jRq83j0essJo3OykfrMUoeBsERHLGgtTXNbIGMPq6jzsiyBwCndSo6iuKAeqDFlcSm4m/MNAQgVnlflZyFZII24WPjgslEiEM0Z/uqYSFaoKsuJe2titNaMiLwuqCMrW6ouVEWfOnt43DMaAy9rK575zgjVolOiJwyLqMZMDQwZ72CWNImGcfvoHZ+K/pdYYg+mawmyciKG/5qE3juOeFzrx7vGJmM6xWPl8HD/ddgSFOQqcHWW/mUgM1DuGDHE4WWQmrC443D6UB4Kz1J0lFHHHWaiyxmBrqvOxtEQ5Ly7ULCS/fLkHQwY7fnDFSihkod8SN/kvwkYzFKRXb52yfPrU51ZBIZOgfSDyFUPh6Avx+pubNTDY3IFy/Ol0FheKqd8sJSg4W0T0/sxZKvecidqq8nFCbwtk8+YiBmd1YQZnEgnDysrcuFyJmm0YiPhaS0tUgaxTuA4NGZGXLZ9z+uRMGGPY2lKKN4/qoup1C6Vba8HSMPZzBasrzkG/3ga31xfW/TnneLp9CGcuKQy8sUqlBo0Sg5N2ONyRDaiZTrzYsLo6vEmNoqr8rLS62h9Kx6ABMglDS1nkFxOC1RTmYNzshDXKv7e7TghB2a9f7YnpHIvVv/cOYl+/AV97XzNyMmLrJWksUUEhlaRkEIdYAiZ+/2wsVUIhlaTVxEatf83AXJkzUYkqk8oak6hzxISH3+zFtWurcPqSglnv21iiBGPRjdMPNUZfpJBJsLxcnZDMmdfHMTBhD0xqDHbO0iJIJSxkaWOqp3svZhScLSJ6ixNZcimyFalv7myLsOzw+LgVOQppRJvqV1XloWvUFPOb7oOzDAMRNZUoI/6mfXBYKJWMtp57a0sJXB4f3ugO3dAbCY/Xh2NjFjSF2W8mqi9WwuPjYZd7tA8Y0Kuz4orVFdEcM+4aNEpwjpj7E/f1GyCXMiwvjyx4qSrIxojRHnZwmyodg0b/nkJpTM8jXsGNpu9s1OjAwIQddcU5ePuYHrv7KHsWCaPNjXtf7MJpNfn44JrKmJ9PnIzbkYKhIOKOM/ECjzgUJJXTI6cb8wdnmjAyZwBQkpuJMXP0i45J+Hw+jm88dQC5WXJ87X3Nc94/WyFDdUE2jmgjGwpicXowbnaG7DcTrarMw4EhIzxx/jkwanLA5fXN2EuXmyXH2pr8GYMzn49jwupCkSr1F/MXIwrOFpFUL6AO1lqZCwlDyHT6dH16K2qLciIKZFZV5sLt5egciW7CkujAoBG1IYaBiJpK1dBbXYG9IHNxerw4MmqOqt9MdFpNPvKz5Xjp0GjUzxHsxIQNLq8v7GEgonp/f9qxMPu2nt43hAyZBBetLI34jIkgrg2Ite9sX/8kWsrUEQcvlflZ8HFgxBD5FfMxkwM/39YddgY6Wpxz7B8wRL18Oph4Bbd/IvK+s13+YOwHV6xEQY4Cv3qV+nMi8dNtR2CwufDdy5ZDIolPk39rRS4ODpmSvkhdXDgtTjwFhJ1R6TQURGtyQiphKAyzz7tElYkJqyviNTMkco/vGcC+fgP+7+JlyA/zfVFzqSrissa+MKt+2qryYPcPCYunE/7Xr50hcwYIpY1do2aMGKdWb0zaXPD6OGXOUoSCs0VEb3WlRUkjAORkyNBYogq772yusoCZiIMmYi25OTBknHNJdLN/0mC42bMerQVuL4+q30wkk0qwZVkJXu0ai0vWpdt/9sYoyhoB4HgYi1TdXh+e6xjB+S0lUY9jj7faomxIWGzBmcfrw4EhY8QljYCwiBqIbpz+n9/pwy9f6cH7fvkG3j6mi/jx4erT22ByeKJePh2s2v8moS+KzNnuvglkK6RYW5OPmzfU4Y3u8agW2i9GB4eM+Nu7J3D9mTUxXRSarrUyFxanJ6x///E0bHAgWyGdctFsZUUuzA5P2kwD1ZocKFZmQBpmIFyaK7wRHjNR9izR3ujWoTI/K6IKjqZSNfp01oiqccSWjLkyZ5FWE4VL/D5bE+L1NzdrAADbu6ZW4OgstOMslSg4W0T0VmfKh4EEW12dh/0DhjmvuLo8PgxM2MLuNxOVqoWpkLF8s5trGIhIHKcf7lU1sVRyRYxvkra2lMDk8OC93tjLu7q1FjB2cvdXuNSZchSrMsLKnO3oGceE1YUr2tKjpBEQyqFqCmOb2NittcDm8oa9fDpYYBF1FMHZjh4dGjRK5ChkuO7hnfjx/7oSUh4plq3FI3OmzpSjIEcR1cTGXX2TWF2dB5lUguvPqkFethy/od6zOfl8HN9+5iDysxW4Y2tTXJ9b/DtxIMmDOMRJjcHVFOL36XTpO9OaQ+84m4m4gkPsVSOJMzBpw5IIq3GaS1XwceHiargCwdkc+zvFVT3x3ht4Qm+FQipBaYi+xwaNEpX5WaeUNopVQBScpQYFZ4vIhMWV8jH6wdqq8mByzH3FtX/CBh8HloS540zEGENrZR7aY/hmFxgGMkfGoFiVgcIcRSD7NJeDw0aoMmUzNulGYsPSYmTKJXEpbeweM6MqPzuqnsS6opywrpw/uXcI+dlynNtYHM0RE0aY2Bh9cLZvQFw+HXnmrCw3E1IJi3goyKTVhQNDRnxgVTme+9w5uPq0Svx2+zFc88A7GIhhGuJM9g8YkSmXRJxVDUUYpx/ZGU0ON7pGTVhbIzTuKzNk+MT6JXi5cwyHhtPjzXiieLy+mHpnn9g7iL3+ISCzlWdHo744B1lyacLGgIcyZLCfMlBIHFCSLsHZmMkR9s5DAIE30FrKnCXcwIQtcGEsXOIuzkiWUffprCjLzUSWYvZyd8YYVlXlYV+/IaIzzeWE3oaqgqyQ2VvGGDY3a/DWUd2U7zFicFZMPWcpQcHZIsE5h87qSvkC6mBiCdhcZUknx+hH/sawrSoXx8etMDncET8WCMpwhbEourFEha4wJzYeHDJhebk65uWOWQopNiwtxrbD2pj7LLpHzVG/+a7XzD1O3+xwY9thLS5dVT7ryOJUaNAo0aezRp112tdvQEGOAlUFkU+flPmvakZa1vjWMR04FyZu5WTIcN9Vq/DrD6/GUa0F7//lDjzTPhTxWULpGDRgeXkuZNL4/LnVFuZEHJztPTEJH8eUqWo3nF0LVYYMv1nAvWc+H8cn/7wb5/xoe+D7USTEISBrqvPiMgRkOplUghUV6qQHRMMGOyryp/57U8gkWFaWPkNBtCZHRJmzEsqcJYXZ4cakzR0oKQ9XbWEOMmSSiIZ/9erDb8loq8xFt9YMmyt+E5hDjdEPtqlJA7vbi51BFTjjZsqcpVJ6vUMiCWN1eeHy+NJmIAggTPlTZsjm3O3RqxPe9C+Z4xvMTAIlN1H+sBaHgYTTH9VUqkKP1jxnmabH60PniCnmkkbR1pYSDBsdODQc/eATl8eHXp01UJ4ZqbqiHBhs7lkHU/z34CicHh8uT5MpjcEa/BMno+1V2d03gdVVeVEH21UFWRiYjCxztqNbB3WmDK1BFw4uXVWOF27fgKUlStz+z3Z86fH9UY+sF3m8PhwcNsa8fDpYdUE2ho32iAYf7O6bhFTCAr0ZgDBt7OPra/HiwdGoRlzPB3/beQKvd4/D4fbiww++i3eP6yN6/M+2HcGkzYXvXrYibkNApltZkYdDw/GfNBeK3eWF3uqacQ3JiopcHBwyJn1AyXROjxeTNjdKVOFnzvKz5VBIJRScJZhYpRDpxTSphGFpiRJHIlib06uzztlvJlpVlQcfFy7exgPnws+0mSY1BjurvhCZcgm2B5U2jluckEtZ3DPtJDwUnC0Sen+KujCNroJIJQytlblhZc4KcxTIzY78m4T4hjLaoQEHhoxYGWafTXOpCjaXNzBFLJSj4xY4Pb6wsnHh2LKsBBKGmEob+/RWeHw86uAsMLFxluzZU/uGUFuYHfGS5mQQd7sdjWIZ9bFxC/r0Nmxsir5Usyo/O6JSRM45dvSMY31D0SnZrKqCbDz2qbPwuc0NeGLvIC759ZsxZRJ6xixwuH1YFYd+M1FtUTY4R0SlnLv6JrC8XH3Kbq5PnLMEOQopfrM9PbJnPh/HoztPxKXUsldnxQ9f6MK5jcV46YvnoiQ3Ex/7w3thL58/NGzEX989gY+eWRO37zczWVWVC4fbF/dJc6EMG6fuOAu2siIXZqcnpkXn8TAWWEAdfnDGGINGnUHBWYKJ/b2RZs4AoKlEHXZv+aTVBYPNHXa/vHgxOV5DQcbNTtjd3jnbJzLlUpxdX4RXu8YCFTg6swtFyoyYq3tIdCg4WyQCC6jTKHMGCENBOkfMsLtCX0E/Ph75pEZRXrYCS4pyotrDc3IYSHgTFRvDrEcXr4rFMqkxWEGOAmtrC/BSmG/YZnIkMKkxyuDMX3J6PERwNmK0453jely+uiItv9nXF4vBWeRvLsU3yluWlUT9+pX52RgzO8PuKzqus2LY6MCGpTMHhDKpBHdubcLfbzoTdpcXV/7+LTz0xvGoSl9PDgOJ35v7msCus/CGgjg9XrQPGLCu9tRFsXnZClx/Vi2e7xiOeVddrGwuDz796B5886mDuP6R92Lq/fP6OL70+H7IpQz3fbAV5XlZePxTZ2FZmRq3/m0PHt89MOvjhSEgh5CfrcCdF8R3CMh04iCOePfLhDI0OXXH2ZSzVKbHUJAxc2Q7zkQl6kyMUnCWUOK/y0h7zgDhIuy42RnW+pJefXjDQETFqgxU5GXF1CcfTLxAEU5v+6ZmDfonbIHecVpAnVoUnC0SE/6xqOlU1ggAbVX58Po4Ds5ylVnccRat1srcqJrVD0TQbwacDGy65yh5ODhkRLZCGlUPXShbW0rQNWpGf5RleT1aMyTs5Fj8SFXkZ0Ehk+DY+Mxvtp9tHwbnwOVpNKUxWE6GDOW5mVEFZy8f1mJ5uXrGN4rhEstrhgzhZZJ2+BePb1haNOv9zqovxIu3b8CmJg3ueaETLxyIPLu6f1AYXhPuG4xw1PjfFIVbRnpwyASnx4d1tTMPXLlpwxJkyCT4bQqzZyNGO66+/x1sO6zFZzfVw+P14ea/7I66rPShHcex58QkvnvZCpTmCtmX/BwF/n7TGTi7vhBf/ncHHnzjWMjHP7F3EHtOTOKr72uOquogEkuKclBVkIUXD44k9HVEJxdQn5qVaixRQSGT4EAKFmMH00aROQOEoSA0Sj+xBiftyFFIkR/Fv4vmsvCHgog7ziIZZtZWlRe3zFlfmJMiAWCTv/JDLG0UgrP0er+4mFBwtkjorWJZY3r9YxP7R9pDXHG1Oj3QmpxRZ84AYFVlHkZNjohLRcQf7uEGZ8oMGSrzs+YseTg4ZERLmTrs3Tfh2NoiLHR+6XB0pY1HtGbUFuZEvEBZJJUwLCnMCZk5e2rfEFZX58UUZCdaQ4kq4rIsvcWJPf2TOD+GrBkQNE4/zEzLjh4daguzw7rym5+jwO8/ehrqinLw+9ePRpw96xg0CEvj4/j3tSBHAVWGLOzM2W7/8unTak7NnAFC0/pHz6jBM+3DUY3oj1XHoAGX/eYtnNDb8MgN6/DlC5vxm4+sQbfWjC/+qz3i/qeuURN+9lI3Llpeisvayqd8LCdDhodvWIuLW8vwgxe68MMXO0/5MzXahSEgq6vzcFUChoBMxxjD5W0VeOuoLikleUMGOyQMM44Hl0slWFaW/AEl04lfh0iDM406A6MmR9os0l6IxEmN0VRxBCY2jsxd2tirs0LCIiufbKvKw+CkPTAtMRYn9DZIJeyUwTkzqczPRmOJMjBSnzJnqTVncMYYq2KMbWeMdTLGDjHGbp/28S8xxjhjbPZLuCSlTpY1ptc/tmJVBirzswKjyKcTJzVGuuMs2KoqIbiK9GrUgSEjlhTlRLQsublUNWvmzOvjODxiinv/R3VhNppLVWH3okzXo7VEXdIoqtfkzJg56xwxoWvUjCvTcBBIsIZiYeJkJG+khRp94IKWGIOzfHHX2dyZM5fHh3eO60OWNM5EKmG45dw6HBwy4a2j4Q+UcLi96Boxx2W/WTDGGKoLs8PuC9rVN4m6ohwUq0J//7rl3DpIJQy/2x46m5QILxwYwTUPvAO5VIJ/f/osbPIvdT23sRj/d3ELXjqsxc+2dYf9fC6PD3c+th+qTBnuuWLFjG8gM2RS/OpDq3HdGdV44PXj+NoTB6YM4/j5tm5M2lz4XgKHgEx3xeoK+DjiOiU0lCGDHaXqzJDTQ1dWqHFwyJTSoSBakzBQIdLsTKk6EzaXF5YYB/mQ0AYmbaiMot8MAIqVGSjIUYQ1gKhXZ0VVQXZE04lXxXEZdZ/eioq8LMjDnLK7qVmD93onYHK4obe4UDTL91uSWOH8iXkA3Mk5XwbgTACfZYy1AELgBuACAP2JOyKJB73FhWyFdM5dG6nQVpUXMnPWG0VZwHTLy3ORIZPgsd2DEV2NPDgUeRDVWKLC8XErXJ6Zp5b16qywubxYXh6ffrNgW1tKsKtvIqxa+GAOtxd9emvMO6zqipTon7Cd8rk/vW8IMgnDxa3lIR6ZHho0SjjcvrBLCwHg5U4tStWZMf95alQZUEglYY3T39c/CZvLO2dJ43RXrKmARpWB+18PP3jpHDHB4+NYFcd+M1G44/R9Po7dJyawNkRJo0ijzsSH11Xhib2DEa8liAbnHL9+pQefeXQvWsrUeOa29Wgunfr34OPra/GhdVX4zfajeHb/cFjP+5tXe3Bo2IQfXLly1gFOUgnD9y9fgc9vWYp/7R7AZx7dC4fbi0PDRvzlnT5cd0Zih4BMV1esxKqqPDy1L7zPMxZDk6eO0Q/WWpEHi9ODvhRkUUVjJgc0qsyIszM0Tj+xOOcYmLCjOop+M0C4sNQU5tqcXt3cY+ynW1GhhoTFJzjrn7BFtEt1c5MGHh/HfzpG4PFxypyl0JzBGed8hHO+1/9rM4BOAOIl8J8D+AoAyr+nuQmrK+36zURtVXkYNs5cdtgbQc10KJlyKe64oBEvd2rxdJhXdfUWZ0TDQERNpSp4fDzkYAJxgttcS62jsXV5KXwceKUzsuzZsXELfPzkQJNo1Wty4PVx9E+cfEPk9XE80z6MjU3Fafv3T9SgiWwoiMPtxRvdOpzfool5yInEX3oyGMb0wh09OkglDGfWF0b0GhkyKT5xzhK8eVQX9vTGDv/9ViVgwmZNoTChcq7x68fGLTDY3Fg7wzCQ6T51Xj0YQ0QBaDQcbi+++K92/HRbNy5vK8ffbz5zxjcyjDF897IVOL22AF9+fP+cg4n2Dxjw29eO4co1Fbhweemc52CM4Y4LGnHXpUKG7sY/vodvPX0QedkKfGlrYoeAzOTK1RX+THl8RoGHMmw8dQF1MDEoTWVpo9Yc2Y4zUQktok4ovdUFu9sb1U5KUThrczjn6NNFPswsWyFDY4kK7THu6uOcRxwcnlaTD3WmDI/5hw1Rz1nqRNRzxhirBbAawE7G2AcADHHO98/xmFsYY7sZY7vHx8ejPymJic7iTKsx+sHEZdQzTfrq1Qlp+Wh7oUQ3bajDaTX5uOuZQ2FdkYx0GIhIvHIeqrTx4JARGTIJGorjNwxEtLxcjfLczIhLG3u0QjASa1ljXZEY3JwMznYe12PU5MAVqxPf9xKrpREGZ+8c08Pu9sbcbyaqzM8KjHiezY6ecayuyouo3Fb0kTOqocqQ4f5ZBkkE2z9oQLEqY8benljVFGbD4+MYMc7+73FXn1DyPNOkxunK87Jw1WlVeGzXIEbneN5o6SxOXPfwTjzdPow7L2jEz69tm/X7k0Imwe8/ugZFygzc/JfdIb//ONxe3PFYOzSqDNx16fKIzvTx9Uvwyw+1YXffJPb2G/C1ixI/BGQml7SWQSZheGpv4kobvT6OEYNjxjH6oqUlSmTIJCldRq01OSPuNwMQCOgoc5YYgUmNUZY1AifX5sz2/Xrc7ITV5Y2qX14cChJL36HB5obZ4YkocyaTSnBuY3HgvVhxmr5nXAzCDs4YY0oATwD4AoRSx28C+PZcj+OcP8g5X8s5X1tcHP0eIBKbCasr7cboi5aXqyGXshl3kR2P4srTTKQShh9f1QqX14evPdEx5ze9g1EGZ0uKciCTsJBDQQ4MGdFcpg7ZKxELxhguaCnBGz3js64mmK5ba4ZMwmKexidOejyuOxncPLlvCKoMGbYs08T03MmQn6NAYY4i7OBsW6cWOQopzoowgxVKZX72nDvyJq0udAwZI+o3C6bOlOO6M2vw4oGRsAZn7B8wYFVlbkLWH4jj9OcqPdvVN4EiZQZqw3yT8ZmN9fBxjgfCDEAjcWTUjMt+8xYODRvx24+swee2LA3ra1OozMDDN6yF2eHBLX/dM+PKhJ/87wiOjVtx31WtUS1+vaytAn/6+On43OYGXHVaai6GFCozcF5jMZ5uH4I3Qf1e42YnPD4+a+ZMHArSkcrMmckRZXAmPIbG6SeG2NcbzRh9UWAoyCx9Z4GWjCjev6yqyoPR7g57mu1MxO+rcy2gnm5z88mf1bP1+JLECusdImNMDiEwe5Rz/iSAegBLAOxnjPUBqASwlzE2dx0GSYl0LmvMlEuxrEyN9mlDQTjn6B23xCU4A4SeiK9c2IztR8bx+O7BWe8bzTAQQLhKXl+sRPcM37R9Po5DQyasSEC/mWjr8lI43D7s6Ak/S92tNaOuOCeipuWZqDLl0KgycMyfObO7vPjvwVG8b2VpzJnPZKnXKNETxiJqn4/jlU4tzm0sRoYsPp9bVUEWJqyuWUevv31MD86BDY3Rz1/6xPpayCQSPPjG8VnvZ3a4cVxnjfswEJF4Rbdvjjcgu/omsK42P+wAsaogG1esrsDfd/YHdk3Fg9HuxtX3vw2314fHPnUWLm4ti+jxy8rU+Nk1bdg/YDjlAtG7x/V45K1eXH9mTdSBNwCcs7QId25tStoQkJlcsaYCWpMT7x4Pf/BMJIYMwt+XuSbQtVbm4tCQMSVDQWwuD8wOT8Q7zgBhGqcqQ0bj9BNEzJxVhjHBMBSxymS2oSAxBWfiMuoY1kH0+z/PcC9qic5rLIb4rZZ6zlInnGmNDMAjADo55z8DAM75Ac65hnNeyzmvBTAIYA3nPLo53iShOOfQW1xpN0Y/2OqqPHQMGqdcbZ2wumByeOIWnAHAjWfX4owlBfje84dnHfxwYNAYdTN9Y6lqxitqA5M2mJ2ewMLWRDh9SQHUmbKwSxtdHh86R8xYGmNJo6i+WBnInL3cqYXF6ZkXJY2ipRoljo5Z5s6sDhuhNTnjVtIIBE9sDB2s7OgZhypThtYY/g5p1Jm4ck0FHt8ziHFz6DeAB4aM4Dy+y6eDlagykSGToH+WzNmI0Y7BSXtY/WbBPrupAW6vDw/v6I31mAGvd4/D5PDgt9etiTpgvWhFKe68oBFPtw/j9/6+OIvTgy//ez+qC7Lx9fc3x+28qXL+shKoMmR4MkGljUMGIeCerawREKoerC5vYKluMomBVYkqunLgktzMhJXlLnYDEzYU5iiQkyGL+jlyMmSoLsiePTjTW6GQSqLaf9lYokSWXDpjNVG4+nQ2MBZ5hrBQmYFVlXmQSVhUGXwSH+FcKl8P4HoAmxlj7f7/3p/gc5E4sjg9cHl9aVvWCABt1XmwubxTerXEtHw8gzOJhOHHV62Cl/OQ5Y16ixPDRkfUb4CbS1UYMthhdrin3H5wSGiST+QENblUgs3NGrzcqZ110ILD7cWf3urFeT/ejiGDHWfHqTSvrjgHx/zBzVP7hlCWm4kzlkT2xjqVGjRKmBwejM+xY+blw1pIGAJj0+NBvJI7EGIoCOccO3p0WF9fFHNZ7C3n1sHt9eHPb/eFvI84DCRRmTOJhKGmMHvWzJnYb3Z6hMFZbVEOPrCqHH995wSMNvfcDwjDq51aFOQosKZ69qmRc7ltcwMuXVWOH//vCF4+rMU9/+nE4KQdP716FbIV0b9hTBeZcinet7IU/z04ElF5dbiGJsUF1HNnzoCTJerJFO2OM1GJOgPaOGZ9yUkDkzZUxlDSKGouVaFzlsE3fTorqguzo9pnKpNKsLIiN6bg7ITeijJ1ZlRVKzdvqMO166pSmoFf7MKZ1vgm55xxzls5523+/16Ydp9azrkucccksdBb0nPHWbC2KuENT/A3o+Pj8Q/OAGEn2Nffvww7enR4dOepWyCiHQYiavJnobq1U3uXDgwZIZcyLI1xZP1cti4vxaTNjT0nTt0dZ3F6cP/rx3DOj17F3c8dRlVBNv7yidPxkdOr4/La9cVCcNMzZsHr3eO4rK1iXn2DD3di47bOMaytKYhrqbB4hTPUGPjjOiuGDPaYShpFdcVKXNhSir+80xdyn1LHoAFVBVkJLYeuLshB/yzB2e6+CWQrpFhWFnlm94aza2F3e/FKV3S7/4J5fRyvdY9jY2NxzMvjGWO474OtWFGei9v+sRf/eK8ft2yoizg7mM6uWF0Jq8uLlw7Hv5hm2GBHbpYcyjkyHw3FSmTKJYGLDMmk9Weko5nWKDwuE1rKnCXEwIQdVTGUNIqaS1Xo01ln7B8FohujH2xVVS4ODZtCruWZS59eCA6jcXFrGe65YmVUjyXxEf+pBCTtiAuoC9K4rLG2MBt52fIp+856dVbIJCym2vBQPnpGNc5pKMIPXugM1KCLxCutyyMcoy8Sm4WnlzwcGjaisUQVtx6lUM5tLIZCKplS2miwufDzbd1Yf++ruPfFLiwrU+Nft5yJxz51Fs5tLI7bwAdxKMgvX+6B18dx5Zr0Xjw93VKN8Gc3W3A2OGlD54gJ57fEd8hJYY4CWXJpyMzZjm6hj/DcGHqSgt26sR4mhwf/fG/mNZX7B4wJy5qJaguzcWLCGrIvaFffJNZU50eVKVxVmYcSdQb+dyj2AGFf/yQMNjc2x2mwTZZCioc+thbqTDmaSlT44gWNcXnedHHGkgKU52biqX3xL20cNtjnLGkEhOxDS5k6JZmzMX/mTBN15iwTY2ZnSpdoL0ReH8ewwR7TMBBRU6kaPj7zzwqfj6NPbwv8PIzGqqo8uDy+sJZdz6R/whbzkC+SOhScLQLiUuKiNM6cMcbQVpWHfUFDQXr9ZQGJmmz4o6taIWEMX3p8/5Qfgh2D0Q0DEVXkZSFHIZ1Sosk5x8EhY0L7zUTKDBnWNxTipcNajJuduPfFLqy/91X88pUenL6kAM98dj3++skzcEZdfEoZg9X7VwT858AIWsrUMY/nT7YSdQaUGbJZg7NXOscAIK79ZoDwd7KqIPQ4/TeP6lBTmB2XNxaAMK75zLoCPLyj95Srs+Kev0Qsnw5WU5QDh9uHsRl630wON7pGTWGN0J+JRMKwtaUUr3dHNr10Jq92jUEmYTEN65iuNDcT2754Hp74zNnzZmBOuCQShstWV2BHj27WvsZoDBlm33EWbGVFLg4OGxM2OTIUrcmBTLkE6szoylRLVBnw+HjgwiqJjxGjHR4fj2mMvmi2iY3DRjtcHl9smTP/hbHpg9LCYXa4obO4Ip7USNIHBWeLgN7fP5POmTNAeLPYM2YJ9Gr16qyoi3NJY7CKvCx865Jl2Nk7gb+80xe4PdYgSiJhWFqimrKIddjowKTNjeVJCM4AobSxf8KG9fe+igfeOIbNy0rw3y9swEMfW5uQhcKiirwsZPinPl6xen5lzQAhQKr3DwUJ5eVOLeqKc1CXgF11ocbpuzw+vHNMjw1LYy9pDHbrefUYNTnwzLTl7InuNxPV+APNmcb67zkxCc6BdbXR93hdGMX00pm82jWGtbX5cW+Qz82euzxvvrpydQW8Po5n9w/H9XmHDPawqylWVgq9zL268NZjxIu44yzaioTSXHERNZU2xpNYlRDLAmpRbWE2FDIJjszQd9anEy6wxdKSUZmfhfLcTOzoibxjSBzBH+mkRpI+KDhbBMSrb+k8EAQQllFzLkxK9PmE7fbx7jeb7pq1VdjUVIx7/9uFXp0VOv8wkFgzXM2lKhwZNQcGjojLUBM5Rj/Y1pYS1BXn4ANt5Xj5jvPw6w+vDizITiSJhGFJUQ4kDPhAW3nCXy8RlmqU6AkRnJkcbrx7XI8L4pw1E1XlZ2FwwnbKoJp9/ZOwurxxzdwAwtjk5lIVHnjj+JTs8f5BAxhL7PAaAIEryzPt89ndNwGZhKGtOi/q5z+jTphe+lKEi9mDDRns6Bo1Y0tzYv7MF6qlJSqsqFDjqX2zry2JhMkhLNYtzwuvXFD8Pp7svjOtyRH1pEbgZDlkPFdBkJOTcKvjUH0gk0qwVKOcMXMmXgyI5f0LYwybl2nw5lFdyL62UMTvp9H2nJHUo+BsEdBbXMhRSNO+dKbNf5V+34ABIyYHnB4flhQldngGYwz3frAVCqkEX3p8Pzr8e0VifVPaWKLCpM0dmPp3aNgIqYRhWVlygrNCZQZevXMjfnL1qkCpYbK8f2UZPnR6ddSTylKtQaPEuNkJo/3UKX9vdI/D7eU4vyVBwVlBNsxOzymvvaNHB6mExW3htYgxhlvPq8fRMQte6RoL3N4xaERDsTLhWZ3yvEzIJGzGRdS7+iaxvCI3pgmGcqkEW5aV4JU5ppfO5lX/1yWekzkXiytWV+LgkAk92uj6ZqYbNoQ3qVFUX5yDLLk0MOQpWcbMzqh2nIlKxUXURtp1Fk+DEzZIWPh/f+bS5L8IO12vzoYsuTTqgTCiLctKYHN5I94ZeGIiugXUJH1QcLYITFidaV/SCAglPnVFOdjXb0BvgiY1zqREnYnvXLYce05M4rvPHQYArIhyGIioedpQkINDRizVKNM+QI6Hz29Zih/M40lPDcWhJza+fFiL/Gx5zOPUQwk1Tn/HUR3aqvKi7oOczSWtZajIy8L9/r1bnHN0DBoSXtIICFefK/OzcGLaUB6nx4v2AQPW1cT+dd7aUoJJmzswlj9Sr3ZqUVOYjfoYmvsXq0tXlUHCELfBIOIY/XAGggD+oSDl6kDlQjJwzoXMWQwXp4pVGWAsPcsak92/F08Dk3aU5WZBHqc+9uZSFcbMzkBfv6hXZ0FtUU7Mg7bOqitEllwauEAUrhM6G4qUGQu2ZHoxoOBsEdBbXWk9Rj9YW3Ue2gcMcSkLiMTlbRW4oKVEmLBUlANVjG+Cp09sPDhswvLy5PSbkdiIqw6Ojk29Iur2+vBq1xg2N5fEPE49lMr8U8fpG2wudAwa4t5vJpJJJbh5wxLsOTGJ3X0TGDY6oLO4sKoqOX9fawpzTuk5OzhkhMvji8t4+fOaipEhk0Q1tdHu8uLtY3psatLEbaLpYqJRZWLD0mI80z4cl8mDYuYs3OAMEEobDw2bkhZUWJwe2FzemLImcqkEhTkZaRec7e6bQMu3/4shw8wTZdPdwIQtrtOfm/ytAl3T+s7E9xGxypRLsb6hCK90js24kzWUPr2V+s3mOQrOFgG9xZX2/Wai1VV50FmcePOoLi5lAeFijOEHV6xEQY4Cp8Xhan2hMgNFSgWOjJoxZnJg3OyMORtHkqMyX2j0np452903CZPDgwviPEI/mDiJMXhi41tH9eAcce83C3bNuirkZ8tx/+vH0OHfNZiMzBkA1BRm44R+ap+dmOWKZRiIKFshw4alRdh2WBvRGxwAePuYDk6PD1viNEJ/MbpyTQWGDHbs7J2I+bkGDXYopBIUKcP/ubCyIhd2txfHx5MzFERrEnecxVbWXaJOv+Ds3eN6OD2+wPeI+WZg0ha3abcAsGyGtTlur08YY18Un9fZskyDIYP9lL2pszmht1G/2TxHwdkiMGF1oXAelDUCJ5dRv9o1hiVxKAuIRLEqA//7wrm4+wPL4/J8TaUqHNGaY15qTZJLKmGoK8o5JTh7uVMLhUyS0CApN0sOdaZsSlnjjp5xqDJlCR1rn62Q4Yaza/Fy5xie2DsIuZRFtfg5GjWFOTA7PJi0neyz29U7gbriHBRG8CZ8NluXl2LIYMeh4VMnq83m1a4xZCukOH3JwlkQnWxbW0qRo5Di6TiUNg4bHCjLy4xosX1rZXKHggR2nMUwEAQQ+s5GTenVc9bpD0Jmm2abrhxuL7QmZ1zG6IuKVRnIz5ZPCc4GJ+3w+njc+uU3+3tdX+4Mb6iRw+3FqMlBO87mOQrOFjjOOfRWJwrmSVljc5kKGTIJ3F6OJSno8ShWZSAnTnXajSUqdGvN6Bg0gjGgJUnDQEjsGqZNbOSc4+VOLdbXF8bt70cowjh9W+B1d/TocHZ9YUL2/QW74axaZMmleLlzDMvK1Alfli4Sx+mLQ0F8Po7dJyaxriZ+AdH5y0ogYYiotJFzjle7xrBhaVHSvhYLUZZCigtXlOKFAyMRT52bbmjSFlFJIwDUFSuRrUjeUBCtf8JirFUfGnVmINBLF10jwsWNo0nKQsaTuKIkHmP0RYwxNJWqpkxs7NOJ/fLxCQJL1JlYWZEbdt9Zv79/t4YyZ/MaBWcLnNnpgdvL501Zo1wqCYw/TuSOs2RoLlXB4fbhxYMjqCvKSfibehI/SzUqDBnsgeXFR8csOKG3JWxKYzBhEbXwRqJXZ8WQwZ7QbJ0oP0eBa9dVATiZbUgGsfyn3z/++ei4BUa7G+vimK0qyFFgXW0BXjoU/kj9rlEzRowOGqEfB1euroTZ6Qn76n8owwZHxJP2pBKG5eXq5AVn/myXJsayxlJ1JvRWF5ye2ALaeHG4vej1Bx7HUhCc9WjN0FmizySKpeLxLGsEgOZSNbq15kBP5fFAcBa/KcmbmzXY2z95yuCRmYjBIWXO5jcKzhY4vcW/42yelDUCwjJqIHnDQBJFbBbu1lqopHGeadAowfnJNyHb/G8qk/FGvcqfOROzZgBwbhKCMwC4acMSqDJkOKchOa8HCJlCxk5mznb1Cb1J8eg3C3bh8lIc0ZoDb17mIl6p3ticvK/FQnVWfSFK1Bl4am/0pY0ujw9asyPizBkglJQfHjZFvU4hElqTA8oMWcyT8sTM27g5PUobe7QW+DhQnpuJY2PWuAx4icRHH9mJ7z9/OOrHD/ozSvEsawSE9gWbyxvIzPXprFBnypCfHb/JuluWacA58NqRubNnlDlbGCg4W+AmrMI39oJ5kjkDELhi3liSnJ6XRFmqOXnlbAVNapxXGjRTx+m/fFiL1spclOYmfndbZX4WHG4fdBYXdvSMo6YwO2nN3ZX52dj37Qtw0YrSpLweIEwkK1NnBhan7uqdQLEqIy6LYoNd4M96vnQ4vNLGVzqFP/NYe4eIkL26vK0Cr3ePQx9l9kNrcoDzyCY1ilorhaEgx8bDC8xjMWaKbceZqMT/vUabJn1n4kTCi1vLYHd7MWxM3sRGvcUJrcmJvf2GqJ9jYNIOhUwCjSq+LR7iZGbx69Ors2JJsTKu/fIrynNRrMqYsosylD69FblZcuRlz5/3fORUFJwtcGLmLJLpVqm2taUEz962ft5nm3IyZIE3mMtpUuO8UluUDamE4eiYBeNmJ/YNGHD+suSUt4llN706K945psc5DYkZoR9KonvbZhI8Tn9X3yROry2I+zCgqoJsLC9X439hlDZOWF3YN2DApiaa0hgvl6+ugMfH8XzHSFSPFzMTFVGMQhdL5ZNR2qg1OVASh4BefI50mdjYNWpGllyKzf7qgWQOBREnFfZP2GCwzV3aNxNxjH4kw2TCIV5EFoeC9OqsWBLni2kSCcOWZg3eODIOl2f27O8JvS19xujfdx+wffvU27ZvF24ns6LgbIHT+2uU51PmjDGWtDHeiSZ+46YdZ/NLhkyKmoJsHB2zYHvXGDhH0oOzZ/cPweryJqXfLNVqi4Rx+sMGO4YMdqyNc0mj6MLlpdjbP4kx8+xveF87IvyZ0wj9+FlWpkZzqQpPRjm1UdxxFmnPGSD0/+QopDgwaIjqtSOhNTvisgJGfI70Cc5MaCxVodG/BzIZWUhR8M7JaKduDkza4l7SCADKDBmqCrLQNWqGw59RrE1AS8bmZg3MTg92982+kqJPb0VNuvSbrVsHXHPNyQBt+3bh9+vWpfZc8wAFZwvcxDwMzhaSD66pwEfOqEZuVvzqz0ly1GuU6BkzY1unFhV5WUkbLS+WbT29bxhSCcNZ9YVJed1Uqi7Igd7qwmtHxgEA6+KwfHomW5eXgHPg5cOzlwe92jWGImUGlSPH2ZVrKrB/wBDVQAlx8XFZFKXFwlCQ3IRnzjjn0JqcMe84A4Sf2XIpw2gaBGecc3SOmLGsVIWCHAXysuVJz5xlyYWJqR1RBtgDE/a4TmoM1lSiRteoCf0TNnCemH759Q1FUMgks5Y2ujw+DE3a06ffbNMm4LHHgMsuA/Lzhf8/9phwO5kVBWcLnM7ihDJDhkw5jYJOhfetLMMPrliZ6mOQKCzVKHFCb8OOnnGcv0yTtJ17ORkyFOYoYHF6sKoyd1EE9mIZzr/3DCBHIUVzaWIC4aYSFWoKs2cdqe/2+vB69zg2NxfHvQRqsbusrQKMAc+0D0f82GGDHUXKjKh/lq2oyMWhBA8FMdrdcHl8MU9qBIQKEo0qE2Np0HM2bnFiwupCU6kKjDE0FCtxLKnBmRnLylSoK8rB/igyZyaHG0a7OyGZMwBYVqZCn96GTv+qgUQEZzkZMpxVVzjrSP0hgx0+jvTJnAGAVgtYLIDBAJjNwD/+AThSf8Eh3VFwtsBNWF2UNSMkCg0aJTw+Dofbl5QR+sEq/aWNi6GkEUBg4MnefgPW1OQnrO+NMYatLSV4+5gOZod7xvvsOTEJs8MT6K0h8VOizsTamny8fDjykfpDBntU/WaiFRVqOD2+wDj4RBCHd8SjrBEASnMzMWpM/RvZrhGhrLDZP4G4QaNM6q6znjELGktUaK3MjSpzNjCRmDH6oqZSFbw+jpc7hcApEWWNgFBm3auz4niIr7048TZtes7+9jfgIx8BpFLgy18GsrKAhx4CzjoLOHYs1adLaxScLXB6i2tejdEnJF2IExuVGTKcsSS5pYVV/jeh5zYmdxhIqgRf6U1USaPowuWlcHs5tvtLKKd7tWsMcinDOUsXx9c+2TY1a3B4xBRx0DFksKMiL/qMVEu5EFgc9mc3EkHsD4tHWaPwPBmBpdapJA67EDPaDRolJqyusPZuxUrnz9otLVGhtTIPWpMz4j68gQn/AuoEZc7Er8v2rjEUKRVQZyam2mFzs9AD+0rnzNmzE/4LD2mROfvzn4HrrwdkMuCZZ4QhIP/5D6BWA0ePAmvWAE8+mepTpi0KzhY4vdU1bxZQE5JO6ouF4Oy8pmIoZMn9VtlamYvy3EysWiCDceaizJAFJsomahiIaHV1PoqUCrwUorTxlU4tzqwrjHlPFZmZ+AZzexg7m0Sccwwb7FGN0RfVFyuhkEpweDgJwVmc1i+UqDOhTYPMWeeoCaXqTOT730uI3xuTsYy6WysEho0lSqyqEnpA9w8YInqOk5mzxPSc1RbmQCGTwOL0JHQ/a2V+NppLVXila+bM84kJG3IUUhSl+oL8I48AH/840NAgBGbvf79w+6ZNwNNPA5/9LNDUBHzwg8CddwLumasYFjMKzha4CasThTnzZ4w+IekiJ0OG+65qxRfPb0z6a9+8oQ6vfXlTSsbap0pNYTZkEobVVYkNzqQShgtaSvDakXE4Pd4pHzuht+LYuJVG6CdQU4kK5bmZ2B7GzibRhNUFh9sX1aRGkVwqQWOpMqGZszH/wuh47DkDhODM6vLC4vTE5fmi1TViRnPQQKTpeyATqcc/Rr+xRIWWslxIJSziiY0DkzaoMmQJ69+VSSVo8AestQnOWm1u1mBX3ySM9lMDmhN6G6oLc5LWHz2jBx4AbroJuPBCoKMDeN/7pn580ybg3nuBHTuA224DfvYzYONGYHAwJcdNV4vnJ/8ixDkXes5SfRWFkHnqmrVVgTciycQYS3q2LtW2tpTgyjUVyFIkfnjR1uWlsDg9ePuofsrtYrM9jdBPHMYYNjVr8OZR3SnBcSjDBiF7FEtwBgAtZWocHjaBcx7T84SiNTmQmyWP2wCuUn95ZCr7ztxeH46OWQLLlgFhomymXJKU4Kxba4Y6UwaNKgNZCikaS1TYH2Hf2cCEDZUF2QkNWsTSxiXFiQ3OtizTwOvjeKP71LLsPr01tf1mv/0tcOutwMUXA089JfSYhZKRAfz618A//ykEcU1Np+4/W8Q70RbXT/9FxuTwwO3lVNZICEl7nzqvHvddtSopr3V2vVC2+NLhqaWNr3aNoa44Jz16NhawTU0a2FxevNc7+84m0ZBBKEuLpawREIIzvdWFcXNiJiBqTfHZcSYSM3BjKRyn36uzwuX1YZl/GAggLEWuK1ImpayxR2vB0hJVILBaVSmsRIgkwB6YtAf6eBNFDF6XJPh7R1tVPgpyFHilc2ppo9fHMTBhS933rl/8QsiEXXYZ8MQTQGaYpb3XXgvs3g1oNMBXvwrccAPg9S76nWgUnC1geovwA4gGghBCyEkZMik2NhVj22EtvD7hTZ7V6cHO4xPY0kxZs0Q7u6EQCplk1rHgwYb8mbOYgzP/3rpDCSptjNeOM5GYOUvlUJAucRjItD2P9RplwjNnnHN0j5kDi68BoLUyDwabOzDkI5znGJy0oTpBkxpF6xuKkJslR2tVXkJfRyph2NhUjNe6x6eshRgx2uH28uRkzu677+RiaQD4yU+AL34RWLFC2GOWEeEFiqYm4NAhYOtW4C9/AerrgauuWtQ70Sg4W8BOLqCmnjNCCAm2dXkpdBYX9vVPAgDePKqDy+vDJgrOEi5bIexsei3ExMzphibtyFZIkZcdW8+QGGAkaijImMkBTZyGgQAnpz6OGlO366xrxAS5VMiUBWsoVmLIYIfdFV5pajR0FhcMNjeWak4Ghq2V/qEgYZY2jluccLh9CRujL1pRkYv9d22N+QJCOLY0l8Bgc2Nf0GCUE3ohu1ydjOBs3Tohq7V9u9A/9uUvCwHZz34GKKJMBmRnA//9L/CBDwAnTgA2G2BKXH9ouqPgbAHTWYTgjMoaCSFkqk1NxZBLWWAh9audY1BlyBI+yp8INjcLO5vC2Ts2bLCjPC8r5p4hdaYcVQVZCRkK4vNxjJmdcS1rzMmQQZUhi3h0fDx1jZqFSZfTemAbNEpwntiJjT2BSY0ng7OmUhUUMknY+84CY/QTNKkxFTY0FkEmYVNG6p/ccZaEssZNm4Ss1qWXAl//uhCYPf88cMEFsT3va68Bb78NfOYzgMcDXH65MNnRHl6WdCGh4GwBEzNnVNZICCFTqTLlOLu+CC8d1sLn43j1yBjObSqGfBFNyEwlcSJmOKWNQzGO0Q/WUqZGZwIyZxM2Fzw+HteyRkDoO0tpcDZiCgy7CCYOSkpkcBY8Rl8kl0rQUqbG/jAnNg5O+sfoJ2jHWSqoM+U4fUkBXg0aqX9Cb4NCJgmUwibcpk3AJz4h/PpLXwLOPz+25xN7zB57TBgs8vzzwkCR3/0OOP104ODB2M88j8z5U4gxVsUY284Y62SMHWKM3e6//XuMsQ7GWDtj7CXGWHnij0siMWEVSiEKKHNGCCGnuHB5KU7obXhi7yDGzU5sphH6SVNdmI364pywRuqLmbN4aCnLRa/eCmucx9OfXEAd3zaCEnVmyoIzo82NYaMDzWXqUz5WW5QNCQOOJbDvrHvMgtwsOYpVU7+mqypzcXDIGOgXnY2446xyAQVnALBlWQm6tZbA53dCb0VNQTYkkiSN0d++HfjHP4BvfUsYnx/cgxaNXbum9phdeKGwtPqTnwTGxoRSyt//HkjQpNV0E84lQg+AOznnywCcCeCzjLEWAD/mnLdyztsAPA/g24k7JomGzuKCKkOGDFniR1MTQsh8c36LBowBP3yxC4wBG5uKU32kRWVzswY7e/WzBkp2lxd6qwuVcZq211KuBucnB13Ey5hJ3HEW38xFqToTWlNqes6O+DNXM2XOMmRSVBdk42iCyxobS5SnlLO2VubB5vKGlbUbmLCjSJmRlBUdySQOLhKnNp7Q21CTrDH6wVmu735X+L/Ygxatr3zl1OEfmzYBDz8sjNrfuFEod1y5UlhkPf08C2zk/pzBGed8hHO+1/9rM4BOABWc8+C6gBwAiyOcnUcmrC4qaSSEkBA0qkysqc7HhNWFtqo8FCppeFIybWrWwO3lePOoLuR9ho1Cv0l5XnyCnpZyIQsU776zk5mzeJc1ZmLM7IAvjCxRvHWNCl+jZTNkzgChtPHY2Nw9g9HgnKPbP0Z/ulVV/qEgQQMxQhmYtC2ofjNRbVEO6opz8ErXGDjn6NNbkzdGf3qWS+xB27UrMa9XUiJk0X76U6CrC/jgB4Gf/1z42AIduR9RcT1jrBbAagA7/b+/hzE2AOA6UOYs7eitTippJISQWVy4vAQAaIR+CqyrLYAqQzZraePQpBCcVeTFJytQnpuJ3Cx53Cc2itmt4jgH+KXqDLi9HBM2V1yfNxydI2bkZ8uhUc38OdVrlOjVWaeMdI+XcbMTRrsbSzXKUz5WV6SEMkOGjjD6zgYmbQuq3yzYlmYNdh6fQK/OCofbl7wF1KGyXF/5SuJeUyIB7rgDeO89oKxM+PXll5/M4C2wkfthB2eMMSWAJwB8Qcyacc6/yTmvAvAogNtCPO4Wxthuxtju8fHwxuaS+NBbXDRGnxBCZnFZWwXOqivEZW0VqT7KoiOXSrChsQjbj4yFXCo8bIhv5owxhpYydfwzZ2YHCnMUp0w1jJWYiUtF31nXqAnNpeqQUzLri5VweX0YmIz/NL0efy9b4wyZM4mEYUWFes6JjR6vD8MGx4LMnAFC35nL68OjO/sBIHULqJNpzRohe1ZZCTzzDHDjjQsuMAPCDM4YY3IIgdmjnPMnZ7jL3wF8cKbHcs4f5Jyv5ZyvLS6mev5kmrC6UERljYQQElKJOhP/uOXMhO9BIjPb2KSB1uTEoRCZrCGDHRKGuE6haylXo2vEFNeMz5jJEfd+MwAoyc30P39y+858Po4jo2Y0zdBvJhInNiZiGbU4qXFpyamZMwBYVZmHzhEzXJ7Qf4YjRge8Pr5gM2en1eRDnSnDv3YNAEDyes5SbdcuwOL/O/fb38Y+jCQNhTOtkQF4BEAn5/xnQbcvDbrbBwB0xf94JFqcc0xYXVTWSAghJG2JQ1heOzJzaeOQwY5SdSZkcVxx0FKmhtPjC+yGigetKb47zkSBRdRJzpwNTNpgc3mxrGzu4CwR4/S7tRbkZctDlomurMyFy+sL9MXNRJxkuFAvvMilEpzXpIHF6YFMwpKyADvlxB6zJ58Ebr4ZcDqFHrQFFqCF891uPYDrAWz2j81vZ4y9H8C9jLGDjLEOAFsB3J7Ig5LImOweeHycGtwJIYSkLY0qE62VuSH3nQ0b7KiI06RG0cmhIPGb2Kg1OVCiin/mTOz3SnZZozjNsrl05mEggLBvS6PKSEjmrEdrRqNGFbKkclVlHgDM2nc24N9xVr1AgzPgZK9sZX5WXC9gpK3gYSTf/z6gVAJLlyZuGEmKhDOt8U3OORPH5vv/e4Fz/kHO+Qr/7ZdyzoeScWASHp1/x1khZc4IIYSksU1NGuwbMGDCeurQi6E47jgT1RcroZBK4jYUxOP1QWdJTOZMLpWgSKlIfnA2YgZjM/d8BasvVsY9OBMmNZpDljQCQjCSny2fte9sYMIOqYShLDdJi5lTYGNTMSRskfSbAVOHkWg0wLe/LQwJWbkyteeKs0UQZi9O4g85GqVPCCEknW1u1oBz4PXuqdkzr49j1OiIe3CmkEnQoFHGbSiI3uqCj8d/x5moJAW7zrpGTVhSmDPnfjBhnL4l5ECXaIyZnTA5PLMGhowxtFbmzZk5K8uNb0lsusnLVuC2zUtx9drKVB8lNT73OSFzdscdgNud6tPEzcL9G7vI6S3CN3LqOSOEEJLOVlbkokipwPauqROdx81OuL08Ib00LeXquGXOErXjTFSizsSoMflljbMNAxE1aJQwOz0YN8cveJxrGIhoVWUuurVm2FwzLzEfmFi4Y/SD3XFBIy5pLU/1MVJDoTi5/+x3v0v1aeKGgrMFSi9mzmiUPiGEkDQmkTCc16jB693jUyYoDhnEHWcJCM7K1NBZnBgzxx70iFmtRJQ1Cs+bGZdzhsvm8qBPb52130yUiImN3drQY/SDtVbmwccRctLnwKR9wY7RJ0EuuQTYuhW4+25AF3qh/XxCwdkCNWERgjPKnBFCCEl3m5s1MNrd2DdgCNwWCM7iPBAECBoKEofsWeIzZxnQWVyzjo2Ppx6tBZwDzbNMahTVF/uDszhObDw6Jiy/nqtnvrUqFwCwP+jvjMjh9mLc7FwUmbNFjzHg5z8HzGahB20BoOBsgdJbXVBlyuK+EJMQQgiJtw2NRZBK2JSpjScXUMc/OFtWJk5sjD04GzM5IGGJG8AlBn3jluT0nYnj6ZeFkTkrUWdAmSGLe+ZsaUnoSY0ijSoTZbmZM/adDU4u7DH6ZJqWFuDTnwYeeAA4cCDVp4kZvXNfoPRWF4pojD4hhJB5QJ0px9qafGwPCs6GJu3IzZJDmSGL++vlZslRmZ8Vp8yZE0XKjIQNnhAXcCdrYmPniBk5Cikqw8hYMsZQr1HGbdeZOKmxcY5+M1FrZe6MExsHJoTAnsoaF5HvfAfIywO+8AUgjgNqUoGCswVKb3FSSSMhhJB5Y3OzBl2j5kDGbDgBY/SDtZSp45I505odCStpBACNv5dNm6ShIF2jJjSWqiCRzJ65EjXEcZy+1uSEeY5JjcFaK/PQp7fBaJs6qa9fXEBNZY2LR0GBEKC9+irwzDOpPk1MKDhboCasLtpxRgghZN7Y7F+ou/2IkD0bMtgTMgxE1FKuRq/OGnLaX7i0psTsOBMlM3PGOUfXqDmsYSCiek0OtCYnTI7YR5kHJjVqwg3OhL6zjiHDlNsHJmzIkElQrKIKokXl1luFEsc77wScyV0/EU8UnC1QOouLdpwRQgiZNxo0SlTmZwVKG4XgLHEZqZYyNTgXxsbHYszkSNiOMwDIz1ZALmUYTcKuM63JCYPNjWVhDAMRNfiHghyLQ/ZMDM7CLmusyAOAU/rOBiZtqCrInrNvjSwwMhnwi18Ax48L/5+nKDhbgHw+jkmbi8oaCSGEzBuMMWxu1uCto3qMm4XytkRMahTFY2Kjy+OD3upCiSpxwZlEwqBRZWIsCZkzcRhIJJkzcZz+sXFrzK/fo7WgMEeBwjB75nOz5agtzD6l72xgwo6qBP7dIWnsgguASy8Fvv99YHQ01aeJCgVnC5DJ4YbXx2nHGSGEkHllU5MGdrcXT+0bBJCYSY2iirwsqDNl6Iyh70ycoJjIskbx+UcjDM7+0zGC7z1/GDyC4QhiFjGcBdSi6oJsyKUsLn1n3WPmOZdPT9damRcyc0YWqeZmwG4HvvnNk7dt3w7cd1/qzhQBCs7S3PFxCzb/5LUZ93iEovPvOKOyRkIIIfPJWfWFyJRL8OjOfgCJWUAtYoxhWYxDQRK940xUmpsZUc8Z5xw/3XYEj7zZi+c6RsJ+XNeICRV5WcjNkof9GJlUgtrCnJiDM845jmotYQ8DEbVW5mLE6Ags6jba3DA7PDQMZDF73/sAhQL4wx+APXuEwOyaa4B161J9srBQcJbGOOf4v6cP4rjOiqf2DYX9uAmrPzijzBkhhJB5JFMuxdn1RTihF6btJTI4A4TSxq4RM7y+6EZvi6WGmgRnzjSqTGgj6Dk7NGzC8XErMuUSfPe5wzDawxvW0TVqjihrJmqIwzj9UZMDZqcHSzWRZc5WVeUBADoGhOzZQGDHGZU1LlqbNgH/+pewoPraa4XA7LHHhNvnAQrO0tgz7cN4+5ge6kwZXunShl2aoPeXWVDPGSGEkPlmk39qo0IqSfi+zpYyNexuL/r00fVLiQFTMjJnFqcHFmd4kyWf3jcEuZTh4Y+tw4TViR//r2vOx7g8Phwds6A5yuCsf8IGp8cb8WNF3VohuFsaYeZsebkaEoZA39mAf4x+JWXOFrdLLwWuvx44dkxYUD1PAjOAgrO0ZbS58f3/HEZbVR6+fFEzBibsYZcM6P2ZsyIqaySEEDLPbGoqBgCU5WWGvWsrWrEOBdGaHJBJGAqyE/vzVuxpC2coiNfH8VzHMM5r1OCcpUW44exaPLqzH/v6J2d93HGdBR4fR3NZ+MNARA0aJbw+Hsh4RqMnMKkxsuAsWyFDY4kK+wenZ84oOFvUtm8HXngB+Na3gN//Xvj9PEHBWZr68UtdmLC68P3LV+D8ZcJVxFf844XnIpY15lPmjBBCyDxTmZ+NZWVqLCnKSfhrLdWoIJeyqPvOtCYnNKqMhAeRYmYunKEgO4/roTU5cVlbOQDgjgsaoVFl4JtPHYTH6wv5uK4RIThaFkXmrN4/Tj+WvrNurRlFSkVUVT8rK3LRMWgA5xwDE3aoM2UR9c2RBUbsMXvsMeC73xX+f8018yZAo+AsDe3rn8SjO/tx49lLsKIiF2W5WWgpU+OVTm1Yj9dbnFBnyiCX0h8vIYSQ+ecPN67FfR9sTfjrKGQSNGhUUWfOxsyJ3XEmEoOzsTD6zp5uH0KOQorzl5UAAFSZctx96XIcHjHhT2/3hXxc56gJCqkkqqC4rlh4TGzBmSXs5dPTtVblYdLmxuCknSY1EmDXrqk9Zps2Cb/ftSu15woTvXtPMx6vD9986iBKVJm4Y2tj4PYtyzTYc2ISk/6s2Gz0VlfC6/QJIYSQRCnLzUpK0AMIfWfRZ84cCR+jD4SfOXO4vXjx4CguXFGKLIU0cPtFK0qxuVmDn23rxpDBPuNju0bMaNAoIYviwm62QoaKvKyoh4JwznF0zBL28unpVlXmAgD2DxrQP2GjSY2L3Ve+cmqP2aZNwu3zAAVnaebP75zA4RETvn1pC5QZssDtW5aVwMeB17vH53wOvYUWUBNCCCHhaClXY9zsDIxij4TW5Ez4MBAAUGbIoMyQzTlO/7UjYzA7PLisrWLK7YwxfOcDy+HjHHc/e2jGx3aNmtBcFl3mChD6zqLNnA0bHbA4PREPAxE1l6qhkEqwf8CAwUk7TWok8xoFZ2lk1OjAz146go1NxXjfitIpH2utyEWRUhFW39mE1UU7zgghhJAwtPgHYHT6e67C5XB7YbS7kxKcAcK4/rmCs6f3DaNIqcD6+sJTPlZVkI3btzRi22EtXjo0OuVjk1YXtCYnlpVGPgxEVF8sjNP3RbGWoDvKYSAihUyCZWUqvNI5BpfHh2oqayTz2KIPzsZMDhwaNs59xyT47vOH4PFxfPcDK8DY1OZiiYRhU5MGrx8Zg3uWhl4A0FudKKAdZ4QQQsicxOAs0r4zsf9Lo0rOz9tS9ey7zox2N149MoZLWstDlibetGEJmkpUuPvZQ7AGjeXvGhWCo1gzZw63L2TZ5GyOimP0I9xxFqy1Mg/HdcJKhEoKzsg8tuiDs4//aRe++dTBVB8D24+M4YUDo/j8lqWoLpz5m8qWZRqYHB7s7gs9Dtfn45i0uWmMPiGEEBKG3Gw5KvKy0Blh35nWXwaZrMxZiToTo8bQmbP/HRyFy+MLTGmciVwqwQ+uXIFhowO/eLk7cHvXqPC5N8eQOWvwB1bR9J0JkxozYpoy3ervOwNAPWdkXlv0wdkVqyvQPmDA0bHIyhniyeH24tvPHER9cQ5u3lAX8n7nLC2GQirBq12hpzYa7W54fZx6zgghhJAwLYtiKIhYYpjM4GzM7ADnM5cNPt0+hJrCbLRV5c36PKfVFODDp1fhD2/1BbKFXSNmFOYoUBxDFlAMzqLpO+uOYRiIaFXQ512ZTz1nZP5a9MHZ5asrIJMwPL5nMGVn+M2rRzEwYcf3L18JhSz0H4kyQ4Yz6gpm7TvTW4WSBwrOCCGEkPC0lKtxfNwCu8sb9mPEEsNkTGsUX8ft5YFdplPP4sA7x/W4rK3ilLaImXz1ombkZcnxjacOwOfjMQ8DAYT3HfnZ8ogzZ5xzHNWao+43E9UXK5GtkEKjykCmXDr3AwhJU4s+OCtSZmBjkwZP7R2adTljohwdM+OBN47hyjUVOGuGBt7ptjRrcHzcil5/XfV0eovwTZtG6RNCCCHhaSlTw8eBI9rwq2jGTA4oZJKkLTsu9WfoZuo7e27/MDjHrCWNwfKyFfjmxcvQPmDAoztP4IjWHFNJo6hBo8SxsZnfn4QyZLDD6vJiaYyZM6mEYW1tAZqiWKJNSDpZ9MEZAFx1WiXGzE7sOKpL6utyzvF/Tx9EtkKGb7x/WViP2dwsLJUMtZBa77+iRpkzQgghJDzLyyMfCiLuOAsnUxUP4t437Qwj/59pH8bKilzUF4cf4FyxugJn1xfie//phMPtQ3McgpoGjRJHI8yc9fiHgcSaOQOAX39oNX7z4TUxPw8hqUTBGYDNzRrkZ8vx7ySXNj61bwjvHp/AVy9qDjvTVV2YjaUaJV4NUdooBmc0Sp8QQggJT2V+FlQZMhweCX96s9bkRIkqOf1mAFCa6w/Opg0FOTZuwYEhY9hZMxFjDN+7fAXgb2FbVhZ75qy+WIkJq2vG0stQAmP0NbEHZ7nZcuRmJyeTSUiizBmcMcaqGGPbGWOdjLFDjLHb/bf/mDHWxRjrYIw9xRjLS/hpE0Qhk+CytgpsO6SF0eZOymsabC7c859OrK7Ow4fWVUX02C3LSvBe7wRMjlPPqrcI5Q752RScEUIIIeFgjGFZuTqyzJnZkbRhIABQ7L+IO72s8Zl9Q2AMuHRVZMEZIARTt5+/FPnZ8sBAj1jURzEUpFtrgUaVQUEVIX7hZM48AO7knC8DcCaAzzLGWgBsA7CCc94KoBvA1xN3zMS7em0lXF4fnt0/lJTXe3hHLyZsLnz/8hWQSCIridiyTAOPj2NH96llmBNWF/Ky5ZCH2HFCCCGEkFO1lKnRNWqGN8wlymMmJzRJGgYCCBeSi5QKjAYtouac45n9wzi7vjDqQPGzmxrw3jfPj8sQjYbiyMfp94zFPgyEkIVkznfwnPMRzvle/6/NADoBVHDOX+KcixsM3wVQmbhjJt7y8lwsK1MnpbTR5HDjz+/04aLlpVhenjv3A6ZZXZWHvGz5jH1nequL+s0IIYSQCLWUq2FzeXFCP/dAC4vTA4vTk9TMGQBoVJkYCwrO2gcMOKG34bK2ipieN14XdCvyspAll4adOfP5OI6OWeKStSNkoYjoXyNjrBbAagA7p33oEwBejNOZUuaq0yqxf9AYqH9OlL++cwJmhwef3dQQ1eNlUgk2NhZj+5GxU67w6S1OFFJwRgghhESkxd9zNde+M5PDjb+9ewJA8sboi0rUGVMyZ8+0D0Mhk+CiFaVJPUcoEglDXXFO2MHZkMEOm8tLmTNCgoQdnDHGlACeAPAFzrkp6PZvQih9fDTE425hjO1mjO0eHx+P9bwJdXlbOWQShicSmD2zu7z4w5u92NhUjBUVkWfNRFuWlWDS5kb7wOSU2yesLhTm0Bh9QgghJBJLS5SQSdiMfWecc+w5MYEvPb4fp9/zMu59sQvLy9U4Y8ncK3DiqTQ3M9Bz5vH68HzHMLY0a6DOTJ9+rfpiZdjBWc+YfxhIjGP0CVlIZOHciTEmhxCYPco5fzLo9hsAXAJgCw+xsp5z/iCABwFg7dq14RVyp0ihMgObmzV4ct8QvnxhE2QJ6Nv6x3v90FtdUWfNROc2FkMqYXilcwyn1RQEbtdbXFhbS5kzQgghJBIZMikaNMopmTODzYUn9w7hn7v60a21IEchxRWrK/Hh06uwsiI3aWP0RRpVJvRWJ9xeH94+pofO4oq5pDHeGjRKPLt/GHaXF1mK2fvYuv1j9JdS5oyQgDmDMyZ853kEQCfn/GdBt18E4KsAzuOc2xJ3xOS66rRKvHRYizd6xgM7xeLF5fHhwTeO4/QlBVhXWzD3A2aRmyXHutp8vNI5hq9c1AxAqN2etLlQRGWNhBBCSMRaytR486gO7x7X45/v9eOFg6NweXxYVZWHe69ciUtXlSMnI6zr2glRmpsJzoFxsxPP7BuCKlOGjU3FKTvPTMT+sW2dWpy7tAh5s0yP7taaUaLOSNoib0Lmg3C+w6wHcD2AA4yxdv9t3wDwKwAZALb5rxy9yzm/NRGHTKZNzRoU5ijw7z2DcQ/Ontw7iFGTA/dd1RqX59vSXIJ7XujEwIQNVQXZMNjd8HFaQE0IIYREo6VcjSf3DeFDD74LVaYMH15XhQ+dXh2XHWDxIPa49emt+N+hUVzSWh6XKYvxtKI8F4wBn//HPgBAYY4C9cVK1BXnTPl/ZX4WerQW6jcjZJo5gzPO+ZsAZsrbvxD/46SeXCrsPPvbuycwaXUhP06Bjsfrw+9fP4aVFbnYsLQoLs+5eZkG97zQ+f/t3WtwXGUdx/HvP2kS04SWhDSlTYG2gJQ65dYqFwGh4AUcKWJR7ozXUV86OuLo4AtHZ3D0jSI6DoOioxZhZOwM6IyWKiAFwbFQKS20FWhpbZNSaBM0vT2+2BNJa9Juym727O73M3Mmm2fP2fPs+TU8/PecfQ7L127jpnNn/u8eZ8cUeUNrSZL0psvmTePpTa9z0duncPm8aYe9LG+8Dc0O+csnXmZg974x33h6PBx/zEQe/fJC1mzZyYbeAdb39rO+t58/rN7KkoGN/1uvqTHYuz/x8fNmVbC3Uv5U7tx8jl29YAZ3/eWfLH16MzefN7Mkr/nAqi28tP0NfnTD/JJdo37ilHZmdbWx7LmsOBvYDeBsjZIkHYGeo1v5/rVnVroboxoqzh5ctYWpk1o4e/b4TkhSrJ6jW+k5upVLTj2wfcfAbjb09bM+K9o27fg3V52Vr+/MSZVmcTaCU6dN4h3TC/c8K0Vxtn9/4o7l6zm5u533zS3tpZIL53Tz8xUvMTC4l+39heKss93iTJKkWtM5sZmmxmDPvsQVp0+nsWF8JyR5qzrampnf1nnARGaSDlT66QhrxOL5M1j1yuus+deh73dSjGVrtrF26y4+f/GJNJT4P6SXzOlm9779PLquj1cHsssanUpfkqSa09AQdB9VOHuWt1kaJZWGxdkoFp3RQ1PjW7/nWUqJ25ev47jOVj50WumvDV8ws5OjWibw0HPb6MvOnHVMdNYjSZJqUU9HKyd1t/OO6fmYpERSaVmcjaKzrZmFc7q5/++vsGff/iN+ncfWb+fpja/x2fecWJb7pjVPaODCU6bw0NptbB8YpGNiU1n2I0mSKu+2j5zGnTctGPd7rEkaH/5f/CFcPf84+vp38+e1vUf8Grc/tI6pk1pYPH9GCXt2oEvmdNO7a5CHn+9zGn1JkmrYrK42Zna1VbobksrE4uwQ3nPKFLraC/c8OxJ/e2kHKzZs59MXzKZlQvmm473olG4i4OVX33AafUmSJKlKWZwdQlNjA1ee0cOyNVt5NZumfizuWL6OjolNXHf28WXo3Zs625o56/gOwGn0JUmSpGplcXYYixfMYM++xNKVr4xpu9Wbd7JszTY+8e5ZTGwu/x0LFs7pBvCyRkmSJKlKWZwdxpxjJzGvZzL3jvHSxjv+tI72lgncVKKbWB/OpacW7p/mZY2SJElSdbI4K8Li+TN4dvNOVm8u7p5nG3r7eWDVFm489wQmt47PtPZvn9rO1z54Kled6X1PJEmSpGpkcVaEK06fTlNj8LMVL7L5tX+ze++hp9b/4Z/W09zYwCfPnzVOPYSI4FMXzHYGJ0mSJKlKlf/LUDWgo62Z9809liVPbmTJkxsBmNzaxJSjWuhqb6arvSV73MKk1ibu//sr3HDOCXR5iaEkSZKkIlmcFelbV83jyjN76OsfpG/XIL39g/T1D9K7a5BnN++kb9cguwb3AtAyoYHPXDi7wj2WJEmSVE0szoo0ubWJ986desh1/rNnH727BpnQGEyb3DpOPZMkSZJUCyzOSuhtTY0c1zmx0t2QJEmSVIWcEESSJEmScsDiTJIkSZJywOJMkiRJknLA4kySJEmScsDiTJIkSZJywOJMkiRJknLA4kySJEmScsDiTJIkSZJywOJMkiRJknLA4kySJEmSciBSSuO3s4he4KVx22HxuoC+SndCZWfOtc+M64M51wdzrg/mXPvM+P+dkFKaMtIT41qc5VVEPJVSWlDpfqi8zLn2mXF9MOf6YM71wZxrnxmPjZc1SpIkSVIOWJxJkiRJUg5YnBX8uNId0Lgw59pnxvXBnOuDOdcHc659ZjwGfudMkiRJknLAM2eSJEmSlANVVZxFxAciYm1ErIuIW4a13xMRK7PlxYhYOcr2nRHxh4h4IfvZkbVfP2z7lRGxPyLOGGH7X2T7/0dE3BURTVl7RMT3sn49ExFnlecI1Icc5zwnIlZExGBEfLE8775+5Djn67O/42ci4rGIOL08R6A+5DjnRVnGKyPiqYg4vzxHoPaVMeOmiLg7IlZFxHMR8ZVRtp8VEU9k298TEc1Zu2NzCeU4Z8fmEspxzvUzNqeUqmIBGoH1wGygGXgamDvCet8Fbh3lNb4N3JI9vgW4bYR15gEbRtn+ciCy5VfA54a1/y5rPwd4otLHq1qXnOfcDbwT+CbwxUofq2pecp7zeUBH9vgy/55rNud23ry0/zRgTaWPVzUu5cwYuA5Ykj2eCLwIzBxh+18D12SPf+TYXHc5OzbXR851MzZX05mzdwHrUkobUkq7gSXAouErREQAH6UwAI9kEXB39vhu4MoR1rl2tO1TSg+mDPBXYMaw1/1Z9tTjwNERMa3od6bhcptzSmlbSulJYM+Y3pFGkuecH0sp7chWe5w3/841dnnOuT9rA2gD/AL2kSlnxgloi4gJQCuwG9g5wmsvBO4bYXvH5tLJbc6OzSWV55zrZmyupuKsB9g47PdNWdtwFwBbU0ovjPIaU1NKWwCyn90jrPMxRv8HBxROzQI3Ar8fQ99UnDznrNKplpw/SeGTdx2ZXOccER+OiDXAA8AnDrW9RlXOjO8DBoAtwMvAd1JKrx607THAaymlvSPs37G5dPKcs0qnWnKu6bF5QqU7MAYxQtvBn3SO+ulpUTuIOBt4I6X0j8OsegfwcErpkTH0TcXJc84qndznHBEXUxgA/C7Skct1ziml+4H7I+JC4BvApUfajzpWzozfBewDpgMdwCMR8ceU0oYi9+/YXDp5zlmlk/uc62FsrqYzZ5uA44b9PgPYPPRLdpr0KuCeYW0/yb64+GDWtHXokobs57aD9nENh//09evAFOALxfZNY5LnnFU6uc45Ik4D7gQWpZS2j+F96UC5znlISulh4MSI6CrmTekA5cz4OuD3KaU9KaVtwF+ABQftv4/C5YpDHzYP379jc+nkOWeVTq5zrpexuZqKsyeBk7NZXJopDMhLhz1/KYUvdG8aakgpfTyldEZK6fKsaSlwc/b4ZuC3Q+tGRANwNYXra0cUEZ8C3g9cm1LaP+yppcBNUXAO8PrQKV2NWZ5zVunkNueIOB74DXBjSun5t/Aele+cT8q+30AUZvFrBmp2sC+jcmb8MrAwG1vbKEzqsWb4zrPvDS4HFo+wvWNz6eQ5Z5VObnOuq7E55WBWkmIXCjMvPU9hJpmvHvTcT4HPHmb7Y4BlwAvZz85hz10EPH6Y7fdm+16ZLbdm7QH8IHtuFbCg0seqmpcc53wshU+VdgKvZY8nVfp4VeuS45zvBHYMa3+q0seqmpcc5/xl4NmsbQVwfqWPVbUu5cqYwoya92Y5rQa+NMr2sylM9rIuW78la3dsro+cHZvrI+e6GZuHphGWJEmSJFVQNV3WKEmSJEk1y+JMkiRJknLA4kySJEmScsDiTJIkSZJywOJMkiRJknLA4kySJEmScsDiTJIkSZJywOJMkiRJknLgv7qb5SztvGM+AAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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17FL1T/lxatKPw8OeSh8KIZmu99B0oMJHQghRyqn06qQJb5guhF8EqDi7yD016MbmtnrUm7RFv8a6FgoFWanmizO6GidV/5RwEjzli1b4SMjFbsQTwmC6KBuk4oyQmiVuHUlxUCjIRYCKs4tYIJrA8+fnikppzNbTZIGKAf2072zFGRGLs9lQhY9k5RDHRkz76QOSVNbjp4Wu2ap6AxVnhNSw05N+WPUaAMAIfV7XPCrOLmIHz84gkeIl7TcDAINWje5Gc6btTlYOsXPmiyTgj1CggBRicUadM1Jpe0+70O0wYfc6JwZdAXDOK31IhBCZxZMpDLkCuG5dEwDad3YxoOLsIvbkwAz0GhW2ddlKfq11zVZFB1G/+yeH8A+/eEGx178YJZIpjM2F0e0QRijQ0kZpxA7xtJ+KM1I5kXgSTw+5sXudE71OC+ZCccwEY5U+LEKIzITANY4965xQMSrOLgZUnF3Enhp0Y+dqOwxadcmvta7FinOeEMKxpAxHdqHD5+bw9NCMIq99sZrwRpBMcVyxRhijQImN+fkjcYx7IzDp1JgJRpGgjdmkQp45M4NIPIU964XiDKBQEEJq0al0UuPGtjq01Bkw6qFljbWOirOL1LQ/gtNT/pL3m4nWNVvBOTCgwDDjaCIJdyCKYTfF9ctJ3G8mFmfj1DnLSwwDuWKNA5wD7gB1KkhlPH5qGkatGrtW2zPF2aCLijNCas3pST80KoY1jRa0203UObsIUHF2kXp6UOhClbrfTLRWTGxUYN+ZmEyUSHGcowGMshH3m23rtEGnVmGUirO8BtJLd8V/N1M+CgUh5cc5x2Onp3F1rwMGrRqt9QaYdGoKBSGkBp2e9KOnyQKdRoV2mxGjFAhS86g4u0g9OehGg0mLS1fVyfJ63Q4zdBqVIvvOsvdCDUzRyYdczntC0KgYWhsMaG0w0LJGCU5P+WHUqjP7NGnfGamEIVcQI54wdq9zAgAYY+hpslBxRkgNOjXpz4wsareZMOmLIJagVUS1jIqzixDnHE8NunF1TyNUKibLa6pVDH1OiyKJjRNZA5IH6ORDNuc9IbTZjNCoVWizGSkQRIKBqQD6mi1YVW8AUNuds/4pP277z6fhogK06ogR+rvT6W0A0NNkpj1nhNQYfySOsblwVnFmpFlnFwEqzi5CZ9xBTHgjsu03EymV2CjuhXJa9VScyWjEE0KnXUhqbK03UudMgtNTfvQ5rXCYdWCsdjtnnHP84y9fxKFzszg+OlfpwyGL7D09jbXNFrTbTJnbep0WjHsjCEYTFTwyQoicxHOq9VnFGUCzzmodFWcXoacG3QDk228mWtdixZQvirmQvCEJ494IHGYdNrbV07IdGZ33hDInd202I6b9UUQTyqRt1oLZYAwufxTrWizQqFVotOgxXaOds8dOTWfSUSfoCm1VCUQTOHjWgz3rnQtuzyQ2UigIITVDXI0kds460p/ZtO+stlFxdhF6csCNDrsRnQ5T/gcXQKlQkPG5MFY1GNDntGDIFUAyRYNWS+WPxDEbimc6Z20NwtU4WiqRm3gFs69Z+D53WvU12TmLJ1P4/G9PYnWjGWoVo++JKvPkgDsz8yhbJrGRLmARUjNOT/ph1Wsyn9Gr6g1QqxglNtY4Ks4uMolkCs+cmZG9awYIyxoByL60ccIbRmu9ET1OC2KJVCYCnhRvxCP8YM8UZ+mlErS0Mbf+9Emv+H3eXGeoyT1nPzt4HkOuID71qvVwWvWYrME/40r2+OlpWA0aXJ4OpRF1OczQqBgVZ4TUkFOTfqxtsYIxIR9Ao1YJs87os7qmUXF2kTkx5oU/kpB9vxkgXNGxGjQ4LWNxxjnH2GwYrQ1G9KWvDNO+s9KJMfpicdbekF4qQaEgOfWnr2CKYSC12DnzhuP4+p8GcMUaO152aTNa6g3UOasinHPsPT2N6/qaoFUv/PjWqlXocpioOCOkRnDOcTorqVHUbjPSReoaR8XZRUbcb3ZVj/zFGWNMCAWZlO/kwBdJIBhLorXBkFm2o8Sg64vNyKLirKXeAMaoc7ac/ik/+potmSuYzjoD3IEoEjU0GP07ewcxG4rhM6+5FIwxtNQZMOGl74lq8dKED1O+6IKUxmy9TgsNoiakRkz5ovCG45kwEFG7jQZR1zoqzi4yTw66saG1DnazTpHXX9tixalJHziXZ1+YeGLY2mCE1aDFqnoDXRmWwXlPCHUGDepNWgCATqOC06qnOP0cOOfon/JjbfP8h6TTqgfnwExQ3gCcShnxhPDDp4bxxq3t2NhWDwDUOasyj592AQCuX6Y4OzcTohlIhFSBRDKFr/zhFNyB4lZYnJr0AZhfSi/qsBsx5Y9QgFcNo+LsIhKKJXDk3Jwi+81E61us8EUSmPLJs9xLjNFfVS/siep10qBVOZz3hC4IhGlrMGa+3mQhdyCG2VA8EwYCCHvOgNqZdfal35+CSgV87BXrMretqjcgGEvCH4lX8MiIaO+paWxqq4fTaljy/l6nBckUx7mZYJmPjJDqlkxxfPj/juLEqLds7/nShA/f3juE+w+NFPV8MVxtfUvdgtvbbSZwvnAGLKkteYszxlgHY2wvY+wkY+xFxtgH07f/I2NsjDF2NP3fq5U/XFKK54ZnEUumFNlvJhI7C3LtOxtP//ARk4rE4ixFiY0lGZkNZSJ5RW02E3XOchhIfz+vW9Q5A4BpmS5EVNLhc7P49fEJvOu6HrTUz5/4t9RTime1mA3GcOT87AUR+tl6m4TvT7qARchCY7NhPPz8GH73wkTZ3tOV3pO8v99V1PNPT/rRUmfIrHARibPOaGlj7ZLSOUsA+Ajn/BIAVwB4P2Ps0vR9X+Ocb0n/91vFjpLI4qlBN3RqFXZ02xV7j0xxlm7Hl2p8LgyNiqEpfSLc57QiFEtivAz7YI6cn63Jk9JUimPUE87sNxO1NRgxMRdZcYXv+ZkQQjFlB++KFxvWNlsyt2U6Z/6V/T3COce//uYlNFn1ePd1axbcJ4af0Kyzyts/4EKKA3tyLGkEgB6nGQAVZ4QsJl54PFfGIA1xOePhc7NFDYc/tUQYCECDqC8GeYszzvkE5/xI+td+ACcBtCl9YER+Tw64cXmXDUadWrH3sJt1aLLqcVqmUJAJbwTNdcJcDwDoay5PYiPnHG//wUF87Y/9ir5PJUz5I4glU+hYXJzZjIglU3AVuT6+Ek6MenHTV/fhO3uHFH2f/qkAGkzazEUCAGi06MDYyu+c/fr4BJ4/P4ePvnwtzHrNgvta0gVoLV6kWGkeP+2C3azD5vaGnI8x6YR5SBQKQshC4pL9ci75dQeE/cjxJMeBszMFPTeRTGHQFbggDAQQfi4Ls86oOKtVBe05Y4x1A9gK4ED6prsZY8cZYz9gjNlyP5NU2kwgipcmfLimT7kljaL1LVbZZp2NzYXR2jC/zKq3KT1odUrZk48pXxT+SAJna3DvxvmZhUmNorb013mlLJXwhuN4332HEUumFO8U9E/5sdY5P2sGEObNOMx6TK/gzlkknsSXfn8K61usuO3yjgvud9YJxSjNOqusZIpjX78L169tylyoyqWH9uUScoH54iwkW2BZPi5/FCadGgatCvv73QU9d3gmiFgitSCESqRRq9DaQLPOapnk4owxZgHwEIAPcc59AP4TQA+ALQAmAPx7jue9izF2iDF2yOUqbt0tKd3TQ8JVGyX3m4nWNgvFWVKG5XETXmHGmchm1qHRolf85GMofeVZLGRqyeIZZ6K29KyzlRAKwjnHxx88hom5CDrtJozOKff3lElqbLFccJ/Tql/RnbMfPz2M0dkwPvOaS5c86ddr1Gi06GhZY4UdG52DJxhbdr+ZqLfJgiEX7cslJNt4+meYP5LAbKg8AUeuQBTNdQZcscaB/QOFnf+eSoeBLLWsERBmk1JxVrskFWeMMS2EwuxezvnDAMA5n+KcJznnKQD/BWDnUs/lnN/DOd/OOd/e1JR7rTxR1lODblgNGmxKR2QraV2zFdFEKlMEFCuV4pj0RjJJjaJep1nxWWdicTbpiyASr6242hFPCCqGBUUvICxrBLAiQkF+8NQw/vDiFD75qvW4bm2joh9Sk74I/JHEklcwm+v0K3bP2Uwgim89Nogb1juX7agLcfrV/z1Ryx4/NQ0VA66TsPKh12lBJJ4qy75cQlaK7IuO5Vra6PZH0WjR4dq+JpxxBQtahnh60g+1imXmuy7WbjPSssYaJiWtkQH4PoCTnPOvZt2+KuthtwB4Qf7DI3J5asiNq3oceZfEyEG80iPGwBbLHYginuSZ5XaiPqcVA9MBRZcmDGV15kbKuIG4HEZmw1hVb4ROs/Cfv0WvQb1RW/WDqI+cn8UXfnsSL7u0Ge+4ZjXabSbMheKKxb33p5fQLlWcOa2GFds5+48/DyAUT+LvXr1+2ccJg6hXZgFaK/aedmFbpw0NpvzzKcWTOVraSMi88blw5t/GuTKtiHEHomi06HH9WuGiyhMD0pc2npr0o9thgkG7dEZAu82EKV+05i4eE4GUztnVAO4EcMOi2PwvM8ZOMMaOA9gD4P8peaCkeMkUx4gnjHWLZmUoRQztKLU4G1s04yz79f2RBKb9yp0UD7mCmeKl1A5gtTnvCaHDblzyvrYGY1V3zmaDMXzgvufRUm/Av912GRhjmeQqpY67f1JMaly6c+YORGVZwltOg9N+3HvgPN66sxO9zqWXzYha6g2056yCpn0RnBjzSlrSCFBxRshinHOMz4Wxa7UdjJWzOIuhyapHT5MFq+oNeKKApY2nJ/0XzDfLJn6Gr4RtCKRwUtIan+ScM8755uzYfM75nZzzTenbX885L9/wCFIQsaNQb9TmeaQ8TDoNOu2mkkNBxKv1i5ffiScfAwqGggy5ArhyjQNA+X6Ql8t5T+iC/Wai1gZj1XbOUimOjzxwDNP+CL791m2Z2S/t6XltIx6FirMpPxotetjNF3YtmuoMSHFhieBK8sXfnYZJq8aHburL+9hV9UbMheJ0hbZCHk/PSNqzTlpxZjfrYDfrMkuzCbnY+cIJBGNJrG40Y1WdoSzLGqOJJLzhOBotejDGcG1fI54ccCORTOV9bjCawHlPKOd+M2D+c4/2ndWmgtIaycrkCwvzNcpVnAHC0sZSB1GLV4Ral1jWCAhX/5UQiCYw4Y1gR7cNFr1Gls7Z+ZkQDp71yHB0pQnHknD5ozmLs3ab0DkrV5pVIe554gweOzWNz7zmUlzW0ZC5vSMzkFOZIrp/yr9gvlm25nS0/tQKWtqYSKawr38ab9rRAYdFn/fxFKdfWY+fnkZznR6XrFq+w5mtt4kSGwkRjWXOJYzodJjKMutsJh2j35j+GXvd2ib4IgkcH/Pmfa54YXv54owGUdcyKs4uAt5weTtngBAKctYdRDRR/NX28bkITDr1BcfdaNGh3qhVbNbZWZdwVa3XaUGn3STLVbbP/fYlvOl7z+C/9p8p+bVKIQ6tXDzjTNTWYEQgmoAvouxQ50I9N+zBV/5wGq/ZtAp/eWXXgvvsZh2MWrUiH1KpFMfAdGDJJY0A4EwXLispTv+cJ4R4kuOSVdKWObfQIOqKiSdTeKLfjT3rnAvGOOTT4zRXRXGWTPEVt+SX1J7xrOKs22EuS+dMHEDdaBFWXFzd0wjGgCckROqLW0KWmnEmaq4zQKNiNIi6RlFxdhEQi7M6gybPI+WztsWKZIpjaLr4H4Ljc0KM/uKTEsYY+pwWxYozcTlQT5MFXQ6TLJ2zgekAtGqGz/32JL7wu5MV60zlmnEmyiQ2VtHVuJlAFHffdwQdNiO+cOumJb8flEquGpsLIxRL5izOmutWXudMPGnPlQK2mFicTfqq53viYnFoeBb+aELyfjNRT5MFs6F4xZfb/s3/HMKnf36iosdAVq5hdxB333cEwWhpFwvF5NLWBgM6HSa4AzEESnzNfDLFWXp1hc2sw+a2ekmR+qcm/TDp1OiwLf05DQBqFUNrg5E6ZzWKirOLgE/cc2YqX+dMvOJzespX9GtMeMNYVW9Y8r6+ZuWW7Qy5AlCrGDodJnTaTRiZDZc0MyieTOH8TAjvuGYN7tjVie/tO4OPP3hc0tpzuUnpnAHVE6efSnF86P+OYjYUx7fv2IY6w9Lfw0JxJv8xzy8vWbqQEfYTrKzOWcHFWWZZ48opQGvF46enoVWzgudTVksoyMkJHw4OV34592KReBI/ffYc7aOscl959DR+fXwCx0bmSnqdsbkwdGoVGs16dDvMAJSP03f7hWWNTVlLx69b24SjI3OZC+a5nJ70o6/ZClWedO0OO8Xp1yoqzi4ClVjWuLrRDI2KZWLIizE2F8kUC4v1Oq3wBGOKXBkecgXQaTdBr1Gj02FCLJEqKa1uxBNCIsXR67TgX9+wER+8sQ8PHB7Fe356pOwnB+c9IZh0ajiWCLcAsjtn1fED/9t7B/HEgBuffd2l2NCae0Zfu82kyMgDcd9krkRDrVoFh1m34jpnq+oNsOilddLNeg3qDBqadVYBe09PY+dqu+S/K1GmOKtgKAjnHO5AFOdnQohX4ELUcp4ZmsFnHnkBf/8ITQCqVoPTAfz2hJAzV+oesfG5CFY1GKBSscyqkfMKB3250ucmTdb54uzaviYkUxzPDM3kfB7nHKen/FifY7VGNhpEXbuoOLsIzC9rLF9xplWrsLrRjIEiQ0GiiSTcgegFMfqiPgWvDA9NB9HTJFxd67KLV9mK/0F+Jr2HbU2TGYwx/L+XrcU/37wBfz41hb/8/sG8V9HkNJJOasy1f8Vh1kGvUVW8c5ZIpvDQ4VF87U/9uHlLK966s3PZx3fYjfBFErJ/LQemhEJmuQsbTVYDXCuscya1ayZaVW+kPWdlNjYXRv9UQHJKY7bWeiOMWnVFO2fecBzxJEcixasu8VZccvbA4VHcf2ikwkdDlvKdxwdh0KihUbGSv3/G58JoTZ9LdDmE4mxY4e9JdyAKi16zYE7Z1s4GWPSaZZc2ugJReIKxZcNARO02I1x+mnVWi6g4uwh4w3FoVAwm3dLDDJWyttladOdsMhOjv/SyxkycvswnH8kUx1l3ED1NwuuLP8jPe4pfAnHGnd7D1jh/QvyXV3bjG2/ZiudHZvHm7z2D6TLNkRJmnOVex84YQ1uDEeNzlTkRd/mj+OafB3Dtl/fiIw8cw7qWOnz+lgv3mS0mxgrLvVdOXF6ynOY6/YrpnKVSvKjirJlmnZXd46enAQC71zUV/FyVilU8FMSVNYey0ssrF5sNCUvOtnU24B9+8QJOTRa//J7I7/xMCL84Oo47dnWiw24q6fMXmN+/DgBWgxYOs67k18zH5Y9mwkBEWrUKV/Y4sL/flXPfuZQwEFG7nRIbaxUVZxcBXziOeqO2oLQvOaxttmJkNoRQrPCNt9nRt0tZVW+AWSf/leHR2RBiyVSmOFtVLyQilRIKcsYVhMOsu2DP3+sua8UP7tqB854Qbv3u0xh2K/thwTlfdsaZqM1mxGgZO2eccxwa9uBvf/Y8rvrin/Hvf+xHr9OCe+68HL/+wDUwS1jS1a5AnH4yxTHkCmBdjhh9kdOqx9QKKVzGvWGE48nCO2d1BorSL7PHT7vQ1mDM/CwqVG+TBUNVUpxV28w1TzAOrZrhe3duR51Bi/f99IjiARFEuv/cNwi1iuFd161JJyYX/3M9nkxhyhdBW9aF3i6HCcNu5TtnjUuMKrmurxGjs+GcnTuxOJPWORNnnVVXZ5qUjoqzi4A3HEddGfebidY2W8B5cVdNJ+aWHkAtYoyht9mKAZlnnWWSGp3CckaNWoU2m7GkD4chVyDnCda1fU342d9cgUAkgdu++zRekDADpViuQBSReCp/cVamQdShWAI/O3ger/7Gk7jtu89g7+lpvO2KLvz5I9fjJ+/YhZdvaIE6z4ZokRIDOc97QogmUhI6Zwa4A9EVERmeCQMp8IS/pd4AVyBadXuHalU0kcRTg27sWd9U9EW1XqcF495IyUl3xRL33KhY9RVns8EYbCYdmqx6fPMvtmJ4JohPPnS8Kuc7XmzG5sJ48PAo3rKjA846g5CYPBMq+u9myhdBii88l+hymGVJYV6OOxBbujhbK3TCn8ixtPHUpB+NFr2kGZQdNIi6ZlFxdhGoVHEmntQWs7RRnEuSK60REPadDZQQOLIUMfp/TdYSxE57aXH6Z1xBrEnvYVvKZR0NeOA9V0GvUeMt9zyLE6PKFGgjHuFr2mFfuuAVtTUY4Q4ot459LhTDv/z6Jez6/J/xqYdPgHOOz9+yCQf+7kZ89nUbiuoU2ExamHVqWWe+iFcwc8Xoi5xWPVIcFY8tl0IszvIVnIutqjeAc2DaX/1/xlpwaHgWoVgSu9cWvt9MJHZHK1UYiZ2zDa31GHIpP1eqEJ5QDPZ0KNKuNQ589BXr8OvjE/jJs+cqfGTknn1DAIB3X98DQPj89UcTmA0Vt594fIkLvV0OE8a94ZLmsObjDkQXhIHMv7cZnXYT9ueYd9Y/5Ze0pBEQPnu0akbFWQ2i4uwi4IskyprUKOp2mKBTq4oKBRn3RuAw6xZspl2sz2nBtD8qawjEkCsAh1kHW1aaYZej+GUV3lAcM8HYssUZIJxIPfjeK8EA3HdQmRMEMc0wX+dM/BAbV2Bp4x9fmsLLvrYfP3p6GNevbcL9774Sv/vgtXjrrk6YdMXP4RNmncmbXCV+3/blWQI4P4i6+guXwekA7GZd5sRUqmZx1hklNpbF46enoVOrcFWvo+jXqHScvssfhU6jwtbOBgxNB6qqKyV2zkTvua4He9Y14V9+/VLJse2keNP+CH723Ahu3daeSWruKjH6fnyJLRLdDjM4n79gKbdYIoW5UHzJzhkAXNvXiGeG3IglFq5ESKY4+qf8kpY0AsLe0rYGitOvRVScXQTEPWflplGrsKbJnJkVVYjxuTBW5QgDESlx8rHUEsQuuxnecBzeIq7cDaXDQLI7cbmsqjdi52o7DpxRZi6Q2P1rX2awJZAVpy9jceYNxfHh/zuKv/mfQ3CYdfjF+6/Gt966DTtX22XbCyn3rLPTU36024x597w5reIg6urfkzU4HSh4SSMw38GmWWfl8fhpF3autpd0waLLIYwzqVhxFoiiyaJHr9OCQDRRVRcvsjtngHCS+9U3bYHTasD77ztS1M96Urr/2n8GyRTHe3f3ZG7rTodyFXuBdH7/+vz5RGfmNZXp6M4ExQHUS18Eu25tE4KxJJ4/P7vg9vOeECLxlOTiDEiPkaHOWc2h4uwi4A3HUWco/kO+FH1FJjZOeOejb3O+dnr21KCM+86GXMHMfjORmG5YzNLG7Bh9KXautuOMO6jIUOPznhCa6/TLdiOB+UHUcnXOHjs1hZd/fR9+cWwcf3tDL3559zXY2JZ7ZlmxhOJMviuIA1MBrJOw/K95hXTOOOcYmA6gN0/AyVJW1QnfExPUOVPc6GwIA9OBolIas2nVKnQ5TBXtnDVZ9ZmLXZUMJ1lsNhiDzbzwgqXNrMO33roVU74IPvLA0arq9F0MPMEYfvrsedx8WWumWwbMf/4WW5yNz4VhM2kXXOiYH0StTMdJHECdq3N2ZY8DahW7IFL/dDo1VOqyRkD43KuWuaREPlSc1TjOecU6ZwCw1mnB2Fy44E3p43ORnGEgojabEQatSrZ9Z55gDJ5g7MLOmXiVrYjo3SFXABoVWza+PtvO1XYAwHNnZ/M8snBSkhoBIfxBxUqPpfeG4/joA8fw1z86hAajDo+872p8+OXroNMo82On3WaCP5KQ5ap3PJnCGXdA0t4s8QO42jtn7kAM3nC8qM5ZnVEDo1ZNiY1l8Php4YSt1OIMEFYXVGoQtRAlnlWcVUkoSDLFMReOw266sKuxtdOGv3v1JfjTyWncs/9MBY7u4vX9J88gkkjifXt6Ftxu0KrRUmco6vMXWBijL7KZtLDqNYp1zsQ5ermKszqDFls7GvDEwMJ9Z6cm/WBs/sKzFB12E9yBGMIxmnVWS6g4q3GhWBKJFK9ccZa+AlTIPDJfJI5ANJFzxplIrWLoabLINuvsjJjUuOjktbOEK3dnXAF0OUzQqqX9U9vYVg+jVo2DZ2cKfq98RvLMOBNp1Sq01BlKitPfe3oar/jafvz8+THcvacXv/zA1djULn+3LJsYdCJHKMiwO4h4kmNdS/5CRqdRwWHWVX3nbD4MpPDijDGGlnoDJqq8AK0Fj592od1WfIR+tl6nBednQhVJ2RQDEZrr9LDoNVUz68wbjoNzLNhXnO2uq7rxqo0t+PIfTuO5YWWWmJOFvKE4fvz0Obx64yr0LlGYdKYTG4ux1IVexhi6Gk04p1Bio5hU6lwiEER03domnBjzwhOMZW47PelHt8MMYwEzadsz2xCoe1ZLqDircWJYRiXSGoH5pLtC9p0ttYE3l16nRbYP/aEcxZlZr0GjRZ8J1CiEkNQo/SRLq1bh8i4bDpyV96Qgmkhi0hfJRO/m02YrLk7fF4nj4w8ew1/98DlYDRr8/H1X4aOvWAe9RvkB6HLG6Z/OhIFIu4LZZNWXbZB4scTlv4XOOBO11BkwRZ0zRUUTSTw95MbudcVH6GfrdVqQSHHFOgS5JJIpzARjaLLqwRhDT5O5ahIbxZPhXKE4jDF86bbN6LAZcfd9RzJdEKKcHz8zjEA0gbtv6F3y/i578YXU+Fw4s1R/4WualVvWmKdzBgihIJwDTw7Od89OT/olLaXPJhZnSoWbkMqg4qzGicVZpTpnnXYT9BoV+ielF2fijLNVefacAUKSXjHLJpcy5ApCp1FlAjGyddoLn3WWTHGcmwlJ3m8m2rnajtNTfsyFYvkfLNHYbBic509qFLU2GIsKBLnrBwfx4OFRvG93D379t9dgc3tDwa9RLDkHUfdPBaBi0guZ5jrDiuicWfQatNQt35HOZVW9ARNUnClKjgj9bL1N4r7c8natPMEYOEcmSrynyVI1yxpn0z9XbUssaxTVGbT49h3bMBuK49M/P1GuQ7soBaIJ/OCps7jpkmZcsqpuycd0OUxw+aMIxQr7nPdF4vDnWIXT5TBhdDaEhAJdZZc/CrNOvWwHbHN7A+qNWjzRLyxjjsSTGJ4JFhQGAtAg6lpFxVmN81W4OBOXHvYXcHIgFgVLXe1aTFwCIccH/9B0AGsazUsOPi5maOXobAixZAo9EpIas+1abQfnwomaXMRjF1Oq8mlrMGLSGylosPLYXBhHzs/ho69Yh4+/cn1ZumXZ6o1aWPQaWTpn/ZN+dDnMecNTRE6rvur3nA1MB9DjtBTdkWmpNwgDXVfAsO2VSo4I/WxiuFG5izNxWVdTunPQ47RgwhtBoEIDsbPl65yJNrTW4w1bWnH4nPz7f8m8nz57DnOhOD6Qo2sGAJ3pAI9CP4OXW4XT5TAhnuSKXHByB2JoXGZJIyCcG13T24gnBtxCWNNUACleWBgIIPwb02lUNOusxlBxVuMq3TkDgLXNloJmnU14w9Co2JIDHBcT98/IEQqyVIy+qNNe+NDKQpMaRZd1NECnVuGgjPsdpM44E7XZjEikeEGpkfvTVwBvuqS58AOUgTDrTJ7Exv5pP9YWsDeruc4AdyBWUDFbbsXG6ItW1RuQSHG4g8V1CH91bJwCRfLYK0OEfjaTToO2BmP5i7N0Fzm7cwbM7+utpNl0cZZrz1m2Jqses6E4XZBQSDiWxH8/cQbXrW3CZR0NOR/XVeS+7+WLM+USG93pMJx8ru1rxKQvgoHpAE6lkxoL7ZypVAztDfKOkSGVR8VZjcvsOTNUrjjra7ZiwhuBLyItRW98LoLmOsOSHazFuuwmaNWs5FCQaCKJ854QenIUUl0OEzgvbD+T2M0rZM8ZIKRTbelokHXf2XlPCHqNKnMlOx+xa1nIvrN9p11YVW/IO7RZSXIMoo7Ekxh2BzP7JaVw1umRTPHMfJtq4w3HMe2PFr3fDJgfGTBVxKyz2WAMH/jZ8/jS708V/f61bnQ2hEEZIvQX66lAYmOmOEv/vOlNd/CqYWmjJ72scam0xsXsZuHftdTPLlKYnx08D3cgtmzXDJhPTC40FGQsvUViyT1n6dccVmA/pjs94y+fa9cK/9b397twetIPg1a1YIyAVG0yj5EhlUfFWY3zRYRlJJXtnKUTGyV2t4ToW2n7YjRqFVY3mku+MnxuJoQUF05klpL5cChgWcWQKwibSZt3+cxSdq6244Uxr2zLgM6nkxpVEgpeIDsBSlqhE0+m8NSgG9evlSfIoFjiIOpSZhSdcQWR4iisOLOmZ535qrM4yyQ1llCciXtAi5l1dnJCuCr82xMTdKKbw3yEvjz7zUQ9TWYMTQfL2v0RlzWKQ3g77cJy8aHpyoeCzAZjMGhVkhLx7OlZaDNB+fb/EkE0kcT39g9h12o7dnTbl31sg0mHeqO24Dj98bkwtGq2ZKHUbDVAr1EVNb80H3cgmnMAdba2BiN6mszYP+DG6Sk/+pxWSRelF6NB1LWHirMa5w3HwRhgrdAQagCZ5WFSlzaOey+cS7KcPqe15EHU4oDUXMsaM4OoC7hyd8YVKLhrJtq52o5kiuOITPsdRjxhyUsagfllIFK7UM+fn4M/msD1a+W96l+odpsRgWgi0zEuhpgsWmjnDIAiw8PlIH5/l9I5a6kXCtDJIvbWvZQuzqKJFH51bLzoY6hl8xH6hV85X06v04JwPInxMg4Qd/mjsOg1meWZOk1lB2Jn8wSXnnG2FLtZ+Hc9S8WZ7B44NIopXxR/e2OfpMd3OUxFLWtsqTcseVFSpWLotJsw7Jb3gkE8mcJsKC5pWSMAXNvXhANnZvDiuK/gJY2idpsRnmBMlmA0Uh2oOKtxvnAcFr1GcsdECR02EwxaFfoldM5SKY5Jb0RSUqOo12nBeU8IkXjxQxjF5TarG5c+MWqy6GHSqQv6cDjjDmJNjtfLZ1uXDWoVw0EZljZyzoUZZ0ukUOZi0mlgM2kld8729U9DrWK4qrex2MOUhRxx+v1TfmhULOf3wlLEJX9V2zlzBaDTqCQPQ1+Kw6yDVs2K2kB/csKPJqse61usuP/QaNHHUKvkjtDPJu4zLGdh5PJHL9gzXC2JjbOhmKT9ZoDwPQ9Q50wJ//3EGWztbMBVPdLCbzrtxRVnrcucS3Q55I/TnwkI3ytSi7Pr1jYimkjBE4wVHAYiEn+uF5OwTKoTFWc1zhuOV3RJIyBcoepzWjEgobvlDkQRT3K0SVzWCAihICk+H8BRjCFXEK31Bpj1S3cYGROusp2XuKzCH4nD5Y8W3Tmz6DXY2FonS3E2FxLihAs9MW+zGTMbqvPZ3+/Gts6Gin+vzc98Kf4Dt3/Kj9WNZug00n88istmpooozn7/wiR+/8Jkwc8rxMCUP2cSqVQqFYPTaigq1OPkhA+XrKrD7ds7cGxkDqcLGK1xMXjurLwR+tnEbmnZizPLhcXZ8ExQkejyQniCMclLzcUizkPFmayE2PgQblzvlHwxosthwthcuKCB6uNzkWVTn7scJpzzBEtaBr+YlBln2a5Y44BWLXwNSumcARSnX0uoOKtxvioozgChgJJyQjbulT7jLPPa6Th9KcVfLkOuQM79ZiKhOJP2w6/YpMZsO1fbcXRkrqSOIJAVo19ocdYgbRC1OxDFiTFvxZc0AvNXEEvrnAWwtsAPSZ1GBbtZV/CyRs45/v4XL+A9Pz2M7+4bKui5hRh0BUpa0ihaVV94cRZLpDAw7cclq6x4w5ZWaNUMDxwaKflYaoncEfrZHBY9bCZtWbtW7sBSnTMz4kle8b0xs6HYsjPOsonLH6k4k5cYGNNcwMzFLrsZyRSXfMEwkUxh0hdZdotEt8OESDwl64zKzBgJCWnTgLBKZXuXsOeu1OKMBlHXDirOapw3HK9oUqNobbMV0/4ovKHl9wItF32bS3ejCSpW/JVhzjmGpnPH6IvE4kzKVTbxRKiU/SM7VzsQS6ZwbGSu6NcACp9xJmprEK5U5vvzPjEgBBlcr8BV/0LVG7WwGjRFX0EMxRIYmQ1hrbPwD0lh1llhH/IT3ghc/iha6w344u9O4Yu/OyXrVVxAiKsenQ1nLmKUoqXeUPCesyFXAPEkx6Wr6uCw6HHTJc34+fNjiCUq20GpJo/3u7BrjXwR+ov1Oi0VX9ZYiQ7eUmYL6JwZdWoYtWoqzmQmzoQspDgTP7+kLkOc9keRTPFlzyU6FYjTdy9KKpXirbs6cdMlzoKek63Joodeo6LOWQ2h4qzGVcOyRmA+FKQ/T3drvjiT/kNbr1Gj21F8YuOUL4pgLJm3y9VVwFW2M64g1CqGTnsJxVm3HYyh5KWNYnHWYSt8WWMolsRcnoJ632kXHGYdNrTWFX2MciolTn9wOgDOgXUthXeZnHUGuArsnImF97fu2IY7dnXiu/uG8Hc/f0HWeWlDLuHPJFfnbMJbWBqmmNR4ySrh++NN2zswE4zhsVPTJR9PLRAj9JXsPJezOIvEk/BFEmi0LCyAxCXeldx3Fk+m4IskJHfOAGFYNQWCyEu8iCUGKUkhJiafk7h6Rcq5RLcCcfpucc+ZhLRG0esua8V/v31H0ftNGWPpOH3qnNUKKs5qnC9SHcWZeNW+P09i4/hcBCaduuBj7nVaip51Nt/lytM5K+Aq2xl3AJ12U0H7lharN2mxrtla8jDq0dkQGi26nPvpchH3/S23yTiV4tg/4MZ1a5sqGjqTrb2EDykxtKavgKRGUTGds6Mjc9CpVdjQWod/fcNGvH9PD3528Dz+9mfPy9ZZEr+/5SjOWuqNiMRTBaVhnpzwQadRZcJxru1rRHOdvmaXNha6DFmpCP1sPU0WzIbimAkoH1jjzrGsq96oRZNVn0kOrQTxQpMYkS+Fw6KjQBCZZTpnVukXYZutBug0KpyXWEiJn1vL7TlrbTBCrWIFz09bjssfhUmnVqwLnkuHDDM+SfWg4qzGecNx1JsqX5y1NRhh0qnzzjoTZpwZC76C1NdswbA7WNQJrdTirMsufdbZGVfxSY3Zdq224/C52YI2QS923hPKpBgWoq0hfwLUC+NeeIKxqthvJmq3GTEyK2356WL9U37o1KrM33Uhmuv0cAWiBc2TOjoyh0ta66DXqMEYw8desR6ffvUl+M2JCbzjx88hFCs9GnlwOgC1iqG7sfikRlFLXeFx+icn/FjbbIFGLXzcaNQq3LqtHXtPT2dO0mrF0ZE5XPZPj+K/9p+R/BylIvSzlXNJYWYA9RJ7bnqazBXtnM2mB1BLTWsEAJtJR8saZTblj0CnVqGhgHMTMfpe6hLE8fQA6lXLFGdatQrtNqPMnbOo5DAQObXTIOqaQsVZDYsmkojEU6ir4IwzkZDYaMnbOZvwhrGqXvrVNFGf04pEiuNcET9kh6YDMOvUaM6zxKK1wQgVQ94rd6kUx1l3sKQwENHO1Q6EYkm8MOYt+jXOe0IFh4EAwrJGAMuGguw77QJjQjekWnTYTAjFkpjNsxxzKScnfOhxzhcShXBaDUimuOSr7MkUx4kxL7Z2NCy4/W+uW4Mv37oZTw268bb/PpB3n2Y+A1MBdNlN0GvyD93NR5x1JjVOn3MuJDW2LFzyevv2DqQ48PCRsZKPqZr88KmziCZS+NxvT+Knz57L+3gxQn/POumpdcXIFGdlKIwyxZnlwp/j4vJKufdVSiUWWVLnnAFCnD4VZ/Ka9kXhrNMX/D3f7ZAeyjU+F0a9UQtLnhUjhQR9SbFUGE45tNtMmA3FEaBZZzWBirMa5gsL/0irYVkjIISC5Jt1NpYn+jYX8eSjmKWNZ9xB9DgteT8odBoVWhuMede8j82FEU2kio7Rz7ZjtQ1A8fvO4skUxuciRRVnNpMWRq162c7Zvn4XNrXVw1GBK4W5FBsrnEimcOTcLC7vaijqfZsLHEQ9MO1HKJbEZR31F9z3ph0d+M4d2/DCmA9vvucZTJfQYRqUkEQqlXjhRGpio8sfxUwwltlvJlrdaMaObhseODRSsRN1uc0EovjdiUncsasTN6x34u9/8QIePrL8TLdMhP46ZTvPrfVGGLXqsnTOxD03S3fOLPBFEpnHlJu4d6ygzhkVZ7Kb9kcKCgMRddqFuWRSfmaIq3Dy6XaYZR1ELXTOpH9/yYXi9GsLFWc1TNwXUldFxZk7EM35QRdNJOEORAuK0Rf1NFnAikxslJLUKOpy5F9WIS7bkWNZo9NqwJpGc9HF2cRcBMkUL6o4EzcZ5+qceUNxHDk/W1VLGoHiB1GfnPAjGEtiR7e9qPdtshY2iFoMA9nSYVvy/lduXIUf3LUD5z0h3PbdZ4raFxFPpjDsDqJPpuKsyaqHiknvnL20KAwk2+3bO3DGHcThc7OyHFul3X9oFLFkCndd1Y3v3LENV65x4KMPHMPvX5jI+RwxQv9KiYN4i6VSMfQ4iw9NKoTYOXMscYLaU+FQEE96WaPUtEbxseF4EuFYaSNNyLwpXzTvSpWldDlMCMeTme+x5YzNhSXNS+1ymOCLJDAXkqcAdwdiFVvWCACjFKdfE6g4q2HVVpz1iYmNOZY2ilfjC0lqFBl1arTbjAV3zoLRBMa9Ecn7PTrt5rxLIOZnnMlzQrxztR0Hhz1FJfhlkhqLKM4AYSlnrs7ZU0NupDiqrjhrK/IKohi8snN1ccVZoZ2zoyNzqDNoMolhS7mmrxH3vnMXfJE4bvvu0wX/mc7NBJFIcVnCQABhj0ajRY8picXZyQnh3/qlSxRnr9m0CiadGvfXQDBIKsVx38Fz2LXajr5mKwxaNf7rL7djS0cDPvCz5/H46aWTKZWO0M92WXsDDg3PKr7syRWIwGbSQrvE0mCxg1up4kzsnBWy18khDqKW6eSdCIEgzgLCQESdBSQ2Su2cdaWDvoZlCAWJJ1PwBCtTnM3P+KTOWS2g4qyG+dLFWTUtawSAgRzFmbiBt5AZZ9n6nNacr53L2fRyhkI6Z55gDP5I7n1AZ9wB1Bk0si1t2LnaDn8kIWmI92Ijs8XNOBO1LVOc7TvtgtWgwZZFe6Yqrd6oRZ1BU/BAzufOetBuMxbVuQXml3FJTWw8OuLFZR0NeZfTbu204f/edSVmgjH89NnzBR2T2CmRqzgD0nH6EpdZnpzwoa3BuGQokVmvwWs3r8Kvj08guML3SewbcGHEE8bbrujK3GbWa/DDv9qJPqcV7/7JYTx7ZmbBc0Y8ykfoZ7tlaxvC8SR+/8Kkou+z1Iwz0ao6A0y68iyvXIonGIdFrylo/6W4BNJToaWYtSYUS8AfSRQUoy8Sg5ryrV4JRBPwRRISizPxNUtf2iiuCmqswJ4zh1kHg1ZFiY01goqzGuaLVFdxtqreAKtek3PfWTEDqLP1OS044w4iUUCyYSapUeLJa6eExMYzriDWNOXfwybVrjXCkqeDZ2fyPPJC5z0haNUsk7JXqHabEZ5g7IIlPZxz7Ot34dq+xqLCM5TWYTcVdAWRc45D5zzYWeSSRkCYt2czaSV1zkKxBPqn/BeEgeSyrsWK69c24RdHxwpKgxTTUaVefJCipd6ASa+0E4CTEz5csir3WII3be9AKJbEb07kXvq3Etz77Dk0WvR4xYaWBbfXG7X4yTt2osNuwjt+9ByOZg2Uf7xf+Qj9bJd32dDlMOXdB1eq5YozlYphTZMZQy759vgUYjYUg62AGH2AOmdyE5d9FxKjL2q3mSSFck0UcC7RKbHgk2I+DKf8e84YY2i3mTIXZMnKlvesijHWwRjbyxg7yRh7kTH2wUX3f5Qxxhlj1RPXRgDML2usluKMMYbe5tyJjRPpE75i0hoBocCKJVIYKeDK0dB0ACo2f/Usn0xxtswPcqE4ky8Wu63BiLYGY1Hzzs57QmhLz3Ip9r2BC+P0+6cCmPRFqm5Jo6jQWWdn3UG4AzFsL6E4A4Q9glI6Zy+M+ZBMcVxWQNfxlq1tmPBGLujALGfQFUBbg7HgGXfLWVVvlLTnLBJP4ow7uOR+M9HlXTasaTSXfeZZPJnCtD+C/ik/nj0zU1Ia6thcGI+dmsabd7QvOdfQYdHj3nfugsOix9t/cDAzlHvf6Wl02JWN0M/GGMMbt7bjmTMzy4b8lMoViKJpmWVdPU2Wis068wRjBSU1Almds6DyM+IuBpkZZ0VcMNRpVFhVLy2UC4CkPWcGrRotdQZZirNcM/7KpZQZn6S6SPnETgD4COf8CGPMCuAwY+yPnPOXGGMdAF4GoLC1NqQsxAjuOkN1FGcAsNZpxR9PTi1539hcJN2aLy7yW1w2eeTcLFZLDOMYcgXRWUDMeFeeNe+BaAKTvoisnQpAmHe2f8AFznlBHbkRT6jo/WZAVpz+XHjB0rh9/cIemuuqtjgzYX+/W/LX67nMfrOlwzmkctbpMS1hs7oYBlJIcfayS5th0Wvw8+fHcFWvtGthg9MBWZc0AsJJlT+SQDCaWLboG5gKIJniyxZnjDHcvr0DX/r9KZxxBWTbpwkI+8C+vXcQZ91BzIZi8ITimA3GMBuKwR9ZuIxSxYBf3n0NNrZdmJyZz88OnAcH8Bc7O3M+prnOgHvfuQtv+t4zuPP7B/CTd+zC00MzuHVbu6IR+ovdsrUNX/tTPx55fgzv39Mr++tzzpftnAFCcfaLo+MIx5Iw6kof71CI2VCsoDAQIKtzFixtpAURTKV/PhYTCAJIC+UqdIuE8Jqld3PFFNJK7DkDhDEyz5+fq8h7E3nl7Zxxzic450fSv/YDOAmgLX331wB8HEBtZCHXGF8kDqNWveTV3EpZ22KFJxjLXGHKNuENY1URYSCiTW31WNtswbcfH5S8tHHIJT2pEQCsBi3sZl3OZY1nxTAQGZIas+1cbYc7ECt4OVCxM85Emc7Zoqtx+/pdWNdsLXp/ltLabUaE40nJEdgHz87CbtaVXFQ7rQZJsfdHR+bQbjMW9CFu0Krxqo0t+N0Lk5KS41IpjiGX/MVZJk4/z5/z5DJJjdlu3dYGtYrhwcPyLrd7cdyHf/9jP54cdMMdiKHOoMHWzgbcuq0d/++mtfiXmzfgm3+xFT/6qx2wm3X49CMvFBy6E0uk8L/PnccN65x5B7132E346Tt3AQBu/c+nyxKhv1inw4Sd3XY8fGRUkREGwZgwW3O54qy3gqEgxXTO6gxaqFWMOmcyEX8+Ootcat/lyB/KNT4XhlrFJIeOdDlMkkJG8hGXNVaqOGu3GeENxzNbWsjKVdBZO2OsG8BWAAcYY68HMMY5P6bEgZHSecPxqlnSKFq7TGLj+FwYrSWc7KtVDB95+TqccQUlDbdNpnhmxlkhOuymnMsaz7jTMfoyd87EBMFCIvV9kTjmQvGSijOnVQ+1imFsbv7PG4wm8NzZWVxf5hPLQognylKXuD437MH2LlvJXYzmOj1c/mjefWFHR+YK6pqJbtnWhkA0kbP7nG1sLoxIPCV7cdYicdbZSxM+mHTqzCb+XJx1Buxe24SHjowWtF80n2OjcwCAh957FX71gWvwk3fswn+8ZSv+8fUb8MGb+nDnld143WWt2L3Oic+85lIcG5nDzw4WtgjkDy9Owh2ILQgCWU5PkwU/eccuaNUq6DTKR+gv5Y3b2jDkCuL4aPFLOXPJ7LnJ0zkDKlOczQZjBc04A4R9cjaTlmadyWTaH4VBq0Kdobil1lJCucbnwmipM0hezt/lMMPlj5YcTOQORGHUqmVdRl6IzBgZitNf8SQXZ4wxC4CHAHwIwlLHTwP4BwnPexdj7BBj7JDL5Sr2OEkRvOE46oyV+SGRy3xi44UfzBNzkaLDQEQvv7QZl3U04Ot/6kckvnx3YWw2jFgiVfCejy67Cec8S3ewhlzBgvawSbW60YxGi76gUJCR9JXAUoozjVqFljpDZpkIADx7ZgaxZKpq95sBQIddepz+lC+C855Q0RH62ZxWPRIpvmx4gMsfxdhcWHIYSLYrVjuwqt6AR57Pf/FBTMSTa8aZSOyc5dt3dnLCh3UtVqgknCDdvr0DU74onhhwy3KMAHB8dA42kzYz/2c5N29pxVU9Dnzp96ckzVAS/fTZc+iwGwta3nvJqjo8/L6r8MO7dpQlQn+xV29eBZ1GpUgwiJTOQZdDCHUodyhIJJ5EMJYseFkjIMw6o+JMHlM+YQB1sRfCpCQ2js2FCxrJI35e5+vI5eMORNFoLX8YiIgGUdcOScUZY0wLoTC7l3P+MIAeAKsBHGOMDQNoB3CEMday+Lmc83s459s559ubmqr3ZK4W+cKJquucOa161Bk0OL2oc+aLxOGPJoqacZaNMYaPv2Idxr0R3Hdg+avgmaTGArtcXQ4TxuciiC9xlf+MK4B2m6nofXO5MMawa7UdB856JC9HGilxxplo8SDqff0uGLVqbO8ubX+WksTlmFI2R4v7zYodPp1N3OS+3CDqYvabiVQqhpu3tGFfv2vJpcHZBqaFf2NK7DkD5jf2L4Vznk5qXH5Jo+iG9U44zDpZZ54dH/ViU3v+UQWA8O/rX96wEdF4Cp/7zUuSXn9gyo8DZz14686uggN3eposuFrivkG51Rm0ePmlzfjlsXHEEvJ1KgFpnTODVo0Ou6nsnbO59B5sW4HLGgEqzuQkzDgrftlfZtbZMsXZuFfajDNRd3rWWan7ztx5wnCUNl+cUedspZOS1sgAfB/ASc75VwGAc36Cc+7knHdzzrsBjALYxjlXdoAKKUg1LmtkjGFt84XzyCZKnHGW7ereRlzd68C39w4uO3C12OKs025CMsUz0f/Z5E5qzLZztR0T3ojkH7ylDqAWtS+adbav34WrehwFzQoqN6tBiwaTVtIVxOfOemDUqnFpq7RCYjni7J6pZeL0j43OQa1i2NhaePgEICxLS6Y4fnVsfNnHDU4H0GjRoaGIk9HlGLTCyICJZeL0x70R+CIJycWZTqPCLVvb8KeTU5jJU3RKEY4lMTAdwGXt0r/GPU0WvOf6NXjk6DieHszfwbv3wHno1Cq8aXt7KYdaEbdua8dsKJ5zOHaxXOnv+3wnqL0VSGwUiyt7gVH6wnOoOJPLtC9a9H4zYH5odK7VK8kUx6S3sFU4Ugo+Kdz+ygygFtnNOph06qotzkY8oYJn0V6spHTOrgZwJ4AbGGNH0/+9WuHjIjLwhuNVldQo6mu2on8qsKADJBY6cgVMfPTl6zATjOGHT57N+ZghVwB2s67gPQi55qKkUhxn3AGsaZS3UyEqdN/ZeU8I9UZtyQV6m82ISV8EiWQKw+4gzs2Eqnq/majdZpQ0iPrg8Cy2dTVAK8O8NnEDumuZztnRkTmsa7YWnVS3ttmKDa11eZc2KpHUKGqpNy675+zkuBAGcukyM84Wu317B+JJjkeOLl90SvHShBfJFMfm9oaCnve+Pb3otJvwmUdeQDSRe1l0KJbAQ4dH8apNLXBU8GSsWNf2NaLRopO0N7cQrkAUahXL253qSc+kLDSApRSz6aXG1DmrrClfpKgZZyKLXgOHWZdz37c7EEU8yQsqzurSQV/DJRZnrkC0IgOoRcKsM2NVLmt8cdyLN/7n0/jAz54vaFbnxUpKWuOTnHPGOd/MOd+S/u+3ix7TzTmXb7MAkYUvHEddlXXOACEUxBuOL9jbMe4V55LIU5xt7bTh5Zc24579ZzCb40N1aDpY1Iyh+St3C38ATvgiiMRTinXO1jVbUW/USirOBqf9eGpwpqT9ZqLWBqNwNdIXwb704Nxq3m8mam/IP4jaG47j1KRPliWNwPxyrlxL/lIpjmNFhoFku2VrG46NenMuDeOcY0DB4mxVvWHZPWdiUuO6FundyHUtVlzW0YAHDo2UnCR4bEQIu9hcQOcMELqC/3zzBpxxB3HPvjM5H/fLo+PwRxOSg0CqjUatws1b2vDnU1OYk3G4stA50OXdZ9jTZEYskbogBVZJ852zIoozkw5z4XhZi8laFIgmEIwli47RF3UuE6dfyIyzBa9pN+F8jm6cFIlkCrOhynbOAKQHUVdX5+zpITfe8r1noVExfPMvtkrah3yxq56MdSKrZIrDH62+PWfAfChIf1YoyPhcGBoVk3V440devg6BWALf3Te05P2FxuiLnFY99BoVzi9an37GJSY1KlOcqVQMO7rtyw6jjsST+Lc/nMar/uMJzASi+OCNfSW/b3ac/r5+F7odpkyBWs067MJAzuVO9I+cmwXnwE6ZijODVo0GkzbnrLPhmSB8kURRYSDZXn9ZK1QM+HmOzofLH4U/kkCvzKmhopZ6w7J7zk5O+tDlMMFSYGrZbdvacGrSX/J+pOOjc2iu0xc16Hb3Oides2kVvrl3cMk9KJxz/PTAOaxrtmJ7V/Xuu8znjdvaEE9y/Or4hGyv6QosP+NMJF40GHSVb4lTpnNWZCAI55C1kL0YTZcwgDpbl92UM7xDXIVT6BaJbocJw+7iO06eYAycA02WygWCAKi6ztlvjk/grh88h5Z6Ax5671Xoa5a+muJiRsVZjRJjZqu7OJv/YB6fExKcCt1Yv5x1LVbcsqUNP3p6+IITydlgDDPBWFHFmUrF0Gm/8MrdmXT6mFInxIAwjPqsO7jkLK39/S684uv78a29g3jt5lY89tHduOnS5pLfUxxEPTwTxDNDMyuiawYIVxCjiVRmMOhSDg57oFExbO2U7yTbadXnLFyOlhAGsuA96gy4pq8JjxwdW3KJSCapUaEPwpY6A9yBWM6lfycn/LikgK6Z6No+4XvrQAEjI5ZyfMyLTW0NRT//7197KXRqFf7hFy9eUNwfG/XihTEf3nZFZ1kHSMvt0lV1WN9ilTW10eWPSuociEu/h6bLl9gods4aivhMtKf/TLS0sTRT6eXezpI7Z2aMe8NL/vwptjjrdJgxkeM1pXAF8ofhlEO7zQh/JAFvuPKzzv7nmWHc/bMj2Nxejwfec6UsmQIXCyrOapT4D7MalzU2WnSwmbSZNDkgPeOsxKTGpfy/l61FinN8488DC24X55H1OIvrAHU5Lrxyd8YVgEWvUfSHs7jvLPvkddoXwd33HcFf/uAg1IzhvnfuwtfevEW25RVi5+yR58cRjidXxH4zQFqs8KFhDza21Re9/2spzXWGnJ2zYyNzMOvUsiw3vGVrK0Znwzh0bvaC+wbSxZlye85yp1KGYgkMzwQlh4Fk63KY4LTqC5rnt5gvEscZV7CgMJDFWuoN+MjL12Jfvwu/PbEw5+qnz56DSafGG7a2Ff361YAxhjdua8Pz5+cyXf9SufzS0upsZh0cZl1ZExtngzHUG7XQFLG3VBxcTcVZaab98nTOuh0mcL50KuH4XARWvabg/fbdDhNSOV5TCvEiYKWXNXakZ509f/7Cz4Vy4Zzj3x89jX/4xYu4cb0TP3nHLtmDqWodFWc1yhcWUgqrsXPGGMuEgogKjb6VqsNuwlt2dOL/nhtZsERJvGJbTOdMfN3zntCCq+pn3EJSo5JX0ze01sGkU+PgWQ+SKY6fPDOMG/99Hx59aQr/76a1+N2HrsVVMkd0G7RqNFp0eObMDHRqFa5YU/7BucXIN4g6Ek/i2IgXO2QeCdBk1S/Z2QSAo6NebGqvl6VD/IoNLTDp1Pj58xd2PganA7AaNCVFVi9nuVlnpyb94By4pIAwEBFjDLvWOHDgjPSREYu9kB6uvLnE7uSdV3RhQ2sd/ulXL2ZWIsyFYvjVsXG8YWsbrFUYtlSom7e0CctjJczNyyeV4kKUuMTvuZ4mS1mLM08oXtR+M2B+nxoVZ6URVxSU+nMpM5dsiX1nwoyzws8llntNKdwSZvyVw1U9jei0m/CB+57PrNQop0QyhU89fALffGwQb97ege++7XJZL35eLKg4q1Fi56waizNACAXpn/SDc45UOvpWrqTGxT5wQy80aoav/bE/c9uQKwCdWpU5gS9Ul92EUCy5YMnc0HQAaxqV3YulUatweZcNj52axhu/8xT+/hcvYnNHPf7woevwwZv6FIu3Fz/sdq62V2RwbjHydc6Oj3oRS6ZkCwMRNdcZ4ApEL1huGE0kcXLcV/KSRpFJp8ErN7Tg18cnLhi4PjDtR6/TotiFArE4m1yiCBXDQIrpnAHC99ikLyIpaXMpx8eE4mxTW/GdM0D4t/a5WzbBFYjiq+mfHQ8eHkU0kcLbdq3MIJDFmtPLYx8+svTy2ELMheNIpLj04sxpySy/LYfZYAw2U3Gfh2JxNkPFWUmmfFGYdOqC96Iu1mnPPZes2FU44j7q4SJnnYnLGiuZ1ggA9SYt/vddV8Bm1uHO/z5Q1gItHEviPT89gv99bgQfuKEXX7x1U1GdakLFWc2aX9ZYnSfSa5ut8EcTmPRFMtG3haYrSeWsM+Cuq1bjF8fGcWpSOHEccgWwutFcdAdD/EEupjuFYgmMeyNYo+B+M9Gu1XaMzYUxNhfGf7xlC376jl1YrXBRKC5tXCn7zQDArNfAbtblXKYi5/DpbE6rHvEkzwQQiE5O+BFLprClwHj35bxhaxv8kQT2nlo4r2pwOqjo3kdxWdLkErPOTk74YDVoMsVxoXZllu7OFPX846Nz6LAbi+6SZNvS0YA7dnXix08P48SoF/ceOI9tnQ2yzMSrFrdua8PYXHjZoCEp3AXuuelpMmM2FC9bN8oTjBX9PWFLz0bLlfxLpJnyCXvLS71o1GgR5nktTkwGxOKs8J89DrMOZp266Flnbn8UBq0K5iroErU2GOcLtO+Xp0CbC8Vw5/cP4M+npvDPN2/AR16+bkXvya00Ks5qlK+KA0EAoM85n9g4nl4apVTnDADec/0aWPQa/NsfhCvgQ65g0fvNgPmhleK+s7NuoUhTKqkx251XdOMfXnsp/vzh3bh5S1tZfgBmirMVst9MJCRXLV2cHTzrQZ/TUlR623LEwmXxvrOj6T0AWzobZHuvq3sb4bTq8XDWsrS5UAzuQBR9zcoVZ1aDFha9ZslljWIYSLHfl71NFthM0kZGLOXYiLfg+WbL+dgr1sNu1uEdP34OZ91B3HllbXTNRC+/tAVmnbrkYBBxNIqUPWeA0DkDULaljbOhWFEzzgBAr1HDqtdQ56xE076oLEutGVs6lCsUS2A2FC+qOGOMocthXrIbJ4W4pLdaCpLWBiN+9q4rYDMJBdoxBQs0dyCKN33vGRwf9eJbf7ENf3llt2LvdbGg4qxGrYRljQAwMOUvOl2pEA0mHd593Rr86eQUnj0zg/OeUNH7zQDhpJ+x+UHUYlKjUgOos9WbtPjra1ajvsglOsV4w9Y2vOf6HvQpFDChlFyxwskUx5Fzs9ixWt6uGTC/n2JxYuOxUS+cVj1aStwMn02tYrh5SysePz2duao/qHAYiKil3nDBIOpUiuPUhK+o/WYilYph52p7UYmNM4EoxubC2FziksZs9UYt/v61l2LaH4XNpMWrNq6S7bWrgVGnxqs3rcJvT0wiHCsuqQ7IKs4knnyLnd2hMixt5JzDE4yVdCHGZtZd0A0nhZn2R0oOAxF1OUwXFFLjc8LPo2LnpXY3mpbsxknhDlR+xtlibekCrcGkxdsULND++4mzGHIF8aO/2oHXbK6tn4+VQsVZjfKG49CqGYzayrfYl+Kw6OEw69C/oDhTZlmj6K+uXo1Giw4ff/A4kileUnGm16ixqs6Q2Tx8xhUEY1B8eWGlbGyrxydftb5qrgpK1W4zYXQ2fMF+mpMTPvijCdnmm2XL1Tk7NjKHLR0Nsn8N37BVmFf16xPCvKpMcdak7DyZpQZRj8yGEIwli95vJtq52oHznhAmllg2uRxxv5mcnTNAmCv3Fzs78OGXrYWhSn+mluKN29oRiCbw6EuT+R+cg1icSd1z09ZghF6jKsu+s3A8iWgiVXTnDBD2nVVLIIg3FMf2f/0TnhhwVfpQJOOcY8oXLXkAtajLYcbIop/tpV7o7bSbMeIJFTVs3B2QNkai3NoajPjfd12pWIGWSKbw8JFR7F7bJHsY2cWMirMa5Q3HUWfQVvXJ9Np0YuP4XAQmnVrxLp9Zr8Hde3ozSxFLKc4AYWmjeJXtjDuA1nojpRJVmXabEbFEKrMfRnRI3G+mQOdM7BxkJzZ6Q3GccQdlCwPJdumqOqxrtuLn6WVpg9MBGLSqzHw6pbTUXTiIutQwEJG476zQpY0nRr1gDNjYJu+eMMYYvvDGzbizRpfr7FptR1uDEQ/nGGouhSsQhV6jglVi2INKxbCmTImNYlFlNxf/GWM36zCzzMzEcuqf9sMdiOLIublKH4pk/mgC4XgSTqs8F2E77SbEEqkFoUSlXujtdpgQT/KCLwoB0mf8VcLiAu346Jxsr/3EgBvT/ihu394u22sSKs5qli8cr9oljaK1zUJal7iBtxyF5F/s6swseSh1f1iX3Zwp9IZcgbLsNyOF6cgRp//c8Cxa6w1FL39ZjkErXGjI7pwdS38YblGgOGOM4ZZtbThyfg7D7iAGpgNY02iRdaD7UlrqhXluiWQqc9tL4z6omDAAvhSXrKqDVa8peGnj8dE5rGk010TMfTmpVAy3bG3DEwOunGMg8nH5C99z09NkxpBL+UHUs0FhmX+pnbNqWdY4nN7jvNwMx2ojfl+VOoBaJEbfZ+87G58LQ8WKn6PWucRrSpFIpuAJxdBkqd5ZXm0NRvzsb4Qljnf8t3wF2gOHR2A363DD+mZZXo8IqDirUd5wvCoHUGfra7YiEE3g8PnZTDS30vQaNb546ya8+7o1MJca5+swweWPIhhN4KwrWHInjshvqTh9zjkODnsU6ZqJnFb9gq7S0ZE5MAZsKmEw8nJu3tIKxoBHjo5hcDqg+H4zQCjOkim+YJzESxN+rG40l7z0T61i2N5tK6hzxjnHsVEvLpN5SePF4pZtbUhx4BdHx4t6vlicFaKnyYKR2dAFoyDk5gmJnbPiT54dZh1mgrGi5+/JSbwoWOzA5EqYSg+sl2vPWfeixGQAGJsT9rRpi4xvF1+z0OLME4qBc+n7LSul3WbCz/7mCtQbtXibDAXabDCGP700jZu3tEKnoXJCTvTVrFG+FVCcrW0Wrq67/FFFOhi5XNvXhE+9+pKSX6fTLlxlO3RuFsFYkjpnVagtU5zNn8ScmwnB5Y/KHqGfrbnOsLBzNjKHniYL6hTq6KyqN+LKNQ48cGgUY3PhsgS3zA+inv/anpzwlbykUbRztQOD04ELlqTmMuWLwuWPKlYA17qeJgu2dDTgoSJTG92BqOSkRlGv0wLO59NulSKG5ZQaCBJLpBAqITRFLsPp4mF0buV0zsSLVXIVZ6vqDdCo2AWds1KCxVrqDNBpVAUnNrr9wvdXtS5rzNZuM+F/33UF6oxavOt/DiOaKP77+RdHxxBLpnD75R0yHiEBqDirWb5IYkUsaxQpGaOvFHFZhThjqhxJjaQwJp0GDrNuQedMnOe0U+HO2XT6SrHQ0ZlTZEljtlu2CvOqAOWTGgGgpU74NyuedHnDcYzNhWUszoS/n0MS52+JS0flDgO5mNy6rQ2nJv14adxX8HOL7ZwBysfpZ/aclbisMfu1KkksHibmIkWFV1SCeLFKjih9QBgS324zLkhXHPeWVpypVEtH9OfjrpIB1FK120z40q2bMemL4MHDxY/QeODwKDa01tXU3MdqQcVZjfKG46iv0gHUogaTLvNhrnRSoxK67EKn7PHTQnFWytw0opzFs86eO+tBg0mr6JBmZ50B0/4IOOcYnQ3DHYgpEgaS7ZUbW6BPLy1RcsaZqCXTOROKs1PpMJBLZSrONrXVw6BV4dkz0oqz46NzUKsYNtCJQtFeu1lYHvunk1MFPS+e3nNTaOdgdaMZjAFD0wp3zkIxqBhKWk3iSBdn1TDrbNgdhFGrRiLFLwjlUcqkN4Ib/u3xogvpKV8EVr2m5O0E2Tod5kxicirFMTEXKflcostuwnCBnbNMUukK6JyJrupxYEtHA767b2jBvmGpXhr34cVxH26/nIJAlEDFWQ3inGfSGqud2D1TcsaZUupNWtQbtRieCcGkU8s6v4rIp91uWlCcHTo3i+1ddqgUDMxwWvWIJzlmQ/H5MBCFOzpWgxYv39ACnVqFLofyFwpsJi10GlVm1plcSY0inUaFy7uk7zs7PurF2mZrTUbdl4vNrMOaRjNOpEcSSOUJFrfnxqhTo63BWJbOWYNJV1JIjrgkcrbCxdlcKAZfJJHZM1uufWdHR2Zxxh3EM0MzRT1/2heVLQxEJBZSnHO4g1HEkqmSt0h0OYSgr0L2FmY6Z1UcCLIYYwzv39OLEU8Yvzpe+D7TBw6PQKdW4eYtbQocHaHirAYFY0kkU7zqlzUC8/vOVmJxBszvOxOuAFfv2IKLWbvNiLH0PJxpfwRn3UHs6LYp+p7ivoopXwTHRuag06iwvoTBzFL9w2svxf+8Y2fRG+ILwRhbMOvs5IQfNpNWtjlGALCz24GTkz54w/FlH8c5x4kxLy6j/WYl29RWjxOjhRVnhQ6gztbrtCg+62w2FIPNVNrnYbV0zsT9Zlf3OACUL7FxxCMUgcX+XU35IrLF6Iu6HCb4IwnMheKZAdStJW6RWN1kRiiWLKjodafHSFhk7AqWw43rnVjfYsV39g5dMAt0ObFECr84Oo6bLnWWtI+T5EbFWQ3ypU9kVkJx9rJLmrGj21bWQBA5idG7ayipsWq120yIJVNwBaI4NDwLQJn5ZtnEK8TT/iiOjsxhY2tdWQqmJqseV6xxKP4+opY6Q2bO0MlJIQxEzosUO1fbwTlw+Nzy3bMRTxhzoTjtN5PBpvYGTPoimPZLXy5XSnHW02TBGXegoJPDQnmCsZKSGoHq6ZyJ+82u6hEG/parcyYmRBZdnPkjsl64AeYvjp7zhEoeQC26Iv3Z8NSgW/Jz3IFYwWMkqoFKxfDe3T0YmA7g0ZekL2V+7NQUPMEYBYEoiIqzGuRdQcXZVb2NeOA9V63YGNau9IfDmkbab1atxDj9EU8IB896YNCqsLFV2Q5Lc/oK8fhcWOjoKLzfrFJW1Rsw6Y0gkUzh9KRftiWNoq2dDdCpVTiQZ9/ZfBgIdc5KJX4NXyhgaaMrvayr0LRGQCjOIvEUxosY/CvVbDBe0owzALDqNdCqWeU7Z+4QGBP2lTZZ9Rgrc3E2MO0v+Lmcc0z5orIlNYq6MtH3wUxxVuqF3l6nBc11ejxRUHFWvQOo83nt5lZ0O0z49t5ByUs5Hzg0CqdVj2v7GhU+uovXyjwjJssSi7Nqj9KvBV2ZzhkVZ9WqIytO/7lhD7Z22BS/GCB2zp4ccCMSTyme1Fgpzeni7Kw7iGgiJXtxZtCqcVlHfd5h1MdHhaWjpQ6/JkKgC2PCHj6pSuucCT87lVza6AmV3jljjMFu1sETlDbaQSnnPEG01Blg0KqFsKMyxemPpJdPTvmi8EWWX2a8mDccRyyRglPm4kzsnJ2fCWFsLgyzTo26EoPQGGO4urcRTw+6JXdzXf6VW5yp092zE2Ne7B/IX5BO+yN4vN+FN25rh6YMq0EuVvSVrUEraVnjSnd5lx1tDUZc3qXsHiZSvHab8AF+ctKHkxM+xZc0AkJRUWfQZJI8a7U4W1VnQCyZwtPpkIBLFNhXt3O1HS+MeRGMJnI+5vioF5euKs/S0Vpn1mvQ22QpaN+Zyx+F1aApKoxFHPsw5FImsZFzjtlgTJa9MTaTDp5gYYWJ3M7NhDIXBdttprIsa0ylOEY94aILaTFGX+5ljUadGs11+syyxtYGoyxLC6/ta8RsKI6XJqSNlHAHomiyrty9V7dsbceqegO+vXcw72N/fmQMyRTH7dsppVFJ9ElWg1bSssaVrtdpwVOfvCFTAJDqY9Cq0WjR49fHJpDiwE4Fh09nc9YZEIwlYTNpM1d4a01LevP9Y6emoVExRear7VztQCLF8fz5uSXvT6Y4Xhjz0pJGGW1qr8fxApc1FrOkERDmhzWYtIolNvqjCSRSvKQZZyKHpQo6ZzNBdKeX87U1GDE+F1Z81tmUP4JYMoUb1jsBFF6cyT2AOluXXYjTH5+LyBYsdnV6P9+TEpY2JlMcnmDhYySqiU6jwruuW4ODZz14bpm5kpxzPHB4FJd32TIzCokyqDirQZlljSsgSp+Qcmi3GTE2F4ZaxbC1s6Es7yleJb6so2HFbRSXalV61tkzZ2bQ67RAr5E/xv7yLhvUKoYDZ5eO8D7jCiAYS1IYiIw2tdXD5Y9KnqHl8keLHsDLGENPkwVDCi1rFAM85OqczYYq1zkLRBNwB2KZvVbtNiPiSV5QeEsxxKTGq3obodOoiijO0p0zmdMaASGU65wnmOmcycFZZ8C6ZiuelLDMzxOMIVXEGIlq85YdnXCYdfjWY7m7Z0dH5jA4HaDZZmVAxVkN8kUSYAywGlZWrCshShFDQTa01sk6BHU5Ymz0ZTVcNIiDqGOJlGzDpxez6DXY2FqXc9+ZuDeKOmfyEb+WUveduf3Rkk5Oe5rMinXOPOnizG4u/WKlw6zDTKBynTMxqXF+WeP8floliWEgqx1mrGk0F905k3vOGSCEck35opgJxtBW4gDqbFf3NuLgsAeReHLZx83POFvZxZlRp8ZfX7Ma+/pdOcOAHjg8CoNWhddsXlXmo7v4UHFWg3zhOKx6jaJDdglZScRlpzvKtKQRmD8R2VKmTl0lNFr0mcG+coeBZNu52o6jI3NLnigdH52DSaemZTYyunRVPVQMOJFOwczH5S9+WSMgLA93B2KYC8mfhDibfs1S0xoBwG7WwxdJIJ5MlfxaxTiXnnGWvecMgOKJjSMeISGytcGIXqel4MTGaV8EdUXuScxHHGcDyDsv9dq+RsQSqcz4lVxqpTgDgDuv7ILVoFly71kknsSvjo3j1RtXwUqrshRHxVkN8objqC9x4CYhtaTDLnxol7M462mywKhVY0sNd87UKobmdMdE2eLMgVgitWQn59ioFxvb6jNFIimdUafG2mYrTkjYdxaJJ+GPJkrsnCkXCiIGeJSa1ii8hvC5OqtAESnFcKZzNr+sEVB+EPWIJ4TWeiN0GhX6nFaMzoYRji3fUcqmRIy+SPxaAPIWZztX26FRsbz7zsSk0kbLyg0EEdUZtLjrqm78/sVJDC4qwP/w4iT8kQRuoyCQsqDirAZ5w3Hab0ZIlt3rnLh5SyuuKeNcllu3tWP/x/fIstelmolLG5VIahTt6LaBMeDAmYX7zuLJFF6a8GFzGy1plNvGtnqcGPPmnX1USoy+aL44k39po5x7zuxm4c/oqdCss/MzITRadLCkl2aLYUflWNYoFoK9Tgs4L+zvShhArVBxlhW2VOqMs2xmvQbbOm14ctC17OMynbMVvudM9FdXr4ZBo8Z3Hh9acPsDh0bRbjPiitWOCh3ZxYWKsxrkC8cpqZGQLG0NRvzHW7ZmTmrKQa1iK36TuBTtNhNa6gxwKLisp8Gkw7pmKw4uShI7PelHLJHC5hodVVBJm9vr4Q7EMOFdPmyilAHUog67CTq1SpFQEE8oBo2KwSrDv31bunNWqeJseCa4oFMEAG02o+LF2chsKJM429dceCE97Ysqst8MABpMWlgNGjAmfxrkNX2NeHHct+zftzsQg16jkuX7qxrYzTq8dVcnfnF0HCPpvYZjc2E8NeTGbZe303aZMqHirAZ5qTgjhJTJx1+5Dv/99u2Kv8+u1XYcPje7YL+PuOzuMgoDkd2mdDcy39JGOTpnahXD6kZlQkHEGWdyJKY6Ktw5y55xJmq3GRVd1hiJJzHli2aKs26HGWoVw8CUtL8rzoU0SaU6Z4wxdDvMcFr10GnkPaW9urcRnAPPDC2dFAsIYTiNFn1NJfL+zbVroGYM39svdM8eOjwKzoXVIKQ8qDirQbSskRBSLu02EzaWYVnhztUOhGJJvDg+Pxj2+Ogc6o21O0euki5ZVQe1iuUdRi1HcQYAPU6zQnvOYrLMOAPm961VojiLxJOY8EYyM85E7TYjxuciSCk060ws/DrS/8Z0GhW6HCbJoSCzoTjiSQ6ngqsIbrzEiRvWN8v+upe118Oq1yy7tNEVKH6MRLVqqTfg1svbcf+hUUz5Injw8Ciu6nFkvgeI8qg4q0G+CAWCEEJqy47VNgAL950dGxGGT9fSVetqYdAKoSD5hlG7/FEwVnrgRk+TBec9IUQT0oMmpJgLxTPLEUvVYKrcskZxidmFnTMTYslUZnmp/O8rLJnMPjHvbbJIjtNXcgC16EM3rcUX3rhJ9tfVqFW4osexbCiIkFRae/uK33t9D5IpjrvvO4LznhBupyCQsqLirMZEE0lE4ila1kgIqSlOqwFrmsw4mJ53Fokn0T/lp/lmCtrcVo8To3PLhoK4A1HYTTpo1aWdTvQ6LUimeCYuXi6eUEyWpEYA0KpVqDdqK1KcDWdi9C/snAHKJTaKM87ExFtA2Hc2PBNCLJF/pMB8cbYyu0vX9jVixBPOzJhbzB2I1USM/mKdDhNef1krnhuehVWvwSs30GyzcqLirMZ4w0JscB0VZ4SQGrNrtR0Hhz1IpjhemvAhkeLY1NZQ6cOqWRvb6zEbimNsLnfghKvEAdSiTGKjzKEgs8GYLDPORHazriLFmVgcdC/qnHUoPIh6xBOCQataEPgyX0jnX4Y67RM6ek6rcp0zJV3dKyT8LtU9S6Y4PEF5vv+r0Xt39wAAXnvZKhh18s+oI7lRcVZjfOEEAKDOUBvJQYQQItq52g5/JIFTkz4cH5kDAFzWQZ0zpYgjCpbbd+YKyHNyurpR6AjJGQqSSnHMytg5AypZnIVQZ9CgYVGhKc72Uqo4O+8Rkhqzlw73OYWxGQMSCmmxc6ZUWqPS1jSa0VpvwJMDFxZns6EYUrw2BlAvZW2zFf/7rivwiVeur/ShXHSoOKsxYueMljUSQmrNzvSMnYNnPTg+5kWjRY8WBfeyXOzWr7JCq2bL7jtzpdPqSmXWa9Bab5A1FMQXiSPFUROds+GZILobzRfcbtJp4DDrFF3W2GFb2K1b0yQch5R9Z9P+KGwmLfSaldl5YYzh6t5GPD00g+Si0JXMjLMaLc4A4Io1jgsuCBDl5S3OGGMdjLG9jLGTjLEXGWMfTN/+L4yx44yxo4yxRxljrcofLsnHR8UZIaRGtTUY0W4zCsXZqBeXURiIovQaIRTkhRzFGedctmWNANDjlB40IYVYRMnaOTNVrnO2eL+ZqF2hWWecc4zOhi9I6TPpNGi3GSV3zpQMAymHa/oa4Q3HL/h3ICaVNtZgIAipLCmdswSAj3DOLwFwBYD3M8YuBfAVzvlmzvkWAL8G8A/KHSaRivacEUJq2c7Vdjw9NIMhVwCb2xsqfTg1b3N7PY6PepcMBfFHE4gmUiUNoM7W02TBkCuwbABJIWZDQhFlk7M4s+gwG4rJdoxSxJMpjM2FL9hvJmq3mTCmQHE2G4ojEE0sGaHeK7GQnpKxeK+UXPvOMp2zFf7nI9Unb3HGOZ/gnB9J/9oP4CSANs65L+thZgDl+0lFcvJFqHNGCKldu1bb4Q3HwTkoqbEMNrU1wBuOZyLVs8k140zU47QgFEtiMr1PqVSeoPB5KNecMwBwmHWIJzn80YRsr5nP2GwYyRTPOc+v3WbE6FxY9llnYlLjUu/b5xQK6cVL/RabroHOWaNFj0tW1V2w78ztj2XuJ0ROBe05Y4x1A9gK4ED6959jjI0AuAPUOasK3lC6c0ZDqAkhNUjcdwZQcVYO4tf4xBJLG91yF2cF7GWSYjYods7k+zwU9695AuVb2jgsJjUusecMEIqzWCKV6eTIZWSZ4qzXaUEskVp2r1sqxTHtj67YGP1s1/Q6cPjcLMKx+Tl87kAUOo2KAtiI7CQXZ4wxC4CHAHxI7Jpxzj/NOe8AcC+Au3M8712MsUOMsUMuV+4p60Qe3nAcRq0aOg1lvRBCak+3wwSnVY+2BiMcdMVacWubrdCpVTg+NnfBfeLgY7mKs16nvHH6npACe87S+4tmyrjv7NzM0gOoRW3pOP0RmZc2ip0zcZZatl4xsXEq99/VTDCGZIqv+M4ZICxtjCVTODjsydzmCkTRZNHTvlciO0ln8IwxLYTC7F7O+cNLPOQ+ALcu9VzO+T2c8+2c8+1NTU3FHymRxBeJ05JGQkjNYozhAzf04j3Xr6n0oVwUdBoV1q+yLhmnPx+IIE9x1mTRw2rQyJbYOBuMQa9RwaiVLylQXCI5W8bibHgmCJNOnXNvX3s6TVHuxMbR2RAaLTqY9Rd2hsRCenCZ0QeZGP0VOuMs287VdujUKjyVte9MSCqlMBAiv7y9WCZcEvg+gJOc869m3d7HOR9I//b1AE4pc4ikEN4wFWeEkNp255XdlT6Ei8qmtnr88tg4OOcLugQufxQaFUODTJ85jLFMKIgcPEFhxpmcnQ2xC1fOxMbzMxfOGsvWlp51ttyw8KLe1xNaMgwEEPa1O636ZTtnYvFeC8saTToNtnU14ImsfWfuQAyt9Su/8CTVR0rn7GoAdwK4IR2bf5Qx9moAX2SMvcAYOw7g5QA+qOSBEmmoOCOEECKnTW318EcSmeV1InHGmUolX/HT0yRfnP5sKCbrjDMAcKQ7JeKSyXIYngmiO0eMPiDMiLObdbLH6S814yxbX7NFUuesFpY1AsC1fU04OeHL7O1zB+SZ8UfIYlLSGp/knDMxNj/9328557dyzjemb38d53ysHAdMlucLJ1BnpM2phBBC5LEpHQqyeBi1KyB/THqv04JpfzSTPFwKsXMmJ6NWDb1GVbbOWTLFMeIJo6sxd5EEyD/rLJFMYXwukjMhEgB6mywYms49+mDKV1tDmsVI/aeHZpBKcXiCsRU/JoBUJ0qNqDHecJxmnBFCCJHN2mYrdBoVTozOLbjdrUBxJiY2npFh39lsKC7rjDNAWHrpMOswU6a0xklfBLFkCl323J0zQCzO5NtzNuGNIJni6LBfGAYi6m22IhBN5Bx9MOWPwGHW1UxA2aa2etQZNHhywIXZkBB2QnvOiBJq418MyfDRskZCCCEy0qpVuGRV3QVx+i5/VLYB1KIeGRMbPcEY7Cb5Pw9tZl1mwLXSzrnTMfo5khpFbQ1GjM2GZRuOLcbo59pzBgidMyB3YuO0LwJnjSxpBAC1iuGqnkY8OeDOJJXSAGqiBCrOakgyJQzGpBlnhBBC5LS5rR4vjPkyg45TKQ53QP5lXZ12EzQqtuxeJikSyRS8Yfk7Z4AQClKuKP1hMUY/x4wzUbvNhGgilSkaSrXcAGpRX3M6sTFHIT3lq40ZZ9mu6WvEuDeC54ZnAdTOkk1SXag4qyH+9Bp96pwRQgiR06b2egSiCZxND0RWalmXVq1Cd6O55M7ZXFj4PJR7zxkAOMy6skXpn5sJQqdRYVWeDpQ4i2xMpn1n5z0haFQMq+pzL2t0mHVoMGkxkOPvatofQXMNxOhnuya97+yR54WYBSrOiBKoOKsh3jAVZ4QQQuS3qU0IBXkhvbRxfgC1/CffPU3mkuP0xeJJ7rRGQFjWWK5AkHMzIXTYjHkTMednnclTnI3MhtFmM0K9zPsyxtDntCxZSCdTHC5/7XXOuhwmtNuMOHxO6JxRIAhRAhVnNYSKM0IIIUroc1qg16hwPD2MWpxhpcTJaU+TBedmQognU0W/hlg8KdU5C0QTiCaSsr/2Yvli9EVt6c6ZXMVZvhh9Ua/TgoFp/wW3zwSiSHHU1J4zQChIxe6ZTq1CnYHSsYn8qDirIb5wAgAorZEQQoisNGoVNrTW4US6OHMHlCvOep0WJFL8grlqhRADO5TqnAHAbLD0uP/lcC58DbokFGcWvQY2k1a2xMaRZQZQZ+t1WjEbimNm0V43MUbfWYOdpWv6hOKs0SLvgHNCRFSc1RDqnBFCCFHK5vYGvDDuzSxZA5TrnAEoaWmjJ6jsnjMAmAnKE76Ri8sfRTieRFeepEZRm0yzzgLRBDzB2LJhIKLedLrm4n1ntTaAOttVPY1gjJIaiXKoOKshVJwRQghRysa2eoRiSZx1B+DyR2HUqmHWqWV/nzXpWWelFGdi56xBgSh9u1k4KVe6c3YunZgotThrbzDJ0jmbj9HPHQYi6nMundg45a/d4sxu1mFHlz0zSoAQudFi2RriS6c11hnpr5UQQoi8NrcLoSDHR71w+aNotCqzrMtq0KK5To+h6eIHUXuCMZh1ahi08hePdrNQ8CndORvOzDjLv6wREBIbH++fBue8pL+XEQkx+qJV9QaYdeoLizNfVOgu1eiQ5h/99Y5lw1IIKQV1zmqINxyHVs1gVODDiBBCyMWtp8kCo1YtFGcB+QdQZ+t1WkqadTYbjCky4wyY75wpndh4biYEtYplwj7yabcZEYmnSp7BJs44kxIIwhgT/q4WFWcufwSNFj006to8zTTpNNBr6FyLKKM2/9VcpLzhOOqNWtqgSgghRHZqFcOG1jq8MCZ0zpSMEe9psuDMdACc86Ke7wnFFNlvBghbB1QMis86O+cJoa3BCK3EAkeuOP0RTwhWvUbyktCeJRIba3EANSHlQsVZDfGG46gz0H4zQgghytjUXo8Xx32Y9EYUL8780UQmeKRQs8GYIkmNgFCkNph0JXeo8jk3E5S83wwA2u1inH5p+85GZsPosJskX+jtc1ox5YtmtlYAQiCIs8YGUBNSLlSc1RBfOE4x+oQQQhSzub0e4XgSvkgCTRblTr7FxMZilzYq2TkDhFAIMXREiv39Luz43J8wNietq8U5x1m3tBlnorYGeWadnfeEJIWBiJYKBaHOGSHFo+KshvjSyxoJIYQQJWxqq8/8WsnOmRjRPjRdXHE2G4wr1jkDALtJh5mA9OLsl8fG4fJH8b19Q5IePxeKwx9JFNQ5sxq0qDdqMVZCccY5x4gnJCkMRNS7qDiLJ1OYCUapc0ZIkag4q1LeUBz/+MsXMVfAlTkvFWeEEEIUtLrRkonPV7I4a67Tw6xTY8hVeGJjNJFEIJrIpCoqwW7WSQ4E4ZzjiQEXGAP+97kRTKdngC1nPkZfeucMEEJBSlnW6PJHEU2kCirOOuwm6DSqTHHmDkTBeW3G6BNSDlScVan/euIMfvT0MH7/wqTk5/giCYrRJ4QQohi1imFDunumZEw6Yww9TktRs87mQsLeJ6XSGgHAbpG+rLF/KoApXxTvvb4HiWQK//3k2bzPOTcjxuhLL5IAsTgrvnMmJjW2F1CcqVUMaxrNGJgSQkGmfMI+QVrWSEhxqDirQr5IHD9+ZhgA8NzwrKTncM6pc0YIIURx4tJGJTtngLDvbHFEuxRiR8uu8LLG2VAcqVT+NMn9/S4AwNuu6MLrL2vFT589lzfpcdgdAmNCV6oQ7TYTRmfDRadcjsxKn3GWra/ZmtkfKHYGqXNGSHGoOKtCP3nmHPyRBHqazDh8ziPpOcFYEskUp+KMEEKIot64rQ23X96OVfXSQyOK0eu0YMIbQSCaKOh5YuGjaOfMrEMyxRckFOayf8CFXqcFrQ1GvG9PL0KxJH741PLds3MzQbTUGQoeot1uMyIcTxY9g+38jNB1E8NFpOptsmB0NoxwLImpdMKmkzpnhBSFirMqE4ol8P0nz2LPuia8aXsHhmdCkqKEvWHhA4Ki9AkhhChpQ2s9vnL7ZVCrlJ2p2dMk7Lc6W+C+M096uaHSaY0A8sbpR+JJHDjrwXV9TQCAtc1WvGJDM3709PCyhd05T6igMBBRqYmN5z2hoorCvmYLOAeGXAFM+yJQMcBhpuKMkGJQcVZlfnZwBJ5gDHff0Ivt3XYAkNQ986WLM+qcEUIIqQVinH6h+84ynTMllzWmi7N8HaoDZz2IJVK4bm1j5ra79/TBF0ngJ8+cy/m8czOFxeiLxEHUUiP7FxuZLSypUZSd2DjlE2bgKV28E1KrqDirItFEEvfsH8Ku1XZc3mXHxrY66DQqHJKw78xLxRkhhJAa0uUwQ61iBe878wSFz8MGk7JpjcJ7LV+c7e93QadRYddqR+a2Te31uH5tE37w5FmEY8kLnuOPxOEOxNBZTOfMVtog6hFPKDPMuhDdWX9Xwowz2m9GSLGoOKsiDx0ew5Qvirtv6AUA6DVqXNZej+fOSS/OaAg1IYSQWqDTqNBlNxXeOQvFYDVooFUrd4pTSHG2s9sOo27hMsEP3NCLmWAMPzt4/oLnnJsRCqtiOmf1Ri3qDJqiljVGE0lM+iJFdc50GhW6HCYMTPsx5YvQjDNCSkDFWZVIJFP47r4hXNZej2t655c/bO+248Ux75JX17LRskZCCCG1Zk1T4XH6nmBM0f1mgLTibMIbxsB0YMGSRtH2bjt2rbbje/uHEE0s/Hw/n5lxVniRBMwnNhZqbDYMzoEOW3Hv2+cU0jWn/VGK0SekBFScVYlfHR/HeU8I79/TC8bm12lv77IhkeI4OjK37POpc0YIIaTW9DjNGHaHkEimJD9nNhRTdL8ZABi0aph06mWLsyf63QCA69Y2LXn/3Tf0YsoXxUOHxxbcPpyecVboAGpRsYOoxaKwmOWUgLDvbHgmBE8wRssaCSkBFWdVIJXi+M7eIaxrtuKmS5oX3Hd5lw1A/lAQXzgOxgCrnoZQE0IIqQ09TRbEkimMFNAJKkfnDBC6Z8sVZ/sGXHBa9VjXbF3y/mt6G3FZRwP+c9/gguLznDuERosOliI/z9vSg6gLnXUmfo2LWdYIAH1OK5LpuW/UOSOkeFScVYFHX5rEwHQA79vTA9WidKMGkw59TgsO5dl35oskYNVrLng+IYQQslKJKYBDBYSCzAaV75wBgGOZ4iyZ4nhywI1r+5oWrIbJxhjD3Xt6MeIJ45fHxjO3n/MEi+6aAcKyxlAsiblQ/hls2UY8Ieg0KjRZiiusxL8rALTnjJASXPTF2UcfOIaPP3isYu/POce39g6i22HCaze3LvmY7d02HD43i1Qq91UwbziOegWTqQghhJBy62ksPE7fE4rBblb+89C2THF2YswLbzi+5H6zbDeud2J9ixXf3juY+Yw/N1PcjDNRu624WWfnZ0LosBmLvsjb02SBWIfSAGpCinfRF2cA8IcXpwpazy6nff0uvDDmw3t39+ScCbK9yw5/JIH+aX/O1/GG4xQGQgghpKbUm7RotOglF2fhWBKReAq2Ci9r3N/vAmPAtX1L7zcTqVQM79/TiyFXEL9/cRKReBIT3gi67KV0zoqL0x+ZDaGjyCWNAGDUqTNDsGnPGSHFu+iLsxvWO+ENx/F8nsANpXx77yBa6w24ZWt7zsds7xb2nT23zLwzbziOOgMVZ4QQQmpLr9MsedaZJyQUS/YKL2vc3+/CprZ6SXvfXr1pFdY0mvHtvYPzMfqNpXTOhOcW3DnzFDeAOluf0wKNipXl609Irbroi7Nr+hqhUTE8dmq67O994MwMnhuexbuuWwOdJvdfRafdhCarHoeHc4eC+KhzRgghpAb1NFkw5ApKCriYTRdL5eic2cw6hOPJC0bd+CLCBd9r+5Zf0ihSqxjeu7sHL4778ONnhgEUn9QICCN1rAZNQZ0zbygOfyRRcnH2yo0teMXGFtr/TkgJLvrirM6gxY5uO/ZWoDj71t5BNFp0eMvOzmUfxxjD9i5b3s4ZFWeEEEJqTU+TBd5wHDN5Bj4D83PHypHW6BBnnYUWHtfTgzNIpjiuy7OkMdsbtrahrcGI+w4IQ6m7S9hzBgBtDUaMzUnvnIkx+u1FzjgTvXlHJ7791m0lvQYhF7uLvjgDhKWNpyb9Bf0gK9WxkTk8MeDGO65ZA4NWnffx27vtGJsLY9IbWfJ+Ks4IIYTUop4CEhtn04VSOdIaxffwBBYWZ/sHXLDoNdiWHoUjhVatwnt29wAA6gwaNJR4/IUOos7MOCuxc0YIKR0VZwD2rHcCQFmXNn577yDqDBq87Yrlu2ai7ekf8oeWmHcWiScRTaRoADUhhJCaI0a0D0oIBSlr58xyYeeMc479/S5c2eOAVl3YKdbtl7fDadVjdWPxSxpF7QXOOhtJL4HssBtLfm9CSGmoOAPQ02RGp91UtqWNpyf9ePSlKdx19WpYJYZ4XNpaB6NWjUNLLG30RYRZJlScEUIIqTWr6gwwatUYmg7mfexsMAbGUJaVJHazEBfvCUYzt511BzE6G8Z1EvebZTNo1fjBXTvwuVs2lXxs7TYjAtEEvGFps87Oe0KwmbSSz0kIIcqh4gzCnq4b1jvx9JAbkXgy/xNK9J+PD8KkU+OvruqW/BytWoUtHQ1Lds586R++tKyREEJIrVGpGNY0mSXF6XtCMTQYtTlH08hJTCScyVrW+MSAGwBw3Vrp+82ybWyrx8a2+pKPrdDExhEZkhoJIfLIW5wxxjoYY3sZYycZYy8yxj6Yvv0rjLFTjLHjjLGfM8YaFD9aBe1Z70QknsIzQzOKvo8nGMNvTkzgzTs6Ck6T2t5tw0vjPgSiiQW3e8PC7+sMGtmOkxBCCKkWPU0WvDDmxR9enFy2GzQbipclqREA6owaqFUss88NECL0uxymktIW5VDorLMRTwjtVJwRUhWkdM4SAD7COb8EwBUA3s8YuxTAHwFs5JxvBtAP4FPKHabydq22w6hVK77v7JdHxxBPcrxpe0fBz93ebUeKA0fPzy24nTpnhBBCatnLLm1GKJbEu39yGFv/+VHc/K0n8eXfn8JTgwtXvMwGY2WbscUYg800P+sslkjhmTMzBaU0KmW+OMvfOUumOMbmwtQ5I6RK5G21cM4nAEykf+1njJ0E0MY5fzTrYc8CuE2ZQywPg1aNq3sb8dipafwz52BMmSURDx4ZxYbWOlyyqq7g527tbABjwHPDHlyTtZ7dS8UZIYSQGva6y1rxig0teP78LJ4amsHTg27cs/8MvvP4EHQaFbZ32XB1byNGZkNY31L452uxsgdRHzrnQSiWlDzfTEn1Ri0seo2k4mzSF0E8yak4I6RKFLQOjjHWDWArgAOL7vprAP+X4znvAvAuAOjslJZMWCk3rHfiTyenMDAdwNpmq+yvf2rShxfGfPjs6y4t6vl1Bi3Wt9Th8LmFoSBUnBFCCKl1Oo0Ku9Y4sGuNAx9+2VoEogk8d9aDpwbdeHLQja/84TQA4Noydq7sWcXZEwNuaFQMV/Y4yvb+uTDGMomN+ZyfSSc1ljjjjBAiD8nFGWPMAuAhAB/inPuybv80hKWP9y71PM75PQDuAYDt27dLy3StkD3rhR/oj52aVqQ4e+jwKDQqhtdf1lr0a2zvsuGhI6NIJFPQpGN6xWWNlNZICCHkYmHRa7BnvTMzDscdiOLwuVls6Wgo2zHYzTqcnBROifb3u7Cty1Y1iYdCcZZ/z9kIzTgjpKpISmtkjGkhFGb3cs4fzrr97QBeC+AOLnWYRhVbVW/EJavqFNl3lkim8PPnx3HDeiccFn3Rr7O924ZQLIlTk/7Mbd5wHCaduuCZKoQQQkitaLTo8YoNLWiuM5TtPe1mHWaDMbj8Ubw47sP1RaY0KqHdZsK5mRAePjKKwekAUqmlT9NGZkNQMWBVQ/m+boSQ3PJ2zpiw+er7AE5yzr+adfsrAXwCwPWcc2lxQCvADeub8N19Z+ANxVFvku/q1/4BF9yBKG67vL2k19nebQcAHBr2ZOJ2veE4LWkkhBBCysxm1mEuHMe+fhcAVMV+M9HVvY24/9AIPnz/MQBCp3FjWx0ua2/A5vYGbG6vR7vNiPOeEFobjHSBl5AqIWVZ49UA7gRwgjF2NH3b3wH4BgA9gD+mwzOe5Zy/R4mDLKcb1jvx7b1D2D/gwutKWH642IOHR2E367B7nbOk12lrMGJVvQHPnZvFXVevBiAMoa6rkmUUhBBCyMXCYdaBc+BXx8ZhN+uwsbX0GWVyedmlzTjxj6/A4HQAx0bncGLUi+Ojc/jhU8OIJVMAhM5fPJHCpvbqOW5CLnZS0hqfBLBUdOFv5T+cytvSYYPNpMXeU9OyFWdzoRj+9NI07riiEzpN6VemtnfbcfDsDHg6VZI6Z4QQQkj52dMz1Z4cdOM1m1ZBVYbh14VQqxjWtVixrsWaGeETTSRxetKP4+li7YUxH165saXCR0oIEdHU4kXUKobr1zbh8X4XkikOtQw/aH91bByxZAq3bittSaNoe5cNvzo2jtHZMDrsJnjDCbQ1GGV5bUIIIYRIIxZnyRTHdVW032w5eo06vayxAUBXpQ+HELIILTBewp71TniCMRwbnZPl9R48PIr1LVZsaJVn9sr2bhsAZCL1feE46oxUZxNCCCHlJBZnQHXtNyOErFxUnC3h+rVNUDFgrwypjQNTfhwb9eK2y9tlG2y9vqUOFr0Gzw17AAjFGS1rJIQQQsrLkS7O1rdYy5oSSQipXVScLaHBpMPlXTZZIvUfPDIKtYrh5i1tMhyZQK1i2NrZgMPnZpFMcfijCSrOCCGEkDJrMOmg16hKDvsihBARFWc57FnvxIvjPkz5IkW/RjLF8cjzY9izrglN1uJnmy1le5cdp6f8GJsNAwAVZ4QQQkiZ6TQq/Px9V+Nvb+yt9KEQQmoEFWc53LBeuApWytLGJwZcmPJFZQsCyba92wbOgb2nheOjKH1CCCGk/C5trYNJR/u+CSHyoOIsh3XNVrTWG0pa2vjQkTE0mLS44RL5lzts6WiAWsXw5/TxUeeMEEIIIYSQlY2KsxwYY9iz3oknB92IJpIFP98bjuMPL07i5staodeoZT8+s16DS1fV4dmhGQBAvYmKM0IIIYQQQlYyKs6WccN6J0KxJA6c8RT83F8fH0cskcKtl8u/pFG0vduGWDIFgJY1EkIIIYQQstJRcbaMq3oaodeoilra+NDhUaxttmBTW70CRybY3mXP/JqWNRJCCCGEkLL48peBvXsX3rZ3r3A7KQkVZ8sw6tS4sseBvaenwTmX/LwhVwBHzs/JOttsKeIwaoCKM0IIIYQQUiY7dgBvetN8gbZ3r/D7HTsqe1w1gIqzPG5Y78S5mRDOuIOSn/PwkVGoGPAGGWebLaW5zoAOuxE6tQoGLf1VEkIIIYSQMtizB7j/fuCWW4A1a4TC7P77hdtJSeiMPo896wqL1E+mOB4+Mobr1zbBWWdQ8tAAAFeuccBZp1e0Q0cIIYQQQsgCe/YAr389cPYssH07FWYyoeIsjw67CX1Oi+R9Z08PuTHhjSgaBJLt06+5FPe+c1dZ3osQQgghhBAAwlLG3/0OaG8HHn1U+I+UjIozCW5Y78TBsx74I/G8j33o8CjqDBrcdElzGY5M2GvW5TCX5b0IIYQQQgjJ7DG7/37gnnuAVAq49dYLQ0JIwWikvQQ3rHfie/vP4BVf248OuwltDUa0NhjRZkv/v8GAVfVGpDjH71+cxG2Xt8OglX+2GSGEEEIIIRX33HPze8w4B7ZtAyYngQMHaHljiag4k2BHtx0fe8U69E/5MT4XxoGzHkz6IkimFiY4mnRqROIp3LqtPEsaCSGEEEIIKbuPf3z+14wBn/kM8MY3Ap2dlTumGsEKiYgv1fbt2/mhQ4fK9n5KSiRTmPZHMT4XxthcGONzEUx4w7DoNfjYK9ZRQAchhBBCCLk4pFLA5s1CF+3ECUBFO6eWwxg7zDnfvtR91DkrkkatQmt6eeOSX1lCCCGEEEIuBioV8OlPA299K/DII0IXjRSFylpCCCGEEEJIad70JqC3F/jc54QOGikKFWeEEEIIIYSQ0qjVwKc+BRw5Avz+95U+mhWLijNCCCGEEEJI6d72NiEU5F/+hbpnRaLijBBCCCGEEFI6nQ74xCeAZ54BHn+80kezIlFxRgghhBBCCJHHX/810NIC/Ou/VvpIViQqzgghhBBCCCHyMBiAj30MeOwxoYNGCkLFGSGEEEIIIUQ+73434HAIyY2kIFScEUIIIYQQQuRjNgMf/jDwm98Azz9f6aNZUag4I4QQQgghhMjr/e8H6uvL3z378peBvXsX3rZ3r3D7CkDFGSGEEEIIIURe9fXABz4APPQQ8OKL5XvfHTuEgdi//rXw+717hd/v2FG+YygBFWeEEEIIIYQQ+alUQkDIF74wf5vSXaw9e4D77wduuQXYuFEozO6/X7h9BaDijBBCCCGEECK/3bsBxoD77gOGhsrXxVq3DkgkhI7de9+7YgozgIozQgghhBBCiBL27AF+8hOAc+C228rXxfrqV4X/v/vdwH/+54V70KoYFWeEEEIIIYQQZdx6K7BrF3D0KPDGNypfmO3dC3zzm4DTKRRm998vFIUrpECj4owQQgghhBCijL17gcFBwGgEfvhDYTi1kp5+WtjrdtttwpJKcQ/ac88p+74yoeKMEEIIIYQQIj9xj9kDDwhLDeNxIahDyS7W9u1AJAK85jXzt+3ZA3z848q9p4zyFmeMsQ7G2F7G2EnG2IuMsQ+mb789/fsUY2y78odKCCGEEEIIWTGee25+j9k73wls2iR00J5+Wrn3/M1vhPdYQSEg2TQSHpMA8BHO+RHGmBXAYcbYHwG8AOCNAL6n5AESQgghhBBCVqDsbpVGA3z968CNNwrLDZXAuTDf7MYbhQJtBcpbnHHOJwBMpH/tZ4ydBNDGOf8jALASv7jxeByjo6OIRCIlvQ4hixkMBrS3t0Or1Vb6UAghhBBCyA03CKEgn/88cNddQGurvK9/6hRw9izwiU/I+7plJKVzlsEY6wawFcCBAp7zLgDvAoDOzs4L7h8dHYXVakV3d3fJhR4hIs45ZmZmMDo6itWrV1f6cAghhBBCCAB85StCd+tTnwJ+/GN5X/vXvxb+/+pXy/u6ZSQ5EIQxZgHwEIAPcc59Up/HOb+Hc76dc769qanpgvsjkQgcDgcVZkRWjDE4HA7qyBJCCCGEVJM1a4CPfAT4n/8BDkju90jzm98AmzcDHR3yvm4ZSSrOGGNaCIXZvZzzh+U+CCrMiBLo+4oQQgghpAp96lNASwvwwQ8CqZQ8rzk7Czz5JPDa18rzehUiJa2RAfg+gJOc868qf0jl97nPfQ4bNmzA5s2bsWXLFhxIV/HvfOc78dJLL8nyHt3d3XC73cs+5vOf/3zBr/ujH/0Id99994LbfvjDH2LLli3YsmULdDodNm3ahC1btuCTn/xkwa9fDl//+tcRCoUqfRiEEEIIIaQcrFbgi18UOmf33SfPaz76KJBMLozQX4EY53z5BzB2DYAnAJwAIJa2fwdAD+CbAJoAzAE4yjl/xXKvtX37dn7o0KEFt508eRKXXHJJMccui2eeeQYf/vCH8fjjj0Ov18PtdiMWi6FV5g2K3d3dOHToEBobG3M+xmKxIBAIFPS6P/rRj3Do0CF861vfKvp9lcY5B+ccKtXS1wKKOcZEIgGNJv+WyUp/fxFCCCGEkCWkUsAVVwBjY8Dp04DFUtrr/eVfAr/9LTA1BajV8hyjQhhjhznnS44iy9s545w/yTlnnPPNnPMt6f9+yzn/Oee8nXOu55w35yvMqtXExAQaGxuh1+sBAI2NjZnCbPfu3RCLSYvFgk984hO4/PLLcdNNN+HgwYPYvXs31qxZg1/+8pcALuxivfa1r8Xjjz9+wXu+4Q1vwOWXX44NGzbgnnvuAQB88pOfRDgcxpYtW3DHHXcAAH76059i586d2LJlC9797ncjmUwCEDpja9euxfXXX4+nnnpK8p/1K1/5Cnbs2IHNmzfjs5/9LABgeHgY69evxzvf+U5s3LgRd9xxB/70pz/h6quvRl9fHw4ePAgA+Md//EfceeeduOGGG9DX14f/+q//yvu6l1xyCd73vvdh27ZtGBkZwXvf+15s374dGzZsyDzuG9/4BsbHx7Fnzx7sSc+jsGT943zwwQdx1113AQDuuusufPjDH8aePXvwiU98AkNDQ3jlK1+Jyy+/HNdeey1OnTol+WtBCCGEEEIqSKUC/uM/gPFx4EtfKu21kkmhMHvVq6q+MMunoLRGpf3Tr17ES+OSs0YkubS1Dp993Yac97/85S/HP//zP2Pt2rW46aab8OY3vxnXX3/9BY8LBoPYvXs3vvSlL+GWW27BZz7zGfzxj3/ESy+9hLe//e14/etfL/mYfvCDH8ButyMcDmPHjh249dZb8cUvfhHf+ta3cPToUQBCx+f//u//8NRTT0Gr1eJ973sf7r33XrzsZS/DZz/7WRw+fBj19fXYs2cPtm7dmvc9H330UQwMDODgwYPgnOP1r3899u/fj87OTgwODuKBBx7APffcgx07duC+++7Dk08+iV/+8pf4/Oc/j0ceeQQAcPz4cTz77LMIBoPYunUrXvOa1+CFF17I+bqnT5/GD3/4Q3znO98BICwftdvtSCaTuPHGG3H8+HH87d/+Lb761a9i7969kjpn/f39+NOf/gS1Wo0bb7wR3/3ud9HX14cDBw7gfe97Hx577DHJfw+EEEIIIaSCrrwSuOMOIcHxHe8AuruLe52DB4GZmRW/3wyosuKsEiwWCw4fPownnngCe/fuxZvf/GZ88YtfzHRrRDqdDq985SsBAJs2bYJer4dWq8WmTZswPDxc0Ht+4xvfwM9//nMAwMjICAYGBuBwOBY85s9//jMOHz6MHTt2AADC4TCcTicOHDiA3bt3Q0y+fPOb34z+/v687/noo4/i0UcfzRRygUAAAwMD6OzsxOrVq7Fp0yYAwIYNG3DjjTeCMXbBn+3mm2+G0WiE0WjEnj17cPDgQTz55JM5X7erqwtXXHFF5vn3338/7rnnHiQSCUxMTOCll17C5s2bC/ra3X777VCr1QgEAnj66adx++23Z+6LRqMFvRYhhBBCCKmwL34R+PnPgY99DHjggeJe49e/Fjpmr1iRC/kWqKribLkOl5LUajV2796N3bt3Y9OmTfjxj398QXGm1Woz6X8qlSqzDFKlUiGRSAAANBoNUlmJM0vFuD/++OP405/+hGeeeQYmkwm7d+9e8nGcc7z97W/HF77whQW3P/LII0WlEHLO8alPfQrvfve7F9w+PDyc+bMs92cDLkw/ZIwt+7pmsznz+7Nnz+Lf/u3f8Nxzz8Fms+Guu+7KGXOf/T6LHyO+ZiqVQkNDQ6bTSAghhBBCVqD2duDqq4EHHwT27QPEFWx79wLPPQd8/OP5X+M3vxFeo6FB0UMtB8lzzmrV6dOnMTAwkPn90aNH0dXVVdRrdXd34+jRo0ilUhgZGcns18rm9Xphs9lgMplw6tQpPPvss/+/vbsPjqLK1zj+PcZgkAAXdqOXiHoT3XiNIcRIIgEBMbsERORlAdEtQQHdWKXUrotXry4SVm6Vr+ULxSVSFCwqdZFlXaCWl1IxATSwRMugKCigISIxsInhRdBNyLl/dM+QwCSZgYTpGZ5PVdfMnO4+fbp/mTo5c06f9q+LjY2lrq4OgNzcXJYvX86BAwcAqKmpYe/evdx4440UFxdTXV1NXV0dfwnyF4a8vDwWLlzon3Dk22+/9ecdrJUrV/Ljjz9SXV1NcXExWVlZQed7+PBhOnXqRNeuXamqqmLt2rX+dZ07d+bIkSP+z5deeik7duygoaHB38N4qi5dupCUlOQ/f2st27ZtC+l8RERERMQDHn7YuQdtyhTn/rGiIhg/HtwRZC365hvYti0qhjSCx3rOwuHo0aM89NBD1NbWcuGFF3L11Vf7J+kIVf/+/f1DBNPS0sjMzDxtm6FDh1JYWEh6ejrXXHNNk2F/999/P+np6WRmZrJkyRJmz57NkCFDaGhoIDY2lrlz59K3b18KCgrIycmhR48eZGZm+icKacmQIUPYsWMHOTk5gDOc84033iAmhJsms7OzGT58OBUVFcyYMYPExEQSExODyrd3795cf/31XHfddSQnJ9O/f/8m5z1s2DB69OhBUVERTz/9NLfddhuXX345aWlpzc5guWTJEh544AFmz55NXV0dEyZMoHfv3kGfj4iIiIh4wNCh8MQT8NRTzlT4H30Ey5aBO1lci9ascV4jfAp9n1an0m9LXpxKX4JTUFBAfHw806dPD3dRQqK/LxEREZEIYC2kpMDu3ZCfD/PmBbffiBHw2WewZw+cwa0/4XBWU+mLiIiIiIi0q+JiqKmBjh1hwQLnodKtOX4c1q93es0ipGHWmvN+WKMEp6CgINxFEBEREZFo5LvHbPlyOHwYRo1yltWrWx7aWFTkNNCi5H4zUM+ZiIiIiIiEU2npyXvMRo50JgY5ftxJa8nq1XDxxSdneIwC6jkTEREREZHwOXW6/JdecoY5rl3r9KR16XL6PtY6zzf71a8gLu5clPKcUM+ZiIiIiIh4R3w8vP66M03+tGmBt/nsM6ioiJpZGn3UOBMREREREW/JyXGm11+8GP7619PXr17tvN5667ktVztT4wyIiYkhIyODtLQ0xo0bx7Fjx844r3vuuYfly5cDMHXqVD7//PNmty0uLqakpMT/ubCwkNdee+2Mj+1TXl5OWlpak7SCggKef/75kPJpq/KIiIiIiIRsxgznQdT33w/79zdd9/e/w/XXw2WXhads7SSyGmfPPuvMytJYUZGTfhY6duxIWVkZ27dvp0OHDhQWFjZZH8xDngNZsGABqampza4/tXGWn5/PxIkTz+hYba2+vt5T5RERERGR80xsLLzxBvz4I9x7LzQ0OOk1NVBSEnVDGiHSGmdZWc40m74Gmm/azaysNjvEgAED2L17N8XFxQwePJi77rqLXr16ceLECR555BGysrJIT0/n1VdfBcBay4MPPkhqairDhw/nwIED/rxuvvlmfA/dXrduHZmZmfTu3Zvc3FzKy8spLCzkxRdfJCMjg02bNjXp3SorK6Nv376kp6czevRovv/+e3+ejz76KNnZ2aSkpLBp06aQz7GlvB9//HEGDRrEyy+/7C/P/v37ycjI8C8xMTHs3buXvXv3kpubS3p6Orm5uVRUVABO7+G0adPo168fycnJ/p5EEREREZGQpKTACy84zz2bO9dJW7fOaahF0RT6Pt6arfF3v4Oyspa3SUyEvDzo0QMqK+Haa2HWLGcJJCPDmfElCPX19axdu5ahQ4cCsHXrVrZv305SUhLz58+na9eulJaW8tNPP9G/f3+GDBnCxx9/zBdffMGnn35KVVUVqampTJ48uUm+Bw8e5L777mPjxo0kJSVRU1ND9+7dyc/PJz4+nunTpwOwfv16/z4TJ05kzpw5DBo0iCeffJJZs2bxknse9fX1bN26lTVr1jBr1izefffd085lz549ZGRk+D9/9913/uO0lHdtbS0bNmwATj7bLDExkTI3LnPnzmXDhg1ceeWVjBgxgokTJzJp0iQWLlzItGnTWLFiBQCVlZW8//777Ny5k9tvv52xY8cGFQMRERERkSZ++1t4+WX4wx8gN9e53ywhAY4edUbQnTrbYwTzVuMsGN26OQ2zigq44grn81k6fvy4vyEzYMAApkyZQklJCdnZ2SQlJQHw9ttv88knn/h7gQ4dOsSuXbvYuHEjd955JzExMSQmJnLLLbeclv+WLVsYOHCgP6/u3bu3WJ5Dhw5RW1vLIPeZDZMmTWLcuHH+9WPGjAHghhtuoLy8PGAeV111lb9BBScbWq3lfccddzRbrg8++IAFCxb4e+s2b97MW2+9BcDdd9/NfzX6YowaNYoLLriA1NRUqqqqWjxfEREREZFmGQOzZ8O4cc5z0KqrnZFzEya0/iy0COOtxlkwPVy+oYwzZsC8eTBzZstPDg+C756zU3Xq1Mn/3lrLnDlzyMvLa7LNmjVrMMa0mL+1ttVtQnHRRRcBzkQm9fX1bZYvND3nxiorK5kyZQqrVq0iPj4+4DaNz9FXRnDOX0RERETkjP361/CnPzltAIDNm2HlyrNuB3hNZN1z5muYLVvmBGfZsqb3oLWjvLw85s2bR11dHQBffvklP/zwAwMHDmTp0qWcOHGCyspKigKUJScnhw0bNvD1118DUFNTA0Dnzp05cuTIadt37dqVbt26+XuoXn/9dX9P19k6k7zr6uoYP348zzzzDCkpKf70fv36sXTpUgCWLFnCTTfd1CZlFBERERE5zR//CJmZzvv8/KhrmIHXes5aU1rqNMh8gRg82PlcWtruwZk6dSrl5eVkZmZirSUhIYEVK1YwevRo3nvvPXr16kVKSkrAhk5CQgLz589nzJgxNDQ0cMkll/DOO+8wYsQIxo4dy8qVK5kzZ06TfRYvXkx+fj7Hjh0jOTmZRYsWtdm5hJp3SUkJpaWlzJw5k5kzZwJOj+Err7zC5MmTee6550hISGjTMoqIiIiINFFU5Nza9Pvfw6JFMGxY1DXQzLkcctanTx/rm73QZ8eOHVx77bXnrAxyftHfl4iIiEgUaDyCbvDg0z9HEGPMR9baPoHWRdawRhEREREROf+0NIIuikTWsEYRERERETn/BJouf/DgiOs1a416zkRERERERDzAE40zTbUu7UF/VyIiIiISScLeOIuLi6O6ulr/SEubstZSXV1NXFxcuIsiIiIiIhKUsN9z1rNnT/bt28fBgwfDXRSJMnFxcfTs2TPcxRARERERCUrYG2exsbEkJSWFuxgiIiIiIiJhFfZhjSIiIiIiIqLGmYiIiIiIiCeocSYiIiIiIuIB5lzOkmiMOQjsPWcHDN7PgX+GuxDSZhTP6KFYRhfFM3ooltFF8YweimVkuNJamxBoxTltnHmVMeZDa22fcJdD2obiGT0Uy+iieEYPxTK6KJ7RQ7GMfBrWKCIiIiIi4gFqnImIiIiIiHiAGmeO+eEugLQpxTN6KJbRRfGMHopldFE8o4diGeF0z5mIiIiIiIgHqOdMRERERETEAyKqcWaMGWqM+cIYs9sY81ij9DeNMWXuUm6MKWtm/+7GmHeMMbvc125u+m8a7V9mjGkwxmQE2H+Je/ztxpiFxphYN90YY15xy/WJMSazfa5AdPFwPP/TGLPZGPOTMWZ6+5x99PFwPH/jfi8/McaUGGN6t88ViB4ejuVIN45lxpgPjTE3tc8ViC7tGM9YY8xiY8ynxpgdxpj/bmb/JGPMP9z93zTGdHDTVXeGyMOxVL15BjwcT9Wb4WStjYgFiAH2AMlAB2AbkBpguxeAJ5vJ41ngMff9Y8AzAbbpBXzVzP63AsZd/g94oFH6Wje9L/CPcF8vry8ej+clQBbwP8D0cF+rSFg8Hs9+QDf3/TB9PyM6lvGcHI6fDuwM9/Xy+tKe8QTuApa67y8GyoH/CLD/MmCC+75QdWdUxlL1ZnTFU/VmGJdI6jnLBnZba7+y1v4LWAqMbLyBMcYA43Eq80BGAovd94uBUQG2ubO5/a21a6wL2Ar0bJTva+6qLcC/GWN6BH1m5yfPxtNae8BaWwrUhXRG5zcvx7PEWvu9u9kWTn5vJTAvx/KomwbQCdBN061rz3haoJMx5kKgI/Av4HCAvG8BlgfYX3VnaDwbS9WbZ8TL8VS9GUaR1Di7DPim0ed9blpjA4Aqa+2uZvK41FpbCeC+XhJgmzto/ksAON3FwN3AuhDKJk15OZ4SukiJ5xScX+qleZ6OpTFmtDFmJ7AamNzS/gK0bzyXAz8AlUAF8Ly1tuaUfX8G1Fpr6wMcX3VnaLwcSwldpMRT9eY5dmG4CxACEyDt1F9Nm/0lNqgDGHMjcMxau72VTf8X2Git3RRC2aQpL8dTQuf5eBpjBuNUMrpPqWWejqW19m/A34wxA4GngF+eaTnOE+0Zz2zgBJAIdAM2GWPetdZ+FeTxVXeGxsuxlNB5Pp6qN8MjknrO9gGXN/rcE9jv++B23Y4B3myUtsi9mXKNm1TlGzLhvh445RgTaP2X3JlAAvBwsGWTgLwcTwmdp+NpjEkHFgAjrbXVIZzX+cjTsfSx1m4ErjLG/DyYkzqPtWc87wLWWWvrrLUHgA+APqcc/584wxV9PwY3Pr7qztB4OZYSOk/HU/Vm+ERS46wU+IU7s0wHnMp9VaP1v8S5OXyfL8Fae6+1NsNae6ubtAqY5L6fBKz0bWuMuQAYhzPmNyBjzFQgD7jTWtvQaNUqYKJx9AUO+bqZpVlejqeEzrPxNMZcAbwF3G2t/fIszvF84eVYXu3eJ4FxZvbrAOifhpa1ZzwrgFvcuq8TzqQeOxsf3L1HsAgYG2B/1Z2h8XIsJXSejafqzTCzHpiVJNgFZ2anL3Fmt3nilHV/BvJb2f9nwHpgl/vavdG6m4Etrexf7x67zF2edNMNMNdd9ynQJ9zXKhIWD8fz33F+0ToM1Lrvu4T7enl98XA8FwDfN0r/MNzXyuuLh2P5KPCZm7YZuCnc1yoSlvaKJ87smX9xY/I58Egz+yfjTOyy293+IjdddWf0xFL1ZnTFU/VmGBfflMQiIiIiIiISRpE0rFFERERERCRqqXEmIiIiIiLiAWqciYiIiIiIeIAaZyIiIiIiIh6gxpmIiIiIiIgHqHEmIiIiIiLiAWqciYiIiIiIeIAaZyIiIiIiIh7w/7XvKANCh9ZBAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
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"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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27di0adOQx01NTcW8efP67//WW2/hmWeegcFgQF1dHUpLS5Gfn2/Xa3fFFVdArVajs7MTmzdvxhVXXNF/W29vr13HIiIiIqKx8WVpAwBg5ZQ4N4/Es3lUcOYuarUaS5cuxdKlS5GXl4eXX375jODM29u7v/qfSqXqT4NUqVQwGAwAAC8vL5hMpv77WCvjvm7dOnz55ZfYsmULAgICsHTpUqv7SSlx/fXX49FHHz1t+wcffOBQFUIpJR566CHcdtttp22vrKzsfy7DPTfgzOqHQohhjxsYGNj/c0VFBf74xz9ix44dCA8Px5o1a4Yscz/wcQbvYzmmyWRCWFgYSkpKRnrqRERERORma0sbkBEdiEkxQSPvfBbzqOBsuBkuVzl06BBUKhWysrIAACUlJUhNTXXoWGlpafjnP/8Jk8mEmpqa/vVaA2m1WoSHhyMgIADl5eXYunVr/23e3t7Q6/Xw9vbG8uXLcfHFF+NHP/oRYmJi0Nraio6ODsydOxf33HMPWlpaEBISgrfffhvTp08fcWyrVq3CL37xC1xzzTUICgpCTU0NvL297Xp+H374IR566CF0dXVh3bp1eOyxx+Dv72/Tcdvb2xEYGIjQ0FA0NDTg008/xdKlSwEAwcHB6Ojo6C9aEhsbi7KyMkyePBnvv/8+goODzzheSEgI0tPT8fbbb+OKK66AlBL79u2z6bUgIiIiorGj7dZjy7EW3Lwow91D8XgeFZy5Q2dnJ+666y5oNBp4eXlh0qRJ/UU67LVw4cL+FMFp06Zh5syZZ+yzevVqPPXUU8jPz8fkyZNPS/u79dZbkZ+fj5kzZ+L111/Hb37zG6xcuRImkwne3t74xz/+gXnz5uGRRx7B/PnzER8fj5kzZ/YXChnOypUrUVZWhvnz5wNQ0jlfe+01qNVqm5/fnDlzcMEFF+DkyZP4xS9+gYSEBCQkJNh03OnTp2PGjBmYOnUqMjIysHDhwtOe93nnnYf4+HgUFxfjsccew4UXXojk5GRMmzYNnZ2dVsfz+uuv44477sBvfvMb6PV6XH311QzOiIiIiDzMukONMJgkVk6NdfdQPJ5Q1qSNjcLCQmmpfmhRVlaG3NzcMRsDOeaRRx5BUFAQfvzjH7t7KHbh7xcRERGRe935+m5sr2zFtoeWQ6Wyf3nORCOE2CWlLLR2m11NqImIiIiIiGzVozdi3aFGrMiNZWBmg7M+rZFs88gjj7h7CEREREQ0zmw51oKuPiNTGm3EmTMiIiIiInKJtaUNCPRRY0Fm5Mg7E4MzIiIiIiJyPpNJ4ovSBizNiYGvl+1F6M5mDM6IiIiIiMjp9lRp0NzZi5VTmNJoKwZnRERERETkdGtL6+GtFijKiXH3UMYNBmcA1Go1CgoKMG3aNFxxxRXQ6XQOH2vNmjV45513AAA333wzSktLh9x33bp12Lx5c//PTz31FF555RWHH9uisrIS06ZNO23bI488gj/+8Y92HcdZ4yEiIiKis4uUEmsPNmBeRiRC/LzdPZxxg9UaAfj7+6OkpAQAcM011+Cpp57Cvffe23+70Wi0q1mzxXPPPTfs7evWrUNQUBAWLFgAALj99tvtfgxXMRgMHjUeIiIiIho/jjV1oqK5CzcuTHP3UMaV8TVz9vjjQHHx6duKi5XtTrJo0SIcPXoU69atQ1FREb773e8iLy8PRqMR999/P2bPno38/Hw8/fTTAJSrAj/4wQ8wZcoUXHDBBWhsbOw/1tKlS2Fpuv3ZZ59h5syZmD59OpYvX47Kyko89dRT+Mtf/oKCggJs3LjxtNmtkpISzJs3D/n5+fj2t7+Ntra2/mM+8MADmDNnDrKzs7Fx40a7n+Nwx/7pT3+KJUuW4G9/+1v/eGpra1FQUND/pVarceLECZw4cQLLly9Hfn4+li9fjpMnTwJQZg/vvvtuLFiwABkZGf0ziURERER0dvj8YAMAYAXXm9llfAVns2cDV155KkArLlZ+nj3bKYc3GAz49NNPkZeXBwDYvn07fvvb36K0tBTPP/88QkNDsWPHDuzYsQPPPvssKioq8P777+PQoUPYv38/nn322dPSFC2amppwyy234N1338XevXvx9ttvIy0tDbfffjt+9KMfoaSkBIsWLTrtPtdddx1+//vfY9++fcjLy8OvfvWr08a5fft2/PWvfz1t+0DHjh07LaB66qmnbDq2RqPB+vXrcd999/VvS0hIQElJCUpKSnDLLbfgsssuQ2pqKn7wgx/guuuuw759+3DNNdfg7rvv7r9PXV0dNm3ahI8//hgPPvignWeCiIiIiMaztaUNmJ4UivhQf3cPZVzxrLTGH/4QMKcXDikhAVi1CoiPB+rqgNxc4Fe/Ur6sKSgA/vrXYQ/Z3d2NgoICAMrM2U033YTNmzdjzpw5SE9PBwCsXbsW+/bt658F0mq1OHLkCDZs2IDvfOc7UKvVSEhIwLJly844/tatW7F48eL+Y0VERAw7Hq1WC41GgyVLlgAArr/+elxxxRX9t1966aUAgFmzZqGystLqMTIzM/tTNYFTTaRHOvZVV1015Li++eYbPPfcc/2zdVu2bMF7770HALj22mvxk5/8pH/fSy65BCqVClOmTEFDQ8Owz5eIiIiIJo6G9h7srdLg/lWT3T2UccezgjNbhIcrgdnJk0BKivLzKA1cczZQYGBg//dSSjz55JNYtWrVaft88sknEEIMe3wp5Yj72MPX1xeAUsjEYDA47bjA6c95oLq6Otx000346KOPEBQUZHWfgc/RMkZAef5EREREdHb4olS5MM8S+vbzrOBshBkuAKdSGX/xC+Bf/wIefhgoKnL50FatWoV//etfWLZsGby9vXH48GEkJiZi8eLFePrpp3HdddehsbERxcXF+O53v3vafefPn48777wTFRUVSE9PR2trKyIiIhAcHIz29vYzHis0NBTh4eHYuHEjFi1ahFdffbV/pmu0HDm2Xq/HlVdeid///vfIzs7u375gwQL85z//wbXXXovXX38d55xzjlPGSERERETj19rSBqRHBWJSjPUL+jQ0zwrORmIJzN56SwnIiopO/9mFbr75ZlRWVmLmzJmQUiI6OhoffPABvv3tb+Prr79GXl4esrOzrQY60dHReOaZZ3DppZfCZDIhJiYGX3zxBS666CJcfvnl+PDDD/Hkk0+edp+XX34Zt99+O3Q6HTIyMvDiiy867bnYe+zNmzdjx44dePjhh/Hwww8DUGYMn3jiCdx44434wx/+gOjoaKeOkYiIiIjGn/YePbYca8YNC9Odmjl2thBjmXJWWFgoLdULLcrKypCbm2vbAR5/XCn+MTAQKy4GduwABqx3IrKw6/eLiIiIiEblo721uPuNPXjn9vkoTBu+zsLZSgixS0pZaO228TVzZi0As8ygERERERGRW609WI+oIB/MSBl9XYiz0fgqpU9ERERERB6p12DEukNNWJEbC7WKKY2OYHBGRERERESjtuVYCzp7DVg5lVUaHeURwRlLrZMr8PeKiIiIaOysLW1AgI8aCzKj3D2UccvtwZmfnx9aWlr4QZqcSkqJlpYW+Pn5uXsoRERERBOeySTxRWkDlk6Ohp+32t3DGbfcXhAkKSkJ1dXVaGpqcvdQaILx8/NDUlKSu4dBRERENOHtrdagqaMX57Lx9Ki4PTjz9vZGenq6u4dBREREREQOWlvaALVKYNlkBmej4fa0RiIiIiIiGt/WHqzHvIwIhAZ4u3so4xqDMyIiIiIiclhLZy+ONXVhSXa0u4cy7jE4IyIiIiIih7Xp+gAAsSEsxDZaDM6IiIiIiMhhGp0eABAW4OPmkYx/DM6IiIiIiMhh2m5zcObP9WajxeCMiIiIiIgcZpk5C2VwNmoMzoiIiIiIyGH9M2es1DhqDM6IiIiIiMhhGnNwFuzH4Gy0GJwREREREZHDtLo+hPh5Qa0S7h7KuDdicCaESBZCFAshyoQQB4UQ95i3FwghtgohSoQQO4UQc1w/XCIiIiIi8iTabj2bTzuJlw37GADcJ6XcLYQIBrBLCPEFgMcB/EpK+akQ4nzzz0tdN1QiIiIiIvI0mm49wvxZRt8ZRgzOpJR1AOrM33cIIcoAJAKQAELMu4UCqHXVIImIiIiIyDNpu/UsBuIktsyc9RNCpAGYAWAbgB8C+FwI8Uco6ZELnD04IiIiIiLybFqdHglh/u4exoRgc0EQIUQQgHcB/FBK2Q7gDgA/klImA/gRgOeHuN+t5jVpO5uampwxZiIiIiIi8hBKWiNnzpzBpuBMCOENJTB7XUr5nnnz9QAs378NwGpBECnlM1LKQillYXR09GjHS0REREREHkJKqRQEYXDmFLZUaxRQZsXKpJR/HnBTLYAl5u+XATji/OEREREREZGn6uw1wGiSXHPmJLasOVsI4FoA+4UQJeZtPwVwC4C/CSG8APQAuNUlIyQiIiIiIo+kNTegZrVG57ClWuMmAEN1lJvl3OEQEREREdF4odEpwVkI0xqdwuaCIERERERERAO1W2bOmNboFAzOiIiIiIjIIRpzcMaCIM7B4IyIiIiIiBxiSWvkzJlzMDgjIiIiIiKHsCCIczE4IyIiIiIih2i6++CjVsHPm2GFM/BVJCIiIiIih7R36xEa4A2lNTKNFoMzIiIiIiJyiEanZzEQJ2JwRkREREREDtHo9AhjcOY0DM6IiIiIiMgh2m49KzU6EYMzIiIiIiJyiLZbjxDOnDkNgzMiIiIiInKItlvPMvpOxOCMiIiIiIjspjea0NlrYEEQJ2JwRkREREREdmu3NKDmmjOnYXBGRERERER205iDM86cOQ+DMyIiIiIisptGZw7OOHPmNAzOiIiIiIjIbv1pjZw5cxoGZ0REREREZDdNdx8ApjU6E4MzIiIiIiKym1ZnKQjCUvrOwuCMiIiIiIjsZikIEuLn5eaRTBwMzoiIiIiIyG4anR7Bvl7wUjOkcBa+kkREREREZLf2bj0rNToZgzMiIiIiIrKbplvPYiBOxuCMiIiIiIjspu3WI4wzZ07F4IyIiIiIiOym0fVx5szJGJwREREREZHdtN0GhPqzjL4zMTgjIiIiIiK7SCmh7e5jWqOTMTgjIiIiIiK76PqM0Bsl0xqdjMEZERERERHZRWtuQB3G4MypGJwREREREZFdNDolOOPMmXMxOCMiIiIiIrtYZs7YhNq5GJwREREREZFdtN19ADhz5mwMzoiIiIiIyC6WtMawAJbSdyYGZ0REREREZBcWBHENBmdERERERGQXTbceXiqBAB+1u4cyoTA4IyIiIiIiu2i79QgL8IYQwt1DmVAYnBERERERkV20Oj1CmNLodCMGZ0KIZCFEsRCiTAhxUAhxz4Db7hJCHDJvf9y1QyUiIiIiIk+g6e7jejMX8LJhHwOA+6SUu4UQwQB2CSG+ABAL4GIA+VLKXiFEjCsHSu7Rozdi14k2LMiM5LQ1EREREQFQ0hpjgv3cPYwJZ8SZMyllnZRyt/n7DgBlABIB3AHgMSllr/m2RlcOlMbesaZOXPKPb3DNc9uw7nCTu4dDRERERB5Co9Ozx5kL2LXmTAiRBmAGgG0AsgEsEkJsE0KsF0LMdsH4yE0+LKnBRU9uQkN7DwJ91Phsf727h0REREREHkLbzeDMFWwOzoQQQQDeBfBDKWU7lJTIcADzANwP4C1hJe9NCHGrEGKnEGJnUxNnXzxdj96Ih97bh3v+U4KpCSH45J5FWDElFl+UNcBgNLl7eERERETkZkaTREePgcGZC9gUnAkhvKEEZq9LKd8zb64G8J5UbAdgAhA1+L5SymeklIVSysLo6GhnjZtc4Gijksb4xvYq3LE0E2/cMg/xof5YPTUOrV192FHZ5u4hEhEREZGbtVsaUAcwOHM2W6o1CgDPAyiTUv55wE0fAFhm3icbgA+AZheMkcbAB3tq8K2/K2mML94wGw+szoGXWvn1WDI5Gr5eKnx+kKmNRERERGc7DYMzl7Fl5mwhgGsBLBNClJi/zgfwAoAMIcQBAP8BcL2UUrpwrOQCPXojHnx3H3745qk0xqLJpxfeDPDxwpLsaHx2oB4mE08xERER0dlMo+sDAKY1usCIpfSllJsADFVD/XvOHQ6Npcb2Hlz3wnaU13fg+0szce+52f2zZYOtnhaHtaUN2FejRUFy2NgOlIiIiIg8htY8cxbq7+PmkUw8tvQ5ownqL18exvGmLrx4w+wzZssGW54TCy+VwGcH6hmcEREREZ3FTgVnnDlzNrtK6dPEUd2mwzu7qnHV7OQRAzMACA3wxvzMSHx2oA7MXiUiIiI6e2m55sxlGJydpZ5afwwAcPvSTJvvs2pqHCpbdDjc0OmqYRERERGRh9PoOHPmKgzOzkL12h68taMal89KRmKYv833WzklFkIAnx1g1UYiIiKis5VGp0egjxreQ9QqIMfxFT0LPbX+GExS4vt2zJoBQEyIH2alhLOkvod7Z1c1fvifPe4eBtFZr89gcvcQiIhcQtutR1gAi4G4AoOzs0xjew/e2H4S356RiOSIALvvv3paHErr2nGyReeC0ZEz/Gf7SXxQUovWrj53D8UjaXR9WPmX9dhe0eruodAEdrJFh4Jfr+XFLCKakLTdfQhhSqNLMDg7yzyz4Tj0RhPuLJrk0P1XTY0DAH7g8FA9eiP2VmsAAHtOtrl3MB5qbWkDDjd0ovhQo7uHQhPYS5sroesz4r3d1e4eChGR02m79QhjcOYSDM7OIs2dvXht2wlcUpCItKhAh46RHBGAqQkh+MxFwdkn++vQ1WtwybHPBrtPtkFvlP3f05nWHmwAAJTVtbt5JDRRdfYa8PbOKqgEsP5wE7r7jO4eEhENo7pNh52VzKawh0anZzEQF2FwdhZ5bmMFeg0m3LnMsVkzi9VT47DrRBsa23ucNDLFofoOfP/13XhhU4VTj3s22V7RCpUA0qMCsfuExt3D8Ti6PgM2HmkCwOCMXOe93dXo6DXgvpWT0aM3Yf3hJncPiYiG8eC7+3H9C9u5TtQOypozBmeuwODsLNHa1YdXtlTiovwEZEYHjepYq6eZUxtLG5wxtH4lVcpMz5flTDdz1LbjrZiSEIJFWVHYW62Bwcg3moE2HG5Cr8GEZTkxaGjv5bo8cjqTSeKlbyoxPTkMty7OQFiAN9YyDZzIY1W36fDNsWZ09Rm5HMBGUkpouvUIZXDmEgzOzhIvbKqArs+IH4xy1gwAJsUEISM6EJ87uaR+SZUGALC3SoPGDufOyp0Neg1G7D7ZhrnpkZiZEg5dnxGHGjrcPSyPsvZgA8ICvHHd/FQAE2v2zGA04XeflOHlzZXuHspZbcORJhxv7sKNC9PgrVZheU4svixrgJ4XSog80ru7agAAKgFsOtrs5tGMDz16E/oMJqY1ugiDs7OAVqfHS5srcX5eHLJjg0d9PCEEVk2Nw5bjLdDonDfzsOekpr/v2tdlnD2z1/5qLXoNJsxJj8DMlHAAwO6TGvcOyoPojSZ8WdaA5TmxyEsMBQCU1k6M4KzPYMJdb+zBMxuO43mmBbvVi99UIjrYF+dNiwegZBq09xiw9XiLm0dGRIOZTBLv7K7CwswoFCSHYcMRBme20HYrDajD/FlK3xUYnJ0FXtxcgc5eA35QlOW0Y66eGgejSeJLJwVRuj4DDjd04LKZiUgM83facc8m28yl4eekRSA5wh9RQT7Yc4IpGhbbK1rR3mPAqqmxiAzyRUyw74SYOevRG3HHa7vw6YF65CWG4mSrDlqd3t3DOisda+rE+sNN+N7cVPh4KW+vi7KiEOCjZoVbOmt09RrGzdqtrRUtqGrtxhWFSViUFY391RqnXnSeqDTdymvEmTPXYHA2wbX36PHCpgqsnBKLKQkhTjtuflIo4kP98JmTUhv3V2thkkBBShiW58Zg09Em9OhZ4cwe2ypaMTk2GOGBPhBCYEZKOPaYU0UJWHuwHn7eKizKigYA5MaHoHScB2fdfUbc8spOfFXeiN9cMg0/WT0ZAHCgVuvmkZ2dXtlcCR+1Ct+dm9K/zc9bjSXZ0Vh7sAEmk3Tj6IhcT0qJi57chN99Uubuodjk7Z3VCPbzwqqpcViUFQWTBDYf4yz3SCwXAFkQxDUYnE1wr2yuRHuPAXcvd96sGXAqtXHDkSanlL639OaanhSG5bmx6NGb8A1zv21mMJqwq7IVczMi+rfNTAlHRXMXi15A+cCwtrQBi7Oi4e+jBqAEZ8eaOsfNFd7BOnsNWPPidnxztBl/uDwf35uXimkJSrrmvmoGZ2OtvUePd3ZV48Lp8YgO9j3tttXT4tDY0cuLJTTh1bf34HhzFz4/WA8pPftiRHuPHp8eqMO3pifAz1uN6clhCPb1wkamNo5IY05r5MyZazA4m8A6ew14blMFlufEYJp5jY0zrZ4Whz6DCesOjb5MdEmVBskR/ogM8sW8jAgE+qhdntr4ZWkDTrR0ufQxxsrB2nZ09RkxJ31gcBYGYHw0o+7o0eOL0gaXvZnvr9GiTtvT30QdAHLjg6E3Shxt7HTJY7pSe48e1z2/DTtPtOGvV8/AFYXJAIDwQB8khfvjQA2Ds7H29s5qdPUZccOC9DNuK8qJgbdaMLWRJry9Vcrfnjptj8f/bf14bx169CZcaf776a1WYV5mJDYeafL4wNLdLDNnDM5cg8HZBPbqlhPQ6PS4y8mzZhaz0yIQGejjlIbUJSc1KEhWilj4eqmxODsaX5e77sO6tluP217bhT+uPeyS44+1bRVKGsbA4Cw/KQxeKuHxzah3nWjD+U9sxC2v7HRZOsnagw1QqwSW5cT0b5sSr6T5jrd1Z21dfbjm2W3YX6PFP747E9+annDa7flJodjP4GxMGU0SL2+uRGFqOPKSzrwQFuLnjQWZUeNiNoFoNPZVa6ASyvee3t/v7V1VyI4NQv6A/7OLs6JQ3daNEy06N47M8/UXBGFao0swOJugdH0GPLfxOBZnR6MgOcwlj6FWCZw7JRZflzWMan1YY3sParU9p41zeW4sGtp7caDGNR+cNx1phtEksfV4y4T4sLS9ohUZUYGICfbr3+bvo0ZufIjHNqM2miSe+OoIrnx6C0wmpYzxdnNRE2f7/GA95qRFIDzwVGWp9KhA+HqpxlVw1tzZi+88uxWHGjrwzLWF/T0HB5rGoiBjrri8ESdbdVizMG3IfVZNjcOJFh3bW9CEtq9ai9z4EGTFBHl0cHa0sQN7TmpwZWEyhBD9288xr0neeMRzx+4JNN19UKsEgny93D2UCYnB2QT1yf56tHT14S4n9DUbzqppcejqM2LzMcdztC39zQqST129KpocDZUAvihzbqNri+JDSspkU0cvjjeP79RGo0lie8Xp680sZqaEeWQz6uo2Ha5+Zgv+/MVhXJgfj09/uAg5cSHYecL5wdnxpk4caezEqqmxp233UqswOS543BQFaWjvwVVPb0FlSxdeuH42igbMAg5kaRPA2bOx89LmSsSH+p2WNjvYuVNiIQScVkSJyNOYTBJ7qzXITwrDkuxobKtoRXefZxb2entnNbxUApfMSDxte1pkAJLC/bnubATabj1C/b1PC2zJeRicTVDHmzrhpRL9/a5cZUFmJIJ9vUb1gWNvtQZeKoGpCaeCs8ggX8xMCcdXLgjOTCaJdYea+lMZRtt/qKRKg21unIErr29He4/htJRGi5mpnteM+qO9tTjvbxtRVteBv1w1HX+7egZC/LxRmBaOPSedH0iuLVV+h8618sE5Ny4EZXXt42L29Puv70a9tgev3DgX52RFDbkfg7OxdbihA5uONuN781LhrR76LTU62BeFqeH4/KBrLjgRuVtlSxc6egwoSA7F4uxo9BlM2FrheZUP9UYT3t1dg2U5MYgKOr14jxACi7KisOVYi8dd1PQkGp0eYVxv5jIMziaoWk034kL9oFa59qqGr5cay3Jj8EVpg8N/yEqqNMiJD4aft/q07ctzY3Gwth112m5nDLXfwdp2NHf24vr5aYgN8cWWUaxzMpkkbnllJ656ZiuueGqLWxYSW1IB56ZHnnGbJzWj7uw14L639uLuN/ZgUkwQPrl7Eb49I6n/9sK0COj6jCirc24gufag0v/L0uB8oNz4YLTp9Gho73XqYzpbnbYbu0604QfLsqwG4QOFBfggOYJFQcbKS5sr4eulwnfmpIy476qpcSira8dJN61n6RxH/ado7JXXt6PX4PhMl6VKbH5SGOakR8DPW4X1TigY5mzrDzWhubO3v5DSYIuyotHRa+ivIk1n0nbrEcLgzGUYnE1QNZpuqx9GXWHV1Di06fTYXml/SprJJLGvSmt1Xdy5U5S0LWdXbVx3qBFCAEsmR2NeRiS2Hm91OKAqrWtHU0cvLsiPR42mG9c+vx2X/Wsz1h1qHLMgbdvxViSF+yPByvlOCvdHVJCv25tRl1RpcMETG/H+nmrcvWwS3rptPlIiA07bpzBVCSSdmdrY2N6DPVUarJwSa/X23HFSFOTrcuX/gOX/xEjyEkOxr0bjwhERAGh0fXhvdzUuKUhExID1jEOxpD26q2rjJf/4Br/9X6lbHtuaV7eewK2v7HT6BTiy3/t7qrH6rxvxj6+POnyMvdUa+HmrkBUTBD9vNeZlRGKDi9dubTnWgi9K7ZuNfmtnFaKCfLF0crTV2xdkRkIIYMNhpjYORdutZzEQF2JwNkHVtHUjMXxsgrMl2dHw8VLZ/QcSAI43d6Kj19BfqXGgzOggpEYGOD21sfhQI/KTwhAV5Iv5GZFo7uzFsSbH1p1ZFjw/fNEUrLt/KX5zyTQ0tPdizYs7cMk/N7u04iSg9O/aXtlqddYMUFI0ZqaEjXnFxubOXqw9WI9HPy3DFU9txmX/2gyDUeI/t87HvSsnW03/SgjzR2KYP3ZWOm+sX5Q1QEpg5RBrgXLMwZmnrzv7qqwRKREByIwOsmn/vMQwVLV2Q6NjjztXenNHFXr0pmELgQyUHBGAKfEhbgnONLo+HG3sxCcHPKdi5EclNVhb2oDz/rYRXzrw/kHOseFwE+5/e5/y/SjWWu2t0mBaQii8zH/fF2dF43hTF6paXTdT/H8fl+LWV3fif/vqbNq/ubMXX5c34tKZiUOmIYcF+CA/KQyb2Gt1SBqdnmX0XYjB2QSkN5pQ396DpDGaOQv09cKiSVFYe9D+QGSPOd1uYDEQCyEElufEYvOxFqc0ugaA1q4+7KnSoMh8xWxehhLUOLrubP2hJkxNCEFMsB98vdT43rxUFP94KR69NA8tnb248aWduPgf3+BLF/XwOtrYidauPswdJtVtZmo4Klt0aOl0Teqe0SRRXt+O17edwL1vlWDpH4pR+Jsvceuru/DCpgoYTBK3LMrAJ/csGjElb1ZqOHaecHwmc7C1BxuQFhmA7FjrQU2ovzeSwv09euasu8+Ib442Y3lujM2Lry3rzlxV7ZSUxu+vbDmBeRkR/TOwtlg9LQ67TrahsaPHhaM7U3m9ki7c1NGLg7We8XtR0azDoqwoJIb54+ZXduLX/y1l2uUY21+txe2v7cKkmCCsWZCG/TVadDrwfqs3mnCwth3TB2TBLDG/z7qqaqOuz4Dy+nZ4q1T44Zt7sMmGwPKDPTUwmCSumJU07H6LJkWhpEqD9h5WvbVG2801Z67E4GwCqtf2wCQxZjNngFKJrEbTbfcMxN5qDYJ9vZARZf3D84opMegzmJxWOUlZEwYUTVbSw1IjAxAX4octDgRn2m49dp1sOyM1wse8/qT4x0vx+8vy0Kbrw82v7MT97+xzynMYaJtlvZmVSo0WlnVne5y47sxgNGHdoUbc8589KPjVWqz+60b87P0D2HC4CdmxwXjovBy8c/t87H9kFd7//kI8eF6OTVfZZqeFo6G9F9Vto09z6ujRY/OxZqycGjdsUJMbH+LRwdk3R5vRazBheY711ExrpiUqwQJTG13ny7IG1Gi6scZK0+nhrJoaBynhUKbBaJQP+B0vLnduqrgjOnr0aO7sxfzMSLz3/QVYsyANL3xTgcuf2owTLeO7gu54caKlCze8tB3hAT54+cY5WDklFkaTxA4HWpocbuhAr8F0Ws+wjKhAJIX7Y4OLgrP91VqYJPDYZXnIjA7Cra/uxF5z9WdrpJR4a2cVCpLDkBUbPOyxF2VFwWiSo1qTPlGZTBLtPXqEBoycyk2OYXA2AdVolA+2iWEBI+zpPMtzlTLRa+2sRFZSpUF+cihUQxQumZ0WgWA/L6elNhaXNyIy0Kd/ZkEIgXkZEQ5VW9x8VOmVtiTb+jogb7UKV81Owdf3LcW181Lx7u5qVDi5bP+2ilbEhvgiJWLoc52fFOqUZtRSShyo0eL/Pi7FvEe/xpoXd6C4vBHn58Xjz1dOx/r7l2LHz1bgmesKcduSTBSmRZxR5GUkhWlKkOmMdWfFh5qgN8oh15tZ5MaHoKK5y2NLPn9V3oAgX68RZx0HCgvwQUpEAIuCuNCL31QiMcwf547w+zVYdmwQ0iIDxrykfnl9ByICfZCfFNrfSsSdLE1+0yMD4eulxiPfmoqnr52FyuYuXPDEJvx3b62bRzixNXf24roXtsNoknjlpjmIDfHDzNRw+KhVDl2stBQDmZ4U1r9NCIHF2dHYfKzFJTOiljY8i7Oj8fKNcxAR6IMbXtqBo42dQ47xcEMnrhyiEMhAM1LCEeijZr8zKzp6DJASTGt0IQZnE1CNedYhIcxvhD2dJzrYF7NSwu26GtyjN6K8rmPYJtneahWWTo5B8aFGmEyjS3UzmiTWH27CksnRpwWD8zMj0dzZh2NN1v+gD2X94SYE+3lhZkrYsPt5q1W4a/kkeKtUeGFThSNDt0pKie0VLZibHjnszJCftxpTEkIcDs5qNd3457qjWPmXDbjwyU14ZUslZqWG4anvzcSOn6/A7y/Px6Uzk5AaGTjqnifZscEI9vXCDiesO1t7sB5RQb6YMUI7iSnxwTBJeFS7AQspJb4qa8Ti7Cj4eNn35zovMZTl9F1k6/EWbKtoxXXzU+2uiCuEwKppcdhyrAXa7rFLmSqr70BOXDCKJsdgT5UGrV3uXY9ouVCVFhXYv23V1Dh8cs8iZMcG4a439uCh9/Z57EWT8ayr14AbXtyBhvYePL9mdv9aVj9vNWakhDk0W7S3SoNQf2+kDir0tCQ7Gp29Bpesey6p0vQXvYoN8cOrN82FSgDXv7DdapGZt3dVwc9bhQunx494bB8vFeZlRNqUKnm20XQrfzuY1ug6DM4mIMvMmbXqfa60cmosSuvabV78e7BWC4NJWi0GMtCK3Bg0d/ahZJRlbfdWa9Cm0/enNFpY1p1tOW77bI2USq+0cyZF9S9+Hk5MsB8uLkjA27uq0OakD0UnWnRoaO+1aUZlZko49lZp7Wp3cLSxE995ZisW/v5rPP7ZIYT6e+M3l0zDjp+twNPXFmL1tHj4etk3MzYStUpgZmo4do0yOOs1GLHuUBPOnRIz4odnT67YeKCmHY0dvVhmR0qjxbTEUFS1djvt940UHT16/PjtvUiNDMD35qU6dIxVU+NgMMkxSy80mSQO13dgclwwinJiICXcPiPQH5xFBp62PSk8AG/eNh93LM3EG9urcPE/NuGwB144Ga/0RhPueH03Suva8Y/vzjyjF+r8zEgcrNXafeFgb7UW+UmhZ1ygW5AZCS+VcElqY0mV5rSLb+lRgXjphjnQdutx3fPbTyuI1KM34sOSWpw3LR4hfrYFFYuyolDZonNpQZPxSKNTfjc4c+Y6DM4moFpNN6KCfO1OKRutc6coFfFsnT2zrIGabqUYyEBLs5UP2KOt5rWuvBEqofzBHSglIgDxoX7YasfVwsMNnahv78GSbOuleK25eVEGevQmvL7thM33GY6lv9m8YdabWcxICUO33thfFGAkUkr89P39KK1rxw+XZ2PD/UV4544F+N68VIS5OM+8MDUchxo6oNU5Pquw+VgLOnsNWDnFepXGgZLDAxDoo/bI4Oyr8gYIgf4CNvawrP04UGv/7FlzZy8qnZyCO1H85uMy1Gq68ecrpyPQ18uhYxQkhSE2xHfMUhtPturQrTciNy4E+YmhiAz0cfu6s8rmLsSH+sHf58z3KW+1Cg+szsErN85Ba1cfbnllpxtGOPFIKfHAO/uw4XATfvftaViee+ZFn/kZkTDJU+8vtujuM+JwQ8dpKY0WwX7emJka7vSiIPXaHtRpe87IvJmWGIpnryvEiVYdbnhpB3R9SnGTzw/Wo6PHMGIhkIHOyVL+7tqz5n3jkSY8+O4+j6mI6gqWwJ2l9F2HwdkEVKMZuzL6A6VHBSIrJsjm4GxvtRaJYf6ICR4+/TI0wBuz08Lx1Sj7nRUfasLMlPAzggtl3Vkkttqx7mydec3GEjs+NE+OC1Zy47ecGFWjT4utFS2IDPSxqbz6qaIgts1IbTzSjO0VrbhvZTbuWZF1Rk8yV7KsOxtNGszagw0I9FFjwSTrLQYGUqkEcjy0KMhXZY2YkRyGyCBfu+87LUEJzhxJbbz/7b246MlNqNeObUVBT/dlaQPe3FmF25dkYlaq7WsAB1OpBFZOicP6w01jkrZXXq/8bufEB0OlEliSHY31h5tgHGWq+GhUtHSdMWs22OLsaNx0TgZOtOjQwap5o/b7zw7hvT01uO/cbFw123rT9IKUMPh6qexKbSyt08JokqcVAxloSXY0DtYqPUGdpaRKeX+wtixifmYknrh6BvZWaXDHa7uhN5rw9s5qJIX792fK2CIzOhAJoX42zzLXa3tw1xt78J8dVSirm7izvZpuzpy5GoOzCaimrXvMyugPtnJqLLZXttqUSlVS1TbirJnFitxYHGrocDi9oLGjB/trtCjKsV68Y35GJFq6+oZcSDzY+sNNmBwbjPhQ+17nWxalo6mjFx+VjH6x+7bjrZiTHmHTOq+kcH9EB/titw0VG6WU+OPaQ0gM88fVQ7yBu1JBchi8VAI7HGhqDihrC78obcDSnBib0y6nxIegvK7Do652NrQrv7PWrm7bIjTAGykRAdhfbV9w1trVhw1HmtHRa8BP39/vUa+JO7V09uLB9/YhNz4EP1yRPerjrZoah2690eVNegGgrK4DKgFkxSgV6opyYtCm0/cXVHCHyuau09abDSXdvE9lM1PLRuOFTRV4av0xfG9eCn6wbNKQ+/l6qVGYFm5XUZCSKuVvzFDrxy0ZJs5Mpd1TpYG3WmBqgvU2FqunxeF3387D+sNNuO3VXfjmWDMun5U0ZPExa4QQOCcrCt+Yi38Nx2SSuO/tEvTolYstrmof4AksM2ehnDlzGQZnE4yU0m0zZwCwckocjCaJr0dImWnp7EVVa/ewxUAGsnxA/dLBqo0bDitpCYPL3lvY0++sq9eAHZWtQx5rOOdMikJOXDCe31Qxqg+91W061Gi6ba7gZ08z6rWlDdhXrcU9K7LsLkLhDP4+akxNDHW4GXVJVRuaO3tHrNI4UG58CDp6DXaV8DeaJO58fTd+83GpU68IW1j+Dy3PtX5BwRZ5SfYXBfnsQD2M5j5AX5c34v09NQ4//kQhpcTP3j+A9m4D/nLVdKf8v5ibEYFQf+8xaUh9qL4DaZGB/SmEi7OioRKnMgDGmlanR5tOj/SokWfkM6KV4Ox4s30Fm+iUls5e/OZ/pTh3Six+9a1pI17Qm5ceibK6dpvXq+6r1iAuxA8xIdazYKbEhyAqyMepAUvJSQ2mxIcMu3zj6jkp+Mnqyf1/Sy+3I6XRYlFWNNp7DNg3wpr35zYdxzdHW/DIRVORExfssvYBnkBrXsvHmTPXYXA2wTR39qHXYEKim2bO8hJDERvii7Wlw3/g2Gv+QzdSMRCL9KhAZEYHOpzaWHyoETHBvpgyRLPY5Ah/JITa1u9s87EW6I3SrvVmFkII3HROOsrrO0bVu82yHmBuuu0pGjNTwnGiRYfmYZpRG00Sf157GBnRgbh0RqLD4xutwtRw7K3WOJT+ufZgA7zVYshZUmty45UZBXua82443IT/7a/Dc5sqsOjxr/Hb/5UO+9ra66uyRiSG+WPyCP14hpOXGIrqNvuKgny8rxYZUYF47LJ8zEoNxyMfHURj+9md3vj+nhp8drAe963MRk6c7Q2nh+OtVmF5bgy+KmuE3o5CPY4or29HTvyp36PQAG/MSg13W0n9ihbrxUCsSYkIgBBwehuSs8nB2naYJHDDwjSbqovOz1TeV7ZV2DZ7ts9cDGQoKpXA4qxobDzSPOqqy4DSZ3N/jdami7t3LMnE/asm49bFGUgKtz89f+GkKAiBYas2HqjR4g+fH8LqqXG4anYylmRHY+eJVnQ50Mx7PNB26+HvrXZ6QTA6hcHZBHOqx5l7gjOVSuDcKbHYcLi5f3rfmpKTGqhVor9Zri1W5MZiW0UL2u1ce2AwmrDhcBOKJscMecVQCIF5mZHYerx1xBmt9YcbEeCjxqw02wLLwb5VkIDoYF88u/G4Q/cHlOAsxM8Lk+Ns/+A+M3XkZtQf76vFoYYO/GhFtk1VKF1ldlo4eg0mHKixbx2YlBKfH6zH/MwomytyAcp6QCHsq9j4xvaTiArywdofLcb50+Lx/KYKLPp9MR79pGzUQVqP3ohNR5uwPHfo31lbWPr52Tp71tTRi63HW3BhfjzUKoHHL89Hr8GEn31wYEKkNxpNEiVVGrs+INZquvHwhwcxOy0cNy/KcOp4Vk+Ng7Zbb9OMvaN0fQacaNWdEVQunRyjVAN1Q+BdYZ4Fs8yKDcfPW43EMH8GZ6Ng+bs21MXJwfKTwuDvrbZp3ZlWp0dFcxemjxAoLc6ORmtXn0MFigY73NAJXZ8RBSO0sQGU9/Y7iybhofNyHXqsiEAfTEsIHfJiqq7PgLv/sweRgb547LI8CKGs6dQbJ24Da41Oz2IgLsbgbII51ePMPcEZoKQ2duuNw15pKqnWIjs2GAE+tlc7WzElFnqjtDtdYPdJDTp6DCjKGX6ma15GJFq7+nBkmHVnlhL6CzKjHL5q5OulxpoFadh4pLl/ob69tlUo683s6bGUlzh8M2q90YS/fHEYufEhuCBv5D4wrmQptrDLzmbUB2vbUdmisyulEQACfLyQHhloc3DW2NGDr8obcdmsJGTHBuPPVxXgi3uXYPW0ODy78bgSpH1ahhYHg7Qtx1rQozc5vN7Mwt6iIJ8eqINJAhdOTwAAZEYH4d5zs/FFaQM+GudNgY0miXvfKsEl//gG3/7nNzattzKZJO5/Zy+MUuJPVxTY3dNsJIuzoxHi54XXt5506nEHOtzQCSmBnEEXciwtRdYdGvv0q4pmHVQCSI6wbSYjPSqQwdkolNa1IzHM3+ZKuz5eKpvXne2r0QCA1UqNAy3KUmag1jvh983yf9fWzJvROicrCrtPtqHTykzYb/5XhormLvz5yun9r++stHAE+Kgn7LozTbeeKY0uxuBsgqnRKIum3bXmDFCCnGBfryFTG6WU2FulQYGNxUAsZqaEIzzA2+7UxuJDjfBSCSycFDXsfvNtWHd2vLkL1W3ddlVptOaauSnw91bjuY32N6VubO9BRXOXXSmNgHIFempCCHafsB6cvburGpUtOtx3brZdi6ZdITrYF2mRAXY3o35pcyX8vdW4KD/B7sfMjQ9BmY3B8ju7qmE0SVxVmNy/LTM6CH+5qgBrf7QEK6fG4pkNx7Ho8WI89mm53bO9X5Y1IMBHjbk2rikcSmiA0hT2gI3B2cd765AVE4TsAamUNy/KwPTkMDzy0UGXrK0D4PJZOaNJ4sdv78WHJbW4YlYSarU9uOQf3+CBd/YNG0C/sqUS3xxtwS8unOKSiqV+3mpcNz8Nn5fW41iTa9ZUlZsvOAyeOcuND0ZciJ9bUhsrm7uQEOZv8wWujKhAVDR1TYjZW3corW3v7+doq/mZkTjc0DliFsA+c8GhvGHSGgEgMsgXeYmhTglYSqraEBbgjbQxqiK8KCsKBpM8o93O5wfr8e9tJ3Hr4gwsGPD5wtdLjfkZkRM2ONMyOHO5EYMzIUSyEKJYCFEmhDgohLhn0O0/FkJIIcTwn3xpTNRqehDs6+XW/zg+XioszVHWUlircFTR3AVtt97mYiAWapVA0eQYFB9qtKuZcnF5I2anRSB4hDS3pHB/JIb5D5uKYLnqt9SB9WYDhQX44IrCJHxYUmN3WtE283ozW4uBDDQjJRz7qs9sRt2jN+JvXx1BQXLYqApQOFNhWgR2nWiz+QNZY0cPPiqpxRWFSQ5VkcqND0ZVa/eIJbullHhzRxXmpkcgw0obg0kxQfjb1TPwxY8WY0VuLJ7ecAzff223zc9DSqWgzqKsKKf0KpyWGNr/AWo49doe7DjRigsHBbZqlcAfL89HV68RD390YNTjGeytHVWY/PPPsOQPxbj2+W34xQcH8NzG41h7sB6HGzqGTY+2hdEkcf/be/H+nhrcv2oy/nDFdHx93xLcujgD7+6uRtEf1+HlzZVn/J842tiJRz8tx7KcGFw9O3mIo4/emoVp8Far8Nwo0pyHU17fgUAfNZIGXbATQqAoR1kH5Oo1b4NVtnT1V2G0RXpUIDp6DWjuZEN1e/XojTjW1IkpQ1Q1HIotFysBYG+VBulRgTZ95licFY09VRq7G1wPVlKlQUFy2KhSvu0xKzUc/t5qbDp6Khuoob0HD767D9MSQ3DfuZPPuM+SydE42aqbkP0itUxrdDlbZs4MAO6TUuYCmAfgTiHEFEAJ3ACcC8B1ORlkl+o291VqHGjllFi0dPVZTaGzFAMZKUfdmhVTYqHR6bFriNmfweq03Siv7xgxpRE41e9sW0XrkGtS1h1uQkZ0oM3pOMO5cWE6DCaJV7bY15R6e0UrAn3UQ5YQHs7M1HCrzaj/ve0k6rQ9uH/V5DF7wxtJYWo4Wrv6cNzGN7fXt55En9GENQvSHHo8y4eXkRp1bzneghMtOlw9Z/gP7JNigvHEd2bg/y6ehk1Hm/Hv7bb9mSyta0edtgfLc0aX0miRnxiKGs3IRUH+t78OUgIXTj8zpTUrNhj3rMjCJ/vr8cn+OqeMy+L1bScQHaxcVdfo9PiwpAa/+V8Zbn11F1b+ZQNyfvEZ5v7uS6x5cbvNM4AWRpPET97Z19/b6c4ipYR4sJ83fnp+Lj774SLkJ4Xh4Y8O4sInN2Gb+YOowWjCfW+VIMBH3b+OxFWignxxxawkvLvL/gs1tiira8fkuGCrs+FLJ8egs9fgcGVUR0gpUdE8co+zgdLNF0GY2mi/Q/UdMElgSrx9hYXyEkMR5Os14rqpfdVaTB9h1sxiyeRoGE0Sm486Xgyro0ePI42ddl/cHQ1fLzXmZkT0t70wmSTue2svevQm/O3qGVart1oKhk3E2TPOnLneiMGZlLJOSrnb/H0HgDIAljJufwHwEwDMNfAQNZputxUDGWjp5Gh4qwXWWikTXXJSg0AfdX/PHXsszo5GsJ8XHnpvv00fZCzrKZZOtm02aF5GxJDrznr0Rmw73uJQlUZr0qICsXJKLF7bdgK6PturOm2raMGstAiHCnbMNC+gHhg06/oM+Oe6o5ifETli6udYsjSj3mXDB8cevRGvbT2B5TkxVmezbGFJ+xlp3dmbO6oQ4ueF86bZti7vmrkpWDgpEr/7X5lNffq+Nqft2lNtcji2FgX5eF8tcuNDhmxqftviDOQlhuIXHxxAqx3VH4dT2dyFvdVaXL8gFX//7kz8965zsO+RVSj55bn44M6F+NvVBbjv3GycMykaB2q0+NbfN+HX/y21uvZjMJNJ4sF39+Hd3dX40Yps3LU864x9JsUE49Wb5uCp781ER48BVz2zFff8Zw8e/bQce6u1+O238xATbL08uDPdujgDBpMJL3xT6dTjSilxqKEDk4eoMLlwUhS81WJMS+q3dvWho8dgU48ziwzzvhUsp2+3U8VA7FtG4KVWYfYI684a2ntQ396D/BHWm1nMSA5DsJ/XqAKWfdVaSDl0TzVXOWdSFI43daFG043nN1Vg09Fm/PKiKUP+vUyNDERqZMCEDM403X02r18kx9j16U4IkQZgBoBtQohvAaiRUu51xcDIMTVtOo+YOQv288b8zCisLW04I52rpFqLvKRQhxbXB/l64cU1s1Hf3oNrnts2YsGF4nKlHHlWjG0f2Ifrd7bleAt6DSabAz1b3LwoAxqdHu/uqrZp/9auPhxu6HR4LVJimD9ign1PW3f24jeVaO7sw49XnZma4U6Z0YEID/C2qRn1R3tr0dLVhxvPSXf48eJC/BAW4I3SYcrpa3R9+PRAPS6dmWRzyqEQAr+/LB8A8MC7+0asFPhleSOmJ4chOtjX9sEPY6oNwVl1mw57TmpwYf7QAaeXWoU/XJGP9h49Hv7ooFPGZikyMjiVMizABwXJYbi4IBF3Lc/Cn66cjq/uW4rvzk3Bi5srsOJP6/HZgbohU0VNJomH3tuPt3dV4+7lWbhnxZmBmYUQAqunxePLe5fg7uVZ+PRAPZ7fVIFLChJw/hgVxkmNDMR5efF4feuJEdNq7dHQ3guNTt/fKmKwIF8vzEmPGNN1Z5bZrww7grOEMH/4qFU2z6LTKaV17Qj29TojrdUW8zMjcbypCw1DXAjday7MMd3G9eNeahUWZkZhw+Emh9cPnioGEubQ/R212HxR9pn1x/D45+VYNTV2xHTnJdnR2HKsxaGWMJ6qR29Ej97EmTMXszk4E0IEAXgXwA+hpDr+DMAvbbjfrUKInUKInU1NE+8Kgifp6NGjvcfgETNngJLaeKJFd9osVK/BiLLadodSGi0K0yLw/PWzUdWmwzXPbYNGZ/0qfq/BiG+ONmPp5Gib05KSIwKQFG593dn6Q03w9VKNukjDQIWp4ZieHIbnN1VYXZ832Kn+Zo6NQWlGHY7d5nL62m49nl5/DMtzYjArdWwqX9lKCIFZqREjprBKKfHCpgrkxAVjQaZ9RVIGP15uXMiwM2fv7a5Bn8GEq+xcg5QUHoCfXTAFm4+14PVh0hubOnqxt0qD5U6aNQOURqFpkQHYP8y6s//tU1IVRyqkkhMXgh8UZeG/e2tH3TxZSomP9tZiTlqETdVlQ/298ZtL8vDeHQsQHuiD21/bjZte3nnGbKTJJPHT9/fjzZ1VuGvZJPxomMBsIH8fNe49Nxtf/mgJ7js3G7++ZJpDz8tRty/OREevAf/e5rxVApYCN8P1ZiuaHIPDDZ2obht5VtcZLMGZPTNnapVAamQAKpoYnNnLUgzEkSJP8zOUTIqh1p3tq9ZCrRJ2zcotmRyNWm0Pjg5TFXk4e05qkBEVOOYzN1kxQYgN8cXLW04gItAHj12aP+LniiXZ0ejWG8c0bdjV2s3rBRmcuZZNwZkQwhtKYPa6lPI9AJkA0gHsFUJUAkgCsFsIETf4vlLKZ6SUhVLKwuho56SDkXX9Pc48YOYMAM41lzMfmNpYWtuOPqMJM0Z51Wt+ZiSeva4Qx5u7cO3z260uMN5Z2YauPmN/yWhbKevOWs6Y4dhwuAnzMyOdUqTBQgiBWxalo7JFhy/LGobcr0dvxGcH6vH0hmPw9VLZnEZizczUMJxsVZpRP7fxONp7DLh3ZbbDx3OlwrRwHG/uGrZi2JZjLSiv78CNC9NHvTYoNz4Ehxo6rAbKlkIg05PD7K58BgDfmZOMRVlRePSTodMbi8uVGQxnF2WZlhg67MzZx/vqkJ8UalNFwu8XZSI3PgQ/e//AkBdGbFFW14GjjZ24qMC+ypozUsLx3x8sxM8vyMXW4y049y/r8a91x6A3mmAySfzsgwP4z44q3FmUiXvPzbb7dyIlMgB3Lc+yq0+eM+QlhWLhpEg8v6nCaVfay+uU9ZPD9UO0pM8Wj1FJ/cqWLqhVwu6ZnPSoQM6c2clkkiira7e7GIjFlIQQhPgNve5sb7UGk2OD4e9j+3vi4lGsxZJS9hcDGWtCCCzKUsb+5ysLEB44cnA4LyMS3moxoVIbNebPWiwI4lq2VGsUAJ4HUCal/DMASCn3SyljpJRpUso0ANUAZkopR3cplUbFE3qcDRQb4oeC5DCsLT0VdJxKgwgb9fEXZUXjqe/NRHl9O9a8uP2MdSjrDjXCR63Cgkn2zabMy4hEm06Pw42nCkOcbNHheHOX09abDbR6ahwSw/zPqNbWozfi84P1uPuNPZj1f1/g9td24USLDg+szrG6ANlWM1OUGbIvSxvwwqYKXJAfj6kJ9q1HGCuzzY2+h7vy+PymCkQG+uBbdn7ItyY3Phg9epPVwgN7qjQ41NDhcOU+IQQeuywfKiHwk3espzd+Vd6A+FA/m5vF2irPXBTE2lqxyuYu7K/RDpvSOJC3WoU/XpEPja4Pv/641OExfbS3FmqVwPnTzrimNyIvtQo3L8rAl/cuweKsaPz+s3Jc8MRG/PDNEryx/STuWJqJH6/0nOI2trp9SSYaO3rx4R7n9JQ7VN+OhFC/Ya9yZ0QFIiUiAOvKxya1sbJZh+Rwf3jbuWY2IzoIJ1q6bMowIMXJVh26+oxDprWORK0SmJMeaXXdmZRSKQZiZ0ucxDB/TIoJcihgqdF0o7mz16bm067w45WT8dpNc21emx3o64XZaRF292b1ZFrOnI0JW/46LgRwLYBlQogS89f5Lh4XOcAyc5bkIcEZoMye7avWok6rjK2kSoPYEF/EhzpnjMtyYvHkd2ZiX7UWN7y4/bTCGsWHmjA3I8KuRteAUhQEwGk9TdYfVj64uCI481KrcOM56dhR2YZtx1vw+cF63PMfJSC77dVd2HikCd8qSMRrN83F9p8uH9W6KkCZRfFWC/zmf2Xo1hvxoxWeOWsGKGP18VIN2Yy6orkLX5U34pp5qU6Z0bRcYbaW2vjm9ioE+Khx0XTHg8DEMH/8/IJcbDnegte2nV6ls0dvxMYjzViWE+P0oMLSg8ja7Nn/zNUXL7CjN9zUhFDcvCgD7+2ucSg9SUqJ/+6txTmTohAZ5PjauoQwfzxzXSGeva4QXb1GfLS3FrctycBPPKjqqD3OmRSFqQkheGrDsRHXJtqivL4DOSME+kIIFE2OxjfHmkfdtsAWFc1ddqU0WmREBUJvlP0XIWlkpQ4WAxlofmYkTrToUKs5/XU/0aKDtlvvUBbHkuxobKtoRXeffb9ve8zp+O6YOQOAuFA/nJNlX9GsJdnRKK/vQL3W/kqsnnghQqMzz5z5syCIK9lSrXGTlFJIKfOllAXmr08G7ZMmpXS8Nio5RY2mGz5qFaJG8WHH2VZNVVIbvzTPnu2t1jr9D+vqaXH429UF2HWiDTe/vBM9eiOqWnU42thpd0ojoKwPSo7wP+1q4bpDTUiJCLCrN489rpqdjGA/L1z1zFbc9uourD/chIumJ+DVm+Zg+89W4NFL83BOVpRDFRoH8/NWY0pCKDp7Dbh0ZhIm2VgsxR18vdSYnhQ6ZDPqF7+pgI9ahe/NS3HK402KCYKXSpwRnHX2GvDffbW4KD8BQb72BfuDXTU7GYuzo/HoJ+U42XIqvXFbRSt0fUaX9JmbZi4KYq0U/X/31mJmSpjda1VvWZQOXy8Vnt1gf3+u3SfbUKPpxrdGEegOdO6UWHxx72K8ees8PLg6Z1wGZoASKN22JBPHm7rwxTBpzrboM5hwtLETOcOkNFoszYlBj97U30PRVaSUqGyxr4y+RXq0cp/jrNhos7K6dqhVAlmxjv+Nt1ysHJzaaGmJk29jGf2BFmdHo89gwtaK4cv0D1ZSpYGPl2rYNZSexpLGae/s2cf7ajHpZ58g9xefYeFjX+PCJzfi2ue34e439uCRjw7ib18ewatbKvF1+ZlF11yJM2djY/Sf9Mhj1LR1IyHMz6GFv66SGR2EjKhArC1tgEbXh4rmLqekNA52YX4C/nTldGw53oJbX93VX6zA0XLk89JP9TvrNRix+ZhSQt9VH/qCfL3wyEVT8Z05KXjlxjnY8bMVeOyyfCzKirY7/ccWc9Mj4K0WuMdKeXFPU5gWgYO12jOusmp1ery9sxoXTU9wWrlzXy81JsUEnRGc/XdvLXR9xhF7m9lCCIHHLs2Dl0rg/nf29s+QfFXWAD9vFRZkOr+dQYifUhRkn/kDlcXRxk6U13ecUS3RFpFBvriyMBnv76kZsprbUD4qqYWvlworpzqnlxsABPh4YW5G5LgNzCzOnxaH5Ah/PLX+2Kg+dB1r6oTBJEecOQOUhsO+Xqr+NY+u0tjRC12fERnRDgRn/eX0ue7MVqW17ZgUHTSqrILcuBCEBXifkdq4r1oLP28VsmPtT5mcmx7h0O9bSZUGeeZsivEiJy4YMcG+WH/E9uCsR2/Eo5+UIzM6CN+bl4J5GZGICfZDR48B+6o1eG93Nf7y5WH84sODuPGlnfjmqH1B7mhY1hmHcs2ZS42f33AaUY3GMxpQDySEwLlTY7HlWAs2HlEmV12VkvDtGUn4/aX52HC4CY9+Wo60SMdnuuZlREKj0+NQQwd2VrahW2/E0smuLWhz2awkPHppHhZnuyYgG+iuZZPwyd2LnNJM29UKU8OhN8r+K7UW/9lxEt16I248J82pj5cbH9KfDtT/WNtPYnJssNN+dxPC/PGLC6dgW0UrXtlSCSklviprxDmTopxacGagvKQwHKg5/Xl9vK8WQgAX2LjebLCbF6XDYDLhRTv6cxmMJvxvfx2W5cQgeIyLbowHXmoVbl2UgT0nNUPOGNvikLmZui0zZ37eaizIjMTX5Y0uvQrfX6nRgZmzyEAfBPt5uT0423y0GbN/++WwRYo8RekoioFYqFQCc9MjzqjYuLdKg6kJoQ69V/l5q7Fqahze3VWNNht7JuqNJhyocX7mjasJIbAkOxqbjjTbnKb4ypZK1Gi68etvTcXPLpiCP105HS+smY0P7lyIdfcXYd8jq3Dkt+dh60PLEeLnhXd329aKxxm03XoIAQSPMoOEhsfgbAKpafOMBtSDrZwSC4NJ4omvjkCIU01xXeHK2cn4v0umwWiSWJbj+FX5eZmn+p1ZCotYeqBNBMF+3shy4IqnO1hK/A8sqW8wmvDy5krMy4hwejGT3PhgNLT39hfPKK1tx95qLa6ek+zUWZkrCpOwdHI0fv/ZIawtbUCNphvLc503kzRYXmIIajTd/b0BpZT4eF8dZqdFIDbEsZlHR/pzbTnegubOPqelNE5EVxQmIzLQB0+tP+bwMcrq2+GjVtl8gWpZTgxOtupcGvxUmo/tyEUzIQQyogLdHpz9b38dmjp6hywv7ylau/pQp+1xSnGh+RmRqG7r7q8yazCacKBW61BKo8VdyyZBpzfiuU22pUWX13Wg12Aad8EZoKQ2arv1Z1xgtEaj68Pfvz6KJdnRWDBM4RFvtQpxoX64cHoCPjtQf0ZBNFfRdusR6u/tURlaExGDswmi12BEY0cvEsM8byakIDkcUUG+ONLYiayYIJdfLb92Xio+vHMhfrzK8UIXiWH+SIkIwJZjLVh/uAmz08MRyCtFbhEW4IOsmKDTmlF/drAetdoe3HROhtMfz1Im35La+OaOk/DxUuHbMxKd+jhCCDx6aR681AJ3vbEHgPIB2VWmDWpGfajBXMrewVkzi9sWZ6Cj14A3hunfNtBHJbUI8vVyOOX4bODnrcb1C9LwdXlj/wyYvcrrOjApJsjmmY2lk11fUr+ipQs+apXDFYXTowJx3M29zixrr0bqv+hulr9fjrT9GGy+OdXaktp4pLETPfrRBUpZscE4Py8eL28+YVNLjj1Vyus9HoOzcyZFQSWUXqkj+ee6Y+joNeDB83JsOvZlMxPRrTfiU3NhJ1fT6PQI43ozl2NwNkHUaZQ1H56W1ggo5XjPnaK88Y/VH9bpyWF2V2kcbF5GBDYeacbhhk4szeYHSXcqTFOaUVvWZ72wqQKpkQEuCWYGBmc9eiPe31OD86bFuaTpaXyoP3554RT0GUzISwx1eAbLFoOLgny8tw4qAayeNrrgLD8pDAsybevP1Wsw4rOD9Vg5NdZl6ZsTxXXzUxHgo8bTGxybPTtU32FTSqNFckQAJsUEYd0h1607q2zuQnKEP9QOXnVPjwpCrbZ7TKpKWlOn7e7vtTYWwVlLZy+ueGqzQxVRTwVno8+QyI4NQmSgT38F4339xUDCRnXcu5dlobPXgOc3VYy4b8lJDaKCfO3uj+cJwgN9MD05bMT2ATWabry0uRKXzkiyOaiemRKOtMiAMUtttMyckWsxOJsg+htQe2BaI3CqIbUrioG4yryMSHSbPwQscfF6MxpeYWo4OnoMONzYgT0n27D7pAY3LEhz+EPecKKCfBET7IvSunZ8eqAO7T0GXD3bOdUgrbl8VhJuW5KBO4smuewxAKUoSHpUIPbXaM0pjbWYnxmJ6ODRV3e9fUkmGtp78WHJ8P251h1qQkePgSmNNggL8MHVs1PwUUlt/993W7V19aG+vQc5dn4wL5ocjW3HW9HlohSpymbdqCrepkcHQkqljLs7WGbNluXE4GBt+2mtW1xh/eEm7Khswzu77P/gXVrbjrgQv1G1qrAQQmBehtLvTGkErUWInxfSbGhaP5zJccE4Py8OL31TCa1u+LRoS/Pp8VrwZ3FWNPZVa4ZdY/entYcAAPeutD3rRwiBS2cmYevxVlS3uf7/haZbj1AXXKik0zE4myAsvV88NThbkh2D3307z+mpYa5kWWOWEOqHLA8uN382mJ2mlHPeWdmGF76pRLCvFy4vHH3lxKHkxoegrK4Db2yvQlpkQH85aVcQQuCh83Kx2oFmzPaalhiK/dVaHKxtR2WLzqEqjdYsyopCbnwIntlwfNj+XB/trUVEoI/NTVzPdjctUnoaPr9x5JmFgcr7i4HYl9JWNDkGfUYTvjnq/M44JpNSRn80wVlGf8VG95TT33ysBeEB3vjevBQYTRIlVRqXPt6240oq91cOtFVwRjGQgeZlRqJO24MTLTrsq9YgP8k5gdLdy7PQ0WvA898M/Tuu1elxvLkLM9zUfNoZlkyOhkkCm4b4v1Va247399TghoVpdn+Os3yu+mBPzajHORKtro8zZ2OAwdkEUaPphhBKk0RPpFYJfHduyqhTDcdSQpg/8pNCcdH0hHF7tW6iSI7wR0ywL/67txaf7K/D1XOSR91vbDi58SE43NCB7RWtuGp2yoQ5//mJoajV9uCVLZXwUgmsnuqcgFAIgduXZOBoYye+HqI8dlevAV+VNeD8vDiXVyOdKBLD/PGt6Qn4z46TNq3LsSivV1La7J05K0yLQJCvl0vWndW196DXYHKoAbWF5b7H3VAUREqJLcdaMD8zErNSlIs1u0ZRTdMW2ytboVYJHGnsPK0n4kh69EYcbex0SjEQi/nmi5XrDinrIKcnO6cQU05cCFZPjcOL31T099AarMScRjke15tZTE8KQ6i/95CpjY99Vo4QP298f4n9GRTJEQGYmx6Bd3fXuLznmbaba87GAt8hJ4gaTTdig/3GVf+P8eDDOxfavDCXXEcIgcK0cGyraIWUEtfNT3Pp4+XGB8NokvBSCVw2a/zM9o7Esu7snV3VWDgpCuGBzktPOT8vHolh/kOukfqitAE9ehO+NX3ivJ5j4bYlmdD1GfHy5hM23+dQfQciAn0QbWdKm4+XCudMisK6Q84vqd9fqdGBMvoWQb5eiAn2RYUbioKcbNWhRtON+ZlRCA3wRnZsEHa6cN1ZY3sPKpq78B1zb8Uv7Zg9O9qo9LhzRjEQi8zoQEQH++KlzZUwmOSo15sNdPfyLHT0GPDiELNne062QQjHGl57CrVK4JysKGw43HTG/61NR5qx4XAT7lo2yeH+YZfNTEJFcxf2uHA212SSSnDGHmcux0/yE0RNm+f1OJsIhBATZtZkvCtMVa5Wr54W5/L+bJYrzstzY5zW4NoTTE1UnpdJAheOskrjYN5qFW5elI4dlW3YdaL1jNs/LKlBfKgfCs2tEcg2k+OCsSI3Fi9urrC5XHaZuRiII3+7inKiUaftwaEGx6pEDqW/x9koZs4ApWKjO8rpbzavN1tgbrMyKzUCu0+2DZvGOxrbKpT/Q1fMSsakmCB8VW57cFZaq8ycOjOtUQiB+RmRqDTP4E13YnA2JSEEK6fE4oVNFWi30pKjpEozJpWeXW1JdjQaO3r7044BJeB59NMyJIb549r5qQ4f+7y8OPh5q/CuA+sTbdXZZ4BJgmmNY4DB2QRRo/HMHmdEzrJ0cjSignxx+5JMlz9WRnQQrpufinuWO96OwROF+HkjIyoQPmoVVjoppXGgq2YnIyzAG0+vP713UVtXHzYeacZF0xPYH8cBdy2bBI1Oj9e2jjx7ZjRJHK7vsHu9mUV/Sf1y56Y2VjZ3wddLhbhRViTNiA5yW3AWG+Lbv+5tYJEiV9he0YpAHzWmJoRgeW4Mth1vtRq4WFNa144AHzVSnXwRa745MI0J9nX6Eoq7l2ehvceAlwY1tJdSYq+5GMh4tyRbKSw2MLXxo721OFjbjh+vyoavl+MVbIP9vLFqahz+u7d2xKq5jrIUbWFw5noMziYAk0miTsuZM5rYMqKDsPPnK5yaTjMUtUrg1xdPc+qVZ0/x3bkpuGVxukveYAN8vHDdvFR8UdZwWvnvTw7UwWCSrNLooOnJYVicHY3nNh5Hd9/wH7xOturQrTfavd7MIjbED1PiQ1A8xNpBR1W2dCEtMnDUwXlGVCBauvpGrO7nTMp6s2YsyIzqn42cZZ4B3umidWfbK1oxKy0CXmoVVuTGwmCS2DBCKXaL0rp25MaHOP1CiGXdmSuqLk9LDMWK3Fg8v6nitIb2J1p0aNPpUZA8/mfcY0P8kBMX3H8eew1G/HHtIUyJD8HFTkj3vmxmEtp7DPiqzDXtMDQMzsYMg7MJoLGjF3qj5MwZEY3o5kUZuH+V69ZRXrcgDT5qFZ7beGr27KOSWmREB2LqBAx2x8pdyyahubMP/x6h2fchSzEQO3qcDVaUE41dJ9ucGgAdbx5dpUaL9P6iIGNXsfFIYyeaO/v6Z44AIDUyAFFBPtjtgnVnbV19ONTQgbnpSir3zJRwhAd42/ShW0qJstp2pxYDsUiNDMAF+fG41EVVl+9ZngVttx4vb67s32apiDkRZs4AZfZsR6XSruLVLSdQ3daNh87PcUogvXBSFGJDfPGei3qeWQq2uKLnJ52OwdkEUKNRcsA5c0ZE7hYV5IsrC5Px3u4aNLb3oF7bg+2VrfgWq56Oyuy0CMzLiMAzG44N24S5rK4DKgFkxTgenC3LiYHRJLHxqHNSGw1GE6padaNebwYovc4AjGlq42Zz+fMFA4IzIQRmpYa7pCjI9kplvdkcc3CmVgkUTY5B8aFGGEdY41bd1o2OXoNTi4FYCCHwj+/OxHl5zl2vapGXFIrlOTF4btOp9ZV7TrYhwEeN7NiJ0c5mSXY09EaJzw/W4+/FR7EoKwqLspzTR1WtErhkRiLWHWpCc2evU445kKZbqRjLgiCux+BsAqjR9ADw3B5nRHR2uXlROgwmE174phIf76uFlGBKoxPcvSwLDe29eHuYRf/l9e1IiwqEv4/j61cKksMRFuDttHVntZoe6I0S6VGjXwOVHB4AtUqMbXB2rAUpEQFICj99/IWpETjZqkNjR49TH297RSt8vVSnVSdcnhsLjU6P3SeHDwYPuqAYyFi6Z0UWNLpTs2clVRrkJYbCa4K035iVFg5/bzV++eFBaLv1eGC1c7MYLpuZBINJ4qOSWqceFzg1c8a0RtebGL/tZzlPb0BNRGeX1MhAnJcXj9e3nsDbO6sxLTEEGdET48q3O83PjMTMlDA8te4Y+gwmq/uU13cg18FiIBZqlcDirGisP9zolGqEFS3mSo2jKKNv4eOlQnK4/5j1OjOaJLYebzlt1sxiVpqyDsrZ/c62V7RiRkrYaQUiFmVHwUslRiypX1bXDpUAJsc6PnPqTvlJYSiarKyvbO3qQ2ldOwrGcfPpwXy91FiQGYnOXgMuKUjsb2/iLNmxwchLDMW7Lkht5JqzscPgbAKo0egQFuCNQBc25SUissdtizPQ0WvAoYYOzpo5iRACdy3PQo2mGx/sqTnj9q5eA0626jB5FOvNLIpyotHc2YcDtdpRH6u/x5kT0hotxxmrXmcHa7Vo7zGctt7MYlpCKHy9VE5Nbezo0eNgrRZz0k9/vBA/b8zNiBhx3VlpXTsyooNGNXPqbvesyEabTo+H3tsHvVFixgRZb2Zxfl48gn29cO+5rqkGfOnMRBysbe9vRu8s7d16+Hqp4Oc9fn+3xgsGZxNATRvL6BORZ8lPCuufbbgwn8GZsyzNjkZeYij+se4oDMbTZ88ON3RAytEVA7FYnBUNIYCvnVC1saK5C4E+akQH29cUeyjpUUo5fWc3yrbG0t/MWnDm46XC9KQwpwZnO0+0wSTRXwxkoOU5sTja2IkTLUMHpqUuKgYylgqSw7AkOxqfH2ww/zz+KzUOdOnMROz8xQqX9ev81vQEeKkE3tt95gWc0dDo2IB6rDA4mwDY44yIPNHvvp2HJ78zAwn8++Q0Qgj8YNkknGjR4b/7Tl9XYmlu64xiEJFBvpieFIbiQ6Nfd1bR3IW0qECnFYRJjw5Et96IhnbnFz0YbPOxFmTFBA3ZjH5WWjgO1mhHbHFgq+0VrfBSCcxMOTMgWZEbCwD4cojZM61OjxpNt0uKgYy1e1ZkAQDiQ/2c3lPN3YQQo+ppNpLIIF8snRyD9/fUnHEBZzQ03X0I82elxrHA4Gyck1IqM2es1EhEHiYtKhAXMaXR6c7NjUVOXDD+/vXR06r3HarvQKCP2mkX65blxGBftWbUld8qW7qcUqnRImOMyun3GUzYUdFqdb2ZxayUcBhMEnurNU55zO0VrchPCrWalpgSGYCsmCB8NcS6s9K68V0MZKCZKeG4dGYiLi5wTdn+ie7yWYlo6ujFJnOlUWfQduu53myMMDgb57TdenT1GTlzRkR0llCpBO4smoRjTV347EB9//ayunZMjgt2WvPhoskxkBI2Nz+2Rm80obqtG+lOKAZiYVm75uqKjXurNejWGzE/M2rIfSzNqHc5IbWxu8+IfdWaM9abDbQ8NxbbK1rR3nNmD7r+4GwCzJwBwJ+vLMCD57muJ+NEVpQTg1B/b6emNmp0eoQyrXFMMDgb56rNlRqTOHNGRHTWOD8vHhnRgXjy6yMwmSSklCiv70COEz+YT00IQVSQ76hSG6tadTCapFNnzuJC/ODnrXJ5UZDNR1sgBDAv48z1XxbhgT7IjA50SnC252Qb9EZpdb2ZxYrcGBhMEuutnJOyunZEB/s6bW0fjV++Xmp8a3oCPj9YbzWQd0Q7Z87GDIOzca5WowRnXNNBRHT2UKsE7lw6CeX1HfiyrAH17T3QduuR64RiIBYqlcDSydFYf6jR4bUrlS2WSo3OK36gUgmkRQa6fOZs87FmTE0IQVjA8OtsClMjsOtE26jbDmyraIVKnCrRb82MlHBEBPpYTW2cCMVAyHkunZmIXoMJn+6vG3a/rl4DjjZ2QNdnGHY/TbceYQzOxgRrr49zNRr2OCMiOhtdXJCAv311BH8vPoofrVDKck8eZY+zwYomx+CdXdXYU6XB7LShZ3SGUtGsA+CcHmcDZUQHoryuw6nHHKi7z4g9JzVYszBtxH1npYXjzZ1VONbUiaxR9BfbXtGKKQkhCPEb+gOw2hwwf1WmBMyW5sx9BhOONHZgyeRohx+fJpaC5DBkRAfi3V01uLIwGXXaHhxv6sKxpk4ca+rs/75OqzRRF0JJGZ4SH4IpCSH9/8YE+6HPYIKuz8iZszHC4Gycq2nrhp+3ChGBrKBDRHQ28VKr8P2lmXjwvf14ZsNxAHBKj7OBzsmKglolUFze6GBw1okQPy+nv0dlRAVh7cEG6I0meKudnwS060Qb+owmqyX0Bys0rzvbeaLN4eCs12DE7pNtuGZu6oj7rsiNxXu7a7DrRBvmZijjO9rYCb1RTohKjeQcQghcNjMJf/j8EKY+/Dl0AyqKBvt6ISMmCPMzIpEZE4SEMD+caNGhtLYdJVUafLzv1GxbVJAvsmKCAICl9McIg7NxzlJG31kliomIaPy4dGYSnvjqCLYcb0FimL/Tr2yH+nujMDUcxYea8JPV9hdnqGzWId2JZfQt0qMCYTBJVLXqkBEdZNN9jCYJtY3FUjYfa4aXStgUkKZHBSIy0Ac7K9vwnTkpNh1/sP3VWvQaTJgzzHozi0VZUfBWC3xV3tgfnE20YiDkHFfPTkZ5fQeignyQGR2EjOhATIoOQnSw77D/J7U6Pcrq21Fa246Dte0orWtHsK+XU9e00tAYnHkYKSV0fUYE+tp2amo03UgMd00jQyIi8mw+XircvjQTv/zwoFOaT1tTlBODxz4tR722x+6eUxXNXSgcZg2Vo9KjT1VstCU4e2FTBf657hheu3kOcmxI/dx8rAXTk8MQZMN7sRACM1PDsetE68gDH8K2CuW+tgRnwX7emJcRiS/LGvDT83MBKMVA/LxV/ZUsiQCl59mT35lh9/1CA5TfsXkZI88ck/OxIIgHMZokbn9tFxY/XmxzdZ2aNjagJiI6m11ZmIxJMUFYlDV0yffRKJocAwBYd8h68+Oh9OiNqNV2O329GXCq15ktRUG03Xr89cvDaO7sxZoXdvQX0hpKe48e+6o1w/Y3G6wwNRyVLTo0dTjWE257RSuyY4NsTv9cnhOD401d/c+/tLYdOXEhNs8MEpHnYnDmIaSUePijA/j8YANauvrwzs7qEe/T3WdES1cfy+gTEZ3F/LzV+OJHi7FmYbpLjp8dG4SEUD98XW5fcFbVqoOUcMlsTliAD8IDvHHchuDs+U0VaO8x4M9XTkdXrwFrXtwOrW7oC6A7KlphkrBpvZmFpd/Z7pP2l9Q3GE3YWdlq06yZxfLcWADAV2UNkFKitK59QjSfJiIGZx7jn+uO4bWtJ3H7kkzMSg3Hy1sqRyzLy0qNREQEwKXrjoUQWJoTg2+ONqPXYBz5DmaWwMmZPc4GSo8KHLHXmUbXhxc2VeC8aXG4dGYSnr5uFiqau3DLqzvRo7f+XDYfa4GPlwozU2xPx5yWGAoftcqhfmelde3o6jMO23x6sOSIAEyODcaXZQ2o1ZrbKHA9ENGEwODMA7yzqxp/+PwQLilIwE9WTcaaBWk40aLDusPDX6VkjzMiIhoLRZNj0NVnxM5K24OPSnNwlu6CtEYASI8KGjGt8ZkNx9HVZ8APza0GFmRG4U9XFmB7RSvufasERisXQTcfa0Fhajj8vNU2j8XPW428pFDsrLR/3dl283qz4ZpPW7M8NwY7Ktuw5VgLABYDIZooGJy52YbDTXjw3X1YOCkSj18+HSqVwOppcYgN8cWL31QOe9/+mTOmNRIRkQstnBQJH7UKxXakNla2dCEi0AehLiq/nREdiPr2HnT1Wm+e29LZi5c2V+LC/ITTWgx8a3oCfn5BLj7ZX4//+7gUUp4K0Fq7+lBW127XejOLwtRwHKhpH3JGbijbKlqRFhmA2BD7iq0sz42F0STx9PpjEAIuKwhDRGPrrA/OOnr0/Vf3xtqBGi3ueG0XsmKD8dT3ZsHHSzkd3moVrp2Xio1HmnG0cegmmzVt3VCrBGKDfcdqyEREdBYK8PHC3IwIFNtRFKSiuQtpka6rJmxZy1bZYv09/OkNx9GjN+Ke5Vln3HbzogzcdE46Xtpc2d8jDgC2HldmoeZn2l9cZVZqOPqMJuyv0dp8H5NJYoed680sCpLDEBnogyONnUiPDLS5yjMRebazOjiTUuLqZ7bih2+WnHblbCxUtepww0s7EBbgg5dumI1gv9OvLF49JwU+ahVe3nxiyGPUaLoRF+IHLxc04CQiIhqoaHIMjjV14WSLzqb9K5t1LltvBpwKzqylNjZ29OCVLZW4pCARk2Ksl9r/2fm5uDA/Ho9+Wo739yhFuDYfa0agjxr5SaF2j8dSFMSe1M/DjR3Q6PSYa8d6Mwu1SqAoR6mkmctiIEQTxln9qV4Igevnp6GkSoPPDtSP2eO2dfXh+he3o1dvxEs3zLaayhAV5IuLpifg3d3V0HZbrypV09bNlEYiIhoTlkDAltkzXZ8B9e09LltvBqC/RL+1oiD/WncMeqPE3VZmzSxUKoE/XTkd8zIicP/b+7DxSBM2H2vBnPQIeDtw0TMyyBcZUYF29Tvbbkd/M2tW5CrnhOvNiCaOszo4A4DLZiUhOzYIj39+CHqjyeWP16M34qaXd6C6rRvPXT8bWbFD54ivWZAGXZ8Rb++ssnp7jaYbSSwGQkREYyA9KhBpkQE2BWeVzcrsmitnzvx91EgI9Ttj5qxe24PXt53EZTMTR3x8Xy81nr62EJNignDbq7twvKkLCxxIabSYlRqOXSfabM7G2VbRioRQP4db4izJjsGlMxJxfl68Q/cnIs8zYnAmhEgWQhQLIcqEEAeFEPeYt/9BCFEuhNgnhHhfCBHm8tG6gFol8MDqHFQ0d+E/O6wHQc5iNEnc/cYe7KnS4K9XFYx4pSwvKRSFqeF4ZcuJMypKGYwm1Lf3cOaMiIjGTFFODLYca0F33/BFLyzrwFzR42yg9OjAM3qd/aP4KEwmibuWDT1rNlCovzdeumEOwvyV5QX29DcbbFZqONp0epv6r0kpse24st7M0VYI/j5q/PmqApe/zkQ0dmyZOTMAuE9KmQtgHoA7hRBTAHwBYJqUMh/AYQAPuW6YrrUsJwZz0iLwty+PDFn1yRke/7wca0sb8MsLp9h8lWvNwjScbNVh3aArlfXtPTCaJHucERHRmCmaHINeg6m/cMZQKlzc48wiIyoIx5s6+2eqajTd+M+Ok7hydjKSI2wvRhIX6ofXb5mH/7t4KqaOYv1WYZqy7myXDevOKpq70NzZi7kZjgeDRDTxjFjaR0pZB6DO/H2HEKIMQKKUcu2A3bYCuNw1Q3Q9IQQePD8Hl/5zM57bWIF7Vth2tc0eW4+34JkNx/GdOSm4YWG6zfdbNTUOcSF+eGlzJZbnxvZvr9X0AGCPMyIiGjtz0iPg763G458fwv/218FbLaBWCXipVOZ/BbzUAhuPNCM62BdBLq4gmB4ViPYeA9p0ekQE+uDvXx+BgMAPiiY5dKzRzkBlRAUhLMAbO0+04srZycPuO9r1ZkQ0Mdn1V1MIkQZgBoBtg266EcCbQ9znVgC3AkBKSor9IxwjM1PCcd60ODyz4RiumZeCqCDnlafv6NHjvrf2IiUiAD+/INeu+3qrVbh2fir+8PkhHGno6F+jVqNR8vmZ1khERGPFz1uNNQvT8L99ddhyrAV6owlGk4TBJGE0ydN+vrggweXjSY+2VGzsRGePH97eWY1r5qa47cKlSiUwKyUcO0+MPHO2raIVUUE+yGBKIhENYHNwJoQIAvAugB9KKdsHbP8ZlNTH163dT0r5DIBnAKCwsHBs69Xb6f5Vk7G2tAFPfHUEv754mtOO++v/lqJO2423b1/gUB+Sq2cn429fHcHLWyrxm0vyACiVGgEwrZGIiMbUA6tz8MDqnGH3Gav2NJbA5lhTF97YXgW1SuD7DsyaOdOstHB8Vd6I331ShoRQP8SH+SM+1A/xof6IDPSBSqWsL9teMbr1ZkQ0MdkUKQghvKEEZq9LKd8bsP16ABcCWC7HulGYC2REB+Hq2cn497aTuHFhulNy5dcerMfbu6pxZ1Fmfw8Ue0UG+eLi6Ql4d1cN7l+Vg1B/b9RouhEV5AM/b/Wox0hERORMYxVwJIb5w1stUFzeiM8P1uOGhelW29OMpZVT4vDurmq8tLkSfYbTq0D7qFWIC/VDbIgvajTduHVxhptGSUSeasTgTCh/YZ8HUCal/POA7asBPABgiZTSto6U48A9K7Lw/p4a/GHtIfzjuzNHdazmzl489N5+TIkPwT3Ls0d1rOsXpOHtXdV4e2cVbl6Ugeq2bs6aERHRWc1LrUJKRAA+PVAPf281bl+S6e4hYVJMEL66bymklGjp6kO9tge1mm7UaXtQq+1GnaYH9doeTIkPwYopsSMfkIjOKrbMnC0EcC2A/UKIEvO2nwJ4AoAvgC/MV8i2Silvd8Ugx1JMsB9uXpSBJ746glsXaTA9Ocyh40gp8dB7+9HRY8C/bymAj9foWspNSwzF7LRwvLylEjcsTEeNphs5cUP3SCMiIjobpEcF4VhTF65bkIroYOetFx8tIQSignwRFeSLaYmh7h4OEY0TI0YMUspNUkohpcyXUhaYvz6RUk6SUiYP2DbuAzOLWxdnIDLQB49+WuZw3vzbu6rxRWkD7l81GZOdFEStWZCOqtZufFXWgFoNZ86IiIimJYYgxM8Lty12/6wZEdFojW46Z4IK8vXC3cuzsPV4K9YdbrL7/lWtOvz6v6WYmx6Bm86xvWz+SFZOjUV8qB/+8uUR9OhNDM6IiOis9/2lk7Du/iJEBPq4eyhERKPG4GwI35mTgtTIAPz+03IYTbbPnhlNEve9vRcA8McrpvdXZXIGb7UK35uXirI6pVhmYrjtDTaJiIgmIh8vFQMzIpowGJwNwcdLhR+vnIzy+g68v6fG5vu9sKkC2yta8cuLpiA5wvnB03fmpPSvX0sIc29FKiIiIiIich4GZ8O4IC8e+Umh+PPaQ+jRG0fc/1B9B/7w+SGcOyUWV8xKcsmYIgJ9cElBAoQAkjhzRkREREQ0YdjfEfksolIJPHheDr777Db8+uNSnDMpCqH+3gjx80aIvxdC/LwR7OcFL7UKfQYTfvhmCYL9vPDopXku7fHy0/Nzcd60eIT6e7vsMYiIiIiIaGwxOBvBgswoXJAXj39vO4l/bztpdZ9AHzX8vNVo6erDM9fOQlSQa0v5hgX4oCgnxqWPQUREREREY4vBmQ2e/M4M/OyCXLT36NHebUB7tx7abv2pn3v0aO/WIz8pFCunxrl7uERERERENA4xOLOBSiWQEOaPBLB0PRERERERuQYLghAREREREXkABmdEREREREQegMEZERERERGRB2BwRkRERERE5AEYnBEREREREXkABmdEREREREQegMEZERERERGRB2BwRkRERERE5AEYnBEREREREXkABmdEREREREQegMEZERERERGRB2BwRkRERERE5AEYnBEREREREXkABmdEREREREQegMEZERERERGRB2BwRkRERERE5AEYnBEREREREXkABmdEREREREQegMEZERERERFNDI8/DhQXn76tuFjZPg4wOCMiIiIioolh9mzgyitPBWjFxcrPs2e7d1w28nL3AIiIiIiIiJyiqAh46y3g/POBxYuB3buVn4uK3D0ym3DmjIiIiIiIJg6dDujpAdauBe64Y9wEZgCDMyIiIiIimii0WmDNGkCtBh56CPjXv85cg+bBGJwREREREdHEcM01QHMz8OSTwO9+p6Q0DlyD5uEYnBERERER0fj35ZfA//4HXHWVks4InFqDtmOHe8dmIyGlHLMHKywslDt37hyzxyMiIiIiorNAZyeQlwf4+AAlJYC/v7tHNCQhxC4pZaG121itkYiIiIiIxref/hQ4cQLYuNGjA7ORMK2RiIiIiIjGr40blTVmd90FLFzo7tGMyojBmRAiWQhRLIQoE0IcFELcY94eIYT4QghxxPxvuOuHS0REREREZNbdDdx0E5CerhQAGedsmTkzALhPSpkLYB6AO4UQUwA8COArKWUWgK/MPxMREREREY2Nhx8GjhwBnn0WCAx092hGbcTgTEpZJ6Xcbf6+A0AZgEQAFwN42bzbywAucdEYiYiIiIiITrd9O/CnPwG33gosX+7u0TiFXWvOhBBpAGYA2AYgVkpZBygBHICYIe5zqxBipxBiZ1NT0yiHS0REREREZ73eXuDGG4GEBODxx909GqexOTgTQgQBeBfAD6WU7bbeT0r5jJSyUEpZGB0d7cgYiYiIiIjobPf446eaSf/2t8DBg8D3vw88/bR7x+VENgVnQghvKIHZ61LK98ybG4QQ8ebb4wE0umaIRERERER01ps9G7jySmV92aOPAitXAn/+s7J9grClWqMA8DyAMinlnwfc9BGA683fXw/gQ+cPj4iIiIiIJryBs2IWxcWnUhb1ekClUtaW3X474OsL7NoFvPUWUFQ09uN1EVtmzhYCuBbAMiFEifnrfACPAThXCHEEwLnmn4mIiIiIiOxjmRWzBGjFxcDllwMtLcr26Ghg6VLgvfeAlBSgq0tJaZxAgRkACCnlmD1YYWGh3Llz55g9HhERERERjRPFxcBllwF5ecDmzYDBoGyPjwfOPx+44ALAxwdYswa44w7gX/8alzNnQohdUspCa7d5jfVgiIiIiIiIzpCdDXR0ABs2AImJSvriBRcABQWAEErwduWVpwKyoqLTf54A7CqlT0RERERE5BK3367Mln3/+0qp/IULgRkzlMAMAHbsOD0QKypSft6xw31jdjKmNRIRERERkXu98gpw/fXAt7+trCsbPEs2gQyX1siZMyIiIiIicq8//Qnw81PWkQETclbMFlxzRkRERERE7rN7N7BvH/DznwOxsae2W9aVnUU4c0ZERERERO7z058CERHAj3/s7pG4HWfOiIiIiIjIPdatAz7/HPjDH4DQUHePxu04c0ZERERERGNPSuChh5Sy+Xfe6e7ReATOnBERERER0dj773+BrVuBZ58F/P3dPRqPwJkzIiIiIiIaW0ajstYsOxtYs8bdo/EYnDkjIiIiIqKx9frrwMGDSrl8L4YkFpw5IyIiIiKisdPbCzz8MDBrFnDZZe4ejUdhmEpERERERGPnmWeAykrg6acBFeeKBuKrQUREREREY6OzE/jNb5Tm0uee6+7ReBzOnBERERER0dj461+Bxkbgo48AIdw9Go/DmTMiIiIiInKdxx8HiouBlhal2fQllwA6nbKdTsOZMyIiIiIicp3Zs4ErrwSWLVPSGr/1LeXnt95y98g8DmfOiIiIiIjINUwmpTpjZqYSjE2bBvzkJ8r3RUXuHp3H4cwZERERERE55vHHlZmxgYFWcTGwcSMQFgb84x/A4cNAXByweDGwYQPwi18wMBsCgzMiIiIiInKMJWXRMhP28svA7bcrt/X0APPmKQ2no6KAa65RArN//UvZlwHaGZjWSEREREREjikqAt54QynykZEBrFkDGAxKwLZjB7BlCxAfrwRmb70F/PrXyr9XXqnMsNFpOHNGRERERESOy8wE2tuVr6VLgTffBGJiTt2+Y8fpa8yKipSfd+zg7NkgDM6IiIiIiMhxlZVAaChw553AM88ABw+eHpz95Cdn3odpjVYxrZGIiIiIiBxTXKykKL7/PvDb3zJlcZQYnBERERERkWOGS1kkuwkp5Zg9WGFhody5c+eYPR4REREREZEnEULsklIWWruNM2dEREREREQegMEZERERERGRB2BwRkRERERE5AEYnBEREREREXkABmdEREREREQegMEZERERERGRB2BwRkRERERE5AEYnBEREREREXmAMW1CLYRoAnBizB7QdlEAmt09CLILz9n4wvM1/vCcjT88Z+MPz9n4wvM1/njqOUuVUkZbu2FMgzNPJYTYOVSXbvJMPGfjC8/X+MNzNv7wnI0/PGfjC8/X+DMezxnTGomIiIiIiDwAgzMiIiIiIiIPwOBM8Yy7B0B24zkbX3i+xh+es/GH52z84TkbX3i+xp9xd8645oyIiIiIiMgDcOaMiIiIiIjIA4yr4EwIsVoIcUgIcVQI8eCA7W8KIUrMX5VCiJIh7h8hhPhCCHHE/G+4efs1A+5fIoQwCSEKrNz/dfPjHxBCvCCE8DZvF0KIJ8zj2ieEmOmaV2D88eBzliOE2CKE6BVC/Ng1z3588uBzdo35/9c+IcRmIcR017wC448Hn7OLzeerRAixUwhxjmtegfHFhefLWwjxshBivxCiTAjx0BD3TxdCbDPf/00hhI95O9/LhuDB54zvZUPw4HPG9zIrPPh8jf37mJRyXHwBUAM4BiADgA+AvQCmWNnvTwB+OcQxHgfwoPn7BwH83so+eQCOD3H/8wEI89cbAO4YsP1T8/Z5ALa5+/XyhC8PP2cxAGYD+C2AH7v7tfKULw8/ZwsAhJu/P4//z8bFOQvCqfT5fADl7n693P3lyvMF4LsA/mP+PgBAJYA0K/d/C8DV5u+f4nvZuD5nfC8bf+eM72Xj63yN+fvYeJo5mwPgqJTyuJSyD8B/AFw8cAchhABwJZQPB9ZcDOBl8/cvA7jEyj7fGer+UspPpBmA7QCSBhz3FfNNWwGECSHibX5mE5fHnjMpZaOUcgcAvV3PaOLz5HO2WUrZZt5tK079/zvbefI56zRvA4BAAFzk7NrzJQEECiG8APgD6APQbuXYywC8Y+X+fC+zzmPPGd/LhuTJ54zvZWfy5PM15u9j4yk4SwRQNeDnavO2gRYBaJBSHhniGLFSyjoAMP8bY2WfqzD0iQegTJECuBbAZ3aM7WzkyeeMrBsv5+wmKFf4ycPPmRDi20KIcgD/A3DjcPc/S7jyfL0DoAtAHYCTAP4opWwddN9IABoppcHK4/O9zDpPPmdk3Xg5Z3wvU3j0+Rrr97HxFJwJK9sGR69DXtm16QGEmAtAJ6U8MMKu/wSwQUq50Y6xnY08+ZyRdR5/zoQQRVDe0B5wdAwTjEefMynl+1LKHChXIf/P0TFMIK48X3MAGAEkAEgHcJ8QIsOOx+d7mXWefM7IOo8/Z3wvO41Hn6+xfh8bT8FZNYDkAT8nAai1/GCerrwUwJsDtr1oXsD3iXlTgyVFw/xv46DHuBojXxl+GEA0gHttHdtZzJPPGVnn0edMCJEP4DkAF0spW+x4XhOZR58zCynlBgCZQogoW57UBObK8/VdAJ9JKfVSykYA3wAoHPT4zVDSFb2sPD7fy6zz5HNG1nn0OeN72Rk8+nxZjNX72HgKznYAyDJXU/GB8mHhowG3r4CySK/askFKeYOUskBKeb5500cArjd/fz2ADy37CiFUAK6AkudqlRDiZgCrAHxHSmkacNNHAK4TinkAtJap1bOcJ58zss5jz5kQIgXAewCulVIeHsVznGg8+ZxNMufyQyiV/3wAnO0fRFx5vk4CWGZ+LwqEUtSjfOCDm9dOFAO43Mr9+V5mnSefM7LOY88Z38us8uTzNfbvY9IDqrTY+gWlktRhKBVdfjbotpcA3D7C/SMBfAXgiPnfiAG3LQWwdYT7G8yPXWL++qV5uwDwD/Nt+wEUuvu18pQvDz5ncVCu1LQD0Ji/D3H36+UJXx58zp4D0DZg+053v1ae8uXB5+wBAAfN27YAOMfdr5UnfLnqfEGpKva2+TUvBXD/EPfPgFK45ah5f1/zdr6Xjb9zxvey8XfO+F42vs7XmL+PWUpDEhERERERkRuNp7RGIiIiIiKiCYvBGRERERERkQdgcEZEREREROQBGJwRERERERF5AAZnREREREREHoDBGRERERERkQdgcEZEREREROQBGJwRERERERF5gP8H1iUdKw0rULgAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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cfPHF2effe++9+Pa3v41kMomRkREcOnQImzdvVvS9u/HGG6HVahEMBvHEE0/gxhtvzN4Xi8UUHasQJYszzvkIgJHMvwOMscMAegG8A8BnOeexzH3jqpwRUTe+vusEYsk0AGBwunEGIwmCIAiCIBYqWq0WV111Fa666ips2rQJP/zhD+cVZ3q9Puv8p9Fosm2QGo0GyWQSAKDT6ZBOp7PPyWfh/sgjj+DPf/4znnzySVgsFlx11VV5H8c5x2233Yb//M//nHX7b37zm7IcCDnn+NjHPoZ3vetds24/c+ZM9r0Ue2/AfOdDxljR41qt1uzXp0+fxhe/+EXs3bsXbrcbt99+e0GL+9zXmfsYccx0Og2Xy4V9+/aVeuuKUTTJxhhbBmArgN0AvgDgCsbYpwFEAXyIc743z3PeCeCdALBkyZJKz5eoEgNTYfxszzm8YUc/fvnsEAanI/U+JYIgCIIgiJpRTOGqFkePHoVGo8GqVasAAPv27cPSpUvLOtayZcvwjW98A+l0GkNDQ9l5rVx8Ph/cbjcsFguOHDmCp556KnufXq9HIpGAXq/HNddcg1e96lX4p3/6J3R0dGBqagqBQAAXXXQR3v/+92NychIOhwP33Xcfzj///JLndu211+KTn/wkbr75ZthsNgwNDUGv1yt6f7/97W/xsY99DKFQCI888gg++9nPwmw2yzqu3++H1WqF0+nE2NgY/vCHP+Cqq64CANjtdgQCgaxpSWdnJw4fPow1a9bg17/+Nex2+7zjORwOLF++HPfddx9uvPFGcM6xf/9+Wd+LUsguzhhjNgC/BPCPnHM/Y0wHwA3gYgA7ANzLGFuR6aPMwjn/NoBvA8D27ds5iIbkK38+DsYY3n/Nauw+NYUBUs4IgiAIgiCqSjAYxHvf+154vV7odDqcd955WZMOpVx22WXZFsGNGzdi27Zt8x5z3XXX4a677sLmzZuxZs2aWW1/73znO7F582Zs27YN99xzD/7jP/4DL33pS5FOp6HX6/E///M/uPjii3HnnXfikksuQXd3N7Zt25Y1CinGS1/6Uhw+fBiXXHIJAKmd8yc/+Qm0Wq3s93fhhRfi+uuvx7lz5/DJT34SPT096OnpkXXc888/H1u3bsWGDRuwYsUKXHbZZbPe98te9jJ0d3dj165d+OxnP4tXvOIV6O/vx8aNGxEMBvOezz333IM77rgD//Ef/4FEIoE3vOENqhRnbE4tlf9BjOkB/B7Ag5zz/8rc9kdIbY2PZL4+CeBiznlBq7/t27dz4TpDNA4nxgN46Zf/hrddvhyfuH49brl7N3yRBO5/z+X1PjWCIAiCIIiqcfjwYaxbt67ep0GU4M4774TNZsOHPvShep+KYvL9jjHGnuGcb8/3+JJW+kxqvLwbwGFRmGX4DYCrM49ZDcAAoHi6HdGQ/NefjsGs1+KOq84DAPS5LdTWSBAEQRAEQRA1Rk5b42UAbgFwgDG2L3PbxwF8D8D3GGMvAIgDuG1uSyPR+BwY9OGBA6N43zWr0GI1AAD63GZMheIIxZKwGimnnCAIgiAIgqgfd955Z71PoWbIcWt8DEAhW5Y3q3s6RK354kNH4bLo8fYrlmdv63ObAQBD3ghWd84fgiQIgiAIgiAIQn1KtjUSC5c9p6fw12MTuOPKlXCYZpxt+lssAMhOnyAIgiAIgiBqCRVnixTOOb7w4BG024249ZJls+4TyhnNnREEQRAEQRBE7aDibJHy12MT2HtmGu+7+jyYDbPtRtttRhh1GirOCIIgCIIgCKKGUHG2CEmnOb7w4FH0uc14/Y75weCMMfS6zdTWSBAEQRAEUWW0Wi22bNmCjRs34sYbb0Q4XP766/bbb8cvfvELAMDb3/52HDp0qOBjH3nkETzxxBPZr++66y786Ec/Kvu1BWfOnMHGjRtn3XbnnXfii1/8oqLjqHU+zQZZ8S1C/nhwFAeH/fjijefDoMtfn5OdPkEQBEEQRPUxm83Yt28fAODmm2/GXXfdhQ984APZ+1OplKKwZsF3v/vdovc/8sgjsNlsuPTSSwEA7373uxW/RrVIJpMNdT61hJSzRUYqzfGlh47ivA4bbtjaW/BxfW4zFWcEQRAEQRCCz38e2LVr9m27dkm3q8QVV1yBEydO4JFHHsHOnTvxpje9CZs2bUIqlcI///M/Y8eOHdi8eTO+9a1vAZA8BN7znvdg/fr1uP766zE+Pp491lVXXYWnn34aAPDHP/4R27Ztw/nnn49rrrkGZ86cwV133YUvf/nL2LJlCx599NFZ6ta+fftw8cUXY/PmzbjhhhswPT2dPeZHPvIRXHjhhVi9ejUeffRRxe+x2LE//vGP48orr8R///d/Z89neHgYW7Zsyf6n1Wpx9uxZnD17Ftdccw02b96Ma665BufOnQMgqYfve9/7cOmll2LFihVZJbFZoOJskfHr54ZwciKED75kNbSaQgkJs7POCIIgCIIgFj07dgA33TRToO3aJX29Y4cqh08mk/jDH/6ATZs2AQD27NmDT3/60zh06BDuvvtuOJ1O7N27F3v37sV3vvMdnD59Gr/+9a9x9OhRHDhwAN/5zndmtSkKJiYm8I53vAO//OUv8fzzz+O+++7DsmXL8O53vxv/9E//hH379uGKK66Y9Zxbb70Vn/vc57B//35s2rQJ//qv/zrrPPfs2YOvfOUrs27P5eTJk7MKqrvuukvWsb1eL/7617/igx/8YPa2np4e7Nu3D/v27cM73vEOvPa1r8XSpUvxnve8B7feeiv279+Pm2++Ge973/uyzxkZGcFjjz2G3//+9/joRz+q8CdRX6itcZHxjUdOYGOvA9dt7Cr6uD63sNOPYE0XZZ0RBEEQBLHA+cd/BDLthQXp6QGuvRbo7gZGRoB164B//Vfpv3xs2QJ85StFDxmJRLBlyxYAknL2tre9DU888QQuvPBCLF8u5dA+9NBD2L9/f1YF8vl8OH78OP72t7/hjW98I7RaLXp6enD11VfPO/5TTz2FF73oRdljtbS0FD0fn88Hr9eLK6+8EgBw22234cYbb8ze/5rXvAYAcMEFF+DMmTN5j7Fy5cpsqyYwEyJd6tivf/3rC57X448/ju9+97tZte7JJ5/Er371KwDALbfcgg9/+MPZx7761a+GRqPB+vXrMTY2VvT9NhpUnC0ipkJxnJoI4WMvWwvGCqtmQK6dfpiKM4IgCIIgCABwu6XC7Nw5YMkS6esKyZ05y8VqtWb/zTnH1772NVx77bWzHvPAAw+UXNNxzks+RglGoxGAZGSSTKrbYZX7nnMZGRnB2972Ntx///2w2Wx5H5P7HsU5AtL7byaoOFtEHBz2AQA29jpLPpayzgiCIAiCWFSUULgAzLQyfvKTwDe/CXzqU8DOnVU/tWuvvRbf/OY3cfXVV0Ov1+PYsWPo7e3Fi170InzrW9/CrbfeivHxcezatQtvetObZj33kksuwT/8wz/g9OnTWL58OaamptDS0gK73Q6/3z/vtZxOJ9xuNx599FFcccUV+PGPf5xVuiqlnGMnEgncdNNN+NznPofVq1dnb7/00kvx85//HLfccgvuueceXH755aqcY72h4mwRcXBY+gPc0OMo+diZrDOy0ycIgiAIgsgWZvfeKxVkO3fO/rqKvP3tb8eZM2ewbds2cM7R3t6O3/zmN7jhhhvwl7/8BZs2bcLq1avzFjrt7e349re/jde85jVIp9Po6OjAn/70J/zd3/0dXve61+G3v/0tvva1r816zg9/+EO8+93vRjgcxooVK/D9739ftfei9NhPPPEE9u7di0996lP41Kc+BUBSDL/61a/irW99K77whS+gvb1d1XOsJ6yWUt/27du5cI0has97f/Ycnj07jcc/Or8fOR9Xf+kRrOm045tvvqDKZ0YQBEEQBFF7Dh8+jHXr1sl78Oc/L5l/5BZiu3YBe/cCOfNOBJFLvt8xxtgznPPt+R5Pytki4uCwD+tlqGaCfso6IwiCIAiCkMhXgAkFjSBUgqz0FwmhWBKnPSFs7Ck9byaQss6orZEgCIIgCIIgagEVZ4uEwyN+cC5v3kzQ57ZgOpxAkLLOCIIgCIIgCKLqUHG2SMiagfQqKc4kx8Yham0kCIIgCGKB0mxW60TzUM7vFhVni4SDwz60WA3ocphkPyc364wgCIIgCGKhYTKZMDk5SQUaoTqcc0xOTsJkkr/2BsgQZNHwwpAfG3ocikII+9wWAMDAFBVnBEEQBEEsPPr6+jA4OIiJiYl6nwqxADGZTOjr61P0HCrOFgHxZBrHxwN40eoVip7XZjNkss6orbER+O2+IWg1DK/Y3FPvUyGIBUM4noRJp4VGI3/jiiCIhYNer8fy5cvrfRoEkYXaGhcBx8YCSKS4IjMQAGCMZRwbqThrBL75yEnc/djpep8GQSwYookULv3sX/C/Tw/U+1QIgiAIAgAVZ4uCg8M+AMDGXvk2+oI+twWDXmprbAQ8wRgmg/F6n0bDEImn8IcDI/U+DaKJOT4WhDecwKGMYRJBEARB1BsqzhYBB4f9sBl1WNpiUfxcUs4ag2QqjclQHJ5grN6n0jD8Zt8Q7rjnWZydDNX7VIgm5cioVJSN+OgzjiAIgmgMqDhbBBwc9mNdt72smYo+twXecAKBaKIKZ0bIZTIUB+dAOJ5COE65cwBwLmNUMxkiNZEoj2NjAQDAsDda5zMhCIIgCAkqzhY4qTTH4RE/NvQob2kEcrLOvLSzXE/G/TOKGbU2Soj8PW+Yvh9EeRwZlYozUs4IgiCIRoGKswXOaU8I4XhKsRmIoD/TCjk4RYuXejIRnNnZp9ZGCZG/5w2TqkuUx9FMcTYdTiAST9X5bAiCIAiCirMFjzADqVQ5oyDq+kLK2XyEmjtNxRlRBtOhOMYDMazrljauhkk9IwiCIBoAKs4WOIeG/TBoNVjVaSvr+a1WA0x69bPOTk4EcXiEHNLkMhHIKc5CpJzFkimMZQpWH7U1EmUgWhp3rmkHAIzQ3BlBEATRAFBxtsA5OOzH6i4b9NryftRS1plF9eLs3353CB+673lVj7mQGQ/EYNZrAQAeUs5mLaS9EVLOCOUIM5CdazsAkHJGEARBNAZUnC1gOOd4YdiHjWW2NAr63GYMqNzWOOaPYmCKWiXlMhGIoddths2oo5kzYNZmAbU1EuVwZDQAl0WPzX3S5yMpZwRRO366+xx+/NTZep8GQTQkVJwtYIZ9UXjDibLNQATVyDqbCsXhjybJol8m44Eo2m1GtNkMNHMGYCgTjN5mM5JbI1EWR0f9WNNph1GnRZvNSI6NBFFDvv/4adxDxRlB5IWKswXMwSHJDGR9xcqZBb5IAn6VCinOOaYy2VSULySPiWAMHQ4jWm1GUs4gKWcaBqzrtpNbI6EYzjmOjQWxpssOAOhxmSguhCBqRCrNcXYyjBEfXf8JIh9UnC1gXhj2ZxewlZDNOlNJPfNHkkimOQBgmBZEJeGcYyIQQ7vNiFYrKWeA9LvY5TBJylmEvh+EMganIwjGktnirNtpooUiQdSIoekI4qk0fBGKsCCIfFBxtoA5NOzDinYbLAZdRcfpc2eyzlQqznLdBmm3ujSBWBLRRBodDiPa7EZya4T0u9jntsBl0cMbWhjKWTqzYUFUH2EGsjarnJkx4o2Ac/oZEES1OeUJZv896qdNEYKYCxVnC5iDw/6K580A9bPOREsjQMWZHISNfrvdiDarAVOhOFKLfCE/5I2g122Gy2xAIJZEIpWu9ylVhCcYw/ZP/xl/fGGk3qeyKBA2+qs7M8WZ04xQPAV/NFnP0yKIRcGpiVD23zTrSRDzoeJsgTIZjGHEF1WlOFM76yzXCp7aGksjAqg77Ca02oxIc2B6EZtgJFJpjPgi6HOb4bbqAQC+JrfTv+/pQUyF4jg6Giz9YKJijo4G0Osyw26Sfn+6XSYAtFAkiFqQq5yNkXJGEPOg4myBcnBYCniu1EYfyM06U1c5W9pqoeJMBhPBHOXMZgSART13NuqLIs2BXpcZTrO0uG5mU5B0muOneyTXssVcdNeSo6OB7LwZAHQ7pe4A+jwiiOpz2hPC6k4bANCsJ0HkgYqzBYooztaroJwBQL+KdvqTmWJjY6+T3BplMJ7ZWeywG9FqMwCY+R4uRkQrrDRzJn0/mtlO/9ETHgxMSe9pMtS876NZiCfTODkRnFWc9WSUM/o8Iojqc2oihI09TthNOoxRcUYQ86DibIFycNiHXpc5u3itFEk5U8sQJA67UYcVbVaM+qNINvm8ULWZCMZg0GrgNOvRlinOJhZxcSZ+D3vdZrgtza+c3fPUWbRaDdjY68A0FWdV57QnhGSaZ81AAKllWKth1NZIEFUmHE9ixBfFinYruaQSRAGoOFugHFLJDETQ5zarlnU2FYqj1WZAj8uMVJpjLLB4Cw05TPhjaLcbwRhDq5XaGkWkQ4/LBJdZKlabtR1wxBfBw0fGceP2fnQ5TLPMcojqcGRU6irIVc60GoYuhwkjpJwRRFU57ZHMQJa32dDlNNPMGUHkgYqzBUgwlsQpTwgbeyufNxNk7fSnKt9ZngzF0GKVijOA5jxKMRGMoc0uFWVOsx46DVvUdvqD02F02I0w6rRwNbkhyP/uHUAqzfGmC5fAbTE0bZHZTBwdDUCnYVjRZpt1e7fThGFSzgiiqginxhXtVnQ5jKScEUQeqDhbgBwekXaG1VbOAHXs9CeDcbRYjejNznnQgqgY4/4YOjLFmUbD0GI1wBNYvIv4IW8k+/toN+qg1bCmLGqSqTT+d+8AXrS6HUtaLWjJxCRQ1lZ1OToawIp2Kwy62Ze/bpeZZs4IosoI5WxZqxVdTjMmgrGmj0IhCLWh4mwBcnDIBwDYoIJTo2CmOFNDOYujzTajnKk1y7ZQmQhKbY2CVtviDqIenI6gN6PkMsbgMuubcuZs19EJjPiiuPmiJQAAt9WAWDKNSCJV5zNb2BwZDWBN1/yNqx6nSXICXeQZggRRTU5NBNHrMsNs0KLLYQLnM1meBEFIUHG2AHlh2I82mwGdDmPpB8ukxWqAWa+tuJDinGM6FEeL1QCLQQe3RU/KWRHiyTSmQvGscgYAbTbDrKy4xUQqzbMZZwKnpTmLs3t2n0Wnw4hr1nYAAFoy5j00d1Y9AtEEhryRWWYggh6XGfFUmhwziaYlFEtmO2calVOeEFa0WwFIrcQA2ekTxFyoOFuAHBz2Y32PE4wx1Y4pZZ2ZK25r9EeSSKY5WjN5XT0uMxVnRRAKWfus4swIzyJ1axwPRJFIcfS6Zoozt8UAb6S5FtQDU2H89dgEXr9jCXRa6WPYbc2Ym4Sar9BsFo6NSeG3azrnF2czC0X6PCKakx89eRav+vrjCMWS9T6VvHDOcXoihOVtUnHW6ZD+5sgUhCBmQ8XZAiOWTOH4WEDVeTNBnwpZZ55MsdGaWYj2uMzZ3CpiPuN+6fvVYTdlb2u1GhatW6NwasxVzlxmfdMVND/bcw4MwBt29Gdva8mYm0w14fxcs3B0NABgtlOjYMagiBaKRHMy4osgnkrjxHiw3qeSl4lgDIFYEivaSDkjiGKULM4YY/2MsV2MscOMsYOMsfdnbr+TMTbEGNuX+e/l1T9dohTHx4JIpnmVijNLxcqZaNlqyRRnvS4zhqYjVTNBODcZbuphY9GLP0s5sxsRSaQQjjfm7iiAqmXXDeYrziyGpnJrjCfTuPfpAVy9tjNbEACSAggAU4t4nrDaHB31w2rQzlJeBWKhSEo+0ayITbtjY4E6n0l+ZpwaJadUl0UPg05DyhlBzEGOcpYE8EHO+ToAFwP4B8bY+sx9X+acb8n890DVzpKQzQsZM5CNKpqBCPrcZvijyYoWwuLi0WqbKc5C8RT8UfULDX80gRd/+a+47+lB1Y9dK8YDQjnLMQTJFLaNqp55gjFc/J8P496nB1Q/tlBZe12W7G0uix7eJlKbHjo0Ck8wjpsvXjLrdrFhMdVkKmAzcWQ0gNVddmg081u+W6wGGHUaamskmhbRBn+8QZWzmYwzSTljjFEQNUHkoWRxxjkf4Zw/m/l3AMBhAL3VPjGiPA4O+2Ez6rCkxVL6wQoRWWdDFbQ2TmbbGmdmzoDq7FYPTkUQT6ZxdjKk+rFrhVDO2myzZ84AqUWkEfnqw8fhCcarMpg+OB1Gq9UAs0Gbvc1t0SMUTyGebA6F9Ke7z6HPbcaLVrXPut1h0kuxAGRIURU45zg2FshrBgLMLBSHaaFINCmiM0W07zYapyaCMOg0s5TrLocJY/Q3RxCzUDRzxhhbBmArgN2Zm97DGNvPGPseY8yt9skRyjk47MP6bkfeneFK6W+pPOtsKqP2uDPzNT2ZrLNKCr5CiB3wZm6ZGA9E4c60fghEcdaIytmpiSB+uvscAFTFUXJwerZTIwA4M+2AzWAKcnIiiCdOTuKNFy6Bds7fqEbD4LboaeasSkwEYpgOJ/KagQh6XGaMUFsj0aSI4ux4g7Y1nvaEsLzVOmt90uU0YcRPf3MEkYvs4owxZgPwSwD/yDn3A/gmgJUAtgAYAfClAs97J2PsacbY0xMTE5WfMVGQVJrj8EgA66swbwbMKGcDFSlncdhNOhh1kvLRm1loD1ehlUjsgI83cYbKRGB2xhkw0xI62YDK2RcePAqDToMV7VZ4qvB9H5qOZH9nBG6LVOg3g53+z3afg07DcOP2vrz3uy0GUs6qxJGsGUjhz8duJwVRE81JOs0xFYrDpNdg2BdFINp4n4enJmZs9AVdThPGfLGqzZ0TRDMiqzhjjOkhFWb3cM5/BQCc8zHOeYpzngbwHQAX5nsu5/zbnPPtnPPt7e3t+R5CqMRpTxCRRAobe9WfNwOkRbDFoK1IOZsMxbMzUwDQZjXCoNVUxbFR7IA3c3E2HojNcmoEZmaTGs1O/5mz0/jDC6N414tWYk2nXfW2S845hryR7CaBwGXOKGcNXpxFEyn84tlBXLuha97PVOC2GijnrEoUc2oU9LhMGA9Eq2ZoQxDVwhtJIM2B7UtbADTe3Fkilca5qfD84sxhQjyVps89gshBjlsjA3A3gMOc8//Kub0752E3AHhB/dMjlHBwWJrxqYZTI5CbdVZ+ITUVimUzzgCplavbZarKbrUYMh5v4rbGfMqZSa+F3ahrqCBqzjn+84HDaLcb8fYrllcli20iGEMsmZ7ntOfKKGfTDd4O+MCBEXjDCdx80ZKCj2mxGBr+fTQrR0YDaLcbs5sb+eh2mpHmwFgTb+gQixPh8nrxikxx1mCtjQNTYSTTHMvbbLNuJzt9gpiPHOXsMgC3ALh6jm3+5xljBxhj+wHsBPBP1TxRojSiaBJOSNVAstOvoK0xGJ+3OJLs9Cuz6M+HMBnxR5OIJlKqH7/acM4xEYjNcmoUtNmNmGygncaHDo3h6bPT+KcXr4bVqEObzQhvOKGqSYeYSyxUnPkaXDm7Z/c5rGiz4pKVrQUfIylnjf0+mpViZiCC7swMLM2dEc2GmEHe3OeCSa/JBq43CjM2+rPXJxRELZ//2XUCP37qbL1Pg6gBctwaH+OcM8755lzbfM75LZzzTZnbX8k5H6nFCROFCcWS0GsZTHpt6QeXiaScqdfWCEhD+NVSzoTpwkQT7oT7I0nEU+l5yhkg2elXY6arHBKpND73hyM4r8OGmzKzVOKcJ1XM7BKtr30tc4sz6fepkRWnI6N+PHN2Gm+8cAmkZoT8tFj1mA7Haf5CZVJpyamxmBkIMFP4k2Mj0WyItsA2mxHnddgaLuvslEcqFlfM2Tzudkp/c6ScleYXzwziF880bzQQIR9Fbo1EYxOOp2Ax6Kr6Gn1uMwJlZp2JgWVhaCHocZkxFoiqGhadTnOM+qJYnVmMNeOu3HhAOue8xZnNoGrhUwn/u3cApzwhfPS6tdBppY+UtszP2BNQr2AaLKCcWQ1a6LUM3gYOov7F04MwaDV47QX5jUAEbosBqTSHP9K4AePNyNnJEGLJdNF5M4CCqInmxZMtzgxY3WnH8QZTzk57QmixGrKbaYI2mwEa1pzX6FrjCcQwOKV+lxHReFBxtoAIxpKwGqqnmgEzjo3lqGf+aAKpNEeLdXax0esygXNgVMWds8lQHPFUGlv6JXOUSkxB/nBgBGc8tc9KE2pfvuKszWZsCCv9YCyJr/z5GC5c3oJr1nVkb2/LnLOac2dD0xE4zXrYTfpZtzPG4DQbGjqI+sREEGu67EXnnYCcIOoGfi/NiBwzEACwm/SwG3XU1kg0HTMxNVJxNuqPlrWJWi1OToTmqWYAoNNq0GGnIOpSRBMpBGJJTIbiCMdp826hQ8XZAiIcT8JirL5yBqCsubPJnJ29XHpdmXBrFRdEIuNsS78LQPmmIKk0x3t/9hzefPfumluci4Iyn7Nfq82IqXC87q5y3/nbKXiCcXz85etmteu1i6BsFVsvB6fD81Qzgduib2i3xmFvJJvpV4xscdZA84QLgSOjATAGrOooXpwB0twZtTUSzcZUKAaHSQe9VoPVnZLpxonxxmltPO2Zb6Mv6HKaSDkrQe5GZyVz/0RzQMXZAiIUS9VQOSujOMvs7M1VD8SiVc1WIjHDtr7bCZ2Gla2cjQeiSKY5BqcjeN/Pn0MqXbtZoOLKmQGcA9N1LEjG/VF859FTuH5zd7YIFohzVtNOX7LRz1+cuSz6hp0545xjaDqCngKFZS7ib4OyztTl6GgAy1qtMMv4fOx2mrObOwTRLEyG4lknZLEJ0SimIIFoAhOB2DynRkGXg5SzUuS6M1cy9080B1ScLSDC8WTVZ84qyToTVr/zi7PMEL6KxdloZnHV4zKh3W4suzgTF4yXbezCo8c9+OJDR1U7x1KMB6Iw6jRwmOb/TNts6htuKOUrDx9HIpXGh69dM+8+k14Lm1GnWlsj51KBPDeAWuCyGBpWOfNHkwjFUwVVv1zcFmprrAZyzEAEPS4zRiiImmgypnLMtnpdZlgM2mw7b70p5NQo6HKaVB1rWIjkGoCRcrbwoeJsARGKpWA1Vlc5E1lnA1OVtDXOz+1qtRpUbmuMwqDToMVqQIfdWHbLhLhgvO+aVXjjhUvwzUdO4g8HamNMKjLO8rn7iYtwvebOTowH8L97B3DzRUuxtDX/BbfdblStrdEbTiAcT80LoBa4zI3b1ig2HUg5qw/RRApnJkMl580EPU4TJkPxmsdvjPgi1M5KlE1uTI1Gw7Cqw4bjDdLWeDozs51v5gyQirNgLIlAtDE/wxuB3I3OATIFWfBQcbaAqIVyBmRyycoopEQh4bbMN0XodZsxpOJu9bAvim6nCYwxtNtNZRcJYmHd7TThzleux5Z+Fz503/M1CfgcL5BxBiDbvqJ20LNcPvfHo7DotXjv1ecVfEybzaDa+RVyahS4rQZ4I425sFVSnFkMWhh0GlLOVOT4WBBpXtoMRNDtqo+19zt/9Az+9XcHa/qaROPw1YeP4zXfeLzs50/OcUJe1WlvmLbGUxNBaBiwpDX/5ppwSaW5s8KIa6kUZ0TK2UKHirMFRCiegrXKhiCANHdWTmj0VCgOu0kHg27+r12P06xqW+OIN5L9wO90lN/WOOqLwqTXwGnWw6jT4ptv3gazQYt3/fgZ+Ku8yyeUs3y0Z4uz2i/i/3ZsAn86NIZ3X7UyWyTmo81mVO38hrzS71uhmTOnWY9oIt2QYeMzxVlpQxDGGFosBlLOVOTIqB+A/OKsx1mfIOqB6XC2/YtYXKTSHD9+6iz2DXjLmmtOpzmmw/FZIwOrO22YCMQawsX2pCeE/hYLjLr8nT0iiHrU1xjxMI2IJxiH3ajDynYbFWeLACrOFhDhGljpA5LK5Y8mFRcnnmBsXkujQAqijqgWvjvii6InE27ZYTdhKhRHPKnc2XDELx1HtBZ2O834nzdtw7mpMD7wv88jXUWDEEk5y7+gd5h10GkYJmuonHmCMXzsV/tx2/f3YEmLBW+9bHnRx6vZ1iguRoWKM6HGNmJr45A3CoNWgzZr4UI2F7fVgKlQ472PZuXoaABGnQbLCrTfzqW7DkHUiVQa3nCC8tUWKU+dmsREIIY0R1mf6flialZ1No4pyOmJEJYXaGkEZpQzMuIpzEQwhja7Ef0tZgyQIciCh4qzBUI6zRFOpKpupQ/MtJYNKdy9mQrFC+Y89bhMCMdTqiyuU2mOUX8U3RmlosNRfgvgiDeCLufsAumiFa34xPXr8OfDY/j6rhMVn28+YskUfJFEwbZGxhhaVWwbLEY8mcZ3/nYKO7/wCO57ehBvu2w5fvfey0s637XZjPBFEmUVxXMZnI7AZtTBadbnvd9lkW5vxNbGYW8E3S4TNJr5s4P5aLHqs+Y5ROUcHQtgVacNWpnf/+46KGdi1qwes25E/fntvqHsv8f8yv/288XUrM4WZ/WdO0unuWSjX8CpEZhRzqitsTCeQAxtNgP63BZ4wwmaz1vgUHG2QIgmU+AcNVPOgPKKs9YCxZlQRNQwBZkIxJBKc3RnlTOpwCmntXHUF80eJ5fbL12GG7b24st/PoZdR8YrO+E8FLPRF1Q7iJpzjr8cGcO1X/kbPv3AYexY3oIH/+lF+JdXrC9YJM09P0AdR8nB6Qh6Xea85ijATHE23YCK07A3klVx5eC2GOoakbDQODoawJpOh+zHC4Oi4Rru4ucqzGQpvriIJlL4wwujWJlxMhwPKP/554up6XGaYDPqajIfXYxRfxSRRKqgUyMg/c25LXr63S+C6DyqJGuWaB6oOFsghGLSbmstlLNyCylPcPbAci5q2ukP+2bP+IjWQKW7cqk0x1gglt1Jz4Uxhs/csAnruhx4/8+fwxmPurMiYrEmVL98tNqM8FRpNunEeAC3f38v3vqDp8EY8P237MD3bt+Ble2Fdz/nks06U6G1sVjGGQC4zNLvla9BlTM5ZiCCFquBXPtUYjoUx3gghjVd8n9vgUwQdQ3t9HMV8FrPuhH15ZGjEwhEk3jHFSsAlKec5YupYYxhVaet7m2NpZwaBV1OMylnRfAE42izGdFfQdYs0TxQcbZACMWSAGqjnLVZjTDoNIqKs3wDy7moWZyJjCKheHU6ylPOhAI3t61RYDZo8a1bLoBGw/DunzyDZKry9j2BONd2W2ETiTarYVb2iRrEk2n8++8P4dqvPIpnz03jk69Yjwf/8UXYuaZD8bFEi40arZeD0+GCGWdAjnLWYIpTMpXGqD+KXhlmIIIWqwG+SELV36fFypFMztOaLvnKGVD7IOpcBVzNSJFy+OrDx3Hzd5+q6zksJu5/fgitVgNevbUXQJnKWWYzp3XOXOvqDnvd7fRPTUjF4YoSG3tdDiMpZwWIJ9PwRRJzlDOaO1vIUHG2QAjFpeKsFlb6Gg2T7PQV7Nz4ItLA8tyLh6DVaoBRYcFXCLGoEq1krTYjNAyYULgrNzJHgctHf4sFH3vZWhwZDeD4uHo7lPKUMwMmQzHVTFRCsSTe9sO9uPux07hpez8e+dBVeNvly6HXlvcxIdoaPYHKVCBfJIFANFlUOWtUQ5CxzJC/UuUMALyRxnovzcjRjFPjWplOjYIep6mmQdSzlLM6L1BfGPLhyZOTiMRp9q3aBKIJPHx4HK/Y3J1tpy1LORMxNdbZ7earOm3wBON1VeJPeUKwGLTZTdJCdDnNFERdADEa0GY3oMVqgFmvLStrlmgeqDhbIIQzF9Jqh1ALel1mRTs32Z29Am2NjEkFnxqtRMPeKCwGLRxmqVDVahhabcrt9MUiqctRfGG9pd8NQJptUYvxQAyMoeCMHiAVP9FEOvuzrwRvOI43370bj5/w4POv24z/fM2mojb5csi2NVaonA1lM87yZ+QAgEmvgUGnaQjb6FyUZJwJRKFJdvqVc3DYD7dFX9BYpxA9LjMCMeWOtOXiCcZg0mvQZjPU3bHRH00gzVF3xWUx8NDBMcSSabxyi6SaSQ635SlndpNunlV9I5iCnMo4NRaaFxZ0OaTw91iSNgXmIjY422xGMMbQ36Js/UU0H1ScLRBEW2MtlDNAmjtTonKJnbtCbY2AtCBSSzkTAdSCDnv5xVm+mbNcVrRbodeybAuVGkwEYmi1GqArolqpFUQ96ovipm89iYNDfnzzzRfgpu39FR1PYNJrYTfqKp45E78TxZQzxhjcFn3DKWflFGfib4Tmzipn75kpbF/WUnJhOJdsEHWN1LPJYBytVqMUKVJn9cAfka4lR0aoOKs2v31+GH1uM7YtcQGQXAvLdWvMt5HXEMWZJ1iypRGYuc6Ol/H+FzriGi+6UfrcFpo5W+BQcbZAqIdy5gnKt30W2S2F2hoBqX1QjeJs2BedtxiWijNli55RXwQmvSY7z1QIvVaDle22bAuVGkwEogUz4QSt2Zmu8hfxpz0hvO6uJzA0HcEP3roD127oKvtY+WizGysuHsUOYbGZM0AyBZluMOVsSEEAtSCrnDXYe2k2xvxRnJkM46LlLYqfK4Koa+XYKDKMup2mhlDOAOCwip9nxHwmAjE8fsKDV23pyW4elHOdAiRDkHwbn50OI+wmXd2Ks1gyhcHpSNGMM0Fn5m9ulExB5iG6T9oza4J+N2WdLXSoOFsgzBiC1EY561Xo2FiqrRGQ2tYmArGK2xpGvJF5aleHXfmO5HDGRl/OrvuaLruqbY0TgRg6HMUX9OKDutwg6oPDPtx41xMIx1P42TsvxqUr28o6TjHabIbKlbNpqUgu1uIJSKYgjTanNeyNwGXRK1K0Z5Szxnovzcbu01MAgAvLKM5qrZx5gnG02wzocZkx4o2oNkdaDr7M3xApZ9XlgQMjSKU5XpVpaQQk5UwYUSlhMhifFUAtYIxhdae9bo6NZyfD4BzZmIBizARRU3E2l6xyZpeuDX1uCwLRZPZvlVh4UHG2QJhRzmpUnLmUZW2IFi2hCuRDqAuVDAXHk2lMBGPzssk6HUZMBpVd9EZ9UXSVKJAEa7rsGPZFVfuwHA/EssVXIUShO1lG+9ue01N4w7eegl6rwb3vugSb+1zlnGZJ2lVQzoa8xTPOBC6LvgFnzqKKMs6AGedJCqKujN2nJmEz6rC+W5lTIwB02iUToVo5Nk4GY1Jbo9OMUDyVbS2sNek0RzCz0Xdk1F/XInGh89t9Q1jbZc+2HgKSAVSaK8+GLJYhurrThuNjgbr8LLNOjUUCqAXCFXmMirN5eAJxWAza7CafaPEfmCL1bKFCxdkCYcatsUZtjQqDqCeDMThMOhh0hX/lRMFXSWvjmD8Kzue3kbU7TNJFT0GhMOqLoltmO5pwg1OjfSSd5vAEY0WdGoEZhUWpnf5fjozhlrt3o91hxC/uuBTndSjLgFJCm81YUdslkAmgdhc2AxG4LYaGnDlTMm8GSLN6VoOWlLMK2XN6ChcsdRed2yyETqtBp6M2WWfpNMdkKI42u2EmUqSGNv65BGJJcA70t5gxHU4ontMl5HFuMoxnz3lnqWbATCankrkrzrlUnBXoSlnVYcd0OFHx53A5nMpknC1rK/35bTfqYDFoSTnLgwigFvS3UNbZQoeKswVCOJaCVsNgLFL8qEmXwwSthmHIK2/nZjIUL+n+JxYmSiz65zJj4jF/5gyQn3WWSnOM+qMlzUAEIkdJDVMQbySBRIqXVM6MOi0cJp0i5eyhg6N454+ewepOO+571yXZgrhatNmM8EUSFbWqlgqgFjgzhiCNtNsvqX7y580Ebmvjzc81E5PBGI6PB3HRCuUtjYJazX+JmJE2mzG7GVSvuTN/Rvm/cFkrAODwCM2dVYPf7R8GAPzd+d2zbu/IZnLKL1D8kSSSaV7QbEsoc8frMHd2aiKEDrsRdlPxuW1AasHscpow6qeCYy5ScTbz86Wss4UPFWcLhFA8CYtBq9iVrFx0Wg26HCYFylnhtgvBzMKk/J2zQtlkM8WZvGN7giKAWl7x0uM0wW7SqWIKIifjTCApU/J3Wb/72GksabHgp++4qGKrfDkIO/3JMndtw/EkpkJxWUWk22JAPJVGRKZJTbXxR6V8NqXKGSBFKJBbY/nsPSPNm5VjBiLodtUmiFr8/bbajNnf83o5NgozEPF9U9OBlpDgnOM3zw1hxzI3+uZ0BHRm2uiVzEeLFshCytnqLqkzoh6mIKc9IayQMW8m6HKYKOssD3OVM6dZD5tRR8rZAoaKswVCKJasmRmIoFeBnf5UKF7URh+QlKB2u7GiXWNR2M1TzhzK2kWyFugylTPGGNZ0qmMKIgrIUsoZIF2Q5RZnqTTHwSEfrljVJmsnUw3aKrT7F8W/HOXMZZbe03SDtDYKM4lyijNSzipj9+kpmPQabOp1lX2MHqcJI75o1ZXYiaxNtgFtNiN0GlZH5Uxqj+9rMaPHacIRUs5U58hoAMfHg9lss1zEZ76StsaZmJr814t2mxEuix7HxmtvCnJqIojlMubNBF1OKs7y4QnG0ZaT1cgYQ5+bss4WMlScLRBC8RQsNbLRF/S5zLJ3buS0NQLI5PxU0tYYgcOkm2eMIi56cnckxQWiS2ZxBkimIEdGKx+8nlHOSr92m80oW5U6NRFEKJ7CpiqZf+RDtGKU69g4qKQ4y5jNNIopSDkZZ4IWS3nK2V+OjNXMxKKR2X1qCtuWuIvOuJai22lGLJmuuoIp/n7bbUZoNVJr10i9irOMcuYw6bG220HKWRW4//lh6DQM12/qnnefQadBi9WAMQVtjVkn5AKbn4wxrO6w17ytcToUx3Q4IcupUdDlMGG8DLfKhUwylcZ0OD4vWoeyzhY2VJwtEMJ1UM763GaM+aNIpNJFH5dOc0yHS7c1AkCvS36rZD6GvfMzzoCZi57ctkYxu6bEaW9tlx2BaLLigWYxF9dul6ecyZ052z/oAwCc3+cs/+QUIt5DucrZYDaAuvRAuXA5bBRTEKEqlzPX57YaMK2wKIgn03jHj57B1/9yQvHrLSR8kQQOj/px0fLWio6TNeeosilIblsjIH3m1MKIJB9i5sxp1mNNlx0nxoOIJ4t/vhPySac57t83jCtWtRXsJOmwGxUpZ6K4LxZTs6rThmNjwZrO4wozEDkZZ4JupwnJNC87HmYhMhWKg3Ogfc7Pt7/FjIGpcEPNWBPqQcXZAiEUT9XMqVHQ6zYjzUtb34uB91JtjYC0kB2qIOdn1D8/40wgBXzK+9Af8UVg1JUOoM5FmIJU2to4EYjBYtDCJiMWodVqxHQ4jmSJAhkADgz5YDFosaK9eu6Mc5lpayxPfRicDsOg1chq8XRnlbPGKM6GvRHoNExWkT2XFqsBoXhKdsg7IBWDqTTHC0M+xa+3kHj6zBQ4Ly/fLBcxt1pt50RPMAathmXbcntcprq5NfqjUlujw6TH2i47kmmOkxP1ychaiDx7bhpD3sg8l8ZcOhwmRYYgInKj2PV1dacdvkii4sxJJWRt9BVcb8TMHQVRzzDT9jxfOQvFUw1zvSPUhYqzBUI4nqxZxpmg1yXPzrXUwHIuPa7KWolGvNFsgOxc2hUVZ5JToxKDlTUZV6xKW4HGAzHZC/o2mwGcA1MyWvn2D3qxsccJraY2pjGAZAtvN+rKXhQMTUfQ4zJBI+OcRSHdKLNaw94Iupymsr7f5RSa5zKZN4dHAyXV7IXMntNTMGg12LrEVdFxxNxqtVsMpQBhQ/Z3vNtlxqgvWpfWLpHTaDPpsK5bONDS3Jla/HbfMEx6DV6yvrPgYxQrZ6E4bEYdjLrCm7OrOqUC6WgNWxtPeULQaRj6ZbSkC7J/czR3lkVsbLbZ5xZnmawzmjtbkFBxtkAIx+qjnAGl7VyzbRcFBpZz6akg6yyaSGEyFC9o4tFhN2FC5o7cqC86z1SkFE6LHt1OU8WOjROBaNZdshRiN63U3FkylcbBYT821bClUdBuN2Z3/5QiZZzJ+zk4M8qDWkHglVKoxVYOLVbpvSgJoxXFWTyZxvGxxat2PHV6Cuf3O2HSV/Z52Go1wKDVVH2hONeJrcdlRjKTdVhr/JEE7EYdtBqG5W1WGLQaHBmhuTM1SKTS+L8DI3jxus6iG6mdDunzUm5xLsdsS9jpH6vh58LpiRCWtFoU5Qxmg6hJOcsickznKmf9bso6W8hQcbZACMVrP3Mm2n5KFVIzblLy2hqB8nJ+Zkw88i+IOxyScpaWcdETyplShClIJShRzlpluiEeHw8ilkxjcx2KszabUXFQtmDIG0Gfq/S8GSCpdGa9tnEMQXyRsnPkhHI2rSCIemBqZpNksbY2hmJJvDDkq7ilEQA0GXOOatvaTwTjszKMxOZSORtUleKPJuDIbHLotRqc12EjUxCVeOyEB1OheNGWRkDaREyluezukclg4QBqQZvNiBaroaamIKc8QaxQ4NQISBsiei0j5SwHT46bay5yN8eJ5oSKswVCOJaqeVujUadFh91Y0sDDk7nIzP1wyUdvVjlT/uEs5jQKK2dGJDPmJMVIpTnG/FFFTo2CNV12nJwIVtRWNhGIocMu77XFRbmUcnYgYwayqbcOxZldvt1/LtFEChOBmGzlDJBaG5VY6UcTKfz4yTOIxNXNRkulOUZ90Xl5e3IRGxly2lUF5ybDWNFmhc2ow4FFWpw9c3YaqTSv2AxE0OOqfhD1ZB7lDJiJYqgl/kgSdtPMdWRtt53aGlXi/n3DcJr1uHJ1e9HHdSoMop4MyTPbWtVhq1nWWSrNcWYyrMipEZA2RDrsZKefiycYg1GnmTeD7jTr4TDpMDBFytlChIqzBQDnXFLOamylD8jLOpvKFA5uGRcQl0UPs15b1oJILGYKzZyJYeNSc2eTwRiSaV7wOMVY22VHIsVxOuNUpZRoIoVANKlg5kyecrZ/yAu7UYdlrcoulmrQbjOWNXM2nHVqVFKcGRTNaT1ydAKf/O1BfPzXB1R1vZoISL9D5bc1CuVMQXE2FcbSVgvW9zjwwvDiLM72nJ6CVsOwbalbleP1OM1VnTnjnGfaGnOVs/K7ByrFH01k24MBYF2XA2P+GAWiV0g0kcKDB0fx8k1dJeMd2u3KMjmnQjFZXSmrO+04XiPHxmFvBPFkWpFTo4CyzmbjCUo2+vnm3/tbLKScLVCoOFsAxJJppDlgqXFbIzDjrliMqVAMTrMeehm954wx9JRppy/ynYq5NQKlizPRxtQtI2dsLms6xRB9eTuUEwps9AHAYdJBr2Ul7fT3D/qwsdcpy1hDbdpsRvijScSSytQp0UuvpDXQZdYramsUTnS/fm4IP3nqrKLzK8ZQBRlngLQryhhkL4o55xiYCmNJiwWbep04POKX5eC50Nhzegobe52ynE7l0O0yYayKuUvheArRRHqWcuYw62A1aOvi2OiPzLQ1ApJyBpApSKWcnQwjHE/h0pVtJR+rRDnjnGdmzkpfL1Z32hCIJavuhBhPpvE/u6Q4D2FEooQup4ncGnPwBGPzzEAEUhA1KWcLESrOFgChmGR/XC/lbNgbKTrH5ZHZdjFzTEtZC5NhXxQtVkNBI4CO7I5k8Q/+0cxrl9PWuLLDCq2GlW0KIi7IcoszxhharcVnumLJFA6P+LG5v/YtjcCMy5TcsGyBKHD6WuTNnAGA26qHV4EhyKmJEDodRly9tgP/9vtDePbctKJzLMRwBRlnAKDTauA062U7T3rDCQRiSfRnirNoIo0Ti8wCPZpIYd+AFxepMG8m6HaakUpzRdbmSpibcQZIf9PdLnNdlLNANAmHKac4y8SDkClIZYi/YzkKl/jsH5OhnAViSSRSXNb1tRamIMPeCG761pP4+d4BvOOK5di2RLmC3eWQlDPK75KYCMTmZZwJ+jNB1PS9WnhQcbYACMUkRaIeylmf24JEihdVo6aCpd2kcuktc85jxFs44wyQDEEAGcpZpj2yHNXDqNNiRZu17KwzoZzJdWsEpJmuYsrZsdEgEimOzb2uss6pUkRGmdLWxsHpMLQahk4F3wun2aBYOVvZbsOXb9qCbqcZf/+TZ1VxyRO/v+WYyghaLAbZyplwalzaasXGXmlB/cLQ4lI79g14EU+lVS3OsllnVZr/KjTs3+My18UUQVLOZq4j7XYj2mwGUs4qRHwmycnNNOq0cFv0sjYE5ARQC0RxVi1TkMdPePCKrz2G42MBfOPmbfjE9esVRdEIup0mRBIp+CPJKpxl8yHaGvPR5zYjknGpJhYWVJwtAELxjHJWYyt9AOjLGngU7nueDMVkXTwEPU4zPMG4ogBeQDgsFi6oTHot7CZdaeXMH4VBp4FbQQB1LpU4No4rbGsEpIiCySIFxf4hLwDUxakRmFHOlBY9Q9NSsa3Eitlt0cMbTsjaSeSc41SmOHNa9Pjmm7dhOhzHe3/6XMUtgcPeCBwmHeym8n6HAGlGU65ydjZTnC1psWB5mw0Wg3bROTbuPjUFxoDtS9Uszqo7/5XNMJqz+OpxVt+IZC6pNEcgNls5AyT1jBwbK0PMwQoX1lJ02E2ylDM5AdQCt9WANptRdVOQdJrjf3adwC1370aL1YDfvudyvHxTd9nHoyDqGSTXzliR4kzqKsl16iUWBlScLQDCmeLMUmO3RiDXzrXwQkJuT7yg3AXRsDdS0h2v02EqqZyVE0Cdy9ouOwanIwjGlO/8TQRi0DB5mXCCVpshu8jLx4FBH1wWvSJjDTURqoDS4mxwWrkVvcuiRzLNZX3vJ0Nx+KNJrMg4im3oceLTN2zCk6cm8cWHjil63bkMVZBxJnBbDLJbQcXFub/FDK2GYUOPo+kdG//td4dw011Pyi6U95yZxNouB5xlbqrkYyYUt1rFWf4Mox5XeRtUlRCMSn8zuTNngPR5dnQ0UJdQ7IXCtNLiLBP7UgolGaKANHemZlujL5LAO3/8DL7w4FG8fFM3fvsPl+G8DuVzZrmIboNq/c01E9PhONK8sNN1fwtlnS1UqDhbAIi2xnooZ2LxXOjDIZ3Ja5Fjo589plsUZ/J3zkKxJPzRZMng6A576YteqfbIUqzJzGmU09o47o+h1WaEVoFxR7vNCE8wVlAt2j/ow6ZeZ9nFZqW0ldnWOOSVH0AtcGUWP3IcG0+OS4uUle0zi4nXXdCHN120BHf99ST++MKootfOZdhbfsaZoMUqf+bs3GQYbTZjtrV5Q48Th4b9TbugTqbS+OWzg9hzZgo/fLK0UUs8mcYzZ6dVbWkEJMMdq0FbvbbGQP5ZJPH5U0vXOhHe7jDN3uRb2+1ALJnGmcnyHGgJqa3RoNPApJe35Oqwm0p2eAA5GaIyr6+SY2NAlRmlwyN+vPLrj+GRo+P41N+tx9feuFWVOB8Kop4hu3lToJNGzuY40ZxQcbYAyCpndZg5sxp1cFv0BR0bvZEE0lxe24WgnCBqsctWSjmTirPiH/ql2iNLsbZL6u0vpzibCMYUzZsBknIWS6YRypPVFU2kcGwsULeWRmCmnbSYujeXQDSBEV90VuEkB1dm119OcXYqE3ewYk4Wz6f+bj3O73PiQ/c9j1NlmmoM+yKVK2dWA6ZD8lo0z02FsaRl5vU29ToRSaTKPv968+w5L3yRBNpsRvzXQ0dL7qIfGPIhmlB33gyYMeeo1i7+ZMbJdq69evYzsIbqgT+aKc7yKGcAmYJUwnQ4DrdFL3uDrMMhxY8UM9oCkJ01kmu4tarThlA8VXHA+QMHRnDDNx5HNJHCz995Md5y2XLVNv+EcRcFUc9s3hRqa7Rl1l8DZKe/4KDibAGQVc7q4NYIZLLOCuzcKOmJF3Q6TGAMGFRwARE72yWVM4cJ4/7CKlM6E0BdiXLW6zLDatCW5dg4HogqmjcDZlpa8jk2Hh7xI5nm2FQnMxBBu82ICQVtjcczqpYYYpeLyNLzRkoXgifHgzDpNdlcKYFRp8U33nwB9FqGd//kmezmh1xCsSS84UTFxVmLxYB4Kn/RPZdzGRt9waZMMd6srY0PHxmDTsPwo7deiBTnuPP+g0Ufv/v0JADgQpWLM0BSsaq1UJybcZZ9TZfy7oFK8WeVs9nF2XkdNmg1jExBKsAbTshuaQSATrsRyTQvGUI/GYzDatAWdCiey4wpSGWbNp954DCWt9nw+/dege3L1P2bM+g0aLMZKesMhduec5Gyzkg5W2hQcbYAEItHNVoKyqFY1lmhgfdiGHQadNiNZSlnpYqqDrsRsWQa/mj+BbdHBFBXUJxpNAyryzQFmQgoV86yVvWh+cXP/kFpcV5P5QyQzlFJW+OxzPdO7NrLRShn0zKVs+VttrzZb70uM772xm04MR7ER3+pLKBaropbCrlB1PFkGiO+yKzibEWbFSa9pmkdG/9yeBwXrWjB+h4H3nfNKjx4cAx/PjRW8PF7Tk/hvA7bLEt6teitoq29J5DfiS07d1NDUxChnDnnKGcmveRAe5iUs7LxhhPzvq/F6HDIC6KeCsVktzQCwOoOYadf/s8ykUpj2BvBi9d1KN5IlEuX00iGIJgpztqLfK5JWWeknC00qDhbAIiddWsd2hoBoNdlwVCBrI1sT7wC5Uw6prIF0bA3CsZKZ5OJi0mhfn6xQ95VQVsjkBmiV9jbn0pzeILxMpQzYbgxfxG/f9CHNpuxomJTDcRcnFyOjgVgMWjLMASRvhc+GbNapyaC81oac7l8VRs++NI1uP/5YfxIxtyTYCijdlQ+cya9l1J2+sPeCNJ8ZjgckHLS1nc7mtKx8dxkGMfHg7h6bScA4B1XrMDqThs+df/BvCpmKs3x9Bn1580EvRlzjnIMfkrhKeDEZtJr0Wo11LatMSIMQeZfR9Z2O0g5qwCprVFJ90gm66xEC/6kQrMtp0WPNpsRpybKnx8c8Ualzxu3/PxJpXQ5zKScQRpzMGg1ef8mBSLrrFQLLNFcUHG2AAjHkmAMsoeN1UZkbeRbRCrtiRf0KCzORnwRtNuM0JewXRc2vYVMQeQqcKVY02mHN5yQ5bglmA7HkUrzbM+9XMTiLl/xc2DIi8199TMDEbTZDEWDsudybCyAVZ32vKpWMZwylbNYMoVzU+GSM213XLkSF69owV1/PSm70M5mnKkwcwagZGvTuRwb/Vw29TpxcNjXdBfth49ICtk1azsAAHqtBp+5YROGvBF85c/H5z3+0LAfwViyKi2NALC53wUAeE6lgPJcPIH8bY2A+AysYVtjgZkzYMaBVjyGUIY3koDbqkA5y1wDJkoqZ3HF19YlLeaKZpSESlNN919SziQ8gThabYai1+8+txnxZFqVfE6icaDibAEQiqdgNejqtgAXjkH5WhtF/pa7LOUsKnthOeKLyloMi5bBQqYgQjmruDjLODYqaW0ULSxKlTOhsMy1XQ/FkjgxHsSm3vq2NAJSAemPJmVbgx8dDWJNp3JLZoNOA6tBW9IQ5NxkGGkOrCyinAFSi+r1m3sw4ovitEfebvOwNwINg6Lw7Hy0WOS1NWaLs9bZxdmGXidC8VTW+KRZ+MuRcaxot2JZ28zPZvuyFrxhRz/ufuw0Dg3PVnDEvNlFy1urcj7blrigYcDe01OqHjeWTMEfTRZs+e6ucdaZP5IAY4AtTweGaC8+RnlniuGcwxuOw2mWfw0U14BSjoWTwXKKM0v2M6McRGHX31I95azbaYY3nKhplEQjIs2kFr+OZLPOqLVxQUHF2QIgHE/CUgcbfYFo38pnCjIVisNp1pdUtObS4zIjnkrDk2eOKh/D3gh6ZBRUpXr5R31SALXSNsy5rCljMSMMM5TOnBl0GjjN+nlB1IdG/Ejz+s+bATOLjckShQYgFfSeYEyxGYjAZTHAW0JtOplxMVzRVroAvGyltOh//OSkrNcf8kbQ5VAWnp0Pt8y2xoGpMAxaDTrnKK6iKD84XNvWxlgyhSdOenC2DOv1YCyJp05N4sXrOufd99GXrYXLrMcnfnNg1qbN7tNTWNpqKdnSXC52kx7rexzYc0bd4kz8XAvNyYnuATVsz+XgjyZhN+ryqtVru6XNpsNUnCkmFE8hkeJwK8jfM+m1cJr1RTsvOJdiapTMnAFSUTXiiyIhMz9wLgNTEWg1rKqt8tkg6hq0Nh4bC+Dne85V/XXKoZBhUC79LWSnvxCh4mwBEIyl6mYGAsy0N+RVzkKSLK+UXgVuZZxz2fb3NqMOFoO24EVvuMIAakGL1YB2u1Ghcia913KGrFttBnjmLOKFGUijKGdAfkfJuRzNDKuvUWgGInBZ9PBGiitnJyfy2+jnY3mbFd1OE5444ZH1+lIYeuUtPw6TDloNK1mcnZsKo6/FPG9RvarDBqNOgwOD1S/OhrwR3LP7LN7+w6ex9d/+hDd9Zzfe+oO9iguLx45PIJHiuDrT0piLy2LAJ65fh+fOefHTzGIqnebYe2YKF6rsGDeXHcta8Nw5L+LJ8ha0+ZixyS7U1mhCKJ4qaF6kNv5IIm9LIwD0OE2wm3Q4MkJzZ0oRG0VKZs4Aae6smHIWjCURT6UVK2f9LRak0hwjZbbMDk6HVdl8KsZMEHX1i7Nv/+0UPvqrAw0ZOyJHOet1URD1QqTkXxdjrJ8xtosxdpgxdpAx9v4593+IMcYZY23VO02iGOFYfZUzp1kPq0Gb98NhMhhTfPEAgL7MbtCJ8dIfmP5IEuF4SrY7XrEg6lGfpHqogWQKIn8xI5SzcoqzNqtxXuFzYNCLLocpqxbWE+EoKcexUaiNa8pUztwWQ8nw5lMTIXQ5TLI2NRhjuHRlG548NSmrzXbYG1WlOGOMyXovc230BTqtBmu7HVWx048n03jipAefeeAwXvrlv+Kyz/4Fn/j1Czgy6sdrtvXiHVcsx8mJEB6TWdAKHj48DodJhwuWuvPef8PWXly6shWf++MRjAeiODYegDecwEUrqtPSKLhwWQtiyTQODHlVO6boCiimnAGoWsbaXHyRxDwbfQFjDOu6HGU50C52RIu1S4FyBmSCqIt8Xs6YbSm7XojPinLb4AamI1m1plrUMohabF7d+/Rg1V9LCek0x2QwXjCAWmA2aNFmM2CgglZVovGQs/WRBPBBzvk6ABcD+AfG2HpAKtwAvARAY2rCi4RQPFk3p0ZAunD3ufNnbUyF4mW1CK7usKPXZcZv9w2VfOyIX5h4yLtgdNhNBT/0R3yVZZzlsqbTjuNjQaRkzs2N+2MZZU/5z7LNbpjXMrh/0JfNu6o3ouCUM7R8dCwIl0Vftk2z06KHr8TM2ckSTo1zuXxVK7zhBA6VUA7SaY4RFQKoBS1WfVHljHOOc5P5izMA2NTrwKFhv6qmIAeHfbjgPyR17PuPn0aH3YR/uX4d/vyBF+HRD+/Ef7x6Ez507Rq02Qz44RNnZB83nebYdXQcV67pKNgGzRjDv796I2KJNP7j94exJzMHVi2nRoHIctpzWj1TELGZUsgmW3ye1WruzB9NFHWFW9ttx9HRQNMZzNQbsbniUqicdTiMBV2FgfLNtsSsWLlzZ4PT4eycU7UQG6TVVs4i8RSOj0sbDr98dhDJMls9q4EvkkAyzWXFEBVafxHNS8nijHM+wjl/NvPvAIDDAHozd38ZwIcB0Kd1HQnHU7DUKYBa0OvOn3U2GYyXlT2k0TC87oI+PHbCUzBDTSDaM7rlKmeO/Jlb2QBqlRbWa7rsiCXTOCNz9mYiqDzjTNBqNc6aOfNHEzjlCWFzA7Q0Arl2/zKUs7EAVnfay24tdZdoa+Sc49REsKRTYy6XrpQaAx4voQR5gjEkUhy9FWacCdwWA6ZDhd+LL5JAIJYsUpw5EYglcVbFXdU9p6cQiCbxtTduxb7/91L85O0X4e1XrMB5HTM/M6NOizdeuAQPHxnHuUl5r/38oBeeYDzr0liIle023HHVStz//DC+99hp9DhNVXWOA6TNhRVtVuxVce4smwFpz7+4VtLarQb+SLKgcgYAa7scCMaSJT+PidkI51glM2eAtIk4EYwVLIaFAZTSsYEuhwl6LSurOIsmUhjzx6pqow9Ima12kw6jVVaNxVz2Tdv7MBGIYdfRiaq+nhJmAqhL/3wp62zhoahpmDG2DMBWALsZY68EMMQ5f74aJ0bIJxSrr3IGZIKo53w4pNMc02HlblKC113QB86BXz5TvN1AZAH1KFDO8u1IekLSwlot5WxtxrHxqMxWoAl/rGQLQyFabQZMhxPZIW+RbyVswOuNSa+F3aQr2dbIOcex0UDZLY0A4DJLhiAFFzWhOPzRpCLlrNNhwnkdtpKmIGLhqp5yZihqpV/IRl+woUcqztVsbTzjCcFm1OEVm7uLtoXefNFSaBnDj548I+u4fzkyDg0DrlrTXvKxd1y1EsvbrDgzGcaFy1tq4lR74fIWPH1mSjXlaDIYg1mvLaiUt9uN0GlYTZWzYkHJa7ulv8nDNHemCF+Zylmnw4hEihdsa57KtMUq7UzRahh6Xeay2uDE51u12xoBqYistp2+uE6+9+pVaLMZ8b97B6r6ekqYkBFALehvsWDIG5HdpUM0PrKLM8aYDcAvAfwjpFbHTwD4fzKe907G2NOMsacnJhpnV2IhEY6n6jpzBkjKmT+anJWD440kkObKLx6C/hYLLl3Zil88M1h0QTTijUKnYbLb4DocRoTiKYTmhMoKZyi1Zs5WddqgYfLt9CtRzkTrg7BdP9BAZiCCdrsxb1B2LiO+KAKxJFaXaQYCSLMdaQ4ECoQGn8zMMSpRzgDJtXHv6amiphBC5VCzOCtmpV/IRl+wutMOg1aDgyoWZ6cnw1jWZilZEHU5Tbh2YxfufXogb3j0XB4+PI7tS1tkLWJNei0+/eqNAIDLV5Uu5tRgx7IW+KPJrGFNpXiCsYKqGSAtojsdppqYIgDFDUGAmRlQuZtNhIRQzooVvvkQWWeF5s5m2hqVXzP6WyxlFWeida7abY2A9PlRbbfGA0M+tNkM6HOb8doLerHr6HjRVtJaMqOsy2lrNCOR4gUjgojmQ1ZxxhjTQyrM7uGc/wrASgDLATzPGDsDoA/As4yxrrnP5Zx/m3O+nXO+vb29NhfRxUYolqyrWyOQ49iY0/cs2uzKaWsU3Li9D+emwthdJGNo2BdBp8MErczA4pmss9kXPbUX1ia9FstarTg6Km+nedwfLXvOSrQ+iA/0/UM+9LnNFUcCqEmbzZjdDSxE1qmxEuUss7gvZKcvcr+UKGcAcOl5bYgkUkXDiIeroJxNF1EBRXFWqM3IoNNgbbdddeVsWau8793tly6DP5rEr58rPjs64ovg0IgfV68r3tKYy6XnteFv/7wTN2ztLf1gFRAh12q1NnqC8ZLzJD0uU03aCJOpNELxVNG2RqtRh6WtFjIFUch0OA6bUQeDTpm7YaejeNbZVDAOs14Lcxkbs/0tFgyUMaMkCrqFpJxt7HWCMYabtvcjleb45bOl59xrgZhJlTtzBkgxB8TCQI5bIwNwN4DDnPP/AgDO+QHOeQfnfBnnfBmAQQDbOOejVT1bYh6cc4TjKVjrPXOWJ+us3IHlXK7b0A27UYf7nincbjDijSrKOBI7knMveqK/Xc28pDVddlk7zaFYEqF4KntuShEFsOhTPzDoa4h8s1zabfMdJecinBpXlxFALRCzHYWCqE+OB2HSa2S3wQouXtEKDSuedzbkjcBm1MFhUmezxG0xIM0xS5HOZWAqjDaboejmzIYeJ14Y8qmSlxVPpjE4HZZdnG1f6sb6bgd++MSZoq//8OFxACg5bzaXJa0W2ZsyldLnNqPLYcqakFSKJxgrqXr0uMw1cWsMZOz6ixmCAJID7WGZm02EhC+cUOzUCMhTzsrdfFvSYsFUKI5gge6CQgxOR6DXsrKvU0rodpowEYhVzaQjmkjh+Hgw212yst2GHcvcuO/pgZplCxbDE4xBq2FwyVBc+90i64zmzhYKcrZyLgNwC4CrGWP7Mv+9vMrnRcgknkojmeZlOfypSW+erLNyB5ZzMRu0eMX5PXjgwAgCBRaoI76IojmxDkd+5WzEF4VBq0GLwtmAYqzpsuPsVLhkW5eYxSpfORMhzzF4w3GcmwpjU6+rrGNVi3a7POWs02FUPJ+Ri1gIFZrVOOUJYXmbLW/YbjGcZj029TqL5p1JGWeV5+QJWkoEUZ+dDGfd1wqxqdcJfzSpyq7qwHQYaQ4sa5NXnDHGcPuly3BsLIgnTxUuav9yZBxLWiw4r6P8orzaMMawY3kL9p6ZUmXx5gnG0V6krRGQHBtHfdGqOySK4r+YcgZIc7RnPCFE4qmqns9CYjocL684E9epAurRZCguyywiH1k7fYWtjQPTYfS6zDXZEOl0mpDmKHnNKJdDI36k0hwbc1r/b9rej1OeEPaeUc+VtVw8mRgiOdcp0alBjo0LBzlujY9xzhnnfDPnfEvmvwfmPGYZ51xZoA2hCuGYdJG01nnmrM1qhEGnmVWclTuwPJebtvchmkjj//aPzLtPBFAraSPrFDuScy56Iz5JgVO6aC/G2i47OAeOjxXPaxMXoLLdGjMX6clgPNvC1mjKWZvNgEA0iWii8MJOODVWgijsfAUcG08ptNHP5dLz2rBvwFtwx3lYRRt9AHCXKM4KZZzlInaG1WhtPJtxHl3eJn/m5JVbeuC26Ava6kfiKTx+woOr13bUxNijEi5c5saYP1ZxoZtKc0yFSitnvS4TEikuy+W0EvwRoZwVLyLWdduR5sjajxOlmQ4nFAdQA1JbvMOkK6icTYVi5c9zu8uz0x+cKr0ZpBbVDqIWZiC5c9nXb+6GzahrCGMQOW3PApNeiw67kbLOFhDVi3gnakIoo8hY6jxzpsk4QOVrayznwpTLln4Xzuuw4d6n539gToXiiCXTipQzh1nq/5/rHDjqU9YeKYc1MhwbJ4MxfO4PR8AYsFymIjEXu1EHg1YDTzCO/RkzkI09jVacFc86S6U5jo8FK5o3A5BtA8lnpBFLpnBuKoyVZX6fL1vZhmSaY8/p/CqQWgHUAqHi5ivOEqk0hr2RksXZ6i4b9FqmSnF22iNd/OW2NQLSwuH1O5bgT4fG8rbdPHHSg1gyjWsUzJvVix2ZubM9Fc6decNxpHlpm2yRdVbtubMZ5axUW6P0eXZkhIozufgiibI7ATodhTM5p4JxxQHUgnKVs8HpSNVjKwRLWqTPGGFupTb7B31otRpmrR0sBh3+7vwe/N+B4YKt5LVCMgyS//Ptb6Gss4UEFWdNTiirnNW3OAPmZ21MBqV2jkKBsnJhjOHGC/rw7DkvTozPVqDErprcAGpxvA67cb4hiC+CHpWLsyUtFpj0moJD9CfGg7jhG0/gwJAPX3/jtrJ3JRljaLUZ4AnGsH/Qi2WtFjjLaKWpJjNB1IVVoFgyXZFTIzDjipYv6+zcpNSWt7LM9rnty9ww6DR4/MT84iyaSGEqFM/OX6qB21q4RXPYG0Gao+TvjFGnxepOOw4OV77IOeMJwW7SKd6xf/PFSwAAP3nq3Lz7Hj4yDqtBmzXcaGRWd9jhNOsLFudykevEJgr9ajs2CpW5lHK2pMUCs15Lc2cKmA7HFWecCToc869TgNQxMhmKlz0y4LToYTfpFBVnoVgSk6F4TZwaAWBluxWbep24Z/fZqsyA5ZqB5PL6Hf2IJtL43fPDqr+mEjyBmKK21T63GYNeUs4WClScNTkzyll92xqBTNbZrLbG8geW53LDtl5oNWyeMciMO56yokoqzmYWPCKAukuhSUQptBqG1Z12HB2bv5h54oQHr/nG4wjHk/j5Oy/G9Zu7K3qtNpsURC2ZgbgqOlY1yCpnBdp0hLpYqXKm02pgN+nyGoKcnJCK+xVt5RVnJr0W25e684ZRl/u7WIyZmbM8hWaJjLNcNvU6cUAFU5AzkyEsb7Mqbj/sc1vwkvWd+Pnec7PaWjnn+MvhcbxodTuMuvp/hpVCo2HYscxd8UxK1sm2pCGI9LtU7awzv8ziTKNhWNNlbxjlLBJPZbMdG5FUmkvKmUIbfUGn3YRx//zPy1A8hVgyXZHZ1pIWi6K2RnFtr5VyxhjDLRcvxbGxoGomPAJhBpKv9f/8PifWdNpxbx1bGznn0kyqAqfrfrcFw95o1QxUiNpCxVmTE24g5azXZYYnGM8uviZDMbSV2XYxlw67CTvXtONXzw7N+vApRzkTxxvLuehNhuKqBlDnsqZzvmPjvU8P4Nbv7UGnw4Rf//1l2LrEXfHrtNoMODoawLAv2nDzZsCMSlBowPtYxkZ/VQVOjQKXRZ/XSv/kRHk2+rlcdl4bjowG5rVnZqMYVCzwzXotjDpNXuVMSXG2odcJbzhRcdvLaQU2+nO57dJl8IYTuH/fzI70wWE/Rv1RXK3QpbGe7FjWgtOeUEWZQtmA2RKGIE6zHhaDNvu7VS3ktjUC0tzZkVF/Qzja3fitJ/DFh47W+zQK4o8kwLnyAGpBu0PaRJz7vZ7KKK+VbH72u5XZ6c/Y6NdGOQOAvzu/B06zHj966qyqx81nBiJgjOGmHf14ftCHI3VSiP3RJOKptOyZM0AqmlNpXvX4AaI2UHHW5GSVszobggDzHRsng+opZwBw4/Z+TARi+NvxmTDzYV8EBq1G8Q5ip8M4yxBE2FVXpTjrssMTjMMTjCGd5vj8H4/gw7/Yj0tWtuIXd1yq2sWu1WrEcKZYbaTwaUE2i62QcjYWwJIWiyrOo26LIRv+msupiRC6HKaKcgEvXdkKAHhyjqW+2hlnQKZd1WrIO3N2bioMg1aDThmh6eL3oZLWxlgyhWFvBMsKBF6X4pIVrVjTacf3c2z1/3JkHIwBV61pouIs0375dAXqWbatscTiizGGbqepBspZEhoG2GT8XazptGM6nChoVFFLTk2EsqHyjYhorRbtyUrptEuGMHM/yyZDIkO0AuWsVQqilltkD5TIVKwGZoMWN17QhwdfGFU1HDqfGUguN2zthV7L6mYMIjb+ioXUz4WyzhYWVJw1OcKivd4h1MDMh4PYnZ8KxdFSwcVjLlev7UCr1YB79w5mbxMZZ0odFjscJvhznAPLVeDkIIbo9w968d6fPYdvPHISb7xwCb53+47sfJQaiA9yxiSlpNEw6iT3sUKGIMdGK3dqFDjN+rwzZycrcGoUbOp1wm7U4YmTs1sbh7wRMKZuTh4gOTbmMzcZmAqjzy3P1nptlx06TWWmIANTymz058IYw22XLsPhEX+2LfDhI+M4v89VdoREPdjY44RJr6mo1WoyGINOw0pa1wO1yTrzRxNwmPWy2lXXdkufZ4dH6jt3FkumEI6nssZTjYhQvF3m8q6DM7EvswsTsVlTriEIIGVjxZLpecZYhRicjsCk15Rt318uN1+8FMk0x8/2qFcoHchjBpJLi9WAl67vwq+fG0IsWfvYCCUB1AIRDE5ZZwsDKs6anKwhSCPMnAnlbDoiWUWH42hTUTnTazW4YWsvHj4ylp3ZUJpxJhCLQXFhGs0UZ2ovrAFJOQOA9/70OTzwwgg+/vK1+MwNGys2SpmLaCFd2W6TtQNeD9oKZJ3Fkimc9oSwtkIzEIHbYpjX1sg5x6mJIFa2V9Y2qdNqcNGK1nmmIMPeCDrtJtV/ri1WA6YKtDXKVV1Nei1WddpxYKj8BfUZ4dRYZnEGAK/e2gOHSYcfPnEGE4EYnh/w4sVN4NKYi0GnwdZ+N/ZW4NjoCcbQapOZYeQ0Y6jabY2RhKxCEUB2A0W0CNcLMVNaKGaiERCfQeXknAHIquJjc+bOshmilbQ1tiiz0x+YDqPPbal53MXyNitetLodP91zVrX5wgMFzEByuWlHP7zhBP50aEyV11SCXGU9l26nGYxR1tlCgYqzJiernDXAzFmn3QithmHIG4Y3HAfnlWeczeXG7f1IpDh+k5lbKde6XOSJiR3Jctsj5dBuN6LdbkSKc3zz5m1454tWVuUCJ1pcNjegaiZotxnhCcxfTJ32hJBM84qdGgXSzNncVqA4/NFkxcoZAFx2XivOTYVnuZ1JGWfqF/duS37l7Nxk6YyzXDb2OHCwAlOQMyLjrMyZM0Cyqn79jn788eAofrZHcm68em1n2cerFzuWt+DwiB+BMu22lWQY9bjM8ARjVd3B90eTcJjlXUPcFj0MWk1FM3dq0BzFWaatscyZs+x1ak5Ln1ALK2prVFic1dJGfy63XLwUY/4YHj5ceaEkzEBKtf5ffl4bepymurQ2ZtsaFRRnBp0GXQ4TBkg5WxBQcdbkCOXMrK+/cqbTSh8OQ9ORmbYLBR8ucljTZcfmPifue3oAqYzDYjnKWUc2iHpGOet0GlUNoM7lO7dux+/fezmu21iZI2MxxAf5pgY0AxG02Y152xrVcmoUuCwG+KMJpNIzhYiYTalUOQMkUxAAs1ob1c44E7RYDfNat3zhBPzRpKLibFOfE5OheNm27Kc9ITjN+mwwdrnccvEypDnHVx8+jm6nCeu61fmZ15ILl7UgzYFnzpY3dzYZjKFV5mdjd6bgH62inb4S5Ywxhna7ERN5XARriWgZDESTiCcb06FOzIqVq5xlr1NzWg+nQjGY9JqK5nN73ZLSIndGaWAqXNN5s1yuXtuBXpcZP3qycmOQw0XMQHLRahhet70fj53w1LxV0BOMQcOUb273uynrbKFAxVmTE44nYTFoq1ZUKKXPLdnpZ2X5KihRN27vx5HRAB45Oo5kmqO7HOUs08svAj5HvFF0O6q3KygFaVd3Ebqhx4ELlrpxTQMrEe22/G2NR0cD0GlY2SHcc3GZ9eB8xiIcAE55KndqFKzqsKHdbsy2NnLOMeSNqJpxJnBbDAhEk7NaerJOjQrMOcRipNy5szOToYpaGgVLWi24Zm0HkmmOq9d21LxNSg22LXVBp2FltzZKypm8z0bxO1VNx0Z/VH5xBhRuT64luW3L+dxMGwFvOA4Ng6LvbS5mgxZ2ky6vclYqhqEURp0WXQ6TLOXMF5E2g8RcU63RahjedNESPHFyEifGK4txEJ9/cjYxb7ygDwDwi2cGSzxSXTzBGFqsBlnzxLn0uc0YouJsQUDFWZMTiqdUcbdTi163GYOzlDP1i7NXbu6BQafBVx8+DgBlBUe3WAzQaVh2R3LEH8nuUDcrrTYjfnnHpYoW7LWmzSYVGrlZV4Bko7+i3QqDTp2PpHzhzSfHgzDpNapY3TPGcNnKVjxxcjIbCBtPpquknM1/L0ps9AXruhzQMOBgucWZJ4zlKv1uvfXy5QCAl2+qnpJcTSwGHTb0OssyBeGcYyIYk92yJDoDqunY6IskZLc1ApmcyLorZzMbL43a2ugNJ+A06yvaPO10mObNnKmVISrZ6ZcuzoRyVKsA6ny8fkc/DFpN3iB7JRwY9KHFapC1buhvseCylW247+lBpNO1i46YCMhve86lr8WCEV+kobP/CHlQcdbkhGLJhjADEfS5zBjzR7OKVKW7e/lwWvS4bkMXnh+UFpnlOCxqNFJrznhAsrcf88WqYgZCzEYYscxtbTw6pp5TIzDjjuado5wtb7OppjJfel4bPMEYjo0Fq2KjLxBthNM5QdTnysgcMhu0WNVhL0s5iyZSGPZFVFHOAODSlW3Y8/Frsu2hzciFy9x4fsA3b6OhFMGY1IYnVzkTv1PVdGz0R5KK1J2OBlDOcjcrGrU4mw7Hy543E3TYjfPm+9SKqelvscyamy2EaH2sV1sjILXtv3xTF375zCBCsWTZx5FjBpLLTTv6MeSN4PE57rzVxKNg8yaXPrcZaS51AhHNDRVnTU4o1njKWZpL4bKANDxeDW7c3pf9d7kmDB2Z4mwyFEc8lUa3jLwoojLEBUe0vQLSBsPAVES1eTNgZsbDl7O7fkoFG/1cRGHx2AlPtjirRk5eS2Zxl7sAPTcVRqvVoNiVc2OvEweGlAcIn5sKg3OUHUCdj44m/3vbsawF8VQa+weVFbtKndhMei1arIaqOTbGk2lEEik4FMR6tNuNmMqoxfUi92+7Ue30veEEnBVeAwspZ5WYgQj6W8wY9UdLms3MKGf1aWsU3HLJUgRiSfw2J8heCTNmIA7Zz3np+k5YDFo8fHi8rNcsB6k4U/7zFT8fMgVpfqg4a3LC8SSsDRBALeh1STtr+we9cFn00KlsKy64dGUbel1mmPXasrPC2u0mjPuj2UH7cmbXCGWIBWluts7xjFGHWk6NgGQIAszsrseSKZybCmOlSsoPIM0CLWu14IkTnuzCuRozZ6I1OFcpGFBgo5/Lpl4HPMGYYlOQ05l5PbWUs4XAjmVSGLXSuTOhGss1BAGkDahqKWfCcVLJ56gwqhBhyPVgOhzPtkFP1VnFK4RaytlEIDZrQ2UyFFPFWXhJiwWco+Sc0uB0BDajrmxjE7XYtsSNdd0O/OjJM2W5zgozkFJOjbmY9Fqs73bg4HD5GZFK4JyXr5xl1l9DVQ6tJ6oPFWdNTiiegqWBMq3Ezs2JiWBVbOkFWg3DB16yGm+6aEnZhgIdDumiJxY91VA9iNnka2s8prJTIzCj2Aor63OTUoDyyo7KnRpzufS8Nuw+PYWBqTDMem1VFi+FlDMl82aCLUvcAIB9A15Fzzurgo3+QsNtNWBVh03x3Nlk1iZb/udjt9NctZkzf1RqEVMyc9aetXivZ3GWwNIWCxgDpsLlRRpUG284UfFnQofDhHgqnf0sC8eTiCbSFQVQC+Ta6YvA+3qb9zDGcOslS3FkNFCWU+oLWTMQl6Lnbehx4OCwvyZzZ6F4CtFEGm125T/fLqcJjJUutonGh4qzJiccayzlTJhqcF6debNcXntBHz75ivVlP7/DbsRkKJ69MJUzu0YoQ7TieHKUs6NjAZj0mrKUoELYTXowNuPodnJCUudWtKlbnF22sg3BWBIPHhxFj8tUlcVLVgXMFGfJVBpD3khZxdn6bgcMOg2eVbiwOe0Jw23RV9yitdDYsbwFz56dnhXZUIqJTFtju4Kd8V6XuWpzJMLRVOnMGTBbAa813rDU2ucy6zFVRwWvGN5wPDv/Wi4zmZzSe1QjgFogPnMHZChn9TQDyeVVW3pgN+rKstU/MCTfDCSXDb1OhOMpnJ6sfvC6uDaWo5wZdBp02I1VNQ8iagMVZ01OuMHcGo06bfZiokZPfDXpzMy8HBjyQa9lVVX6CAmjTguHSTfLTODYWACrOuyKbYOLodUwOEz6rCHIyQn1bPRzuWRlKwBgxFedjDNAuuDajTpMZQrNYW8UqTQvqzgz6DTY1OvEcwqVszMedWz0FxoXLmtBIJbE4RG/7OcI5UxJXly304RALAl/maHXxRDHVDpzBszP36ol0+EE3BYDWqyGhjQEiSfTCMVTFc9di+uUMNkS83VqGIK024ww6jRFTUE45xiYDtfNRn8uFoMOr72gD394YUTx5sCBIb8iMxDBxh6pDfKFMp1uleApQ1nPpddlprbGBQAVZ01OKJ6ErYHcGgHJFARQ5+JRTUQRuX/Qh06HqWGy4hY67XOCqI+OquvUKHBb9Fm77VMTIXQ5TLCq3ALcYjVgfbc0XF6NeTOBO2cBWo5TYy7blrhwYMinyMzhzGSIWhrzcOFy5XNnnmAMboseegXzuFnHxiqoZ/5Ipq1RSc6ZTRRn9XOFEy2DrVZjVk1qJLwR6ZxcFV4H5ypnQiVUY/NTo2Hoc5uLFmfT4QTC8VTDKGeAZAySSHHc+/SA7OdEEykcHwsoMgMRrOq0waDVZI3OqslMcVZe51EPFWcLAirOmpxwrLFmzoCZLJRGV6LEUPtpT0iV7CtCHm02IzwBaeEyHYpjPBDDmi512w0BwGkxzGprVFs1E1y+SnJtrJZyBuQvzsrNs9u6xI14Mo1DMtWeSDyFEV+UlLM89LjM6HWZlRVngbgiMxDpdTJZZ1UwBZlRzuRfRww6DdwWfd3aGjnnUsugxQC3Vd+QIdRiRsxVpmGVoMMxuxCeaWtUZ2xgSYul6MyZKNz66+zUmMvKdhsuO68V9zx1FkmZmV6HR/xIKjQDEei1GqztttfEFCTb9lzGzBkgbY6PeKM1zWUj1IeKsyYmnkwjnko31MwZMKMgKF2A1Bpx0QNAGWc1pC0nI+nYmGQGUi3lzBtOgHOOUxNBrGxXvwAEgEszrY3VLM5aLDML0HNTYei1DF1lWtFvy5iCPHdO3tzZ2SmpJXRpA4eb15MLl7dgz+lp2e5xkyHlNtnid6sasyTlzJwB0uZWvdoag7EkkmkOt0WPFquxIdsaxYxopW6NFoMOdqMua74i3muLSmMDpbLOBhoggDoft1y8DMO+KP5yRJ7FvWhJ3FhGcQYAG3qceKGMGBKliJmzcjuP+lxmxFPpeVmiRHNBxVkTE4lL2SSNNHMGNE9bY6vVANF6Tk6NtaPdZsxegERxtkZFG32By6yHNxLHZCgOfzRZPeXsvDZ84uXrcO2GzqocH5CUMxFCLTmnWcqe0etymtDtNOHZc15Zjz+TsdFfTspZXnYsa4EnGMvGDZTCE4wrblnqsJug1bCqtDX6IgloNQwWhZt8wu22HmRVKYsBrVYDpsOJhlMKxLyrGg6uHY6ZIOrJkBQhoNam7JIWC/zR5KzcuFwGM2YhjTJzJnjxug50OUz43uOnZRVMB4Z8cFv0Zbefb+hxwBdJZL8f1aKctudcxEbOILU2NjVUnDUxobg0K2BtsJmzZZkd9s4GD5nVaTXZ1hAqzmpHu92IQCyJaCKFo2MB2E26slWgYrgsBnhDCZzM5KitqJJyptNq8I4XrYBdofKghBbL7LbGSp0tty5xyVbOzkxKO+fU1pifC5dLSqTc1kZPQHmGkVYjKaVVUc6iCThMOsUmCe22+hdnbosBbqsBqTSvillKJYiWaiXGL4XosM8EUU8G42izGlRzhu0vYac/MBWGy6Kv6udbOei0Gtxx1Uo8dWoKP91zruTjyzUDEQjFrdqtjeVmnAnE5jg5NjY3VJw1MaGYVJw1mnJ22co2fO/27dixzF3vUylJZ6a1sYtmzmqGaOmaCMRwbDSINZ32KlnQ6xGIJbPq3MoqKWe1oMVmQCSRQiSeymScVfb7um2JG4PTEVmGDmc8IbRaDYrb3hYLK9ttaLMZ8NiJyZKPjSZSCMSSZTmxdTtN1Zk5iyQVBVAL2h3zw5FrhWjxlQxBpO/lZIO1Nk6rNHMGSNcp8bc6FYqp1tIIAP1uYaefvziTbPQb8/p4y8VLccWqNvz77w/hRGYTLh8zZiDltTQCwNouyVH4haHqmoKUo6znIpRByjprbqg4a2JCmbbGRlPONBqGq9d21j2wUg7CCYuUs9qRG0R9dCyA1VVoaQRmZj2eO+eFSa9patMXEUR9ZjIEXyRRlo1+LluXuABI35tSnCYb/aIwxvDidZ3YdWQcsWSq6GNFAVHO4qvHZcZwNdwaowlFNvqCdpsR8VQavkjtFStRnEkzZ/ND2huB6XAcBq1GcbtoPjocknLGOcdUKK5KALVAtCsWVM6mw9kCrtHQaBi+eOP5MOu1eP/PnyvoQHtkNFC2GYjApNdiVYcNL9RCOSvTDASQMj7tJh0pZ00OFWdNTLhBlbNmQjg2ivBsovqIhenBYT98kQTWVMEMBJiZ9Xjm3DSWt9maOipBtEbtH/QCAJa0VFYsbehxQq9leFZGa+OZyRCWkY1+Ua7d2IVgLIknSqhnYtayHLOkbpcJoz71Xdj8kURZqmhHphW5Hq2NuTNnojirlp1+Os3x231Dsl0BBb6M1b8am5QddiPiyTT8kSQmQ3FVnZDtJj3cFn1eU5B0mje0cgZI4xOfe+1mHBz240t/Opr3MQcqNAMRbOhxVt1OX2p7ruznS1lnzQ8VZ01MVjmj4qxsVnfZ0WYzoE3FnUiiOKI4e/yEB0B1nBoBaeEGAGcnw1UzA6kVYgG6b0BaZFSqnJn0WqzvcZZUzsLxJMb8MSxva8yd80bh0pWtsBl1ePDgaNHHTYbKD5jtFS5sIXWLIX80qchGX9CezTqrfXGWbWs0zyhn1bLT3316Cu//+T48etyj6HnT4bgqZiDATCE8FohiMhhX3WyrkJ2+JxhDPJmueMa12rx0QxfedNESfPtvp/DEifk/pxcGJTOQSovMjb0OTARiGPdXJ98vEk8hFE9V1NYIAH1uc9WNS4jqQsVZExPOGIJYGqytsZm47ZKl2PWhq5paVWk2RHjqk6cklaEaTo3A7FmPlU3elidaNPcNeAGo45y2bYkL+we9SBRRBM54pAXbUlLOimLUaXH12g48dGgMqSLKlsj3K2fx1e0Ug/7qLgzLV87qF0TtDSdgN+mg02qq3tYoFIhiWWD5mA4nshtElSLa789OhhFJpFQJoM6lr8WSdzEv5tAata0xl3+5fh2Wt1nxgXufz5qxCA4M+SoyAxFs6JGUt2q1Ngr7+/YKizOpBZqKs2aGirMmJhSTlDNbg4VQNxM6rabhXKgWOkadFk6zlEHWbjdWLXIhN19oZUd1nBprhfgeHRsLoMVqUOV3dusSN6KJNI6OBgo+5swk2ejL5bqNXZgKxYu6Nnqyylk5xZmknoz6VC7Oyp05yxQM9WlrnFGlTHotrAZt1doaxzIqyWABw4xC+MIJuFVSzoTz8ZFMcLyabY2ApJwNTofnbSwMTEkL/EZuaxRYDDp89Q1bMRmK4WO/OpA1qokmUjhWoRmIYH2PAwCqZgoi8j/b7JW3NfqjSQQazME0l1MTQdz115P1Po2GhYqzJiarnDVYCDVBlEK0dVVr3gwAnDkLoxVtzV2cOc16MAak0ly1FqNtGVOQYnNnojgjQ5DSXLm6HUadBn98oXBroycQh9WghbmMz2yxQB9TsaUqlkwhmkjDYVK+wWc36mDSa7LhyLVkOpyYtfnSYjNUra1xJOOQqbRNbDoch8usrnJ2eFQqCtQ0BAGk4iyR4hid87s12KAB1IXY2OvEB1+6Bn94YRT3PTMIQB0zEIHNqMOKNms20FptxExqpW2NIuusEefO4sk0vvbwcVz334/iG7tO1EV5bwaoOGtihHJGhiBEsyF23as1bwYADpMuG9Tc7DNnWg3LtmlWOm8m6HWZ0W43Fp07O+MJoc1mJHVeBlajDi9a3Y6HDo4WtJf3BGNlmYEAklqi17J5C+hK8EekDb5ylDPGGDrspuxufy2RlLOc4sxiqJqVvlAqlRRnnHN4wwm4rOooZ1ajDjajDodHJJVb7W6DrJ3+nNbNgakI2myGsjYT6sU7r1iBS1a04s77D+KMJ6SaGYhgQ2/1TEE8wfLbnnNp1Kyzp89M4fqvPoov/ekYXrK+E3/+wJVZUzZiNlScNTHheBImvSa7ACWIZkFcfNZ0VU/RYozBadajy2GCdQEUF8KxsdKMMwFjDNuWuIorZ54wmYEo4NoNXRj2RbMLwrlMhsp3YtNopGJoTMW2RhHcXG6GXbvdWEflbOacW6wGTKlslCIY8Slva4wkUoin0rPUvUrpsBuzSnY12hqB+XN1g95w06hmAo2G4Us3nQ+9VoP3/+8+7DvnhUsFMxDBxh4HhrwRTFdhM0DMnFU6U9jXYFlnvkgCn/j1AbzuricRiiVx923b8T9v2pY1uiHmQ8VZExOKJ8mpkWhKRHFWTeUMkHKQml01E4isM7WUM0CaOzs7GcZkAfXjNNnoK+LF6zqg1bCCrY2eQGUBs50OY7ZYUAN/JqOsnBBqQCoY6qacmXOLMyOmqjxzNh1OIJSJrymFCKBWa+YMkAxYhCCrtiFIt8sEDQMG8yhnje7UmI8elxmfuWETnh/w4lfPDWKTCmYgAqHAVUM98wRjcJh0MOoqUyrbbEYYtBoMVSEXUQmcc/zhwAhe8l9/xc/2nMPbLl+OP33gSlyzrrOu59UMUHHWxIRjKXJqJJqS5W1WmPVarKpycfb//m4D/vnaNVV9jVohWpnUXCxtW+IGMOMCmUswlsREIEbzZgpwWQy4ZEUr/vhC/tbGStoaAaDLaVJ15swfFW2N5W3yScpZbReAyVQa/mhydlujVY+pKsycxZIpeIJxrO6UFH65MzxCVXGqNHMGzGRyGrQa1duM9VoNelzmWcpZKs0x7G3sjLNiXL+5Gzde0AfO1WtpBIANwhSkCo6NlQZQCzQahm6Xqa4zZ8PeCN7xo6dxxz3Pos1mxG/+4TJ88hXrF0QXSy2g4qyJCcZIOSOakzdeuAQPf/DKqs8yXbm6HVszBUiz02JVXznb1OuEVpM/jPqMh5way+HajV045QnhxHhw1u2pNMdUOI72ClSPLocZo/5owZk2pQjlrNy2xg67Ef5oEtFESpXzkYMvMl+VarEaEU2ksyZZaiFaNi9Y2gJAfmujtwrKWWcmuqDFalBNBcql323BQE4b3Kg/imSaN4WNfiHufOUGvHZbH161pUe1Y7osBvS6zFUxBfEEK1PWc+l1mTGk0GFULbzhOF7+1Ufx2AkPPv7ytbj/PZdhc5+rLufSrFBx1sSE4ylyaiSaEoNOk3WUIuTR5zbDbtJl867UwGzQYl23Pa8piJhvWdravIuzenDt+k4whnmtjVOhODhHRTvjXU4jwvEUAjLb60qRnTkrs62xHnb62ZbBnLkrMYOltp2+aCHdvlTa4JFrCuKNSOfhVnE2TChn1YoemRtELcxBmlU5AyQjlS/ddD7WdjlUPe7GXkfV2horzTgTSFln9WlrvGf3OXjDCdz3rkvxzhethE5LpYZS6DvWxITiSZKICWKR8PYrVuAP779CdQOgbUvceH7AOy/j6OyktDijmTNldDhM2NrvwoOHZhdn2WH/CmzQs3b6Ks2dZd0ay1bOpPMZr2Fx5ssUPrPbGqsTRC2cMTf1OWHUaWQXZ6KAdJVZ9OZDhH6rPW8m6G8xYyIQQyQuqaDivTbjzFm12djjxGlPSPUcMU+gfMOgufS6zBgLRBFPplU5nlziyTR++MQZXLGqDZv61GsnXWxQcdbEhGOknBHEYsGk11bFOW3rEhdCcSmoNZfTnhA67EbaACqD6zZ24YUh/yxr8smsTXYlbY2ZIGqV5rz80QT0WgaTvrylQF2Us9D8wkcoVGrPnY1mMs66nCb0us3y2xpD8wvIShGFsNpOjQJRhIn3ODAVBmNAj4sc9eYiZthEtIEaxJIp+KNJVdsaOVc/tL4Uv3t+GOOBGN5xxYqavu5Cg4qzJobcGgmCqBRhCjJ37uyMJ0RmIGVy7YYuAMCDB2fUsxmb7MoMQQCo5tjojyTgMOnLnmHqyBZntVsAirDpXJt6UbCo7dg44ovCatDCbtShz22RbU3ujSRgNWhh0Km3xJqZOVM3gFow105/YDqMTrupYufAhciG3owpiIpzZ9nNGxUMQYCZrLNamoJwzvGdR09hTacdV6xqq9nrLkSoOGtiwvEU7WoTBFERS1osaLEa5s2dnZkMYTm1NJbF0lYr1nU78hZnlcyUqN7WGE2WPW8GSIWmhtVWORNmG7kBzy226rQ1jvmj6HKawBhDn9usoK0xrqpqBkiFuUGrqZqSJZQzofYOTkfQr1Km4kKjw25Cu92oqmOj+HxQUzkDalucPXFyEkdGA3jbFcurYlqzmKDirIkJxZJkpU8QREXkC6MORBPwBOOknFXAdRu68PTZ6Wzh4gnGodeysm3rAam11W3Rq9bW6Isk4DCVfz5aDUOL1VjTmbPpcBw6DYM9Z2PSbtRBr2WqtzWO+KJZA55elxmTobgsR0hvOAGXik6NAGAx6PC7916Omy9aqupxBa1WAywGLc5NSYv5wanmC6CuJRt7HDg4pJ4pyExxpk5RL1T24RoWZ9999BTabEZV3TEXK1ScNSnJVBqxZJraGgmCqJitS9w4NRGCN7O4PeORds+Xt9HirFyu3dgJzoE/HRoDkMk4sxor3lHudKiXdeaPJCpSzoBMEHUtlbOIVPjkfh8ZY3BbDKq3NY76olm1UrgWymlt9Ibjs9ou1WJNlx3mKs2ZM8YydvphxJNpjPqj6G9ip8Zqs7HXiePjgayBSqU8fHgcBp1GtegSk16LdrtRditupZwYD2DX0QnceslSaoVVASrOmpRwJleGDEEIgqiUrUtcAIDnMmHUp7M2+qSclcuaTjuWtVrwx0xr42QwhjZ75Qv2LqdJVUOQSouzdnttlTNvOA5nnnNusRowqWJbYyrNMR6IodspijNhmCGnOFNfOasF/S0WDEyFMeKLIM2BPnJqLMiGHifSHDgyWrl65g3H8ctnB/HqLT2qtsP2usw1a2u8+7HTMOo0ePPF1VF2FxtUnDUp4ZhUnNHMGUEQlXJ+nwsahuzc2dlMADXZ6JcPYwzXbuzCEyc88EUSqgXMdjlMGPWpUwz5I8mybfQFHXYjxmtpCBJK5FWlWm0GTIXUKxI9wRhSaZ5tDxMqkhzHRmnmrBmLMzPOTYUxkGltbOaMs2qzMWMKokbe2U/3nEM0kcZbL19e8bFy6XWZa9LW6AnG8Mtnh/DaC/qqlsO32KDirEkJZkJISTkjCKJSrEYd1nQ58Fxm7uz0ZAhdDlPVWqgWC9dt6EIyzfGXI2PZtsZK6XSYMBmKqZJfJClnlW3wtduN8ATjSM/JyasWhcw23BZDNl9MDYQjplDO2mxGGGRknaXTHL5I/gKy0VnSYkE4nsLzg14AQD/NnBWk12WGy6LHwQpNQRKpNH70xFlcfl6b6mHZvW5JOeO8un+bP3nqLOLJNN6mcnG5mKHirEkRQ8k0c0YQhBpsXeLCvnNepNM8Y6NPC7NKOb/PhS6HCX98YRSTwbhqbY2co2K1KppIIZ5Mq6KcpdJcdTOOQnjDCbjzqFKtVgMmg+opZyLjTMycaTQMvS4zBksoEYFoEmmubsZZrRB2+k+enIRWw7KFKTEfxhg29DjwQoWmIA8cGMGoP4q3Xr5MnRPLocdpQiyZhkflWcxcookUfvzkWVyztgMr221Ve53FRsnijDHWzxjbxRg7zBg7yBh7f+b2f2eM7WeM7WOMPcQYI3uWGhLKtDWSWyNBEGqwbYkbgVgSJyeCODMZVm0wfTGj0TC8dEMn/nJkHPFUGm0qKGeiza5SUxB/VFKZKjYEyRQvtTIF8Ubi2dDpXFqsRvijSSRSlSuKwHzlDIAsO32Rw+aq8PtaD4Sd/t4zU+h2mqDT0v59MTb2OHF0NFC2is05x/ceO40VbVZctbpD5bMDejPKZzVbG3/z3BAmQ3G8XW7o9Oc/D+zaNfu2Xbuk24kscv7ykgA+yDlfB+BiAP/AGFsP4Auc882c8y0Afg/g/1XvNIm5kHJGEISaCFOQR45OYCoUp3kzlbhuQxcSKamtSBXlLFMMVTp35o9I15BKrPQBqa0RQE1MQaKJFKKJdH5DkIwF+bRKpiCj/igMWs2sGZo+txlDJWbOsiHZ1iYszjKL+VgyTS2NMtjQ60Q8lcbx8UBZz3/m7DSeH/ThLZctg0ajfi5YtbPOOOf47mOnsaHHgYtXtMh70o4dwE03AX/5C5BKSYXZTTdJtxNZShZnnPMRzvmzmX8HABwG0Ms5z9VyrQBq03BOAABCcWEIQsoZQRCVs7zVCqdZj189NwQAlHGmEhcub8maQ6hlCAKgYsdG1ZSzTHFWC+UsW/jkaRlsydymVnvlqC+KTufs6IM+twWeYLyofbo3kgnJbsK2RrNBm/0dJTOQ0mzsyZiClNna+L3HT8Nh0uG1F/SpeVpZRHFWLeXskWMTODEexNuVhE5fdRXwwQ8C114LGI3Ay18OfOlLwM6dVTnHZkWRZs0YWwZgK4Ddma8/zRgbAHAzSDmrKeGsIQgpZwRBVI5Gw7B1iQuHR6SFBrU1qoNOq8FL1nUCgCqGIC6LHgadpuK2Rl+miKh05mxGOau+Y+N0SDrnfDNnQuFSK+tsxBdFt2N2gZLNOvMWVs+8TdzWCABLWqT32E82+iVZ1mqF1aAtyxRkYCqMP74wijdetKRq6ziHWQebUScr/qEc7n70NLocJly/SeZU065dwOWXAx/7GGC1SspZIgHcfjvw2tcCu3dX5TybEdnFGWPMBuCXAP5RqGac809wzvsB3APgPQWe907G2NOMsacnJibUOGcCucoZFWcEQajD1n539t9LaHGmGm++eCkuXN6iSsHLGMvY6VeonGWKs3wtgkqwGKQFYC2UM28kU/gUsNIHoFrW2agvmp3vE4jibKDIYnemgGw+5QyYKcpIOSuNRsOwoceJF8qw0//Rk2fAGMNtlyxT/8QyMMaqlnV2aNiPx054cNuly2DQ5ZQS+WbKvvY14LzzgKuvBs6eBf7pnwC9HvjkJwGnE3jTm6Q2x4svBq68EnjLW4CHH559jEU2lyarOGOM6SEVZvdwzn+V5yE/BfDafM/lnH+bc76dc769vb29/DMlZhEmK32CIFRm21IXAMnly6Snzxa1OL/fhXvfdYlq0QSqFGfRzMxZhVb6QO2CqL0Zq/x881yiGJpWoa2Rc45Rf3SeW2GvSypchooUZ95IAoxV3i5aL8SmDCln8ljf48ChYT9SCqIkgrEkfr5nAC/f1I0eV3WL4B6XqSptjXc/dhoWgxZvunDJ7DvETNmuXcAzz0gF1/veB0xOAl/5CnD33cCPfwzcey/wb/8G/OIXwIMPAj/5CfDlLwOnTwM/+IHU9viRj0jK2iKcS5Pj1sgA3A3gMOf8v3JuX5XzsFcCOKL+6RGFCMVTMOg00JObEkEQKnF+vwuM0bxZo9PlNFU+c6ZSWyMgFWcT/trNnLnM+XLOpPcxqUJb43Q4gXgynbXRF3TYjdBrWdE2MW84DqdZD20VDB5qwaZeJ8x6Ldmiy2RjrxORRAqnPUHZz7nv6QEEYkm89bJl1TuxDCLrTE3G/FHc//wQbtreD+fcFuOdO4G77gKuuw7Yvh3Yswd4xzuAwUHg/e8Hnn9eKszEjNnOndLXBw8C//iPwMmTUvG2ZImklF15pVSY5T5nESBny+wyALcAOMAY25e57eMA3sYYWwMgDeAsgHdX5QyJvITjSVhJNSMIQkUcJj1evrE769xINCZdThNGD0bBOZc/iD8HfzQBg06jikLabjfiUBmtXUoRypkrz8yZTquBy6LHlAptjSOZjLO5ylk266yIY+N0ONG082YA8JL1nXjmky+meXaZbOyVTEFeGPLjvA57ycen0hw/eOIMti1xYesSd8nHV0qPywxvOIFQLKnaGMxPd59DMs3xlkLF5fXXS+2KExPAhz40ux3xwx+e//idO2cKL70eePObgZtvBtatA558Evj4xxdVYQbIKM44548ByPfp/4D6p0PIJRRL0YcnQRCq8z83b6v3KRAl6HSYEE+mpUDmPJlfcvBHkqqoZoCkKD1SoZInh+lQHGa9tmBB2WI1qFKciZbRuTNngOTYWEo5a0anRgFjjNYWCjiv3QajToMXhnx49dbeko9/+PAYzk6G8c/XrqnB2c12bFzVWbp4lMPjJzzY0u/C0kJxK08+CXAuzZR985vAy16mvLh65BFgZET699e/Drz4xYuqQKOeuCZF2gUh5YwgCGKxoYadvj+aUGXeDAA67CaE4imEMrPQ1cIbSeR1ahS0WFQqzvwigHr+PFCpIGpvuPg5EgsLnVaDtd0OHJSpHN/92Gn0usy4bkNXlc9MQhi7DKrU2hhPprF/yIcLCql+Yj5MzJTde+/MDJpcxDF+8QugvR3YskX5MZocKs6alFA8SbtbBEEQi5Aup2RfX1FxFkmoppy11yjrzBuOw1lElVJTOdMwoM02/7X63GZ4gjFEE/mzzqabXDkjlLOxx4EXhnw4NVF87uyFIR92n57CbZcuha5GfgE9KmedHRrxI55MY9vSAsXZ3r35Z8r27pX/IuIYL3mJ5OT41FOSkYiSYzQ5VJw1KeF4ipQzgiCIRYgwqqjEsdEfTarmKJgNog5WtzibLqFKtdoMqljpj/ii6LCb8i6ge7NZZ/kXu95wIu9MHLFwednGboQTKVz9pb/ipm89iV89O5g3qPx7j0sOh6/fsSTPUapDh90EnYYVdRhVwrNnpwEA2wopZx/+8Pz2w50788+aFSL3GLfeCsTjUoujkmM0OVScNSmhGClnBEEQi5EOuwmMVVacBSIJOEzqXEOyQdRVdmycDseL5oe1WA2YDsfBuXxb83zkyzgT9Lkli/l8rY2JVBrBWLJpM86I8rh8VRue/NjV+Mh1azHuj+ID9z6PCz/zZ3zyNy/ghSEpoHo8EMXvnh/GjRf0VZwtqASthqHbZVLNsfHZc9PocZoK/n2oztatwIYNwI9+VJvXaxBodd+khOMpcmskCIJYhBh0GrRajRiroK3RF0motkjMKmeB6pqClFKl3BYDUmkOfyQ53+JbAaP+KFZ15LeSz87w5HFsLOYmSSxsOuwm3HHVSrz7yhXYfXoK/7t3APc+PYAfP3UWG3sd6LCbkExz3H7Z8pqfW4/TrFpb43PnvNhaqKWxGjAmqWcf+Qhw4oQUZr0IIOWsSQnH1bNFJQiCIJqLLqex7JkzznnGEESdIsJtMUCnYVUNok6nObwllLPWzIzYZKiy8xj1RedlnAk67KaCWWdekcNGytmihTGGi1e04suv34I9H38x/u1VG5BKA385Mo5r1nZieR0yJHvdZlXaGkd9UQx5I4VbGqvFzTdLRdqPf1zb160jtLpvUkKxFBVnBEEQi5Quh6moa2Axook0EimumiGIRsPQZjNWtTgLxJJI8+KqVItVUvCmQnGsaC/zdaIJBGPJeRlnAq2GoceV37HRmwn2JrdGAgCcFj1uvWQZbrl4KY6PBwsW/NWm12XGqD+KRCoNfQVGJM+ek+bNLqilcgYAvb2Slf6PfgR86lOAZuHrSgv/HS5AUmmOSCIFC7U1EgRBLEo6Haay2xr9UamIUMtKH5Dmzqrp1ihHlWrJ3FeJY2OxjDOBZKc/v61xOvO6NHNG5MIYw+pOe01nzXLpdZmR5qioDRqQzECMOg3WdztUOjMF3HorcOYM8PjjtX/tOkDFWRMSyVj4WskQhCAIYlHS5TBhOpwoaOleDH9G4VFLOQOkubNqKmfT4dKqVItNheIss4DtKqJy9Lryt4mJmbN6LcIJIh9Zh9EKWxufPTeNTb1OGHR1KB1uuAGwWheNMQgVZ01IOBP0aSErfYIgiEWJUHbK2Q2fUc5ULM4c9VfOWq1i5qz84mzEVziAWtDntmA8MD/rbDpzjm4rKWdE45DNOvOVX5zFkim8MOQvnG9WbaxW4HWvk/LPIuqYmzQyVJw1IaE4KWcEQRCLGVGclWOn749IG3xqWekDQLvNiMlQDMlUWrVj5uKVoZyZ9FpYDFpV2ho7HMaCjxGOjXMd8LyRBPRaRk7KREPR66pcOTs47Ec8lca2JS6VzqoMbr0V8PuB+++v3znUCCrOmpCQUM7oAkAQBLEoEW135Tg2VkM5a3eYwHllLYXFmJbphOi2GLKzX+Uw6o+i1WqASV/4+loo68wbjsNpNoAxVvbrE4TamPRatNkMFWWdlQyfrgVXXQX09y+K1kYqzpoQUZyRWyNBEMTipLOStsYqzJy12zJB1FVqbZwOJ8BY6XmuVpuhorbGYjb6gpmss9mL3elQgpwaiYakx2XGkLd8Q5DnznnR6zKjo06OkwAkl8Y3vxl48EFgdLR+51EDqDhrQsKZtkZSzgiCIBYndqMOFoMWoz7lxZAvor5bo2gDHK9SELU3HIfDpIdWU1yVarEaKlLvRnzRgjb6gk6HCToNm+fY6I0Uz2EjiHohmdjMdxiVy7Pnpus3b5bLLbcAqRTws5/V+0yqChVnTUgoTsoZQRDEYoYxhq4y7fT90SRMeg2MOvU2+IRyVi1TEG9YnipVaXE26osUtdEHpKyzbpdpXpuYN5yAk5QzogGRlLMIOOeKnzvsjWDEF63vvJlg3Tpgx44F39pIxVkTEo6RckYQBLHY6XKaMFKGA5s/klC1pRGQcs4AYNxfrbbGeMl5M0DKOiu3OIsmUpgOJ0oqZwDQ57LMb2sMx6mtkWhIel1mRBPpbCSFEuoWPl2IW28F9u0D9u+v95lUDSrOmhChnNlIOSMIgli0SMqZ8mLIH02oagYCSKYDDpMOE8HqKWcuOcqZzYBIIoVIXHn+m1AhS82cAfmDqCV1j9oaicajkqyzZ896YdJrsK4e4dP5eMMbAJ0O+PGP630mVYOKsyZkZuaMijOCIIjFSqdTamtMp5W1KvkjSVVt9AUdDlNVlTM5hc9M1pny85CTcSboc1sw5o8hlpSux5F4CrFkWpa6RxC1JmunX4Zj47PnprG51wW9tkFKhrY24PrrgZ/8BEgm6302VaFBvtOEEkKxJPRaVp+UdoIgCKIh6HKYkExzxe6E1VDOAKDDbqy7ciYKuOmQ8vYtkXFWauYMyM06k54zY/VPbY1E41FucRZNpHBw2IetS11VOKsy+fznga1bJcfGhx+Wbtu1S7p9gUCr+wbh3GQYDx8ek/XYcDxFqhlBEMQiR7TfKTUFqcbMGSDNnVXDrTGRSiMYS8pTzmzlK2ciM05JcSZaG0VxRjNnRCPisuhhMWgVtzUeHPYhkeL1zTeby44dwNe/DthskjHIrl3ATTdJty8QqDhrEP7t94fw7p88g0QqXfKxoVgSVjIDIQiCWNSIIkIoPnLxR5Oq2ugLOuxGTARiZTnCFcObMTGQNXNmlYxJyjEFGfVFYTfqZM1z987JOvNlz5HaGonGgzGGHpcZwwqVs2fPegHUOXx6Ljt3AvfeK7U03nsvcOON0v937qz3makGFWcNgC+cwF+PjSOR4jjjCZV8fDiegoXMQAiCIBY1wlVwVIFyxjmvqnIWTaQRiKk7B+LNtgzKcGvMzJyVU5yNyLDRF3Q5TNBqWFaJmFZQQBJEPejN2Okr4dlz0+hvMWfdWBuGnTsltSyZBK66akEVZgAVZw3BHw+OIJGSdhqPjQVLPj5IyhlBEMSip81mhFbDFClnkUQKyTSHsyozZ1JhI8cURIm6JgofOS2DDpMOOg0rWzmTW5zptBp0O0152hpJOSMakx6FxRnnHM+cnW4s1UywaxfwwAOAyQT8/vfS1wsIKs4agPufH0avywwNA46NBUo+PhxP0swZQRDEIkerYWi3GRUpZ76IVOhUwxBE7K6XCqIOxZK44vO78OMnz8g6rldB4cMYg7vMIOpRfxRdMmz0BZKdfqatMfN9rUbRSxBq0Oc2YyoUlx0zMeSNYDwQa7ziTMyY3Xuv9H+DQfr/AirQqDirM+OBKJ48OYnXbOvFkhYLjo+XLs5CsRSsRlLOCIIgFjvCTl8u/ojUcliNtsYOEURdwhTknt1nMTgdwV+PTcg6rpKZM0Cy01fqYJlIpTEeiMkKoBb0uWeCqKdDcZj1Wpj0dG0mGhOljo3PnvMCaKDwacHevTMzZq98JRAIAJ/4hHT7AoHklzrzf/tHkObAK8/vwZHRgKy2RlLOCIIgCADochhxaqL0rLLAHxXKWTUMQaTCpphyFk2k8J1HTwMADgz5ZB13WsHMGSDNnU0rLM4kIxOgS0bGmaDPbcZYIIpYMoXpcIKcGomGpscl4h8iOK/DVvLxz56dhlmvxdoue7VPTRkf/vDMv1/6Ukk5GxgAvvSl+p2TypByVmfuf34Y67odWNVpx+pOG854Qognizs2huIpWMkQhCAIYtHT5TApamv0i7bGKihnDrMOBp2maHF239MDmAjE8JL1nRjzx2SpftPhBPRaJnvWupy2xpkAavnKWa/LDM6BEW8UvkicnBqJhkY4jMpVzp47N43NfU7oGiV8Oh92O3DN/2/v/qOjvO47j7/vjDRIGv0YSaCRkAQYJBlDhDB27DjGP3Da2nVj0R+pE8frpql30yTO6ea03jStm6Sn3f1j4ybn7K7d+HSz3U1P0sRuGwdcO02dhiRgx9guBoQNRvgXCBAg0O+RNCPN3T+eGSHEjDQz0jDPaD6vc3QknnkezdV8gUffud/7vR+CHTtgkbvE5pKLX/Gl7/j5EK8dH6CzYyUAbcEKJqOWd+bp2BhSQxAREcGZ6Rken2Q0xQ6JF2fOFj85M8ZZA5csOYtMRXniZ2+zZVWAT926FoCunvlnzwZCTuJjjElpHJmUNcaTxGBaa87KAOeX3f5QhGq/Zs7EvYIVyy7pMDoXZ/PpIba4raQxkc5OeOstOHw41yNZNErOcuiZg6cAuKejAYDWOmfqeK6mINGoJRRRK30REYH6KmedV6qzZxfXnGXnHuJsRJ04OfvBayc5OTDG5+5oYePKSjwmtdLGgTRLBmv8PgbHIintGxqXyczZzI2o+0NhAqWaORP3KvJ6qK8sSWmvs66Tg0xGXbb5dDL33ON83rkzt+NYRErOcmjn/lNct7p6+t23tSv8eAx0z5GcjU9OYS2aORMRkemZnjMpttOPlzVWZKGsES5uRD3bVNTy1z99iw0NlWy7uo4yXxEtdeUpJWf9ofRKBuN7ncUbiaSid3CMZUWetPYpa6hy9jrr6R9jIBTRHmfieo2BUt44PcTE5NwdG/e91w/AtasCV2BUC9TYCNdf75Q2LhFKznLkzd5h3jwzPF3SCFBS7GVNrX/OpiCjE84/KM2ciYhIvPV7yjNn4xFKi734irJz+3dmzi4fy3Ndp3mnb5TP3dEyXZ7Y3hjgYM/gvHueDYQiBNIow8xkI+rTsT3OUi2dhIszEccvhBgIhbXHmbjeb2xp5EjvMPf/772cH0m+NnTf8X5W15axvNxlm08n09kJe/dCb2+uR7IolJzlyM4DJ/EYuLu94ZLjrcFyjs7RTj8UdkpSNHMmIiLxTZPTKWvM5l5cdRUl9IcilzS2stby+K5jrFvh566N9dPH2xsr6RuZ4Mw8m1b3p5n4xJOz86Pzb4YddybNPc7imqpLOXJ6mKhNvdW/SK7cd8Mq/td919J1cpDtj7/Am72X/77pbD49kB8ljXHbtzsNQZ59NtcjWRRKznLAWsszB05zc8vy6U0749qCFbzbN8p4JPGU80hs0bda6YuISJmviIqSopTLGgfHIllpox8Xv6f1zXhX/t8On+VI7zAPbWvB47k4M9XeFADgYM9A0u9nrXVmztJotlHrd8aQ7sxZOuvN4hqrSzl2zql2UbdGyQf3dKzkyd+/iYnJKL/1jRfZdeTsJY/39I/RNzLBlnwoaYxrb4fVq5dMaaOSM5i39nax7T8xwPELIe6ZUdIY1xasIGpJum9NKLazuzahFhERcNY+pVPWmI02+nEXN6J2kjNrLY/tOkZzTeklZfwAGxoq8XrMnOvOxiJThKeiac2cxbsmprrXWTRqnZmzNPY4i2uqLmMq6pRlap8zyRebmwPs/NzNrK4t48FvvcI3d789XV6877iz3iwvOjXGGeOUNj7/PIRCuR7NghV8cvYfvrmXz3573xV9zp0HTuHzerhzRnlHXFvQ6djYnaS0cVQzZyIiMkOwsoTeVBuCjEey0kY/rq7SSc7iTUFeOHae/ScG+PRt6y7bL6nU56V1nqYg/bGmHukkPvFELtV2+hdCYSJTlvrK9NfXxDs2gsoaJb80VJXyD5++iV/eEOS/PnuYP326i/BklH3v9VPm83J10GWbT89n+3YYH4cf/zjXI1mwgk/O6qtK2H9iYN4FyYtlKmr554Onuf3qFQnr/q9a7qfIY5K209fMmYiIzJTORtRDY5NZa6MPF8sa401BHtvVTbByGR+5rinh+e2NVXTN0RQkPvtVlUab+mKvh6rS4pTLGuOJbWYzZzOTM5U1Sn4p8xXxjfuv47O3r+O7L5/gd/52Ly++dZ6OpoC7N59O5NZboapqSZQ25tkrv/g6mgOcHw3Tk8KmfIth79vnOTc8Qefmy0saAXxFHtYsT96xMT5z5tfMmYiI4LzJeG54gskU9vXK9sxZvLvbueEJ/v29C7z09gU+des6lhUlfkNxU1MV50fD0/uMzTaQwcwZOBtRp5qcZbLHWVxzbCscQN0aJS95PIYv3LWer9/bwb73Bug+O8KW1YFcDyt9xcVw993wzDMwdWWXKy22gk/ONscWJB+YY0HyYtp54BR+n5cPrQ8mPactWJ50r7OLM2dKzkRExClrjFroG5k7GbHWMjSW3TVnxV4PNX4fZ4cneOwnx6jx+7jvhuak57+vsQqAgz2JSxsHxpyfqdqfXuJTnUZy1jvovDmbSXJWX1VCvMdJNrtgimTbb25p4u//041sWRXgw5sSTyC4XmcnnDvntNXPYwWfnK1vqMBX5GH/8YGsP1d4MsoPD/XyyxuClM7RCr+1roL3LoQSdmwcDcfXnKmsUUREUt/rbDQ8RdSS1W6N4DQFefFYH7vePMeDW6+ac430NQ2VFHkMXScHEj4eX3OW7nqumnSSs6FxvB5DbQZ7OhXH9jqrLCnC60l9jzQRN7p+TQ3f/+zNXNNQmeuhZOauu6CoCHbuzPVIFqTgk7Nir4f3ray8IjNnPz96jsGxCNs3N855XluwAmvh2NnLSxtDE1N4PYZlWdpAVERE8sv0XmfzNAUZGnMSnWzOnIGz7uzd8yEqSop44KbVc55bUuylLVhB18mhhI8PxBKsQBprzsApa0y1IcjpwXGCFcsyTq6aqsvSntkTkSwIBOD225WcLQUdzQG6Tg6mVK+/EDsPnKK6rJitrcvnPK8tWA4k7tg4Gp6kzOfFGL1DJyIiF5OzM/PMnMVLB2uynEjEm4L87gfXpJQIOk1BEjfm6g9F8Pu8+NJ8Q7La76N/NJxSs6/ewfHp1zATH31/M/fdsCrj60VkEXV2wuHD0N2d65FkTMkZzn4P45EobyZZ57UYQuFJnn/jDL/a3kDxPB1w1iz3U+w1CZuChCam1AxERESm1ZT5KPaapE01AAZDEb6y8xBXByu47eoVWR3P2uV+KkqK+OTNV6V0fntTFf2hSMLGXAOhcEZdEGv9PiajlqHxyXnP7R0cpyGDTo1xv3VdE5++bV3G14vIIursdD7n8eyZkjOc5AzgwInke60s1I8Pn2UsMnXZJpyJFHs9XLXcn7ApyGh4kjK10RcRkRiPx1BXUTLnzNmfP/M650fCfO3ejqSdExfLp25dx08fvj3lGbr2WFOQQwn2OxsYi0xvKp2O+HPPt+7MWkvv0DjBysxnzkTERVavho4OJWf5blVNGdVlxRw4MZC159i5/xT1lSXcsKYmpfNbgxUJZ85GJyY1cyYiIpeor0q+EfWPXu/l6ddO8tC2lunuiNnkK/Kk1VxjfUMFxV7DwQTJWX8onFGL+lSTs6HxSULhqYw6NYqIS3V2wp490NeX65FkRMkZYIyhoznA/iwlZ0PjEX529Cwf3tSAJ8UFx211FZzoDzEWvrRj42h4Sp0aRUTkEvWViWfOLoyGeeTpLjY0VPLQtpYcjGx+y4q8XF1fQVeCdvoDoUhGLepTTc4ubkCt5ExkyejshGgUnnsu1yPJyLzJmTGm2Rizyxhz2BjzujHmP8eOP2qMOWKMOWiMedoYE8j6aLOooynA0bPDjEzMX5+erheP9RGZsvzKxvqUr2kLlifs2BgKT2qPMxERuUR9VQm9Q+OXNcD40o5DDI5F+PpHO9JuqnEltTdW0XVy8LLxL3zmbGLO807H9jhTciayhFx3HaxcCTt25HokGUnlf+pJ4I+stdcAHwAeMsZsAJ4H3met3QQcBf4ke8PMvs3NAaxNXPO+ULu7+/D7vFy7KpDyNa3BCgCOzlp3FprQzJmIiFyqvrKEUHiK4RlvMP7zwVM8e/A0n/+lNtbXu3vfovbGAINjEU5cuNgUZCpqGRyLUJ3mHmcAtX6nrHK+dvrx2cZ6rTkTWToefdRJ0H70IxiPVRTs2gVf/Wpux5WieZMza+1pa+2+2NfDwGGg0Vr7r9ba+F3gJaApe8PMvo5YU5BslDbuOdbHTetq5+3SONOa2jJ8Xg9HZ7XTHw1rzZmIiFwqOGuvs3PDE3zpB4foaKri929dm8uhpWRTk7MW7uCMzaiHxyNYS0bdGkt9XkqLvfTPk5zFO1yqIYjIEvL+98PPfgajo05StmsX3HuvczwPpFXjYIxZA1wL7J310O8BP1ykMeVEjd/HqpqyRW8KcuJCiPfOh9jaMvfeZrMVeT2sXeGne1ZTkNDElMoaRUTkEvGZn95Bp7Txkae7GA1P8bV7OyhK443BXGkLVuDzeuiaUb3SH3I2zc6kWyM49/X5Zs56B8dZXr7M1SWfIpKmbdvgqaecr//sz5zE7KmnnON5IOX/jYwx5cA/AZ+31g7NOP4ITunjd5Jc9yljzKvGmFfPnTu30PFm1ebmwKInZ7u7nU4xW1vT31fG6dh4cebMWuvMnKmVvoiIzDCdnA2N84P9J/nXN87w8K+00VJXkeORpcZX5GF9w6VNQfpDTmIVKM1s0+wav2/OhiBTUcvbfaPUV6XeWVJE8sSddzrJ2L598JnP5E1iBikmZ8aYYpzE7DvW2u/POP4J4MPA/Xb2Kt4Ya+3fWGuvt9Zev2JFdje+XKiO5gCnBsc5O8deMena3X2OhqoS1q3wp31tW105Pf1jjMbWEExMRolaKFNZo4iIzFBX6SQYB3sG+MqO17ludTUPbnV/OeNMs5uCDMSTswzWnMHcydmr715g++N7ePmdC9y0tjazAYuIe+3aBV1d8KUvwTe+4fw5T6TSrdEA/wc4bK39+ozjdwF/DHRaa0PZG+KVs7nZqXlfrHVnU1HLi2+dZ2vLcpyXMT3xpiDxjo3xJE0zZyIiMlNJsZcav49vv3Sc8FSURz+yCW+KW7e4RXtjFcPjk7x33vmVYiBe1pjBmjNInJz1Do7z+e+9xkee+AV9w2H+x8c286d3X7OwgYuIu8TXmD31FPzFXzif7703bxK0VGbObgYeAO4wxuyPfdwNPAZUAM/Hjj2RzYFeCRtXVlHkMRzoGViU79d1cpDBsQi3tGU2Y9gWLAcudmwMxfY808yZiIjMFm9q8YU717N2RXmOR5O+9ummIE5pY/8iJmfjkSke+0k32/7qpzx3qJfPbWvhJw/fxvbNjRm9eSoiLvbKK5euMYuvQXvlldyOK0Xz/pZvrd0DJPqfKz93dptDSbGX9Q0VizZztqfbWWN387rMSiZW1/rxFXnojs+chWMzZ2qlLyIis9x4VQ0rq0r43Q+uyfVQMtIWrMBX5KGrZ4DOjpUMhMJ4DFSUZPaGZI3fRyg8xc4Dp3j0R0c4cWGMOzcGeeTuDayqLVvk0YuIa3zhC5cf27Ytb9adaQpmlo6mADv3nyIatXgWWBLy8+4+Nq6spLY8s8XGXo9h3Yry6ZmzeFljmbo1iojILH/euTHXQ1iQYq+Haxoqpzs29ofCVJUWZ3wvro1tRP0H332NtmA5337wRra2ptc5WUTkSlPv2Fk2NwcYnpjk7b6R+U+ew+jEJK8d71/wjaAtWD7dTn90wilr1MyZiIgsRZsaqzh0coho1NIfimRc0giwZXU1rXXlfOWeDTz7B7coMRORvKDkbJbN05tRD8594jz2vnOeyJTllpaFdahsC1ZwcmCMkYlJQrGyRq05ExGRpai9qYqRiUnePT/KYCiScadGcO6fz//hbXzy5qsozoO93kREQMnZZdauKKd8WdGC9zvb3d3HsiIP16+pXtD3aa1zFnV3nxm+OHOmbo0iIrIEtTc6TUG6Tg7SHwovaOZMRCQfKTmbxesxbGqqWnBTkD3dfdxwVQ0lxQtLpNpi7fS7z4xMz5z5teZMRESWoNa6cpYVeTjYM8hAKEJAyZmIFBglZwl0NAc4fHqI8chURtefHhyj++wItyxCfXtzTRnLijwcPTPMaDi+5kzJmYiILD1FXg8bV1ZOz5wtpKxRRCQfKTlLoKMpwGTU8sbpoYyu39PdB8DWBa43A2cmr6WunKNnRwhNTGIMlBQrbCIisjS1N1bR1TNIKDxFtZIzESkw+i0/gWtXBQDYf3wgo+v3HOtjebmP9fUVizKetmAFR3udmTO/r0gbZoqIyJLV3hRgLFa5orJGESk0Ss4SCFaWUF9ZwoGegbSvjUYtLxzrY2vL8gXvkxbXGiynd2ic3sFxytRGX0RElrB4UxBADUFEpOAoOUuio7kqo46NR3qH6RsJs7V14SWNcW11zgzc/hMDagYiIiJL2roVfkpjzbS05kxECo2SsyQ2N1fz7vkQ/aPhtK7b3X0OgK0ti7fZZbxj48mBMc2ciYjIkhZvCgJKzkSk8Cg5S6Kj2SmrSLe0cc+xPlrryqmvKlm0sTRVl06/i6hOjSIistS9L1baqLJGESk0Ss6SaG+swhg4cGIw5WvGI1O8/M4Fti5CC/2ZPLGOjQBl2oBaRESWuF+/tpG7NtZTV7Es10MREbmilJwlUVFSTGtdOftP9Kd8zavv9jMxGeXWRVxvFtcadJIzzZyJiMhSt7k5wBMPXEeRV7+miEhh0f96c+hoCnCgZxBrbUrn7+4+R7HXcOPamkUfS3zdmdaciYiIiIgsTUrO5rB5VYALo2F6+sdSOn93dx9bVlVTloXZrbb4zJm6NYqIiIiILElKzubQ0RQA4LUUWur3jUzwxukhblnk9WZxrbF2+n6tORMRERERWZKUnM3h6voKlhV5Utrv7IVjfQCLur/ZTI2BUn5tUwMfXJed5E9ERERERHJLNXJzKPZ6aG9MbTPqPd19VJUW0x5r/7vYPB7D4x/fkpXvLSIiIiIiuaeZs3l0NAfoOjlIZCqa9BxrLbu7+7i5pRavx1zB0YmIiIiIyFKh5GweHc0BJiajfHnH6zz5ynH+/b1+Bscil5zz1rkReofG2dqSnZJGERERERFZ+lTWOI+tLcvpaA7w/X09fPfl49PH6yqW0VJXTmtdORdCTrKWrWYgIiIiIiKy9Ck5m0eN38eOh25mKmrp6Q9x7OwI3WdHpj//076TjExM0hYsp7mmLNfDFRERERGRPKXkLEVej2F1rZ/VtX4+dE1w+ri1lt6hccqK9VKKiIiIiEjmlFEskDGGhqrSXA9DRERERETynBqCiIiIiIiIuICSMxERERERERdQciYiIiIiIuICSs5ERERERERcQMmZiIiIiIiICyg5ExERERERcQElZyIiIiIiIi6g5ExERERERMQFlJyJiIiIiIi4gJIzERERERERFzDW2iv3ZMacA967Yk+YuuVAX64HIQkpNu6kuLiXYuNOiot7KTbupdi4k+KycKuttSsSPXBFkzO3Msa8aq29PtfjkMspNu6kuLiXYuNOiot7KTbupdi4k+KSXSprFBERERERcQElZyIiIiIiIi6g5MzxN7kegCSl2LiT4uJeio07KS7updi4l2LjTopLFmnNmYiIiIiIiAto5kxERERERMQF8io5M8bcZYx50xhzzBjzxRnHnzTG7I99vGuM2Z/k+hpjzPPGmO7Y5+rY8ftnXL/fGBM1xmxOcP13Ys9/yBjzt8aY4thxY4z5n7FxHTTGbMnOK+BeLo7NemPML4wxE8aYh7Pz07ubi2Nzf+zfy0FjzIvGmI7svALu5OK4bI/FZL8x5lVjzNbsvALulcXYFBtjvmWM6TLGHDbG/EmS668yxuyNXf+kMcYXO17Q9xoXx0X3GffGpqDvM+Dq2BT8vSYpa21efABe4C1gLeADDgAbEpz3NeDLSb7HV4Evxr7+IvDfE5zTDryd5Pq7ARP7+C7wmRnHfxg7/gFgb65fL8VmOjZ1wPuB/wY8nOvXSrG5JDYfBKpjX/9qIf27cXlcyrlY8r4JOJLr12upxAb4OPC92NdlwLvAmgTXPwV8LPb1E7rXuD4uus+4NzYFe5/Jg9gU9L1mro98mjm7AThmrX3bWhsGvgdsn3mCMcYA9+L8opHIduBbsa+/Bfx6gnPuS3a9tfY5GwO8DDTN+L5/F3voJSBgjGlI+SfLf66NjbX2rLX2FSCS1k+0dLg5Ni9aa/tjp73ExX9PhcDNcRmJHQPwA4W2MDmbsbGA3xhTBJQCYWAowfe+A/jHBNcX8r3GtXHRfcbVsSnk+wy4OzaFfq9JKp+Ss0bgxIw/98SOzXQLcMZa253kewSttacBYp/rEpzzUZL/BQWcqVzgAeBf0hjbUubm2BS6fInNgzgzAoXC1XExxvyGMeYI8Czwe3NdvwRlMzb/CIwCp4HjwF9Zay/MurYWGLDWTiZ4/kK+17g5LoUuX2JTaPcZcHlsCvxek1Q+JWcmwbHZWXbSd4lTegJjbgRC1tpD85z618DPrbW70xjbUubm2BQ618fGGLMN56b5x5mOIQ+5Oi7W2qettetx3uH8y0zHkKeyGZsbgClgJXAV8EfGmLVpPH8h32vcHJdC5/rYFOh9BlwemwK/1ySVT8lZD9A8489NwKn4H2LTqr8JPDnj2P+NLTR8LnboTLwEJPb57Kzn+Bjzv8v8FWAF8Iepjq0AuDk2hc7VsTHGbAK+CWy31p5P4+fKd66OS5y19ufAOmPM8lR+qCUim7H5OPAv1tqItfYs8AJw/azn78MpVyxK8PyFfK9xc1wKnatjU8D3GXB5bOIK9F6TVD4lZ68ArbGuLz6cXzx2znj8l3AWE/bED1hrP2mt3WytvTt2aCfwidjXnwB2xM81xniA38apx03IGPMfgTuB+6y10RkP7QR+xzg+AAzGp4ALhJtjU+hcGxtjzCrg+8AD1tqjC/gZ85Gb49ISWyeAcboB+oBC+oUmm7E5DtwRu1f4cZp6HJn55LE1GLuAjyS4vpDvNW6OS6FzbWwK/D4D7o5Nod9rkrMu6EqS6gdOp6qjOJ1nHpn12P8DPj3P9bXAvwHdsc81Mx67HXhpnusnY8+9P/bx5dhxAzwee6wLuD7Xr5ViMx2bepx3joaAgdjXlbl+vRQbC847mf0zjr+a69dKcbHglP28Hjv2C2Brrl+rpRIbnO5k/xB7fd8A/kuS69fiNGk5Fjt/Wex4Qd9rXBwX3WfcG5uCvs+4PDYFf69J9hFvYSkiIiIiIiI5lE9ljSIiIiIiIkuWkjMREREREREXUHImIiIiIiLiAkrOREREREREXEDJmYiIiIiIiAsoORMREREREXEBJWciIiIiIiIuoORMRERERETEBf4/wRS14cI4FgcAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
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"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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aWtDe3o5NmzYBADZs2ICrr74ajLF5P9sNN9wArVYLrVaLK6+8Evv378dzzz2X9ritra246KKLEs//wx/+gJ/+9KeIRCIYHh7GiRMn0NnZmdN7d/PNN0Mul8Pj8eCFF17AzTffnLgvGAzmdKx05GSWZIy1AdgKYB+AbwG4jDH2VQABAJ/inL9YlFURJYFzjs8/cBQapQwyGdA17l3sJREEQRAEQSxr5HI5du3ahV27dmHTpk34zW9+M684UyqVieQ/mUyWsEHKZDJEIhEAgEKhQCwWSzwnVYT7008/jSeeeAJ79uyBTqfDrl27Uj6Oc47bbrsNX/va12bd/uCDD+aVQMg5x2c/+1m8733vm3X7+fPnEz9Lpp8NmJ98yBjLeFy9Xp/4/56eHnz729/Giy++iKqqKrzjHe9IG3Gf/DpzHyMeMxaLwWKx4NChQ9l+9JyRXJwxxgwA/gzgY5xzF2NMAaAKwEUALgDwB8ZYR1yqS37eewG8FwBaWlqKtnAid/54YAD7ehz4+k2bcM/eXnRPUHFGEARBEAQBIKPCVSpOnz4NmUyGVatWAQAOHTqE1tbWvI7V1taGH//4x4jFYhgcHEz0ayXjdDpRVVUFnU6HU6dOYe/evYn7lEolwuEwlEolrr76atxwww34+Mc/jpqaGjgcDrjdbuzcuRMf/ehHMTk5CZPJhD/+8Y/YvHlz1rVde+21+MIXvoBbbrkFBoMBg4ODUCqVOf18f/nLX/DZz34WXq8XTz/9NL7+9a9Dq9VKOq7L5YJer4fZbMbo6Cj+9re/YdeuXQAAo9EIt9udCC2pra3FyZMnsWbNGjzwwAMwGo3zjmcymdDe3o4//vGPuPnmm8E5x5EjRyS9F9mQVJwxxpQQCrP7OOf3x28eAHB/vBjbzxiLAbABGE9+Luf8pwB+CgA7duygBIpFYsITxFcfPYkL26rx5h3NeL5rEof6pxZ7WQRBEARBEMsWj8eDD3/4w5ienoZCocDKlSsTIR25cumllyYsghs3bsS2bdvmPebVr3417r77bnR2dmLNmjWzbH/vfe970dnZiW3btuG+++7DV77yFVxzzTWIxWJQKpX47//+b1x00UW48847cfHFF6O+vh7btm1LBIVk4pprrsHJkydx8cUXAxDsnPfeey/kcrnkn+/CCy/Eddddh76+PnzhC19AQ0MDGhoaJB138+bN2Lp1KzZs2ICOjg5ceumls37u17zmNaivr8fu3bvx9a9/Hddffz2am5uxceNGeDyelOu577778IEPfABf+cpXEA6H8da3vrUoxRmbI3TNf4Cg7f0GgINz/rGk298PoIFz/kXG2GoATwJomaucJbNjxw4ups4QC8vHfv8yHjk6jL999DKsrDHiu/84gx8+dRYn/9+roVFK/8UgCIIgCIJYKpw8eRLr1q1b7GUQWbjzzjthMBjwqU99arGXkjOpPmOMsYOc8x2pHi9lCPWlAG4FcBVj7FD8z2sB/BJAB2PsGIDfA7gtU2FGLB7Pnh3Hg4eG8IErVmBljSDNrrDrwTnQ5yifoXsEQRAEQRAEsZyRktb4HIB0nX9vK+5yiGITCEfx+QeOocOmx79duTJxe4dNmOHQPe7B6tr5XlqCIAiCIAiCKAfuvPPOxV7CglGa0dZE2fCDJ8+iz+HD/75n5yz7YptNmIJOoSAEQRAEQRAEUR5IsTUSFcqpERd++s9uvGl707x5ZkaNEjVGNbopTp8gCIIgCIIgygIqzpYosRjH5+4/CpNWic+/NnWja7tNj+7x1Ak0BEEQBEEQBEEsLFScLVHu29+Hl/qm8Z/XrUOVXpXyMR12A3rI1kgQBEEQBEEQZQEVZ0uQaIzjO4+fxiUrrLhxa2Pax3XY9JjyhTHlDS3g6giCIAiCIAgRuVyOLVu2YOPGjbj55pvh8+WfpP2Od7wDf/rTnwAAt99+O06cOJH2sU8//TReeOGFxP/ffffd+O1vf5v3a4ucP38eGzdunHXbnXfeiW9/+9s5HadY66k0KBBkCXJqxIVpXxhv3tEMYUxdajrsegBCKMj2NOoaQRAEQRAEUTq0Wi0OHToEALjllltw99134xOf+ETi/mg0mtOwZpGf//znGe9/+umnYTAYcMkllwAA3v/+9+f8GqUiEomU1XoWElLOliAHzk8BALa3VmV8XId9Jk6fIAiCIAiCyMA3vwns3j37tt27hduLxGWXXYZz587h6aefxpVXXol//dd/xaZNmxCNRvHv//7vuOCCC9DZ2Ymf/OQnAADOOT70oQ9h/fr1uO666zA2NpY41q5du3DgwAEAwGOPPYZt27Zh8+bNuPrqq3H+/Hncfffd+N73voctW7bg2WefnaVuHTp0CBdddBE6Oztx4403YmpqKnHMT3/607jwwguxevVqPPvsszn/jJmO/bnPfQ5XXHEFvv/97yfWMzQ0hC1btiT+yOVy9Pb2ore3F1dffTU6Oztx9dVXo6+vD4CgHn7kIx/BJZdcgo6OjoSSWClQcbYEOdA7hTqTBk1V2oyPa6rSQiFjFKdPEARBEASRjQsuAN785pkCbfdu4f8vuKAoh49EIvjb3/6GTZs2AQD279+Pr371qzhx4gR+8YtfwGw248UXX8SLL76In/3sZ+jp6cEDDzyA06dP4+jRo/jZz342y6YoMj4+jve85z3485//jMOHD+OPf/wj2tra8P73vx8f//jHcejQIVx22WWznvP2t78d3/jGN3DkyBFs2rQJX/rSl2atc//+/bjrrrtm3Z5MV1fXrILq7rvvlnTs6elpPPPMM/jkJz+ZuK2hoQGHDh3CoUOH8J73vAdvfOMb0draig996EN4+9vfjiNHjuCWW27BRz7ykcRzhoeH8dxzz+Hhhx/GZz7zmRz/JRYXsjUuQQ6ed2B7W1VGSyMAKOUytFh16KE4fYIgCIIgljsf+xgQtxempaEBuPZaoL4eGB4G1q0DvvQl4U8qtmwB7ror4yH9fj+2bNkCQFDO3v3ud+OFF17AhRdeiPb2dgDA448/jiNHjiRUIKfTibNnz+Kf//wn/uVf/gVyuRwNDQ246qqr5h1/7969uPzyyxPHqq6uzrgep9OJ6elpXHHFFQCA2267DTfffHPi/ptuugkAsH37dpw/fz7lMVasWJGwagIzQ6SzHfstb3lL2nU9//zz+PnPf55Q6/bs2YP7778fAHDrrbfiP/7jPxKPfcMb3gCZTIb169djdHQ0489bblBxtsQYnPZjyBnAe7JYGkU6bHp0T5CtkSAIgiAIIitVVUJh1tcHtLQI/18gyT1nyej1+sTfOef44Q9/iGuvvXbWYx599NGsm/Gc86yPyQW1Wg1ACDKJRCJFOy4w+2dOZnh4GO9+97vx0EMPwWAwpHxM8s8orhEQfv5KgmyNS4wD5x0AgAvaMu+KiHTYDTg/6UM0VlkfXIIgCIIgiKJy113A009n/nPHHYDPB3zhC8J/77gj8+OzqGZSufbaa/E///M/CIfDAIAzZ87A6/Xi8ssvx+9//3tEo1EMDw9j99yeOAAXX3wxnnnmGfT09AAAHA7hWtFoNMLtds97vNlsRlVVVUKhuueeexJKV6Hkc+xwOIw3v/nN+MY3voHVq1cnbr/kkkvw+9//HgBw33334RWveEVR1rjYkHK2xDjYOwWdSo61dUZJj++w6RGKxDA07Udzta7EqyMIgiAIQiQQjuJV33sGn3/terx6Y91iL4fIhthj9oc/AFdeKfxJ/v8Scvvtt+P8+fPYtm0bOOew2+148MEHceONN+Kpp57Cpk2bsHr16pSFjt1ux09/+lPcdNNNiMViqKmpwT/+8Q+87nWvw5ve9Cb85S9/wQ9/+MNZz/nNb36D97///fD5fOjo6MCvfvWrov0suR77hRdewIsvvog77rgDd9xxBwBBMfzBD36Ad73rXfjWt74Fu91e1DUuJmwhpb4dO3ZwMTWGKA2v/f6zqNIrcd/tF0l6/L7uSbzlp3vxm3ddiCtW20u8uuUJ5xzffvw0btjSiNW10opmgiAIYulzfMiJ637wHN57eQc+99p1i72cZcnJkyexbp3E9/6b3xTCP5ILsd27gRdfBJL6nQgimVSfMcbYQc75jlSPJ1vjEsIdCOPUiAvbW6VZGgGK018Ixt1B/PfuLjx0aGixl0IQBEGUEV3xQK7Baf8ir4SQxH/8x3yF7MorqTAjigoVZ0uIl/umEePABW3Sm1NtBhWMagW6KbGxZAw5AwCAEVdgkVeysNyztxdv+O/nF3sZBEEQZUvXmLAxOjhFxRlBEAJUnC0hDvROQcaArS3SizPGGDrsevTQrLOSMRzfER1dZsXZC+cmcKh/GqFIbLGXQiwyD7w8gEP904u9DIIoO7rirpUhUs4IgohDxdkS4mCvA2vrTDCoc8t56bAbyNZYQkS7yohzeRVnYsHv8IYWeSXEYhKJxvDZ+4/iJ890LfZSCKLsEG2NY+4ggpHoIq9m+VJpUetE5ZDPZ4uKsyVCJBrDy33T2JGDpVGk3abHkDMAf4hODKVgOF6ULSflLBbjieJswhNc5NUQi0nXuBeBcAy9k77FXgpBlBWxGEf3uAfVehUAYNRJ35WLgUajweTkJBVoRNHhnGNychIajSan51GU/hLh5LAbvlAUOyTON0umwy4M/OuZ8GJ9g6mgdURjHL/b34ebdzRBrZAXdKylgmhXcQUi8Iei0KqW/vsy7AogGLczUnG2vDk26AQA9E56iz4IlSAqmcFpP4KRGK7dYMNDh4cwMO1Di5VG2iw0TU1NGBgYwPj4+GIvhViCaDQaNDU15fQcKs6WCAd6hYGCO1rzU84AoHvCU3BxdrB3Cv/54DFYdEpc39lQ0LGWCkNJdsYRVyDxfi9lepICZiY9ZGtczhwbEoozbyiKCU8IdqN6kVdEEOXBuXg7wWWrhOJsaHr5uCvKCaVSifb29sVeBkEkIFvjEuFA7xQazBo0WLQ5P1csFnqKkNgoJhKep4CRBEPTfjRVCf8uy6XvrGcyqTjzknK2nDk+6IJCJqhlvZP0vUAQImJS42WrhBmjFApCEARAxdmSgHOOA+cdeVkaAUCnUqDerEF3EQqqMbE4o/4SAEAwEsW4O4ht8QTN5dJ31jPuhVYph1ohwwQpZ8uWWIzj+JATl660AaDvBYJIpmvciyqdEnVmDexGNRVnBEEAoOJsSTAw5ceoK5hXGIhIh11flOJslJSzWYgN3ltbLACWz6yzngkP2m162Axq6jlbxvRMeuENRXHthjrIZYyUM4JIomvcgxV2AwCgwaKlQdQEQQCg4mxJcLB3CgCwPY9+M5EOmxCnX2ha0ZhbuBCnHXKBIadwsl1VY4ReJV8+ytmEF+12PawGVcX1nB3un8Yn/3AY0RgldxWKGAaytcWCRouWvhcIIonupOKs0aKh4owgCABUnC0JDvQ6YFArsLYu/zCPdpse7kCkYAuaWHxMeILwBCMFHWspINpUGiwa1Jo1y6I4C0Vi6J/yoyOunFVSzxnnHF/663H8+aWBZaNylpJjg06oFTKsqjGg1apDHylnBAEAmPaFMOEJYWWNWJxpMTTtpzh3giCoOFsKHDg/ha0tFshl+UdUJ8fpF8KYKwi1QvhYkbVxZsZZvVmLOpNmWQSC9E/5EI1xtNv0sOorSzl7oWsSL/VNAwDG3ZVTVJYrxwZdWFtvgkIuQ6tVR8oZQcTpiic1rqgRzr0NFi0C4RimfOHFXBZBEGUAFWcVjtMfxulRN3a05hcGItJhE3bvuuMnjHwZdQUS/VU0dFaYY1OlU0KrkqPOpMGoa+lf8Iupn+02PawGNSY9oYrZDf7Bk2ehlAubHFScFQbnHMeGnNgYH8/RZtXD6Q9j2lc5xTpBlIquMeF7MrnnDAAGp8jaSBDLHSrOKpyX+6bAOQoKAwGAxiotVApZQcqZJxiBNxTFhfHUyPNkYcLwtD9x0hVtjbEl3sskfoaEQBAVQtEYXIHyt7ju73FgX48D77pUmHdDxVlh9Dl8cAci2NRoBgC0WgWFgNQzghCUM5VchqYqYeh0o1icUd8ZQSx7qDircA72TkEuY9jSbCnoOHIZQ5tVh64CZp2JMfrtdj3sRjXZGgEMTQdQbxZOunUmDSIxjknv0lYOeiaFeGiLTgWrQQUAmKyAxMYfPnUWNoMKH7pqJQBgzL30Lail5NigCwCwMV6ctVmFi1BKbCQIoThrt+kT7QhicUZx+gRBUHFW4bx43oH19Sbo1YqCj9Vu06NnIn9bo2jZqzFq0G7Vk60RQlpjo0UDAKg1Cf9d6qEgPePexGBzm0ENAGVfkL7cN4Vnz07gPZd1wKhRolqvIuWsQI4NOaGUM6yqFWxbzdU6MAacn6DvBYLoGvcm+s0AwKJTQquUU3FW4ezvceDAecdiL4OocKg4q2DC0RgO9U8XFKGfTIfdgD6HD5FoLK/ni0pDrUmNVqsOPct8h9wdCMMdiKBetDWahEJlyRdnE160x3sYrfp4cVbmytmPnjoHi06Jt13UCgCwG9RUnBXIsUEnVtcaoVbIAQAapRz1Jg0pZ8SyJxiJos/hS/SbAQBjDA0Up1/xfP6Bo/jCX44v9jKICidrccYYa2aM7WaMnWSMHWeMfTR++52MsUHG2KH4n9eWfrlEMieGXAiEYwX3m4m02/QIRzn682xIHhOVM5MGbTY9xt1BeJdxnL6Y1Cj2nNWZBeVsKUe0e4MRjLgCifRPW9zWOF7GiY3HBp148tQY3n1pe0KBthvVGC/zgrKc4Zzj2KAz0W8m0mrVUy8qsezpmxQSbZOLM0A4V5ByVrkEwlF0jXtwZtQNfyi62MshKhgpylkEwCc55+sAXATgg4yx9fH7vsc53xL/82jJVkmk5MW4dF5oUqPIikScfn7WxlFXAFqlHEa1Am2J5v/iXoiNu4MVk/wn7oA2xIsyu0ENGQNGyyhOn3Ne1IAS8d9btDVW6cu/5+xHT52DUaPAbZe2JW6rMZJyVghDzgCmfGFsmFOctdl0ZHcmlj3nxoRzrDjjTKSpSovB6fI5PxC5cWbUjRgHojGO40POxV4OUcFkLc4458Oc85fif3cDOAmgsdQLI7JzsHcKTVXahCJTKDNx+vkVVKPuIGpMajDG0Jpo/i/ehdjgtB8Xf+1JPH16vGjHLCXD07OVM4VcBptBXVbK2eceOIY33v1C0Y6XnNQIAEq5DFU6ZdnOOjs94sZjx0fwzkvaYNIoE7fbjWqMVdBGQLlxdEC4MBFj9EVarXpMekNwB2iWE7F8EWecid+TIg1mLSY8QQTCpLpUIieGXIm/Hx6g4ozIn5x6zhhjbQC2AtgXv+lDjLEjjLFfMsaK460jJME5x4HeKewoUr8ZIKgcFp0S3XmmLI65Aqg1CoVim634ytmZETciMY5TI+6iHbOUDDv9kDFBhRGpM2swUiazzp46NYrf7e/D0QEnokVSz8QZZ6JyCkCYdeYtj595Lj/afQ56lRzvekX7rNvtRjVCkcoYAVCOHB9yQi5jWFc/pzirLv6mDUFUGl3jXjSYNfOCvMSNvOEyclcQ0jkx7IJBrUCtSY0jA9OLvRyigpFcnDHGDAD+DOBjnHMXgP8BsALAFgDDAL6T5nnvZYwdYIwdGB+vDMWjEuh3+DHuDmJ7W3EsjSIdNn3eg6jH4soZABjUCtgMxY3TF4MEKsWTPzjtR51JA4V85tes1qRJjBxYTFyBMD53/zEwBiHev0i2w55JL+rNGmhV8sRtVr0KE+7yU866xj14+MgQbr24DRadatZ99nhBTdbG/Dg26MSqGgM0Svms28VZZ1ScEQsN57xom1CF0jXuwYo5lkZgpjirlHMcMZuTwy6sqzdic5MFR0g5IwpAUnHGGFNCKMzu45zfDwCc81HOeZRzHgPwMwAXpnou5/ynnPMdnPMddru9WOte9oj9ZhcUKQxEpN1myMvWyDnHqCuAGuOMxbLdpivqwNleh3CsSkmzGp4OJJIaRepMmrKwNf7XIycx5g7gQ1cKM72KtVMrJDXOturYDGpMlKFy9t+7z0GtkOH2y9rn3Wc3UHFWCMeGXNjQYJ53u2h3plAQYqH544EBXPS1JxGK5JdGXCw45+ga88wLAwGEnjOgcs5xpeTogLOiitRYjOPksBvr6k3Y3GxBz4QXTh/Zt4n8kJLWyAD8AsBJzvl3k26vT3rYjQCOFX95RDpe7p+CUa3A6hpjUY/bYddjzB2EJ8eURU8wAl8omoiLB+LJbEVUzvrihV6lfGEPOf2JnVCRWpMa077wovYUPHt2HL9/sR/vubwD126oA1Da4sxqUJVdz1nfpA9/OTSEf72wNTGLLRlRAabExtwZdQUw7g5iY6Np3n16tQJ2o5ri9IkF59iQE+PuIPqnFle1HXUF4Q1FEwFcydSaNGCscs5xpeS99xzANx87tdjLkEz/lA+eYATr603Y3GQBABwZnF7UNRGVixTl7FIAtwK4ak5s/jcZY0cZY0cAXAng46VcKDGbMVcQjVVayGSsqMdNJDbmqJ6NxRUGcdAyALRZdRhzB+ELFadvp5KUs1iMY9gZSCQ1iiz2IGpPMILP/PkoOux6fPyVq2fi/Z2Fv6dT3hCmfeGUypnTH170Hetkfvz0OchlDO+7oiPl/XaD8L6UgwW10jg2KNh55sboi7RZi6uoE4QURuIbUPkGXhULMQwklXKmUshQY1RjMM9xNkuFUCSGEVcAZ0bza7FYDMQwkPUNJmxqEr77yNpI5Isi2wM4588BSFUBUHT+IuIJRmDUZP3nyxlxeHD3hCfxBSMFsdioSVLOxFCQ3knfvGCAXInFOPocPqgUMrgDEbgC4VnpeuXGpDeEUCQ2TzmbKYYCif6bheQbfzuFIacff3r/xdAo5VDJZVDJZRguQhEiBsl02OcrZwAw5QvNKt4Xi2lfCH9+aQBvvaAl7XpMWgVUchkpZ3lwbNAFxpD2d77VqsezZ6n/mFhYRuMbiEJPde2irUMszubG6Is0WLQYKsJmWSUz5g6Ac8GJwTmHYOAqb04OuyCXMayuNUKjlKPdpsfh/unFXhZRoeSU1kiUD+5ABAZ18YuzVqsOjOW+u5gYQG1MVs7iiY1FsDaOuYMIRWLY1mIBkJ/t4ysPn8Cn/3QEkWjpFZzh+Mm1fo5yVmdavEHUe7sncc/eXrzzknZsj8/Gk8kYas3qxK5yIczE6M++6LDqy6t/6+yYB+Eox9XratI+hjEmDKIukzVXEkcHneiw6ecl0Ym0WXUYdRVPUScIKYyWiXJ2bswDY9zem4pGixZDy3zWmXg+8oejZdGjLYUTwy502PSJEKTNTWYcpsRGIk+oOKtQBOWs+MqRRilHo0WbuNCWyphb+AKd3XMmNv8XbmESe1Qu7rAByK84+9uxEfzfgX585v6jRR28nApxffN6zsyiXW5hL/r9oSg+/ecjaKnW4VPXrp51X71JW5Ses54JDxQylmhqF7HFlbNJb3n0nYmbBW1ZlEsbFWd5cXzImdbSCMwkNvY5yNpILAzRGE+o4N0TxbfKjbkDuG9fr6S5iF3jHnTUGNKqQY0WLQan/ct6xmJyQbbYxbRUTgy5sD5prmNnkwWjruCitTAQlQ0VZxWKOxCGoQS2RgDosBtyPoGNuoLQqeSz1DyjRgmbQVUU5UzsN7tkpRUAMJjjzmIoEsOw04/mai3+dHAAd/71eElPfkNzBlCLGNUK6FTyBd8N/Pbjp9E76cM33tgJnWr256bOrCmactZSrYNSPvtrxRoP3ChWXH+h9E76IJcxNM4pIudSQ8VZzkx4ghh2BrAxQ3E2o6hTcUYsDBOeIKIxDqWcleRi/4GXBvH5B45hT9dk1sd2jXlThoGINFi0CEVimCizEKWFJPl8lO9on4Vk2hfCkDOA9UlW7s3NwncgWRuJfKDirEJxBUrTcwYIs856xr05FS+jrkA8aWr2bmCrVV+U2Oy++AX15iYLlHKWc8P00LQfMQ585KpVeO/lHfjtnl58/bFTJSvQhqb9UCtkqNLNVjcZY6hd4Dj9g70O/PL5HrztohZcvMI67/76eHFW6HvRM+FL9Bkmk1DOyuRi4/ykF01V2nlF5FzI1pg7YhhIqhh9kRarOIi69Dvi/lAUt//mQKLPh1ieiOrF1uYqTHpDRY84H407IX67pzfj4zzBCEZcgZRhICI060xID9YoZdCp5OiqAOXsxPBMGIjI+noz5DJG1kYiL6g4q0CCkShCkRiMJeg5A4B2mx7eUDSnC9MxVzClh77Nqi/KwNlehw8NFg1UChnqzdqcT1yiharVqsdnX7MWt+xswU+e6caPnjpX8NpSMewMoNGiTWldqTWpE/0PpcbpC+Pf/3QEDWYtPvOadSkfU2fWIBSNwVGA7TAW4zifIkYfEAaSqxSyspl11jvpkxTGYjeo4fCFEF6AHsWlwvF4YtmGFDH6ImatEtV6VUINLyWnRlx44uQodp8aK/lrEeWLqMSIm1NdRbY2jsZt/f84OZroN05Fd4akRpFGKs4w4gqgwaxFu02fc4vFYiAmNSaHIGlVcqypNVJiI5EXVJxVIJ6A0Ehfip4zAIkL7Fy+FMfcgZTJd21WHUZcAfhDhc316pv0orVaWFeDRZPziUu8EGyp1oExhi/fsBE3bW3Ed/5xBr94rqegtaVicNqPekvqJMCFGEQ97g7i6387hUu/8RTOT3jxtZs2pQ2QEUNLCuk7G3UH4A9HUxZnjDHY9CpMuBdfOeOc4/ykF21x9SYTdqManKOgonW5cWzQiTarLmuSaku1bkGUM1ExKcYGEVG5iJ+DS+LFWbGtjeOuIJqrtYhxjt/t70/7uJmkxvSbQ2JxVgkjY0rFiFO4nsinxWIxODHsQo1RPW9m5uZmM44MOJd1/yCRH1ScVSDigOhSpDUCuRdnnHOMuoKoTaWciXH6jsJOhn0OX8IO1WDJXTnrj8fw18TXKJMxfPNNnXjNxjp8+eET+N3+voLWN5dhpx8N5tQ9TbVmDcZcwZJ8YQ9N+3HnQ8fxim88hZ/8swu71tjxyEcuw+Wr7WmfUx9fZyF9Z+JcvI4UxRkg9J1NloFyNuULwx2ISFPOjOWVMlkJHB10YkOGfjORNqtuQXrOxM90MazVxMJQirCmEVcAchnDlhYLFDJW9D6mMXcAm5ss2LXajt/t70urtneNeSGXMbRUp//+MWkV0Kvky744qzdr0GHTY2DKj0C4sM3dUjM3DESks8kCpz9Mm0NEzlBxVoG4E8pZaYqzBosWKrlMcnHmDkbgD0dnzTgTKUacvisQxpQvjNZqoThrsmgx4grkZDfrm/Shec7QboVchu+/dSt2rbHjcw8cxV8ODea9xmRCkRjG3EHUW1IXZ3UmwUY4VcS+h/MTXnz6T0dwxbd24969vXj95gY8+Ykr8KN/3ZZ1xlxCOStAzRNnnLWnaXS3GlRl0XMmXqRLUc5qqDjLiWlfCANTfmzM0G8m0mrVY8jpRzBS2osu8TNNyZCVwW9eOI9d334a3mBxxyyMOIOoMaqhVsjRYtUVXTkbcwdRa9Lg1otbMe4O4vHjoykfd27Mg1arDipF+ksvxlheG5BLhViMY9QVQJ1Zgw67HpyXt/IdisTQNe6ZFQYisrnJAgDUd0bkDBVnFYi7xLZGuYyhxaqTXJyNucQY/fk2vlZb4XH6fZNiv9iMchbjyCmits/hQ0v1/AtylUKGu9+2HTvbq/GJPxzGwV5H3usUGXUJAzQbM9gagcKUKhFXIIxP/uEwrvrO03jg0CD+5cIWPP3vu/CtmzejI0NfQzJWgxoKGcNIAYNPeya80CrlqDWm/pltBnVZpDWKVrpclDNxTASRGbHfLFOMvkibTQfOgX5HaS9Axd7OgSk/9Q5WACeGXOhz+PDLIlvNk233HbbiWuU8wQh8oShqjGpcsboGTVVa3LP3fMrHdo17MvabiTRWLd9ZZxPeICIxjjqzJvFelXNi49kxN8JRnnITdHWtARqlDIf7qe+MyA0qzioQd0BQXEqlnAHIqRE31QBqEZNGCateVVB/ibhr1lw9U5wBkHzy4pyjP01xBgiz3X5+2wWQyxj+nmbHMxfEHc/6DLZGILfiMhUnhlx43Q+fw18ODeL2yzrw3KevxP+7YSOaqrKrQsnIZUKCZCE9Zz0TXrTZ9LOUyWSsBhUmvKFF996fn/BBxoDm6swx+gAS/QOknEnjaCKpMbNSC8wUx6XuOxN7O6MxvuBKxKH+afzLT/fS5ycHxFmIP/lnd1F7PYUeJuH3eYVdj/OTPkSLZJ8Uv8drTGrIZQy37GzF3m4Hzo66Zz0uEo3h/KRXUnG2nJUzcdOyzqRJtEV0l3EoiBgGksrWqJDLsKHBjCOknBE5QsVZBSL2nJW6OOt1SDuBjaYYQJ1Maw4qXCrEfjXxgk6cTzU4LU2Nm/aF4Q5GEsVdKgxqBTY0mHCobzrvdYqIRc7cGWci4g5uIaEgfzjQjxt//DwC4Sh+/96L8LnXrktZHEul0FlnQlJj+vfXplcjFInBXWS7Uq70TnrRYNFCrZBnfaxGKYdJo6CLa4kcG3Si0aJFlV6V9bEJu3OJ7UojzkDCnlrq15rL/p5J7OmexKf+eLjkQ++XClO+EJqqtPCFIvjx7uIl6Y64AgnHQrtNj1AkVrTiZ+7m5FsuaIZKIcM9e2fH6vdP+RGO8owzzkQaLVpMekMFB2lVIuL5s96shUGtQK1JXdaDqE8Ou6FVyhPfaXPpbDLj2JATEVLuiRyg4qwCEW2NpQoEAXI7gYkzXmpS2BoBIRSkEM94v8MHq16V+HnFoA2pyllyjH4mNjdZcHSw8C9RsZG7IY2tscaoBmP52RoD4Sg+/acj+I8/HcH21io8/OHLsKOtuqD1AoUVZ+FoDH0OX8qkRhFrmcw6Oz/pS3sSTYXdqMZ4GdgxK4HjQy5szBChn0yVTgmjRlFS5YxzjhFXABe2C78ffQscCiL2lD5zZhy/fuH8gr52pTLlDWFLswVv3NaE3+7tLUoohi8UgTsQSTgWRLt3sWbfibZncROgWq/C9Zvqcf9Lg4mNVADoGovH6NdIUc6EtQ4VYDWvVEQlstYsvJ/FtqEWmxPDTqypM0KexjWypdmCQDiGM6Pl+zMQ5QcVZxVIIq2xxMoZIC3lbMwVhF4lT1sstln1GHbmH6ffOzmT1AgI80Oq9SrJJ+6+pBj9TGxtscAfjuLsWGFfosNOPyw6JXSq1O+HUi6DVa/O2dbYO+nFTT9+Af93oB8fvHIF7nn3zpSz5fKh3qTBkNOfl+1wYMqPSIyj3Zb+okO0CC5231nvpDfRuyiFGqOGlDMJuAJh9Ex4JfWbAULoQZtVX1I1y+kPIxCOYXOTBRqlbMFDBaa8IdgMarxyXQ2+/rdTCfsTkZ5JbwjVehU+9qrVAAe+/8SZgo8pbh6KyllHXLkqlhojfj8kb06+7eJWeIIRPPjyTMiUWAyuyPA9KdJoEb6jlqO1cdgZgELGYNPHizO7Ht3j3kW3xKeCc542qVGkMx4KQtZGIheoOKtAXIEwVAqZJGtWvuQSpz+aZsaZiOgbzzcxrXfSl0hqFMll1pn4utn6jBLJSv3TOa8xmeHpQNp+M5E6c27F2ePHR3D9D5/DwJQPv7htB/792rVpd+ryoc6sQSAcg9Ofe4JkT3xXU4pyNrGIypnTJ6R+5qycUXGWlROJ4dPSijNAsDuXUs0SbcP1Fg1aqnULbmt0eEOw6lX45ps2w6JT4iO/f3lZ2tSkEokK3z9VOhUaLVrcenEr/nRwAOfG3NmfnAHRESCeo6x6FUwaRdHUmDF3EGqFDKakzdKtzRZsaDDh3r29iaKia9wDm0ENsy57kFdCOVuGxZk440zsX+6wG+D0h8ty3uTgtB+uQCRlUqOIMPdRgcM0jJrIASrOKhB3IDLrRFAKaoxq6FRyScXZmCuQUcERY8vzmTUUisQw7PSjZc4FdaNFi8EpicXZpA82gzqtkiXSatXBrFUWHHs7OO1Pm9QoIgyilnbRf+/eXrz3noNoterwyEcuw9XragtaXyrEYjKfUJDuLDPOgBnlbGIRlbPziaRG6cqZ3ajGGBVnWTkWDwOREqMv0mbVlzRFMTlYoNWqR1+BsxZzZdoXhkWnRLVehe+8eTPOjXnw1UdPLOgaKgnRBipu5HzwypXQqRT49t8LU89G56QJM8aE4cZFUs5GXQHUmNRgbGazjDGGWy9qxakRNw70TgEQYvQzDZ9OptakgYxB0jnu7KgbL/dN5bf4MkSccSaSUDrLMBTk5LCwcZBJOWOMobPJQsoZkRNUnFUgnkCkpP1mwIztSJJy5gpmVM5aC5h1NjDlQ4zPtySKaVZSrA5CjH72dD7GGDY3W3CowNjbYWd25azWpJGsnN27txebmy340/svyRhqUgh15vzj/XsmvLDolBmDIKp0i99zlphxlqGInIvdqIYvFC363KWlxrNnJ9BUpc3JZtti1SFSwhTFZMWktVqH3knfggZzOHyCRQ8ALltlx3sv78C9e/vw+PGRBVtDJTHlE74bxO+Kar0K7728A48dH8GhAtwMooJaN+eCv1jF2ZgrmDKM6YYtjTBqFLhnj6CedY1LS2oEBOt7rUmDQQl91R/7v0P45B8P57zucmXEFUj0BwIzm37lGKd/YsgFxoC1dcaMj9vcbMapEXfZD9MmygcqzioQdyBcshlnybTbsxdnnPP4DJn0F2VmrbB7nI+tqNcxe8aZSKNFC28oCpc/+0VzuhlnqdjSZMaZUTd8ofwuxr3BCJz+cNqkRpFakwYObyjrEN5pXwinR9145doaaJSls7EmBlHnWZxlsjQCwjw5s1aJSe/iqVBiz5HUzwIA2POI03/q1ChcgeINGC93xtwBPHt2HK/f3JDT80qd2DiSpJi02vQIxofDLxTTvhAsupkNi09dswYbG0349J+PFDxGYyki2taqkzZ53v2Kdlj1Knzjb6fy7jkacQZgUCtmbWiusBsw4goUZdNlzD2TCJqMViXHzdub8bdjwzg96obTH5ZcnAHCOS7bxsXZUTeOD7nQLzFZudzhnGPY6Ud90mZvU5UOKrmsLJWzE8NOtFv1WV05nU0WRGM8MQuSILJBxVkF4gmWXjkDhB2rgSk/QpH0tiNXIIJAOJZROQOE4iof5axfLM5SKGcAsoaCJGyREi/INzcX9iU67Myc1CgiNqePZbE27u9xgHNgZ4c1r/VIxW5UQ8aQ1yBqKcUZANgMqkVXzurNmpyKXFEJkprYOOYK4F2/PoD/eborrzVWIg8dGkKMAzdta8zpeaLduVSJjaOuAGwGFVQKWeL7Ix9rdT5wzjHlC6NaP7OJplLI8P23bkUgHMMn/nCI4vXnkKo406sV+PBVK7GnexLPnp3I67hjbsF2mExHDj3V2Y+f3jlyy0UtCEc5vvrISQDSkhpFGizarGmNDx4SAkfCUZ4491QyLr9wPZGscsplDK1WXVnG6Z8YdmGdhLmOmykUhMgRKs4qEHcgUtIZZyJtVj2iMY7+qfQ722PxHeBsdqZ2qz6vi7DeSR+0Svm84zdKLM6Gpv2IcUi2A3YWGAoi2lCy2hpFG2GWHfR9PQ6oFDJ0Nknv5ckHpVwGu1Gds3LmD0Ux7AygXULIhtWgXtSes95JX079ZgASF3VSlTMxLvmpk2O5La6CeeDlQXQ2mbGyJrO1Zy52oxpapRznJ0qjnA07Z4KKRJWub4FCQVyBCKIxnrDoiaywG3DH69bj+XOT+Nmz3QuylkohVXEGAP+yswVNVVp88++n8ipoR5wzM85EihWn7w9F4Q5E0p7/VtgNeMVKW6KwlDLjTKTBosXwdCDtzxyLcTz48hAs8YCRfAO3yolhl3A+Ty7OANGGWl62RlcgjH6HP2MYiEidWYMaoxpHKBSEkAgVZxWIUJwtjK0RyNwrJtqEsitnegw5Azl7rnsnBUticrM1MKOcZbN9SI3RF7Eb1Wi0aPFynsXZcJYZZyLixUI2e9P+Hge2NltKamlMrMmszXkwtqhEtEu46LAZVItcnHlzSmoEZmyNYxLfFzFZ7vSoGwMZNjWWCmfitqobt+ammgFCj2erVVcy5Sw5WKDBooFCxhZMOZvyzu6fSuYtFzTjNRvr8K2/n6ad9CTE98wyJ81QrZDjE69ajWODLjx6bDjn4466gvOKs1arDowVHqc/d8ZZKm69uBUAoFHKEjM6pdBo0SAUjaX9zjzQO4XBaT/efWk7gBmXSSUzM4B6fjHd5/CV1SDnU2IYiITiDBBcOYUmQRPLByrOKhCh56z0ypmohmSyfsxNwkpHm00ojnLd3etzeGfNOBOx6gW7ktTiLNsA6mS2FPAlOjTtB2PZ3w/xYiFTAIcrEMbxIWfJLY0iDWZNzsqZ+NmQYmu06tWYXKQ4ZHcgjAlPKKfPASBcXMtlTLKt8dy4B4p4BPTuU0tfPbv/pUHIZQyvy7HfTESYdVY6W6P4e6iQy9BUpU30sJaaRLiFfv4mGmMMX7tpE2wGNb7+t1MLsp5KYNIbglGtSDki5oYtjVhTa8R3Hj+TU7pnLMaFz8Gci32NUo6mKm3BtsaxFDPO5nL12hrUmzVYYTck4uGl0FiV2R3y4KFBaJVy3HZpGxQytiSUs0TC6pwitt2mRzjK0S8xoXkhODEkqGCZkhqT2dxkRveEN69xNcTyg4qzCoNzDk9wYWyNVXoVLDplluIsfnLKYmtsyyOxkXOeNsxDJmNotGgxkKU463f4oFLIsq4vmc3NZgxM+fNSeYacAdQaNVDKM/9qmbQKaJSyjMrZwfNTiHHgovbqnNeRD3VmTc5pjeJnQ4oiZTWoMO0LS7q4GnMFcMnXnpw1xLUQxDCQthxtjTIZg82gkmxrPDfmwaYmM1qtOjy5xIuzaIzjwZcHccVqe2JUQq602nTod/iLHmYQCEcx5QvPUkxa8rRW58Pc5MG5WHQq7OyozmgZX25M+UJpE1/lMoZ/v3YNeia8+OOBAcnHdPhCiMT4POUMANpthoJnnY1JOP8p5DLc/bbt+OqNm3I69ow7ZP53cigSwyNHhnHthlqYNEo0VmnR5yifwiVfRpwBMDb//VxhL7/ExhPDLlj1KsnXFmLLhDh2hCAyQcVZheELRRHjWJBAEABZ4/RHXUISlj7LemaS2aRfHI27gwiEY2n7hKQMou6d9KG5SpvTjuWW5ioA+TXvDk37UZ/F0ggIu+fZZp3t7ZmEUs6wtaUq53XkQ71ZA08wAncOSYPd417UmTRZ//2BmVlnUxLUswO9UxhyBvDJPx4uSvS4WJzlqpwBuQ2iPjfmxUq7AVetrcELXZN5p35WAnu7JzHiCuRlaRRps+oRisaKHmYwmiI+vc0qxOnnm/qXC1Ne4XcoXXEGCL1VjkUMyCk3HN7QvH6zZK5eV4OVNQY8fkL698HMOIX5F9AdNj16xr0FfR5EW2M2p8TmZgu2NFtyOvZM6NX8Av7p02Nw+sO4If6711KtWzLKmc2gnre52WETegSLEeBSLE4Ou7G+wTSv5SIdYt94oXNUieUBFWcVhjsgXOwtRM8ZIJzAMqld4+7gvCSsVJh1SlTplDnFZvdm6RdrMGePGs4lRl9kY6MJMoa85p0NOwNZY/RFakwajGZQqvb3ONDZZIFWVfp+M2DGSpKLetYz4ZFkaQSEnjMAmJBwQXpy2AW5jGFjgwkf+t+X8fy5/JLaRPIZQC1SY9RIsjU6fWFMeIJYWWPA1WtrEYrE8MK5yZxfr1K4/6VBGNUKvGp9/kPRxX+PYgd1zNijkpSzah3cgQimfaW3Fc3YGtMXGzaDGt5QlGYfxclWnDHGsK7ehHNj0tWTTLb7FXY9vKFowv2RD2PuIJRyhipd8c/HJo0SRrUipXL24KFBWPUqXLbSBkAIvFoSPWeuwLx+M0D4ParSKdFVJomN4WgMp0fdWCex3wwQ1PI2q476zghJUHFWYXiCwoWFYQFsjYDg9R5yBuAPpb6AGHUJNj4ptOZoK8qmdjRYtBhzB9NG/XPO0Z9HcaZTKbC61pjzlyjnwkDdhhQnl1QIylnqQsgXiuDogBM7F8jSCOQ366xnwispDAQQ0hoBSLKLnhhyocOmx2/edSHabXq857cH8HLflOR1zaV30gu7US1J4ZuL3aDOOvIAAM6NCw3iK2sMuLC9GnqVfMlaG/2hKB47NozXbKorKKymVLPOEoOHTcnKWe7qfb5M+UKQyxhMGb6nxUJksfowy40pbyij0ggAK+0GDE77056P5pJqALWImNhYiFVu1BWA3aCWrJ7kSmOVdl7PmSsQxhMnx/C6zQ1QxBWmlmodHN5QTq6HcmQ0RbKmSIfdUDa2xu5xL0KRmOQwEJHOJgslNhKSoOKswnAllLMFsjXaMl/QjKaYIZOOdps+p9jsvkkvZGwmNn8ujVVacJ5e6Zn2heEORiTH6CezpdmCwwPTOVlehKHSMcnKWZ1Zg1FXIOVrHOydQiTGceECFmdSQkqSmfaFMOULS4rRB4QQFwCSBlGfHHZhXb0JFp0K97z7QtiNarzjVy/i1Eh+8+fOT/py7jcTsRuFIJNsfVHijv7KGgNUChkuW2XH7lNjC2KjKwYOCT+jyOMnRuANRXHj1qaCXrPOpIFKISt6L1gq5Syh0i2AwuDwhlGlU2a8aBeLM7I2Chtbk94QrIYsxVmNAZxLj8Afjfcw2VP0RHbEN5W6CrDKjbuDsGexNBZCQ4pB1I8dHUEoEsMbkuzE4gZkpVsbh53+lIU0ILh4ymUQ9Ynh3MJARDqbzBh2BhJ2WIJIBxVnFYZHLM4WqOdMtKylsjZyzjHqSj+Acy6tVh2GnH7JNp5ehw8NFi1UitQf02yzznKN0U9mc7MF075wTie7IYkzzkRqTRoEI7GU6U37exyQyxh2tC1ccSb+O0pVzkSLiWRbY7xxOtsg6mlfCEPOQOLEV2PS4N5374RWKcetv9if1zDz3klvXv1mgFCcRWM8YVVLx7kxD1QKGZqqhM/bVWtrMOIK4MRwfgXlQuIOhHHVd57GO361X1KBdv9Lg2i0aAtWdmUyhpZqXdHVrBFXAHqVfJb9u7laiE8v1Vy1ZKZ92VUg0eYrZbNiqeMPRxGMxLIrZzW5zScbdQVhM6gTClMydSYNdCp5QWrMmCuYU9hUrjRYNPPObw+8PIh2mx6bk2Zfiue4SrY2+kIRuAKRtMVZu12PcXewLNTBk8NuqBSyxDBzqYh9h4fzaJkglhdUnFUYC91zJipnqXasXP4IQpGY5JNTm1UPzqWfQMQZZ+nINussUZzloZhsjicrHcrB2jgUDzVIp/TNJaFUpbA27ut2YGODacGCXwBApZDBZlBjxCUtnEFMnZK6e2hUK6CSy7L2nInFTLKfv7lah3tvvxCRaAy3/HxfTgESvlAEo65gQcoZkH0Q9bkxDzpsesjj4TO71toBVEak/v0vDWLaF8azZyfwvX+cyfjYMXcAz54dxxu2NuQUtJMOMaijmIw4U8en15k06HWUfvfdIcGiV62XtlmxHJgZQJ35vNZm00HGILnvbMSV3ibHGEO7TV/QrLMxdyBl2EixaLBoMe0LwxsUzvvDTj/29kzihi0Ns1TZ5iWgnI2kmXEmIoaCFDqbrhicGHJhTa0xZdGfiQ0NZihkrCCLPrE8oOKswljonjODWoEaozqlWjEqMalKZMYiKe0E0u/wZQxwEL/ES6Gcra41QKOU5bTDJRaJUtIaAaDOLJzU59oIA+EoDvVPL6ilUaQ+h1lnhwemYTOo055M58IYg1XCIOqTaYZ7rqwx4rfv2gmnP4y3/XwfJiWOOshn1l0yNVKLs3FPYmdfeJ4Gm5vMZd93xjnHb/ecx+YmM96yoxk/2n0OT5wYTfv4hw4NIcZRsKVRpDU+66yY9s+RNMECLdXFLwRTMe0Lp5xxlkzC1kg9Z0nFWeZCR62Qo9Wql1ycJc+6S0WHPf84/WBEGNdQI7HnOh/EjT5xM+qhQ0PgHHjDltkJqWatEhadckkUZ+n+vcQ4/cVObOSc48SwK+d+MwDQquTY0GDCgV4qzojMUHFWYbgXuOcMEIqqVF+IYhKWdOVMKJKk9Jd4ghFMekNoqU5/Qa1RymEzqNMqZ/0OH2wGNXSq3N8rhVyGTY1mHOqX/iU67AxApZAlequyIZ6E5s46e7lvGqFoDDvbF2b4dDJ1Zg2GU6SDpeLIgBObm8w5NcNbDaqsRdWJIRdsBnVCsUpmU5MZv7htBwam/HjXr19ETIIFT7SxSZnFlgpxHWMZirNAOIqBKf+s4gwArlxbg0P905ILyWz0THhxYsgFpz9ctGLmha5JdI178faL2/ClGzZgY6MJH//DobT20QdeHkRnk3nez5ovbVYdAuFYxvc3V0adqS/K26z6BSnOHBJsjSaNAko5o0AQSFfOAGCF3ZCbcmZOf37qsOkxMCXdap+MuFlTSlvjjHVf+E5+8NAQtjRbEhudyQhx+pU762zYmbktoMUqqKaLHQoy5g7C4Q3l3G8msr21Gof7p9MGmREEQMVZxSEWZ4Y8Co586bDpU/aEiAl2UpUziy77UGuRXonR540pPPkiQoy+NIthKjY3WXBsyCVpaDKARFKj1GJF3HEdcc6+KN3f4wBjwAWLppxlP8F7ghF0jXsSgzWlYtWrs16MCmEgxrT37+yw4o7XbcDhAaekmTGJz5ItP1ujOJ8tk3LWPe4F55hXsFy9thacA0+fHs/rtZMJRqJ47fefxWt/8Cw2f+lxbLrzcVzzvWfwzl/tx+cfOIr/3n0urwCS3+45j2q9Ctd11kOjlON/btkOuYzh/fcenJeKd2bUjeNDroJmm81FVDSLtSMejXGMuoMp7WwtVh0mPEF4gqWbP8c5F3rOsmzSMMZQrc++WbEckKqcAcLv2PlJLyJZvpcD4Sim5wwin0uHXbDa51Owi5sJUgOx8iEx62zKj9MjbpwcTv+7V+lx+qkSVpNRK+RortYVFOBSDE4Mzbfd58KOtioEIzEcH6K+MyI9VJxVGO5ABAa1oii9HlJpt+kx4QnNC64QbY25nJxaJe5ci3OPslkSU6VZiWTrWcvG5mYLQpEYTo+4JT1+aNovOakRQEJlG52T3LSvZxLr6kwwaxemrzCZOrMGrkAk0eOQjqMDTnAOdDabMz5uLjaDOmOPTSgSw7kxT9Zdyes21UMhY/j78fT2O5Hzkz5Y9SqY8uzT1KsV0KvkGYuzc+MzSY3JbGgwocaoxlNFsDb2TvrgD0fxzkvb8PnXrsObtjehzarHmDuIR48O41t/P413/vpFPHhoUPIxB6f9+MeJUbzlguZEJH5ztQ53vWULTo+68fkHjs4q9u5/aRByGcPrNjcU/POIrKkTCvHjQ8UJTpn0BBGN8ZS2RlE9LfZctWQ8wQjCUS5p9lW1Xk22RiQVZ1nURkD4HQtHeWIOZjrEzcOaDMXZCrs43Dh3NSZx/BLaGmuMashlDEPTfjx4SPjdu66zPuVjW6p1GJjySU5cLTdGnAGYtcqMcz07CuwRLAZiT/TaDBuImdjRWgVASGQmiHRkLc4YY82Msd2MsZOMseOMsY/Ouf9TjDHOGLOVbpmEiCcYXlBLI5DUKzZnx2rMFYRRrcjJNthu1UlTziSGeTRahDkwc9WCUCSGYae/oOJMTFaSGgoy7AxITmoUqZ0ziDoUieGlvqlF6TcDZvr40s1fEzkSV6w256ic2eI9Z+nUna5xD0LR7PNjzDolLl5hxePHR7IqRUJSY/6fA0CwNmYaRH1uzAMZm59cKZMxXLmmBv88My5ZgU2HaOd547YmvOfyDtz5+g346dt34JGPXIaXv3gNjn/pWmxqNOMbfzsNX0iaMvS/+3oBALfsbJl1+641Nfjo1atw/8uDuHdfHwAgFuP4y6FBXLHanlATi0GtSYNGixYvFeliZSTD4OHWHKzV+SIOuc5mawSE3weyNc7MhZNybhM3QLJZG7MpMcDMuS2f4cbjeWxO5opCLkOdSYOBKR/+8vIgLl9lS/u711KtQzjKcwpLKieE82fmQrfdZkDPhEeSnb1UnB11o8GsyXuzr8akQXO1FgfOU3FGpEeKchYB8EnO+ToAFwH4IGNsPSAUbgBeBaCvdEskkhGVs4WkI82ss1GX9BlnIusbTBic9uPsaGY1qnfShyqdMusXYINFi0A4hinfbFVvaNqPGEdeM85Emqq0sOpVkoZRR6IxjLoCaJQYBiJSZ549iPro4DQC4Rgu6lic4qzOJBSX2WadHRlwoqlKmwg1kIrVoEIwEktrKzsZ35WU0mx9zfpadE94s8Zq90768u43E6kxahIXY6noGvOgpVoHtWL+ru9V62rgDkbw4nlHQWvINrpAr1bgi69bjxFXAHc/0531eMFIFL/f34+r19Um4v+T+chVq3DlGjv+31+P46W+KeztnsSwM1BUS6PIttYqvFSkBLPhFDPORMTNnmyqSyGIKpCU4qxar6IofcykW0pxhIjBEJKLswwX/Aa1ArUmdV5qzJg7CBkTrNqlpNGixZMnxzDkDMyabTaXSp91NuJKP+NMpMOuRyAcw3CWzcNScmbUg1W1+almIjtaq3Ggd6piZmASC0/W4oxzPsw5fyn+dzeAkwDEb4jvAfgPAPQJWyDcgciCK2fifKC5J7Axt/QZZyI3b2+GVinHT/+Z+eKx3+FDi4QL6nRx+oUkNYowxrA5Pow6G6PuIGIcqM/B1gjElbOkE83ebuEC/oIFnG+WjLhzmS2x8fDAdM6qGTBzIZPO2nhy2AWVQiZpdtqr1tcBQEZrYyAcxZDTn3dSo4jdqM4YWHFuzJM2IOMVK21QyWV46mRh1saucQ/qTBroM2zOXNBWjes66/GTZ7rS9mKKPHp0GJPeEN5+cWvK+2Uyhu+9ZQtqTRp88L6X8Mvne2BUK/Cq9bUF/Ryp2N5iwbAzkNainAujGS7KTRolqvWqkoaCOOLz8LL1nAFCcUZDqIXiTEoYCCCMkakzadCVpTgbzZL+J9Jhyy+xcdQVgM2gTozOKBUNFg3cwQh0KnnG3718Zp35QhH8/NnusuhVG3EGsypn4uDwnkWyNkZjHF3jHqyuLSwMaXtrFSY8wYotpInSk1PPGWOsDcBWAPsYY68HMMg5P5zlOe9ljB1gjB0YHy+8KX654w5GYFigGWciGqUcjRZtSuUs1+KsSq/Cm3c04cFDg/NSCpPpdXjRKqGwaqpKPYi6kBlnyWxusuDsmCdrgEAiRl9irLxInUmDCU8okdy0r8eBVTUGWItoG8tpPaKtMYM1ZtITxMCUH51NufWbAYJyBqQfvHtiWPr8mDqzBluaLfj78ZG0j+l3+MC5MB+pEOxGddqes0g0hp4JL1akKc70agV2dlTjqdOFFWfd497ExUkmPvuateAAvvG3Uxkf95sXetFh1+PSFekd6RadCne/bTsc3hCeODmG12yqS/SmFZNt8T6MYqhnI84AFDIGWxpFo9WqK7GtUVTOsn9P2wxqeEPRvNIClxJT3rAkpVFkZY0h0eeZjlFXABqlDKYsm5kddn080Ce3PeYxd7CklkYRcQPy1RvqMrYQ1Js1UMhYThf8Dx8exlceOYld334an/zD4UVLQgxFYpjwZN/sFXsE8x1/UCh9Dh+CkVjhylmb8H1H1kYiHZKLM8aYAcCfAXwMgtXx8wC+mO15nPOfcs53cM532O32fNdJxHEHFr7nDBCsVMm9YpxzjLmCecUI335ZB6Ixjl89fz7l/eFoDEPTAUl9QslpVsn0O3xQKWSoLbBZe3OzGZwLARiZEIszqQOoRcQBpmPuACLRGA6ed2DnIlkaAaEQr9arMipnR+LDp3NNagRmkg9TDaLmnOPksDun+THXbKjFkQFnWsVFnKlXDOXMHYikvIjun/IjFI0lLhxScfXaGnSPe9PG02eDc2HHNtNriDRV6fDeyzrw0OEhHOxNbaU8MjCNQ/3TuPWi1qxWso2NZnzlDRuhVsjw1gtbMj42X9bVm6BRyorSJD/iDKDGqE77c7WWeNaZwytYrKVYfsXHLPe+s0lvMLFxI4WVNQZ0jXkyFlTiAOps6bkddgOc/nDOwSzC+a90YSAiouX4hix2YoVchsYqbU5x+ocHpmFUK3DbxW145OgQrv7uM/jw717GqZHihPNIRdyozba5WWNUQ6+SL1ooyJl4O8aqAseIrK4xwqhR0LwzIi2SijPGmBJCYXYf5/x+ACsAtAM4zBg7D6AJwEuMsbpSLZQQ8AQiMC5wzxkwU5yJJ0OnP4xQNJYxCSsdzdU6vGZTPe7b2wt3IDzv/sEpP6IxLsmSWKVTQqOUpbQ1NldpC061FK172ayNQ/E5NDnbGs3irLMgjg+54A1FF2W+WTJ1Jk3GnrMj/U4wJswcyxWxOEtlaxx1CfNjMsXoz+XaDcJXzj/SDE0WFZK2QgNBMsTpi70vmeZ+XbVWsCPlm9o44QnBHYhIUs4A4AO7VqDGqMb/++uJlM3zv93TC51KjjdulzZI+uYdzTh8xzXY1lKV07qlopTL0NlkwUt90wUfS5htlf57qdWqx5DTj2CkNGrVtC8EGYOkwIDEIOplbm2c8uWmnK2oMcAbimbcRJLq7BB/p7pz3DgRbP2lV86u66zH12/ahMtWZs9cE2adSd94ODroxKYmM774uvV47tNX4f1XrMBTJ0fx6ruexXt/eyDrpmSxmLEiZz5/MsbQYTdk7TMuFeJ3faHKmUzGsK2lKu3mGUFISWtkAH4B4CTn/LsAwDk/yjmv4Zy3cc7bAAwA2MY5T+8vIorCYvScAUJx5g5EEju8o4kZZ/mdnN53eQfcwQh+v79/3n29OfSLMcaEOH3n/OKskH4zkSq9Cq1WHQ5luWgcdvph0ihyDmupSxpEva9nEgCwc5GSGkWEWWcZirOBaaywG/IKphEvRidSJB+KYSC5zI9ZYTdgZY0hrbXx/KQXZq0Slhwu/FJhj3/OUyU2SinOWqw6rKwx5F2ciXajDgnKGSBYKf/j1WtxeMA5L1rf4Q3hocNDuHFrY06JY6WwMyazvbUKxwedBVv8shdnOnAODEyVJtXO4Q3BIjHcwpbF5rsciMY4pnwhWHMIF1ppz57YmO1zILLCFrfK5XDBH4nGMOkNwr4AyplZq8RbL2yR9HlqrtahT6JlNxiJ4uSwK7HJZjOo8elXr8Xzn7kKH3vlKuztnsTrfvQcvviXYwWtXwqJEB8JxXT7Isbpnxl1o9GiLUoo247WKpwZ9cDpm79BTRBSlLNLAdwK4CrG2KH4n9eWeF1ECiLRGPzhKAzqhZ9/NTdOX9zpytfW0dlkwcUdVvzy+Z5Ev5WIuPMn1YrWaNHOsjVyztFX4IyzZDY3ZQ4FGXUFsLd7MqcZZyLiyWjEGcC+bgfabfq81MhiMjdBMhnOOQ4POPPqNwOE2W4mjSLl4F1xfsy6LDPO5nLN+lrs63FgKoUtSUhqLPxzICpn4myjZM6NeVBjVGctdK5aW4N9PZN5DUAWd/VXSFTOAOCmrY3Y3GTGNx47NWtu3R8O9CMUieHtF7flvI5Ssq2lCpEYx9HB/HfrOecYcWZWTMTvlVL1nU37wrBI6DcDZoYuZ5r9t9Rx+sPgXFqAiki2OH3OOUZdqQeRz6WxSguVQpbTBf+EJwTOkZetv5S0VOsw5QvDlcKRMpczIx6EoxydjZZZt1t0Knzslavx/Geuwms21uH3+/sLHgOSjZEMCatz6bALyvdi9GkKSY2FWRpFtrcVr8+WWHpISWt8jnPOOOednPMt8T+PznlMG+d8onTLJAAkLuoWQzkT4/S75xRnhdg63ntFB4adAfz18NCs2/smvVArZJJPfMKss5liYtoXhjsYKShGP5nNzUKSXKoAk78fH8Gr7/on+hw+fPiqVTkf26JTQqWQYdjpx/7zjkVXzQBBOXN4QylPfsPOACY8wbySGkVsRjUmUhRSJ4ZdaKrS5jw/5toNdYjGeEpV6vykt+B+M2DmIiylcjaePqkxmavW1iAc5XjubO7BSF1jHmiUMjTkMEdPJmP44uvWY9QVxE+e6QIgqBT37OnFzvbqxPDncmFbiwVAYcNZ3cEIfKFoxt6VmVlnpek7c3hDkoYpAzMBOct5EHViAHUOxZnNoIJZq0wbCjLtCyMUkWa7l8sY2qy6nGadjYkzzsqwOAOkJTYeGZwGgLQbbUaNEtdsqEUoGsu7V1Yqw84AdCp51vAWQHAPcD5/tE+pmUlqLM735pZmC+QyRsOoiZTklNZILC7uwOIVZ40WLRQylggFEWPFC2mI3rXajjW1Rvzs2e5Zjd29cdVLar9Yg0WLCU8wUUwUI0Y/mS3Nwskred6ZLxTBZ+8/ivfdcxANFi0e/vBluK6zPudjM8ZQZ9Lg6dPjcAciixoGIiL6/lMVo+Lw6XyVMwCw6dUplbOTw66cwkBEOpvMqDdr5lkbQ5EYBqf8RVHOqvUqMDa/54xzjq4MMfrJbG+tgkmjwJN5ROp3T3jRZtXn3EO5vbUar9vcgJ/8sxsDUz7sPjWGwWk/brukLec1lBqrQY02q66gYdRS4tOtehUMakXJirMpX0iyCmRUK6CUswUPBPnO46fLpt8ln+KMMSYkNqZRzqQMoE4m1zh9UUFfbJfDXHIqzvqdqNIpE4nHqVhTK3wfnxrJPJe0UEbjFtRs4S1A0kbxAlsbeye9CEViBYeBiOhUCmxoMOFAmfweEuUFFWcVxGIWZwq5DC1WXWIHbcwVgEmjgFaVfx8KYwzvubwDp0bceObMjJrQ5/BJSmoUEe2EojWiWDH6IhsazFDIWMLaeHTAiet/8Bx+/2If3ndFBx74t0slXZyno86kwdn4RcaFixwGAmSedXZ4wAmFjOXUFzYXq0E1z8blC0XQM+HN67iMMVyzvhb/PDsOf2hG7RuY8iHGC09qBITPv1WvmlecjbqC8AQjkv79lXIZLl9tx+7T4ylDOjLRLTGpMRWfec1aAMA3HjuN3+w5j1qTuiSzyoqBOIw63+GsUnpXGGNoqS5dnP6ULyQpRl9cS7VelXKzolSEIjH88KlzePDloewPXgByGdqdzEq7Ie2ss5kB1NKUrQ67Hn2TPsn2PXFzciECQXJBPOdJCQU5MujEpiZLxoJoRY0echkreXrjsNMvvZAWA1wWOBTkzKjwesVSzgBhw+5Q/3TJbaNE5UHFWQUhJhsaF3jOmUi7dSZOf9QVLMqu4es3N6DOpEkMpeacx8M8pF9Qi/H14qwz8cTUXFWc4kyjlGNtvREv9U7jf57uwo0/fh6+UBT33b4Tn33NOqgUhf0aibNymqq0OUfxl4KZWWeplbO19caCwiGsBtW8QJDTI25wnlsYSDLXbKhDIBzDP5Msg6IyUuiMMxG7UTOvOEuEgUgsnHatqcGEJ4gzY9J3ooORKPocvpz6zZJptGjxvss78NfDQ3j27ARu2dkKpYQ5covBtpYqTHhC6M8hDjyZkUQkd+bfozabLhE8VEw450LyYA4qkFWvXlBboxg+IlrzFpspX+7KGSD0nU16Qyl7TaUOoBZpt+kRiXHJITFj7gAYm0mfLRdMGiUsOmXW4iwQjuLMqBudjZkdEGqFHB02PU6XWDkbcUoLbwEExanerFlw5exsPEa/kI3YuexorUYgHMOJoYUdXUCUP+V5hiZSIvacFSMpKB/abXqcn/QiFuMYdQeKsmuoUsjwzkvb8ELXJI4OODHuCcIXiqKlWnqRMrc463f4YDOooC/i+7S5yYI93ZP4xmOncO2GOjz2sctwSYbhvbkg7hgudoS+iLieucpZLMZxZMCZ13yzZKx6NaZ8YUSSdgtPDgsnvg05hoGIXNheDbNWOcvaKPYkFEM5A8RB1LPfk3NjuZ2wt8b7qo70Sw+96JsUFECpSY2peP+uFag1qaGUM7z1wua8j1NqtseHUR/sy8/qI16UZxsO3FKtR7/Dh2iOCmY2fKEoQpFYTiqQ1aBaUFujuMEwlmao+kKTj60RSAoFSaGgiGnCUm334u+WVDVm1BVEtU5VlpscQpx+5iLzxLAL0RiXNA5lTZ2xpLbGaIxjzB3MOuMsmXabHl0l7oOby9kxDxot2qJeVySGUVPfGTGH8vtmIdKymLZGAGi36xEIxzDiCmDMFSx4wLPIv+xsgVGtwE/+2ZXwyudyQS141WcGQRcrRj+ZV62vhc2gwrfe1Ikf/evWgqPZkxF3DMshDAQQYthNGgVG5ownOD/phTsQweYC+s0AIRAEABy+mQvSE8NOGNWKjP0PmVDKZbh6XQ2ePDmWsIj0TvpgUCtyiujOhN2gnq+cjXtg1ChglxgM0G7Vw6hWZJ2bl4wYVCB1xlkqdCoFfnzLNnz75s0LMjg3X1bXGmFQK/BS73Rezx92BeKzDzMru21WHcJRjmFnceP0EypQDt8P1XrVgkbpJ4qzFMmji4HDG4JOJc9Zjc+U2DjiCsCqV0l2Nayw59bHNO4OSP6dX2ikxOmL88uk9A6vqzdhYMqfV8qsFCY9QURiXLKtERC+C3vGMw8hLzZnRt1YXaSkRpFakwaNFm3Z9H8S5QMVZxWEW1TOFqs4ixdMPRNejLkDRWuGNmmU+NedLXj06DCeOyvM+sqlX0wVT3YU4/RLUZztWlODA//5Kty8o1lS03IurG8wQauU4xWriqPEFYN6s3aecnYkcUK3FHRsW7xYSu47Oznsxrp6U0Hv7TXr6+D0h/Fij3CiE5IadUX797Ib1Rj3BGddEJyLh4FIfQ2ZjGFTkznxXkpBDCpotxWmAG5vrcYNWxoLOkapkcsYtjRb8k4wG3UGsg6yBWa+X4odCjLlFaznUqP0gbitcQGj9EVL8bg7uKAXt+lweEM5q2aA4JjQKGUpizOpA6hFLDoVqvUqyaEgY+7i2PpLQUu1DgNT/oyq8OGBadgMakkF0Zp4j1WprI2JPtEckmg7bAa4kuaulppINIbucW9R+81EdrRV4cD5/PtsiaUJFWcVhNhzlmvUeLFoj+8uvtw3hXCUFzVG+J2XtkMuY7j7mS4whpwVFHEQdTgaw9C0v+jFWSm5ZIUNR++8Jq85aaWiLsUg6sMD09AoZQWnVVnjfRriRWIsxnFq2IV19YWd+K5YbYdGKUtYG4UZZ8WxNAJCcRaOcjj9MzOEzo15JfebiWxqMuPUiAvBiLQ5PV1jXtSa1IvWa7rQbGux4NSIa9ZsNqkMOwOok2C3bkvMOitycZZH/5TVoII3FF2wuU2ichaKxmZ9lheLfIszmYyhw5Y6sTGXHiaRVTUGyb0/gnOkPJWzlmodIrHMqvDR+KxKKZtK4siNUoWCzPSJ5qacAQuX2Njr8CEUjWFVKYqz1iqMuYOS+x2J5QEVZxWEJxCBQsagLjCAIl9qjRpolDLsiysTuexMZqPOrMHrNzfCH46iwayFWpGbxaXBosXQdACDU37EOIo242yhUJRZ70J9iuLsyIATGxvMBa9VnO0kKmd9Dh+8oWhBCZAAoFXJcfkqOx4/MYpwNIb+HFM/s5GYdRa/uHX6wpjwBHNuEN/cZEE4ynFqWNpOdPeEBx224tppypltrVWI8dmjK6QiRHJn3+SoM2mgUsiKntgoFme52J7FwmShVICJJJWuHPrOhHTL/KzH6eL0x/Loid7eWoXjQ65Zia+piMU4xj3BrH2Ni4W4MZkuFMQbjODcuEfyOJSmKi0MakXJlLORHMNbACS+D3tyGH9QCGIYSLFtjYDgaABAkfrELMrripDIiDsQgVGjKLqtTioyGUObVY8D5wXLUbFjhN97eQeA/OaTCYOo/YkEtkpSzsqROrMGE54gQhGhfysSjeH4UOFhIIAw5wyYUc5ODgs7suvzDANJ5toNdRh2BvDYsRFEYrzoyhkwU5ydG88vvUu8KDoioe+Mc47ucW9B/WaVxtZmoUn+pb7crI3BSBST3pAkq5ZMxtBcpS2BrTEP5Sz+2IWyNib3TaaaZbjQTHpCefeFrqwxYHDaD19oRmUNRWKY8IRy3jy8oK0akRjHoSybApPeEKIxXra9m9lmnR0fcoFz6bMqGWNYXWsoWSjIsDMApZzl9BlosGiglDOcL9GswrmIMfr5jjPJxJo6I4xqReK6iiAAKs4qCk8wsmj9ZiLtNj38cftNsU9Oa+qM+OCVK3Dzjqacn9to0SIUieHl+AVdsWacLVca5gyiPjPqQSAcw+bmwsJAAMCknT1498SwCzJWnPkxV6+rgVzGEqMZiqmcicWZqDYkYvRzLM4aLVpY9SocltB3NukNwekPl+SioFwx65RYVWPIue9MDLiQOtuqzapPJHoWC4cvDMYAszaHnjNRSV6gUJBxTzCxsVYOoSC5DO2ei/i7l2xvE0cE5BIwAQhjHBgDDpzPrGCIxy+mrb+Y1Js1UMhYWuVM3BTamCVGP5k1dab4uJPi90WJ/YEymfRNZ4VchuaqmbmrpebMqBtNVcVNahSRyxi2tOTfZ0ssTag4qyDcgTCM6sXtO0kOJSiFrePfr12Lm7blXpyJ/Vp7uyehksuKliS5XEnMOosXZ+IJvRjKGWMMVr06MXj35LALK+yGgmaniVh0Kuxsr8bRQaHwaSswRCOZecrZmAcqhQxNOc7TY4yhs8ksSTnrLkJSYyWyraUKL/dP5zSse2bwsLTezRarDn0OX1EvOKd9IZi1SshzuNCsjivJcwezl4oJdxDr4xbixbY1BsJR+ELRvHrOgNSJjeKGUm2OPWdmnRKra4x4MctFsvielautUSGXobFKmzZO/+igE/VmTU6bq2vrjHD6w4kRBcVk2OnPqd9MpM2mXzDl7NyYpyRhICI7WqtxetRdFj2gRHlAxVkF4Q6Uh3IGCDvDxbiYLhYNFuHL/aW+aTRVa3PahSPmI54sxb6zwwNOmDQKtBVJiRIGUQsXo2JSY7G4dkMdAECrlBd1d9uoVkCtkGHcM1Ocddj0OV2Ii3Q2WXBuzJM19EKcu7SclDNA6P+Z9oXRncPOuNi7IlUxabPq4QtFE/+excDhzb1/SlTOFmoQ9bg7iFarHnqVfNEHUec740ykzSr8/iUXZyPOuIKaR0/0jrYqvNw7lTHpcDzHGWqLQUuGOP0jA05sykE1A4TiDChNKMiIM7dkTZFWqw69k96SpxyKSY2rStBvJrKjrQqcI+H8IQgqzioIdyACU5kUZ8XuNyuUJotQNIQiMbRSv1nBJJSzeOLXkYFpdDZZitbvaDUIytm0L4TBaX9Ri7NXra8FgKLG6AOC4lVjUif1nHlytjSKdDaZEeNC/0cmuie8UClkZZXkuRBsa7UAyK3vLNfiTLQ+9xVx910It8jN3WBUz7b5lpJAOAp3MAK7UY0ak6YkytmEJ5g1VENELM7yDQRRKWRordalVs7yLM7cwUjG8AuxoC3XOWdAfNZZCluj0x9Gz4QXm5stOR1vbZ3w/VzsvjPOOYadgbyUs3ZbfHOlxOrv+UkhqXF1TemUsy3NFshlrKTWxnF3EN987NSCpcIShUHFWQXhDoYXPU5bLM7KbdfQpFVArxKUPAoDKRyjRgmDWoFhZwCBcBSnR9ySG8ilYIsrZyfjiYXFCAMRabBocelKK7a2VBXtmCLiIOpAOIqBKX8BxZkFQPZQkK4C1LlKpsNmgEmjwEs5XKyMuALQKuUwaaVtYJUiTn/KG8650GCMCYOoi6jgpUO8kLUb1MLcvhLY1G788fP47j9OS3qsWJyJ6mE+rKgx4Nz47OJMpZDlXCQDgr0MQMahwKOuYNk5R+bSUq3DlC8MV2C2Te543O6dq3Jm1ilRZ9IUPbHR6Q8jGInlNONMpDVp7mopmUlqLF1xplcrsK7eWNJQkHv29uLHT3fhHydGS/YaRPGg4qyC8AQiMJSgITUXqvUqWHTKvHa6SgljLKEuVFqMfrlSZ9ZgxBnAiWEXIjFelH4zEZtBjUlvECfiSY2Fzjiby2/ftRP/dePGoh4TEHbLx9wBdI17wHnuYSDJx2kwa7KGgnRPLK+kRhGZjGFba1VuyplLmG0lVS1ttGghYyhqnH6+4RZWvXpBbI1iQqrNqEJN/LNcTHyhCPod/kTPZzbE0QP5KmeA8Dt4fsKLcFRIlh1xCTH6+ajmTVVa1JrUeDHDRfKYO1C2YSAirWkSG4/kWZwBQmBXsZWz4RzV7mTa48VZsUN95pJIaqwp7ffwjtZqHOqfTnyOiwnnHI8cGQIAPBz/L1HeUHFWIXDOE1H6iwljDD97+w585OpVi7qOVIjFGSlnxUGcdXYkHi1djKRGEatehUA4hoO9DtgMqqIrsXIZK8nICbtRUM7yTWpMprPJklE5C0Vi6HP4ltWMs2S2tVThzKhHcpO80Lsi/aJZpRCCE3rTpNrlQz62RkBQjhbC1jijnAmBEMW2NQ7GB+l2SRwOXGjPGQCstBsQifGEAjriDOR1sQ8I57cdbdUZ7WVj7vKdcSbSnKY4OzrgRHO1Nq8NhLV1RnSNeYpaPCSsyHls9i5UnP6ZMTeaq7XQqUp77bW9tQr+cDQxWqaYnB51o2vcC7tRjd2nx+HJ0utMLD5UnFUIwUgMkRhf9EAQQJgHU47qVGNVvDijGP2iUGcSlLMjA07YDOq8L3hSYTUIFzfPn5ssar9ZqbEbNJjyhXFqxA0Zm51emiudzWb0Tvow7Ut9Ud7n8CIa48tSOQOEixUAWedOiYw4A6jP0R7VWl28xDd/KIpAOJbXhW+1XrUgUfpiCI/dqEatSQ1fKFrUC7WBeHE27g7Os9SlwuENQZbj6IG5zE1sFIqn/L+rdrRWYXDaj6Hp1GmHY65g2acBJ/op5xRnhwem0dloyeuYa+qMCEVjRY2vFxNW83HiLFSc/tlRd0n7zUR2tAnfd6WwNj5yZBgyBnz5ho0IRWJ4gqyNZQ8VZxWCeKJb7J6zcmal3QCNUvjCJgqn3qzBmDuAl/unsbnJXFQlSuwxcfrDiVjvSkDcMd/bPYmWah3Uivz7TjYn+s5SW8BE9WG5JTWKbG62QMYgqUk+FuMYc+ee+tZqTZ9qlyuFWPSsevWCDKEWlTOrQZX4LI8VcRD1wNRMMdAtQT1zeEOw6FQF9VSuiBdngtWYF6ScAcLmIwAcSPG545xj3B2EvcyVM5NGCYtOOas4c3hDGJjyY1OevcOlCAUZdgbAWP7hKqWO0w9HY+iZ8GJVCfvNROrNWjRatEUPBREsjcO4ZIUN16yvRZ1JQ9bGCoCKswrBExB2N42L3HNWztxyUQse/9gVJRkUuRypM2sR40LDdTH7zQAhkECkmGEgpUZc95EBZ0GWRmBmCGy6/pzlOuNMxKBWYE2dSVK89KQ3hHCUoy7Hi+ZWqxCcUIz5QoUkD1oNKnhD0ZInqY17AqjSKaGUyxJW4mJaGweS1KbupJCOdORrA03GoFag3qzBuTEPXIEI/OFoQcXZ2joj9Cp5ymHU074wQtFY2QVipaKlWjcr7Eb8nsk32GlFjRBMVMw4/RGnH3aDGkp5fpeipY7T7530IhzlWF3CGP1ktrdW4UCvo6g/z8lhN7onvLiusx4yGcN1nfX455kJmqlW5lBxViG4xeKsDGyN5YpaISdLYxFJtpp0FrHfDJidzlZRtsb4Dm80xgtWtMxaJdptehxOY9vrHvfAblQva7V8W4sFh/qmM86dAmbi03NNfRNV9mTFJ1+mfcLFTj79U+JzSt13NuEOwRbfYBBDLYpanE350WjRQiFj6JJQnE16QrDqC1ehVtYYcG7Mk/cA6mQUchm2tlSlDAVJDKAu80AQQOg7S+45Oxrvb92YRxgIIJxfO2z6oiY2jriCBYWLlTpOXwwDKWVSYzIXtFVh1BVEf5oB4vnwyNEhyGUsMf/zus56hKIxSm0sc6g4qxDEvoDFTmsklg/JTdqbi6yciRejKoUMHQX0bS00yfabFQUqZ4Cwi53e1ujBimWqmolsbxXmTp0dy3xBmG+wwExwQuEXQ46ErTGPQJD470OprY3jnmDiM5xQzopoaxyc8qPdpkeLVYeusey2RiHdsvDNhxV2A7rGPQWl/yWzo60Kp0dc8/rmxHTLSijOWqp1GJjyJzY2jgw40WHTw1TAZk+xExtHnP68wkBE2kocp39m1A3GFs5aflGHFQCwp3uiKMfjnOPhI8O4ZIU1cc7d2mxBo0VL1sYyh4qzCsFNPWfEAiPuaDZVaQtKU0uFWiGHUaPAmlojFHlaWhYDW5Ids1BbIyAkNo64AikvkIUY/eXZbyayLT6r7qXe6YyPG3bld1HeFA8RKo5yFi/O8onSN4jKWWlDQSaSijOTVgGVQlYS5WyF3YDuiezKmcMbQnWRlDNfKIpDfdMAkFNqZyp2tFYjxoGX48cTGYvPhctnwPVC01qtQyTGMewUNh6ODDjz7jcTWVtnxMCUv2ghMsMF9ge2lThO/+yoB81VOmhVCzPTbmWNATaDGi90TRbleMeHXOid9OH6zvrEbYwxXN9Zj+fOTqQNoyIWn8q5KlrmuMjWSCwwZq0SWqW86KqZyNaWKlyx2l6SY5eK5OG2xSjONscvlubOO3N4Q5j2hStKVSwFrVYdrHpV1ib5UWcAchnLOVjArFXCqFYkUgYLQew5s+SRPCgWKJOlVs7cwcQGA2NMmHVWJOUsEI5iwhNEU5UWHXY9zk/4MtpRYzGOKV8Y1UVQzsTfxee7BMWh0OJpS4sFchnDwTl9ZwlbY5kHggAzI2X6HD6MuQIYcQXymm+WzJp4KEgxrI3eYATuQCSvAdQipY7TPzPqXrB+M0D4nbxkhRV7uiaL0nf28JFhKGQM16yvm3X7dZ31iMQ4/n58pODXIEoDFWcVgoeKM2KBYYzh2zdvxkdfWZqZdr9914X41LVrSnLsUmI3qlFjVBdkDxLZ0GCGXMbmzTsT+3WWa1KjCGMMW1uqcDBLk/yIKwC7QZ1z6h9jDE1zenPyZdoXhkmjyEsJFpWzUg6i9gYj8IWiswpYYRB1cZSzwXgYSGOVoJyForGMiqQ7EEE0xgsaQC0iFmcv903BolNCoyxM6TCoFVhXb5zXdzbqCsCgVpR85lUxSJ51JoaBbG62FHTMtXVC71UxQkEKidEXKWWcfiiycEmNyVyywooxd1DyrMB0cM7xyNEhXLrSNk/N39RoRku1Dg8fGS7oNYjSQcVZhSAGglDPGbGQXNdZv2DN0JXCxkYzLl5hLcqxtCo5VtUY5vWddVNxluCqtTU4P+nDHw8MpH3MiDOQdwhEc5UW/UWwNTq8obwsjYCQwquUs5IGgkx4hCIs2ZpbzEHUovrYVKVL9EpmCgURLZzJ4UD5YtWrYNEp44mdxbEc7mitxqH+6VlDl8fdwYroNwOEokchY+hz+HBkwAkZQ8FjS5qqtDCoFUVRzsQ+0UJVzlLF6Z+f9CISW7ikRhHx3LKnq7C+s6ODTvQ7/LguydIoIlobX+iaxKSn9PMVidyh4qxC8ATD0CrlFdWfQxBLke++eQu+/9atRTueEAoyPUsZ6h73QqWQJQarL2feekEzLllhxR0PHU97sT/iCqA+z4u8piod+h3+gm1EQix8foUGY0yYdVbCnjMx0W6WcmYqnq1xMFGcadFhEy5oM806K2Qu3FwYY1gZ38goZAB1MjvaquAPR3FiaEYlGnMH8p7JtdAo5ML3R++koJytrDEUPGaGMYbVtYaihIKI4S2FKGdA6eL0z8aTGlctwADqZFqqdWi0aAvuO3vkyDCUcoZr51gaRa7vbEA0xvEYWRvLErrSrxDcgQhZGgliCdLZZMGULzyr76lr3IM2q66g4bxLBZmM4btv3gK1UoaP/v5lhCKxeY8ZdQbyTn1rrtbCH44WbCksdGZXtV5V0p4zUTmzG2bbGl2BSFHmqw1M+aCQMdSaNKjSq1CtV2VUzhze/EcPpEK0NuY66y4dO1qFYdQvJvWdjbmDFREGItJSrYsrZ9PY1GgpyjHX1JlwesRdcDE0M/6isPdTjNMvZrANMJPUWIze4lxgjOHiFVbs6Z5ELMsIkXSIKY2vWGmDOc130rp6Izpsejx8mKyN5QgVZxWCOxiBgYozglhyiIErh5P6zrrHvQn1gRAu4L7xxk4cG3ThO4+fnnWfJxiBOxjJ+6JZnHXWX2AoyJQ3nLetERDsfaW0NYrKmc04s0YxTr8Yc6IGp/2ot2gSGwor7PqMcfqiSlj84qw4xVOdWYOmKm0ijIZzjjFX5dgaAaHv7OSwCxOeUN7Dp+eyts4Ipz+MUVdhn5k9XZOoM2kK7g9MJDYWue/s7JgbLdW6gteXD5essGLaF85boTw84MTgtB/XdTakfYxobdzXM5kYEUGUD1ScVQiCckYx+gSx1FhTZ4RKLkv0nYWjMfQ5fFhRs7yTGudy7YY63LKzBT/5ZzeeOzvTjzFSoD0qOTihEAqxNQJC31QpA0HGPSHIGGYNfbabxEHUhV+ciTH6Ih22zHH6xVbOxLmDhQygnssFbdV48fwUOOdwByPwh6MVkdQo0lqtQzgqqC+FxuiLrClCKMje7kk8d24C735Fe8HrKVWc/plRz4JbGkXEvrMX8uw7e+TIEJRyhletr834uOs3NyDGgceOkbWx3KDirELwBMIwUhgIQSw5VAoZ1jWYEomNfQ4fIjFOylkK/vO69VhZY8An/nAo0cgu2qPyVc7EWWeFhIIEwlH4QtGCCo1qvbqkzfnj7iCq9apZVtnaxCDqwl93YMqHprgKCQAravSY8ITg9IVTPt7hDUKjlBUt+XBbcxV2tFZhZ3txwnoAoe9swhOMx9HHY/SNlWVrBACFjBUcBiIyk9iYn6rDOce3/34atSY1br24teD1lCJOPxSJ4fyEd8HDQETqzVp02PTYk0ffGeccjxwZxuWr7DBnGeuxutaIVTUGsjaWIVScVQjUc0YQS5fNTWYcG3QhFuPoGhPUhg47KWdz0ark+MFbt2LaF8an/3wEnPNEsEC+vSt6tQLVelVBs86m4wWIpYCeM6tBBW8oWpT+r1QkzzgTqUkoZ4UVZ8GI0PPTlBRgIyaNdqVRzxzeMKqLEAYiYtYp8acPXFLUHqGZvrOphLpYabZGQLgIL5Y9z6JToc6kyTux8enT4zjQO4UPX7WqKGsqRZx+z4SY1Lh4ScUXrbBiX48Dkej8HttMvNw/jSFnIGVKYyqu72zAi72OhAOBKA+yFmeMsWbG2G7G2EnG2HHG2Efjt3+ZMXaEMXaIMfY4Yyy9uZUoGE8wQjH6BLFE2dRohicYQfeEB93xi4wOitFPyfoGEz7zmrV44uQY7t3bOxMsUECvUXOVtiBbo2hHLKTYsOpLO+tswhOclzRYrVNBIWMF2xqHpwPgHLNtjWJxNpa6OJvy5T96YKFYVWOASaPAwV5Hoi+vWGmQC0GLVSjOitVvJrKmzpiXchaLcXz78dNortbizTuai7aeYsfpnxkVfrZVi6ScAULfmScYScyok8ojR4ahksvwyiyWRpHrN9eDc+DRo6SelRNSlLMIgE9yztcBuAjABxlj6wF8i3PeyTnfAuBhAF8s3TIJ6jkjiKWLOBz2cL8T3eMe2AzqrJaU5cw7L23DrjV2fOWRk3ju7ATMWiW0qvx34ZuqdQUqZ0JBZSmgOBMtkaVKbBx3B2clNQJCEqbNoC443EEcQJ1sa2yu0kIpZ4nNhrlMekNF6zcrFTIZw/bWKkE5E22NFdRzZtIo8elXr8XbL24r6nHX1hnRNeaZNQNOCo8dH8HxIRc+dvVqqBTFM261WfVFjdM/O+aBjC3unMmLOsS+M+nWxliM49Gjw7h8tR0mideLK+wGrKs34eEjQ3mtkygNWX87OOfDnPOX4n93AzgJoJFzntwNqgdQ3CETRIJojAvKGdkaCWJJssJugE4lx5GBaXSNe8nSmAXGGL5982YYNUrs6Z4sOKGvqUqLwSl/3tHVjnhxVkixIQ5jnizBrDPOeUrlDIjPOivQ1jgQ79dLtjUq5DK0WvXplbMKKM4AYEdbNc6NeXB61A2NUlZxvd8f2LUC6xuK028msqbOiFA0lpOVMBrj+O4/zmBljQFv2NpY1PW02XRFjdM/O+pGq1W/KEmNIjaDGmvrjNjbLb04e7l/CsPOAK6XaGkUub6zHi/1TSc2WYjFJ6etC8ZYG4CtAPbF//+rjLF+ALeAlLOS4Q1FAAAmKs4IYkkilzFsbDTj8ICgnK2g4iwrNoMa3765E0DhCX3NVTqEojGM5mnvm4r3nBUy50xMUSyFrdEdjCAYic3rOQOEHqpCB1EPTvkhY/P7/lbY9WlnnU15C0u3XCh2tFYBAP5xYhQ1Rg0Yo9mDa/IIBXnw5UGcG/PgE69aXfT5jcWO0z8z6l7w+WapuHiFFS+edyAYkdaH+vCRYagUMly9rian1xGLuUePkLWxXJBcnDHGDAD+DOBjomrGOf8857wZwH0APpTmee9ljB1gjB0YHx8vxpqXHe6AUJxRIAhBLF2EUBAnpnzhRbXTVBK71tTgm2/qxPsv7yjoOGJwQr7WxilvEWyNhtLZGsV+qVTKmd2oKXjO2cCUH3UmDZTy2ZcUHXYD+hy+efa3YCQKdzCS6LMrZzY3W6CUMzj94YoKAyklK2sMkMuY5Dj9UCSGu548g42NJrx6Q13R11PMOP1gJIrzk75FS2pM5uIOKwLhGA71TWd9bDgaw6NHh7FrtT3nFphWqx6bGs34K1kbywZJxRljTAmhMLuPc35/iof8L4A3pnou5/ynnPMdnPMddrs9/5UuYzzx4sygph4UgliqdDZZEInb6sjWKJ0372jGJSttBR0jEaefZyjIlC8Eo1pRUB+NUa2AUs5KMoh6QhxAnUY5m/SGcu4fSmZgyj+r30xkhd2AcJTPe1/FdMtyDwQBAI1Sjo2NQqBGvuMalhpqhRwdNr3kxMY/HOhHv8OPT16zBrIiq2ZAceP0z4x4EI1xrKkrrhU0H3Z2WCFj0vrOhHCkIN56YX5BKzdsacCRASfOpbEhEwuLlLRGBuAXAE5yzr+bdPuqpIe9HsCp4i+PAAB3QDiRkXJGEEuX5EQ1mnG2sIgpg/2O/JUzi76wzTPGGKx6NRwl6Dkb96RXzsSAi4kCZqwNTvtn9ZuJiJsM3eOzFQ1RHawE5QwQhlEDqd+/5YrUxMZAOIofPnUWO1qrsGt1aTboixmnv69HKIQujP+bLyZmrRIbG81Z551NeUO464mzeMVKG65ck5ulUeT1WxogY8ADLw/k9XyiuEjZ5rsUwK0ArorH5h9ijL0WwNcZY8cYY0cAXAPgo6Vc6HLGHYwrZ1ScEcSSpaVaB4tOCaWcpbzQJUqHRilHrUmd9yDqKV9xZnZV61UlsTVOZLA11hQ4iDocjWHY6Udjis/sivgmw9y+s6l4gEolKGcAsD3ed1ZJSY2lZm2dEQNTfnji1yfpuGePoOh86to1Je3XK1ac/t5uB1qturznJhabi1dY8XL/FPyh9H1ndz1xBu5AGF+4fn3e73GNUYPLV9vxwEuDeQcjEcVDSlrjc5xzJsbmx/88yjl/I+d8Y/z213HOBxdiwcsRseeMAkEIYunCGMP2liqsqjFCIS9ezDQhjeYqXSJ1MFemfKGC+s1ErAZVSWyN454g5DIGS4rxDGIfVb5JdyPOAGIcKTcUzDolbAb1POUsMReuQoqzne3VaLRosaXJsthLKRtE218ma6MnGMH/PNOFy1bZEtHwpaIYcfqxGMeL5x3Y2b74qpnIJStsCEc5DvQ6Ut5/ZtSNe/f14ZadrYmglny5aVsThpyBnBIiidJAVwAVAPWcEcTy4Gtv3ISfvn37Yi9jWdJUpc3f1ugrTiy8Va8qSVrjhDsEm0GVst9HVIPyHUQthqg0Wub3nAGCtXGuclZpxZlFp8Lzn7mq4N7GpcTaeCHwzzPjGJz2IxCer+z88rkeOLwhfOqaNSVfTzHi9E+NuOH0h7GzvbSFZC5c0FYFhYyl7DvjnOPLD5+AXiXHx1+1uuDXumZ9LYxqBf78Emktiw1JMRUA9ZwRxPJAtJgRC09ztQ4PHR5COBqblzqYjSlvGJYCYvRFqvVqTBbQ+5WOcU8wZRgIIISEMJa/rTHVjLNkVtgNeOzY7IhusThLpeQRlUGjRYsqnRLff/Isvv/kWQCAQa2A1aCCVa+C1aDGnq5JXLO+FpubLSVfT3Kcfr7BLWK/2c6O8lHOdCoFtjRbUhZnu0+P4dmzE/jC9euLstGhUcpxXWc9Hjo8hC+/YQN0KrrmXCzona8APMEIZAzQqRZvICJBEMRSprlKhxgHhqcDaLGmVoFSEYrE4AlGitJzZjWo4A1FEQhHizoAd9ydegA1ACjlMlTrVHkrDuLg2npL6gviFXY9pnxhOJKGTk/5QjBrlWTfrWBkMoaHPvQKnBpxY9ITxKQ3hAlPEJOeECa9QfQ7fLAZVPj3a0uvmgGz4/R35mmh3NftQKNFmzJ5dDG5ZIUVP9p9Dq5AGKZ4TH4oEsNXHj6JDrseb7+4tWivddO2Jvz+xX78/fgIbtzaVLTjErlBxVkF4A5EYFAraPglQRBEiWiqFpSfgSlfTsXZdDzcwlIkWyMgKEsNluKFwkx4ggkbWipqTBqMF2BrrDWpoVakLibFmX3d4x5U6wVFYtIbqpikRiI9zdW6xIzAxabQOH3OOfafd2DXmvIb+XTxCht+8NQ57O924JXrawEA9+ztRfeEF798x46clf5M7GitQnO1Fve/NEjF2SJC21YVgCsQznmoIEEQBCGd5vhuea6JjVPxmV3FSmsEijuImnOOCU8Qtgwx8DVGNUbztDUOpplxJpIqTn/KG6qYpEaiMlDIZWiuzj9O/+yYBw5vCBeVUb+ZyNYWC9QKGfbEgzoc3hC+/8QZXL7annd0fjpkMoYbtzbhuXMTGHHmt2FDFA4VZxWAJxChfjOCIIgSUm/WQC5jOYeCiP1TVUXoObMa4sVZlllnrkAYv36+R1LktdMfRjjKYU/TcwYIxVnegSDTvsScuFQ0VemgkstmhYIkWxwJoli0WfXoybM429ddfv1mIhqlHDvaqhJ9Z9/7xxl4Q1F84bp1JXFU3bS1EZwDDx6iYJDFgoqzCsBNxRlBEERJUchlqDdrclbOpos4s8uqFwqobImN9+3tw51/PYGDfVNZjzke7yXLqJyZ1JjwhBDNcb5RNMYxPB3IOJdPLmNos+nQlaScObyhoiiNBJGMEKfvyytOf2+PA3UmDVrKxKY5l0tW2HBy2IU9XZO4b18v3razBatqC4vOT0ebTY/trVX488GBgkYTEPlDxVkF4AkKPWcEQRBE6RBmneWonInFWTFsjQZptsYnT44CAI4OOLMeczye/phZOdMgGuM5x/iPugKIxHjKAdTJrLAb0B1XzjjnmPKRrZEoPm02Hfzh3OP0OefY1+3Azo7qsu3tF+fEfeC+gzBqlPjYKwuPzs/ETdsacXbMg2ODrpK+DpEaKs4qADf1nBEEQZQcYdZZrsqZ0HNWjCh9o1oBpZxlHETt8IbwUlwxOzoooTiLX6jajemLoZlB1LlZG8VCNlu63Qq7Ab0OH0KRGNzBCMJRToEgRNFJjtPPhe4JLyY8wbKabzaXziYz9Co5pn1hfOyVq0q+uXH9pgaoFDL8+aWBkr4OkRoqzioATzACA9kaCYIgSkpztQ5j7mDKgbrpcHhD0KnkRYm+Z4zBqlfDkaHnbPepMcS4MGcqp+LMkH7208wg6twUh8FpoZDN1HMGCKEg0RhHn8OHKW/xbKAEkUxynH4u7Ot2ACjPfjMRpVyGK9bYsabWiLddVLzo/HSYdUq8al1tYvYjsbBQcVYBuKjnjCAIouQ0J+L0pVsbp3yholgaRar1qoy2xidPjaLGqMabtjeha9wDbzCS8XgTnhBUchlM2vTnEHH4+XiOiY0DDlE5y25rBICucU/COlmtJzcIUVzyjdPf1zMJm0GNDpu+RCsrDt998xY88MFLihqdn4mbtjXC4Q3hmdPjC/J6xAxUnJU5wUgUoUgMRuo5IwiCKClinP5ADqEgQix88QoNq0GV1tYYisTwzzMTuHpdDTY3m8E5cHwoc0/IuDsIm0GVsZfGnqetcXDaD5tBnVU1TI7TnynO0vfAEUQ+5BOnXwn9ZiIapRw61cJdC16+2g6rXoX7XyZr40JDxVmZ4wkIu6LUc0YQBFFamhKzzqQrZw5fuKjKmVWvShvMsa9nEp5gBFevrcXGRjOA7H1nE55govhKh0Yph0mjyNnWODDlzxoGAgjnrxqjerZyRmmNRAnINU6/z+HDiCuAi9rL19K4WCjlMrx+SwOeODEGZ7y3llgYqDhbJGIxjjsfOo6Xs0Qhu+PFGaU1EgRBlJYaoxoqhQwDOYSCTPuKO7OrWq/GpCd1kfTkyTGoFTJcutKGGqMGdSYNjg5MZzyeoJxlV6lqTBqM5WprnPJltTSKdNj16E4uzgxUnBHFJ9c4/Zl+s/INA1lM3ritCaFoDA8fHVrspSwrqDhbJP55dhy/fuE8/nCgP+PjPEFROaPijCAIopTIZAxNFm1Os84c3uL2nFkNKnhD0XmhJJxzPHlqFJeutEGrEmyEGxvNWZWzcQnKGZD7IOpYjGNoOoCmLGEgIivsBnTFbY0quQx6VeEBKgQxl1zj9Pf2TKJar8KqGkOJV1aZbGgwYXWtAfe/VLqB1JFoDO4AKXPJUHG2SNyzpxdAdkuKK/6BpbRGgiCI0tNUrUO/Q5qtMRyNwR2IFN3WCMwfRH12zIN+hx9Xr6tJ3Lap0YzuCW9iE28u4uwy6cWZdOVs3BNEKBrLQTkzwOkP49yYB1V6Zdn39xCVSa5x+vu6Hbiwrfz7zRYLxhhu2taEg71TOY8oyIQ3GMFjx4bxiT8cwgVffQJ3PXG2aMdeClBxtgj0O3x46vQYjGoFTo+4M8Y2iz1nJuo5IwiCKDlNVVrJgSDijLNiBoKIFsm5iY1PxAdPX722NnFbZ1M8FCTNJt+UL4RojOdka5RqB5M640xkRTwU5GDfFIWBECWj3SY9Tn9gyofBaX9ZR+iXAzdubYSMoeCZZ2OuAP53Xx/e+av92Prlf+D9976EJ0+OYdeaGly+2l6k1S4NSI5ZBO7d2wsZY/j4q1bj/z18AqdH3NjcbEn5WHeAbI0EQRALRXOVDlO+sDBfMkuv77QvPrOrqLZGoXCZnDPr7MmTY9jYaEKdeWZeWXIoSKqemQmPOIBamnIWisbg9IdhkfDziAWslEAQYCZOf9oXxoYGk6TnEESu1Julx+kn+s3KePh0OVBr0uAVq+y4/6VBfPyVqyGT5aYyHjjvwJcfOYnD/dMAhJElb9vZiletr8UFbVVQLNBogEqC3pEFJhCO4v8O9ONV62pxzQZhB/RIBmujaFehQBCCIIjSI84665cQCiJaD0tta5z0BPFS39Qs1QwQiq56syatPV4cQC1VOQOkD6IWlbNsA6hFGi1aqBXCJUcx3y+CSCaXOP19PZMwa5VYW2dcgJVVNm/c1ojBaT/29kzm9DzOOb74l+MYcfrxqWtW4+8fuxz//Pcr8cXXrcfFK6xUmKWB3pUF5q+HhzDtC+Ptl7Si0aJFtV6VMW3LTT1nBEEQC4Y460xKcTZVClujYb6tcffpcXAOvHJd7bzHZwoFyVU5AyA5sXFw2o8qnRJ6iRuHMhlLWM6sRUy3JIi5SI3T39fjwAVt1TkrQcuRazfUwahW4E8Hc7M2Hh5w4sSwCx+6ahU+dNUqrKkzUn+fBKg4W2Du2duLVTUGXNxhBWMMmxrNODqYfoioOxiBSiGDWkHJVgRBEKVGDLgYkDDrbKoEtkajWgGVXDZrEPWTJ0dRa1JjY+N8O2Bnoxnd496UaWeicpZTcSYxsXFgyi+530xEtDZWUXFGlJB2m1CcnRl1p33MiDOA3kkfLqJ+M0lolHJcv7kBfzs6kjaAKBW/29cHnUqON2xpKOHqlh5UnC0gh/qncWTAiVsvbk3sHGxqNOPMaPpQEHcgAhOpZgRBEAtCtV4FnUouKU6/FMUZYwzVehUc8Z6zYCSKf54Zx1Vra1LuOG9sEvrOjg/N3+QbdwehUUqLrc/V1jg45ZNsaRQRQ0FIOSNKyS07W2DWKvHG/3kBe7pS2/D2xe151G8mnTdtb4I/HMWjR4clPd4VCOOhw0N4/eYGGCnULieoOFtAfrvnPPQqOW7c2pi4bVOTGdEYx4nh1OqZJ5C9KZ0gCIIoDowxNFdJi9Of8oagUcoSc8eKRbVelbA17u9xwBuKzus3E9kkhoIMzLc2TniEGH0pNiKDWgGdSi7J1sg5x+C0X3KMvkgHKWfEAtBhN+CBD16KOpMGt/1yPx46PH+A8t5uB4xqBdZTOI1ktrVY0GHTS7Y2/uXlQfjDUfzrzpYSr2zpQcUZIDk6uBAc3hAePjKMm7Y1zdpB6GxKf2IFhJ4z2nEgCIJYOJqrpcXpT/nCqC5BuIXVoErYGp88OQa1QoZLV9pSPtZmUKMhTSjIuDsoKQxEROog6klvCIGw9BlnIttbq9Bo0WJ9PV0QE6Wl0aLFn95/Cba0WPCR372Mn/2ze9a13r6eSexoq4Kc+s0kwxjDG7c3YX+PA31Z0jA557hvXx82NJgSG0iEdJZ9cfZv9x3ER35/qOSv838v9iMUieHWi1tn3V5n0sBmUONI2uKMlDOCIIiFpKlKh4Epf9aNuylvSFLsfK5Y9So4vCFwzvHEyVG8YqUtozqXLhRkwhOEPafiTCPJ1phIasyx56y5WofnP3NVQkEjiFJi1inx23ddiOs66/HVR0/iS389gWiMY8wdQPe4N+X4CSIzN25tBJMw8+xQ/zROjbjxrztbKAAkD5Z9caZTKfDc2XHEYqVTz6Ixjnv39uKijmqsrp0d2coYQ2eTGcfSpG15ghGacUYQBLGANFVp4QlGEkOm0zHlCyWGRheTar0ak54gzox6MDDlx9UpUhqT6Wwyo2fCC9ecUJBxd1BSGIiI3aROhIhkQlQVc1XOCGKh0Sjl+OFbt+L2V7Tj1y+cx4f+9yU8e2YCALCzncJAcqXBosUrVtrw55cGMl43/+++PuhVctywpTHtY4j0LPvi7OIOK6Z8YZwaSZ/qUyhPnRrD4LQfb7+4LeX9GxvNODvmhi80PwHHHYhQjD5BEMQC0lwdj9PPYm2c8oVh0RXfdm41qOANRfFIvPH+6nU1GR8vDqNO3uSLRGNw+EK52xpd2W2NgwnljIozovyRyRj+8/r1+ML16/HY8RF85v4j0Knkid8bIjfetL0JA1N+7OtxpLzf6Q/jr0eG8PotjeT8yhMqzlYIsvae7twG6+XCb/ecR61JjVetT7372dloRowDJ1KkbbkDYZio54wgCGLBmJl1ljkUpFTKmZhm+OeDA9jUaEZtPEkxHZtSFGeCLVJajL5IjVEDbyiaNSp7YMoPk0ZB5yaionj3K9rxo3/ZBsYYLuqwQkkDkPPimvWZZ549+PIgAuEY/vVCCgLJl2X/yWywaNFm1WFP10RJjt897sGzZydwy87WtF8Em+KhIHP7zjjnZGskCIJYYJqqxVln6ZWzaIzD6Q+XpOdMLPgGp/1ZVTMAsBrUaLRoZ51DxN6xXJUzAFnVs8Fpf879ZgRRDlzXWY8nP3EFvvmmzsVeSsWiVclx/eZ6/O3YMLxzNnI45/jd/j5sajQnrm2J3Fn2xRkgqGf7ehyIlqDv7N69fVDKGd56YXPax9SaNKg1qef1nflCUcQ4SBYmCIJYQEwaJcxaZUZb4wtdEzkrU1KxJhVU6SL057KpcXbv8oRH+gBqkRqTOIg6c9/ZwJSP+s2IiqW5WpfTpgUxnzdua4IvFMXfjo3Muv2lvpkgECJ/qDgDcFGHFe5ABMeHUody5IsvFMEfD/bj1RvrUWPMbks5Mqc4cweEHQmK0icIglhYmqu1aW2NZ0fd+Lf7XsLqWgNu2NJQ9NcWbY21JjU2NkqLnd/UZMb5SR+cfiEURAz2yDWtEchcnHHOMTCV+4wzgiCWDttbq9Bm1eFPB/tn3S4Ggbx+c/G/F5cTVJxhpu/shTST5PPlL4eG4A5E8PY58fmp2NRoQde4Z5bX3xMUTrIUCEIQBLGwNFl0KZWzMXcA7/jVi9Ao5fjlOy4oSd+V1SAUZ1etrZUcQy2GGxyPb/JNxIdY24zSbZe1puy2xmlfGL5QFI0WKs4IYrnCGMObtjdhb7cD/Q7he9LpC+PhI0O4YWsj9OT4KoisxRljrJkxtpsxdpIxdpwx9tH47d9ijJ1ijB1hjD3AGLOUfLUlosaowcoaA/YUuTj7w4F+rKk1YkdrVdbHdjaZwfnMiRUAXAnljD7kBEEQC4kwiNo/Ky7aF4rg9t8cgMMbwi9vuwBNJeq7MmqU+OabOvHhq1ZKfo4YCiLOOxt3B2FQK6BTST9/mLVKqBSyjHH64oyzUv3sBEFUBjdua5o18+z+lwcQjFAQSDGQopxFAHySc74OwEUAPsgYWw/gHwA2cs47AZwB8NnSLbP0XNxhxYvnHQhHY0U53rDTj5f7pvH6LQ2Sdj43zjmxAkm2RtqBIAiCWFCaq3UIRWKJ3q1ojOMjvzuEY4NO/PBftpa82f3NO5rRkIM6Va1XCaEgYnHmCcJmyC2shDEGu0Gd0dY4OE0zzgiCABotWly6Ymbm2e/292Fzk5lGFBSBrMUZ53yYc/5S/O9uACcBNHLOH+ecix68vQCaSrfM0nPJCit8oSiODEwX5XiPxZskX72xTtLj7UY1GsyaWcWZh3rOCIIgFoVEnH7c2viVR07giZOjuON1G/DKNGNRFpvOpplQkIkcB1CL1JjUGHOntzXOKGdUnBHEcueN2xvR7/Djf57pwplRDwWBFImces4YY20AtgLYN+eudwH4W5rnvJcxdoAxdmB8fDyvRS4EOzvi886KZG3827ERrK41YIXdIPk5GxvNODqQrJxRzxlBEMRiIBYf/Q4/fvlcD371/Hm8+xXtuO2StsVdWAY2NprRO+mD0xeOK2d5FGdGNcZcqZWzF8878OChQRjUCpi1tGlIEMudazfUwaBW4DuPn4ZBrcD1nRQEUgwkF2eMMQOAPwP4GOfclXT75yFYH+9L9TzO+U855zs45zvsdnuh6y0Z1XoV1tWbijKMetwdxIvnHXjNxvqcntfZZEb3hBeueFEmhoNQzxlBEMTCIvZU/e/+Pnz5kRO4dkMtPvfadYu8qsx0xq2Wx4acmPDkqZwZNbNsjZxz7D49hpvvfgE3370HQ9MB3PG69ZKDSgiCWLroVApct6keMQ68YWsDBYEUCUnvImNMCaEwu49zfn/S7bcBuB7A1Zzz4g8JW2Au7rDivn29CEaiUCvkeR/n78dHwDnwmk3SLI0im5osAIBjg05cssKWCATR59DQTRAEQRSOViWHzaDG/h4HNjdbcNdbtkIuK++CZGODUJwd7J3CtC+cU4y+SI1RDac/DH8oiidOjuJ/nu7CiWEXGswa3Pm69XjLBS3QqvI/PxIEsbS49eJW7D49htsublvspSwZsl71M2F77BcATnLOv5t0+6sBfBrAFZzz9JM6K4iLV1jxy+d78HLfNC6K2xzz4bFjI2i36bGm1pjT8xJpWwNCceYJRGBQK8r+goAgCGIpsrrWAI1Shp+/fUdFFCRVehWaqrR4+vQYAMCWZ88ZAFz1nacx7Aygw67Ht97UiRu2NEKloOk7BEHMZmOjGfs//8rFXsaSQookcymAWwEcZYwdit/2OQA/AKAG8I+4vWEv5/z9pVjkQnFhezVkTJh3lm9xNuUNYU/3JN57eUfOtg8xbUsMBXEHwmRpJAiCWCR+fMs2MMYqqr+qs8mMv8UDqfJRzlbWCH3SNoMaX7x+Pa7ZUEcbhARBEAtI1it/zvlzAFJ9Mz9a/OUsLmatEhsbzdjbNQn8//buPsiusj7g+Pe32SRsErOQEDAm4S1qFUMwuqsEcGTTgQIdJaJGDEEdWplS7MiIQrCj4ktn2rSotQN0HFBRoEl4qXWmkCkDq6AIZFEkgaC8iLwYkgAJCQRCsvv0j3OXbMLd7N3N7jnn3nw/M3f23Ofcc8/v/nLnyf72Oc9zThzae9z60Dq6exKnDnK+Wa/Z01tfL85e2paNnEmS8rf/uMEtRV8Gs6a1cvOqrDgbysjZew+dxK8vnsebJ+7nvDJJKoDXKOxm7hGT+e1TG3nlte4hHX/L6rVMP6CFWdMmDun4o6bvXG1ry6s7HDmTJNVs9rT9X98eyoIgAFNbWyzMJKkgFme7mTtzMtu7E11/emHQx25+dTu/fPQ5Tn7Xm4f8H1vvf6yrnnmRLdt2MMF7nEmSatT3D4ODvQm1JKl4Fme7aT9sEs1NMaT7nd2+Zj3bu9OgV2nsq3dRkAee2eScM0nSoOw/bgyHTBrHxP2a92rVYUlSMSzOdjN+bDOzp7cO6X5nN69ay8ETxzJnxgFDPn/ruNEcMmkcq595Mbus0TlnkqRBeP/hkzhiyoSiw5AkDYG/+Vdx7MwDueIXjw1qQY6Xt+3gF3/YwBntM2jay5Wtjpreyu+e2sRLzjmTJA3SN06bxfaenqLDkCQNgSNnVcydOZnunsTKP9Y+7+znv9/Ath09nDzEVRr7mj2tlac3vsIr27uZMNY5Z5Kk2rWMGcVE5ytLUl2yOKvivYcewJhRTdz12HM1H3PL6rVMHj+G9x0+aa/Pf9T01te3HTmTJEmS9g0WZ1XsN3oUcw7Zv+Z5Z69u7+b2h9cP2806Z03bWZxNsDiTJEmS9gkWZ/2YO3MyD/55My9u3T7ga+/4wwa2vtbNKbOGvkpjXxP3G83hB46vbFucSZIkSfsCi7N+HDvzQFKCu/848OjZitXP0toymrkzJw/b+XuX1H+T8wYkSZKkfYLFWT+OntHKfqObBrzf2Ws7erh1zTpOPPJgRo8avnTOrsw7q3W1SEmSJEn1zd/8+zG2eRRth07i7gHmnf3qsefY8uqOYbuksdcpR01l1TMv8vaD3zSs7ytJkiSpnBw524O5Myfz8LNbeP6lbf2+ZsWqZ5kwtpnj33bgsJ572v4t/PsZc2gZM2pY31eSJElSOTlytge9c8g+d91vOWp6K4dMGschk8Zx6ORxvGX/FgL4v4eeZd47DmJss0WUJEmSpKGzONuD2dNaOX3ONH739CZ+dNdGXtvR8/q+poApbxrLxq3bOfWo4b2kUZIkSdK+x+JsD5pHNfHtT7wbgJ6exLotr/Lk81v50wtbeeqFrTz5wlZ29CRO+IuDig1UkiRJUt2zOKtRU1MwtbWFqa0tvP+I4VsyX5IkSZLABUEkSZIkqRQsziRJkiSpBCzOJEmSJKkELM4kSZIkqQQsziRJkiSpBCzOJEmSJKkELM4kSZIkDZ8lS6Czc9e2zs6sXXtkcSZJkiRp+LS3w4IFOwu0zs7seXt7sXHVAW9CLUmSJGn4dHTA8uVw+ukwdy6sXJk97+goOrLSc+RMkiRJ0vDq6IDDD4dbboGPftTCrEYWZ5IkSZKGV2cnPPkktLTAVVfB7bcXHVFdsDiTJEmSNHx655hdfz1873uwYwfMn//GRUL0BhZnkiRJkoZP3zlmZ58NxxwDTU1wxx1FR1Z6LggiSZIkafhceOHO7aYmuPxyaGuD9euLi6lODDhyFhEzIqIzItZExIMR8flK+8crz3siom3kQ5UkSZJUd+bMgfPOgyuugPvuKzqaUqvlssYdwAUppXcCxwDnRcSRwGrgdMDxSUmSJEn9++Y34aCD4Nxzobu76GhKa8DiLKW0NqX0m8r2FmANMC2ltCal9PuRDlCSJElSnWtthUsvzeajXXll0dGU1qAWBImIw4A5wD0jEo0kSZKkxrRwIZxwAlx8MWzYUHQ0pVRzcRYRE4AbgfNTSpsHcdw5EdEVEV0b/EeQJEmS9k0RcNllsGULLF5cdDSlVFNxFhGjyQqza1NKNw3mBCml76eU2lJKbVOmTBlKjJIkSZIawZFHwnHHwQ9+AHfdtbO9sxOWLMm2lyx54z3R+u5vYLWs1hjAVcCalNK3Rz4kSZIkSQ3rwguzJfbPOiu7QXXvTavb27P97e3Z894Cbff9DSxSSnt+QcTxwJ3AKqCn0vxlYCzwH8AUYBNwf0rpr/b0Xm1tbamrq2svQ5YkSZJU177+dbjkEpgyBTZuhEMPhZaWrFjr7s4ufdywAc45B66/fudNrRtARNyXUqp6K7IBi7PhZHEmSZIkiZTggx+EO++Et741u9yxuRlGjdr587rroKcHvvIV+MY3io542OypOBvUao2SJEmStNd+/nNYsyYrvDZtgvPPhxtvzEbIrrsOzj47K9LGj89uXr37HLQGZXEmSZIkKT+9c8iWL89GxJYvrz7H7Mtfhpdfzuao9d3fwCzOJEmSJOVn5cpd55B1dGTPV67cdf9FF2U3r169etf9Dcw5Z5IkSZLK6bOfhaVLYd06GDeu6GiGhXPOJEmSJNWfRYvgpZfgZz8rOpJcWJxJkiRJKqcPfABmzIBrrik6klxYnEmSJEkqp6YmWLgQVqzI7nvW4CzOJEmSJJXXokXZjamXLSs6khFncSZJkiSpvGbNgqOPhmuvLTqSEWdxJkmSJKncFi2Cu++GRx8tOpIRZXEmSZIkqdw++UmIaPjRM4szSZIkSeU2bVp2s+prroEc79OcN4szSZIkSeW3aFF2WeO99xYdyYixOJMkSZJUfqefDvvt19CXNlqcSZIkSSq/1lb48Idh6VLYvr3oaEaExZkkSZKk+nDmmdnNqG+9tehIRoTFmSRJkqT6cPLJMGlStjBIA7I4kyRJklQfvvtdOP54+OlPYcuWrK2zE5YsKTKqYWNxJkmSJKk+tLfDHXfAK69kBVpnJyxYkLU3gOaiA5AkSZKkmnR0wI03woknwiWXwObNsHx51t4AHDmTJEmSVD/mzYPjjoPHH4czzmiYwgwsziRJkiTVk85OePBBaGqCK6/MnjcIizNJkiRJ9aF3jtkNN8CnPgUpwcc/3jAFmsWZJEmSpPqwcuXOOWZf+AJs2wbz52ftDSBSSrmdrK2tLXV1deV2PkmSJEkN7KSTYPVqeOIJGDOm6GhqEhH3pZTaqu1z5EySJElSfbrgAli7FpYuLTqSYWFxJkmSJKk+nXQSzJoFl16azT+rcxZnkiRJkupTRDb37IEH4Lbbio5mr1mcSZIkSapfCxfCwQdno2d1zuJMkiRJUv0aOxY+9zlYsSK7/1kdsziTJEmSVN/OPRdaWuA73yk6kr0yYHEWETMiojMi1kTEgxHx+Ur7pIi4NSIeqfw8YOTDlSRJkqTdTJ4Mn/kM/OQnsG5d0dEMWS0jZzuAC1JK7wSOAc6LiCOBxcBtKaW3AbdVnkuSJElS/s4/H7Zvh8suKzqSIRuwOEsprU0p/aayvQVYA0wDTgOurrzsamD+CMUoSZIkSXv29rfDhz4El18Or7xSdDRDMqg5ZxFxGDAHuAc4OKW0FrICDjho2KOTJEmSpFpNnQrPPw8//vHOts5OWLKkuJgGoebiLCImADcC56eUNg/iuHMioisiujZs2DCUGCVJkiRpYAsWQHMzfOtb0NOTFWYLFkB7e9GR1aSm4iwiRpMVZtemlG6qNK+LiKmV/VOB9dWOTSl9P6XUllJqmzJlynDELEmSJElvNG8eLF4MTz+d3f9swQJYvhw6OoqOrCa1rNYYwFXAmpTSt/vs+hnw6cr2p4H/Gf7wJEmSJGkQvvY1mDgRli3Lltivk8IMahs5Ow44C5gXEfdXHqcC/wycGBGPACdWnkuSJElSce68E5qa4EtfgiuuyC5trBPNA70gpfRLIPrZ/ZfDG44kSZIkDVHvHLObbspGzE45pa4ubRzUao2SJEmSVForV+5aiHV0ZM9Xriw2rhpFSim3k7W1taWurq7czidJkiRJZRIR96WU2qrtc+RMkiRJkkrA4kySJEmSSsDiTJIkSZJKwOJMkiRJkkrA4kySJEmSSsDiTJIkSZJKwOJMkiRJkkrA4kySJEmSSiDXm1BHxAbgT7mdsHYHAs8VHcQ+xHzny3znz5zny3zny3zny3znz5zna1/M96EppSnVduRanJVVRHT1d5duDT/znS/znT9zni/znS/znS/znT9zni/zvSsva5QkSZKkErA4kyRJkqQSsDjLfL/oAPYx5jtf5jt/5jxf5jtf5jtf5jt/5jxf5rsP55xJkiRJUgk4ciZJkiRJJVBXxVlEnBwRv4+IRyNicZ/2ZRFxf+XxRETc38/xkyLi1oh4pPLzgEr7mX2Ovz8ieiLi3VWOv7Zy/tUR8YOIGF1pj4j4XiWuByLiPSOTgfyVOOfviIhfR8S2iPjiyHz6/JU432dWvtsPRMRdEXH0yGQgXyXO92mVXN8fEV0RcfzIZCBfI5jv0RFxdUSsiog1EXFxP8cfHhH3VI5fFhFjKu324fnn3D68+vEjlW/78OrHj1S+7cOrHz8i+e6zvz0iuiPiY8P4sfOXUqqLBzAKeAw4AhgD/A44ssrrLgW+2s97LAEWV7YXA/9S5TVHAY/3c/ypQFQe/wWc26f9lkr7McA9RedrH8j5QUA78E/AF4vO1T6Q72OBAyrbpzTCd7zk+Z7AzsvOZwMPF52vMucbWAgsrWyPA54ADqty/HLgjMr2f9qHF5pz+/B8820fnm++7cNzzHef+G4HbgY+VnS+9uZRTyNn7wMeTSk9nlJ6DVgKnNb3BRERwAKyX3KqOQ24urJ9NTC/yms+2d/xKaWbUwVwLzC9z/v+uLLrbmD/iJha8ycrr9LmPKW0PqW0Etg+qE9UbmXO910ppY2Vl93Nzu9+PStzvl+qtAGMBxphcvBI5jsB4yOiGWgBXgM2V3nvecANVY63D8855/bhuefbPry6kcq3fXh1I9WHA/wDcCOwfpCfq3TqqTibBjzV5/nTlba+PgCsSyk90s97HJxSWgtQ+XlQldd8gv6/VEA2/AqcBawYRGz1qMw5b0T1ku+/IRtlqHelzndEfCQiHgb+Fzh7T8fXiZHM9w3Ay8Ba4Eng31JKL+x27GRgU0ppR5Xz24fnn/NGVC/5tg/facTybR9e1YjkOyKmAR8hG02re/VUnEWVtt3/EtHvX6hrOkHE+4GtKaXVA7z0cuCOlNKdg4itHpU5542o9PmOiA6y/9gvGmoMJVLqfKeU/jul9A6yvwx+c6gxlMhI5vt9QDfwFuBw4IKIOGIQ57cPH7y9zXkjKn2+7cNrttf5tg8flL3N93eBi1JK3UM4d+nUU3H2NDCjz/PpwJ97n1SGQk8HlvVp+2FlcuLNlaZ1vZeqVH7uPvR5BgP/hftrwBTgC7XGVsfKnPNGVOp8R8Rs4ErgtJTS84P4XGVV6nz3SindAcyMiANr+VAlNpL5XgisSCltTymtB34FtO12/ufILldsrnJ++/CdbXnlvBGVOt/24cV8v+3Dc8l3G7A0Ip4APgZcHhHz9+KzFiuVYOJbLQ+gGXicrKLunYj4rj77TwZ+McB7/Cu7TkRc0mdfE9kX74g9HP+3wF1Ay27tf82uk8nvLTpfjZ7zPvsvoXEmk5c238AhwKPAsUXnaR/J91vZOZn8PcAzvc/r9TGS+SYbBfhhpQ8eDzwEzK5y/PXsOpn87yvb9uE557zPfvvwHPKNfXje+bYPzzHfu73mR9T5giCFBzDIL8apwB/IVov5xyr/GH83wPGTgduARyo/J/XZdwJw9wDH76ic+/7K46uV9gAuq+xbBbQVnat9IOdvJvvFdzOwqbI9seh8NXC+rwQ29mnvKjpXDZ7vi4AHK22/Bo4vOldlzjfZymjXV3L2EPClfo4/gmzhlUcrrx9babcPzz/n9uH55ts+PN9824fnmO8qcdR1cdZb1UuSJEmSClRPc84kSZIkqWFZnEmSJElSCVicSZIkSVIJWJxJkiRJUglYnEmSJElSCVicSZIkSVIJWJxJkiRJUglYnEmSJElSCfw/hM0WFBuO9K4AAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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tVkOpVGLVqlXo7u5OaE0//elP8fe//x0A0NfXh1OnTqGkpGTabV588UUcPHgQm4LpZ6fTCaPRiL1792Lbtm0Q90PecMMNOHnyZNznfP755/H8889j3bp1AAC73Y5Tp06hrq4OjY2NWLVqFQBgxYoVuOyyy8AYm/W1XX311cjLy0NeXh62b9+Offv2YdeuXVEft76+Hueff37o/o8++igefPBB+Hw+mEwmHD9+HKtXr07oe3fddddBLpfDbrdj9+7duO6660LXud3uhB6LEEIIIYQsMD09wJVXCv/+7GeBX/5SyO4t4gzfvAr4skUul2Pbtm3Ytm0bVq1ahYcffnhWwKdUKkNdH2UyWagEVCaThfaTKRSKaXvVIo0E2LlzJ1544QXs2bMHWq0W27Zti3g7zjluueUWfPvb3552+ZNPPplU90nOOb70pS/hYx/72LTLu7u7Q19LrK8NmN31kjEW83Hz8/ND/z979iy+//3vY//+/SgqKsKtt94adWRC+PPMvI34mIFAAIWFhWhvb4/3pRNCCCGEkMXAbgcuvRSwWIDf/ha49VbgHe9Y9GWd8yrgi5WJy5TOzk7IZDK0tLQAANrb21FfX5/UYzU0NOAXv/gFAoEABgYGQvvfwlksFhQVFUGr1eLEiRN4/fXXQ9cplUp4vV4olUpcdtlluPrqq/HpT38aRqMRExMTsNlsOO+883DXXXdhfHwcBoMBjz32GNasWRN3bVdccQXuuece3HjjjdDpdBgYGIBSqUzo63vqqafwpS99CVNTU9i5cye+853vIC8vT9LjWq1W5Ofno6CgAMPDw3j22Wexbds2AIBer4fNZkNpaSkAoLy8HB0dHWhtbcXf//536PX6WY9nMBjQ2NiIxx57DNdddx045zhy5Iik7wUhhBBCCFlgAgHgppuAs2eBb39bCPaAc3v69u+ngG+xstvt+MQnPgGz2QyFQoHm5uZQI5VEbd26NVQeuXLlSqxfv37Wbd72trfhgQcewOrVq9Ha2jqt5PGjH/0oVq9ejfXr1+ORRx7BN77xDbz1rW9FIBCAUqnEz3/+c5x//vn46le/igsuuACVlZVYv359qJlLLG9961vR0dGBCy64AIBQyvrHP/4Rcrlc8te3efNmXHnllejt7cU999yDqqoqVFVVSXrcNWvWYN26dVixYgWampqwdevWaV/329/+dlRWVmLHjh34zne+g6uuugq1tbVYuXIl7HZ7xPU88sgjuOOOO/CNb3wDXq8X73vf+yjgI4QQQghZjL7yFeDJJ4Ef/xi4667p1y3ykk4m7BucGxs3buRi10tRR0cHli1bNmdrIMn56le/Cp1Oh89+9rPZXkpC6PVFCCGEELLA/fGPQnbvIx8BfvUrIIntT3OJMXaQc75xrp5P0uB1QgghhBBCCJkXwoerv/46cNttwOrVQEPDvA/2smHRl3QSab761a9mewmEEEIIIYScG67+s58J5ZvFxcDAABDcYkSmo4CPEEIIIYQQkju2bwcefhh417sAuRzIzwcef3xR79OLhUo6CSGEEEIIIbnD5QJ+8AOhM6fHA9x5JwV7MVDARwghhBBCCMkNXi9www3ASy8BOh1wzz3CcHVxTx+ZhQI+QgghhBBCyPwXCAjz9Z5+Wgj2nnoK+N//FebsXX89BX1RUMAHQC6XY+3atVi5ciWuu+46OByOpB/r1ltvxd/+9jcAwG233Ybjx49Hve3OnTuxe/fu0P8feOAB/P73v0/6uUXd3d1YuXLltMu++tWv4vvf/35Cj5Ou9RBCCCGEEJISzoGPfxz405+At71NCPrEMs7w4epkFmraAiAvLw/t7e0AgBtvvBEPPPAAPvOZz4Su9/v9CQ0oF/3617+Oef3OnTuh0+mwZcsWAMDtt9+e8HNkis/nm1frIYQQQgghixTnwBe+ADzwAPDFLwLf/vbs2yzy4eqx5FaGL3zmhmjHDuHyNLnoootw+vRp7Ny5E9u3b8cHPvABrFq1Cn6/H5/73OewadMmrF69Gr/61a8AAJxz3HnnnVi+fDmuvPJKjIyMhB5r27ZtEAfN//vf/8b69euxZs0aXHbZZeju7sYDDzyAH/3oR1i7di1effXVaVm49vZ2nH/++Vi9ejXe/e53Y3JyMvSYX/jCF7B582YsXboUr776asJfY6zH/p//+R9ccskl+MlPfhJaz+DgINauXRv6I5fL0dPTg56eHlx22WVYvXo1LrvsMvT29gIQspyf/OQnsWXLFjQ1NYUynoQQQgghhMQ185j/W98Cvvc9YezCt76VvXXlqNwK+MSZG+ILYMcO4f+bNqXl4X0+H5599lmsWrUKALBv3z5885vfxPHjx/Gb3/wGBQUF2L9/P/bv34+HHnoIZ8+exd///nd0dnbizTffxEMPPTStRFM0OjqKj3zkI3j88cdx+PBhPPbYY2hoaMDtt9+OT3/602hvb8dFF1007T4333wzvvvd7+LIkSNYtWoVvva1r01b5759+/DjH/942uXhurq6pgVpDzzwgKTHNpvNePnll3H33XeHLquqqkJ7ezva29vxkY98BO95z3tQX1+PO++8EzfffDOOHDmCG2+8EZ/85CdD9zGZTNi1axeeeeYZfPGLX0zwJ0EIIYQQQhat8GP+++8HvvIVQK0Gvv51GqyehPlV0vmpTwHB0sqoqqqAK64AKisBkwlYtgz42teEP5GsXQv8+McxH9LpdGLt2rUAhAzfhz/8YezevRubN29GY2MjAOD555/HkSNHQtkqi8WCU6dO4ZVXXsH73/9+yOVyVFVV4dJLL531+K+//jouvvji0GMVFxfHXI/FYoHZbMYll1wCALjllltw3XXXha6/9tprAQAbNmxAd3d3xMdYsmRJqEwVODc4Pd5j33DDDVHX9dprr+HXv/51KKu4Z88ePPHEEwCAm266CZ///OdDt73mmmsgk8mwfPlyDA8Px/x6CSGEEEIICRH35F19NWCzASoV8MwzwGWXZXtlOWl+BXxSFBUJwV5vL1BXJ/w/ReF7+MLl5+eH/s05x/33348rrrhi2m3+9a9/gcU508A5j3ubRKjVagBCsxmfz5e2xwWmf83hTCYTPvzhD+Ppp5+GTqeLeJvwr1FcIyB8/YQQQgghhEi2fTtQUiIEfHffDVx+ebZXlLPmV0nnj38M7NwZ+8+99wIOhzBzw+EQ/h/r9nGye1JdccUV+OUvfwmv1wsAOHnyJKampnDxxRfjL3/5C/x+P0wmE3ZEaAd7wQUX4OWXX8bZs2cBABMTEwAAvV4Pm8026/YFBQUoKioKZdL+8Ic/hDJyqUrmsb1eL66//np897vfxdKlS0OXb9myBX/5y18AAI888gguvPDCtKyREEIIIYQsci+8AHR3Axs3Ag89RCMXUpBbGT5xz96jj57rxBP+/wy67bbb0N3djfXr14NzjrKyMjz55JN497vfjZdeegmrVq3C0qVLIwZPZWVlePDBB3HttdciEAjAaDTiP//5D975znfive99L5566incf//90+7z8MMP4/bbb4fD4UBTUxN+97vfpe1rSfSxd+/ejf379+Pee+/FvffeC0DIbP70pz/Fhz70IXzve99DWVlZWtdICCGEEEIWqR07AHHL0ac/LVT3zdEx/0LE5rLcbuPGjVzsWinq6OjAsmXLpD3AffcJmzjDf9A7dggzN8L2jxEiSuj1RQghhBBCsu+++4DBQeAnPwHOnAEaGxfUMT9j7CDnfONcPV9uZfgi/YBp5gYhhBBCCCELx+c/D9x0E1BRATQ0CJfRMX/S5tcePkIIIYQQQgjZs0eYu0djGFJGAR8hhBBCCCFk/hgZAbq6gC1bsr2SBWFeBHzUtp9kAr2uCCGEEEJy0J49wt8XXJDddSwQWQ/4NBoNxsfH6eCcpBXnHOPj49BoNNleCiGEEEIIScTu3YBSCWzYkO2VLAhxm7YwxmoB/B5ABYAAgAc55z9hjK0F8AAADQAfgP/mnO9LdAE1NTXo7+/H6OhoonclJCaNRoOamppsL4MQQgghhCRizx5g/XqATtynhZQunT4Ad3PODzHG9AAOMsb+A+A+AF/jnD/LGHtH8P/bEl2AUqlEY2NjoncjhBBCCCGELDQejzB+4Y47sr2SBSNuwMc5NwEwBf9tY4x1AKgGwAEYgjcrADCYqUUSQgghhBBCFoHDhwGXi/bvpVFCc/gYYw0A1gHYC+BTAJ5jjH0fwl5AaqNDCCGEEEIISd7u3cLfFPCljeSmLYwxHYDHAXyKc24FcAeAT3POawF8GsBvotzvo4yxA4yxA7RPjxBCCCGEEBLVnj1AbS1AfRjSRlLAxxhTQgj2HuGcPxG8+BYA4r8fA7A50n055w9yzjdyzjeWlZWlul5CCCGEEELIQrV7N83fS7O4AR9jjEHI3nVwzn8YdtUggEuC/74UwKn0L48QQgghhBCyKPT3A319VM6ZZlL28G0FcBOANxlj7cHL/gfARwD8hDGmAOAC8NGMrJAQQgghhBCy8IkD1ynDl1ZSunTuAsCiXE3TEAkhhBBCCCGp27NHmL23Zk22V7KgSG7aQgghhBBCCCEZs3s3sGkToFJleyULCgV8hBBCCCGEkOxyuYBDh2j/XgZQwEcIIYQQQgjJroMHAa+X9u9lAAV8hBBCCCGEkOwSG7ZQhi/tKOAjhBBCCCGEZNfu3cCSJYDRmO2VLDgU8BFCCCGEEEKyh3Mhw0fZvYyggI8QQgghhBCSPd3dwNAQ7d/LEAr4CCGEEEIIIdlD+/cyigI+QgghhBBCSPbs3g3odMDKldleyYJEAR8hhBBCCCEke/bsATZvBhSKbK9kQaKAjxBCCCGEEJIdU1PA4cO0fy+DKOAjhBBCCCGEZMf+/YDfT/v3MogCPkIIIYQQQsjcuu8+YMcOYf8eAJx/vvD/++7L7roWICqUJYQQQgghhMytTZuA668HmpuBtjahrPP664FHH832yhYcyvARQgghhBBC5tb27cBf/wrs3QtoNOeCve3bs72yBYcCPkIIIYQQQsjca2gAOAfa24E77qBgL0Mo4COEEEIIIYTMvd//Xvj7wx8GfvlLYQ8fSTsK+AghhBBCCCFzS2zQIpMBP/uZUM55/fUU9GUABXyEEEIIIYSQubV/P7BiBbB6tbCHb/t2Iejbvz/bK1twKOAjhBBCCCGEzK3PfQ7o7gbWrz932fbtwOc/n7UlLVQU8BFCCCGEEELmVl8fMDYGbNiQ7ZUseBTwEUIIIYQQQubWoUPC3+EZPpIRFPARQgghhBBC5tbBg4BcDqxZk+2VLHgU8BFCCCGEEELm1qFDwLJlQF5etley4MUN+BhjtYyxHYyxDsbYMcbYXcHL/8oYaw/+6WaMtWd8tYQQQgghhJDcxrmQ4aP9e3NCIeE2PgB3c84PMcb0AA4yxv7DOb9BvAFj7AcALJlaJCGEEEIIIWSBMJmA4WHavzdH4gZ8nHMTAFPw3zbGWAeAagDHAYAxxgBcD+DSDK6TEEIIIYQQshBQw5Y5ldAePsZYA4B1APaGXXwRgGHO+ak0rosQQgghhBCyEB08CDAGrF2b7ZUsCpIDPsaYDsDjAD7FObeGXfV+AH+Ocb+PMsYOMMYOjI6OJr9SQgghhBBCSO47dAhobQV0umyvZFGQFPAxxpQQgr1HOOdPhF2uAHAtgL9Guy/n/EHO+UbO+caysrJU10sIIYQQQgjJZdSwZU5J6dLJAPwGQAfn/Iczrr4cwAnOeX8mFkcIIYQQQghZQIaHgYEB2r83h6Rk+LYCuAnApWFjGN4RvO59iFHOSQghhBBCCCEhYsMWyvDNGSldOncBYFGuuzXdCyKEEEIIIYQsUGLARw1b5kxCXToJIYQQQgghJGkHDwItLUBBQbZXsmhQwEcIIYQQQgiZG4cO0f69OUYBHyGEEEIIISTzxseBnh4K+OYYBXyEEEIIIYSQzKOGLVlBAR8hhBBCCCEk88SAb9267K5jkaGAjxBCCCGEEJJ5Bw8CjY1AcXG2V7KoUMBHCCGEEEIIyTxq2JIVFPARQgghhBBCMstsBrq6aP9eFlDARwghhBBCCMmsN94Q/qYM35yjgI8QQgghhBCSWQcPCn9TwDfnKOAjhBBCCCGEZNahQ0BtLVBWlu2VLDoU8BFCCCGEEEIyixq2ZA0FfIQQQgghhJDMsdmAkyepYUuWUMBHCCGEEEIIyZz2doBzyvBlCQV8hBBCCCGEkMwRG7ZQhi8rKOAjhBBCCCGEZM6hQ0BlJVBRke2VLEoU8BFCCCGEEELS6777gB07hH8fPChk93bsEC4nc4oCPkIIIYQQQkh6bdoEXH898K9/ASdOAEVFwv83bcr2yhYdRbYXQAghhBBCCFlgtm8HHn0UuPZaIBAAnnoKePJJ4XIypyjDRwghhBBCCEm/7duB888X/v3BD1KwlyUU8BFCCCGEEELSb8cOYP9+4OabgUceObenj8wpCvgIIYQQQggh6bVjh7Bn77HHgIcfFso7r7+egr4soICPEEIIIYQQkl779wtBnljGKe7p278/u+tahBjnfM6ebOPGjfzAgQNz9nyEEEIIIYQQMp8wxg5yzjfO1fNRho8QQgghhBBCFqi4AR9jrJYxtoMx1sEYO8YYuyvsuk8wxjqDl9MUxXnOH+Dw+gPZXgYhhBBCCCFkjkiZw+cDcDfn/BBjTA/gIGPsPwDKAVwNYDXn3M0YM2ZyoSQ1gQDHbQ/vh9Xlw+N3bMn2cgghhBBCCCFzIG7Axzk3ATAF/21jjHUAqAbwEQDf4Zy7g9eNZHKhJDUPvnoGOzpHoZAxeHwBqBRUzUsIIYQQQshCl9BRP2OsAcA6AHsBLAVwEWNsL2PsZcbYpgysj6TBG72T+P5znSg3qOELcHSN2rO9JEIIIYQQQsgckBzwMcZ0AB4H8CnOuRVCdrAIwPkAPgfgUcYYi3C/jzLGDjDGDoyOjqZp2UQqq8uLT/7lDZQbNPjZB9YDADqHbFleFSGEEEIIIWQuSAr4GGNKCMHeI5zzJ4IX9wN4ggv2AQgAKJ15X875g5zzjZzzjWVlZelaN5GAc44v//0oBs0u/PT967C2thBKOcMJCvgIIYQQQghZFKR06WQAfgOgg3P+w7CrngRwafA2SwGoAIxlYI0kSY8d6Mc/Dg/iM29Zig31RVDKZVhSpsPJYQr4CCGEEEJIdnHO8fE/HcK/jw5leykLmpQM31YANwG4lDHWHvzzDgC/BdDEGDsK4C8AbuFzOcWdxHR6xIZ7nz6GC5pKcPslS0KXt1boqaSTEEIIIYRk3eF+C/55xIRJhyfbS1nQpHTp3AVg1t68oP9K73JIOri8ftz5pzeQp5Ljx+9bC7ns3I+vtUKPp9oHYXV5YdAos7hKQgghhBCymD1xqB8qhQxXrq7M9lIWNOrNvwB9+18dODFkw/evW41yg2badW0VegDAScryEUIIIYSQLHH7/Hj68CDeuryckhAZRgHfAvP8sSE8vKcHH9raiEvbymddv7RcCPiocQshhBBCCMmWHSdGYXZ48Z4NNdleyoJHAd8C4vL68YXHj2BFlQFfeHtrxNtUF+ZBr1bQPj5CCCGEEJI1TxzqR5lejYuaZzX5J2lGAd8Csr97ApMOLz771laoFfKIt2GMYSk1biGEEEIIIVkyMeXBjs4RXLO2Cgo5hSOZRt/hBWTXqTGo5DKc11Qc83atFXp0DttATVUJIYQQQshc+8fhQXj9HNeup3LOuUAB3wLy6qkxrK8vhFYVu/lqW4UeFqcXw1b3HK2MEEIIIYQQweOH+rGs0oBllYZsL2VRoIBvgRi3u3HcZMVFLWVxb9saatxizfSyCCGEEEIICTk1bMORfgves74620tZNCjgWyBe6xoHAGyVsPG1NTiagfbxEUIIIdLRVghCUvfEGwOQyxiuXksB31yhgG+B2HVqFAV5SqyqLoh720KtCuUGNQV8hBBCiEQnh21oveffODlMn52EJMsf4Pj7oQFcsrQMZXp1tpezaFDAtwBwzrHr1Bi2LCmBXMYk3ae1wkCz+NIsEOC45uev4Z9HTNleCiGEkDTbe3YCHl8A7X3mbC+FkJy1p2scQ1YXrqVyzjlFAd8CcGZsCoMWFy5skT7HpK1Cj9Ojdvj8gQyubHEZsrrQ3mfGnjNj2V7KnHiqfQDHBi3ZXgYhhMyJDpOw77133JHllRCSux4/1A+9RoHLl5VneymLCgV8C8CuU0KAcVFz/IYtotZyPTy+ALrpgytteieE72X/pDPLK8k8f4Dj8387gt+8ejbbSyFzyOsP4D/Hh2kfE1mUTgQDvp4J+twkJBl2tw//PjqEq1ZXQaOMPC+aZAYFfAvArtNjqCvWoq5EK/k+1Lgl/cSAb2ARBHw941Nw+wIYsdFoj8XkyTcG8JHfH8DhfsrsksUlEOChbRC941NZXg0huenfR4fg9Prx3g1UzjnXKODLcT5/AK93jUvqzhmu2aiDjAGdNJohbcQynwGzc8FnQMSmBcNWV5ZXQubSge5JAMBJOlFEFpneCQccHj/yVXLK8BGSpMcP9qO+RIv1dUXZXsqiQwFfjjvcb4bN7cNFCezfAwCNUo6G0vy0NG555eQobv/DwQUf5MQjZvgcHj/MDm+WV5NZnUN2ABTwLTYHe4WA7/SoPcsrIWRuiXNrt7cZYXZ4YVng7/GEpFv/pAN7zozj2nU1YExag0GSPhTw5bhXT42BMWDLkpKE79tWoUdnGtpLv9gxjH8fG8KgZXEf/PdOOCA2SR0wL+yyTjHDZ3X54PL6s7waMhfMDg9OjwiB3ilqS08WmeMmG2QMeMtyodFEzwSVdRKSiCffGAAA6s6ZJRTw5bhdp8awuroAhVpVwvdtLTcEy1R8Ka1BDPQW+0Fg34QDK6qEOYj9kwu75Kdz2AbxBN2IlfbxLQaHgtm9ygINTo1Qho8sLh0mKxpL87G0XNj/3kMNzwiRjHOOJw4NYHNjMWqLpfebIOlDAV8Os7m8eKPPnNA4hnCtFXpwDpwcTu3gbTCYzTqV4uPkMrvbh/EpTyjTupA7dbp9fpwdm8LqmkIAwLBtcWd2F4sD3ZNQyBiuWVeNAbMz5RNFhOSSE0NWtFUaUBc8WO2lfXyESNbeZ8aZsSm8d31NtpeyaFHAl8P2npmAP8BxYQLjGMKJnTpTbcAQCvhGFm+GT2zYsqqmAPkq+YIu6TwzOgV/gOPi4ImGXNnH9+tXz+Ajvz+Q7WXkrIM9k1hRZcDq6gJwLrwOCFkMbC4v+iacWF5pQL5agVKdGj3UqZMQyf59dAhKOcPbVlVkeymLFgV8OWzX6THkKeVYX1+Y1P3rirXQKGUpNW5xeHyYDG5eX8xlXuLZ3vrifFQX5S3o0Qzi/r0Lm8WAb/6XdPZPOnDfc5146cQIfP5AtpeTc7z+AA73m7G+vgjNRh2AxX2Chywu4viiZZXCSdL6Ei2VdBKSgJdOjGBzYzEMGmW2l7JoUcCXw149NYrNjcVQK5IbXimXMSwt16NzOPnRDINmIbtTpFXi9LB90Xbq7AsGfHXFWlQX5i3oDF/nkA0KGcO6uiKoFDKM5ECG77v/7oTHF4A/wDFm92R7OTnn+KAVLm8AG+uLUV+SD4WMhRq4ELLQdQQHrrdVGAAIAR+VdBIiTd+EA6dG7Njeasz2UhY1CvhylMniRNfoVMLjGGZqLdenNHxdLOe8qKUMNrcPQzlw8J8JvRMOGDQKFGiVQoZvAQd8J4dtaCrLh0ohQ7lBPe9LOg/2TOIfhwexpkZoqGOyLNyfTaYc7BEatqyvL4RKIUNDaf6i3rNLFpfjJhsK8pSoLNAAECo5TBYXdSgmRIIdnSMAgEvbKODLJgr4ctSrp8YAIOmGLaLWCj3G7B6M2ZMryxMDvouXCvsIF+tBYM+EA3Ulwmb+6kItzA4v7O6F2dSic9gW6lRXrtdgxDZ/SzoDAY6vP3McRr0a91y1HAAwtMjHhyTjYM8kqgvzUFmQBwBoLtNRho8sGieGrGir0Idmh9UH3+v7KMtHSFwvnRhBQ4kWTWW6bC9lUaOAL0ftOjWGMr0arcED72SJJSrJZvkGzU4whlCmcbHu4+ubcKC+OB8AUF0kHBQvxH18U24f+iacodedcZ5n+P5xZBDtfWZ87opWLAl+2Cz2eZGJ4pzjQM8ENtQXhS5rKdehZ8IBt48yHGRhCwQ4OodsWFZpCF0mntyjfXy54+iABXc/ehhe2sM9p5weP/Z0jWM7ZfeyLm7AxxirZYztYIx1MMaOMcbuCl7+VcbYAGOsPfjnHZlfLgGED6DXTo/hwubS0BnHZC2tEA6Ckw74LC6U6zUoN2hQkq9alLP4/AGO/klHaLZMjRjwmRfewYAY0C8Ndng16jXzdg6f0+PHd589gZXVBrxnfQ0KtUqoFTIMUUlnQgbMTgxb3dMCvmajDv4AR/fYwnuNExKuZ8IBh8eP5WEBX33wvb6HMnw5428H+/H4oX4cHbBkeymLyu6uMbh9Adq/Nw9IyfD5ANzNOV8G4HwAH2eMLQ9e9yPO+drgn39lbJVkmhNDNoxPebC1ObVyTgAo06lRnK9KKcNXVSjsa2g26hZlhm/I6oLXz0PzmWoKF26GTxzhIWb4yg0a2Nw+TM3D8tVfv3oGgxYX7rlyOWQyBsYYKgs0MFGGLyHi/r1pGT6j8POnTp1koTshNmypPFdNU5yvgk6tQC+NZsgZ7X1mAMC+sxPZXcgis6NzBFqVHOc1FWd7KYte3ICPc27inB8K/tsGoANAdaYXRqLbdXoUwLm2+KlgjKG1XI8TSWbmhIBPCHBaynU4NWxLW6fOw31mnPetFzA6j/eIAQjNYxIDvlKdGiq5DP3zoHHLiNWF9/xyN3Z3jaXl8TqHbdAoZaFsZrlBLTzPPPsZDVtd+OXLXXjbigqc11QSuryiQEN7+BJ0qGcSWpUcbRXnDnibyvLB2OLds0sWjw6TFTKG0L5lQPjcrCvWUoYvR7h9fhwfFAJ3CvjmDuccO06MYmtzadLd5En6JLSHjzHWAGAdgL3Bi+5kjB1hjP2WMVYU/Z4knV49NYYWow4VwY5hqWqt0OPUsA2BQGKBWiDAMWhxoToY8C0t18Pq8qXt4H/PmXEMW904Nji/SzDEjfviRn6ZjKGqUJP1DJ/PH8An/vwGDvZM4rXT6Qn4Tg7b0GLUQy4TSonLDcJrcL6NZvjec53w+Tm+9I62aZdXFuRRhi9BB3omsa6uEAr5uY8LjVKOumItTo9SwEcWtuMmG5rKdNAopx+w1pdo0Ut7+HLC8UErPP4AjHo19nVPwJ/gsQ5JzslhOwbMTurOOU9IDvgYYzoAjwP4FOfcCuCXAJYAWAvABOAHUe73UcbYAcbYgdHR0dRXvMi5vH7sOzuRcnfOcG0Vejg8fvRNJvbhNT7lgccXCGX4QgOZ03TWX+wCON83xvdOOCCXsVDLbkBo3NKf5YDvB/85ib1nJ6CUM/RNpGctnUO2aWe6jXohwzc8jzJ8RwcsePxQP27d2oD6kvxp11UUaDBsdSV8cmOxmnL70GGyYkPd7PN5LUYdTlOGj2QI5xz/PGKCx5fdJhtih86Z6kvy0TfpoOAhB7zRawYAfHBrI2wuX0qjqIh0L50QxjHQ/r35QVLAxxhTQgj2HuGcPwEAnPNhzrmfcx4A8BCAzZHuyzl/kHO+kXO+saysLF3rXrQO9kzC7QukPH8vXGvww+xEgm+C4kiGUElnmvf1dI3mSsDnRHVh3rQMSE2hNquz+F7sGMYvd3bh/ZvrsLG+GP0JBvORTE55MGJzo7XiXGtl4zzL8HHO8b/PHEeRVoU7L22edX1VgQa+AMfY1PwJUOez9j4zAhzY0DB7/8USow5nxuzwUdc7kgFvDljw8T8dwj/fHMzaGqwuL/onndM6dIrqS7Tw+vminev5kxdO4acvnsr2MiRp7zOjwqDBO9dUAgD2d1NZ51zYcWIEyysNaatGI6mR0qWTAfgNgA7O+Q/DLq8Mu9m7ARxN//LITGId+voIZ9yT1RLM2CR61utcwCf8MpfqVCjSKtPSuIVzHsrw9U7M743xveNTof17ouqiPIza3FkZzNs34cCn/9qOFVUG3PvO5agtzkNfGrKNJ4P7PMMzfAaNAhqlbN6MZnju2BD2nZ3Ap9+yFAaNctb1FcE5crSPT5qDPZNgDFhbWzjruhajHl4/Ry/tYyIZIL6uOkzZy8aIn4nLKiNk+IoX92iGpw4P4G8H+7O9DEna+8xYV1eImiItqgvzaB/fHLA4vDjYO4ntbZTomS+kZPi2ArgJwKUzRjDcxxh7kzF2BMB2AJ/O5EKJwOz0QC5jKMibfTCbLJ1agdriPHQm2LhFzGCJe/gYY2gx6tMymmHU7obNJXR+nO8fqL1hQ9dF4vdkrveLuX1+/Pcjh8AB/OLG9dAo5agt0qYl+BQDvtaK6c0Lyg0aDM+D0Qycc9z3704sLdfh/ZtqI95GLLulfXzSHOiZxFKjPuL7TYtYwr0IO/OSzBP3QCdaeZJOHcEOnZEyfIt5Fh/nHAOTTvROOGCfhx2aw43b3eidcIROWm1uLMbesxNpay5HInv51Cj8AU779+YRKV06d3HOGed8dfgIBs75TZzzVcHL38U5N83Fghc7i9MLg0aR8vy9mVrLDUlk+FzIU8qnHQw2l+twctie8pupmN1rq9Cjd8KR8J6rxw704V0/24WzY5nNDlpdXkw6vBEzfMDcj2b4+jPH8eaABT+4bk1o/1pNsbCWVPcUdg7boNcoUGGYXp5RrtdgxJb9AGp8yoMzY1O4YVPdtPLacGJpCWX44gsEON7omcSGhsjVBEuCAd9pCvhIBognFDuHrFlbQ4fJikKtctZ7HiA0gFLKGXrmeQVKJoza3XAH91aenOezd8VxDOuCVVGbG4sxZndn/NhgsdtxYgRFWiXW1lI/x/kioS6dJPssTl9as3uitgo9zo5Nwe2TngUyWYQZfOHBZ4tRB4vTizG7J6X1dI0Kb8aXLTPC7Qsk3PnzhY5hHOm34L2/3I0j/eaU1hKL2KFzVsBXKAZZc3f296n2Afzx9V589OImvHVFRejy2iJhbYk25Znp5JAdreX6WScbygzqeTF8XWwWtLRcF/U2xVoVVHIZBhfpvptEnBqxw+b2RWzYAgiVAVUFGgr4SEaIJ8uGrW5MTqX2eRIuEOD4yO8PYEewoUQsHSYb2ipmv+cBgFzGUFu0ODt1hp88nO8NUN7oNUMuY1hVXQBACPgAGs+QSf4Ax8snR3HJ0rJQR2+SfRTw5RiL05uRgK+1Qg9/gCd08BY+g08k7u9Ktayza8SOfJUcmxuFGWrdCQ647RqdwpqaAuSp5Hjfg6/jlZOZ6RAbLeCrKNBAxjBnjVtODdvwpSfexKaGInzuitZp19UEA75UMnycc3QO27A0Qre6cr1mXuzhE0cEiM2DIpHJGMoL1JThk+BAj3BAtDFKhg8Amsv1czJ8vXPIRoHlItM/6YROrQCQ3rLOMbsb/zk+jK//83jMDpv+AEfnkC1iOaeorkS7KEs6+8L27c73gK+9z4y2Cj3yVMJYjabSfJTqVBTwZdDhfjMmpjzYTuWc8woFfDnG4vTCkKGAD0isPGPAfG4Gnyhd+3q6Ru1YYtShIbhPIpGzqD5/AD3jU7hgSSmeuGML6kvy8aH/248n3xhIaU2RiB/2tTMCPqVchsqCvDkp6bS5vLjjkUPQquT42QfWQzmjnNGoV0OlkKE/heYaIzY3LE4vWssjBHwGNaY8/qzv5Tg9bINerQgNg4+m0kCz+KQ42DOJUp1q1smMcM1lOpwesWd8zMXnHz+CLz1xJKPPQeYPzjkGzM5QN+p0lnUOBn/3z4xO4dmj0Xei9IxPwen1xwz46ou16J1wLLr9YOLJw9ZyPU5kseQ2nkCA43CfeVrTKcYYNjUI+/hIZuw4MQIZAy5ZSg1b5hMK+HKMNUMZvoaSfCjlDCclztVyef0Ys7tnZfjK9GoYNIqUz/qfHrGjuUyHqsI8yGWJ7ZPom3TC6+dYUpYPo0GDv37sfGxsKMKn/tqOX796JqV1zdQ74UChVhnxZ1JdmIf+DGb4ukbt+N9/HMfW77yEM6N2/OR960KD0MPJZAw1hanNBRTP4i6NGPAJz5ntLN+pEeEkQbz9rRUFGsrwSXCwZxIb6otifj9bynVweQMZz2T3jk+lZW8wyQ1Wpw92tw/r64pQqFUm3FAsFrG7tE6twM93dEV9TYlZxWUVsTJ8+bC7fZhIY8lpLuifdKIkX4V1dYXoHLLN29/LrlGhLH3djLL0zY3FGDA7szo6aSF76cQINtQXoVCryvZSSBgK+HJMpgI+lUKGxtJ8nJRYniEeMM8M+BhjWFquT2n4ut3tg8niwhKjDkq5DNWFeQmVzZwJlvaJTSUMGiX+74Ob8Y5VFfjGPzvw7X91pC0j0TvhiJoBqS5Kf4bP5w/g30dNuPHXr+OyH7yMP7zejUtajfjbHVuwtTn6bMbqoryU9vCdG8kwe3+cMZhRy/Y+vlMj9lCGOZbKYMA3Xw9S5oNRmxs94w5sqI+94b5lDhq32N0+TDq8sDi9GLVnf68omc7rD+DogCWtj9lvFt6rqovy0FahT2tJpxjwferyFnSYrKHh0DN1mKyQMeGkRjRiBUrPIhtN0j/pQE3wZzPp8GI0wT32c0UcuD5zrIy4j28/ZfnSbtjqwrFBK7bRsPV5hwK+HMI5z9gePkDI3pyUmJmbOYMvXEu5LqWSzlDAViZ80NaXaBOa9SUObF9Seu6DWqOU4/73r8dN59fjV6+cwWcfOwxvGgZG9004ZpVziqoL8zBkdaVlMPWI1YUfv3ASF353B27/4yGcHZ3C565oxe4vXob7378u7lzG2mLttH0XieocsqFUp0aJbna5pFEfHL6exU6dZocHozZ3zIMzUUWBBh5/YNGdlU/Eod5JAMCG+tkD18M1h0q4M7ePJ/ykCe3jm39eOD6Mq+7fhWffTF+jbvFnXl2Yh7YKA04O2dJ2ks5kEbpL37KlATVFebj/pdMRT/50mGxoKtNBo5RHfaz60GiGxdXxcWDSiZoiLVqD2c9sjs6I5Y0+MwwaBZpK86dd3lZhgF6joLLODBCbIdE4hvmHAr4c4vD44QvwjOzhA4R6/L4JJ6Yk7MWaOYMvXLNRj4kpD8aTPBsvBmzNRuFNuj7BjfFdI1Mo1alQoJ3+fZLLGP736hX41OUteOKNAfzzSGoHKD5/AP2TzpgZPn+AYyiFUsdAgOOPr/dg+/d34scvnEJrhR4P3bwRr37hUnx8ezPK9LH3q4lqivIw6fAmvc/u5LANrRWRgylxz1w2SzrFQCBWwxZRZUF2ZiTmkoM9k1DJZVhZHb2cDQAKtSqU6tQpZfTjGTCf+93vmsOAz+cP4E97e7O+N3W+Ez8LvvqPY2n7XoU+X4ry0Fqhx5THn/JYGZHJ4kRloQZKuQy3X7IE7X1m7O4an3W7DpM15v49QGiIxdjimsUXCHD0m52oKRYyfMD8bdzyRu8k1tQWQjajU6RcJuzj23d29s+dpOalEyOoLNCEXhtk/qCAL4dYnF4AyFiGr0XssCnhoGrQLBwsi3PNpj1Oio1bukamIJcx1BUHA77ifFicXpgd0jIyZ8bsaCqLHJwwxvDJS1tQkKfE7q6xpNYnMllc8AU46mNk+IDkZ/H1TTjwX7/Zi688eRTr6orw0t2X4OEPbcZblpcn3Oq4NtSpM/EDk0CA4+SwPeL+PUDYC6NVybM6fF18rTVLLOkEaBZfLAd7JrGqpgBqRfTshqjFqAt1SM0E8UBfLmNzmuE71GvG//z9TXziT4didnNc7EZtbsiY0Njph8+fTMtjDkw6oVHKUJKvCjUUS1dzkAGzC1XBkz7v3VADo16Nn710etptLE4vBsxOLKuMfdCqUcpRYdAsqtEMo3Y3PL4Aaoq0KMpXwahXo2MeNm6ZcvtwctiGdTPKOUWbG4vRNTqFMSoTTxu3z49dp8ewvc2Y9lnRJHUU8OWQTAd8iXTqNFmcKNWpIx4Qpjqa4fSIHfUlWqgUwsuzLlQ2I+1DtWt0CkvK8qNeL5Olp0tXtJEMohpx+HqCG8MDAY7f7+nGFT9+BUf6Lfj2tavwhw9vjhrESiGupX8i8eCzf9IJp9cfsUMnIATR5YbsjmY4PWJHnlIeMeM8kxjwmebBKIn5yOX1481+CzbG2b8najbqcDqDDVX6J51QK2RYWWXIaGA5k1iivKNzFN95tmPOnjfXjNrcqCzIw43n1eH/dp9Ny36+geDIH3FPOJC+LJLJ7AxtRdAo5fjoxU3Yc2YcB3vOfR50SmjYIqor1i6qPXziSUPxM6W1Qj8vM3xvDlgQ4JjVsEW0qYH28aXb/rOTcHj8uJT2781LFPDlkEwHfHXFWqgVMkmNWwbMTlRH2L8HCCV+erUi+QzfqD20fw8I2ych4UN1csqDiSnPtPtHcn5TMXrGHSllecR9hdH28FUlkeHrGZ/C+x96Hf/vqWPY2FCM5z59Md6/uS7ls2XiGpNp3CJ2yIs0g09k1KsxksWN+6dG7Gg26maV7kRSolNDIWMwUYe2iI4NWuDxB7BeYsDXUq6Dze3LWIa3f9KB6qI8NBtTawaVqHG7UFHwrjVVeOjVs/jr/t45e+5cMmp3o0yvxueuaENxvgpf/vubKWdEhc8X4f1Tp1agtjgPJ9LQqdPjC2DU7g6VdQPAB86rQ3G+alqWr8MkZKzilXQCiW85yHVixr02GPC1VehxasSelr3q6SQ2bFkTJcO3qroAGqWM9vGl0UsnRqBSyLCluSTbSyERUMCXQzId8MllDM1GnaQW2JGGrosYY2gu1yV1cObzB9A9PjWtNE/MoPVK2Bh/Zkx4zqYYGT4AOC840H1vCjX8PRMOKGQslDGaSaOUo1SnlpTh45zjd6+dxRU/fgXHTVbc957VePiDmyRlrKQoyVchTylHXxIZPjHjG6sDptGgwUg2M3zDNknlnIDwOi830GiGaA50iw1bpGf4gMw1VBmYFA7+m406jNjcsLq8GXmemcbsQqni969bg4taSvGVJ49i7xna8zPTqE0I+ArylLjnquU43G/Bn/alFhz3TzpDGSRAaLKRjizSsNUFzqc3G9OqFPjQ1gbs6BwNZSc7TFYUaZVxZ3oCQH1JPsbsbkl73xeC/lBDHeFzubXCAI8vgO55FvS2902ioUSL4vzIowFUChnW1xXRAPY02t89gY31RdCqFNleComAAr4ckumADxAat8QL1DjnGDS7ogZ8ALDUqE8qw9c74QjO0Dt38K5VKVCmV0s6i9o1IgSF8TJ8y6sM0KtT69LVOyG0plbIo/8aVRflSQr4dneN42v/OI7zm0rw/KcvxvWbatNaA88YQ01RXlJ7+DqHbKguzINeE/11V65XY9jqzsqoA5vLi0GLS3LABwh7T6lpS2R7zoyjoUSL0ggdWSPJdKfO/mBHwEwHljON2d0ozldDpZDhZx9Yj9piLW7/48FFtV9LijG7O/RaedeaKmxtLsF9/z6RdNdeh0eYaxd+squtQo+zY1Nwef0prfVcd+npn103b2mAXqPAz3cIWb6OIRvaKgyS3oNDJyTjVKA8eqAPF933Ejy++ZUJS1T/pAOlOjXyVMJ2jvnYuIVzjjd6zbPGMcy0ubEYHUPW0LEVSc2g2Yn6ktgn20n2UMCXQ6zBN6VMdekEhMYtQ1ZXzDdAs8MLp9cfM+BrKddhzO7GZIKt709Hab7RUCJtn0TXmB0quQw1RZHLLEVyGcPGhqKUztjHGskgkjrw/IWOYagVMjzwXxumlRulU22xFn1JNJAROnTGbl5QbtDA6fXDloWz3F2jQpAvZQafqKJAk1D31LNjUzBZFn4JaN+EAy+fHMVVq6sk36dMJ2R3UhnFEo3D48P4lAc1RXlZCPg8KNUJ2YGCPCV+c8smBDjw4Yf3wzZHWcb5zh/gmJjyhLoFM8bw9atXwu0N4Jv/TG7foxiUhb+Ht1bo4Q/wlH/24kmeme+xBo0St1zQgH8fG0LnkA2dQ/E7dIoagge4sUYzBAIcv9zZhb4JJ3oncnuEw8zsa7NRBxkDOudR4xaTxYURmzvq/j3R5sZicA4c6pmco5UtXC6vH+NTnqgVTyT7KODLIRanF4wBenXm0uVi6/1YDVfOjWSI/ovdnGSnTvHgfWZJZl1xvqRZR10jU2go1UrqYnleUwm6RqeSHhoba+i6SMzwxZsh9XLnKM5rKok58ylVyWT4vP4Aukajd+gUnRu+PvdZM/G12hJnjeEqDRqYLE7JGcnb/3AQn/jTG0mtL5f8cW8PZIzhA+fVSb4PY0zo1JmBQEzc/1pTlIfaojyoFLI5G80QnrkCgMbSfPzyv9bj7NgUPvHnN6hzJ4DxKTcCHNPGwzSV6XDHtiV4qn0Qu04l3gk5VDJYND3DB6SeRRq0RJ8f+6ELG6FRyPGlJ47A5Q3E7dApktJUbNfpMZwdEz6/To/kdsDXF6xsEWmUcjSU5s+rWXzRBq7PtK62CEo5o318aSA2baOAb/6igC+HWJ1eGDRKSY0pkhXqiBYj4ItWFhPpcRIt8zo9Yke5QQ3DjPLB+hIthq3uuCU9Z2Y0fInlvEahS1cyNfzCmAhv3ICvpigPHl8AY1PRg8recQfOjE1h29KyhNeRiNoiLWwuHywO6dmJnvEpeP086gw+UWj4ehZGM5wesUOlkIWaCEhRUaCByxuQVMrj8PhwcsSGAz2TSZXE5gqX14+/7u/DW5eXx/zdjqSlPDMBX39YwKeQy9BUmp+RTGIkQsA3ff/PliWl+NrVK7CzcxTf+hd17hRPlpXN+D7dsW0JGkq0uOepowmXYUaa8dpQkg+VQiZpf3ksg2YnCvKUEfcYFeercON5dTgUDBakZvgK8pQo1CpjVqD84fUeFAbnwor7zHNRIMAxYHbOqqBpq9Cn/LNJp/a+SagUsrg/wzyVHKuqC2geXxqIo7oS/ewgc4cCvhxicXozun8PED5k81XymJ06pQR8lQUa5KvkCTdumdmhUyR26oy1T8LrD6B3wiE54FtZXQCtSp5U45Z4IxlEUmbxvXxyBACwrTXDAV+xsJZEOnV2Dgk/v3gZvtDw9ST37aTi9IgdTaX5MfdSziS+dqXs4+scskFMBP7ziCmpNeaCpw8Pwuzw4pYtDQnfd0mZDhNTHoyneaZV/4zyviUZyiRGMmbzRNzHeON59bh1SwN+s+ssnjjUPydrma9CAZ9++vdJo5TjG9eswtmxKTzwcldCjzkw6YQi2FhJpJDL0GLUpZxFMsXZe/6Ri5ugkstCDcykqi/WRt3bOWB24sWOYXxgcx3KDerQPvNcNGJzw+vn0zJ8gNBUp3fCMW8a17zRa8bKKkNotFMsmxtLcKTfAqcntf2hi92QVXivjjSbmcwPFPDlEIvTC0NeZrsfMcbQUq7HyRiB2qDFBZVCGIob63Gay/UJZfg45+gaiRzwiYFVrLKZnnEHfAEet0OnSCmXYUN9cl26xMBTLOeJplrCLL6dnaOoK9aisTSzm51rkhi+3jlsg4zFb4JjDB6cSWnN7/UH8MPnO0Otz1N1asSeUDkncO5DSUqnzg6T8BquLNDgH0cGE19gDuCc4+Hd3Wgt14cy34kQv//pDsb6Jx1QyWUoCwZezWU69E06Um7eEY/D44PT60dJlMY1X7lyGZpK8/H04YX5epBqLDi6okw3+yDvwpZSXL22Cr/Y0RUqZ5RiwOxERYFmVlm+MO8ttfeMQYsLVTEOSMsNGnz04iZc2mZMqLy+riQfPVH25v1pbw8AYfzDkjIduuZwlmS6zZzBJ2qt0INzaTN8M83rD+DNAUvc/Xui8xqL4QtwvNFL+/hSIWb4qKRz/qKAL4fMRYYPEDp1xnrjHgzOSIrXwazFmNhohlGbGza3L+KZ1XoJG+PFD1KpGT5AeLM/MWRLuLlMvBl8ongZPpfXj91d49jWWpbWrpyR1AYDvkRGM5wcsqGhND/uwY9OrYBOrZA0fP1QzyR++tJpXP/AnpTb3Ds9fvRNOtCc4FD60PB1CQHfcZMFerUCH76wEUcHrDiTwwds0RzqncSxQStu3lKf1OuwJck9u/H0TzpRXZQXKmNvNurAOXBmNLNZkjGb8H4ws6RTpJDL0FCan/T+34VC/PpL9ZG/T1++chlkMuDh3d2SH1McwzFTW4Uew9bEG4GFGzQ7URlj7zkAfPaKVjx088aEHre+WItBswveGbPo3D6hTPrStnLUFGnRVJaPM6P2rHQzTofQDL7i2SWdwPzo1HnCZIPbF4i7f0+0oaEIjIH28aVoyOKKWi5N5gcK+HLIXAV8LeU6jE95MBalPGvQ7JR0FmdpuTA3S+qeMTE7EClgK9IqodcoYpZ0nonS8CWW85qEeXz7uhN7s++dcKBIq5y113AmvUYJg0YRtVPn/u4JOL1+XJLh/XsAYMhTQK9WJJThOzlsQ6vE7JnRoJa0h6+9zwwAKNGpcPNv9+E/x4clr2emrlE7OBdes4ko06khY5DUebPDZMOySgOuXF0JAHhmAZZ1Pry7B3qNAtesrU7q/mIJd/ozfNMP/sWf8+kMB92jdjGQiT6awqhXY4QCPuSr5FEP8ox6DVZWFeDYoEXyYw6YndMatohaK4T9WMmWdTo8Plic3ox0Qa4r0cIf4LNO7P376BDG7B7cdEE9AOGzzeryhTKjuUb87JgZkNcWaaFVyedF45b2PiFTt66uUNLtDRolllcaaB5fikwWaceFJHso4MshFqdvbjJ8wbN10bJ88WbwiVqMiTVuETN0kTJ8jDHUl2hjDnftGrXDqFfHnBc30+qaAqgVMuw9k2DANx6/Q6eoukgbtaRzZ+coVHIZLlhSktDzJ4MxhpoERjO4vH50j0/F3b8nEg6A42fM2vvMqC3OwxP/vRVtlQbc/seDeOxAn6TnmEkMMBIZyQAIGRqjPv4svkCAo8NkxfIqAyoL8rC5oRj/WGBlfCNWF/71pgnXbahFfpIdgBkT9jylexbfwIwW8I2l+ZCxzI9mEPcilsWYRWjUqzFudy/qbp1jdves/XszLas0oMNki9upGAA8vgCGrC7URMnwAcm3/xdLziJlD1NVL245mHFC8g97etBQosVFzaUAhA6mAHK2rLNvwokyvXpWxYdMJmwFmQ8Zvjf6zCjVqRP6OW9uLMah3smcn5GYTVKPC0n2UMCXIzjnQpfOOQj4xAP8SI1bvP4Ahm3SfrETHc3QNToFnVoRagAyU31xPnpjlHQm0qFTpFbIsb6uKOHGLb0TDtRJHDBaU5QXtaTz5ZOjOK+peM7KIGqL8kINZ+I5PWJHgCPuDD5RuUEjaQ9fe58Za2uLUJyvwp9uOw9blpTgc387gl8l2NwBEE4mKGQsqWGvFQWauHv4eicccHj8WB7s9vbONZU4NWKfFwc2sbT3mXHvU0clNSL4874++AI8lIVIVrNRn9ZAzOX1Y8zunhbwqRVy1BVrcTpDQ95FYgamJEpJJyA0KglwpL1RTS4ZtbkjNrYJt7zKALvbJ2ke6ZDFBc4RMcNn1KtRpFUm3Q1SzOZnIgsRacvB8UErDvRM4r/Orw+VJC8JVp9kuiQ5U/rNjln790Rt5UKnzmyXq7YHB64nUpq+uaEYbp+w948kZ8jqooYt8xwFfDnC5Q3A4w/MSYbPqBcGKZ+McPAW+kCOsw8CEM6kahPo1Hl6xI4lZflR36jrSrTon3TC5599Fo5zjq7RqYTKOUXnNRXjuMkqqUU/APj8AQyYnagrlnY2q7pQmMU384Owf9KB0yP2OSnnFNUUCd9DKR/KR/qFDz+p7cmFgM8V87GHrS6YLK7Q/op8tQK/vmUjrlxdiW8/ewLf/ldHQgcMp4btaCjNl9SNbabKAk3cks7jwcYy4vfg7asqIWOY11k+zjm+/Pc38fCeHtz5p0MRf19EXn8Aj+ztwSVLy1JuGtRs1GHY6pb8exTPuZEM0zPpzXPQqVMsZy/Jjx7MlImjSBZxWeeoxAwfcO53KZZ+s1gyOLt6gjGG1gp90mWDUrpLJ8uoV0OjlE1rKvaH13ugVsjw3g01ocuqCvKgUcpyNsMnDF2PXNnSWqHHxJQnVA6dDWaHB2fGpiSXc4o2BRtVvZ7invLFyuX1Y2LKE7MhEsk+CvhyhHgQNRcBH2NMaNwS4YM1kQ9NmSyxMq9oIxlE9cVa+AI8Yhne+JQHFqc34QwfIJRzcA4c7JFW1mmyuOAPcMklnTVFebC7fbA6p7es3tk5CgDY1mpMbMEpqC3Og9Prx7iExgf7zo6jTK9GQ5xOpCKjXg23LzDr6wwXaSCuWiHHT9+3Dv91fh1+9coZfP5vR2IGKeFOj9oTLucUVRQIJZ2xAszjg1bIZSy0d6xUp8bW5lL848hg1s9kR7OjcwTHBq3Y3lqGF0+M4H/+/mbUtT53bAgjNjdu2ZJadg84V1abrmAstF9oRkZhiVGHs2NTkl8jyRi3u1GQp4x5IsEYrERYzI1bpJR0tpbrIWPSAr6BsLmLkbRVGHBySFp56EyDZhcYw7RxD+kikzHUFWtDAZ/V5cWTbwzg6rVVKNSqpt2usTQ3O3X6AxyDZmeMn032G7eI+8PXSWzYIirVqdFWocdrp8fSv6hFQKyUqcjA/liSPhTw5Qira+4CPkBojhCpPGPQkthZ0maJnTrtbh9MFheWxDh4F0cgRBrNIJbIxLp/NOvriqCSS9/HJz5/vA6dInEvwcz5dzs7R1FdmBcq85kL50YzxC+v2t89ic0NxZJLY8QDqViz+Nr7zFDKGVZUTc8aymUMX796Je66rAWPHezHPU8di/t8bp8fPeOOpAO+ygINHB4/bDFmR3WYrFhSNr1L6VWrK9Ez7khL+c+ZUTv+8HoPnjs2hPY+M0wW56xOf4ngnOOnL55GdWEeHrx5Iz55WQsePdCP7z/fGfH2D+/uRl2xFpcsTf2kgxgUd6Up4BswRz74bzHq4fXzmA2cUjVm98Qs5wSEExwAJO1bXYjcPj/MDm/cks48lRxNZTocH5QQ8AV/5tE6abZW6DHl8ccccxONyeJEmU6dVDWAFHXF+egNjmZ44mA/nF4/bjq/YdbtlpTl52RJ54jNBa+fh7o9z9Q6TwI+xoDVCQZ8AHBhcykOdE/SPL4khI4LKcM3r1HAlyPmMsMHCG/eNpdv1p4sceN7lcQzOUvL9RiyukIBazRnJIxUaBD3SUSYdySeMW1KoixNo5RjTW0BXpfYpUs80JS6byzSLD63z4/dXWNzMo4hXGj4epyD5f5JBwbMTmxOYCZb6AA4xj6+9r5JLKs0RBzzwBjDp9+yFB84rw6PH+yP2921e8wBf4CjOcEZfCLxbGSsfXzHTdbQ/j3RFSsqoJSztJR1fvvZE7jnyaP42B8O4pqfv4YLvv0Sln7lWWz8xgu46v5XceefDiUUULx2ehztfWbcsW0JlHIZPn15C96/uQ4/39GF3712dtptjw1asL97EjedXz9r5lkyaoq0UCtkaevU1z/phFLOYNRPP4hoTnMmMZJRe/y9aWUSXu8L2bg4gy9Ohg8QG7dIy/AZ9WqoFZHHwIhBRTIzPE0WFyoz2FSivkSL3gkHAgGOP7zeg7W1hVhVUzDrdkvmaJZkuvXHyb6W6NQo1amz2qnzjV4zlhr10CXRfGprSyk8/gD2J9ixmwAms5jho4BvPosb8DHGahljOxhjHYyxY4yxu2Zc/1nGGGeMlWZumUQ8+J2rgE9s3DJzg/yg2YnifBXyVNKG0kot8xKvbzZGD6IqDBqoFLKIGb6uETvUClnSHdjOayzB0QEL7DGyPaLeCQeUcoYKiaVBkWbxHeiehMPjn9NyTkB6hk9sUb2pQXrAF8rwRZnF5w9wvNlviTsf6QOb6+DxB/DMm7EDKrFUONEZfKKqOLP4Jqc8MFlcWD4jG1moVeHiljI8c8SUVGlZuA6TFZcvK8czn7gQv7llI7717lX45KUteMtyI4x6DZ4/Poz/eeKo5PLRn750ChUGDa7bKOwbYozhG9esxBUryvG/zxyfFqT+YU8PNEoZrt9Ym9LXIJLLGNbWFibcACma/kknqgrzZgWjYkY83TP/wo3b3TE7dAJCKXJBnjKre5ayaUxCJ1PR8koDBszOuCdxoo1kEIU+l5IIKgbMzoxmIOpLtHB5A3jq8AC6Rqdw0/mRy6SbyvLBeeRKlflMPEkYLeADhLLOE0l2UU0V5xxvDliwOkKQLcV5jcVQyhmVdSZhyCoOXaeSzvlMSobPB+BuzvkyAOcD+DhjbDkgBIMA3gKgN3NLJMC5DF+8uW/pIn6wnooQ8FVJaNgy83HilUt2jdrjdluUyRhqi/IiDl8/MzaFpjJdqBtaos5rKoY/wHGwZzLubfsmHKgp0krOihTnq5CnlE/L8L18UhjHsGUOxjGE06kVKNIqZ5WXzrS/ewIGjUJyh07g3J6maCWdp0ZsmPL44wZ8K6oMWFquwxOHBmLe7tSwHTKW2NzFcOLZyKEojVs6ZjRsCffONVUwWVw41Bv/9RKN1eVF/6QT6+oKsbK6AJctK8cHzqvDp9+yFN++djV+e+smfP6KVrzQMYyn2uNnE/eeGce+sxP42CVN0zIkchnDT963DpsaivGZR9ux69QYzA4PnmwfwLvXVaNAm773lK3NpThusqY0HFvUP+mIeAJHr1GiwqBJW+loJFJKOoHgKJJFmuE7N3RdQsAXPGnSEScYGDBHHrou0qkVqCvW4kSCnTo55zBluG28uKf7+8+dRJFWGZrbOdOSHB3NIJ4kjPU9bKvQ49SwPSujSkwWFyamPBGzqlJoVQqsryvCq6co4EvUoNmJIq1SciKAZEfcgI9zbuKcHwr+2wagA4A4nfdHAD4PYH52L1hA5rqkszhfhVKdetaZ1EGzK6GzOLXFWlzUUoqHXj0DW4yyztMjdtSVaKGUx35J1pfkR87wjdqTPvAHgA31RVDIGPZK6NLVO+GQvH8PELIs1TNGM+zsHMGmxqKk556lorZYG7ekc+/ZCWxqKE6o1E+rUkCvUUQ9AG6P0LAlEsYYrl1fg4M9k+gei77X5fSIHXXF2ojloVIY9RowFj3DN7NDZ7jLl5dDrZClVNYpNkVqixFUf3BrI9bXFeLep4/FLe28/6XTKNWp8f7NdbOu0yjleOjmjVhSpsPH/nAA3/hnB1zeQMQ9RqnY2lwCzoE9aeh21z8ZvUFEs1GXseHrHl8AFmf8vWmAcJJjse7hEwM+aSWdwms81j6+QEAIymJl+AChrDPRDJ/F6YXT68/oYGjxZOWA2YnrN9VGfV8SP6cSOWFxqHcSf9nXG/MzNNP6Jx3BbqTR329bK/Rw+wLojjE+KVOOBV9bK6qSC/gA4KIW4YTVYh61kowhi4satuSAhPbwMcYaAKwDsJcx9i4AA5zzw3Hu81HG2AHG2IHR0dHkV7rIhTJ8cxTwAcDSct2s4euDcc7ARvL5K9owMeXBQ6+ejXqbrtEpSaV5dcXCPonwEje3z4++CUdSHTpFWpUCK6sLQqWMsfSMT0keySASRzMAwvfw5PDcjmMIF2suICCUap0ZnQq1qk6EOJohkvY+MwrylJLa/1+zthqMAX9/I3qW7/SIHc3G5PbvAYBKIUOpTh3afzDTcZMVRr064oG/Tq3ApW1G/PNNU9LdIsW9Lm0xxl7IZQzfu24NXF4/vvz36KWdh3onsev0GD56cWPUA7KCPCUe/tBmFGpV+NvBfmxuKJ5Vrpqq1TWFyFfJsbsrtbPkLq8fozZ31BbwzUYdukbsGemUOhHMTkoK+PSaRTuWQSzpLJWUCdWgVKeO2alz1O6Gxx+IOHQ9XFuFHmfHpuD2Sd8DN5DBkQyi6sI8yBjAGPBf50XveqtVKVBVoMGZGCezZvra08fwxSfexPnfehH3PHl01ufyXOifdMY90dlWIbyfZKNxy9EBC2Ts3MmFZGxtFnYmvdZF4xkSMWhxUcOWHCA54GOM6QA8DuBTEMo8vwzg/8W7H+f8Qc75Rs75xrKy7BzgLgQWpxd6tSItzRWkWlqux6kRe2ifktXlhc3tS6ikEwBW1RTgytWV+PWrZ0IHCeG8/gC6x6YkddisL9HC4fGHBiMDwl6IAEfK3S7PayrG4X5zzC5dFocXVpcP9cWJPVd1UV6ozXw2xjGEqw3O4ou2/2x/MOhNpGGLyKhXRz0Abu8zY43EgbgVBRpsXVKKJ97oj3hQ7/MHcGbMHuoMmazKAg1MUQLU44PWmAHRO9dUYczuwV6JzX5mOjFkhV6jiPtBuaRMh8++tRX/OT6Mp6NkFO9/8RSKtErcGONAExAC8t9/eDNWVRfgrstbklp3LEq5DJsbi7H7dGoHTINROnSKmo06THn8UbOzqQjN4JMQyJQFX+/zdURHJo3a3DBoFFEbrMy0rFIfs9lKtDEcM7VW6OEP8ISa9ogndTKZ4VMpZGg26nBZW3ncwGiJUfpoBofHh6ODVrxzTRXetrISfz3Qh7f+6BXc8Ks9+OcRU0pdfRMRK+MuainXQcaQlcYtxwYtaCrTQatKvmpmdU0h9BoFXqOyzoSYLE5q2JIDJAV8jDElhGDvEc75EwCWAGgEcJgx1g2gBsAhxlhFpha62Fmd3jnN7gHCB6sjrAV2KoNr737LUrh9AfzspdOzruudcMAX4JIyfGKnzt6wTp1iaUwqGT4AOL+xBF4/xxsx9mWJHToTKekEhLO/kw4vHB4fdnaOoKpAk/Q4gVTVFOXB4w9EbTaxr3sCGqUMK5MojYmW4Zty+3By2Ba3nDPcteur0TfhxIEI+yp7Jhzw+nnK38MKgybiHj6PL4CuUfusDp3htrcaka+SJ13WecJkQ1uFXlIA/KELG7EuSmnnm/0W7OgcxYcvbJRUIrykTId/fOLC0NnsdNvaXIozY1Nxh9rHEm3ouiiTnTpHQ5krKRk+NTy+AKyu+M2eFhopQ9fDLa8y4NSwPWqAIv7MIw1dDyeWQJ8wSQ8qxNdisk29pHrktvPx4/etjXu7JWXSM9Rv9JrhD3Bcu74aP7h+DV7/0mX44tvbMGB24uN/OoQLv/sSfrHzdEZPOsSbwSfSKOVoKMlHZxYatxwdsGJlihULchnDliUl2HV6bFGexEmG0yOMZ8lk9pykh5QunQzAbwB0cM5/CACc8zc550bOeQPnvAFAP4D1nPOhjK52EbM4vXO2f0+0NJg9EcszUgn4msp0uH5jLR7Z2zNr/5h40CYlwyfO4useO/cYYmmMlFLBWDY2FEHGEHU8A+ccr5wSsnNSh66LxA/K7jEHdneN45JW45yOY5i2luDao+3j23d2QphNmMS8KqNBaGIx88PySL8FAZ7YQNwrVlRAq5LjiUP9s64TZzu2pFDSCQQzfBGyRKdGbPD6ecT9e6I8lRxvWV6OZ48OweNL7Cw75xydQ7ZQCVQ8chnD9967Bg6PH1+ZUdr5sx2nYNAocPOWhoTWkClblgTLolLI8oUO/mNk+IDMdOocE/emSQj4xIBndBHu4xuzeRIL+CoN8PgDUTNb4onFeBm+hpJ8qBSyWR2kYxm0uKCUM0lBfCrK9GpJIwGayvIx5fFLKgfed3YCjAn7zAFhf/3tlyzBy5/bjt/euhHNRh3u+3enpO0IyRqyuuAL8KgnYMIls8cyVWN2N4asLqysTn7/nujCljIMmJ3ozrEuqtkinkzJZPacpIeUI7qtAG4CcCljrD345x0ZXheZIRsBX0uww+bJYPv7gWBZTLJnSe+6rAUyxvCj/5ycdnlXaAZf/ICtpigPjAkZntD9R+yoLNCk3ABFr1FiRVVBxMYtZ8emcPNv9+F7z3VifV1h6IBTKjHge/rwIOxuX9b27wEIDc6N1KnT6vKiw2RNaBxDuHK9Bh5/AOYZ7dfb+8wAgDUJBHz5agXetrICzxwxzZpZdTr4mlwSY4yHFBUFebC5fLPGcXQEswfx9ri9c00VLE5vwq28B8xO2Ny+hLqgNht1uPstS/H88WH844gJgFAW+tyxYdy6tXHOOvjG01ahR3G+CrtTaG8+YHZAIWMojxJQlOSrUKhVZiTDNx7cwyetS6dwkLMYO3VKmVUYTsyWR2vcMjDpRKFWGTdgUshlaDHqEiobHDQ7UW7QJN3FOd1CnTolvH4P9ExgWYVh1u+3XMZwaVs57n//egDA4X5z2tcp6pcwkkHUWqFHz4QDDs/cZb3T0bBFdFGw8mHXKeo5IYU4x5ZKOuc/KV06d3HOGed8Ned8bfDPv2bcpoFzTkXPGWR1zX3AZ9AoUVWgCXUTNJmFQchSznxHUlGgwa1bG/D39oFps3q6RqZQblBDL+GAVa2Qo6ogD71hXcC6Ru0pl3OKzmssxht95lCA4fL68cP/nMQVP3oF7b1mfO1dK/DY7VsSzn6JZUqPHeiDQsawtXluxzGEEz+0+ydml9wd7JlEgAvfh2REG83Q3jeJ+hItivPjH0SHu3ZdDWwuH17sGJl2+akRO6oL81LarwGcOys5c/j68UErNEpZqIQ4motaymDQKBIu6xTL0RJtMHDbRU1YW1uIe586ilGbGz976TTyVXJ8aGtDQo+TSTIZwwVNJdjdNZ50WVT/pBOVhRooonTtZYyhJdi4Jd3GbG7kKeWSTiCJr/e5atzSN+HAu362K9QhM5tGbYmVdDaW5kOtkEUP+BJoCCZkkaSXDZrMLlTNoy6CUkczeP0BHOoxY1NDUdTbFOerUFOUhyP9lrSuMVy8EutwbRV6cH6uCmMuHB0QvvZ0NKGqL9GiujAPu2genySDwc/O+fT7RSJLvGaLZEU2MnyAkOU7GXzjHjQLG3NTOUv635c0Q69W4PvPdYYuO51gwFZXrA1l+DjnODM6lXLDFtF5TSXw+AI40m/ByydHccWPX8FPXzyFt6+qwIt3X4JbtjQk1TjHqFdDKWcYn/JgY0ORpOA2UzRKOcr06ogZvn1nJ6CQMayri36AEYs4fH1mxqO9z5zQ/j3RBUtKUGHQzCrrPDWcesMWIEbAZ7KgrcIQ92etUsjwtpUVeP748KwsZCxiOZo4p1IquYzh+9etxpTHj/9+5CD++aYJN29pQKE2sUA607Y0l2DI6kqoE2G4/kknauLs5crUaIYxuxulemnfTzHgmavRDAd6JnCk34LDwYx5tjg9ftjdvoQCPoVchrYKfdRZfAOT0gO+tgo9hq1umB3S5j0OWhKbH5tp5QY18lVydI3G/v04PmiF0+uP2zF5dU1BxgM+xiDpe9gaLFOfywHsxwYtqCvWpuUYiTGGi1pKsbtrPOkOzIuJuAeeMnzzHwV8OcLi9KZ1QLJUrRV6nB61w+cPYDANZ0kLtErcvm0JXugYwYHuCSFgG7EnVCJZX6JFb7C+ftTmhs3tQ1OaMnybGorAGPDpv7bjlt/ug5wxPHLbefjJ+9bBaEj+DU0mY6H5hdnqzhmupigvdNY23P6zE1hVU5D0ANXyYIlbeOMWk8WJYas7qYBPLmO4Zl01dp4cDXVP9Ac4ukbtaWl6I/5MwhuMcM7RYbJJPlt8+bJy2N2+hA64OkxW1BTlJRX4Nxv1+MxblmJ/9yQ0Cjluu7Ax4cfItK3BfXzJlnX2Tzri7uVaUqbDxJQn7TOzxqc8kksV9WoFNErZnGXcxP2mqTTESYexBBrbhFtWacDxQeuszC/nXMjwSSgZBMKDivhlnf4Ax7DVhcp51FSCMYamsvidOvd3C/vy4pXYr64pRO+EQ3IAnKj+SQfK9RpJHVmF2aiyOe3UeXTAipXV6Rsxs7W5FDaXD28OZC6IXigGLS4U56uSnodL5g4FfDnA7fPD5Q3AoJn7Id0tRh08vgB6JhwJldzE8sEtjTDq1fjuv09gJBiwJZLhqy/Jx/iUBzaXN3SGNF0lnYVaFVZXF2DM7sbdb1mKZz91Udq6GYrfu22t2R9PUluknZXhc3n9ONxvTmocgyhSiZvUgevRXLu+Gv4Ax9PtQtnkwKQTbl8g5YYtwLn1hmf4Bi0uWJzemA1bwokHY/vOSm9SkkjDlkhuu7ARV66qxGevaEVJhhtRJEMsi0qmcYvbJzSziLdfKFOdOkdtbpTkS/ueMsbmdBbfcPB1OpiBcRSJGElg6Hq45VUGTDq8GJ5RAWB2eOHw+BPK8AHAiRhjHkRjdje8fj7v5oQtKcvHmTgZvn1nJ1BXrA1VTkSzOtisJFNZPikjGURyGcPS8rlr3GJxetE74UjL/j3R1tA+PirrjMdkdlLDlhxBAV8OEIeuZ6OkszWsBfaQ1YXKNJTF5Knk+ORlLdjfPYmHXjkDAAln+ABh/l6o4UuKzTvCPXTLRrz6+e34xGUtkmdMSbGs0oDG0ny0JljGlwm1xXkYNLumlay095nh9XNsTrJhCyCUixbkKadl+Nr7zFDJZUnvr1harsfKagOeeEMo6zwVbNjSnIaSTo1SjpJ81bQD6I7gHqNYIxnCFeWrsLRch33d0cd5hHN5/TgzNhU6aE2GQi7Dz29cjw/Pw+weIARCW5aUYM+Z8ajzHqMxmV3gPP5+oVDAl+ayzjG7B2USSzqB4OzJOWraMhT8vTKZ50eGL9H93OJJlOOm6YHJQJy5izMZ9WoUaZWSOnWK3aUr59keo6YyHQbMzqhzXznnONAzKamB1soaIdjJVEaqb9Ih+WcDCAH5XAV84p7QdHToFBXnq7CiykD7+CQwWVwU8OUICvhygDUY8M31HD5AOKhiDNh1ehT+AE/brJUbNtWivkSL37x2FkBiGTpxJELvhBDwaVVyVKRQbjmTUa9JqXwzmi++vQ1P37k1a+MYwtUUaeEP8NABJHCu/ffG+uQDPkA4GAsP+N7oM2NZlSGl4PnadTU4OmDFyWFbqBV/op1So6komD6L77jJCsaQUEC2qaEYh3omJe35OD1ihz/A0ZZgw5Zcs6W5BBanF8clZGHCnWsQEfu9pqogD1qVPK0ZvkCAY2Iqse6TwvD1ucm4Dc2TDN9okhk+8XdqZuOW0ND1OPs2RYwxrKwuwKEec9zbimWw821OmPiZd2Ys8uu3a3QKE1OemA1bRAaNEk2l+RnZ2+nzB2CyuCQ1bBG1VhgwPuWZk1LnY4NCkLsiDQ1bwl3YUopDvZOYci++GZuJEAK++fW7RSKjgC8HZDPDp1UpUFukxc5OoUVxuj40lXIZ7n5rKzgHdGoFyg3SDxzCM3xnRqfQVJY/L4KoeFQKWVabtYQLjWYI69S5v3sCreX6lPeKlhvOlbj5/AG82W9JaP5eJO9aWwW5jOGJQwM4NWxHhUGTtjEEM2fxdZisaCjJT2jMx+bGYtjdvtA4h1jEM9+plHTmgnPz+BI7S37u4D/2e41MxrCkTJfWgG/S4UGAC2MfpDLq1XO2hy+U4cvyHr5RmxuMIeGuu3qNEvUl2lm/J/HmLkaytbkUncM2jFhjB7/n5sfOryxEU7DRWLSyztD+PYkl9qtqCjKS4RuyuuAPcNQWS//ZiDN8TyUwKzFZRwcsqDBo0j5j8cLmUnj9HPu6MzffMNc5PD5YnF5q2JIjKODLAdkM+AChpE48IE7HHj7RVasqsbqmAMurDAkFbHqNEsX5KvROTKFr1I6m0vRkehaT0GiG4MG11x/AwZ7JpMcxhBOHrwPAyWE7nF4/1tUVpvSYpTo1ti0tw5NvDKBz2Jq27B4QzPCFHTQeN1kll3OKxH2PUg4OTgxZoVLI0FAi/Yx5Lio3aNBs1OG1rsT28fVPOiGXMUllQs1pHs0wZheaXpQmkLkyGjSwunwJdWlNhs8fCAVaQxZXwqWy6TRmd6NYq4IyytiMWJZVGGZlfQfMTuQp5ShK4GTTheI+qzgnFAbNLuQFS83nk8bSfDAWfTTD/u4JlOSr0FQqbbvC6ppCmCyutGebExnJIBK7D5/KwNiUmY4Nprdhi2hTQzFUChnt44vhXPacAr5cQAFfDsh2wNdace7gOp212jIZwx9vOw8P3rQh4fvWFWvROWTDgNmZtoYti0lVoTDAvi/4YX5s0AqHx4/NjanPBxQyfMIBqThwPdmGLeHevb4aQ1YXjg6kN+CrLMiD2eGF0+OHzeVFz7gj4fl4lQV5qCnKw/6zUgI+G5aW66LOmFtIti4pwf6zE/D4pLc3HzA7UWGIPoMvXLNRh0GLC/Y0lV2NJ9F9UixrzHSWb9TuRoADreV6eP08tI8uG0ZtiZW9hlteZUD3+NS0UrmBYFOQRE78La80oCRfhVfjHJCbLMJMx/lWBaJRylFTlBd1NMP+7glsbCiSvO7V4j6+NDdukVpiHc6oV0OvUeBkhjN8Do8PXaP2tDZsEWmUcmxuKE64QmExMZmDQ9cNVNKZCxb+EccCYHUKH4zZzPABgEGjSHtJokGjTGqGWEOJFu19ZnCe3oYti4VKIUOFQYP+4DxDMVDZ1Jjc/L1w5Xo1vH6OSYcH7X2TKM5XhfZdpuLyZeXQBzvVpmMGn0jc/zlkdYXKLZNpMLO5sRj7g6NGYjmRYofOXHLBklI4vf5Q4C9FfwINIkIDrNOUSRgNBXyJlXQCmZ/FJ+7fE2dkZnMf36g9saHr4ZZXGsD59JEKiYxkEMlkDFuaS7Hr9FjM37lBiyutlSnp1FSqw5kIGb4hiwt9E05JDVtEK6oMkDHgcNoDPgcYS6zpDWNCp85MZ/g6TDYEePr374m2NpfixJBtzvbo5hqxtJwyfLmBAr4cYMli0xbgXMA3nza915XkQ6xoopLO5NQWaUNnb/eenUBjaT6M+tTfuMWGN8NWN9r7zFhTU5CWs+sapRxXra4EgLSMZBCJnWdNFmeo1EzqSIZwmxuKMT7liTlMeczuxqjNnVKHzlxyQVMJZCyxfXz9k9IP/sXAP137+EIlnUlk+DLdqfNcwFcIIL2dOjnn+N1rZzE5JW2O21gKAd+yKrFT57myzmRH/lzUUopRmztmt87Bedw2fkmZDmdGp2aV50qdvxdOq1KgxajHm/3mdC4RfRNCxl2lSOxwscWow6lhW9wTYKkQG7aks0NnuItaxHmiiY+XkeJfb5pw469fhzdHB7yLJZ3xxoaQ+YECvhxgcXqRr5IntV8iHZrK8iGXsXl1lrQ+mDFiTNgLQRJXU5yHvkkHAgGOAz0TkrrBSSE24DkzZsepETvW1qbncQHgwxc24cpVlViVxg948cz1kMWFDpMVRVplUl1fxX18+2Ps41ssDVtEBVolVlYXYHeXtIDP4wtgyCq9I2B9sRZKOUvbaIZxuxsKGUuomkI8STKa4RJLcZ/p+gxk+DqHbfjaP47jbwf7496Wcx4s6Uy8MgMAqgo0KMhToiMY8E25fTA7vAln+IBzB+TR9ll5fAGM2d3ztovgEmM+nF4/TDMaz+zvnoBWJU84c7W6pgBH+i1pDbISybiHaynXY9LhxbjEkwjJODpgQXG+KmMB/fJKA4q0yrhlw8ngnOP+l07jtdPjeLFjJO2PPxdMFhdKaOh6zqCALwdYnN6sbjhXK+S4ek0VtrcZs7aGmcROndWFechT0ZtNMmqKtBiyunDcZIXZ4U3L/j3g3AHwC8eHwTmwNsWGLeGajTr8/Mb1af2Zi8GdyeLC8UFrwk2ERI2l+SjVqbAvxj4+sYytdZFk+AChW+cbvWZJ7c2HLOIMPmkHmAq5DA0l+WnM8LlRolMl9PMvyVdBLmOZz/BZXVDJZWgqzYdGKUtrhq97TMhKn5AwO83u9sHlDSSd4WOMYXmlITSaQZzBl8wJxcqCPCwpy496QD5sFV5P8+lkZTixOmVmWef+7kmsrytKeJ/v6poCjE950noyoH/SGerqnIiW4D7rTO7jOzZoxYok36+lEMuGX4tTNpyMowPW0EmPv+7vTetjzxVxfyzJDRTw5QCL05u1ck7RD29Yi/86vz6rawhXFwz4mqhhS9Jqi/LAOfDkGwMAkNLA9XDGYIbvxRPCWcu1NYVpedxMyVPJUahVon/SiRNDNixLMvvGGMOmhuLYAZ/JilKdKumD5Vy0tbkEvoC09uZi19hEMgrp7NQ5Zvck3IxEJmMo1anmZA9feYEaMhlDVUHetFEiqTo7JnzfTwzFn5kolr2m8hpeVmnAiSEr/AGOgSSagoS7qKUMe8+OR+ySKgaT8/WgVNx/Hv76tTi9ODFkxcYkKi5WB99rj6RpHp/PL2bcE//ZiFtB0jk2JZzb58fJYVvGyjlFFzaXYsjqitpNNVl/PdALtUKGWy6ox8snR0PjQ3KJyeyihi05hAK+HDAfAr75pkynRqlOjZUZ2qy9GIhlc0+2D6LCoElozlIsaoXQXt3m8qGpND/luX5zocKgwZ6uMbh9gaQatog2NxZjwOwMHWjO1Dm8eBq2iDbWF0Mll2GPhPEMoY6AEgdwA0LA1z0+Bbcv9bEIY/bkuk8Kw9czv4dPzEZXFmowmMZZfD3jQobv1LAdvjj7icRupKnMPVteZYDLG0D3+BT6Qxm+5Bo7XdhcCpc3gEM9k7OuE5tKzNeSzjKd0M3yzNi5fb+HeibBeXIn4Noq9VDKGY6kaR6fySLM4EtkJIOo3JDZTp2nhu3w+nnGGraIQuM/0ljW6fT48dQbg7hyVSVuu6gJHMCjB/rS9vhzxWRxUsOWHEIBXw6wZrmkcz5ijOGfn7wQn7ysJdtLyVligDdmd2NzY3Fay2LEss50jGOYC5UFGnSPC1mOZBq2iMQmC5HGM/gDHJ1DtkVVzgkIGdR1dYWSGrf0TzogY0hokG+zUYcAB3qCP79UjNs9KElib5pRr5mTks6KYOBSWZAXaomeDmeDAYfHHwj9Oxox4Estwyf8DhwftKJ/0gGlnIW6nSbq/CUlUMgYXo3w+ho0z+85YYwxNJXppmWP9ndPQCFjSZXCqxVytFUYcCRNjVuSGckgYowFG7dkJsN3NBjUrszASIZwtcVa1Jdo4857TMS/3jTB5vbhhk21qC3W4sLmUjy6vw/+LM7WTNSU2weryzdvT6aQ2SjgywHZ3sM3X5UbNLRZOAUVBg0UMiHI25SGgevhxLLOdO7fyyTxQFoll6U013FZpQF6tSJi+aKQhQosmg6d4bY2l+K4yRq3C2S/OfGOgGLTpniBSjycc2HcQBKZK6NendGmLZzzYIZPWFtVgQbDNlfauvt1j0+FMiUdcfbxifP/kvk+iVqMQiaqw2TFwKQTlQV5kMmSO+GkUyuwvq4oYgbGZHGiUKuEVqVIeq2ZtqQsH10j5167+7snsKK6IOk1r0pj45a+UIl1ctnXTI5mODZohV6tSMvIn3gubinD7q7IZcPJ+Ov+PjSW5ocafb1vUx0GLS68emo0LY8/F8SS8vnaAZfMRgFfDqCAj2SCQi4L7W05L80Bn9imOZcyfIDQ5j/R9uPh5DKGDQ1FEffxiR06U8kg5qqtzSXgHNhzJnZZZ/+kM+GDy3QFfDa3Dx5fIKlSRaNejXG7O2Nn6C1OL9y+QOj3qrJQ2H87bE09y+fw+DBsdeOyNiMUMoYTptj7+EZtbshlDEVJzE8VqRQyNBv1OG6yJj2SIdyFLaU4OmiZdUJh0Oya9xmIJWU6DFldwWY4fhzus2BzCh2TV1cXwObyhSoWUtE/6YSMJb8Hstmow8SUB+MZOBlydNCC5VWGpE8UJOLSZUY4PH7sjbE/W6quUTv2dU/g+o21oaqatywvR3G+Cn/ZlztlnefKpSngyxUU8M1zXn8ADo+fAj6SEbVFWhRqlWhOc/ObZqMORVplzuxXE0sI0xGMbWooxukROyZmHHyeMFkhY8L3ZrFZXVOIfJU87niGgUlnwuVjeo0SZXo1zsaYfyjFuDiDT594IFNm0CDAkZEDWyD8bLpY0qmZdnkqxFLYlnI9mo26uJ06R21ulOSrUj7QXlapx/FBa1I/85kubCkF58BrM15fg2Ynqub5AemSsuAJi9EpvDlggccfwMYUGmiFGrekoayzf9KByoK8pEdCiY1bTqa5rNPnD6DDZMWKDJdzii5oKkGeUo4XO4ZTfqxH9/dBLmN4z4bq0GUqhQzvWV+NFzqGQyXT893M9yQy/1HAN89Zg0PXKeAjmXDn9mZ8/eqVaT9L+uELG/HS3dtSypbNJfEAenkaAr7zoszj6xiyobE0f1GWISvlMmxuLMYrJ8eiliF6/QGYLNKHrodrLM1POcMnliqW5CfRtCWYFcxU4xZxBl9FgfA8YkYsHZ39xJEMjaX5aKvQx83wpTJ0PdzySgNGbG6M2NxJ/czDra4ugF6jwKsnpwd8Jotr3nboFIkl5F2j9lBlQCID12dqKddBrZDhSH/qjVv6J5P7fQxfCwCcHklv45YzY1NweQNYWT03JxQ1Sjm2NpfixY6RlEplPb4AHj/Uj8vajKF97qIbNtXBF+CSZmHOB+Ie4vKCxdNxOtflxtHYImahgI9k0JbmUrxzTVXaH1cpl6EoP/mSr7m2qroAmxuKcWkaZk2uqimASiGbVdbZObT4OnSGu35jLXonHLj36WMRD5qGLC4EEpjBF66pNB9nxlLLIoyl0H1S3LOaqbPzQxYx4Atm+IIBXzoyfGLpX32JFm2VBgxaXLA4vFFvP5pkJ9OZwrvhplrSqZDLsHVJKXaFzUubcvtgcXpRNU9n8InqSrSQyxjOjNpxoHsCzUYdilN471TKZVhRZcCbaQj4Us2+Vhg00KsVac/whRq2ZHgkQ7jLlxkxYHaiM4Wuoy+dGMaY3YP3ba6ddV2zUYfNDcX46/7etM/8ywSTxYlSnRpqxeI7gZmrKOCb5yjgIyTzCrUqPHr7BWgI7gdLhVohx9rawmkZPrvbh94Jx6Js2CJ6+6pK3H7JEvxpby8e3t096/pzHQETb8LQWJqPMbsn9H6ZjLGp5Es6xQ6TmZrFN2RxgbFzz6NTK6DXKNIyfL17bAqlOhX0GmXo9RlrHt+oLX0ZPlGqGT5AKOscMDtDmV5xj1HVPC85UyvkqC3Kw8lhOw70TGJTCvv3RKtrCnF00JLSnlIx455swxZA6NTZXK7DqTRn+I4NWqFRytCUhvdrqcSTgS92jCT9GH/d34cKgwYXt5RFvP6GTbXoHnfg9TOp7xXMNJPFRfv3cgwFfPOceABDc/gIyR3nNRbj2KAVdrcPAEKzqNoWYcOWcJ+/ohVvWV6O/33mOHZ2Tj9wSmboukhs3NKdQlnnmM0NxoDiJJqRiAFQpkYzDFtdKMlXT9tLVVWQh8E0ZPjOjk+hoUT4/ol7WKPt4+Ocp62ks1CrCu2vS2TuYjQXtQTnpQXb54sjGXLhoHRJmQ6vnBqFzeVLqZxTtLqmAA6PP6Vh4UcHLAhwoDbFYHypUZ/24etHByxoqzBAkeTewmQYDRqsrinASyeSC/gGzU68fHIU122sibrud6yqhF6jwF/296ay1Dlhsjhz4neLnEMB3zx3LsM3f9tKE0Km29RQDH+Ah4ZBnzAFA75FnOEDAJmM4cc3rEVrhQGf+NMb0/b29E86wVhyTQCaylLv1Dlmd6NIq0rqIFKtkKMgT5mxPXyRzqZXFmpCWaxU9IxPoT4Y8Bn1ahRplVEzfBanF14/T0tJJyAEmCzBuYvR1Jfko7Y4D68GxzOEMnzzvKQTEF6/Do/Q8j9dAR8AHO4zJ3X/QIDjf585jlKdCm9dUZHSWlrKdRize2Y1sUpWIMBxfNA6Z/v3wl3aZsSh3smkmjP97WA/AlwobY8mTyXHu9dV49mjQzA70vP9yhTK8OUeCvjmOStl+AjJOevriyBj5xq3nBiyQqdWpLxXaSHIVyvw61s2Qq2U4cMPHwi10u+fdKJcn9gMPlFtsRYyJjRzSNa43YPSJIaui4x6dcZKOoetrtBIBlFlQV4oi5UscSRDY6mQYWOMoa3CgA5T5AxfOoauh3v3+mpct6Embc2dLmopw56ucXj9AQyYhTLYmd+3+Uhs3FJh0KTcsRQAmkp1yFfJ8eZAcvv4HjvYhzd6zfifdyxLeTtJS7BT56kU9r6F651wwOb2ZXzgeiSXLysH58DOzsTm5QUCHH/d34cLm0tRG2du4A2bauHxBfD3NwZSWWpG2d0+2Fy+0F5ikhvivssyxmoZYzsYYx2MsWOMsbuCl3+dMXaEMdbOGHueMZb+zg+E9vARkoN0agVWVheEGrecGLKhtUI/JzOjckF1YR5+ddNGmMwu3P7Hg/D4AhgwO5I+2FUr5Kgp0qac4UumQ6fIaFBnrGmLyeIKdegUVRVoMDHlSWkYdPeYUEYbvne1rVKPziEbAhH2f42mYeh6uKtWV+G+965Jy2MBwEXNpbC7fTjcZ4bJ7ESZTp0TnYKXBEe1bGosDs1mS4VMxrCyugCHk2jcMjnlwXeePYHNDcV497rq+HeIoyX4tZ1MU1nnsUEh+zyXDVtEK6oMKDeo8eKJxMYzvNY1hgGzEzdsip7dO/ccBVhdU4C/7Oubt81bxL3DlOHLLVLeCX0A7uacLwNwPoCPM8aWA/ge53w153wtgGcA/L/MLXPxsji90Chl1AmJkByzqaEYb/SZ4fb5ccJkResiL+ecaUN9Eb773lXYe3YC9z59FH0TqXUEFEYzJH9QOWZ3ozSFzJVRr8lISafL64fF6Z1V6pqOTp0940KALO7hA4BlFQY4vX70Tswe3H0uwzc/O/BuWVIKGQNePTUWHMmQGxmIpUY9tCo5ti2N3MwjGatrCtBhssLjizwGJZrvPd8Jq8uH/71mRVqCz8oCDXRqBU6nKcN3dNACpZyFRj7MJcYYLm0z4pWTYwl9X/+yvw+FWiXeuqJc0u3ft6kOncM2vJFkSW6m0Qy+3BQ34OOcmzjnh4L/tgHoAFDNOQ8v8s8HMD9PReQ4i9NL2T1CctCmhmJ4fAE8f2wYVpcPyyjgm+Xd62rw8e1L8Od9fRgwpzbzq7E0H2dHp5I+Kz6WlpJOd9rPyosjGWaWJlYF58ul0qnzrBjwzcjwAZE7dYYCPt38PLNfoFViVU0hdp0ew6DFiep5PoNPVKBV4vX/uQzXrk89oyZaXVMIjy8QahglxeE+M/68rxcf3NKQthEyjDE0G3VpG81wdMCCFqM+ayfBL2srh93tmzV2J5qJKQ+ePzaEa9fVSF7zO9dUIk8px1/39aWy1IwR98dShi+3JFTrwBhrALAOwN7g/7/JGOsDcCMow5cRVqePAj5CcpDYXv0Pe3oAUIfOaO5+SyuuCJ75rk6hW2NTWT6mPP6kyipdXj/sbl9KzUjK9Gp4fAFYnb6kHyOSc2fTZwR8wbPrqXTqFEYyqKFTn2sK1mLUQ8YQcR/fmN0DlVwGwzxuInZRcyna+8zon3TmVAbCoFGmJaMmEhu3SB3A7g9wfOXJoyjTqXHX5S1pWwcALC3X4VQaSjo55ziWpYYtoq3NpVArZJLLOp841A+vn0sq5xTpNUq8c00l/nFkMNTpeT4xWXJnfyw5R3LAxxjTAXgcwKfE7B7n/Muc81oAjwC4M8r9PsoYO8AYOzA6mthGV0IZPkJyVYlOjWajDvuCjVuWllOGLxKZjOFHN6zFXZe1SC55ikQczZBM45Yxuzh0PfkMX1mGZvENWyNn+MTOlqlk+LrHHaGGLaI8lRwNpflRM3ylOlVaA5N0u6ilFP4Ah8cXWNQZiLpiLQrylDjSb5Z0+z/t68WbAxZ85arl0GvSe8zRYtRjzO4ONWhKlsniwsSUJyv790R5Kjm2NpfixY6RuNl8t8+PR/b2Ym1tYcIl/e/bXAeHx4+n2udf8xaT2YXSHNkfS86R9NNijCkhBHuPcM6fiHCTPwF4T6T7cs4f5Jxv5JxvLCtLX336YkEBHyG5a3Oj0GK9ujCPfo9j0KoU+PRblqaUYRMDvmQat4zZg0PXU3h+o14ILtLduEXM8M0cXaBRylGSr0o5w1dfMnt49bIKQ8RZfKNpmsGXSevqiqBVCaVzuTCSIVMYY1hdUyApwzdud+N7/z6BC5pK8M7VlWlfi7jfLtUs3+tnxgEAa2sLU11SSi5tM6J3whF3zuH9L57G2bEp3HVZ4hnTdbWFaKvQ4097e+dd85ZBmsGXk6R06WQAfgOgg3P+w7DLw1/B7wJwIv3LIxanl0YyEJKjNgdnalHDlsyrKsiDSiFLKuAT52qVpBLwGcQMX3oDvmGrC3q1YlrZpSiVWXxTbh9GbO5QoByurUKPnnEHpmaUk43Z5n/Ap1LIcH5TCYDFHfABQlln57AtbifX7zx7Ak6vH19PU6OWmcTRDInsJ4xkZ+coSnWqrIxkCHfZMiMA4IWO6EPY3+y34Jcvd+G9G2qwvc2Y8HMwxnDj+fU4NmhF+zxr3jJEM/hykpQM31YANwG4NDiCoZ0x9g4A32GMHWWMHQHwVgB3ZXKhi5WVMnyE5KxNwQzfYh+4PhdkMobGknycGV1YJZ1DFhfKoxxcCbP4kgv4esaDIxkiZPjE/aadMw7QR+3utA1dz6TLlhmhlDPUxZl5ttCtqi6EP8Bx3DS7PFd0oHsCjx3sx4cvbEKzMTPvU1UFGuSr5DidQobPH+B45dQoLm4py/p4m8qCPCyvNOClKAGfxxfAZx87jFKdCvdcuTzp57lmbRW0Kjke2dub9GNkgjB0fXGfTMlFcXdec853AYj02/Wv9C+HhPMHOGxuHwxprqcnhMyN6sI8/OLG9dgUzPSRzGoszcepkcSzCOko6dSrFdAoZRixprmk0xr9bHpVgQavd40n9bjdwQ6d9SWzgyLxBMUJkw3r64TmQ/4Ax3gOlHQCwPs31eHiljIU58/P8RFzZU2tkAl7dH8fJqc8KNSqUKRVokirgiFPCc6FRi2VBRp84tLmjK2DMYbmcn1KGb7D/WaYHV5sSyJblgmXLTPi5ztOw+wQvq/hfvbSKXQO2/DbWzeiQJv88Zteo8Q166rx+MF+3HPl8pQeK11sLi/sbh9l+HLQ/G21RWCloeuE5Lx3rEr/nhgSWWNZPl48MQyfPwCFXHpDgTG7OxiwJd/qnTEGo14TGk6eLsMWF1qMpRGvqyzMg83tg83lTbjRhlj62hChpLOmKA86tWJa45ZJhwcBjpwI+GQyhtpFnt0DgAqDBk2l+fjL/j78Zf/0Fv+MATqVAja3D7+8cT3yI5QMp9NSow47TybfuG9n5yhkDLi4JfLvwly7bFk57n/pNHZ2juKasAH1Rwcs+PnOLly7vhqXtiXfhEr0gc11+NPeXjx+qB8furAx5cdLVahr8CIvl85FFPDNYxYK+AghRLLG0nx4/RwDZmfEZiTRjNk9KEmhnFNk1KvTmuHz+QMYsblQEaX9eVXY8PVEA77usSmU6dUR9wYyxtBWoceJsNEMYjOaXCjpJALGGP5110UYtrow6fBi0uGB2eHB5JQXZqcXZocHRr0ab1tZkfG1tJTr8NjB/ogZMSl2do5gbW1hUvfNhNXVBSjVqfHiiZFQwCeWcpbkq3DvVSvS8jwrqwuwtrYQj+ztwQe3NmS9Q260MTFk/qOAbx6jgI8QQqRrChvNkFDAZ0vP3jSjQY3OCN0tkzVmF7JqMzt0iqqClw+anQmP/egZd6AhQjmnqK1Sj6faB8E5B2Ps3ND1HMjwkXM0SjnqS/JRX5LddYiNW06N2BMucR+zu3Gk34LPvGVpJpaWFJmM4dK2Mjx7dAhefwBKuQw/23EaJ4Zs+PXNqZVyznTjeXX43N+O4PUzE7hgSXZ/kOIYGAr4cg8N0ZjHQgHfPKjbJoSQ+S40miHBxi3jU+kJ+Mp06rhdOjnnePxgv6TmLkPBGXzRMnyVYRm+RJ0dn4rYsEXUVmGAzeULjX0QG9uUUYaPJKHFKIxmSGYf3yvBUtBtrfNrtNelbeWwuXw40D2JowMW/GLHaVy7rhqXL0+9lDPcO9dUwaBR4JG9PWl93GQM0tD1nEUB3zxmdVGGjxBCpCrOV8GgUSQ8miFtJZ0GDWwuX8w2+EcHrLj7scP43WvdcR9vKDhyIVqGr1yvhowlPnzd7vZh1OaOuH9PtKxSyMh0BvfxhUo6KcNHklBdmId8lRynhhPv1DlfxjHMdFFLKVRyGZ47NoTPPnYYRfkq/L93Jt+VMxqNUo73bqjFc8eG0j7nM1FDFifKdGooE9gjTeYH+onNY1TSSQgh0jHG0FimSyjg8/kDmHR40pPhCwZDsQ7Knj48AAA42D0Z9/GGogxdFynkMhj1moSHr/cEO3RGmsEnEktEO4L7+EZtbuQp5chXJd/YhixejDE0G3UJd9ENjWNYmv1xDDPlqxU4f0kJHt7TjRNDNnz73asytsfwA+fVwevnePRAX/wbZ5DJ4qKGLTmKAr55jAI+QghJTFNpPs6MSs8iTDg84Dw9mStjnFl8gQDHPw6bAADt/Wa4fbEHYpusLijlDMUxDiIrCzUJz+LrHhNm8EUaySDSa5SoKcrDieCexLHgSIZsN40guaulXJ9whi80jqF1foxjmOmyNiM4B96dgVLOcM1GHS5oKsGf9/XCH+AZe554TBYXKqmcMydRwDePWZxeqBSylFqFE0LIYtJUmo9BiwtOT+xgSjRmC87gS8PMttDw9SidOvd1T2DI6sKVqyrh8QVwdMAS8/GGLS6UGzQxMxtVBXkJ7+ETZ/DF2sMHCPv4TgSHdgtD1+dHh0SSm1qMOozY3LA4vJLvs/PEyLwaxzDTNeuq8bGLm/DVd6anK2csN55fh/5JJ145lfx4i1RwzmEyO1FZSAFfLqKAbx6zOr2U3SOEkAQ0lglBjBjUxCM2I0lPhk84EIrWuOXpw4PIU8rxxbe3AQD2xynrHLJGH8kgqiwQMnycSz/r3z02BaNeHXf22rJKPc6MTcHl9WPUlhtD18n8tTTUqVN6WefOk6PzahzDTAV5SnzpHcvmpLneW5dXoFSnwiOv92b8uSKxunyY8vipQ2eOooAvS6R8OFucXhg0NDmDEEKkCnXqlLiPLxTwpWEPX0m+CnIZi1jS6fEF8K83Tbh8eTlqi7VoLM3HgXgBn8WF8jgHV1WFeXD7AphMIGvSHadDp6itwgB/gOP0iB1jdg8FfCQlzaFOndLKOsVxDNvnaTnnXFMpZLh+Yy1eOjGccBl3OvSOC6Xg1YXRS8HJ/EUBXxYcG7Rg5b3Pob3PHPN2FsrwEUJIQsRARmrAN24XSjrT0aVTJmMo1akiNm3ZdXoUZocXV6+pAgBsrC/CwZ4JBKLsx+GcY8gaf79MVeG5WXxSnR1zoKE0/kFbW7BT59EBCyam0tPYhixe1YV50KrkkjN858YxUMAnev/mOnAAf9k391m+17rGAACbGorm/LlJ6ijgy4KfvHAKUx4/XjoxEvN2FPARQkhi8tUKVBg0OCNxFt+Y3Q2VQgZ9nPJGqYx6TcSSzqfbB1GQp8TFS4VZYpsaijHp8OLMWORsh9Xpg8sbiNqhU1RZkNgsPrvbhzF77JEMooaSfKgVMuw6LRzoUYaPpEImC3bqlJjhE8cxrKgyZHhluaO2WIttS8vwl/198PoDc/rcr5wcRVuFHkZq2pKTKOCbYyeGrHj++DAA4ED3RMzbUsBHCCGJayzNx9kogdRMo3Y3ynTp6z5ZplfPatri9Pjx/PFhvH1lBVQK4WN3Y/AsebR9fCZr7Bl8IrGBgskiLcPXPSatYQsAyGUMrRV6vCYGfJThIylqMeolZfjm8ziGbLvxvHqM2Nx4sWN4zp7T4REGzIsnrEjuoYBvjv18Rxd0agXeva4ab/SaY56hsTp9FPARQkiCGsvyEyrpTEc5p8ioV8/K8L14YhgOjx/vCpZzAkJQWpKvirqPLzSDL87Z9NJ8NZRyhkGztAyf1A6dorYKfWh/IA1dJ6lqKddh2OoOjZ2Kpr1vfo9jyKbtbUZUFWjw+z09c/ace89MwOMP4OIWCvhyFQV8c6hr1I5njgzipgvqcfmycji9fhwbtEa8bSDAYXVRho8QQhLVVJqPSYcXk1OeuLcds7vTujfNqFdjYso9bVbWU+2DMOrVOK+pJHQZYwwb6otwoCdypYcY8JXHCfhkMoaKAumz+HqCjRek7OEDhMYtIsrwkVQtLRcat5yOk+V7uXN+j2PIJrmM4ZYtDdjdNY4j/eY5ec6XT45Co5SFKhNI7qGAbw79YkcX1AoZPnxhY+iXJlpZp83tA+eAgQI+QghJSKhTp4TRDGNpni9XZtAgwIHxYPdPi9OLlztHcdXqKshnlKZtaihGz7gjYlfPIau0gA8Q9vFJLek8GxzJoFVJ27MoNm4BaA8fSV2LUXg9nRiKHfDtPDmKdXVF83YcQ7Z94Lw66DUKPPBy15w83yunRnFeYwnNhc5hFPDNkd5xB55sH8CN59WjVKdGuUGDumIt9kcJ+KzBcgfK8BFCSGJCAV+cxi2cc4zb09t9UsyCiWWdzx0dgscfwLvWVs26rXji72CEss5hqwulOlVoz18sVQUa6SWdY1OSGraIxAyfXq2ggz2SsurCPFQX5uHb/zqBZ980RbyNOI5hG+0Xi0qvUeKm8+vx7NEhnBmVtl85Wf2TDpwZnaL9ezmOAr458suXuyCXMXz04qbQZRsbinCgezLiTD4LBXyEEJKU2mIt5DIWdx+fxemFL8BRks6SToMY8AkB2NOHB1FfosWamoJZt11RVQCNUhaxcYvJ4orbsEVUVZiHYatrWhlpNN3jDjRK3L8HAMX5KpQb1JTdI2khkzE8evsFaDbqcMcjh/C1fxyDxze9lwGNY5Dmg1sboZTL8NCrZzL6PK+eEpo2UXltblv0AZ/XH8CIVdqZ0WQNmp3428E+3LCxdlp5zqaGYoxPeSIelFDARwghyVHKZagr1sYN+M4NXU9v0xYAGLG6MWJzYXfXGN61pipiF1CVQoY1NYUR9/ENWVxxG7aIKgvz4Avw0NcTjc3llTySIdzWJaVYTq3xSZpUF+bh0Y9dgA9ubcDvXuvGDQ/uwUDYHtQdNI5BkjK9GtdvrMHjBwcwnMHj2FdOjqKyQINmoy5jz0Eyb9EHfO/95W589m9HMvocD75yBpwDH7ukadrlm0L7+Gaf3RUDPtrDRwghiWsszceZuAGf0NQlnc1IxEzYqM2Nfx4xIcAxrTvnTJsainFs0Iopt2/a5UNWl6T9e4BQ0gnEH74eathSIq1hi+gH16/Bzz6wPqH7EBKLSiHDve9cgV/cuB6nhu248qevYkfnCPwBjldpHINkH71oCXyBAH6762xGHt/nD+C102O4uKUsbaNrSHYs+oBvc2MxXu8an/Vhmy4jNhf+vK8X166vRk3R9A/ZJWU6FGmVEffxUYaPEEKS11iaj+6xKQRilDmKGbF0lnSqFXIUapUYsbnx9OFBtFXo0VKuj3r7jQ1F8Ac4DveZQ5e5vH6YHV5USizplDp8PTSSIcEMHx3okUx5x6pK/OMTF6LCoMEHf7cfd/3lDZgdXmynck5J6kq0uGp1Ff74eg8sjtijLpJxuN8Cq8uHi5ZSOWeuW/QB36Vt5fD4A9gVHCybbr9+9Sy8/gD+e1vzrOsYY9jYUIwDPdEzfBTwEUJI4hpL8+H0+jEcoQOmaMyW/pJOQMgYHuyZxBu95ojNWsKtry8CY9MHsA8n0KETAKoKpWX4xKHr9Qlm+AjJpMbSfDz58a24YWMtnjligowBF9F+Mcluv2QJpjx+/HFv+ufyvXJyFDIGXNhMP49ct+gDvo0NRdBrFHipYyTtjz0x5cEfX+/Bu9ZURT2juqmhCGfHpma15bY4vVDIGLQq6opGCCGJapLQqXN8ygMZA4rS3PrdaFDjuEmYsfrO1bEDPoNGidZy/bR9fGKmTmrTloI8JfKU8ridOs+OOVBukD6SgZC5olHK8d33rsZP3rcW/++q5TSOIQHLqwzY1lqG3+46C5fXn9bHfvXUKFbXFNLPYwFY9AGfUi7DJUvL8FLnSMzSn2T8dtdZOL1+fHz77OyeaGNDMYDZbbmtTmHoOpXSEEJI4hrLhIAv2j4+m8uLHZ0jKDdo0r5XyKgXArUN9UWoLY6fTdvUUIxDPZPw+YVuhWKGT2pJJ2MMlYWauLP4esan0JBAh05C5trVa6tx69bGbC8j59xxyRKMT3nw2IG+tD2mxeFFe5+ZunMuEIs+4AOAy5YZMWpz4+igJW2PaXF68fDubrx9ZUXM/Rsrq/5/e3cfHVV953H8/Z0kJCGEJBAChAAhoAZQHkKgPm3F0FXXrU8tulZW7bHdrU97ds9qV7vdo9v27B/Wbbunq671FKvdstWKbU+7RV1EK1blIXiQZwERJDyEp5AASUhIfvvH3MCIk2QmyU3uXD6vc+ZkcufeuXc+DL/Md373/n55ZKZ/dljueq/gExGR5I3MzSI7Iy3uSJ3Hmlu589lVbNl3jEevm9Ln++4YqbOrwVpiVZYWcKKl7fRk1B09fIme0glQnJfN3gSu4ZuQ5PV7IhJ8cyYMo2JcPj9ZvuP0F0e99c5Hh2h3aP69kOi24DOzsWb2ppltNrONZvb33vLHzWyLma0zs9+YWb7vR+uTK84vImKwrA9P6/zFil0cO3mqy949iI5UNWPsZ4flrm9q1QidIiI9FIkYpYU5n5mUuKG5lTueXcW6mnqeuG0m11w4us/3PaloCDmD0rj2osSee7Z3pke1N4DX/vpmhmSmk5uV+N+A4vws9nVxDV90SoYWxquHTyR0zIx75k6ipq6JP3QyoX2y3t52kNysdGaMze+T55OBlUgP3yngAefcZOBi4D4zmwIsBS50zk0DtgLf8u8w/TUsZxAV4wp4Y0vfFHzt7Y5frvqEyyYNZ2rxZyfbPVu8Ybkb1MMnItIrZYU5n+rha2hu5Y6Fq1hfU8+TCyp8KfYAvlxRwnv/PC/hycqL87MpzstitTeAV21DMyOHJjdy6Oi8bA4eP/mZSaw7dEzJMKFQA7aIhNG88iLOKxrCf/3xI5zr/BKlo40ttHVzCZNzjuVbD3HZxELS03QyYBh0+6/onNvnnHvfu38M2AyMcc79n3Ouo0JZAZT4d5j+q5pcxPo99X0yeeWKjw9TU9fELZVjE1q/Y1jutTHDcuuUThGR3plQmMPuuiZaTrVT39TK7QtXsXFvPU8tqODqqaN8228kYgxNoncOotdzV+88gnOOffXNCQ/Y0qE4Pwvn6PRvWEfhm+yUDCKSGiIR4+4rJrJl/zH+uPXg6eUNza28vqmW7/x+I1f/aDkzvruUexet6XLcio8OnmDP0SZNxxAiSZXtZlYKzARWnvXQXcArnWzzt2ZWbWbVBw8ejLdKIFSVR+d8ebMPevkWV9eQm5nOVVMS+0Axa3wBEeNT8/Gp4BMR6Z0JhTm0tTs27K3n9oUr2bS3nqcWzOIqH4u9nppdWkBtw0lq6pqobWhm1NDspLbvbC6+5tY2Xl5Tw5NvbidiMH6YCj6RsLp+RjHFeVn8aOlWHn9tCzc++Q4zv7uUr/+8mv9Z+QlFQzOZP6uE1zbW8tirWzp9nre3RT+vf/48Xb8XFgmPzWxmQ4CXgX9wzjXELP820dM+F8Xbzjn3DPAMQGVlZd8Og9mHLhiZy5j8bJZtOcCtc8b1+HmONbeyZMM+bppZQnaCUyrkZmVQPmoo1d7ALc45GppPMTRbQ2eLiPRUx0iddz23msaTbTz917OYN3nkAB9VfB0jNq/8+AgHjp1kVF5yp3R2zMXXMVLnx4dOsGjFLha/X8PRxlbKCnP44S0zEv67JCKpJyMtwt98vozv/H4TG/c2ML0kj3vnTuTSiYVUjM8nMz36/3/woDR+snwHEwpz4n7mXb71IGWFOQmNMiypIaGKwswyiBZ7i5xzv45ZfifwRWCe6+qE4RRgZlSVF7F4TQ3NrW1kZfTsj+KS9ftobm3n5srkznCdXVrAS2tqONXWTlNrG23tTj18IiK90DEXX+PJNp6+vYKq8mAWewDnj8wlNzOdVzfsp63dMSqvZz18S9bv41fVu3ln+2HSI8bVU0ex4OJxXFI2XNP8iJwD7ryklIvG5HHBqNxOB3565ItT2HW4kX/57QbGDhvMZTETq5881caKHUe4JcnPsRJsiYzSacBCYLNz7ocxy68BHgKud841+neI/adqchFNrW2s2HG4x8/xUnUNE0fkMDPJUY0qS4fR2NLGpn0N1De1AqjgExHphfzBg7jvyok8+9XZgS72ANIiRsX4ApZ7196MSmJKBoCczHTyB2fw2sZadh5q5MGrzufdh6t4ckEFl04sVLEnco6IRIzK0mFdjvKbnhbhidtmMnHEEO7+xRq2HzgzmvGanXU0tbZpOoaQSeQavsuA24EqM1vr3a4FngBygaXesqf9PND+cEnZcLIz0no8WufHh05QvauO+bPGJv3HtbK0AIDVO+toaIqOhaOCT0Skd755dTmXp8jEwbNLC2jx5tBKtuAD+PGtM1l4ZyXL/+lK7q86j6IePIeInBtyszJY+NVKMtMj3PXcao6caAHgrW0HyUgzLi4bPsBHKH0pkVE6/+ScM+fcNOfcDO+2xDk3yTk3NmbZ3f1xwH7KykjjskmFLNt8oMshbTuzeM1uIgZfqhiT9Laj87IpKcimeueR0z18modPROTc0XEdH5D0KJ0QnSB53uSRpEXUmyci3SspGMwzd1Syv6GZb/x3NSdPtbF86yFmjS8gJ1PjSISJJtc4y7zJRew52sTW2uPdrxyjrd3x8po9XHH+CEb28FvV2aXDWL2zTqd0ioicg6aX5JMeMTLSjOE5gwb6cETkHFAxroAf3Dyd1TvruG/R+2ze16DTOUNIBd9ZrrwgOj1Dsqd1/mn7IfY3NDN/VmJz78VTWVrAoeMnWVdzFFDBJyJyLskelMaFY/Ioys0iol46Eekn100v5oE/P5/XN0c/+2o6hvBRf+1ZRuVlceGYobyxpZZ75k5MeLvFa2rIH5zBF6YU9Xjfc7zTeZZ5/+FU8ImInFu+efUFHPaupRER6S/3V01id10jq3fWMWX00IE+HOljKvjiqCofyRNvbKPuRAsFCZxWU9/Yymsb9/OV2WNPz3HSExNHDCF/cAYf1h4jLWIM0fnTIiLnlNjh0UVE+ouZ8diXp9Hu0BkGIaRTOuOYV15Eu4O3vOGxu/O7dXtpOdXOzZU9P50TvKF0x0dH6xyala5htEVERESkX5iZBn0KKRV8cVw0Jo/CIZksS/A6vsVraigflcvU4t53gXeM0qYROkVEREREpLdU8MURiRhV5SN468MDtHpzInVmW+0xPth9lPmzSvqkR262Nx+frt8TEREREZHeUsHXiarykTQ0n2LNrrou13tpTQ3pEeOmmcnPvRfPhWPyGJQeUcEnIiIiIiK9poKvE5efV0hGmnU5PUNrWzu/fn8PVeVFDB+S2Sf7zUxPY8HnxjH3gp6P9ikiIiIiIgIq+Do1JDOdi8uG8/rmWppa2uKus3zrQQ4dP8n8WSV9uu9Hr5vK1y6f0KfPKSIiIiIi5x6N+9+FL0weyaO/28jkR16lYHAGo/OyKc7PZkx+FqPzs3lj8wEKhwziynL1xomIiIiISPCo4OvCrXPGUpAziN1HGtl7tIm9R5uoqWtk5ceHOdZ8CoBvXFFGRpo6SkVEREREJHhU8HUhMz2N66cXx33sWHMrB46dZGzB4H4+KhERERERkcSo4Ouh3KwMcrM0kqaIiIiIiASXzkUUEREREREJKRV8IiIiIiIiIaWCT0REREREJKRU8ImIiIiIiISUCj4REREREZGQUsEnIiIiIiISUir4REREREREQkoFn4iIiIiISEip4BMREREREQkpFXwiIiIiIiIhZc65/tuZ2UFgV7/tMHGFwKGBPoiQU8b+Ur7+U8b+U8b+Ur7+U8b+U8b+Ur7+KwRynHMj+muH/VrwBZWZVTvnKgf6OMJMGftL+fpPGftPGftL+fpPGftPGftL+fpvIDLWKZ0iIiIiIiIhpYJPREREREQkpFTwRT0z0AdwDlDG/lK+/lPG/lPG/lK+/lPG/lPG/lK+/uv3jHUNn4iIiIiISEiph09ERERERCSkUqrgM7NrzOxDM9tuZg/HLH/RzNZ6t51mtraT7YeZ2VIz2+b9LPCWL4jZfq2ZtZvZjDjbL/L2v8HMnjWzDG+5mdmPveNaZ2YV/iTgvwBnXG5m75nZSTN70J9X3z8CnPEC7/27zszeNbPp/iTgrwDne4OX7Vozqzazy/1JwH8+ZpxhZs+b2Xoz22xm3+pk+wlmttLb/kUzG+QtV1t8Zj2/Mg5FWxzgfEPRDkOgM1ZbfGY9XzKOeXy2mbWZ2fw+fNn9Jqj5mtlcM6uPOYZHun0xzrmUuAFpwEdAGTAI+ACYEme9HwCPdPIc3wce9u4/DDwWZ52LgB2dbH8tYN7tl8A9Mctf8ZZfDKwc6LxCmHERMBv4N+DBgc4qpBlfChR49/8iFd/HAc93CGdOo58GbBnovIKWMXAb8IJ3fzCwEyiNs/2vgFu9+0+rLe7XjFO+LQ54vinfDqdAxmqLfc445vjeAJYA8wc6rzDlC8wF/jeZ15NKPXxzgO3OuR3OuRbgBeCG2BXMzIBbiH7IiucG4Hnv/vPAjXHW+Upn2zvnljgPsAooiXnen3sPrQDyzWx0wq8sOAKbsXPugHNuNdCa1CsKniBn/K5zrs5bbQVn3t+pJMj5HveWAeQAqXoBtZ8ZOyDHzNKBbKAFaIjz3FXA4jjbqy0+w5eMQ9IWBznfMLTDEOyM1Raf4VdbDPB3wMvAgSRfV1AEPd+kpFLBNwbYHfN7jbcs1p8Btc65bZ08x0jn3D4A72dRnHX+is7/4YBoVyxwO/BqEseWCoKccVikSsZfI9pTkmoCna+Z3WRmW4A/AHd1tX2A+ZnxYuAEsA/4BPh359yRs7YdDhx1zp2Ks3+1xWf4lXEYpEq+qdoOQ8AzVlt8mi8Zm9kY4CaivVKpKrD5ei4xsw/M7BUzm9rdi0mlgs/iLDv7W5lOv3VPaAdmnwManXMbuln1KWC5c+7tJI4tFQQ547AIfMZmdiXRDxoP9fQYBlCg83XO/cY5V070W7rv9fQYBpifGc8B2oBiYALwgJmVJbF/tcXd623GYRD4fFO8HYaAZ6y2uFu9zfg/gIecc2092HdQBDnf94HxzrnpwH8Cv+1uh6lU8NUAY2N+LwH2dvzidYt+CXgxZtnPvIsZl3iLajtO7/F+nt3NfCvdf2v/KDAC+MdEjy2FBDnjsAh0xmY2DfgpcINz7nASrysoAp1vB+fccmCimRUm8qICxs+MbwNedc61OucOAO8AlWft/xDRUzXT4+xfbbH/GYdBoPMNQTsMAc+4g9pi3zKuBF4ws53AfOApM7uxF691IAQ2X+dcg3PuuHd/CZDR7XvYBeDCyERuQDqwg2gl3HHx5NSYx68B3urmOR7n0xdPfj/msYj3j1vWxfZfB94Fss9a/pd8eqCAVQOdV9gyjnn8X0nRgQKCnjEwDtgOXDrQOYU030mcGSigAtjT8Xsq3fzMmGhvxs+8tjQH2ARMi7P9S3z6QvZ7vftqi33OOObxlG2Lg5wvIWiHUyBjtcU+Z3zWOs+RmoO2BDZfYFTMe3gO0dNCu3wPD3igSYZ/LbCV6Kg5347zhrq7m+2HA8uAbd7PYTGPzQVWdLP9KW/fa73bI95yA570HlsPVA50ViHMeBTRD9oNwFHv/tCBzitkGf8UqItZXj3QWYUs34eAjd6y94DLBzqroGVMdPS8l7ycNgHf7GT7MqID4mz31s/0lqst9j/jULTFAc43FO1wwDNWW+xzxnGOI+UKviDnC9zvbfsB0cGduv2CqKM6FBERERERkZBJpWv4REREREREJAkq+EREREREREJKBZ+IiIiIiEhIqeATEREREREJKRV8IiIiIiIiIaWCT0REREREJKRU8ImIiIiIiISUCj4REREREZGQ+n8mi6OCkYzI1QAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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x36/X67Fz50689dZb2Lx5M6677jr88Ic/DGeFRCqVCpdccgkAYPny5VCr1VAqlVi+fDm6urqSOqf77rsPzzzzDACgp6cHx48fR1lZ2aTH/Oc//8HOnTtx2mmnAQBcLhcqKiqwdetWbNy4EUajEQBw3XXX4dixYwk/58svv4yXX34Zq1evBgDY7XYcP34cDQ0NaG5uxvLlywEAS5cuxQUXXADG2LSv7YorrkBBQQEKCgqwadMmbNu2DW+//XbM4zY2NmLDhg3hj//HP/6BBx98EH6/HwMDAzh06BBWrFiR1PfummuugVwuh91ux7vvvotrrrkm/D6Px5PUsQghhBBCyCz1yitAQYGQlbv5ZuCznwVUKuG/nTuBO+8EbrkFuP9+ITs3hzN0eRXQ5YpcLsfGjRuxceNGLF++HH/605+mBXRKpTI8NVEmk4VLNGUyGfx+PwBAoVAgGAyGPybayPzXX38dr776Kt577z1otVps3Lgx6uM457jpppvwgx/8YNLbn3322ZSmN3LO8Y1vfAOf/vSnJ729q6sr/LXE+9qA6VMjGWNxj6vT6cJ/7+zsxE9+8hNs374dJSUluPnmm2OuFIj8PFMfIx4zGAyiuLgYe/bsSfSlE0IIIYSQueTAAeD55wGtFvj2t4Wg7eMfn+iZu+su4Mknhb9feumcL7vMq4AuXiYtW44ePQqZTIa2tjYAwJ49e9DY2JjSsZqamvDb3/4WwWAQfX194f6zSBaLBSUlJdBqtThy5Ai2bNkSfp9SqYTP54NSqcQFF1yAK664AnfddRcqKiowNjYGm82G008/HXfeeSdMJhMKCwvxxBNPYOXKlQnP7eKLL8a3v/1tfPSjH4Ver0dfXx+USmVSX99zzz2Hb3zjG3A4HHj99dfxwx/+EAUFBZKOa7VaodPpUFRUhKGhIbz44ovYuHEjAMBgMMBms4UHt1RWVuLw4cNYuHAhnnnmGRgMhmnHKywsRHNzM5544glcc8014Jxj3759kr4XhBBCCCFkFvvSl4T/P/qo0Du3adNE0LZ9++TgbdOmibdTQDc32e12fP7zn4fZbIZCocCCBQvCg0qSddZZZ4XLF5ctW4Y1a9ZMe8wll1yCBx54ACtWrMDChQsnlSTedtttWLFiBdasWYNHH30U3/ve9/C+970PwWAQSqUSv/nNb7Bhwwbcc889OOOMM1BdXY01a9aEh6XE8773vQ+HDx/GGWecAUAoNf3rX/8KuVwu+etbv349LrvsMnR3d+Pb3/42ampqUFNTI+m4K1euxOrVq7F06VK0tLTgrLPOmvR1v//970d1dTU2b96MH/7wh7j88stRX1+PZcuWwW63Rz2fRx99FLfffju+973vwefz4frrr6eAjhBCCCFkLjt1CvjPf4APf1gI5oDJQdtXvzr9Y+Z4ySUT+vJmxrp167g4NVJ0+PBhLF68eMbOgaTmnnvugV6vx3/913/l+lSSQj9fhBBCCCFzyJ13Ar/9LXDyJFBfn+uzSQljbCfnfF2mjjfv1xYQQgghhBBCZoHRUeChh4CPfWzWBnPZMO9LLok099xzT65PgRBCCCGEzGe//rUw1TJaWeU8Rhk6QgghhBBCSH6z24Ff/Qq44gqA2mkmoYCOEEIIIYQQkt9+/3tgbAz4+tdzfSZ5hwI6QgghhBBCSP7yeoGf/Qw491wgYkI8EVAPHSGEEEIIISR/PfYY0NMD/O53uT6TvEQZOgByuRyrVq3CsmXLcM0118DpdKZ8rJtvvhlPPvkkAODWW2/FoUOHYj729ddfx7vvvhv++wMPPIA///nPKX9uUVdXF5YtWzbpbffccw9+8pOfJHWcTJ0PIYQQQgghKQkGgR/9CFixArjkklyfTV6igA5AQUEB9uzZgwMHDkClUuGBBx6Y9H4pi7uj+f3vf48lS5bEfP/UgO4zn/kMPv7xj6f0uTLN7/fn1fkQQgghhJB54t57gc2bhT+/8AJw+DBw+eXAj3+c2/PKU7MroIv8xxVt3iy8PUPOOeccdHR04PXXX8emTZvwkY98BMuXL0cgEMBXvvIVnHbaaVixYgV+F0r5cs5xxx13YMmSJbjsssswPDwcPtbGjRshLlJ/6aWXsGbNGqxcuRIXXHABurq68MADD+DnP/85Vq1ahbfeemtSFm3Pnj3YsGEDVqxYgQ996EMYHx8PH/NrX/sa1q9fj/b2drz11ltJf43xjv3Nb34T5513Hn75y1+Gz6e/vx+rVq0K/yeXy3Hq1CmcOnUKF1xwAVasWIELLrgA3d3dAIQs5Re+8AWceeaZaGlpCWcsCSGEEEIISei004BrrwVeew34wQ+AykrgwQeFt5NpZldAJ/7jikHd5s3C3zP0j+v3+/Hiiy9i+fLlAIBt27bh+9//Pg4dOoSHH34YRUVF2L59O7Zv346HHnoInZ2deOaZZ3D06FHs378fDz300KSMm2hkZASf+tSn8NRTT2Hv3r144okn0NTUhM985jO46667sGfPHpxzzjmTPubjH/84fvSjH2Hfvn1Yvnw5vvOd70w6z23btuEXv/jFpLdHOnHixKQgLDLrGO/YZrMZb7zxBr785S+H31ZTU4M9e/Zgz549+NSnPoUPf/jDaGxsxB133IGPf/zj2LdvHz760Y/iC1/4QvhjBgYG8Pbbb+OFF17A12kaESGEEEIIkWrTJuAf/wCuugrYsgVwOIS/b9qU6zPLS/k1FOWLXwT27In/mJoa4OKLgepqYGBA2EPxne8I/0WzahXwi1/EPaTL5cKqVasACBm6T37yk3j33Xexfv16NDc3AwBefvll7Nu3L5xtslgsOH78ON58803ccMMNkMvlqKmpwfnnnz/t+Fu2bMG5554bPlZpaWnc87FYLDCbzTjvvPMAADfddBOuueaa8PuvuuoqAMDatWvR1dUV9Ritra3YE/G9FBeDJzr2ddddF/O83nnnHfz+978PZwXfe+89PP300wCAG2+8EV+NWPJ45ZVXQiaTYcmSJRgaGor79RJCCCGEEDLJpk1AQwOwfz9wxx0UzMWRXwGdFCUlQjDX3S38I5eUpH1IsYduKp1OF/4z5xy/+tWvcPHFF096zL/+9S8wxuIen3Oe8DHJUKvVAIRhLn6/P2PHBSZ/zZEGBgbwyU9+Ev/85z+h1+ujPibyaxTPERC+fkIIIYQQQiR76SUhmFu9WthB9773UVAXQ34FdAkyaQAmyiy//W3g/vuBu++ekX/ciy++GPfffz/OP/98KJVKHDt2DLW1tTj33HPxu9/9Dh//+McxPDyMzZs34yMf+cikjz3jjDPwuc99Dp2dnWhubsbY2BhKS0thMBhgtVqnfa6ioiKUlJTgrbfewjnnnIO//OUv4YxaulI5ts/nw7XXXosf/ehHaG9vD7/9zDPPxN///nfceOONePTRR3H22Wdn5BwJIYQQQsg8Jl7vA8IgFJlM+DuVXUaVXwFdIuI/rviPuWnTjP3j3nrrrejq6sKaNWvAOYfRaMSzzz6LD33oQ3jttdewfPlytLe3Rw2OjEYjHnzwQVx11VUIBoOoqKjAK6+8gg984AO4+uqr8dxzz+FXv/rVpI/505/+hM985jNwOp1oaWnBI488krGvJdljv/vuu9i+fTvuvvtu3H333QCEzOR9992HW265BT/+8Y9hNBozeo6EEEIIIWSe2r5daJs6dgzYuBGQy4Xr/e3bKaCLgs1kOdy6deu4OPVRdPjwYSxevFjaAe69VxiAEvkPuXmz8I8b0b9FiCipny9CCCGEEJJ7VitQUQHcdhtw3325PpuMY4zt5Jyvy9TxEmboGGP1AP4MoApAEMCDnPNfMsYeB7Aw9LBiAGbO+apMnVhU0YI2MVNHCCGEEEIImf2eew7weIDrr8/1mcwKUkou/QC+zDnfxRgzANjJGHuFcx4eh8gY+ykAS7ZOkhBCCCGEEDJPPPYY0NgInHFGrs9kVki4h45zPsA53xX6sw3AYQC14vuZMNrwWgCPZeskCSGEEEIIIfPA6CjwyivAddcBGZwSP5cltVicMdYEYDWArRFvPgfAEOf8eKonQWPtSTbQzxUhhBBCyCzz1FOA3w/ccEOuz2TWkBzQMcb0AJ4C8EXOeeSs/RsQJzvHGLuNMbaDMbZjZGRk2vs1Gg1MJhNdfJOM4pzDZDJBo9Hk+lQIIYQQQohUf/87sHAhsHJlrs9k1pC0toAxpoQQzD3KOX864u0KAFcBWBvrYznnDwJ4EBCmXE59f11dHXp7exEt2CMkHRqNBnV1dbk+DUIIIYQQIkVfH/DGG8KeaSq3lEzKlEsG4GEAhznnP5vy7gsBHOGc96Z6AkqlEs3Nzal+OCGEEEIIIWQueOIJgHOabpkkKSWXZwG4EcD5jLE9of8uDb3vetAwFEIIIYQQQki6HnsMWL1aKLkkkiXM0HHO3wYQNefJOb850ydECCGEEEIImWdOngS2bQN+9KNcn8msk9SUS0IIIYQQQgjJuMcfF/5/3XXxH0emoYCOEEIIIYQQkluPPQaceaawUJwkhQI6QgghhBBCSO4cPAjs30/DUFJEAR0hhBBCCCEkd/7+d0AmA665JtdnMitRQEcIIYQQQgiZWffeC2zeLKwp+PvfgU2bgMOHhbeTpFBARwghhBBCCJlZp50GXHst8LvfAR0dwrqCa68V3k6SknBtASGEEEIIIYRk1KZNwD/+AVx2mVBu+cgjwmLxTZtyfWazDmXoCCGEEEIIITNv3Trh/8Eg8NnPUjCXIgroCCGEEEIIITPvrrsAlwu45Rbg/vuFnjqSNAroCCGEEEIIITPr+eeBP/wBOP104OGHhfLLa6+loC4FFNARQgghhBBCZtavfiVMuPzNb4S/iz1127fn9rxmIRqKQgghhBBCCJk54+PAtm3AFVcAa9dOvH3TJuqjSwFl6AghhBBCCCEz5+c/BywW4DvfyfWZzAkU0BFCCCGEEEJmhskE/OIXwNVXAytX5vps5gQK6AghhBBCCCEz4yc/Aex24J57cn0mcwYFdIQQQgghhJDsGx4WhqFcfz2wdGmuz2bOoICOEEIIIYQQkn333ivsnft//y/XZzKnUEBHCCGEEEIIya6BAWFFwUc/CixalOuzmVMooCOEEEIIIYRk149+BPh8lJ3LAgroCCGEEEIIIZl3773A5s1AXx/wwAPATTcBPT3C20nG0GJxQgghhBBCSOaddhpw7bXAWWcBgQBwwQXC3//xj1yf2ZxCGTpCCCGEEEJI5m3aJEy1fO45YMUK4M47hWBu06Zcn9mcQgEdIYQQQgghJDve/37g3HOBXbuA22+nYC4LKKAjhBBCCCGEZMeuXcChQ8C3vw3cf7/QU0cyigI6QgghhBBCSOZt3jzRM/fd7wr/v/ZaCuoyjAI6QgghhBBCSOZt3z65Z27TJuHv27fn9rzmGMY5n7FPtm7dOr5jx44Z+3yEEEIIIYQQkk8YYzs55+sydbyEGTrGWD1jbDNj7DBj7CBj7M6I932eMXY09HZaKJHHvP4gnt7VC68/mOtTIYQQQgghhGSIlD10fgBf5pzvYowZAOxkjL0CoBLAFQBWcM49jLGKbJ4oSc/PXz2G+18/Aa1KgUuWVeX6dAghhBBCCCEZkDCg45wPABgI/dnGGDsMoBbApwD8kHPuCb1vOJsnSlK389Q4fvfGCQBAx7ANAAV0hBBCCCGEzAVJDUVhjDUBWA1gK4B2AOcwxrYyxt5gjJ0W42NuY4ztYIztGBkZSfuESXJc3gD+64m9qC4qQIVBjePD9lyfEiGEEEIIISRDJAd0jDE9gKcAfJFzboWQ3SsBsAHAVwD8gzHGpn4c5/xBzvk6zvk6o9GYodMmUv3opSPoHHXgx9eswJKaQhwfooCOEEIIIYSQuUJSQMcYU0II5h7lnD8denMvgKe5YBuAIIDy7JwmScW7HaP447tduPnMJpzZWo62Cj1OjNgRCM7cZFNCCCGEEEJI9kiZcskAPAzgMOf8ZxHvehbA+aHHtANQARjNwjmSFNjcPnzlyX1oLtfha5csAgC0VRjg8QfRN+7K8dkRQgghhBBCMkFKhu4sADcCOJ8xtif036UA/gCghTF2AMDfAdzEZ3KpHYnrey8cxoDFhZ9csxIFKjkAoLVCDwA4PmzL5akRQgghhBBCMkTKlMu3AUzrjQv5WGZPh2TCa0eG8PiOHty+sRVrG0vCb18QDujsuGBxZa5OjxBCCCGEEJIhSU25JPlv3OHF157aj4WVBnzxwrZJ7ysqUKKyUE2DUQghhBBCCJkjpCwWJ7PI3f88iHGHF4/cfBrUCvm09y+o0KNjhAI6QgghhBBC5gLK0M0hLx8cxD/39uMLF7RhWW1R1Me0VRjQMWQDtTsSQgghhBAy+1FAN4c8vasPNUUa3L6xNeZjFlTo4fAGMGBxz+CZEUIIIYQQQrKBAro5Ihjk2NppwlkLyqGUx/5nbYsYjEIIIYQQQgiZ3SigmyOODdsw7vRhQ0tZ3MeJky47KKAjhBBCCCFk1qOAbo7YcsIEADi9pTTu48r0apTqVOigXXSEEEIIIYTMehTQzRHvnTShvrQAdSXahI9dUKGn1QWEEEIIIYTMARTQzQFC/9wYNjTHL7cUtVXocXzYTpMuCSGEkCgCQY6XDw7S6yQhZFaggG4OODpkg1lC/5xoQYUeFpcPo3Zvls9s/vAHgugdd+b6NAghhGTAK4cGcdtfdmLnqfFcnwohhCREAd0csOWktP45UVuFAQBwnProMuapXb04/6dvwOL05fpUCCGEpOnwgPD62GWiG3WEkPxHAd0csCWJ/jkAaKukSZeZdnTQDq8/iJ55kKX72ctH8eqhoVyfBiGEZM2xISGgo8oLQshsQAHdLCf2z50hsdwSACoMahg0ChqMkkHii36/2ZXjM8kuXyCI375+As/u6cv1qZAZ0jPmxAv7+nN9GoTMKDGg6xmb28/phJC5gQK6WS7Z/jkAYIxhQYWeMnQZ1DsuvOgPWNw5PpPs6h5zwh/kGLF5cn0qZIbc/8YJfP6x3XB6/bk+FUJmhMcfCJdazoeqC0LI7EcB3Sw30T8nPaADJiZdkswIZ+gsc/turngTYMROAd18safbDM6BzlFHrk+FkBlxcsSBQJBDq5Kjb3xuP6cTQuYGCuhmuS0nTWgo1aK2uCCpj2urMGDU7sG4I71Jl8NWNz75x+0YS/M4s5nF5YPVLWQvBsxzO0MXDugoQzcvuLwBHA2Vnp0coYCOzA9iueU5beUYsLjgCwRzfEaEEBIfBXSzWHj/nMTplpEWiINRRtLL0m3pHMN/jgxjayhTOB9F3sEdmOMZuhOhgM7m9sPtC+T4bEi2Hei3IBAU9nBRQEfmi2NDNshlDOe2GxHkc783mhAy+1FAN4sdGUy+f060wJiZSZdDoZ6xk/O4HEsst2wp16F/rmfoIm4AUJZu7tvbYwYAGDQKnBylEm0yPxwbsqO5XIeWcuF1kgajEELyHQV0s1iq/XMAUFtcgAKlPO1Jl+IQkBNpZvpmM3EgymlNpRiyusMZjbmGc44Tw3Y0lgnrMaiPbu7b02NGbXEBVtUXUw8dmTeODdnQXqlHfanQykCrCwgh+Y4Culks1f45AJDJhEmX6S4XH7QKwcyJeVyO1Wd2oUApx7LaQviDHKNzNNAZsLjh8AbCKzJmQ4aOc44H3jgR7okhydnTY8bK+iK0lOtwcsQBzufmzQpCRC5vAN1jTrRXGlBVqIFcxmjSJSEk71FAN0ulsn9uqrYMrC4QM3QnR+zz9mKvd9yJupICVBcJgfVc7bcQf1bOaJ09Ad1/Dg/jhy8ewRM7enJ9KrPOqN2D3nEXVtUXo8Woh93jnxX/5oSk48SIHZwD7ZUGKOQy1BRrqOSSEJL3KKCbpY4M2mBx+bChNfmBKKLWCj0GLG7Y3L6UjzFocUPGhCEZo/b5Oemyd9wlBHTFGgBzdxedGNCd3lwGxvI/oAsGOX7y8lEAwJA1v881H4n9cyvritFi1AGY35l4Mj8cHRSy+e2hwWH1JVoquSSE5D0K6GapcP9cc3oZOiD1i7RAkGPY5sHy2qLQceZnH50Q0GlRM9czdCN2FBUoUVmoRqlWlfc9dP/c248jgzaoFTIMWedmkJ1Ne3vMkDFgeV0RWkJDlGgwCpnrjg3boJLL0Fgm3MSoKylAD+2iI4TkOQroZqktJ01oLNOiJoX+OVFbpQEAcDzF/qJRuweBIMeZC8oBzM+x5la3DxaXD3UlBSjWKqFRyuZ0hm5BhR6MMRgN6rzO0Hn9QfzslWNYXF2ICxdXYjiPzzVf7em1oL3SAK1KgepCDTRKGTrn4e84mV+ODdrQYtRBKRcuj+pLtBixeWhNCyEkr1FANwuF98+lkZ0DgPqSAqgUspT76MTAZXV9MTRK2bzM0Ik76GpLCsAYQ01RwZzdRXdi2B5ed5HvAd3jO3rQPebEVy5uR1WRBkNW97zt8UwF5xx7e8xYVV8MQBii1FSmm9frScj8cGzIjvbQzU4AqC8VpvpS2eXsEAhyOL3+XJ8GITOOArpZKBP9cwCgkMvQUq5LOaAbDAUuNcUFaC7X4+Q8DOjElQV1JcKLfnWxZk7uoht3eGFyeLEgVKZr1OdvQOfyBvCr/xzHusYSbFpYgapCDZzeAOweepGXqsvkhMXlCwd0ANBqnJ+/42T+sHv86DO7wv1zgFByCYDKLmeJ+1/vwPk/eWPOrg8iJJaEAR1jrJ4xtpkxdpgxdpAxdmfo7fcwxvoYY3tC/12a/dMlQGb650TC6oL0MnTVRRq0GOfn3Xvxrq34ol89RzN04kLxcEBnUGPE7snLrNcf3+3CsM2Dr16yCIwxVBSqAYD66JKwp2ccALAyIqBrMerQM+6C1x/M0VkRkl1i+0HUDN0YZehmg3c6TBi0umlVDZl3pGTo/AC+zDlfDGADgM8xxpaE3vdzzvmq0H//ytpZkkney0D/nKitwoCecSdc3uT7AwYtbqjkMpTqVGg16tEz5oTHn5k+A845hmfBBXjfuAsapQxlOhUAoKZIg2GbB75A7i96fYFgxgIuMYsbGdB5/UFY3fmV9bK4fHjgjRPYtNCI9c1CBruyUJg+SpMupdvbY4FWJZ90Ydti1CEQ5Ogem383bsj8cHxIeJ6L/Lk36tVQKWSUoZsFgkGOA/0WAMCu7vEcnw0hMythQMc5H+Cc7wr92QbgMIDabJ8YiS4Y5NiWgf450YIKPThPbULloNWNqiINGGNoNeoQ5MApU2buYr55fBQbfvAfnDLl98WjOOGSMQYAqC4uAOe5zwY5PH5cdt9b+O4LhzJyvI5hOzRKWXiJvdEgZL3yrezywTdPwOLy4b8uXhh+20RAl/83CPLFnh4zltUWQS5j4be1lKc3FZeQfHd0SJiKK2blAKF/tK6kgHroZoFTY07YQjcZd56igI7ML0n10DHGmgCsBrA19KY7GGP7GGN/YIyVZPrkyHSHB62wuHzh5c7paqsUL9KSD+gGLG5UhS6WxYu9TPXYHOizIMgn7pjmq16zM1xuCQjlp0Dud9F99/lDODZkx4E+S0aO1zFsR0u5HrLQBX4+BnTDNjf+8HYXPrCyBktrisJvrzCIJZf5c675zOMP4FC/Fasjyi0BoDm0i65zHpZWk/nh2JANbZX6STcyAKFHmpaL57/9ode7hlItdnebc3syhMwwyQEdY0wP4CkAX+ScWwHcD6AVwCoAAwB+GuPjbmOM7WCM7RgZGUn/jOe5LSfHAACnt6Q3EEXUVKaDXMZSCpwGLUKGDkDGFw+LKxB68vyuqLhUXCSWweZyF92L+wfw+I4eaJSyjA1oEVcWiMQgKZ920f36tQ54A0F86aL2SW/XqRUwqBWUoZPoyIAN3kBwUv8cABRqlCjXq2kwCsmKrSdNOf/ZOj5kR3uFYdrb60sK8v61iAD7e81QKWS4dl0dOkcdMOXR6xMh2SYpoGOMKSEEc49yzp8GAM75EOc8wDkPAngIwPpoH8s5f5Bzvo5zvs5oNGbqvOetLSdNaCrToroo/f45AFApZGgq0+L4cHINxJxzDFrc4YyUTq1AVaEmY6sLukKllvl8V9Tm9sHs9IUnXAK5z9ANWFz4+tP7sbKuCDed2YRBqxv+NPv5nF5h8ltkQGfUC19nvmToesaceGxbN65dV4/mct2091cWaTBso4BOij09ZgCYFtABwo2b+bhvkmTfnX/fgx//+2jOPr/F5cOg1Y32qigBXakWZqcPNrcvB2eWW9u7xnDzI9vyoi88kX29FiyuLsTpLUIF0y7K0pF5RMqUSwbgYQCHOec/i3h7dcTDPgTgQOZPj0x1eMAa9UIrHalMuhxzeOENBMMZOkC42MtUhk4s68rnu6J9oSxcbcRwGoNGCYNagYEcZOiCQY4vPb4XvkAQv7h+NRpLhSEWQ2kGXeIFfKtxIqArLFBAJZflTZD081eOQcYY7rygLer7KwvVVHIp0d4eM4wGNWoifrdFrfN0mi3JLq8/iCGbG0dzOJlwYsKlftr7xCqM3nk4GOX1o8N4/ehI3t/ICQY5DvZbsaK2CMtri6CQMRqMQuYVKRm6swDcCOD8KSsK7mWM7WeM7QOwCcBd2TxRIrC4fCjRqjJ6zLYKA06ZnEmNI49cWSAS91SlO1nR4vRhzOEFkN8voL1j4g66ydnS6mIN+nOQoXvorZN476QJ93xgKZrLdagtyUz559QJlwDAGMub5eIdw3Y8s6cPN53ZNOkGQ6RKgwaDOe5rnC329Jqxsq44POgnUku5HmMOL8xObw7OjMxVQ1Y3eGioVqYmJSdLDCbbopZcClUYPfNwdYFYtp9sFc9M6zQ5YPf4sby2CBqlHEtri2gwCplXpEy5fJtzzjjnKyJXFHDOb+ScLw+9/YOc84GZOOH5LBjksHv8KCxQZvS4bZV6BII8XOYohdiPVBVR+tli1MHm9mPUnt7FXmfoPGqLC9A75kw6QPz7tm589/lDaZcaJjKxg0476e252EV3oM+Cn7x8FO9fVoVr1tUBAGqLheAm3YDuxIgdMgY0lU/+OsvzJKDbctIEzoEbNzTGfExFoVBymY978/KJxenDyREHVjcUR31/pntlCQEmnqMCQY6u0dwETceH7NCp5JMqLkTi1Mv5uLpArETJ9wFl4gCw5XXCQKy1DSXY12ueFaWihGRCUlMuSW7ZvX5wDhRqFBk9rph5SeYJW8zQiVMugYmSvHT76DpHhY8/t90Im8cPiyu5voW/b+/BH97pxOcf253VJch9ZhfUChnK9ZMzpjXFBRjI0DASKZxeP77w990o06nxg6uWhzMr4oCWdLOcHcN2NJbpoFbIJ73dqM+PgK5z1DFppUI0lYVq+AIc48751wOTjH19ZgDAyrriqO9vCf2O06RLkkmRPceZzgQ9ubMX+3sTT/s9OmjDgkpDeJJvpBKtEjqVfF6uLhCD7Y4k2zJm2r5eC9QKGdpC1zNrGovh9gVxeMCa4zMjZGZQQDeLWEOBTaEmsxm6VqMejCX3QjpocUMuY+Hx9cDE3ft0a+07RxyQMeDM0GqGZAejdJkcqC0uwIsHBvHZR3dmrYRHnHA5tTStpkgDk8MLt29mSoe+93+H0TnqwM+uW4niiHJcrUqBEq0yIyWXkf1zIqNBjdE8mCJ2csSO5oiVCtHQLjpp9oSGCKyoL4r6/rqSAihkLOfTCMnc0h+qaGAss5kgzjm+9cx+fO2pfQmz88eHbVgYpX9OOC82L1cXBII8XKqe7yWX+3stWFJTCIVcuKxd2yhs0qKySzJfUEA3i1hdwsLMwoLMZug0SjnqS7RJ3YEbsLhRYVBP2tdTU1QAjVKW9sVep8mJuhJtOEBM5q6o2emF2enDzWc24btXLMWrh4dx2593ZiW4EpeKT1UdyhTNRM/Wvw8O4m9bu3HbuS04s7V82vtrigvSCuj8gSC6TI5J/XMio0ENk8Ob9dLWRE6OOsI/K7FUFoq76Cigi2dvrxmtRl3Mm0ZKuQwNZdqsD0joGLZh0bdfDA+qIHPbgNmNogIlGkqTex1KxOz0weMP4tCAFe+dMMV8nMnuwajdi/bK6f1zovrS+bdcfMTmgT/IUaxVonPUkbfli4Egx8F+C1bUTtyIqi4qQE2RhiZdknmDArpZRByZnOkMHSBMr0vmhXTQ6po2gEImY2gu12ek5LKpXBfRtyD9RbTLJDy2qVyHj5/RhB9etRxvHh/BLX/cDqfXn9Z5TdU77pw2EAVAeDpgfxb76Ny+AP62tRtfe2ofltUW4ssXLYz6uNrignAPRCpOjTnhC/CYAR3nCA+wyQWPP4CeMSdao6wqiCRm6IZp0mVMnHPs6bEknKLbUq7HydHsZugO9Fnh9gVpOfA8MWBxobpIg7YKfUYzQYMRN3B+/3ZnzMcdC2UF2+IEdHUlWvSOu+ZVH6742nFumxG+AMcpU34GtJ2jdji8ASyfUiq+urEEuyhDR+YJCuhmEatbCEgMWQjoFlTo0TnqQCAo7cVqIGIHXaSWNMeac87ROeJAS7mQJSgqUCZV5tIV+tzNoQEe169vwE+vWYktJ0246Q/bMrZHyO7xY3zKDjqRmKHLRh/dsM2Nn758FGf84D/45jP7UV+ixa9vWAOVIvqvck1xAfrSuAiJNuFSZNSrQ+eUuyCp2+REkE/0dsUilgYPUoYupj6zC6N2D1YnCOhajTp0mZySnytSId4MoRUJ80O/2Y2a4gIsqDBkNBMk/r6f227Ea0eG0REjWBSDyIVxA7oC2D1+mOdRH65Y3XFeu7BDONb3L9f2hXokl9dOLhVf21CCPrOLJhyTeYECulkk3EOX4ZJLQLhg9/iD6JMwQENcKl5VOD071VquQ89Y6qOnR+weOLyB8HLo+tKCpDJ0naNC/52Y3QOAq9bU4ZfXr8aubjNufHhb0kNWohG/T9EydBPLxTOXoTvUb8WX/7EXZ/3wNfx6cwfWNZXi8ds24J93nIWmONmp2uICOLyB8M2AZIkBXWuUkkYxSBrJYR+dOG0xUcmlWiFHqU5FJZdx7O0RLooSZuiMOnglPlekSryQpF69/PO7N07g79u6M3rMyAxdJjNBQ6EL+f96XzvUChkejpGlOzpog0GjCJdmR5NKxchsJ/4entMmlPPn66TL/X0WFCjl016n1oT66GgfHZkPKKCbRaxZLbkUMhwdI4nvwNk8fji9AVQVTX/xa63QIxjaJ5SKztAFuhik1BVrk9r902VyoKa4YNpExg+srMFvPrIGB/st+Njvt6Y9/VLspaiNEtBplELw0JeBDN3eHjM+8tAWXHrfW/jX/gF8ZH0DNn95Ix76+Dqc3lIWdVdYJPH8Ur34PjFsR1WhJmpWuEIM6HKYoRNL/5oTlFwCwvnScvHY9vSMQ6WQYVFVYdzHNZcLzxXZLLsUs9s0TTP//HXrKfy/fx7M2E42lzeAcacPNcUFaAsNJclUJkicnrmoqhBXranDU7v6og5yOj5kx8JKQ9zn04lddPNnMEq/2QWDRoGKQg3qSgpwPE8nXe7vtWBpxEAU0ZLqQqgVMhqMQuYFCuhmEVu45DLzGbrwyoHhxBdQYvlC5A46UYt4sZfinXXxAq4lIkOXTN9C16gDTWXRL+4vWVaF739oOfb3WbA7zTt2vXEydICQpUsnQxcIcvxmcwc+fP+7OD5sx9cuWYQt37gA37liWdyM3FTi6oJUB6N0jNijllsCQLk+DwK6EQcqDGpJZciVoV10JLq9PcJFUazyXVGmptnGI/bunMpyaedUn39sN57b0zdjn282Mtm98PqD+MGLhzNyPPF5srpIE34dylQmaMjqRrleDZVChk+e3QyvP4i/vHdq0mM45zg2bIvbPwcAdaXiGpj5k6HrM7vD62CE/sb8C+iEgShWLKudPplXpZBhZV0xZejIvEAB3SxidfmgVcmn3YXKhBKdCmU6laTBKOJdz1g9dEDqi4c7TQ6o5LJwIFJfqoXHH5RU1sc5R+eoY9oC7EgXLq4EAOxI846duINO7CObqroo9V10veNO3PDgFvz430dx8bIqvHrXebh9YyuKtMlnZmtCy8VTGYzCOceJ4dgBXYFKDoNakeOAzp6w3FJUWaimkssY/IEg9vdZsCpBuSUAlOlUKNQospqh6ze7oFPJ4Q1kt7QzkssbwPN7+/GVJ/ZhX695Rj7nbOP0CtUZlYVq/Gv/ILacjD05UqqJ15MC6NSKjGaCBq3ucCXJggo9zl9Ugb9uOTVp6vGIzQOz04f2GCsLROGe7nkU0PWbXeHX4rZKA06M2Gf0BosUJ0bscPkCWFEXfdXK6sZiHOizzNgaobnutSNDePjtTri89P3MNxTQzSJWty8r5Zai1gppEyoHQ3dUI5eKi3RqBaoKNSlPuuwccaChTBtehyBmwKSUuYw7fbC6/TEzdABQqlOhxahLe/JV77gTtVF20IlqijUpTbn8595+vP+Xb+FgvwU/vWYlfn3D6pQCOVG5Trg7nUqGbsDihsMbQGuMgA4Q+uhy2UMnrCyIfyEmqirUYMTmybsLknxwbEi4KJIS0DHG0GLUZy1DZ/f4YXX7cXqLsIcy2xM1RSaH8HPsDQRx+193YTyH01vzlckufE8+t2kBaoo0+O7zh9L+fRKfm8SbT5nMBAm93hOvU7ee0wyTw4tndk9kYcUJl/EGoojqSwvmV8mlxRX+d1lQoYfXH8xYqW2mxBqIIlrbUAJfQFhrQNL381eO439eOIRzf7wZf3mvK+32FZI5FNDNIlaXPysDUUQLKvToGLEnLG8U76hWRgnogNCky1QzdKOOSf1QYt+ClDKXzvCEy/gZm3WNJdjZPY5gGhcisXbQiaqLCmBz+2H3SBtGYnP78KXH9+ALj+3Gggo9/nXnOfjw2rqEPXKJyGQMNUWalDJ04QmXcQKmcoM6Zxm6MYewc7BFYglqRaEGQY68WIaeb/aGMlIrp4z9jiWd3/FEBkI/q2ctEAYxzFQfnbh+4/PnL8CIzYM7H99Dwf8UptD3qK6kAF+/dDEODVjxxI6etI45EC7hDwV0GcwEDVrdk16nzmgpw9KaQvz+rZPh5/+joV2HiUouAeH1aL6UXDq9wkTPmoiSSwB5V3Z5oM8CrUoe88beGlownlH9ZhfOaClDc5kO337uIC782Rt4ZncvPVfmAQroZhGbx5eVlQWiVqMeZqcv/KIdS2RfQqzjnJAQGE4VCArTzSIv0OvCAV3igERcWZCox2xtYwnMTl9aI9GFgC56/xwwcbd5QEIgtbfHjEvvewvP7unDnRe04YlPn4HGOFnGZNWkuIsu3soCkdGgxmiOAjqxT7NVYoZOvLCjssvp9nSbUaxVorEs9k2KSK1GPQatbjgk3rBIRn/oAn95bREMGkXWl5iLxOe9jQsrcPcHl+DNYyO47z/HZ+Rzzxam0M2QMp0aH1hRjXWNJfjxv4+GB3alYsDiQrleFR5klalMkNsXgNnpm9QawBjDp85pwYkRB14/NgwAOD5kQ6lOhXK9KuEx60qk9XQHg3zW76vrD7UMiD10C8IBXX6tLtjXa8aymqJwVc9U5Xo1Gsu0FNBlgNsXgMnhxdlt5Xj80xvwyCdOg16twF2P78Wlv3wLrxwamvU/97MZBXSziNXlR2EWBqKIxCfsEwnuwA1Y3FEnXIpajDrY3H6M2pMrWeo3u+ANBCcFZAUqOcr1akkv7l2m0MqCOJkzAFjbWAoA2HlqLKnzEzk8fow5vHEDuurQwJh+Cftv7vrHHgQCHE985gzcdVF7xnska4sLUiq57Bixo1CjiHuhY9TnLkMnXuhLmXAJIDySnCZdTsY5x7auMaysK5acERa/59nInkWW4LUY9TOXoQs9X5XpVPjI+gZ8eE0d7nvtODYfHZ6Rzz8biNntMr0KjDHc/YGlGHN68evXOlI+Zr/ZHX6+BDKXCRJv3EytJLlsRTWqCjV46E1hhcGxIRvaKvSSfvbDPd0JnvP++7kD+MhDW1M88/ww8Xso/NsYNEpUF2nQkUerC/yBIA4NRB+IEmltQwl2dZsp2EhT5HMzYwybFlbghc+fjV/dsBreQBCf+vMOfPwP2yhblyMU0M0iVrcPhQXZy9CJAV1Hgv63WDvoRGLpQ7J9dLFKJutKpO2i6zI5UVeiTTylr1yHYq0y5Tt2YrYrfsmltAxd77gTJ0cc+OQ5LeFAM9NqigswbPMkXeveERqIEu9Cx2hQw+bx56RB+sSoHUo5ixtYR0o2Q+cPBHF4wJry+c0Wu7rH0TnqwKXLqyR/THjSZZYCOhkT/r1aynUzXnJZGgpWvnflMiyqKsQX/74n7/qGcmU0HPQKN0eW1xXh6jV1eOSdzpT/ncQddKJMZYIGp5RyipRyGW4+qwnvnTThQJ8Fx4fsaJdQbglErC6I83pkcfrw5M5e7EqzrD/XpgZ0gPBvk08llx0jdrh9wZgDUUSrG0swYvNIqvQhsYlZ25qIGzAyGcMHVtbg5bvOxSfOasJbx0epCiZHKKCbRWxuf1aHolQXalCglCecdDlgcUedcClqTXGs+dSVBaL6Uq2kRvSuUYekkf4yGcPahpKUJ12KPRTxAomqIg0YS5yhe7dDmBJ3dqhfKBtqiwvA+cQFjlTxJlyKxOXiuehLOzniQGOZTnJGs0yngowBwxJfbJ7fJwyoOdQ/t4O6x7b1QKeS4/IVNZI/pqlMB8ays/i73+xGhUEDpVyG5nId+syuGblhYHJ4oZQzGNRCFUSBSo4HPrYGQc5x+6M7aUoehKEoOpUcBaqJPZ9fuWQhVHIZvv9/qa0xGDC7JwUNYiYo3dUFg9bY05hvWN8AnUqO7/3fIdg8frRXSQvoxOf8eIHBs3v64PUH4fEHMTCLL2zDN1YME9U4bRUGdAzb8yZQDQ9ESRDQrW2gBeOZ0GcWrn0if19FSrkMpzcLN6XHnTRQKhcooJslOOewunxZ2UEnkskYWit0cVcOOL1+WFy+aXc9I9UUFUCjlCV9sdc56oBOJQ8HCaL6EqFkMF4an3OOrlEHmiX2AK1tKsHJEUf4rnwywjvoojypiZRyYaVBogzd2x2jKNerE47MTkd4uXgSZZfjDi9MDq/kgG44B2WXJ0fskgeiAIBCLoMxieXi+3uFQO6lAwMpnd9sYHX78MK+fnxwVS10aunPLRqlHLXFBVnpbxNGpQvPL2ImsMuU/Sydye5BqU41KSPdWKbDz69dhQN9Vtz93MGsn0O+Mzk8KJuyqqXCoMHnzl+AVw8P4a3jI0kdz+b2webxTwu6hExQZjJ00YZ3FRUoce1p9dhyUii7b0/wPCeqCy8Xj52he3x7DzRK4dLq1Axll7OhzyxMCI28YdZWqYfLF0ipJzsbDvRZoFcr0Jyg53xhlQE6lZz66NLUZ3ZDxqZnvUXFWqE9w+xMvaeWpI4CulnC5QvAH+RZLbkEhImG8XroBuPsoBPJZAzN5dJWIETqDGXYppb41Zdq4Q/y8B3XaEwOL2wev+RhIuE7dik8wfeNu6BSyMKLtWOpLi4IT3CLhnOOd0+M4qwFZWlPs4wnleXiYtltwoAuR8vF/YEgusecklcWiCoLNXF/jiIdC02/e+ngYNLnN1s8t6cfbl8Q159Wn/THthj1WVkpMGCZ2H2VzV69qcYc3nApYaQLl1Tic5ta8fiOHvxje3oTHWc7k92Lsig9tbec1YyGUi3+54VD8Aekl3aHd9BNuTmWiUzQoNUNnUoec5DYLWc1Q5yjIbXkcqKnO/pz6YE+Cw4NWPGJs5oBCHtVZ6vIHXQisb9Ryr7ambCv14KlNYWQxRiIIpLLGFY10ILxdPWbXagsFKonoikJBXSUocsNCuhmCatLmCaXzZJLQJhe12d2xZxeJ14Mx8vQAaGx5klehE1dWSCa2EUX+65ol8SVBaKV9cVQyBh2pvAE3zvuQl1xQcIXkZqi+Lvojg7ZMGr3hsezZ4sYfCdzV3ViZUH8C52KUIZO6uL3rSdNSV3wxdIz7oIvwCUvFRdVGDSS6/uPDNqgUcpwbMie8l7FfPf49m4sri5M2IMSTUu5Dp0jjowOGuCco9/inhbQZaO0cyqTI3qwAgBfumghVtUX43dvnsj6eeSzUbsnatCrUcrxzUsX49iQHX/b1i35eOE+rSmvJ22Verh9wbQyQUNWNyrjvE7Vl2px+YoaNJZpUaJLPOFy4uMK0GuO/lr0+PYeqBQyfPrcFqgVsvDr0mzUb5ke0OXTpEtfaCCK1OeutQ0lODxgy8pk3vkiWpAfqSS0M3ecMnQ5QQHdLGELjYXO5h46YOIJO9Yd8XCjeYwddKLWch16xpzw+KX1nXj9QfSOO6MGZPUSylw6Ja4sEGmUciytLcLOrlQCOme4jDGe6qICDJjdMS943z4+CgBZD+g0SuGuclIZumE71ApZwq9TKFGTlqF7/dgIrntwC77w993wpRnUTawsSC6gqyxUSyoPNdk9GLV78JH1jQCAf8/BLN2BPgsO9Flxw/r6lDLELUYdHN5ARsttTQ4vvP5g+AJfq1KgukiTleErU405vCiNcWEvlzGsqi/OSWlxPjE5vDGn3l68tBJntJThF68elzzlLnaGLv3AIVGvNwDce/UKPH37mUkdt64kek+32xfAs3v68P5lVSjWqtBUpkPn6OwcphMM8mm9jYBQUmc0qNPub8yE40N2eP3BhBMuRasbSxAI8vDOTZK8RAFduOQyhVYWkj4K6GYJcc9PNvfQAUBrgpKKqUtg4x0nyIFTJmkvaN1jTgR59AxbTXEBGIvfiN5lckAukz7xEBAWjO/tNSc9/THRUnFRTbEGLl8AFlf0u1XvdIyipVwX3vOTTbXFyS0X7xi2o8Woj7nbR6SQy1CmU0kK6HZ3mwEA/9o/iDv+tivp73sksXerpTz5kssxhzfhjYZjoQuWjQuNWFlXhH8fHErtRPPYY9u6oVbIcMXK2pQ+XvzeZzJ7Kd50iLzAby7P3hLzSPECOgAo16tgc/vn7XCUYJALZakxAjrGGD60phZjDq/kqaADUQZvAELJJYC0Aochiztq/1wkjVI+rScwkVg93S8dGITN7cd1ofLlpnLtjPR+ZsOowwNvIIja4unfv7Y8mXS5v88MAFhRVyzp8WvqhTYL8XWIJCcYFKsnYv9OqRQy6FRyytDlCAV0s8REyWV2M3RNZTrIZSzmRdqgxY2iAiW0qvjnIV7sSS2VirWyABCeJKoLNXFHRXeNOlFfUhCztjuatY0l8PiDONhvkfwxTq8fpgQ76EThXXTm6SV+vkAQWzvHsp6dEyW7XLxDwoRLUbnEXXQH+ixor9Tjng8swb8PDuGzj+6SnMGd6uSoHSVaZVKlUsDELrpE53t0UBiIsrDKgIuXVWFvjzmlXX75yun14597+nHZ8moUaVO7SdSS4jTbeMTvce20gM6e1R1SHn8Ado8fZXEDutxNdM0HFpcPgSCPWnIpWhjqRTsyKC2z1m8RJppOnVRbpFWiwqBOOXAIBjmGbZ6ElSSpEHu6B6aU0z++vQcNpVpsaC4DIFSLdJucs3InV3g8fZSbjW0VenQMZ/f3UYr9fRYY1Ao0lkobhFakVaKtQk+DUVI06hBWHyW6AV2sVcFMPXQ5QQHdLGENl1xmN0OnUsjQWKqNm6FLVMYCAM2hi714EzMjJeqBqyvRojfO6oJOiSsLIq1tFO7YJfME3ydOuJQS0IXuZE194QeAPT1mOL0BnLWgTPLnToe4XFzKi7DLK0wxWyBx4IjRoE7YQ8c5x75eC5bVFuHms5rxP1csxauHh3D7X1ML6k6MOJIeiAIAFeFddAkCuiE7ikMXlZcsFfazvZznZZecc1gk3hn9v30DsHn8uH59Q8qfryq05iSzAd30C8kWox5Wtz+rd33DO+jiBCsTKzrm58WKyTGxVDyWtko9GJsYKJTIoMUdfp6MdqxUA7pRhwf+IE9YSZKKaKsLTpkceO+kCdeuqwv3VjeX6eANBGfljaBoO+hECyoNsHv8kodLZcv+0OtJol72SGsaSrCrezznwehsJD43JwroSnRKGoqSIxTQzRJWt5Chy+baAlGLUR8zoBu0uiS9SOrVClQVaiSXY50cdaBEqwzXYE9VVxp7uTjnHF0mB5okTrgUVRZqUFdSkFRAF15ZIKXkUszQRZl0+fbxUTAGnNEycxk6ty8o6aI4PBBFYobOaFBjNEHGa8gq9KQtD/U73HhGE77/oWV47cgwPv2X5Hd8nRxxJLWyQFQlcbn4sSEb2isNYIyhxahHe6U+76dd/uq1Dpz2/Vex9aQp4WP/vr0HLUYdTmsqSfnzCdNsdRmddNlvdkGtkIWb64GJvZTZHIxisosBnYQM3TztoxMD2XjTfbUqBRpKtTgqMaDrn7JUPFJbhQEdQ7aULr6l9nqnIlpP9xM7eiFjwNVrJ6bFihOXZ2PZZbyALtzfmMM+Oq8/iMMDtqSHOa1tLIHZ6ZN8o5lMiPczEalEq6KSyxyhgG6WsIb6sLI95RIQLuS7TI6o0wgHLR5JGTogNOlS4hNn56g97oTK+hItBq3uqH1XIzYPnN6A5AmXkdY1CgvGpV409JqlZ+iMBjUUMhZ1F907HaNYUVuUcrlbssQn4b44fYiiPT1CgCv1xbLCoMGIzRP3e7i/zzLtmB89vRE/vGo53jg2gk/9eYfkoM7q9mHU7kkpQ1cpIaDjnOPYoC1cPgYAFy+twrbOsZT2Fs4Ep9ePh9/uhDcQxGf+uhOn4lxEHhuyYeepcVx/WmrDUCI1G3UZXSkwYHGjtrhg0nmFJ11mcTCKKfTvGi/7VG6Y3yWXYtAb73sECCsAjkooueRcGLwhlqZPtaBCD4c3EHf1SyyDEnu9UyH2dPeEnksDQY4nd/bivHbjpM8n/tzOxkmXfWYX9GpF1BaPiYE1uQvojg3Z4A1IH4giWhu6gbWjaywbpzWnJRfQ5efr5FxHAd0sYXX7oFLIoFHKs/65FlTo4QtwdE9pbPf6gxi1exI2motajcIuOinBUteoM27JZH2pFpxH36XWFRq8kmzJJQCsbSrFiM0Td+BKpN5xJ1ShpeGJyGUMlYWaaRckdo8fe3rMM9Y/B0wEoFL66HZ1m1GuV0seMGM0qOENBMN9ntHs77NAxoAl1ZNfgK9f34AffXgF3u4YxSf/tB0ub+KgLjwQJckJl4AwVlkpZ3FLLvstbtg8fiysmhzQBTnw6qH8HI7yxI5eWFw+/PSaleAAbvnj9pjDeB7f3gOlnOGqNXVpf95kp9km0md2TSvBqyspgFLOsrqLbkwsJ4yToRPfN28DutD3KNH+zUVVBnSOOhL+TFhcPrh8gTgZutQDB/GGTTYydCqFDFWFGvSGKkbePDaCQas7PAxFVFmoRoFSPisnXQrTDDVRb/iU6dUo1anQkcPVBdFuEErRUq6D0aDGFglVDGSyeEF+pBKtEuN5euNzrqOAbpawuvwzkp0DJkbBTy27FF8kk8nQ2dz+hD0njlA9frwSuvAuuihll+H+uyRLLoGJBeM7Tkm7Y9c77kJtSeIddKLqIs20IHRbpwn+IMfZMxjQJbNcfFf3ONY0FEvO3hjDu+hi30nf32vGggo9ClTTb0hcu64eP71mJd47YcJPXz6a8POlurIAECbxVRg0GI6ToTsWyi5EBnRLawpRV1KQkbLLjmE77vjbLvy/5w7gt6934JndvXjvhAldo46UJigGghy/f/sk1jQU48Nr6/DAx9aie8yJO/62a1qW3eMP4OldvbhoSWXCC3MpWozCNNtuidNsExmwuMKlyiKFXIaGUu2MlFzGG/ihUcph0CjmbQ/dqN0LxiaWB8fSXmlAIMgTVmfEG7wBAG2V4qTL5AOHQasbchlLeoKlVPURPd2Pb+9BmU6F8xdVTnoMYwyNZbNz0mV/lJUFkRZU6HNacrmv14JCjVDemwzGGE5vLsWWk2PUR5ekvvHYQX6kYq0KVrc/I/tmSXIooJslbG5f1idcisTVBVPrzCeWikvL3IglcYkuxMQXvOY4I+jrS8W+hekBSafJAYWMxR2nG8vCKgP0aoXkPjphZYH0NQPVxQXTMnRvHzdBrZBhTWPq/UvJKtEqoVHKEmboRu0enDI5wwNjpBCzlbF2dHHOsb/PGrc85qo1dbhoSSWe29ufcCrcyRFhRUVDafIBHSDcOR+yxQ7oxAl97RUTAR1jDJcsrcLbx0fDOyFT9fzefrywbwDP7enHvS8dxV2P78UND23Bxp+8jkXffgnrvvcq3jg2Ivl4Lx0YRM+YC7ed2woA2NBShu9/aDneOj6Ke54/OOnC5d8HhzDu9OH601IfhhJJ7LOUOtUwHq8/iGGbZ9pOMkB4Lsluhs4LhYwl3PNp1CceADRXmewelGpVCVeZiDdCEpVdisOiYt0gLNWpUKZTxeznjn9sNyoN6oTnmiqxp3vU7sGrh4dw1ZpaqBTTL6eay3WzsuSy3+yKWQoLTKwuyFVQdKDPguV1RSmVjG9oKcOg1T2tAonE129xSVqxJPY/x6oQIdmTMKBjjNUzxjYzxg4zxg4yxu6c8v7/YoxxxtjMpRvmIavbD0OWJ1yKCjXCdL+pL6ThJbASM3StEidddo2KJZOx77ZVFWqglLNwmcvkj3egoVQ7bfS1FHIZw+qGYuyQuGC8b9yZVEBXU6TBoMWNYESQ8k7HKE5rKp2R8lkRYyw86TKeXaHANplgM5yhixHQiQNRViTod7h8RQ1GbB5s64yfLT05akd9SUHUCygpKgs1cUsujw3ZUF2kmdbfePGyKngDQbx+VHqwFc3xYRuayrTYe/f7cOi7F+O1L5+Hv916On56zUp85eKFKNQo8LUn98HuiV3CKuKc48E3T6CpTIuLlkxkCK5dV49Pn9eCv27pxp/e7Qq//e/bulFXUpCx7PDCKgO0KnlGelKGrG5wjqi7r1rKdejK4gj4MYcXJTpVwgvEcn3iAUBzlckeewddpOZyHZRylnAwijgsKmEmKMWSy8os9M+JxJ7ux7f3wB/k08otRU3lOnSPOWdVtsLtC8Dk8Eb9PRS1VehhcflycnPDHwji6JANS2uSK7cUbWgRJktT2WVyEmVtReIqIRqMMvOkXBH5AXyZc74YwAYAn2OMLQGEYA/ARQC6s3eKBBCGosxUhg4QXkinTqgcDN1RldpoXlNUAL1aga2d8Z84O0NT8uJNqZTLGGqKC8KN6JM/PvmVBZHWNpbg6JAtYebF5Q1g1O6VNOFSVF2kgTcQDA9dGLa5cXTINqP9c6IaKQFdtxkKGQtPo5QiUUAn9jssT9DvcMHiChQo5XhhX3/cx51McWWBqLJQg6E4gxaODgoTLqda01CCcr067bLL40N2LAhl/7QqBVqMepy5oBwfXluHz21agJ9cuxJDNjd+/sqxhMfa3jWOvb0WfPKclmnZiK9dvAgXLanEd184hM1Hh3HK5MC7J0y4bl19UqO+41HKZVjbWIKtCYJwKeI13TeX6+D1Z28EvMnhjds/Jyo3qOZ1D128klSRUi5Dq1GfOENndkEhY3FLf9sq9TiewqTLQYs7K/1zorqSAnAOPPSWUOq8oGL68wUgtAH4gzypHaC5JmX4hVgO25GDssvOUQe8/iAWV0f/nifSatShXK/GlpM0GEUqlzeAMYdXUkAnTiqnXXQzL2FAxzkf4JzvCv3ZBuAwgNrQu38O4KsAqBg5y2xuX9Z30EVaUKHHiSklFYMWD7QqOQxqaYGlTMZww/p6PL+3P2651MlRB6oKNdAlOG5dScGkUdGAkKE4ZXImvbIg0trGEnAO7O42x31cn9kZPg+pxPIxsbzovRNCcDuT/XOiWgnLxXedGsfS2qKksoeFGgVUClnsgK7XHHUgylRalQIXLK7AiwcGY97RDgY5OkdTW1kgqizUwObxwxElA+YPBNExYp/UPyeSyxjet7QSm48Mp9TrBghlhZ2jDrRXxg5I1zSU4CPrG/DIO5040Bd/6f2Db55AqU6Fq6MMOJHJGH5x3SosqirE5/+2Gz966YgwWn1d+sNQIq1vKsXRIVvaL+D94RK86CWXQPYmXY45vHFXFojK9ep520MnNUMHSJt0OWBxo7JQE7cssq3CAKvbH/O5JZYhqycrEy5FYguA2emLmZ0DJgZ1JVMu/OTOXrx1fCRn5YyJehuB3E66PDRgBQAsqipM6eMZY9jQUootJ03URyeR+NycTMklZehmXlI1S4yxJgCrAWxljH0QQB/nfG+Cj7mNMbaDMbZjZCS9UqX5zOr2z2iGrtWoh83jn9QXJe6gS6Zu/bZzW6GUy/Dr1zpiPqZr1BG33FJUX6KdVnI5ZPXA5QugWcLHx7KqvhgyBuxI0EfXk8RScVF4F13oRfLt46Mo1iqxpCa1F6N01BYXYNTujRmM+AJB7OszY01DcVLHZYwJvUVxMnSxBqJMdfmKGow5vHj3RPSsbp/ZBY8/mGaGLnbPX5fJCa8/OGllQaRLllbB6Q3g7eOjKX3uUyYH/EGOtjgBHQB89eJFKNWp8K1n9scsM+wYtuPVw8O4cUNjzO+tTq3Awzevg1Ylx7/2D2LTwoq4vTGpWN9cCs4huWw5lokLyekX4uII+M4sDUYx2T2SAzqLyxd1fcpcN2r3SB6ks7DKgD6zK27VgzhJMR4xcDiWRCbI5vbB7vFnNUMnBnQ6lRyXr6iJ+TjxdU1qH92gxY3/emIvbnx4G97/y7fwxI6ejE2QlUrM0MW7eDca1CjUKHA8B5MujwzaoJQztKbxGrChpQwDFuqjk0rqygJgYmgSrS6YeZIDOsaYHsBTAL4IoQzzWwD+X6KP45w/yDlfxzlfZzQaUz3PeU8ouZzZDB0AnIi4AzdgcUvunxMZDWp89PRGPLunL+ZurM5RR9yBKKL6Ui1G7d5Jo+3FO5/plFwaNEosrCoM94/F0pfEUnGReMEyYHGBc453OkZxRktZ1pr145+LmC2MXm54eMAKty+INQ3JD2sxGqIPixAHoiyvLZZ0nI0LjdCrFTHLLsUMTSorC0TxdtEdG5o+4TLShpYyFGoU+HeKZZfihWlbjBItUZFWiW9fvgR7ey3429ZTUR/z8NsnoVbIcOMZjXGPVV1UgN/ftA6NZVp86tyWlM47npX1xVApZAlLqxPpN7tQrFVCq5p+46pcr4JBrchahk5yyWUooBFH+M8XXn8QVrdf0vcIQPiGSLxATHg9iX+BuKBSzARJDxzCKwuymKGrKtRAq5LjAytr4laWGPVq6FTy8GqdRHZ1C69Bn93YCs6Brzy5D2f/aDN+/drxGRsF32d2gTHEXU/EGENbpSEnky6PDFjRatSn3EMNABtaSgFQH51U4rWPlMFzxaEMHZVczjxJvxGMMSWEYO5RzvnTAFoBNAPYyxjrAlAHYBdjrCpbJzqfefwBePzBGS+5BICOiDviQl9C8nf3P3NeCxQyht9snp6lMzu9GHf6JJXQiZmxyCydOCEznZJLQFgwvrt7PG7zeu+4S/IOOlGpTgW1QoYBixudow70W9w56Z8DEi8XFyd9JjPhUmQ0RM/QiQNRltdKy0hqlHK8b0klXjowGDULIk5MTS+gU4fObXpAd3TQBhmb+PmfSqWQ4YLFlXjl8FBKgw6OD9vAGCTdXf7gyhqcvaAc9750FMNTpnKO2Dx4alcfPry2TlLWZEVdMd74yqbwQIBM0ijlWFVfnHCYTSL95ukrC0SMMbRkeIm5yOsPwub2SxpxXx4qOUy2BHC2GwsvXpeeoQMmbpBMFQxyDFrc03YOTmXUq1GsVSZV2jdoEf5tpO5LTYVcxvDMZ8/Cf1++JO7jGGNoKpf+c7u7exwqhQxfvLAdL33xHPz5lvVYVGXAT14+hjN++B98+9kDWe/H6ze7UGFQJwyY2ir0KU0gTdfhARsWV6dX4dJq1KNcr8JW6qOTpN/sgoxJ2+uoVyugkDEqucwBKVMuGYCHARzmnP8MADjn+znnFZzzJs55E4BeAGs45+kvaSLT2NxCr49hBksuKwxq6NWK8BO2PxAaKZ7CXc+KQg1uWN+Ap3f1TeuBSybDJmbGInfRdY06oJLLJJUCxLO2sQQObyDuZLbOUTtqijVJDZRgjIV30b3TIZTp5aJ/DpgIiGMNltjVbUZVoSal72WsgG5frxlA4oEokS5fWQ2r24+3jk8v0T454oBBrUgqqJ6qIvSiNBxl0uXRQRuaynRxewgvXloFs9OXUgBzfMiOhlKtpPJTxhj+58pl8ASC+J8XDk9635/f64IvEMStZzcnfQ7ZcHpzKQ70WyVN5oxlwBJ/ilpzuS7hbrNUiKVBUkouxQFA820wivj1Su2hqy0ugE4lj9lHZ3J44Q0EYwbwIsaYEDgkkQkaTHJfaqrElTeJNJXrYlanTLWr24zltUVQKWRgjOHcdiP+8snT8e8vnosPrKjB49t7cMffdqV76nH1W1ySXgMWVOhhcnhhmsHfhXGHF4NWd8oDUUSMMZzeUkZ9dBL1mYUhQ1ImiTPGUKxVUYYuB6Rk6M4CcCOA8xlje0L/XZrl8yIRrKF9HjNZcskYQ2vEpMtRuxeBIE+5jOX2ja2QRcnSiQFds4SArr40tFw8Yhdd56gDDWXatEsYxaxUtH10Yw4vPv/Ybvz74BDWNpYmfezqImEX3TsdJtQWF6CxLPV+v3RUFmrAGGLe4d11ahxrGotTOrZRr8aY0wvflKzVgT6LpIEokc5eYERRgRIv7BuY9r6To3a0GHUp7R8SGdQKaFXymCWX0SZcRjqv3QiNUpbStMvjw7ZwX5AUzeU6fHZjK57f2483Q7vpnF4//rLlFC5aXJlWL2EmrW8uRSDIE5Ytx9OXoKequVyPPrMr5YE0sUwsFZdecjlqm18XK2JAVy4xoJPJhJK8WAFdoh10kRZUGHBsWPqkS3EaczYzdMloLtOhZ9w17blxKq8/iP19lqg9zAurDPjxNSvxyXOacaDPktUeTqnj6cOTLmcwSyfuu0x1IEqkDS1l6Le4o+62JZMJ/a7Sb/SWaJUYd1CGbqZJmXL5Nueccc5XcM5Xhf7715THNHHOU5sSQBKyhjJ0iZbeZtoC40RJRXipeIovkpWFGtxwWj2e3Nk7KUvXOeqAjAENpYmDHKNeDbVCNqnkMt0Jl6K6kgJUFqonBXScc7ywrx8X/ewNvHRgAF+6qB0//PDypI9dXaxB77gT754YxdkLytMKRtKhUshQadBEDeiGrG70mV0p9c8BQuaC84nSLNH+PgvaKgySMlKR53nJ0iq8cmho2sV7uisLAOFmRWWhJvwzLXL7AugyOdAeo39OVKCSY2N7BV4+ODRpv2AivoAw4bItQcA41e0bW9FSrsO3nzsAty+AJ3f2wuz04bYs9MOlak1DCeQylnLZpc3tg83tj3vRIJbZdknMdkgl/swmk6Gbqf1bYw4vHt16KudZhImgV3pmfGGlIWbJpZRJiqK2Cj3MTl949Usig1Y3irXKGd3zGU9jmRaBIEdvjFJ30aEBK7z+IFbHeQ5eWlMIX4DH/L6mi3NhxYKUaYa5mHR5WJxwmWaGDgDOoD46yaRmbUUlWhUNRcmB1LtKyYzJRYYOAFordBiyemB1+5LeQRfNZza2QsYYfvv6ifDbOkcdqCvRSmpwZoyFVhcI5xIMcnSZHGjKQMaLMYa1jSXhSX3DNjc+89eduONvu1FXUoAXPn8OvnBBG5QpLC+vKSoIfR/9OHNB5nuYkjqXYk3UkstUFopHiraLThyIsiyJnXaiy1dWw+7x4/Wjw+G3Ob1+DFjcaa0sEFUY1NNKLjuG7QhyYFGCgA4ALl5WiUGrG3tDJaVSnDI54AvwpDJ0AKBWyPG9K5fhlMmJX712HL9/qxOrG4pT6nXMFp1agWW1RSkPRhEH9cTL2IhZ/EyXXYoDTqSUE2qUcujVihkruXx2dx++9cyBnGcRkvkeiRZWGWByeKN+r5LJ0IkTYaUO4Bi0eLI64TJZ4s9tokmXu0MDUeLdVBOXaR/qt2bo7CYzObzw+oOokfDvUl2kgU4ln+EMnRXlehUqDOn/+4p9dBTQxRcMcgxIzNqKirVKmKmHbsZRQDcLTPTQzWxAt0Dc/TTikHTBlUh1UQGuPa0OT+7sCWeJhAmX0i/Q60u14R66QasbHn8wrQmXkdY2lqLP7MJDb57ERT97E5uPjuDr71+Ep24/M+bUQykiG//PbM1N/5wo1nLxXaFm/KUprlOIFtANWt1JDUSJdEZLGcp0KjwfUXYpXshnosywslCDoSmDRsTysEQllwBw/qJKyBiw+aj0VSzixD8px5/qzAXl+NDqWvxm8wl0jznx6XNbcpbpjWVDcyn29lhSKomUMiq9OYWdXlKI2adSidmncr1qxnbRiWXBYgCUKya7FyqFTFLPmEh8zoxWdjlgcUOtkEnKiooTYTskTrocsrqzOuEyWVJ30e3uNqO6SBP33BtLtdCp5DjYH38/ZaoGksicMsawoNIwo6sLjgzaMlJuCYT66Jqpjy6RUbsH3kAQtUmsa6IMXW5QQDcLWEO7fGa65LJVnHQ5bMegxQ2VXNoLcDy3b1wAALj/9Q5wzpMP6Eq04ZLNriT676QQMx7f/9dhLKjQ48U7z8FnzmuV1Agcj9j4v6jKEA58cqW2pAD9Zve0UkGxGV+tSK1MSRxSEhnQ7e8VLjqW1xUnfTyFXIb3L6/Ca4eH4fQKNzQysbJAVFmoxpDVPemF/NiQDSqFTFLGt6hAWHUh3lWX4viQXfKEy2i+eeliFGoUaCzT4qIl+TdQeH1zKbyBIPb0mJP+WLEErzrOhaROrUBVoSbjGboxhxcyBhRLnCJcrldjdIamXIoB3dTy4Jk2aveiXKdK6iaCeOMiWkDXb3ahWuJO08pCNQxqheTSvgGLO68ydGU6YeVGolLhXd3jWJ1gB6hMxrC4uhAHs5Sh60ti3xgglF3O1OoCfyCIo4M2SRUUUm1oKUW/xZ2wHHY+6wvfbJP+O1WsEzJ0FCjPLAroZoFclVw2lmqhlDOcGLELL5JJLhWPpra4ANesq8c/tvdib68FTm8gyQxdAaxuPywuHzpN6e+gi7S0phAfXFmDuz+wBP/49BlpLS6NJGbocjXdMlJtcQG8gSBGI/ZoefwB7O+N3owvVbTeoomBKKndUb18RQ1cvgD+c1gouzw5IgREmQjgKws1cPuC4f5UQLj7u8ColxzAr2koxp5us+Q+umPDNtSXSJtwGY3RoMbTnz0Tf7nl9JzsMUxkXWMpGENKfXTiWOzKBDc8mst1ODma2QtIk8OLEq1K8vTacr16xkouh0OBY7QBPjPJ5PBIXlkgMhrUKNOpovZ7SdlBJxIyQdICB18gCJPDkzcDUQBpqwuGbUJQIaWHeWlNIQ4PWJPq35VKSqY8UluFHsM2DywzUF7XZXLC4w+mvbIgkrjG5T0qu4wpmX5XUalWBW8gCKc3swOsSHwU0M0CVrcPchmDNsULwVQp5DI0lenCGbpMlbF8dmMrODi++fR+AMldoIurC3rHnegadUCtkKE6Qy/eSrkM992wGp84qzmjF8ytRj2uXluH69c3ZOyYqRKzheKTNAAc7LfCGwim1ZOlUcph0CgmZ+hSGIgS6bSmUlQY1Hh+r7Bk/OSIAzVFBRkZdjCxumDi+3BsyJZUae3axhLYPH7JmYOOIXvS/XNTLagwoCFHU1ITKdIqsaiqMLWAzuKSNBY7G7voxhyepHrDyg2qGQvowhk6S27XJJjs3qS+R6L2SkN4MmGkAbMr4Q66SG0VehwbSjzpctjmAefZXSqeiqZyXdwM3e5uMwAkzNABQh+dwxvI+HAgQAjoCpTy8HLoRMT+xo6R7JddHhnM3EAU0YIKPcp01EcXT3+SWVtAKLkEpg9JI9lFAd0sYHP7YdAoctIz02rU48SwHYPWzJWx1JVocfXaOhwKTaxKtuQSEFYXdI460VimTWovXC4o5TL85JqVMZdVzySxDj5yuXh4IEqKEy5FkbvohIEolpQGoojkMoZLl1fj9WMjsLl94ZUFmSD+LIulbBaXDwMWd1L9beL3a5eEsktfIIiTo/akJ1zONqc3l2LnqfGEI9qn6je74pZbiprLdTA7fRjP4IXCmMObVCl5uV6Ncacv6a8xFXmTobN7kppwKVpYZcDxIdukbFIgyDFk8yTcQRdpVX0JTA5vwmB+MNTrnW8BXXOZFn3jrpjrBnZ3m6GUs/DQk3iWhPqcs1F2KUwzlF6Js8AoPJ/NRNnl4QErFDKW0ddRYR9dKbaeHKPywBj6zC4Y1IqkKsTEGwI0GGVmUUA3C1hdvhkvtxQtqNDj1JgTgxZ3Rhe1fnbjAihkDCpFckvBxV10vePO0ITLzFzgzxfi9zpyMMqu7nHUFheEs1apMuonAjphIIoXK5JYKB7NB1bWwOsP4uWDQ+gccWSsDLayULg4HQpNuhTLwpLpz2gs06JUp5K0e+2UyZnShMvZ5vTmUrh8AezvS25oQ6Kl4iIxoM9k2aXJ4U0qWBF30WX77rPLGwgPxMplDx3nHKMOr+QddJEWVhng8AYmrUoZtrkRCPKkMnQbwiPm42d/wwFdHpVcAkKGLsiB7oiVPZF2dY9jSU2RpOqD9koDlHIWviGaSX1JTjOsLSmASiEL9zdn05EBG1qN+pT7vGPZ0FKGPrOL+uhi6EtyBx0AlIRukNFglJlFAd0sYHX7Z3wgimhBhR6BIIc3EMzoXc/6Ui1uPrMJZy8oT6q8sahACYNagVMmJ7pNzowNRJkvCjUK6NWKSRdYu06ZMzIC32hQh3voxIEo6WToAKFPrba4AH94pxMObyBjGTpx7LWY+QhPuEwioGOMYU1DMXZKyNAdH5I+QXM2O61ZuPBOpuwyPBZbwvNLS/nE5N1MSSVDB0weAJQNw6EprAoZCwcquWD3+OH1B1MuuQQmD0YJD8BJ4vWkuVyHykJ1wl6ndPelZktTnNUF/kAQ+3rNknuYVQoZ2ioM2cnQSdxBJ5LLGFrKdTgxA6sLDg9YM1puKaI+uvj6za6kJlwCwmJxgAK6mUYB3Sxgc+cuQxeZEclkhg4A/vvyJfjDzacl9TGMMdSWFGB71xi8gcytLJgvGGOoLS4IB3T9ZhcGre60BqKIIksu0x2IImKM4bIV1eGLF/GCPl0FKjkKNYpwD92xIRsMaoWkoCLS6oYSnBxxwJzghUvss2utmNs/r+V6NVqNuqQCOpPDC28gKOkucF1JARQylrGMgD8QhNnpSyqgMxqEx2Z7ubiYPV5UbcCQdfpk2pmSylJxUXuox+poxGCUiR100i8SGWPY0JJ4xPyQVViHILUHbKY0hypJovW9HRm0we2Lv1B8qqU1hTjUb8lomaDHH8CIzZN0NqbFqMt6hs7i9KHf4s7YyoJIbRV6lOpU2Jog+ztf9ZuFMtxkFId66KjkcmZRQDcLWF1CD10uRGZEqpJ4Ac6m+lJtuNG+MU8HROSzyOXiO9NcKB7JaFDD7vHD6fWnPRAl0gdW1IT/nKkMHRDaRRe6aD4yaEN7lSHpPlWxj04cahDLsSEb6ksLoFXl5vd4Jq1vLsP2rjEEJAYgyTTdK+QyNJRp0ZmhDN1YKBBPaihKKEOX7dUFYoZuRV0x/EEOU44GDKSyVFxk0ChRW1wwadJleNdZkq8nG1rKMGLz4EScf/vBDE1jzrQSnQpFBcqoPYATC8WLJR9vSU0hRu3ecI9lJohZ4GQDulajHt1jzpj9gZkgDkRZnIUMnXCzoJQGo0Th9Pox7vQl/TMhroChDN3MooBuFrDmMEOni8haZDpDlypxMAqQuR1080nkcvFd3ePQKGUZGQUduYtuf58Fy9PsnxMtqy1EY5kWBUp5RkupxOXinHMcG7KlVA65sr4IchlLOBilY9geXpA8153eXAqb2x++CEtE/FmU+vzSUq7P2KRLsQ8ulZLLbC8XF282rAz9HmVyMMqgxY3Ht3dLeqz4dZYnubZAtLDKMLnk0uKCViVPuo3gjFBpXLwL70GLO69WFkSKNelyV7cZRoM6qVJHcXhKJheMT+ygS+7712oU2jK6x7KXpTs8IAZ0mc/QARN9dD0xehzTEQxy3PSHbXh6V2/Gj51tYnl0Mj+bgHDjzaBRUIZuhlFANwtYXT4USlx6mw2tFXrIZSzlF/RMEwejaJQyVBry88U7n9WWFGDc6YPT68eubjNW1BVDmebydGBiF93+PgtG7V4sT7N/TsQYw5cuasenzmnO6ETTykINhixujNg8MDt9WFiZfDmnVqXA4mpD3IDOHwji5IgjPOJ7rlsf6qOTWsLUb0nuoqHFqEOnySE5AxjPmD35gE6nVkCrkmd9dcGwzQ2VXBa+0ZDJPrrHtnXja0/tD5c/xhMuuUwhQwcIfXQnRuzhqaADZrfkpeKRGsu0qCrUxO11yuQ05kxrLtOia3R6wLC7exyr64uT+n6ImaqDfZnro0v14l2smugYzl5Ad2TQhhKtEhUJ9lSm6vTmxDcLUrW1cwxvHBvB/a+fmHWTNFNZWSAq0aooQzfDKKDLc/5AEA5vIGcllwCwaWEFzkpyeEk2ibvomsp0eb+yIB+JL9gnRxw42GdJe12BSAzoXjsiLAJPdyBKpCtW1eJL71uYseMBwqTLYZsnPC1uYYr9GWsaSrCn2xwzwDg15oQ3EJw3Gbqa4gLUlxZI7qPrN7ugUUrve2ou18HrD06a1JoqsYwx2ZtVM7FcfNjqgdGgDveaZXLSpTht8ZiEcfOm0NeZTNAbaVGVAb4ADw8EGbAkPzUPEG7snNFahq0x+ug45xi0ZnYacyY1lunQb3HB7ZtYtjzm8KLL5Ey65N2gUaKpTJvRwSji71Oyw89aQn32J0ayNxjl8KANi6sLs1ZKK/bRJZqimopndguZuePD9qwMssmmviQXzUcq0SoxThm6GUUBXZ6ze4Sx1bkquQSAW85uxp9vWZ+zzz+VmKGjlQWpES+m/n1wEP4gz8iES2AioHvj6AjkMpb2QJRsqyzUwB/k4Rfx9hQzaGsaSuDwBib1CUWamHA5PzJ0ALC+qQzbuqTtdhIv8KVerLWEyqwzUXaZSsklAJTrs79cfNjmRmWhGuV6FWQssyWXYkB3PMbPbCSTwwuDRpHyuHgxwyj2PfensQJnQ0spRu1edESZqmh2+uD1B/O25LK5XAc+ZXWB2D+3ur446eMtrSnCwYHMlVz2m10o16uT/nfWqxWoLFRndPJspECQ4+igNSsDUUQyGcPpzZnvo3P7Anhx/yAuWlIJlVyGp2ZZ2WW/2QW5jKWUGS3WqhIOCyOZRQFdnrO6QgFdDksu8019iRaMZXZAxnwi3m17fm8/AGB1BiZcAsIUPBkTLgDbKvQZGYiSTeIuureOj6Bcr0ZZiiXFYoZzZ4x9dOLS3Uzt0JsNTm8uxZjDK+mufZ/ZndSAjGZxF10GMgImhxeMCeVBySjXqzFqy34PXYVBA4VcBqNBndGSy4kMXeKAbtTuSavcvrVCB7mM4diQDV5/EKN2T1ITLiOd0VIOIHpp3ECeLhUXRVtdsLvbDLmMYUVdcdLHW1JTiJ4xFyyuzGRB+swu1CbZPydqNeqzlqE7ZXLA7QtmZSBKpGz00b1yaAg2jx+fOLMJFyyuwD/39IdLj2eDPrMLVYXCc1CyhAwdBXQziQK6PGd1C0/WhTksucw3OrUCf7jpNHzirOZcn8qsVGFQQy5j6DI50VSmzVhvpFzGUBoabZ7JcstsERepH+y3JrVQfKr60gKU61Ux++iODdtRV1IAnXr+/A6H++gklF0mOxbbqFdDr1ZkKEPnQXGBMuly8nLDTJRcusM3HaoKNRkruXR5A+H1Iscl7A8z2VNbKi5SK+RoLtfhyKANQ1Y3OE9+8IaovrQANUWaqKVxYgYzbzN0UVYX7Ooex+Lq1KYBL60RMlaHM7RgvD+FBdKiVqMeJ0fsWekREzO72RqIIhL30Ul5zpLqmd19qC7SYENLGa5aUweTw4s3j41k7PjZlsrKAlGxVgWzg0ouZxIFdHlODOgMOSy5zEebFlWES/xIchRyWXhwQKb650Tiv0mmBqJkU+SFXzoLvxljWN1QEnN1wfEhG9oq5k92DhAGWFQY1An76FLZfcUYQ2OZFqcycCfdZE9uqbioXK/GmNMLf5butrt9AVjd/vBNh8pCTcYydD3jwvetVKdCx1Dii3CTw5PSDrpICysNODZki5homlrgEG8fnRjw5msPXZFWiRKtEp2hwSiBIMfeHnPKz8FLQgFdJvqyOOfoN7tTDuhajDpY3f6sTH49PGCFXMawIMvPoWIf3bsdoxk53qjdgzeOjeCKVbWQyRjOazeiVKfC07v7MnL8mZDOz0SJVgWbxz+rMpKzHQV0eW6i5HL+3N0n2SeWXa7OUP+cSKy1z9TKgmwyRmQmF1ald7GwpqEEnaOOcE+WSJxwmU7AOBsxxnB6Sxm2nozfRzdkETJFye4kayzT4pQpAwGdw5tSsGLUq8D5xB67TBsOrSwQf5+qizKXoesOfd/OX1QBm8cfLlWMxWT3pjzhUtReaUD3mDO8gDrVu/4AsKG1DCaHd1p2cdDiBmPI6xt9TeW6cMnlsSEbHN5AyiXvFQYNjAZ1RlYXmJ0+uHyBtDJ0QGbKoKc6PGBDS7kOGmV2S/hlMoazFpTjrY7RjGQan9/bj0CQ46o1tQAAlUKGD6yoxiuHhjJWJptNwSBPeYARAJTohCQErS6YORTQ5bmJkkvK0JHMES+o1mYhQzcbBqIAwgusWEqW6oRLkThYZveUssvu0ITLbN9dzkfrm0sxaHWjZyz2NMp+S2pjsRvLdOgdd6a9umDMkXqGDkDW+uiGQkvFwxm6Ig1sbj+cXn/axxYzmxcurgAQv48uEOQYc3pT7i8VLawygHOEy81SzdABE/vo3jsxuY9u0OJGuV6dkRUs2dJcNrGLTszop1MlsbSmEIcykKGbmGaYWqAt9rPHW/qeqiODViyaodeTc9vKMWLz4PBA4t7SRJ7Z3YelNYWTbuZdtaYOXn8Q/9o/kPbxs23E7oEvwFOacAkIJZcAaDDKDMrfZz4CQNhBB9BQFJJZy2qLUFWowcI0eseiuWF9Pb516eKs303NlIrQHsN0SyJX1BVBEWXBuDgWfr5l6ABhMAoAbO2MPTkuXIKX5IVkY6kWvgBPe3XBmMOL0hSyT+UGcbl4dvroxAxdZA8dkJlddD1jTujVCqwP7d46Hmd1wZjDC86RVg8dgPDzzNvHR1GoUaTVT1pfqkVtccG0wSj5vINO1FSuw4DFDZc3gF3d4yjVqdBQqk35eEtrCnF82D5pFUIq0tk3BggZdo1SlvHBKFa3D73jrrR6nJNxbrsRAPBGmn1uHcN27Ou14EOraye9fUVdEVqNulmxZDydlQWAMBQFAK0umEEU0OU5m1u4I6ufRwMVSPbdclYz3vjqxozvFlzbWIpbzp49w2pqijVoLNOmPbBEo5RjSU3htEmXHcPCnd75mKFbYNSjTKfCEzt6Y/aaieV+yZdcChmBdMouA0GOcacX5Wlk6MThIpkmDvgQbziEA7oMlF12jzlRX6pFqU6Fcr0Kx4djZyNMDuHrS7eHrqFUC41SBpvHn3LQEEnsowtGZGiHrO68nXApEiddnhpzpLRQfKqlNUUIBLmkaaXxpBvQyWQMzeX6jJdcHg0NRJmpio/KQg0WVRnSHlzyzO5eyBjwwVU1k97OGMNVa+qwvWs8XPqcr9L9mRAnB9Oky5lDAV2es7p9MKgVebPUm8wNMhlLea/UXPKNSxfjvutXZ+RYaxpKsLfHMil4OTZkR23x/JpwKZLJGL5x6WJs6xrDT14+FvUxfWYXSnWqpKf8NZYJWY1TY6mXeJmdQvYptZJL4WOylqGzeaCUs/Bd7spQoJKJXXTdY040hHZ5tlUY4i4XN4WGXKTbQyeXMbRVCFmWTAwtOaO1DONOH45FBKOzIUMnTrrc22PGiRFH0gvFp1qaocEo/RY3VAoZylJcHg8ArUZdxksuj4QmeC7K8sqCSOe1G7Hj1BgcntTKm4NBjmd39+OcNmP4hkykK1fXgjGhJDOfTQR0qf1OlYR+lsYdFNDNFAro8pzV5adyS0KypNWox8oUlvpGs7qhGC5fIDxmGxDGwrfNo4XiU129tg43rG/AA2+cwL8PDk57f7/ZldIFflWhBiqFLK0MXXipeAr9YXq1AhqlLIsll25UGDTh7M1EyWV6ny8Y5OgZc4bL/Nor9egYjj3pUvz60i25FD5XKKDLQIZOLOcV++jcvgDMTt8syNAJ33fxYj6VheKR6ku0MKgVaffRCTvoCtLKFrYa9egdd6Zd/hnp0IANRQXKGQ3Uz203whfgKS8Z39o5hj6zKzwMZara4gJsaC7D07t7s7LmIVP6zW4YNIqUJ6xTyeXMo4Auz9ncPhhoBx0heU8cbiAORgkEOU6M2Odl/1ykez64BCvrivBf/9g7bXfcQIpjsWUyhoZSLU6ZUs8ImEIBXSpZCcaYsFw8C2PaASFDV1E4EWjq1AoY1AoMWtLrGRyxe+DxB8MBXVulAXaPH/0xevPCGbo0Sy4BhPugajIQdNWXalFXMtFHJ/YW5nuGzqBRolyvwpaTY5AxYEWaAZ1MxrC4ujCtSZcubwA7usbQHCoHTVWLUYcgT68Meqojg1YsrjakFWgma11TCQqU8pTLLp/Z3QudSo73LamK+Zir1tTilMkZc3dpPugdd6XcPwcABUo5VAoZDUWZQRTQ5Tmr20cTLgmZBepKCmA0qLErNL2ue8wJr39+TriMpFbI8duPrYVCzvCZv+ycNKmx3+xK+QK/sTS91QVisJJKySWAUECXvR66iinj9yszsLpA/H41hEr/xGFAx2P0YJkcHshlDEUZqBJprxJLLtPP0AHCtMutnWMIBnn4+5LvGToAaAp979srDRnpjV9SU4jDA7aUJ74++OZJDFk9+Mx5rWmdh7i6IFODUYJBjqODNixKcwJxstQKOTa0lOLN48nvo3P7Anhx/yAuWVYdt4z8/curoVHK8NSu/C277DenF9AxJpSMUw/dzKGALs8JJZeUoSMk3zHGsLahJHzXVRxUMN8zdIBQZvTL61fj2LAN33x6PzjnsLp9aQ3JaCzToXvMmXLZ0lh44EfqAV22hqIM2zyTFt8D4i669D5fd2hlwUTJpfCzGWvSpbh4XZaBHu7Tm0vx8TMasXGhMe1jAcJgFLPThyODtnBv4dTvWT4SB/qk2z8nWlpTCJcvMC37LcWQ1Y0H3jiB9y+rwvpQGWuqxNUFmRqM0j3mhNMbwOIZ7J8TndtuROeoI+nBJa8cGoLN449ZbinSqxW4ZGkVXtjbD48/cyWqmdSfxg46UYlWRSWXMyhhQMcYq2eMbWaMHWaMHWSM3Rl6+/8wxvYxxvYwxl5mjNUkOhZJHmXoCJk91jQW45TJiVG7J5z1mO8ZOtG57UZ86cJ2PLunH3/ZcgoDZuEiPNWeqsYyLZzeAEZSzJKJJZclKQZ0RoMqKyWXbl8AFpdveoauUIOhNNcWdI85wdjEKPISnQrlenXMKYmjdm9agzIiaZRyfPeKZWnvtBNtaA3toztpCk9LnQ0ZuuZQH126/XOipTVFAJBS2eVP/n0U/mAQX3//orTPQ6tSoKZIk7HBKEcGhb7AxTnYaRpeX3A8ubLLZ3b3oapQgw2hXYnxfGhNHaxuP147PJzSOWaTw+OH2elLO6Ar1iqp5HIGScnQ+QF8mXO+GMAGAJ9jjC0B8GPO+QrO+SoALwD4f9k7zfnL5vZTDx0hs4TYR7fr1DiOD9tRU6ShlSMRPrdpAS5YVIH/eeEQ/i+0XDfVZcbhSZcpll2OObwo1ChSXkRdrldjzOFJe7n5VGLWr2JKtqmqUIMRe3qfr2fMiZqiAqgUE19ze6Uex4ZjZOgcnvCKhnxTW1yAhlIttpw0YdDihkGtmBW/a6sbSqCSyyRd9EvRVqmHSi5LejDKgT4LntzVi5vPbApnDdPVWpG51QWHBmyQMYSno86klnId6koKkuqjG7V78MaxEVyxukbSVPKzWstQYVDnZdnlgCW9CZciytDNrISvZJzzAc75rtCfbQAOA6jlnEc+e+gA5O+4nlkqGOSwuX005ZKQWWJZbRGUcoZd3WYcG7KjjcotJ5HJGH527SpUFxXgvv8cB5D6nqN0d9GZHN60skXlejWCPPN7loZt4g666T10gSBPq29P2EE3+fvdXmlAx5Ataumqye5Ne2VBNm1oKcW2zjEMWFzh1Q757qwF5dhz90WoT2OheCSlXIb2Kn1Sqws45/j+/x1GcYESd5zflpHzAIRA6MSIIyPTG48MWNFUrkt6pUkmMMZwbrsR750wwRdjh+ZUz+/tRyDIcdXqOkmPV8hluHJ1LV4/OgxTlnpxU9UXqp5Ip4cOAIq1KsrQzaCkbk0yxpoArAawNfT37zPGegB8FJShyziH148gB5VcEjJLCAvGi7CjawwnRuzhoRNkQpFWifs/tgZqhQxyGYu6q0mK2uICyGUs5UmXY2mWE4qZq0wPRhkK9clN7QebWF2Qetlld8TKAtGCCj0c3kDUSZcmuycjEy6z5YzWMlhcPrx7wpT3Ey4jaVWZzSQurS7CwX6L5EDq1cPDeO+kCXdd1J6RgTei1go97B5/RnpLjwzaclJuKTq3zQi7x49dp6RNonxmdx+WVBdiYZX0m3hXramFP8jx/N7+VE8zK/rGhQxdbUm6PXRKmJ2+vF7PMJdIDugYY3oATwH4opid45x/i3NeD+BRAHfE+LjbGGM7GGM7RkZSGwM7X1ndwjQ4GopCyOyxpqEYO06Nw+sP0kCUGJbWFOG+G1bjtnNbJJUnRaNSyFBTrEmr5DLVCZdAxHJxW4YzdNboGToxYBlIMaBzeoUL7anldeLP6NQ+Opc3AIc3kOcZOqFs0eb2z4qBKNmypKYQ406fpCmoXn8Q//uvw2g16nDD+oaMnkdLuXADqyPNskub24fuMScWJxEcZdqZC8oglzG8KaGP7uigDft6LQmHoUy1qKoQS6oL8XSeLRnvN7vSutkmKtGq4A9y2FJc0k6SIymgY4wpIQRzj3LOn47ykL8B+HC0j+WcP8g5X8c5X2c0Zma61Xxhcwu1x6kudiSEzDyxjw4AFszjpeKJXLy0Cl+7JL1hDI2lupQzdCaHJ61gpdyQpQydzQOlnKFEO/ncKouEzzeU4uqCnjHhrvvUUr/2yuirC0yOzC0Vz5bqogI0hXopU1lQP1csrREyWQf7EpddPrr1FDpHHfjWZYtT7h+NpbVCuFmQ7mCU/b3CgBdx4EsuFGqUWNNQjDcS9NFxzvGd5w/CoFHgQ6uTC+gAIUu3r9cSczBRLvSbXagq1KR8s01UHFoubnZQH91MkDLlkgF4GMBhzvnPIt4eWXj9QQBHMn9685vVFcrQUUBHyKyxNmIcOZVcZldjmRanxpLP0AWDHONOX5oZuuwEdMNWD4x69bRVAeU6NRQylvIuuqkrC0TFWhWMBjWOTVldkMml4tkkZulmSw9dNiyuLgRjSNhHZ3Z68YtXj+PsBeXYtLAi4+dRVaiBViVPezDK2x2jUMgYTktzlUK6zm0z4kCfNe7v+LN7+vDuCRO+esmilHpyr1xdC4WM4amdvemcakb1pbmDTiTelKJddDNDyu2ZswDcCOD80IqCPYyxSwH8kDF2gDG2D8D7ANyZzROdj6wu4a4GlVwSMnvUFBegqlCD6iINZdezrLFMC7PTB0uSk9QsLh8CQY7SNIKVQo0CKrks47vohm3uaRMuAWGgTDqrC2IFdICQpTs+ZdKlmKHL55JLQOijAzCreugyTadWoLlMl3B1wa9e64DV7cO3LlsM4V59ZjHG0GLUpZ2he6djFKsbinM+tVRcX/B2jCXjFqcP33vhMFbWF+MjKZavluvV2LSoAk/v7oNf4gCWbBN20KX/+1SiE17/KKCbGVKmXL7NOWfiioLQf//inH+Yc74s9PYPcM7zqwh4DrBSySUhs9KNZzTimrXSpp2R1IUnXY4ldwEp7qBLZygKYwzlelXKe/BiGbZ6pvXPiSoL1Sln6HrGnNCrFSjRTn89aauYPulS3LGXr2sLRO9bUoW7LmzH2QvKc30qObWkpjBuhq5z1IE/v9eF69bVZ3XYSKsxvdUFZqcX+/osOCsP/j2X1RahRKuMub7ghy8dgdnlw/9+aFla5YlXr63DiM0jqV8v2wJBjkGLO+0ddICQ/QcAM60umBGU+sljNnEoCu2hI2RW+dymBbk+hXkhchfdirpiyR83Fgro0im5BACjQZ3x5eJDNjfWxyg1qyrS4Mhgar02wsoCbdTMTFulMOmyz+xCXYnwPQ2XXOZ5hq5AJcedF2Zu9P5stbSmCC/sG8D5P3kdJToVSnUqlGpVKNGpUKZT4bUjw1DKZfjS+9qzeh4t5Xr8c28/XN5ASisH3jthAufIiwBdLmM4p82IN4+PIhjkk8qgd54aw2PbunHr2c1p9/ptWliBUp0KT+7sxfmLKtM97bT0jbvgC/Dwc2s6qORyZlGkkMfEkkvK0BFCyHRi+WCyg1HGQuWE6QZ05Xp1ylMno/H4AzA7fXEydBq8fnQEnPOkS+ZOmRxYEKOnU5x0eXzIHhHQeVCglGd8xD7Jjg+vqcWQ1Y0RuwfjDi96xpzY22PGuNMLX0DIvH71koVpTy5MpLVCB86FjOCSmuQzgW93jEKnkmNlfXHmTy4F57Yb8c+9/Tg8aA0Hbr5AEN965gCqizS466L0A2SVQoYrVtXg0S3dGHd4UZLm81I6xLLdJdXpD6QpKlCCMdBy8RlCz9R5zOr2oUAph0qR2UlUhBAyF2hVClQY1EmvLhBLLtMtJyzXq7G/L37fUjKGY+ygE1UVauD0BmDz+JMalhUMcvSMu3DB4uh3/8XhPceGbNi0SBiWISxez+/sHJlQUajBPR9cOu3tnAtj421uP6pnoM9QXF1wctSeUkD3TscoNrSUZXwCZ6rObRMyhW8cGwkHdI+804kjgzb87sa10GWoz+/qtXV45J0uPL+vHx8/oykjx0zFoQEr5DKGtgxMaJbLGIoKlLRcfIbkx28Micrq8sNA5ZaEEBJTY5k26YBuLFROKDbtp6rcoILJ4UUwmJnFucOhASvGwuiBZlVokmOyg1GGbR54/cFpKwtE4qTLyMEoo3ZPSlP7SH5hjKFQo0RtccG0yanZ0FyuA2PAieHkB6P0jDnRZXLi7Lbcl1uKKgo1WFRlCPfR9Y478fNXjuPCxRV435LMlUcurSnC4upCPJnjaZeH+q1YYNRDo0y+XDaaEq0qXOJOsosCujxm8/hQWEDlloQQEktjmS6loSgGtQJqRXoXLeV6NQJBDrMrdklRIMjx5X/sxc5T4wmPJy4Vr4xRFidm7pIdjBJvwqWovVI/aRedye5FeQ5Lv8jsVKCSo6aoACdHkx+M8u4JYZpkPvTPRTqv3Yidp8bh8Phxzz8PAQDu+eDSjE8KvXptHfb1WnA0xT7ZTDg0YE0psxpLsVZJQ1FmCAV0eczq8tNAFEIIiaOxVIshqwcub0Dyx5gcXpRmoJxQyi66bZ1jeGpXr6Q772KGriJGhk5cnj2YZIZOSkDXVmHA8WF7ONuY7uJ1Mn+1VuhxIoVJl28dH0WFQR2z1zNXzm03whfg+O7zh/Dq4SF88cK2cK9pJl2xqkbYSbcrN1m6MYcXAxY3lmRwCmqJVkVDUWYIBXR5zOqmDB0hhMTTWC6sLuhOYsH4mMOT9kAUICKgi7OL7qUDAwCA3d2JM3RDVjcUMoZSbfRzEzN0Qylk6BhD3GXB7ZUGOEOTLoNBDpPdSyWXJCWtRh1OjjgmrcFIJBjkePeECWcvKM/Kjrx0rGsqQYFSjsd39GBRlQG3nN2clc8T3km3Kzc76Q6F1l5Qhm52ooAuj9ncfppwSQghcTSmMOnSZPemtYNOZDQIx4i1iy4Y5Hjp4CAAYeCIw+OPe7xhmwdGgzpmr5NGKUexVpl8yaXJgZqigrgDttpDQxA6hu2wun3wB3ne76Aj+anFqIfTG0jq5/TwoBVjDm9e7J+bSq2QhxfYf/9Dy7I6sOXqtXUYtedmJ92hAXHCJWXoZiMK6PKY1eWjkktCCImjSVwunsRglDGHN7MZuhi76Hb3mDFk9eBDq2sR5MC+3vgTMYesblQkmERYVajBoCW5ZebdY8645ZaAUHIJCIHnxFJxKrkkyWs1Cr+TyQxGeadD6J/Lx4AOAL52ySL8+iOrsbYx+o7ITIncSTfTDvVbUVOkyejahBKtEk5vAB6/9JJ4khoK6PIU55xKLgkhJIEirRJFBUrJg1E45xh3elGqSz/7VFSghFLOYvbQvbh/ACq5DF8K7ara3RO/7HLE5om5g05UWajBoNWV1Hl2j7kSBnRFWiUqDGocG7LDFPp6yjLwPSLzT6txYnWBVG93mLCgQh+e5JpvFlYZcPmKmqx/HnEn3auHhjE+w9MhMz0QBRAm6AKgsssZQAFdnnL7gvAFeFK7hgghZD5KZnWB1e2HL8Azkn1ijKFMp8ZIlB46zjlePDCIs9vKUV+qRXO5Dru7zXGPN2R1ozLGQBRRshk6p9ePUbsHDWWJhzi0VxpwfNgW3tNHQ1FIKioMaujVCpwYlhbQefwBbOs05d10y1y5em0dvIEg/rm3f8Y+p9sXwIkRR0bLLQGh5BIAlV3OAArocuCFff24/sH3EIizu8jmFu5m0B46QgiJr7FMJzmgE7NPmSi5BIRddNEydAf6rOgzu3DJsioAwOr6YuzpMcccFOHxBzDu9KEixsoCUWWRBiaHBz6JQxN6xoRsXqwddJHaKvU4PmQPB6gU0JFUMMaEwSij0rLmO0+Nw+0LUkAXsrSmCEtmeCfd0UEbAkGe8QxdiVZISow7KEOXbRTQzbBAkOPH/z6KLSfH0BHn7pU1FNBRySUhhMTXWKpFn9klKcgRl9xmLKDTq6MGdC8eGIBcxnDRYmH58KqGYozYPOgzRy+XFIMoKRk6zidWHCQiZWWBqL3SAJcvEO71izVtk5BEWox6yRm6dzpGIZcxnN6S3f602eTqtXXY32fBkUHrjHy+QwOhCZfVRRk97kTJJWXoso0Cuhn2yqGh8J3keGOsLS5hGhoNRSGEkPgay7QIBDn6xhP3loXLCTPUH2bUqzFqm3yxIpZbntFSFh4wsLq+BABill2Gd9AlyNAlu4tOnP4pJaBrC+3/2nLShBKtEoosTvMjc1urUYd+ixtOb/zJroDQP7eqvpimekcI76SboSzdoX4rDGoF6kpirzZJRYkulKGjHrqso2frGfbQWydRX1qAYq0ybj+FNVxySU9whBAST6M46VLCLrpwhi5D5YTlBjVMDs+kUsqjQzZ0jjrC5ZYAsKjaALVChj095qjHGQ6NeI+1VFyU7C66njEnDGpFuPQpnrZKYdJln9lFO+hIWlrEwSgj8csuLU4f9vea83a6Za6U6dU4f1EFntndL7m8Oh2HBqxYXFMYc2VKqqiHbuZQQDeDdp4aw85T47j17Basri+OO/HM5hbuahUVUIaOEELiaSqTvotuLJyhy1zJpS/AYXFN3IF+cf8gGAPet7Qy/DalXIbltUUxKzOkZuiqkszQdY85UV+qlbSsuahAGS75zNT3h8xP4qTLEyPxyy7fO2lCkIP656K4Zl09Ru0evHpoKKufJxjkODxgzfhAFEDYnalRyqjkcgZQQDeDHnqzE0UFSlyzrg6rG0pwPLTANRpr6OKAplwSQkh8RoMaBUq5pMEoJrsXWpUcGqU8I59bnJYZ2Uf30oFBnNZYOi04W91QjAP9Vnj90++4D1ndkMtYwkCqRKuESiGTvLRZyg66SO2hLB0tFSfpaCzTgrHEGbp3OkahVcmxqr54Zk5sFjl/UQVqiwvwx3e7svp5To054fQGMj4QRSQsF6eSy2yjgG6GdI068O9Dg/jYhgZoVQqsbigG58C+nuiLZmkoCiGESMMYQ0OpVmKGzpOxgSiA0EMHACOhPrqTI3YcHbLh/curpj12VX0JvP4gDg9MH3QwbPXAqFcnLHlijKGyUC0pQxcMcvSMuyStLBAtCPXR0YRLkg6NUo76Ei1eOzI8KXs91Tsdozi9uRQqBV2OTiWXMXz8jEZs7RzDof7sDUc52C9ch2YjQwcIg1EoQ5d99Bs0Qx5+uxNKmQw3ndEEAFhZXwzGYg9Gsbr8UMoZ1PQkRwghCUndRWdyeDPaH1YeWgQuZuhePDAIAJP650SrG4oBRH/eH7J5Ek64FFUVaiRl6IZtHnj9QUkrC0Riho6WipN0femidhwesOJDv30HJ6OUXvaZXTg56sDZbcYcnN3scN1p9dAoZfhTFrN0h/qtUMgY2ir1WTl+iVZJGboZQNHCDBh3ePHEzh5cuboGFaGG9kKNEguMeuyO0SBvc/tQqFFK6nsghJD5rrFMi+4xJ4Jx9nsCQsllJvvDxNJEMaB76cAgVtUXo7po+rS46iINKgvVUZ/3h61uGBP0z4kqCzWShqIkM+FS1F5JGTqSGVeursWjt54Os9OHK37zDl4/Ojzp/e8cHwVA/XPxFGtV+NDqOjy7pw/jjuxkuQ4NWLGgQg+1IjNl6FOV6FQ0FGUGzPuAblvnGN44NpLVz/HXLafg9gVx6zktk96+uqEYu7vHoy6atbr9VG5JCCESNZbp4PEHMWSLH+iMObwZLbksLlBCLmMYtXvQM+bE/j4L3h8lOwcI5ZKrQgvGpxpONkNnccdcUi4Sd9A1JhHQLa8txk1nNOL8RRWSP4aQWE5vKcNznzsLtcUFuOWP2/HQmyfDP7dvd4yiXK8O30Qg0d18ZhM8/iD+vr0nK8c/1G/F0prM7p+LVKJVZi0YJRPmfUD3k5eP4t6XjmTt+G5fAH96rwubFhrDpSyi1Q0lGHf6opYJWV0+2kFHCCESNYYnXcYuu+ScY8yR2QydLDTIZNTmxUuhcsv3L6uO+fjVDSU4ZXLCFDFExesPYszhDa8kSKSqSAOPPxi3NwkQVhbIGFBTLH23lEohw3euWJbUxxAST32pFk9/9kxcvLQK3//XYXz5ib1w+wJ4p2MUZy8oo0qkBBZWGXBmaxn+8l4X/BleYTBi82DY5snaQBRAGIpicfkSVk+Q9Mz7gO7ctnIc7LdOmlCWSc/u7sOo3YtPndsy7X3hfooo6wtsbh/toCOEEImaxF10cQaj2D1+eAPBjGboAKHscsTuwYsHBrCkujDuEJLVoWl+e3vN4beN2MWVBRIzdOLqggRll91jTlQXFdDACZJzWpUCv/nIGtx1YTue3tWHS+97CyaHl/bPSXTTmU3ot7jxSoZXGBwKDWjK1kAUQCgbDXLEnOpOMmPeP8uf2y40474dquXOpGCQ46G3TmJZbSHOaCmb9v62CgN0KnnUBeNCySVl6AghRIrqIg0UMhY3QxdeKp7pgM6gxuEBK3Z1m2OWW4qW1xVBLmOTnvfFpeKSM3Shxw0kmHSZ7MoCQrJJJmO488I2PPCxteEprRTQSXPh4sqsrDAQp2dmM6Ar0QrJCRqMkl3zPqBbVlOEEq0Sb2ahj+71Y8M4MeLAp85piVpSIJcxrKwvjh7QuXy0g44QQiRSyGWoKynAqbHYAZ1JXCqe4YEf5XpVOLiKtq4gklalwMJKw6Tn/SGrkKEzSszQiYHfUMKAzkUBHck7lyyrwnOfOwu//shqKu2VSC5juOnMzK8wODRgRW1xAYq02bveLNEKz7c0GCW75n1AJ5MxnN1mxJvHRxM2mCfrwTdPoqZIg0uXx+unKMbhAStc3sCkt1vdPhioh44QQiRrLNPFLbl8cf8AAKCqMLMXkWIgtqBCjwUVhgSPFp739/aYwz0lI7bkMnTi4+KVXDo8fozaPUntoCNkprRVGnD5ippcn8asct26BhQo5RldYXCo35LV/jkAKA4Fi7SLLrvmfUAHCH10o3YPDg/YMnbMfb1mbDk5hlvOboZSHvvbvLq+BP4gx4H+iQXjXn8Qbl+QMnSEEJIEcRddtJtzD7xxAg+91Ykb1jdgcXXioCsZ4nLxROWWolX1xbB5/DgR2s01ZPVAHhquIoVKIUOZThV3dUHPuJCppAwdIXNDkVaJD62pzdgKA6fXj5OjjqyWWwIRGToHlVxmU8KAjjFWzxjbzBg7zBg7yBi7M/T2HzPGjjDG9jHGnmGMFWf9bLNE7KN783jmyi5//1YnDGoFrjutPu7jVkVZNGsLNY7S2gJCCJGusUwHm9s/rVfj0a2n8MMXj+ADK2vwvSuXZXyqXmOZDjIGXLYidjVGpNUNJQAQLrsctrlh1Kshk0k/r8rQ6oJYuk0U0BEy12RyhcHRQRs4B5ZmOUNHJZczQ0qGzg/gy5zzxQA2APgcY2wJgFcALOOcrwBwDMA3snea2VVZqMGiKkPG+uisbh9eOjiID6+tSzipslyvRkOpdlI/hdXtBwAaikIIIUkQ961Fll0+t6cP//3sAZy/qAI/u3Yl5EkETVJdsKgCb3xlExZVSbswainXoVCjCC8YH7J6UCFxB52oqkiDQWvs6cziDjoK6AiZO9or468w4JzjP4eH8OH738UnHtkWd83BQXEgSpYDOoNGARkDzDQUJasSBnSc8wHO+a7Qn20ADgOo5Zy/zDn3hx62BUBd9k4z+85pK8eOrnE4vf7ED07gpQOD8PqDuHJ1raTHCwvGzeG/W0O7hQxqytARQohUTeWTd9H95/AQvvyPvTitqRS//eiauOXv6ZDJGOqTCJxk4YFYQmXGsM2DCoO0/jlRVZEmfsnlmBMGtSLcv0IImRtujrLCIBjkeOnAIC7/1dv45J924JTJic1HR3D/6ydiHufQgBWFGgVqszyYRiZjKNaqKEOXZUm9ujHGmgCsBrB1yrtuAfBihs4pJ85tN8IbCGLrybG0j/XPPf1oLNNiZV2RpMevri/GoNWNAYsLAGALZ+johZgQQqSqK9GCMSGge++ECZ99dBcWVxfi4ZvWQaOU5/r0JlndUIJjQzY4PH4MW93JZ+gKNRhzeOH2Baa9r2PYjq2dY6gv1dLSZkLmmAsWV6KupACPvNuFQJDjhX39uPS+t/CZv+6Ew+PHvVevwHvfOB9XrqrBL/5zfFJLT6RD/VYsqSmckeeIYq2SMnRZJjmgY4zpATwF4Iucc2vE278FoSzz0RgfdxtjbAdjbMfISOZXA2TKaU2lUCtkeCPNssthqxvvnhjFFStrJP+STO2nsIZ76KjkkhBCpNIo5agq1OC1I0O49U/bUV+qxZ9uWZ+w9D0XVjcUI8iBXd3jMDm8qEw2QxeadDkcKrvknOPNYyO4+ZFtuPBnb+DkqAMfOf3/t3fvwXGV9xnHn58uK0uypJUsyVa0wsYXaoytCxEYCLG5JC0BBkMKhHBLJ8lk0pQ0DUkG0nYo0zZ/NAmUJjSTMJCETkmgIYQwwUmBpokJN8cNko1tLgYcLBlfZCzLlmXJst7+sUdiMbrsSnt2zzl8PzM7Wp09R+fdxzvv+Lfve95zQtbbDSC/CgtMnzhzgda//pbOv+03uuFHz+vosRHd8bFWPXHjal3Z3qTiwgL946XLNa9ylr5wf4cODb5z9tmxEacXd/VpWUN6Aw8zVc0Ine/SKujMrFjJYu4+59xDKds/IeliSde4Cdb8d87d5Zxrd86119XVZaPNvphVXKiVC+foyRkujPKLjW9qxEmXtKY33VKSTm6oVKyoYOxblNEpl6xyCQCZmT+nTJ1dB1RdHtN/fmpl1m8ini2tibgk6bHNyWlTmY7Qza1KFnTb9/Xrx+vf0J/+6zpd//31eqG7Tzd++CQ9ffN5uvaM+VltM4BguLK9SdVlxSopKtSdV7fpsS+u1qVtjSpKmVZeOatYd1zVqq79h3XrI5vfcfzrPf06cnTE9+vnRlWXFXNjcZ9NOQRkyWGmeyRtdc7dnrL9Akk3SVrtnJv4Tq4hsmpJrf750a3q7h2Y9pzin3fu1PLGSi2un532MbGiAq1orHrXCB33oQOAzDQn4nq9p1/3fXql5lVlNuqVS9XlMZ1YW67HtuySJM2dxpRLSfrkD3+v4RGnZQ2Vuu2KFl3c0qCSomBNLwWQXVVlxXrmq+crVlgw6eq4py2o0Q3nLta3fr1N5/xJ3di9/zZ7t8rye4XLUfGy2NgiLPBHOiN0H5B0naTzzKzDe1wo6U5JFZIe97Z918+G5sLq0dsXTHPa5faefnXu6NWalvRH50a1NcW1qfuAhoZHdPDIsApMKo9R0AFAJm6+YKl++5VzNX9Oeb6bMqW2prh2e1MmM10U5YSaMi2pn63zltbrgc+coUf/+mz9+fsTFHPAe8Ss4sK0bnXy+fOXqLUprr99aJN29ibXatjyZp9ihQVaVJf+4MNMJEfomHLpp3RWufydc86cc83OuVbvsdY5t9g515Sy7bO5aLCfFtfPVkPVrGkXdI907pSZdHFLevciStV2QrUGh0f04q4+9Q0cVcWs4ozuSQQASK6oFrQFUCYyeh9SKfMpl6WxQj1+42rddX27Vi6cw+InAMZVXFigf7uqVcdGnL74QIeOjTht2dmnJXNnK1bkz8q/x4uXxXTk6IgGht69iBOyIzf/kiFhZvrgklo9ta1n0nt3jMc5p4c7urXyxBo1VGU+XbNt7Abjveo7Msx0SwCIuLam5IJYBSbNKc+soAOAdM2fU65bLzlFz73+lr637tXkCpcNuZluKWnsWmZG6fxDQXecVSfVqe/IsDq7DmR03OadfXptb7/WZLAYSqqGqlmaW1mi59/Yr4NHjrIgCgBE3NKGCpUUFaiuosSXG54DwKjL35/QRSsadNtjL2tf/1DOFkSRklMuJQo6P1HQHefsxbUyy/w6up93dKu40PSR5fOmdV4zU1tTtZ7f0au+gWFuWQAAEVdcWKDWprjvN/YFADPT1y5brvqK5GyAXI7QxcuSI3Tci84/FHTHiZfF1JyIZ3T7gmMjTo907tTqk+rHPrTT0XZCXH/cd1jb9/UzQgcA7wHfvKJFt1/Zmu9mAHgPiJfF9O2Pt+n8pfVq9m6dkgvVZUy59BsF3ThWL6lVx45eHUjzm4T1r7+l3X2DWtP6vhmdd/QG43sODgbyRrgAgOxqqinTgtrgr8gJIBraF9Tonr84TaWx3C0eNXpblq79Azk753sNBd04Vp1UpxEnPfVqT1r7P9LZrbJYoT508twZnXdFY9XYdRRMuQQAAEDYxctiaqop1aYM16dA+ijoxtHSFFdFSVFa19ENDh/T2k279GenzJvxtx2lsUKd3FAhSUy5BAAAQCQ0J+Lq7OrNdzMii4JuHMWFBTpr8Rw9+UqPnHOT7rvu5R4dGDiqS2Y43XLU6DLWlaUUdAAAAAi/lkSVuvYPaN+hwXw3JZIo6Caw6qQ6dfcO6NW9/ZPu93BHt2rKYzp7cW1Wzjt6PzruQwcAAIAoGF2EZSPTLn1BQTeBVUvqJE1++4JDg8N6YstuXbSiQcWF2YnyrEW1qp1doqXzKrLy9wAAAIB8Wt5YJTMx7dInFHQTaKop04m15ZPevuCxzbs0ODwy49UtU82rmqUNf/+hnC4nCwAAAPhldkmRltTPZoTOJ8zrm8SqJbX60fo3dO3dzylRXeo9ysZ+PtyxU43xUp3q3W4AAAAAwLs1J+L6zUt75JyTmeW7OZFCQTeJ689aoH39Q9qxf0BPbN2tnkPvviHi585ZpIICPpQAAADARFoSVXrw/7rU3TugRHVZvpsTKRR0k1hUN1t3Xn3q2O+Hh4a1s3dAO/YPjK3Uc+0Z8/PYQgAAACD4UhdGoaDLLgq6DJTFirS4vkKL61mwBAAAAEjX0oYKFReaOrt6deGKhnw3J1JYFAUAAACAr0qKCrWsoVIbd7AwSrZR0AEAAADwXXMirk3dBzQy4vLdlEihoAMAAADgu+ZElQ4NDuu1nkP5bkqkUNABAAAA8F1LU1yS1Mm0y6yioAMAAADgu0V1s1UeK9TGrt58NyVSKOgAAAAA+K6wwLS8sUqdXYzQZRMFHQAAAICcaGmKa8vOPg0Nj+S7KZFBQQcAAAAgJ5oTVRo6NqKXdh3Md1Mig4IOAAAAQE60JOKSpE6uo8saCjoAAAAAOZGoLlVNeYyFUbKIgg4AAABATpiZmhNV3Logi6Ys6Mysycz+18y2mtlmM/uCt/0K7/cRM2v3v6kAAAAAwq45Edcrew7q8NBwvpsSCemM0A1L+pJz7mRJZ0j6KzNbJukFSR+VtM7H9gEAAACIkJZElUac9EJ3X76bEglTFnTOuTedc3/wnh+UtFVSo3Nuq3PuJb8bCAAAACA6mr2FUbiOLjsyuobOzBZIapP0nC+tAQAAABBpdRUlaoyXqmNHb76bEglpF3RmNlvSTyX9jXMu7fFRM/uMmW0wsw179+6dThsBAAAAREhzokobu1gYJRvSKujMrFjJYu4+59xDmZzAOXeXc67dOddeV1c3nTYCAAAAiJDmRFxvvHVY+/uH8t2U0EtnlUuTdI+krc652/1vEgAAAIAoa0lUSZI2djNKN1PpjNB9QNJ1ks4zsw7vcaGZXWZmXZLOlPSomf23ry0FAAAAEAnLE1UykzZyHd2MFU21g3Pud5Jsgpd/lt3mAAAAAIi6ylnFWlhbrk5WupyxjFa5BAAAAIBsaEnE1dl1QM65fDcl1CjoAAAAAORcc6JKew8OalffkXw3JdQo6AAAAADkXHNTXJLUuYOFUWaCgg4AAABAzi1rqFRRgXEd3QxR0AEAAADIuVnFhVraUKGNFHQzMuUqlwAAAADgh29c3qI55bF8NyPUKOgAAAAA5MXJDZX5bkLoMeUSAAAAAEKKgg4AAAAAQoqCDgAAAABCioIOAAAAAEKKgg4AAAAAQoqCDgAAAABCioIOAAAAAEKKgg4AAAAAQoqCDgAAAABCioIOAAAAAELKnHO5O5nZXkl/zNkJ01crqSffjYgw8vUX+fqHbP1Fvv4iX3+Rr7/I1z9k66908p3vnKvL1glzWtAFlZltcM6157sdUUW+/iJf/5Ctv8jXX+TrL/L1F/n6h2z9lY98mXIJAAAAACFFQQcAAAAAIUVBl3RXvhsQceTrL/L1D9n6i3z9Rb7+Il9/ka9/yNZfOc+Xa+gAAAAAIKQYoQMAAACAkApVQWdmF5jZS2a2zcxuTtn+gJl1eI/tZtYxwfE1Zva4mb3i/az2tl+TcnyHmY2YWes4x9/nnf8FM/u+mRV7283MvuW1a6OZnepPAv4KcL5LzewZMxs0sy/78+79F+B8r/E+txvN7Gkza/EnAX8FON81XrYdZrbBzM72JwF/+ZhvsZnda2abzGyrmX11guNPNLPnvOMfMLOYtz30/W+As6Xvla/50vfK13zpe+Vfvimvn2Zmx8zs8iy+7ZwJar5mdo6ZHUhpwy2TvhHnXCgekgolvSppoaSYpE5Jy8bZ7zZJt0zwN74u6Wbv+c2S/mWcfVZIem2C4y+UZN7jx5L+MmX7L73tZ0h6Lt95RSzfekmnSfqapC/nO6sI5nuWpGrv+Uf4/GY939l6e3p7s6QX851XkPKVdLWk+73nZZK2S1owzvH/Jekq7/l3o9L/Bjxb+l5/86Xv9Tdf+l4f801p368lrZV0eb7zilK+ks6R9It030uYRuhOl7TNOfeac25I0v2S1qTuYGYm6Uol/zM1njWS7vWe3yvp0nH2+fhExzvn1jqPpPWSEil/9z+8l56VFDezhrTfWTAENl/n3B7n3O8lHc3oHQVLkPN92jm339vtWb39uQ6TIOd7yNsmSeWSwnjhsp/5OknlZlYkqVTSkKS+cf72eZIeHOf4sPe/gc2WvneMX/nS9yb5lS99b5Jffa8kfV7STyXtyfB9BUXQ801bmAq6Rkk7Un7v8ral+qCk3c65Vyb4G3Odc29Kkvezfpx9PqaJ/9EkJYdRJV0n6VcZtC3ogpxvFIQl308pOdoRNoHO18wuM7MXJT0q6ZOTHR9Qfub7oKR+SW9KekPSN51zbx137BxJvc654XHOH/b+N8jZRkFY8qXvVfbzpe+V5FO+ZtYo6TIlR5XCKrD5es40s04z+6WZnTLZGwlTQWfjbDv+25YJvz1P6wRmKyUdds69MMWu35G0zjn3ZAZtC7og5xsFgc/XzM5V8j8VN023DXkU6Hydcz9zzi1V8pu3f5puG/LIz3xPl3RM0vsknSjpS2a2MIPzh73/DXK2URD4fOl7JzTjfOl7JzXTfO+QdJNz7tg0zh0UQc73D5LmO+daJH1b0sOTnSxMBV2XpKaU3xOSdo7+4g1pflTSAynbfuBdSLjW27R7dCqO9/P4IeKrNPW37/8gqU7Sjem2LSSCnG8UBDpfM2uWdLekNc65fRm8r6AIdL6jnHPrJC0ys9p03lSA+Jnv1ZJ+5Zw76pzbI+kpSe3Hnb9HyamUReOcP+z9b5CzjYJA50vfKykHn1/6Xl/ybZd0v5ltl3S5pO+Y2aUzeK/5ENh8nXN9zrlD3vO1koon/fy6AFyUmM5DUpGk15SsckcvXDwl5fULJP12ir/xDb3zwsWvp7xW4P3DLpzk+E9LelpS6XHbL9I7L8pfn++8opRvyuu3KrwX5gc2X0knSNom6ax85xTRfBfr7QvzT5XUPfp7WB5+5qvkqMQPvP6zXNIWSc3jHP8TvfPC8c95z0Pd/wY525TX6Xv9+ezS9/qbL32vj/ket88PFc5FUQKbr6R5KZ/f05Wctjnh5zfvYWYY/IWSXlZyRZq/G+fD9Nkpjp8j6X8kveL9rEl57RxJz05x/LB37g7vcYu33ST9u/faJknt+c4qYvnOU/I/032Ser3nlfnOK0L53i1pf8r2DfnOKmL53iRps7ftGUln5zurIOWr5Ep0P/Ey2iLpKxMcv1DJxWa2efuXeNtD3/8GOFv6Xn/zpe/1N1/6Xh/zHacdoSvogpyvpBu8YzuVXDRp0i9+Ris/AAAAAEDIhOkaOgAAAABACgo6AAAAAAgpCjoAAAAACCkKOgAAAAAIKQo6AAAAAAgpCjoAAAAACCkKOgAAAAAIKQo6AAAAAAip/wcJ3pu4MQTksAAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"for loop_idx in range(100):\n",
|
||
" current_timestamp = 1500 + 300*loop_idx\n",
|
||
" print(f\"Timestamp {current_timestamp}\")\n",
|
||
" \n",
|
||
" u_1 = float(df_power.loc[df['time'] == (current_timestamp - 300 * 1), 'Heat'])\n",
|
||
"\n",
|
||
" y_1 = float(df.loc[df['time'] == (current_timestamp - 300 * 1), 'SimulatedTemp'])\n",
|
||
" y_2 = float(df.loc[df['time'] == (current_timestamp - 300 * 2), 'SimulatedTemp'])\n",
|
||
" y_3 = float(df.loc[df['time'] == (current_timestamp - 300 * 3), 'SimulatedTemp'])\n",
|
||
" \n",
|
||
" real_x0 = np.array([u_1, y_1, y_2, y_3])\n",
|
||
" iter_idx = (df['time'] >= current_timestamp)\n",
|
||
" real_W = df[iter_idx].iloc[:N_horizon, [5, 2]].to_numpy()\n",
|
||
"\n",
|
||
" real_p = casadi.vertcat(\n",
|
||
" casadi.vec(real_W),\n",
|
||
" casadi.vec(real_x0)\n",
|
||
" )\n",
|
||
"\n",
|
||
" res = solver(p = real_p, lbg = real_lbg, ubg = real_ubg)\n",
|
||
" \n",
|
||
" df_power.loc[df_power['time'] == current_timestamp, 'Heat'] = res['x'].reshape((N_horizon, -1))[0, 2]\n",
|
||
" \n",
|
||
" power = np.array(df_power[['time', 'Heat']].dropna())\n",
|
||
" eng.workspace['power'] = matlab.double(power.tolist())\n",
|
||
" eng.set_param('polydome', 'StopTime', str(current_timestamp + 300), nargout = 0)\n",
|
||
" eng.workspace['result'] = eng.sim('polydome')\n",
|
||
" \n",
|
||
" \n",
|
||
" dict_simulation = {}\n",
|
||
" dict_simulation['values'] = np.asarray(eng.eval('result.SimulatedTemp.Data')).reshape(-1)\n",
|
||
" dict_simulation['time'] = np.asarray(eng.eval('result.SimulatedTemp.Time')).reshape(-1)\n",
|
||
" \n",
|
||
" df_simulation = pd.DataFrame(dict_simulation)\n",
|
||
" #df_simulation['time'] = df_simulation['time'].astype(int)\n",
|
||
" df_simulation.set_index('time', inplace = True, drop = True)\n",
|
||
" \n",
|
||
" df_simulation['timestamp'] = df.index[0] + df_simulation.index.map(lambda x: pd.Timedelta(seconds = x))\n",
|
||
" df_simulation = df_simulation.reset_index().set_index('timestamp')\n",
|
||
" df_resampled_5 = df_simulation['values'].resample('5min').mean().pad()\n",
|
||
" df_simulation = pd.concat([df['time'], df_resampled_5], axis = 1)\n",
|
||
" \n",
|
||
" df.loc[:, 'SimulatedTemp'] = df_simulation['values']\n",
|
||
" T_sim_horizon = np.array(gpr(res['x'].reshape((N_horizon, -1)).T))\n",
|
||
" simul_idx = (df_simulation['time'] >= current_timestamp) & (df_simulation['time'] < (current_timestamp + N_horizon * 300))\n",
|
||
" \n",
|
||
" \n",
|
||
" df_T_sim_horizon = df_simulation[simul_idx].copy()\n",
|
||
" df_T_sim_horizon.loc[:, 'values'] = T_sim_horizon.reshape((-1, ))\n",
|
||
" \n",
|
||
" plt.figure(figsize = (15, 5))\n",
|
||
" plt.plot(df_simulation.index, df_simulation['values'], label = 'Simulated Temperature')\n",
|
||
" plt.plot(df_T_sim_horizon.index, df_T_sim_horizon['values'], label = 'Prediction Horizon', color = 'red', marker = 'x')\n",
|
||
" #plt.plot(df.index, df['InsideTemp'], label = 'Inside Temperature')\n",
|
||
" #plt.plot(df.index, df['OutsideTemp'], label = 'Outside Temperature')\n",
|
||
" plt.title(f'Temperatures step {current_timestamp}')\n",
|
||
" plt.legend()\n",
|
||
" plt.savefig(f\"sim_{current_timestamp}.png\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|