Note
Go to the end to download the full example code.
Function chaos expansion#
Given a training dataset
whose input samples are generated from OpenTURNS probability distributions,
the FCERegressor can use any linear model fitting algorithm,
including sparse techniques,
to fit a functional chaos expansion (FCE) model of the form
\[Y = \sum_{i\in\mathcal{I}\subset\mathbb{N}^d} w_i\Psi_i(X)\]
where \(\Psi_i(X)=\prod_{j=1}^d\psi_{i,j}(X_j)\) and \(\mathbb{E}[\Psi_i(X)\Psi_j(X)]=\delta_{ij}\) with \(\delta\) the Kronecker delta and \(X\) a random vector.
A particular version of FCE is the polynomial chaos expansion (PCE)
for which the class PCERegressor interfaces
the OpenTURNS algorithm openturns.FunctionalChaosAlgorithm
(see the OpenTURNS documentation).
Note that FCE can also learn Jacobian data in the hope of improving the quality of the surrogate model for the same evaluation budget.
In this example,
we will compare different types of FCERegressor
to approximate the Ishigami function
\[f(X) = \sin(X_1) + 7\sin(X_2)^2 + 0.1X_3^4\sin(X_1)\]
where \(X_1\), \(X_2\) and \(X_3\) are independent and uniformly distributed over the interval \([-\pi,\pi]\).
from __future__ import annotations
from numpy import array
from gemseo import sample_disciplines
from gemseo.algos.doe.openturns.settings.ot_opt_lhs import OT_OPT_LHS_Settings
from gemseo.algos.doe.scipy.settings.mc import MC_Settings
from gemseo.datasets.dataset import Dataset
from gemseo.mlearning.linear_model_fitting.elastic_net_cv_settings import (
ElasticNetCV_Settings,
)
from gemseo.mlearning.linear_model_fitting.lars_cv_settings import LARSCV_Settings
from gemseo.mlearning.linear_model_fitting.lasso_cv_settings import LassoCV_Settings
from gemseo.mlearning.linear_model_fitting.linear_regression_settings import (
LinearRegression_Settings,
)
from gemseo.mlearning.linear_model_fitting.null_space_settings import NullSpace_Settings
from gemseo.mlearning.linear_model_fitting.omp_cv_settings import (
OrthogonalMatchingPursuitCV_Settings,
)
from gemseo.mlearning.linear_model_fitting.ridge_cv_settings import RidgeCV_Settings
from gemseo.mlearning.linear_model_fitting.spgl1_settings import SPGL1_Settings
from gemseo.mlearning.regression.algos.fce import FCERegressor
from gemseo.mlearning.regression.algos.fce_settings import FCERegressor_Settings
from gemseo.mlearning.regression.algos.fce_settings import OrthonormalFunctionBasis
from gemseo.mlearning.regression.algos.pce import PCERegressor
from gemseo.mlearning.regression.algos.pce_settings import PCERegressor_Settings
from gemseo.mlearning.regression.quality.r2_measure import R2Measure
from gemseo.post.dataset.bars import BarPlot
from gemseo.problems.uncertainty.ishigami.ishigami_discipline import IshigamiDiscipline
from gemseo.problems.uncertainty.ishigami.ishigami_space import IshigamiSpace
First, we define the Ishigami discipline and its uncertain space:
discipline = IshigamiDiscipline()
uncertain_space = IshigamiSpace(IshigamiSpace.UniformDistribution.OPENTURNS)
and create a training dataset using an optimized latin hypercube sampling:
training_dataset = sample_disciplines(
[discipline],
uncertain_space,
"y",
algo_settings_model=OT_OPT_LHS_Settings(n_samples=70, eval_jac=True),
)
INFO - 16:19:00: *** Start Sampling execution ***
INFO - 16:19:00: Sampling
INFO - 16:19:00: Disciplines: IshigamiDiscipline
INFO - 16:19:00: MDO formulation: MDF
INFO - 16:19:00: Optimization problem:
INFO - 16:19:00: minimize y(x1, x2, x3)
INFO - 16:19:00: with respect to x1, x2, x3
INFO - 16:19:00: over the design space:
INFO - 16:19:00: +------+------------------------------------------------------------+
INFO - 16:19:00: | Name | Distribution |
INFO - 16:19:00: +------+------------------------------------------------------------+
INFO - 16:19:00: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:00: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:00: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:00: +------+------------------------------------------------------------+
INFO - 16:19:00: Solving optimization problem with algorithm OT_OPT_LHS:
INFO - 16:19:00: 1%|▏ | 1/70 [00:00<00:00, 321.06 it/sec, feas=True, obj=1.45]
INFO - 16:19:00: 3%|▎ | 2/70 [00:00<00:00, 499.20 it/sec, feas=True, obj=1.01]
INFO - 16:19:00: 4%|▍ | 3/70 [00:00<00:00, 617.14 it/sec, feas=True, obj=6.72]
INFO - 16:19:00: 6%|▌ | 4/70 [00:00<00:00, 700.60 it/sec, feas=True, obj=-0.113]
INFO - 16:19:01: 7%|▋ | 5/70 [00:00<00:00, 758.85 it/sec, feas=True, obj=7.68]
INFO - 16:19:01: 9%|▊ | 6/70 [00:00<00:00, 807.14 it/sec, feas=True, obj=1.8]
INFO - 16:19:01: 10%|█ | 7/70 [00:00<00:00, 844.26 it/sec, feas=True, obj=10.3]
INFO - 16:19:01: 11%|█▏ | 8/70 [00:00<00:00, 877.93 it/sec, feas=True, obj=5.96]
INFO - 16:19:01: 13%|█▎ | 9/70 [00:00<00:00, 904.90 it/sec, feas=True, obj=0.0449]
INFO - 16:19:01: 14%|█▍ | 10/70 [00:00<00:00, 926.20 it/sec, feas=True, obj=4.97]
INFO - 16:19:01: 16%|█▌ | 11/70 [00:00<00:00, 943.68 it/sec, feas=True, obj=6.94]
INFO - 16:19:01: 17%|█▋ | 12/70 [00:00<00:00, 960.18 it/sec, feas=True, obj=3.5]
INFO - 16:19:01: 19%|█▊ | 13/70 [00:00<00:00, 972.55 it/sec, feas=True, obj=4.87]
INFO - 16:19:01: 20%|██ | 14/70 [00:00<00:00, 987.44 it/sec, feas=True, obj=4.3]
INFO - 16:19:01: 21%|██▏ | 15/70 [00:00<00:00, 998.15 it/sec, feas=True, obj=2.44]
INFO - 16:19:01: 23%|██▎ | 16/70 [00:00<00:00, 1009.20 it/sec, feas=True, obj=5.7]
INFO - 16:19:01: 24%|██▍ | 17/70 [00:00<00:00, 1017.64 it/sec, feas=True, obj=6.14]
INFO - 16:19:01: 26%|██▌ | 18/70 [00:00<00:00, 1025.25 it/sec, feas=True, obj=5.7]
INFO - 16:19:01: 27%|██▋ | 19/70 [00:00<00:00, 1030.98 it/sec, feas=True, obj=-0.573]
INFO - 16:19:01: 29%|██▊ | 20/70 [00:00<00:00, 1037.63 it/sec, feas=True, obj=5.72]
INFO - 16:19:01: 30%|███ | 21/70 [00:00<00:00, 1044.78 it/sec, feas=True, obj=4.95]
INFO - 16:19:01: 31%|███▏ | 22/70 [00:00<00:00, 1052.03 it/sec, feas=True, obj=1.27]
INFO - 16:19:01: 33%|███▎ | 23/70 [00:00<00:00, 1057.02 it/sec, feas=True, obj=3.54]
INFO - 16:19:01: 34%|███▍ | 24/70 [00:00<00:00, 1061.26 it/sec, feas=True, obj=6.04]
INFO - 16:19:01: 36%|███▌ | 25/70 [00:00<00:00, 1065.90 it/sec, feas=True, obj=7.5]
INFO - 16:19:01: 37%|███▋ | 26/70 [00:00<00:00, 1069.65 it/sec, feas=True, obj=13.2]
INFO - 16:19:01: 39%|███▊ | 27/70 [00:00<00:00, 1075.47 it/sec, feas=True, obj=14.8]
INFO - 16:19:01: 40%|████ | 28/70 [00:00<00:00, 1078.47 it/sec, feas=True, obj=-0.644]
INFO - 16:19:01: 41%|████▏ | 29/70 [00:00<00:00, 1082.91 it/sec, feas=True, obj=4.94]
INFO - 16:19:01: 43%|████▎ | 30/70 [00:00<00:00, 1085.80 it/sec, feas=True, obj=5.5]
INFO - 16:19:01: 44%|████▍ | 31/70 [00:00<00:00, 1089.44 it/sec, feas=True, obj=3.35]
INFO - 16:19:01: 46%|████▌ | 32/70 [00:00<00:00, 1088.60 it/sec, feas=True, obj=4.05]
INFO - 16:19:01: 47%|████▋ | 33/70 [00:00<00:00, 1083.92 it/sec, feas=True, obj=2.43]
INFO - 16:19:01: 49%|████▊ | 34/70 [00:00<00:00, 1084.85 it/sec, feas=True, obj=-0.0246]
INFO - 16:19:01: 50%|█████ | 35/70 [00:00<00:00, 1088.36 it/sec, feas=True, obj=-0.0211]
INFO - 16:19:01: 51%|█████▏ | 36/70 [00:00<00:00, 1083.85 it/sec, feas=True, obj=6.01]
INFO - 16:19:01: 53%|█████▎ | 37/70 [00:00<00:00, 1085.19 it/sec, feas=True, obj=5.03]
INFO - 16:19:01: 54%|█████▍ | 38/70 [00:00<00:00, 1087.24 it/sec, feas=True, obj=0.863]
INFO - 16:19:01: 56%|█████▌ | 39/70 [00:00<00:00, 1090.02 it/sec, feas=True, obj=-0.764]
INFO - 16:19:01: 57%|█████▋ | 40/70 [00:00<00:00, 1092.43 it/sec, feas=True, obj=14.8]
INFO - 16:19:01: 59%|█████▊ | 41/70 [00:00<00:00, 1094.54 it/sec, feas=True, obj=0.87]
INFO - 16:19:01: 60%|██████ | 42/70 [00:00<00:00, 1090.33 it/sec, feas=True, obj=0.829]
INFO - 16:19:01: 61%|██████▏ | 43/70 [00:00<00:00, 1090.87 it/sec, feas=True, obj=5.01]
INFO - 16:19:01: 63%|██████▎ | 44/70 [00:00<00:00, 1092.54 it/sec, feas=True, obj=0.108]
INFO - 16:19:01: 64%|██████▍ | 45/70 [00:00<00:00, 1095.66 it/sec, feas=True, obj=0.948]
INFO - 16:19:01: 66%|██████▌ | 46/70 [00:00<00:00, 1097.70 it/sec, feas=True, obj=1.22]
INFO - 16:19:01: 67%|██████▋ | 47/70 [00:00<00:00, 1100.14 it/sec, feas=True, obj=7.52]
INFO - 16:19:01: 69%|██████▊ | 48/70 [00:00<00:00, 1101.14 it/sec, feas=True, obj=3.97]
INFO - 16:19:01: 70%|███████ | 49/70 [00:00<00:00, 1099.34 it/sec, feas=True, obj=0.768]
INFO - 16:19:01: 71%|███████▏ | 50/70 [00:00<00:00, 1099.64 it/sec, feas=True, obj=-8.26]
INFO - 16:19:01: 73%|███████▎ | 51/70 [00:00<00:00, 1100.69 it/sec, feas=True, obj=-3.5]
INFO - 16:19:01: 74%|███████▍ | 52/70 [00:00<00:00, 1103.02 it/sec, feas=True, obj=7.43]
INFO - 16:19:01: 76%|███████▌ | 53/70 [00:00<00:00, 1104.32 it/sec, feas=True, obj=-2.32]
INFO - 16:19:01: 77%|███████▋ | 54/70 [00:00<00:00, 1106.15 it/sec, feas=True, obj=4.82]
INFO - 16:19:01: 79%|███████▊ | 55/70 [00:00<00:00, 1107.01 it/sec, feas=True, obj=2.5]
INFO - 16:19:01: 80%|████████ | 56/70 [00:00<00:00, 1108.24 it/sec, feas=True, obj=2.58]
INFO - 16:19:01: 81%|████████▏ | 57/70 [00:00<00:00, 1108.98 it/sec, feas=True, obj=-2.55]
INFO - 16:19:01: 83%|████████▎ | 58/70 [00:00<00:00, 1110.94 it/sec, feas=True, obj=2.11]
INFO - 16:19:01: 84%|████████▍ | 59/70 [00:00<00:00, 1111.75 it/sec, feas=True, obj=8.06]
INFO - 16:19:01: 86%|████████▌ | 60/70 [00:00<00:00, 1113.40 it/sec, feas=True, obj=-5.24]
INFO - 16:19:01: 87%|████████▋ | 61/70 [00:00<00:00, 1114.79 it/sec, feas=True, obj=2.4]
INFO - 16:19:01: 89%|████████▊ | 62/70 [00:00<00:00, 1115.63 it/sec, feas=True, obj=3.43]
INFO - 16:19:01: 90%|█████████ | 63/70 [00:00<00:00, 1117.06 it/sec, feas=True, obj=5.99]
INFO - 16:19:01: 91%|█████████▏| 64/70 [00:00<00:00, 1118.38 it/sec, feas=True, obj=0.819]
INFO - 16:19:01: 93%|█████████▎| 65/70 [00:00<00:00, 1116.99 it/sec, feas=True, obj=0.632]
INFO - 16:19:01: 94%|█████████▍| 66/70 [00:00<00:00, 1115.76 it/sec, feas=True, obj=-0.158]
INFO - 16:19:01: 96%|█████████▌| 67/70 [00:00<00:00, 1116.96 it/sec, feas=True, obj=4.05]
INFO - 16:19:01: 97%|█████████▋| 68/70 [00:00<00:00, 1115.44 it/sec, feas=True, obj=7.71]
INFO - 16:19:01: 99%|█████████▊| 69/70 [00:00<00:00, 1115.56 it/sec, feas=True, obj=5.54]
INFO - 16:19:01: 100%|██████████| 70/70 [00:00<00:00, 1111.39 it/sec, feas=True, obj=6.63]
INFO - 16:19:01: Optimization result:
INFO - 16:19:01: Optimizer info:
INFO - 16:19:01: Status: None
INFO - 16:19:01: Message: None
INFO - 16:19:01: Solution:
INFO - 16:19:01: Objective: -8.260663543133736
INFO - 16:19:01: Design space:
INFO - 16:19:01: +------+------------------------------------------------------------+
INFO - 16:19:01: | Name | Distribution |
INFO - 16:19:01: +------+------------------------------------------------------------+
INFO - 16:19:01: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:01: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:01: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:01: +------+------------------------------------------------------------+
INFO - 16:19:01: *** End Sampling execution ***
as well as a validation dataset using Monte Carlo sampling:
validation_dataset = sample_disciplines(
[discipline],
uncertain_space,
"y",
algo_settings_model=MC_Settings(n_samples=1000),
)
INFO - 16:19:01: *** Start Sampling execution ***
INFO - 16:19:01: Sampling
INFO - 16:19:01: Disciplines: IshigamiDiscipline
INFO - 16:19:01: MDO formulation: MDF
INFO - 16:19:01: Optimization problem:
INFO - 16:19:01: minimize y(x1, x2, x3)
INFO - 16:19:01: with respect to x1, x2, x3
INFO - 16:19:01: over the design space:
INFO - 16:19:01: +------+------------------------------------------------------------+
INFO - 16:19:01: | Name | Distribution |
INFO - 16:19:01: +------+------------------------------------------------------------+
INFO - 16:19:01: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:01: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:01: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:01: +------+------------------------------------------------------------+
INFO - 16:19:01: Solving optimization problem with algorithm MC:
INFO - 16:19:01: 1%| | 6/1000 [00:00<00:00, 2738.99 it/sec, feas=True, obj=3.6]
INFO - 16:19:01: 1%| | 7/1000 [00:00<00:00, 2421.25 it/sec, feas=True, obj=5.41]
INFO - 16:19:01: 1%| | 8/1000 [00:00<00:00, 2333.09 it/sec, feas=True, obj=-9.09]
INFO - 16:19:01: 1%| | 9/1000 [00:00<00:00, 2254.73 it/sec, feas=True, obj=7.06]
INFO - 16:19:01: 1%| | 10/1000 [00:00<00:00, 2228.05 it/sec, feas=True, obj=-3.46]
INFO - 16:19:01: 1%| | 11/1000 [00:00<00:00, 2177.83 it/sec, feas=True, obj=3.2]
INFO - 16:19:01: 1%| | 12/1000 [00:00<00:00, 2139.68 it/sec, feas=True, obj=8.62]
INFO - 16:19:01: 1%|▏ | 13/1000 [00:00<00:00, 2115.21 it/sec, feas=True, obj=-0.0229]
INFO - 16:19:01: 1%|▏ | 14/1000 [00:00<00:00, 2094.01 it/sec, feas=True, obj=2.91]
INFO - 16:19:01: 2%|▏ | 15/1000 [00:00<00:00, 2077.42 it/sec, feas=True, obj=8.67]
INFO - 16:19:01: 2%|▏ | 16/1000 [00:00<00:00, 2064.63 it/sec, feas=True, obj=0.13]
INFO - 16:19:01: 2%|▏ | 17/1000 [00:00<00:00, 2053.78 it/sec, feas=True, obj=4.8]
INFO - 16:19:01: 2%|▏ | 18/1000 [00:00<00:00, 2047.33 it/sec, feas=True, obj=8.34]
INFO - 16:19:01: 2%|▏ | 19/1000 [00:00<00:00, 2039.56 it/sec, feas=True, obj=-1.57]
INFO - 16:19:01: 2%|▏ | 20/1000 [00:00<00:00, 2029.32 it/sec, feas=True, obj=4.2]
INFO - 16:19:01: 2%|▏ | 21/1000 [00:00<00:00, 2027.49 it/sec, feas=True, obj=-1.04]
INFO - 16:19:01: 2%|▏ | 22/1000 [00:00<00:00, 2022.72 it/sec, feas=True, obj=6.49]
INFO - 16:19:01: 2%|▏ | 23/1000 [00:00<00:00, 2015.90 it/sec, feas=True, obj=1.83]
INFO - 16:19:01: 2%|▏ | 24/1000 [00:00<00:00, 2007.28 it/sec, feas=True, obj=4.83]
INFO - 16:19:01: 2%|▎ | 25/1000 [00:00<00:00, 2006.61 it/sec, feas=True, obj=2.15]
INFO - 16:19:01: 3%|▎ | 26/1000 [00:00<00:00, 1996.34 it/sec, feas=True, obj=4.61]
INFO - 16:19:01: 3%|▎ | 27/1000 [00:00<00:00, 1992.47 it/sec, feas=True, obj=4.02]
INFO - 16:19:01: 3%|▎ | 28/1000 [00:00<00:00, 1985.10 it/sec, feas=True, obj=4.83]
INFO - 16:19:01: 3%|▎ | 29/1000 [00:00<00:00, 1979.80 it/sec, feas=True, obj=3.43]
INFO - 16:19:01: 3%|▎ | 30/1000 [00:00<00:00, 1975.22 it/sec, feas=True, obj=2.48]
INFO - 16:19:01: 3%|▎ | 31/1000 [00:00<00:00, 1947.25 it/sec, feas=True, obj=6.63]
INFO - 16:19:01: 3%|▎ | 32/1000 [00:00<00:00, 1943.72 it/sec, feas=True, obj=6.92]
INFO - 16:19:01: 3%|▎ | 33/1000 [00:00<00:00, 1941.43 it/sec, feas=True, obj=3.22]
INFO - 16:19:01: 3%|▎ | 34/1000 [00:00<00:00, 1940.28 it/sec, feas=True, obj=5.73]
INFO - 16:19:01: 4%|▎ | 35/1000 [00:00<00:00, 1936.40 it/sec, feas=True, obj=5.62]
INFO - 16:19:01: 4%|▎ | 36/1000 [00:00<00:00, 1931.77 it/sec, feas=True, obj=-1.44]
INFO - 16:19:01: 4%|▎ | 37/1000 [00:00<00:00, 1932.96 it/sec, feas=True, obj=7.02]
INFO - 16:19:01: 4%|▍ | 38/1000 [00:00<00:00, 1929.35 it/sec, feas=True, obj=6.21]
INFO - 16:19:01: 4%|▍ | 39/1000 [00:00<00:00, 1931.10 it/sec, feas=True, obj=4.64]
INFO - 16:19:01: 4%|▍ | 40/1000 [00:00<00:00, 1928.79 it/sec, feas=True, obj=4.71]
INFO - 16:19:01: 4%|▍ | 41/1000 [00:00<00:00, 1930.82 it/sec, feas=True, obj=5.73]
INFO - 16:19:01: 4%|▍ | 42/1000 [00:00<00:00, 1928.12 it/sec, feas=True, obj=-0.0754]
INFO - 16:19:01: 4%|▍ | 43/1000 [00:00<00:00, 1927.24 it/sec, feas=True, obj=5.56]
INFO - 16:19:01: 4%|▍ | 44/1000 [00:00<00:00, 1923.33 it/sec, feas=True, obj=5.03]
INFO - 16:19:01: 4%|▍ | 45/1000 [00:00<00:00, 1924.21 it/sec, feas=True, obj=7.21]
INFO - 16:19:01: 5%|▍ | 46/1000 [00:00<00:00, 1924.01 it/sec, feas=True, obj=8.03]
INFO - 16:19:01: 5%|▍ | 47/1000 [00:00<00:00, 1923.86 it/sec, feas=True, obj=5.56]
INFO - 16:19:01: 5%|▍ | 48/1000 [00:00<00:00, 1923.04 it/sec, feas=True, obj=6.35]
INFO - 16:19:01: 5%|▍ | 49/1000 [00:00<00:00, 1921.56 it/sec, feas=True, obj=6.71]
INFO - 16:19:01: 5%|▌ | 50/1000 [00:00<00:00, 1919.91 it/sec, feas=True, obj=3.52]
INFO - 16:19:01: 5%|▌ | 51/1000 [00:00<00:00, 1916.72 it/sec, feas=True, obj=2.63]
INFO - 16:19:01: 5%|▌ | 52/1000 [00:00<00:00, 1917.58 it/sec, feas=True, obj=4.68]
INFO - 16:19:01: 5%|▌ | 53/1000 [00:00<00:00, 1914.68 it/sec, feas=True, obj=1.07]
INFO - 16:19:01: 5%|▌ | 54/1000 [00:00<00:00, 1916.02 it/sec, feas=True, obj=10.3]
INFO - 16:19:01: 6%|▌ | 55/1000 [00:00<00:00, 1914.71 it/sec, feas=True, obj=6.87]
INFO - 16:19:01: 6%|▌ | 56/1000 [00:00<00:00, 1916.14 it/sec, feas=True, obj=-3.82]
INFO - 16:19:01: 6%|▌ | 57/1000 [00:00<00:00, 1914.36 it/sec, feas=True, obj=-1.58]
INFO - 16:19:01: 6%|▌ | 58/1000 [00:00<00:00, 1915.04 it/sec, feas=True, obj=3.43]
INFO - 16:19:01: 6%|▌ | 59/1000 [00:00<00:00, 1914.13 it/sec, feas=True, obj=-6.44]
INFO - 16:19:01: 6%|▌ | 60/1000 [00:00<00:00, 1915.97 it/sec, feas=True, obj=0.167]
INFO - 16:19:01: 6%|▌ | 61/1000 [00:00<00:00, 1915.09 it/sec, feas=True, obj=2.98]
INFO - 16:19:01: 6%|▌ | 62/1000 [00:00<00:00, 1916.59 it/sec, feas=True, obj=0.771]
INFO - 16:19:01: 6%|▋ | 63/1000 [00:00<00:00, 1915.78 it/sec, feas=True, obj=6.98]
INFO - 16:19:01: 6%|▋ | 64/1000 [00:00<00:00, 1917.63 it/sec, feas=True, obj=6.81]
INFO - 16:19:01: 6%|▋ | 65/1000 [00:00<00:00, 1916.55 it/sec, feas=True, obj=0.257]
INFO - 16:19:01: 7%|▋ | 66/1000 [00:00<00:00, 1918.31 it/sec, feas=True, obj=3.31]
INFO - 16:19:01: 7%|▋ | 67/1000 [00:00<00:00, 1917.18 it/sec, feas=True, obj=6.07]
INFO - 16:19:01: 7%|▋ | 68/1000 [00:00<00:00, 1919.06 it/sec, feas=True, obj=5.87]
INFO - 16:19:01: 7%|▋ | 69/1000 [00:00<00:00, 1917.85 it/sec, feas=True, obj=7.69]
INFO - 16:19:01: 7%|▋ | 70/1000 [00:00<00:00, 1913.76 it/sec, feas=True, obj=5.16]
INFO - 16:19:01: 7%|▋ | 71/1000 [00:00<00:00, 1906.44 it/sec, feas=True, obj=-0.0811]
INFO - 16:19:01: 7%|▋ | 72/1000 [00:00<00:00, 1903.53 it/sec, feas=True, obj=1.25]
INFO - 16:19:01: 7%|▋ | 73/1000 [00:00<00:00, 1903.79 it/sec, feas=True, obj=5.71]
INFO - 16:19:01: 7%|▋ | 74/1000 [00:00<00:00, 1903.68 it/sec, feas=True, obj=8.15]
INFO - 16:19:01: 8%|▊ | 75/1000 [00:00<00:00, 1903.71 it/sec, feas=True, obj=-2.86]
INFO - 16:19:01: 8%|▊ | 76/1000 [00:00<00:00, 1904.59 it/sec, feas=True, obj=10.5]
INFO - 16:19:01: 8%|▊ | 77/1000 [00:00<00:00, 1904.21 it/sec, feas=True, obj=4.87]
INFO - 16:19:01: 8%|▊ | 78/1000 [00:00<00:00, 1904.80 it/sec, feas=True, obj=2.44]
INFO - 16:19:01: 8%|▊ | 79/1000 [00:00<00:00, 1904.79 it/sec, feas=True, obj=6.74]
INFO - 16:19:01: 8%|▊ | 80/1000 [00:00<00:00, 1904.35 it/sec, feas=True, obj=8.51]
INFO - 16:19:01: 8%|▊ | 81/1000 [00:00<00:00, 1903.05 it/sec, feas=True, obj=0.595]
INFO - 16:19:01: 8%|▊ | 82/1000 [00:00<00:00, 1903.62 it/sec, feas=True, obj=7.84]
INFO - 16:19:01: 8%|▊ | 83/1000 [00:00<00:00, 1902.44 it/sec, feas=True, obj=0.842]
INFO - 16:19:01: 8%|▊ | 84/1000 [00:00<00:00, 1903.70 it/sec, feas=True, obj=9.47]
INFO - 16:19:01: 8%|▊ | 85/1000 [00:00<00:00, 1902.40 it/sec, feas=True, obj=4.54]
INFO - 16:19:01: 9%|▊ | 86/1000 [00:00<00:00, 1903.41 it/sec, feas=True, obj=-3.69]
INFO - 16:19:01: 9%|▊ | 87/1000 [00:00<00:00, 1902.18 it/sec, feas=True, obj=0.849]
INFO - 16:19:01: 9%|▉ | 88/1000 [00:00<00:00, 1903.61 it/sec, feas=True, obj=11.9]
INFO - 16:19:01: 9%|▉ | 89/1000 [00:00<00:00, 1903.32 it/sec, feas=True, obj=5.69]
INFO - 16:19:01: 9%|▉ | 90/1000 [00:00<00:00, 1903.70 it/sec, feas=True, obj=7.25]
INFO - 16:19:01: 9%|▉ | 91/1000 [00:00<00:00, 1904.54 it/sec, feas=True, obj=2.17]
INFO - 16:19:01: 9%|▉ | 92/1000 [00:00<00:00, 1904.83 it/sec, feas=True, obj=5.76]
INFO - 16:19:01: 9%|▉ | 93/1000 [00:00<00:00, 1905.64 it/sec, feas=True, obj=5.32]
INFO - 16:19:01: 9%|▉ | 94/1000 [00:00<00:00, 1906.19 it/sec, feas=True, obj=-3.15]
INFO - 16:19:01: 10%|▉ | 95/1000 [00:00<00:00, 1905.64 it/sec, feas=True, obj=9.36]
INFO - 16:19:01: 10%|▉ | 96/1000 [00:00<00:00, 1906.74 it/sec, feas=True, obj=11.9]
INFO - 16:19:01: 10%|▉ | 97/1000 [00:00<00:00, 1906.45 it/sec, feas=True, obj=8.76]
INFO - 16:19:01: 10%|▉ | 98/1000 [00:00<00:00, 1906.86 it/sec, feas=True, obj=-0.112]
INFO - 16:19:01: 10%|▉ | 99/1000 [00:00<00:00, 1906.79 it/sec, feas=True, obj=1.61]
INFO - 16:19:01: 10%|█ | 100/1000 [00:00<00:00, 1906.29 it/sec, feas=True, obj=4.05]
INFO - 16:19:01: 10%|█ | 101/1000 [00:00<00:00, 1905.52 it/sec, feas=True, obj=-0.31]
INFO - 16:19:01: 10%|█ | 102/1000 [00:00<00:00, 1904.38 it/sec, feas=True, obj=1.46]
INFO - 16:19:01: 10%|█ | 103/1000 [00:00<00:00, 1904.79 it/sec, feas=True, obj=1.1]
INFO - 16:19:01: 10%|█ | 104/1000 [00:00<00:00, 1903.22 it/sec, feas=True, obj=2.69]
INFO - 16:19:01: 10%|█ | 105/1000 [00:00<00:00, 1904.14 it/sec, feas=True, obj=7.7]
INFO - 16:19:01: 11%|█ | 106/1000 [00:00<00:00, 1902.90 it/sec, feas=True, obj=4.86]
INFO - 16:19:01: 11%|█ | 107/1000 [00:00<00:00, 1903.93 it/sec, feas=True, obj=-0.104]
INFO - 16:19:01: 11%|█ | 108/1000 [00:00<00:00, 1903.44 it/sec, feas=True, obj=-7.95]
INFO - 16:19:01: 11%|█ | 109/1000 [00:00<00:00, 1903.78 it/sec, feas=True, obj=0.11]
INFO - 16:19:01: 11%|█ | 110/1000 [00:00<00:00, 1901.76 it/sec, feas=True, obj=-0.471]
INFO - 16:19:01: 11%|█ | 111/1000 [00:00<00:00, 1900.66 it/sec, feas=True, obj=-0.843]
INFO - 16:19:01: 11%|█ | 112/1000 [00:00<00:00, 1899.69 it/sec, feas=True, obj=3.99]
INFO - 16:19:01: 11%|█▏ | 113/1000 [00:00<00:00, 1900.42 it/sec, feas=True, obj=5.95]
INFO - 16:19:01: 11%|█▏ | 114/1000 [00:00<00:00, 1899.65 it/sec, feas=True, obj=6.56]
INFO - 16:19:01: 12%|█▏ | 115/1000 [00:00<00:00, 1900.72 it/sec, feas=True, obj=6.04]
INFO - 16:19:01: 12%|█▏ | 116/1000 [00:00<00:00, 1899.56 it/sec, feas=True, obj=0.26]
INFO - 16:19:01: 12%|█▏ | 117/1000 [00:00<00:00, 1899.19 it/sec, feas=True, obj=5.43]
INFO - 16:19:01: 12%|█▏ | 118/1000 [00:00<00:00, 1899.23 it/sec, feas=True, obj=0.706]
INFO - 16:19:01: 12%|█▏ | 119/1000 [00:00<00:00, 1899.16 it/sec, feas=True, obj=-0.861]
INFO - 16:19:01: 12%|█▏ | 120/1000 [00:00<00:00, 1899.45 it/sec, feas=True, obj=5.7]
INFO - 16:19:01: 12%|█▏ | 121/1000 [00:00<00:00, 1899.48 it/sec, feas=True, obj=3.13]
INFO - 16:19:01: 12%|█▏ | 122/1000 [00:00<00:00, 1899.54 it/sec, feas=True, obj=2.51]
INFO - 16:19:01: 12%|█▏ | 123/1000 [00:00<00:00, 1899.77 it/sec, feas=True, obj=0.0431]
INFO - 16:19:01: 12%|█▏ | 124/1000 [00:00<00:00, 1899.89 it/sec, feas=True, obj=4.08]
INFO - 16:19:01: 12%|█▎ | 125/1000 [00:00<00:00, 1900.19 it/sec, feas=True, obj=3.48]
INFO - 16:19:01: 13%|█▎ | 126/1000 [00:00<00:00, 1900.55 it/sec, feas=True, obj=-0.27]
INFO - 16:19:01: 13%|█▎ | 127/1000 [00:00<00:00, 1901.30 it/sec, feas=True, obj=6.25]
INFO - 16:19:01: 13%|█▎ | 128/1000 [00:00<00:00, 1901.57 it/sec, feas=True, obj=9.11]
INFO - 16:19:01: 13%|█▎ | 129/1000 [00:00<00:00, 1901.26 it/sec, feas=True, obj=-0.828]
INFO - 16:19:01: 13%|█▎ | 130/1000 [00:00<00:00, 1901.22 it/sec, feas=True, obj=10.3]
INFO - 16:19:01: 13%|█▎ | 131/1000 [00:00<00:00, 1901.71 it/sec, feas=True, obj=3.98]
INFO - 16:19:01: 13%|█▎ | 132/1000 [00:00<00:00, 1901.86 it/sec, feas=True, obj=2.27]
INFO - 16:19:01: 13%|█▎ | 133/1000 [00:00<00:00, 1902.07 it/sec, feas=True, obj=1.6]
INFO - 16:19:01: 13%|█▎ | 134/1000 [00:00<00:00, 1902.26 it/sec, feas=True, obj=-7.13]
INFO - 16:19:01: 14%|█▎ | 135/1000 [00:00<00:00, 1902.77 it/sec, feas=True, obj=7.82]
INFO - 16:19:01: 14%|█▎ | 136/1000 [00:00<00:00, 1903.05 it/sec, feas=True, obj=4.68]
INFO - 16:19:01: 14%|█▎ | 137/1000 [00:00<00:00, 1903.07 it/sec, feas=True, obj=-0.627]
INFO - 16:19:01: 14%|█▍ | 138/1000 [00:00<00:00, 1903.00 it/sec, feas=True, obj=-4.07]
INFO - 16:19:01: 14%|█▍ | 139/1000 [00:00<00:00, 1902.97 it/sec, feas=True, obj=1.06]
INFO - 16:19:01: 14%|█▍ | 140/1000 [00:00<00:00, 1901.85 it/sec, feas=True, obj=11.1]
INFO - 16:19:01: 14%|█▍ | 141/1000 [00:00<00:00, 1902.58 it/sec, feas=True, obj=1.87]
INFO - 16:19:01: 14%|█▍ | 142/1000 [00:00<00:00, 1901.40 it/sec, feas=True, obj=7.07]
INFO - 16:19:01: 14%|█▍ | 143/1000 [00:00<00:00, 1902.03 it/sec, feas=True, obj=1.79]
INFO - 16:19:01: 14%|█▍ | 144/1000 [00:00<00:00, 1901.38 it/sec, feas=True, obj=5.97]
INFO - 16:19:01: 14%|█▍ | 145/1000 [00:00<00:00, 1897.77 it/sec, feas=True, obj=6.15]
INFO - 16:19:01: 15%|█▍ | 146/1000 [00:00<00:00, 1896.96 it/sec, feas=True, obj=4.61]
INFO - 16:19:01: 15%|█▍ | 147/1000 [00:00<00:00, 1897.71 it/sec, feas=True, obj=0.433]
INFO - 16:19:01: 15%|█▍ | 148/1000 [00:00<00:00, 1896.69 it/sec, feas=True, obj=2.73]
INFO - 16:19:01: 15%|█▍ | 149/1000 [00:00<00:00, 1896.39 it/sec, feas=True, obj=1.83]
INFO - 16:19:01: 15%|█▌ | 150/1000 [00:00<00:00, 1896.29 it/sec, feas=True, obj=5.3]
INFO - 16:19:01: 15%|█▌ | 151/1000 [00:00<00:00, 1896.37 it/sec, feas=True, obj=-0.935]
INFO - 16:19:01: 15%|█▌ | 152/1000 [00:00<00:00, 1896.39 it/sec, feas=True, obj=7.03]
INFO - 16:19:01: 15%|█▌ | 153/1000 [00:00<00:00, 1896.10 it/sec, feas=True, obj=4.91]
INFO - 16:19:01: 15%|█▌ | 154/1000 [00:00<00:00, 1896.32 it/sec, feas=True, obj=5.89]
INFO - 16:19:01: 16%|█▌ | 155/1000 [00:00<00:00, 1896.15 it/sec, feas=True, obj=-1.07]
INFO - 16:19:01: 16%|█▌ | 156/1000 [00:00<00:00, 1896.06 it/sec, feas=True, obj=2.05]
INFO - 16:19:01: 16%|█▌ | 157/1000 [00:00<00:00, 1896.49 it/sec, feas=True, obj=9.08]
INFO - 16:19:01: 16%|█▌ | 158/1000 [00:00<00:00, 1896.63 it/sec, feas=True, obj=1.28]
INFO - 16:19:01: 16%|█▌ | 159/1000 [00:00<00:00, 1896.75 it/sec, feas=True, obj=5.5]
INFO - 16:19:01: 16%|█▌ | 160/1000 [00:00<00:00, 1896.87 it/sec, feas=True, obj=2.86]
INFO - 16:19:01: 16%|█▌ | 161/1000 [00:00<00:00, 1896.90 it/sec, feas=True, obj=2.58]
INFO - 16:19:01: 16%|█▌ | 162/1000 [00:00<00:00, 1896.95 it/sec, feas=True, obj=6.35]
INFO - 16:19:01: 16%|█▋ | 163/1000 [00:00<00:00, 1897.01 it/sec, feas=True, obj=5.03]
INFO - 16:19:01: 16%|█▋ | 164/1000 [00:00<00:00, 1897.15 it/sec, feas=True, obj=4.89]
INFO - 16:19:01: 16%|█▋ | 165/1000 [00:00<00:00, 1897.20 it/sec, feas=True, obj=-0.862]
INFO - 16:19:01: 17%|█▋ | 166/1000 [00:00<00:00, 1897.20 it/sec, feas=True, obj=5.17]
INFO - 16:19:01: 17%|█▋ | 167/1000 [00:00<00:00, 1897.25 it/sec, feas=True, obj=6.54]
INFO - 16:19:01: 17%|█▋ | 168/1000 [00:00<00:00, 1896.56 it/sec, feas=True, obj=5.04]
INFO - 16:19:01: 17%|█▋ | 169/1000 [00:00<00:00, 1897.15 it/sec, feas=True, obj=5.18]
INFO - 16:19:01: 17%|█▋ | 170/1000 [00:00<00:00, 1896.22 it/sec, feas=True, obj=9.72]
INFO - 16:19:01: 17%|█▋ | 171/1000 [00:00<00:00, 1896.80 it/sec, feas=True, obj=4.51]
INFO - 16:19:01: 17%|█▋ | 172/1000 [00:00<00:00, 1896.24 it/sec, feas=True, obj=5.25]
INFO - 16:19:01: 17%|█▋ | 173/1000 [00:00<00:00, 1896.84 it/sec, feas=True, obj=7.58]
INFO - 16:19:01: 17%|█▋ | 174/1000 [00:00<00:00, 1895.01 it/sec, feas=True, obj=-0.152]
INFO - 16:19:01: 18%|█▊ | 175/1000 [00:00<00:00, 1892.22 it/sec, feas=True, obj=0.707]
INFO - 16:19:01: 18%|█▊ | 176/1000 [00:00<00:00, 1891.11 it/sec, feas=True, obj=1.95]
INFO - 16:19:01: 18%|█▊ | 177/1000 [00:00<00:00, 1890.37 it/sec, feas=True, obj=5.37]
INFO - 16:19:01: 18%|█▊ | 178/1000 [00:00<00:00, 1890.32 it/sec, feas=True, obj=9.3]
INFO - 16:19:01: 18%|█▊ | 179/1000 [00:00<00:00, 1890.08 it/sec, feas=True, obj=-6.59]
INFO - 16:19:01: 18%|█▊ | 180/1000 [00:00<00:00, 1890.78 it/sec, feas=True, obj=0.62]
INFO - 16:19:01: 18%|█▊ | 181/1000 [00:00<00:00, 1890.73 it/sec, feas=True, obj=2.86]
INFO - 16:19:01: 18%|█▊ | 182/1000 [00:00<00:00, 1891.43 it/sec, feas=True, obj=7.64]
INFO - 16:19:01: 18%|█▊ | 183/1000 [00:00<00:00, 1891.30 it/sec, feas=True, obj=1.83]
INFO - 16:19:01: 18%|█▊ | 184/1000 [00:00<00:00, 1892.03 it/sec, feas=True, obj=1.15]
INFO - 16:19:01: 18%|█▊ | 185/1000 [00:00<00:00, 1891.56 it/sec, feas=True, obj=4.53]
INFO - 16:19:01: 19%|█▊ | 186/1000 [00:00<00:00, 1892.01 it/sec, feas=True, obj=5.86]
INFO - 16:19:01: 19%|█▊ | 187/1000 [00:00<00:00, 1891.39 it/sec, feas=True, obj=-4.15]
INFO - 16:19:01: 19%|█▉ | 188/1000 [00:00<00:00, 1891.82 it/sec, feas=True, obj=0.77]
INFO - 16:19:01: 19%|█▉ | 189/1000 [00:00<00:00, 1891.43 it/sec, feas=True, obj=3.77]
INFO - 16:19:01: 19%|█▉ | 190/1000 [00:00<00:00, 1892.10 it/sec, feas=True, obj=0.574]
INFO - 16:19:01: 19%|█▉ | 191/1000 [00:00<00:00, 1891.82 it/sec, feas=True, obj=3.27]
INFO - 16:19:01: 19%|█▉ | 192/1000 [00:00<00:00, 1892.12 it/sec, feas=True, obj=1.31]
INFO - 16:19:01: 19%|█▉ | 193/1000 [00:00<00:00, 1891.94 it/sec, feas=True, obj=2.11]
INFO - 16:19:01: 19%|█▉ | 194/1000 [00:00<00:00, 1892.21 it/sec, feas=True, obj=-0.324]
INFO - 16:19:01: 20%|█▉ | 195/1000 [00:00<00:00, 1892.17 it/sec, feas=True, obj=2.6]
INFO - 16:19:01: 20%|█▉ | 196/1000 [00:00<00:00, 1891.66 it/sec, feas=True, obj=7.25]
INFO - 16:19:01: 20%|█▉ | 197/1000 [00:00<00:00, 1892.28 it/sec, feas=True, obj=12.9]
INFO - 16:19:01: 20%|█▉ | 198/1000 [00:00<00:00, 1891.73 it/sec, feas=True, obj=-1.67]
INFO - 16:19:01: 20%|█▉ | 199/1000 [00:00<00:00, 1892.25 it/sec, feas=True, obj=6.19]
INFO - 16:19:01: 20%|██ | 200/1000 [00:00<00:00, 1891.57 it/sec, feas=True, obj=3.52]
INFO - 16:19:01: 20%|██ | 201/1000 [00:00<00:00, 1892.06 it/sec, feas=True, obj=-1.49]
INFO - 16:19:01: 20%|██ | 202/1000 [00:00<00:00, 1891.90 it/sec, feas=True, obj=7.77]
INFO - 16:19:01: 20%|██ | 203/1000 [00:00<00:00, 1892.12 it/sec, feas=True, obj=0.847]
INFO - 16:19:01: 20%|██ | 204/1000 [00:00<00:00, 1892.47 it/sec, feas=True, obj=-2.82]
INFO - 16:19:01: 20%|██ | 205/1000 [00:00<00:00, 1892.59 it/sec, feas=True, obj=6.59]
INFO - 16:19:01: 21%|██ | 206/1000 [00:00<00:00, 1892.62 it/sec, feas=True, obj=4.35]
INFO - 16:19:01: 21%|██ | 207/1000 [00:00<00:00, 1892.52 it/sec, feas=True, obj=-1.95]
INFO - 16:19:01: 21%|██ | 208/1000 [00:00<00:00, 1892.58 it/sec, feas=True, obj=-0.845]
INFO - 16:19:01: 21%|██ | 209/1000 [00:00<00:00, 1891.38 it/sec, feas=True, obj=7.19]
INFO - 16:19:01: 21%|██ | 210/1000 [00:00<00:00, 1889.68 it/sec, feas=True, obj=-0.108]
INFO - 16:19:01: 21%|██ | 211/1000 [00:00<00:00, 1889.06 it/sec, feas=True, obj=1.42]
INFO - 16:19:01: 21%|██ | 212/1000 [00:00<00:00, 1889.25 it/sec, feas=True, obj=0.785]
INFO - 16:19:01: 21%|██▏ | 213/1000 [00:00<00:00, 1888.90 it/sec, feas=True, obj=-4.97]
INFO - 16:19:01: 21%|██▏ | 214/1000 [00:00<00:00, 1889.42 it/sec, feas=True, obj=7.43]
INFO - 16:19:01: 22%|██▏ | 215/1000 [00:00<00:00, 1889.18 it/sec, feas=True, obj=6.26]
INFO - 16:19:01: 22%|██▏ | 216/1000 [00:00<00:00, 1889.41 it/sec, feas=True, obj=2.43]
INFO - 16:19:01: 22%|██▏ | 217/1000 [00:00<00:00, 1889.37 it/sec, feas=True, obj=11.5]
INFO - 16:19:01: 22%|██▏ | 218/1000 [00:00<00:00, 1889.96 it/sec, feas=True, obj=3.62]
INFO - 16:19:01: 22%|██▏ | 219/1000 [00:00<00:00, 1890.05 it/sec, feas=True, obj=5.45]
INFO - 16:19:01: 22%|██▏ | 220/1000 [00:00<00:00, 1890.07 it/sec, feas=True, obj=8.42]
INFO - 16:19:01: 22%|██▏ | 221/1000 [00:00<00:00, 1889.84 it/sec, feas=True, obj=10.5]
INFO - 16:19:01: 22%|██▏ | 222/1000 [00:00<00:00, 1890.35 it/sec, feas=True, obj=4.04]
INFO - 16:19:01: 22%|██▏ | 223/1000 [00:00<00:00, 1890.08 it/sec, feas=True, obj=-1.33]
INFO - 16:19:01: 22%|██▏ | 224/1000 [00:00<00:00, 1890.66 it/sec, feas=True, obj=5.55]
INFO - 16:19:01: 22%|██▎ | 225/1000 [00:00<00:00, 1890.21 it/sec, feas=True, obj=-0.71]
INFO - 16:19:01: 23%|██▎ | 226/1000 [00:00<00:00, 1890.38 it/sec, feas=True, obj=2.84]
INFO - 16:19:01: 23%|██▎ | 227/1000 [00:00<00:00, 1890.24 it/sec, feas=True, obj=1.75]
INFO - 16:19:01: 23%|██▎ | 228/1000 [00:00<00:00, 1889.87 it/sec, feas=True, obj=1.36]
INFO - 16:19:01: 23%|██▎ | 229/1000 [00:00<00:00, 1889.85 it/sec, feas=True, obj=6.32]
INFO - 16:19:01: 23%|██▎ | 230/1000 [00:00<00:00, 1889.69 it/sec, feas=True, obj=6.66]
INFO - 16:19:01: 23%|██▎ | 231/1000 [00:00<00:00, 1889.74 it/sec, feas=True, obj=5.61]
INFO - 16:19:01: 23%|██▎ | 232/1000 [00:00<00:00, 1889.76 it/sec, feas=True, obj=7.2]
INFO - 16:19:01: 23%|██▎ | 233/1000 [00:00<00:00, 1889.91 it/sec, feas=True, obj=6.4]
INFO - 16:19:01: 23%|██▎ | 234/1000 [00:00<00:00, 1890.24 it/sec, feas=True, obj=0.753]
INFO - 16:19:01: 24%|██▎ | 235/1000 [00:00<00:00, 1890.42 it/sec, feas=True, obj=-0.835]
INFO - 16:19:01: 24%|██▎ | 236/1000 [00:00<00:00, 1890.55 it/sec, feas=True, obj=-0.324]
INFO - 16:19:01: 24%|██▎ | 237/1000 [00:00<00:00, 1891.07 it/sec, feas=True, obj=3.91]
INFO - 16:19:01: 24%|██▍ | 238/1000 [00:00<00:00, 1890.94 it/sec, feas=True, obj=6.01]
INFO - 16:19:01: 24%|██▍ | 239/1000 [00:00<00:00, 1891.50 it/sec, feas=True, obj=0.2]
INFO - 16:19:01: 24%|██▍ | 240/1000 [00:00<00:00, 1891.65 it/sec, feas=True, obj=1.91]
INFO - 16:19:01: 24%|██▍ | 241/1000 [00:00<00:00, 1891.61 it/sec, feas=True, obj=5.1]
INFO - 16:19:01: 24%|██▍ | 242/1000 [00:00<00:00, 1891.33 it/sec, feas=True, obj=5.55]
INFO - 16:19:01: 24%|██▍ | 243/1000 [00:00<00:00, 1891.82 it/sec, feas=True, obj=3.32]
INFO - 16:19:01: 24%|██▍ | 244/1000 [00:00<00:00, 1891.37 it/sec, feas=True, obj=8.57]
INFO - 16:19:01: 24%|██▍ | 245/1000 [00:00<00:00, 1891.87 it/sec, feas=True, obj=6.32]
INFO - 16:19:01: 25%|██▍ | 246/1000 [00:00<00:00, 1891.44 it/sec, feas=True, obj=-2.16]
INFO - 16:19:01: 25%|██▍ | 247/1000 [00:00<00:00, 1891.45 it/sec, feas=True, obj=3.75]
INFO - 16:19:01: 25%|██▍ | 248/1000 [00:00<00:00, 1891.57 it/sec, feas=True, obj=2.64]
INFO - 16:19:01: 25%|██▍ | 249/1000 [00:00<00:00, 1891.69 it/sec, feas=True, obj=0.181]
INFO - 16:19:01: 25%|██▌ | 250/1000 [00:00<00:00, 1889.99 it/sec, feas=True, obj=-6.31]
INFO - 16:19:01: 25%|██▌ | 251/1000 [00:00<00:00, 1888.72 it/sec, feas=True, obj=5.01]
INFO - 16:19:01: 25%|██▌ | 252/1000 [00:00<00:00, 1888.92 it/sec, feas=True, obj=8.03]
INFO - 16:19:01: 25%|██▌ | 253/1000 [00:00<00:00, 1888.95 it/sec, feas=True, obj=9.01]
INFO - 16:19:01: 25%|██▌ | 254/1000 [00:00<00:00, 1889.08 it/sec, feas=True, obj=7.08]
INFO - 16:19:01: 26%|██▌ | 255/1000 [00:00<00:00, 1889.16 it/sec, feas=True, obj=-0.461]
INFO - 16:19:01: 26%|██▌ | 256/1000 [00:00<00:00, 1889.28 it/sec, feas=True, obj=-4.41]
INFO - 16:19:01: 26%|██▌ | 257/1000 [00:00<00:00, 1889.23 it/sec, feas=True, obj=-0.98]
INFO - 16:19:01: 26%|██▌ | 258/1000 [00:00<00:00, 1888.82 it/sec, feas=True, obj=7.88]
INFO - 16:19:01: 26%|██▌ | 259/1000 [00:00<00:00, 1887.11 it/sec, feas=True, obj=5.1]
INFO - 16:19:01: 26%|██▌ | 260/1000 [00:00<00:00, 1886.77 it/sec, feas=True, obj=6.42]
INFO - 16:19:01: 26%|██▌ | 261/1000 [00:00<00:00, 1886.93 it/sec, feas=True, obj=2.42]
INFO - 16:19:01: 26%|██▌ | 262/1000 [00:00<00:00, 1886.86 it/sec, feas=True, obj=6.49]
INFO - 16:19:01: 26%|██▋ | 263/1000 [00:00<00:00, 1887.23 it/sec, feas=True, obj=-0.699]
INFO - 16:19:01: 26%|██▋ | 264/1000 [00:00<00:00, 1884.91 it/sec, feas=True, obj=0.137]
INFO - 16:19:01: 26%|██▋ | 265/1000 [00:00<00:00, 1884.02 it/sec, feas=True, obj=9.9]
INFO - 16:19:01: 27%|██▋ | 266/1000 [00:00<00:00, 1883.96 it/sec, feas=True, obj=3.71]
INFO - 16:19:01: 27%|██▋ | 267/1000 [00:00<00:00, 1883.77 it/sec, feas=True, obj=6.84]
INFO - 16:19:01: 27%|██▋ | 268/1000 [00:00<00:00, 1883.78 it/sec, feas=True, obj=1.21]
INFO - 16:19:01: 27%|██▋ | 269/1000 [00:00<00:00, 1884.03 it/sec, feas=True, obj=1.64]
INFO - 16:19:01: 27%|██▋ | 270/1000 [00:00<00:00, 1884.05 it/sec, feas=True, obj=2.2]
INFO - 16:19:01: 27%|██▋ | 271/1000 [00:00<00:00, 1883.92 it/sec, feas=True, obj=14.3]
INFO - 16:19:01: 27%|██▋ | 272/1000 [00:00<00:00, 1884.07 it/sec, feas=True, obj=1.03]
INFO - 16:19:01: 27%|██▋ | 273/1000 [00:00<00:00, 1883.89 it/sec, feas=True, obj=6.25]
INFO - 16:19:01: 27%|██▋ | 274/1000 [00:00<00:00, 1884.26 it/sec, feas=True, obj=3.31]
INFO - 16:19:01: 28%|██▊ | 275/1000 [00:00<00:00, 1883.94 it/sec, feas=True, obj=7.43]
INFO - 16:19:01: 28%|██▊ | 276/1000 [00:00<00:00, 1884.39 it/sec, feas=True, obj=4.7]
INFO - 16:19:01: 28%|██▊ | 277/1000 [00:00<00:00, 1884.02 it/sec, feas=True, obj=-0.0123]
INFO - 16:19:01: 28%|██▊ | 278/1000 [00:00<00:00, 1884.44 it/sec, feas=True, obj=10.9]
INFO - 16:19:01: 28%|██▊ | 279/1000 [00:00<00:00, 1883.95 it/sec, feas=True, obj=2.56]
INFO - 16:19:01: 28%|██▊ | 280/1000 [00:00<00:00, 1884.00 it/sec, feas=True, obj=5.48]
INFO - 16:19:01: 28%|██▊ | 281/1000 [00:00<00:00, 1884.06 it/sec, feas=True, obj=2.51]
INFO - 16:19:01: 28%|██▊ | 282/1000 [00:00<00:00, 1883.86 it/sec, feas=True, obj=5.13]
INFO - 16:19:01: 28%|██▊ | 283/1000 [00:00<00:00, 1883.81 it/sec, feas=True, obj=4.89]
INFO - 16:19:01: 28%|██▊ | 284/1000 [00:00<00:00, 1883.86 it/sec, feas=True, obj=7.69]
INFO - 16:19:01: 28%|██▊ | 285/1000 [00:00<00:00, 1883.82 it/sec, feas=True, obj=3.1]
INFO - 16:19:01: 29%|██▊ | 286/1000 [00:00<00:00, 1884.10 it/sec, feas=True, obj=-5.11]
INFO - 16:19:01: 29%|██▊ | 287/1000 [00:00<00:00, 1884.18 it/sec, feas=True, obj=-0.0286]
INFO - 16:19:01: 29%|██▉ | 288/1000 [00:00<00:00, 1883.78 it/sec, feas=True, obj=1.41]
INFO - 16:19:01: 29%|██▉ | 289/1000 [00:00<00:00, 1883.86 it/sec, feas=True, obj=5.79]
INFO - 16:19:01: 29%|██▉ | 290/1000 [00:00<00:00, 1883.91 it/sec, feas=True, obj=4.71]
INFO - 16:19:01: 29%|██▉ | 291/1000 [00:00<00:00, 1884.06 it/sec, feas=True, obj=6.49]
INFO - 16:19:01: 29%|██▉ | 292/1000 [00:00<00:00, 1883.74 it/sec, feas=True, obj=-7.67]
INFO - 16:19:01: 29%|██▉ | 293/1000 [00:00<00:00, 1884.20 it/sec, feas=True, obj=-0.721]
INFO - 16:19:01: 29%|██▉ | 294/1000 [00:00<00:00, 1883.89 it/sec, feas=True, obj=7.54]
INFO - 16:19:01: 30%|██▉ | 295/1000 [00:00<00:00, 1884.06 it/sec, feas=True, obj=6.14]
INFO - 16:19:01: 30%|██▉ | 296/1000 [00:00<00:00, 1884.03 it/sec, feas=True, obj=-1.73]
INFO - 16:19:01: 30%|██▉ | 297/1000 [00:00<00:00, 1883.97 it/sec, feas=True, obj=8.22]
INFO - 16:19:01: 30%|██▉ | 298/1000 [00:00<00:00, 1884.00 it/sec, feas=True, obj=6.34]
INFO - 16:19:01: 30%|██▉ | 299/1000 [00:00<00:00, 1884.05 it/sec, feas=True, obj=6.14]
INFO - 16:19:01: 30%|███ | 300/1000 [00:00<00:00, 1884.13 it/sec, feas=True, obj=4.71]
INFO - 16:19:01: 30%|███ | 301/1000 [00:00<00:00, 1884.19 it/sec, feas=True, obj=4]
INFO - 16:19:01: 30%|███ | 302/1000 [00:00<00:00, 1884.28 it/sec, feas=True, obj=6.52]
INFO - 16:19:01: 30%|███ | 303/1000 [00:00<00:00, 1884.62 it/sec, feas=True, obj=0.7]
INFO - 16:19:01: 30%|███ | 304/1000 [00:00<00:00, 1884.76 it/sec, feas=True, obj=5.21]
INFO - 16:19:01: 30%|███ | 305/1000 [00:00<00:00, 1885.04 it/sec, feas=True, obj=2.51]
INFO - 16:19:01: 31%|███ | 306/1000 [00:00<00:00, 1885.12 it/sec, feas=True, obj=-0.162]
INFO - 16:19:01: 31%|███ | 307/1000 [00:00<00:00, 1885.15 it/sec, feas=True, obj=2.63]
INFO - 16:19:01: 31%|███ | 308/1000 [00:00<00:00, 1884.36 it/sec, feas=True, obj=4.01]
INFO - 16:19:01: 31%|███ | 309/1000 [00:00<00:00, 1882.74 it/sec, feas=True, obj=2.99]
INFO - 16:19:01: 31%|███ | 310/1000 [00:00<00:00, 1882.30 it/sec, feas=True, obj=2.3]
INFO - 16:19:01: 31%|███ | 311/1000 [00:00<00:00, 1882.69 it/sec, feas=True, obj=3.71]
INFO - 16:19:01: 31%|███ | 312/1000 [00:00<00:00, 1882.46 it/sec, feas=True, obj=5.63]
INFO - 16:19:01: 31%|███▏ | 313/1000 [00:00<00:00, 1882.90 it/sec, feas=True, obj=6.43]
INFO - 16:19:01: 31%|███▏ | 314/1000 [00:00<00:00, 1882.88 it/sec, feas=True, obj=-1.98]
INFO - 16:19:01: 32%|███▏ | 315/1000 [00:00<00:00, 1883.12 it/sec, feas=True, obj=1.03]
INFO - 16:19:01: 32%|███▏ | 316/1000 [00:00<00:00, 1883.03 it/sec, feas=True, obj=-0.511]
INFO - 16:19:01: 32%|███▏ | 317/1000 [00:00<00:00, 1883.27 it/sec, feas=True, obj=-1.34]
INFO - 16:19:01: 32%|███▏ | 318/1000 [00:00<00:00, 1883.27 it/sec, feas=True, obj=6.72]
INFO - 16:19:01: 32%|███▏ | 319/1000 [00:00<00:00, 1883.68 it/sec, feas=True, obj=3.09]
INFO - 16:19:01: 32%|███▏ | 320/1000 [00:00<00:00, 1883.51 it/sec, feas=True, obj=7.12]
INFO - 16:19:01: 32%|███▏ | 321/1000 [00:00<00:00, 1883.62 it/sec, feas=True, obj=5.91]
INFO - 16:19:01: 32%|███▏ | 322/1000 [00:00<00:00, 1882.20 it/sec, feas=True, obj=0.0303]
INFO - 16:19:01: 32%|███▏ | 323/1000 [00:00<00:00, 1882.14 it/sec, feas=True, obj=1.38]
INFO - 16:19:01: 32%|███▏ | 324/1000 [00:00<00:00, 1882.07 it/sec, feas=True, obj=-5.06]
INFO - 16:19:01: 32%|███▎ | 325/1000 [00:00<00:00, 1881.93 it/sec, feas=True, obj=1.18]
INFO - 16:19:01: 33%|███▎ | 326/1000 [00:00<00:00, 1881.96 it/sec, feas=True, obj=0.213]
INFO - 16:19:01: 33%|███▎ | 327/1000 [00:00<00:00, 1881.83 it/sec, feas=True, obj=5.4]
INFO - 16:19:01: 33%|███▎ | 328/1000 [00:00<00:00, 1881.84 it/sec, feas=True, obj=3.09]
INFO - 16:19:01: 33%|███▎ | 329/1000 [00:00<00:00, 1882.08 it/sec, feas=True, obj=1.28]
INFO - 16:19:01: 33%|███▎ | 330/1000 [00:00<00:00, 1882.24 it/sec, feas=True, obj=7.37]
INFO - 16:19:01: 33%|███▎ | 331/1000 [00:00<00:00, 1882.27 it/sec, feas=True, obj=1.31]
INFO - 16:19:01: 33%|███▎ | 332/1000 [00:00<00:00, 1882.34 it/sec, feas=True, obj=2.05]
INFO - 16:19:01: 33%|███▎ | 333/1000 [00:00<00:00, 1882.51 it/sec, feas=True, obj=1.55]
INFO - 16:19:01: 33%|███▎ | 334/1000 [00:00<00:00, 1882.03 it/sec, feas=True, obj=2.46]
INFO - 16:19:01: 34%|███▎ | 335/1000 [00:00<00:00, 1880.93 it/sec, feas=True, obj=1.51]
INFO - 16:19:01: 34%|███▎ | 336/1000 [00:00<00:00, 1880.61 it/sec, feas=True, obj=5.43]
INFO - 16:19:01: 34%|███▎ | 337/1000 [00:00<00:00, 1880.67 it/sec, feas=True, obj=1.14]
INFO - 16:19:01: 34%|███▍ | 338/1000 [00:00<00:00, 1880.60 it/sec, feas=True, obj=7.29]
INFO - 16:19:01: 34%|███▍ | 339/1000 [00:00<00:00, 1880.61 it/sec, feas=True, obj=-0.283]
INFO - 16:19:01: 34%|███▍ | 340/1000 [00:00<00:00, 1880.74 it/sec, feas=True, obj=0.734]
INFO - 16:19:01: 34%|███▍ | 341/1000 [00:00<00:00, 1880.83 it/sec, feas=True, obj=-3.46]
INFO - 16:19:01: 34%|███▍ | 342/1000 [00:00<00:00, 1880.84 it/sec, feas=True, obj=4.12]
INFO - 16:19:01: 34%|███▍ | 343/1000 [00:00<00:00, 1880.93 it/sec, feas=True, obj=3.79]
INFO - 16:19:01: 34%|███▍ | 344/1000 [00:00<00:00, 1881.03 it/sec, feas=True, obj=-3.15]
INFO - 16:19:01: 34%|███▍ | 345/1000 [00:00<00:00, 1881.17 it/sec, feas=True, obj=7.56]
INFO - 16:19:01: 35%|███▍ | 346/1000 [00:00<00:00, 1881.39 it/sec, feas=True, obj=-0.553]
INFO - 16:19:01: 35%|███▍ | 347/1000 [00:00<00:00, 1881.54 it/sec, feas=True, obj=1.43]
INFO - 16:19:01: 35%|███▍ | 348/1000 [00:00<00:00, 1881.80 it/sec, feas=True, obj=-0.851]
INFO - 16:19:01: 35%|███▍ | 349/1000 [00:00<00:00, 1881.90 it/sec, feas=True, obj=8.57]
INFO - 16:19:01: 35%|███▌ | 350/1000 [00:00<00:00, 1881.99 it/sec, feas=True, obj=0.921]
INFO - 16:19:01: 35%|███▌ | 351/1000 [00:00<00:00, 1882.15 it/sec, feas=True, obj=3.01]
INFO - 16:19:01: 35%|███▌ | 352/1000 [00:00<00:00, 1882.19 it/sec, feas=True, obj=4.98]
INFO - 16:19:01: 35%|███▌ | 353/1000 [00:00<00:00, 1882.08 it/sec, feas=True, obj=6.06]
INFO - 16:19:01: 35%|███▌ | 354/1000 [00:00<00:00, 1882.39 it/sec, feas=True, obj=7.26]
INFO - 16:19:01: 36%|███▌ | 355/1000 [00:00<00:00, 1882.12 it/sec, feas=True, obj=5.71]
INFO - 16:19:01: 36%|███▌ | 356/1000 [00:00<00:00, 1882.43 it/sec, feas=True, obj=-5.07]
INFO - 16:19:01: 36%|███▌ | 357/1000 [00:00<00:00, 1882.06 it/sec, feas=True, obj=6.62]
INFO - 16:19:01: 36%|███▌ | 358/1000 [00:00<00:00, 1882.37 it/sec, feas=True, obj=5.86]
INFO - 16:19:01: 36%|███▌ | 359/1000 [00:00<00:00, 1882.18 it/sec, feas=True, obj=6.1]
INFO - 16:19:01: 36%|███▌ | 360/1000 [00:00<00:00, 1882.54 it/sec, feas=True, obj=-0.545]
INFO - 16:19:01: 36%|███▌ | 361/1000 [00:00<00:00, 1882.51 it/sec, feas=True, obj=3.86]
INFO - 16:19:01: 36%|███▌ | 362/1000 [00:00<00:00, 1882.69 it/sec, feas=True, obj=8.51]
INFO - 16:19:01: 36%|███▋ | 363/1000 [00:00<00:00, 1882.59 it/sec, feas=True, obj=5.33]
INFO - 16:19:01: 36%|███▋ | 364/1000 [00:00<00:00, 1882.96 it/sec, feas=True, obj=7.14]
INFO - 16:19:01: 36%|███▋ | 365/1000 [00:00<00:00, 1882.88 it/sec, feas=True, obj=4.01]
INFO - 16:19:01: 37%|███▋ | 366/1000 [00:00<00:00, 1883.10 it/sec, feas=True, obj=2.9]
INFO - 16:19:01: 37%|███▋ | 367/1000 [00:00<00:00, 1883.27 it/sec, feas=True, obj=6.25]
INFO - 16:19:01: 37%|███▋ | 368/1000 [00:00<00:00, 1883.39 it/sec, feas=True, obj=6.85]
INFO - 16:19:01: 37%|███▋ | 369/1000 [00:00<00:00, 1883.53 it/sec, feas=True, obj=4.32]
INFO - 16:19:01: 37%|███▋ | 370/1000 [00:00<00:00, 1883.68 it/sec, feas=True, obj=4.87]
INFO - 16:19:01: 37%|███▋ | 371/1000 [00:00<00:00, 1882.69 it/sec, feas=True, obj=6.43]
INFO - 16:19:01: 37%|███▋ | 372/1000 [00:00<00:00, 1881.49 it/sec, feas=True, obj=2.86]
INFO - 16:19:01: 37%|███▋ | 373/1000 [00:00<00:00, 1880.02 it/sec, feas=True, obj=0.891]
INFO - 16:19:01: 37%|███▋ | 374/1000 [00:00<00:00, 1879.81 it/sec, feas=True, obj=6.47]
INFO - 16:19:01: 38%|███▊ | 375/1000 [00:00<00:00, 1879.95 it/sec, feas=True, obj=-1.87]
INFO - 16:19:01: 38%|███▊ | 376/1000 [00:00<00:00, 1879.69 it/sec, feas=True, obj=-3.28]
INFO - 16:19:01: 38%|███▊ | 377/1000 [00:00<00:00, 1878.04 it/sec, feas=True, obj=0.0745]
INFO - 16:19:01: 38%|███▊ | 378/1000 [00:00<00:00, 1878.08 it/sec, feas=True, obj=5.9]
INFO - 16:19:01: 38%|███▊ | 379/1000 [00:00<00:00, 1878.08 it/sec, feas=True, obj=4.69]
INFO - 16:19:01: 38%|███▊ | 380/1000 [00:00<00:00, 1878.15 it/sec, feas=True, obj=4.66]
INFO - 16:19:01: 38%|███▊ | 381/1000 [00:00<00:00, 1878.11 it/sec, feas=True, obj=6.07]
INFO - 16:19:01: 38%|███▊ | 382/1000 [00:00<00:00, 1878.45 it/sec, feas=True, obj=0.959]
INFO - 16:19:01: 38%|███▊ | 383/1000 [00:00<00:00, 1877.26 it/sec, feas=True, obj=1.9]
INFO - 16:19:01: 38%|███▊ | 384/1000 [00:00<00:00, 1876.61 it/sec, feas=True, obj=7.91]
INFO - 16:19:01: 38%|███▊ | 385/1000 [00:00<00:00, 1876.84 it/sec, feas=True, obj=-0.448]
INFO - 16:19:01: 39%|███▊ | 386/1000 [00:00<00:00, 1876.42 it/sec, feas=True, obj=5.33]
INFO - 16:19:01: 39%|███▊ | 387/1000 [00:00<00:00, 1876.64 it/sec, feas=True, obj=2.88]
INFO - 16:19:01: 39%|███▉ | 388/1000 [00:00<00:00, 1876.60 it/sec, feas=True, obj=0.55]
INFO - 16:19:01: 39%|███▉ | 389/1000 [00:00<00:00, 1876.95 it/sec, feas=True, obj=0.392]
INFO - 16:19:01: 39%|███▉ | 390/1000 [00:00<00:00, 1876.98 it/sec, feas=True, obj=3.32]
INFO - 16:19:01: 39%|███▉ | 391/1000 [00:00<00:00, 1877.02 it/sec, feas=True, obj=7.88]
INFO - 16:19:01: 39%|███▉ | 392/1000 [00:00<00:00, 1877.00 it/sec, feas=True, obj=1.46]
INFO - 16:19:01: 39%|███▉ | 393/1000 [00:00<00:00, 1877.34 it/sec, feas=True, obj=9.49]
INFO - 16:19:01: 39%|███▉ | 394/1000 [00:00<00:00, 1877.25 it/sec, feas=True, obj=-8.6]
INFO - 16:19:01: 40%|███▉ | 395/1000 [00:00<00:00, 1877.58 it/sec, feas=True, obj=6]
INFO - 16:19:01: 40%|███▉ | 396/1000 [00:00<00:00, 1877.43 it/sec, feas=True, obj=6.89]
INFO - 16:19:01: 40%|███▉ | 397/1000 [00:00<00:00, 1877.81 it/sec, feas=True, obj=5.17]
INFO - 16:19:01: 40%|███▉ | 398/1000 [00:00<00:00, 1877.70 it/sec, feas=True, obj=9.21]
INFO - 16:19:01: 40%|███▉ | 399/1000 [00:00<00:00, 1878.03 it/sec, feas=True, obj=8.46]
INFO - 16:19:01: 40%|████ | 400/1000 [00:00<00:00, 1877.86 it/sec, feas=True, obj=9.92]
INFO - 16:19:01: 40%|████ | 401/1000 [00:00<00:00, 1878.23 it/sec, feas=True, obj=2.5]
INFO - 16:19:01: 40%|████ | 402/1000 [00:00<00:00, 1877.84 it/sec, feas=True, obj=2.82]
INFO - 16:19:01: 40%|████ | 403/1000 [00:00<00:00, 1877.88 it/sec, feas=True, obj=9.71]
INFO - 16:19:01: 40%|████ | 404/1000 [00:00<00:00, 1877.89 it/sec, feas=True, obj=-1.54]
INFO - 16:19:01: 40%|████ | 405/1000 [00:00<00:00, 1877.78 it/sec, feas=True, obj=-1.42]
INFO - 16:19:01: 41%|████ | 406/1000 [00:00<00:00, 1877.78 it/sec, feas=True, obj=7.52]
INFO - 16:19:01: 41%|████ | 407/1000 [00:00<00:00, 1877.80 it/sec, feas=True, obj=3.95]
INFO - 16:19:01: 41%|████ | 408/1000 [00:00<00:00, 1877.84 it/sec, feas=True, obj=5.33]
INFO - 16:19:01: 41%|████ | 409/1000 [00:00<00:00, 1877.99 it/sec, feas=True, obj=0.103]
INFO - 16:19:01: 41%|████ | 410/1000 [00:00<00:00, 1877.99 it/sec, feas=True, obj=4.39]
INFO - 16:19:01: 41%|████ | 411/1000 [00:00<00:00, 1878.01 it/sec, feas=True, obj=1.74]
INFO - 16:19:01: 41%|████ | 412/1000 [00:00<00:00, 1878.07 it/sec, feas=True, obj=0.344]
INFO - 16:19:01: 41%|████▏ | 413/1000 [00:00<00:00, 1878.28 it/sec, feas=True, obj=6.49]
INFO - 16:19:01: 41%|████▏ | 414/1000 [00:00<00:00, 1878.34 it/sec, feas=True, obj=4.85]
INFO - 16:19:01: 42%|████▏ | 415/1000 [00:00<00:00, 1878.55 it/sec, feas=True, obj=7.96]
INFO - 16:19:01: 42%|████▏ | 416/1000 [00:00<00:00, 1878.61 it/sec, feas=True, obj=-3.84]
INFO - 16:19:01: 42%|████▏ | 417/1000 [00:00<00:00, 1877.61 it/sec, feas=True, obj=-2.87]
INFO - 16:19:01: 42%|████▏ | 418/1000 [00:00<00:00, 1876.88 it/sec, feas=True, obj=-1.69]
INFO - 16:19:01: 42%|████▏ | 419/1000 [00:00<00:00, 1877.15 it/sec, feas=True, obj=2.32]
INFO - 16:19:01: 42%|████▏ | 420/1000 [00:00<00:00, 1877.07 it/sec, feas=True, obj=8.32]
INFO - 16:19:01: 42%|████▏ | 421/1000 [00:00<00:00, 1877.37 it/sec, feas=True, obj=1.74]
INFO - 16:19:01: 42%|████▏ | 422/1000 [00:00<00:00, 1877.35 it/sec, feas=True, obj=4.05]
INFO - 16:19:01: 42%|████▏ | 423/1000 [00:00<00:00, 1877.62 it/sec, feas=True, obj=2.71]
INFO - 16:19:01: 42%|████▏ | 424/1000 [00:00<00:00, 1876.36 it/sec, feas=True, obj=6.78]
INFO - 16:19:01: 42%|████▎ | 425/1000 [00:00<00:00, 1876.29 it/sec, feas=True, obj=3.95]
INFO - 16:19:01: 43%|████▎ | 426/1000 [00:00<00:00, 1876.02 it/sec, feas=True, obj=-0.0526]
INFO - 16:19:01: 43%|████▎ | 427/1000 [00:00<00:00, 1876.05 it/sec, feas=True, obj=-0.581]
INFO - 16:19:01: 43%|████▎ | 428/1000 [00:00<00:00, 1876.13 it/sec, feas=True, obj=16.2]
INFO - 16:19:01: 43%|████▎ | 429/1000 [00:00<00:00, 1876.15 it/sec, feas=True, obj=1.58]
INFO - 16:19:01: 43%|████▎ | 430/1000 [00:00<00:00, 1876.23 it/sec, feas=True, obj=5.87]
INFO - 16:19:01: 43%|████▎ | 431/1000 [00:00<00:00, 1874.85 it/sec, feas=True, obj=6.51]
INFO - 16:19:01: 43%|████▎ | 432/1000 [00:00<00:00, 1874.87 it/sec, feas=True, obj=4.28]
INFO - 16:19:01: 43%|████▎ | 433/1000 [00:00<00:00, 1875.00 it/sec, feas=True, obj=-6.3]
INFO - 16:19:01: 43%|████▎ | 434/1000 [00:00<00:00, 1875.21 it/sec, feas=True, obj=7.49]
INFO - 16:19:01: 44%|████▎ | 435/1000 [00:00<00:00, 1875.15 it/sec, feas=True, obj=8.03]
INFO - 16:19:01: 44%|████▎ | 436/1000 [00:00<00:00, 1875.45 it/sec, feas=True, obj=1.4]
INFO - 16:19:01: 44%|████▎ | 437/1000 [00:00<00:00, 1875.26 it/sec, feas=True, obj=1.65]
INFO - 16:19:01: 44%|████▍ | 438/1000 [00:00<00:00, 1875.60 it/sec, feas=True, obj=-0.221]
INFO - 16:19:01: 44%|████▍ | 439/1000 [00:00<00:00, 1875.35 it/sec, feas=True, obj=7.25]
INFO - 16:19:01: 44%|████▍ | 440/1000 [00:00<00:00, 1875.39 it/sec, feas=True, obj=5.14]
INFO - 16:19:01: 44%|████▍ | 441/1000 [00:00<00:00, 1875.46 it/sec, feas=True, obj=-0.896]
INFO - 16:19:01: 44%|████▍ | 442/1000 [00:00<00:00, 1875.48 it/sec, feas=True, obj=-0.969]
INFO - 16:19:01: 44%|████▍ | 443/1000 [00:00<00:00, 1875.57 it/sec, feas=True, obj=7.53]
INFO - 16:19:01: 44%|████▍ | 444/1000 [00:00<00:00, 1875.64 it/sec, feas=True, obj=6.62]
INFO - 16:19:01: 44%|████▍ | 445/1000 [00:00<00:00, 1875.63 it/sec, feas=True, obj=3.23]
INFO - 16:19:01: 45%|████▍ | 446/1000 [00:00<00:00, 1875.74 it/sec, feas=True, obj=-10.1]
INFO - 16:19:01: 45%|████▍ | 447/1000 [00:00<00:00, 1875.80 it/sec, feas=True, obj=7.22]
INFO - 16:19:01: 45%|████▍ | 448/1000 [00:00<00:00, 1876.02 it/sec, feas=True, obj=12.9]
INFO - 16:19:01: 45%|████▍ | 449/1000 [00:00<00:00, 1876.10 it/sec, feas=True, obj=7.61]
INFO - 16:19:01: 45%|████▌ | 450/1000 [00:00<00:00, 1876.17 it/sec, feas=True, obj=3.57]
INFO - 16:19:01: 45%|████▌ | 451/1000 [00:00<00:00, 1876.18 it/sec, feas=True, obj=5.91]
INFO - 16:19:01: 45%|████▌ | 452/1000 [00:00<00:00, 1876.34 it/sec, feas=True, obj=-1.97]
INFO - 16:19:01: 45%|████▌ | 453/1000 [00:00<00:00, 1876.37 it/sec, feas=True, obj=7.83]
INFO - 16:19:01: 45%|████▌ | 454/1000 [00:00<00:00, 1876.43 it/sec, feas=True, obj=2.12]
INFO - 16:19:01: 46%|████▌ | 455/1000 [00:00<00:00, 1876.55 it/sec, feas=True, obj=-0.821]
INFO - 16:19:01: 46%|████▌ | 456/1000 [00:00<00:00, 1876.52 it/sec, feas=True, obj=2.27]
INFO - 16:19:01: 46%|████▌ | 457/1000 [00:00<00:00, 1875.96 it/sec, feas=True, obj=7.13]
INFO - 16:19:01: 46%|████▌ | 458/1000 [00:00<00:00, 1875.39 it/sec, feas=True, obj=3.63]
INFO - 16:19:01: 46%|████▌ | 459/1000 [00:00<00:00, 1875.37 it/sec, feas=True, obj=2.21]
INFO - 16:19:01: 46%|████▌ | 460/1000 [00:00<00:00, 1875.66 it/sec, feas=True, obj=3.08]
INFO - 16:19:01: 46%|████▌ | 461/1000 [00:00<00:00, 1875.50 it/sec, feas=True, obj=3.18]
INFO - 16:19:01: 46%|████▌ | 462/1000 [00:00<00:00, 1875.76 it/sec, feas=True, obj=4.64]
INFO - 16:19:01: 46%|████▋ | 463/1000 [00:00<00:00, 1875.68 it/sec, feas=True, obj=0.243]
INFO - 16:19:01: 46%|████▋ | 464/1000 [00:00<00:00, 1875.97 it/sec, feas=True, obj=2.2]
INFO - 16:19:01: 46%|████▋ | 465/1000 [00:00<00:00, 1876.01 it/sec, feas=True, obj=-0.0681]
INFO - 16:19:01: 47%|████▋ | 466/1000 [00:00<00:00, 1876.30 it/sec, feas=True, obj=0.986]
INFO - 16:19:01: 47%|████▋ | 467/1000 [00:00<00:00, 1876.24 it/sec, feas=True, obj=7.39]
INFO - 16:19:01: 47%|████▋ | 468/1000 [00:00<00:00, 1876.33 it/sec, feas=True, obj=6.85]
INFO - 16:19:01: 47%|████▋ | 469/1000 [00:00<00:00, 1876.10 it/sec, feas=True, obj=8.98]
INFO - 16:19:01: 47%|████▋ | 470/1000 [00:00<00:00, 1876.41 it/sec, feas=True, obj=4.98]
INFO - 16:19:01: 47%|████▋ | 471/1000 [00:00<00:00, 1876.26 it/sec, feas=True, obj=0.108]
INFO - 16:19:01: 47%|████▋ | 472/1000 [00:00<00:00, 1876.55 it/sec, feas=True, obj=4.9]
INFO - 16:19:01: 47%|████▋ | 473/1000 [00:00<00:00, 1876.34 it/sec, feas=True, obj=1.98]
INFO - 16:19:01: 47%|████▋ | 474/1000 [00:00<00:00, 1876.38 it/sec, feas=True, obj=-3.79]
INFO - 16:19:01: 48%|████▊ | 475/1000 [00:00<00:00, 1876.37 it/sec, feas=True, obj=13.5]
INFO - 16:19:01: 48%|████▊ | 476/1000 [00:00<00:00, 1876.27 it/sec, feas=True, obj=0.587]
INFO - 16:19:01: 48%|████▊ | 477/1000 [00:00<00:00, 1876.31 it/sec, feas=True, obj=5.28]
INFO - 16:19:01: 48%|████▊ | 478/1000 [00:00<00:00, 1876.38 it/sec, feas=True, obj=6.02]
INFO - 16:19:01: 48%|████▊ | 479/1000 [00:00<00:00, 1876.42 it/sec, feas=True, obj=2.5]
INFO - 16:19:01: 48%|████▊ | 480/1000 [00:00<00:00, 1876.65 it/sec, feas=True, obj=-0.343]
INFO - 16:19:01: 48%|████▊ | 481/1000 [00:00<00:00, 1876.71 it/sec, feas=True, obj=4.72]
INFO - 16:19:01: 48%|████▊ | 482/1000 [00:00<00:00, 1876.91 it/sec, feas=True, obj=6.71]
INFO - 16:19:01: 48%|████▊ | 483/1000 [00:00<00:00, 1876.97 it/sec, feas=True, obj=-2.87]
INFO - 16:19:01: 48%|████▊ | 484/1000 [00:00<00:00, 1876.99 it/sec, feas=True, obj=1]
INFO - 16:19:01: 48%|████▊ | 485/1000 [00:00<00:00, 1877.05 it/sec, feas=True, obj=6.11]
INFO - 16:19:01: 49%|████▊ | 486/1000 [00:00<00:00, 1877.22 it/sec, feas=True, obj=0.946]
INFO - 16:19:01: 49%|████▊ | 487/1000 [00:00<00:00, 1876.15 it/sec, feas=True, obj=2.29]
INFO - 16:19:01: 49%|████▉ | 488/1000 [00:00<00:00, 1876.17 it/sec, feas=True, obj=9.42]
INFO - 16:19:01: 49%|████▉ | 489/1000 [00:00<00:00, 1875.89 it/sec, feas=True, obj=5.01]
INFO - 16:19:01: 49%|████▉ | 490/1000 [00:00<00:00, 1876.08 it/sec, feas=True, obj=1.02]
INFO - 16:19:01: 49%|████▉ | 491/1000 [00:00<00:00, 1875.92 it/sec, feas=True, obj=3.59]
INFO - 16:19:01: 49%|████▉ | 492/1000 [00:00<00:00, 1876.15 it/sec, feas=True, obj=7.01]
INFO - 16:19:01: 49%|████▉ | 493/1000 [00:00<00:00, 1876.14 it/sec, feas=True, obj=8.7]
INFO - 16:19:01: 49%|████▉ | 494/1000 [00:00<00:00, 1876.20 it/sec, feas=True, obj=5.6]
INFO - 16:19:01: 50%|████▉ | 495/1000 [00:00<00:00, 1876.16 it/sec, feas=True, obj=-0.897]
INFO - 16:19:01: 50%|████▉ | 496/1000 [00:00<00:00, 1876.46 it/sec, feas=True, obj=7.82]
INFO - 16:19:01: 50%|████▉ | 497/1000 [00:00<00:00, 1876.49 it/sec, feas=True, obj=6.63]
INFO - 16:19:01: 50%|████▉ | 498/1000 [00:00<00:00, 1876.80 it/sec, feas=True, obj=3.33]
INFO - 16:19:01: 50%|████▉ | 499/1000 [00:00<00:00, 1876.79 it/sec, feas=True, obj=4.36]
INFO - 16:19:01: 50%|█████ | 500/1000 [00:00<00:00, 1877.06 it/sec, feas=True, obj=4.21]
INFO - 16:19:01: 50%|█████ | 501/1000 [00:00<00:00, 1877.08 it/sec, feas=True, obj=3.93]
INFO - 16:19:01: 50%|█████ | 502/1000 [00:00<00:00, 1877.36 it/sec, feas=True, obj=10.4]
INFO - 16:19:01: 50%|█████ | 503/1000 [00:00<00:00, 1877.32 it/sec, feas=True, obj=-1.39]
INFO - 16:19:01: 50%|█████ | 504/1000 [00:00<00:00, 1877.62 it/sec, feas=True, obj=-0.386]
INFO - 16:19:01: 50%|█████ | 505/1000 [00:00<00:00, 1877.54 it/sec, feas=True, obj=4.95]
INFO - 16:19:01: 51%|█████ | 506/1000 [00:00<00:00, 1877.79 it/sec, feas=True, obj=4.8]
INFO - 16:19:01: 51%|█████ | 507/1000 [00:00<00:00, 1877.61 it/sec, feas=True, obj=7.94]
INFO - 16:19:01: 51%|█████ | 508/1000 [00:00<00:00, 1877.90 it/sec, feas=True, obj=1.6]
INFO - 16:19:01: 51%|█████ | 509/1000 [00:00<00:00, 1877.73 it/sec, feas=True, obj=8.91]
INFO - 16:19:01: 51%|█████ | 510/1000 [00:00<00:00, 1877.74 it/sec, feas=True, obj=9.47]
INFO - 16:19:01: 51%|█████ | 511/1000 [00:00<00:00, 1877.73 it/sec, feas=True, obj=-0.535]
INFO - 16:19:01: 51%|█████ | 512/1000 [00:00<00:00, 1877.12 it/sec, feas=True, obj=2.76]
INFO - 16:19:01: 51%|█████▏ | 513/1000 [00:00<00:00, 1876.28 it/sec, feas=True, obj=0.439]
INFO - 16:19:01: 51%|█████▏ | 514/1000 [00:00<00:00, 1876.10 it/sec, feas=True, obj=3.69]
INFO - 16:19:01: 52%|█████▏ | 515/1000 [00:00<00:00, 1876.21 it/sec, feas=True, obj=-1.1]
INFO - 16:19:01: 52%|█████▏ | 516/1000 [00:00<00:00, 1876.03 it/sec, feas=True, obj=2.48]
INFO - 16:19:01: 52%|█████▏ | 517/1000 [00:00<00:00, 1876.10 it/sec, feas=True, obj=2.8]
INFO - 16:19:01: 52%|█████▏ | 518/1000 [00:00<00:00, 1876.03 it/sec, feas=True, obj=13]
INFO - 16:19:01: 52%|█████▏ | 519/1000 [00:00<00:00, 1876.04 it/sec, feas=True, obj=6.01]
INFO - 16:19:01: 52%|█████▏ | 520/1000 [00:00<00:00, 1876.00 it/sec, feas=True, obj=2.49]
INFO - 16:19:01: 52%|█████▏ | 521/1000 [00:00<00:00, 1875.93 it/sec, feas=True, obj=5.92]
INFO - 16:19:01: 52%|█████▏ | 522/1000 [00:00<00:00, 1875.94 it/sec, feas=True, obj=3.4]
INFO - 16:19:01: 52%|█████▏ | 523/1000 [00:00<00:00, 1876.06 it/sec, feas=True, obj=-1.78]
INFO - 16:19:01: 52%|█████▏ | 524/1000 [00:00<00:00, 1876.10 it/sec, feas=True, obj=2.44]
INFO - 16:19:01: 52%|█████▎ | 525/1000 [00:00<00:00, 1876.19 it/sec, feas=True, obj=16]
INFO - 16:19:01: 53%|█████▎ | 526/1000 [00:00<00:00, 1876.25 it/sec, feas=True, obj=6.22]
INFO - 16:19:01: 53%|█████▎ | 527/1000 [00:00<00:00, 1876.13 it/sec, feas=True, obj=7.2]
INFO - 16:19:01: 53%|█████▎ | 528/1000 [00:00<00:00, 1875.07 it/sec, feas=True, obj=4.57]
INFO - 16:19:01: 53%|█████▎ | 529/1000 [00:00<00:00, 1874.57 it/sec, feas=True, obj=6.77]
INFO - 16:19:01: 53%|█████▎ | 530/1000 [00:00<00:00, 1874.45 it/sec, feas=True, obj=13]
INFO - 16:19:01: 53%|█████▎ | 531/1000 [00:00<00:00, 1874.66 it/sec, feas=True, obj=5]
INFO - 16:19:01: 53%|█████▎ | 532/1000 [00:00<00:00, 1874.59 it/sec, feas=True, obj=-0.711]
INFO - 16:19:01: 53%|█████▎ | 533/1000 [00:00<00:00, 1874.68 it/sec, feas=True, obj=-0.543]
INFO - 16:19:01: 53%|█████▎ | 534/1000 [00:00<00:00, 1874.59 it/sec, feas=True, obj=0.469]
INFO - 16:19:01: 54%|█████▎ | 535/1000 [00:00<00:00, 1874.80 it/sec, feas=True, obj=4.16]
INFO - 16:19:01: 54%|█████▎ | 536/1000 [00:00<00:00, 1874.76 it/sec, feas=True, obj=4.73]
INFO - 16:19:01: 54%|█████▎ | 537/1000 [00:00<00:00, 1874.78 it/sec, feas=True, obj=-0.197]
INFO - 16:19:01: 54%|█████▍ | 538/1000 [00:00<00:00, 1874.66 it/sec, feas=True, obj=-2.45]
INFO - 16:19:01: 54%|█████▍ | 539/1000 [00:00<00:00, 1874.91 it/sec, feas=True, obj=2.9]
INFO - 16:19:01: 54%|█████▍ | 540/1000 [00:00<00:00, 1874.76 it/sec, feas=True, obj=4.59]
INFO - 16:19:01: 54%|█████▍ | 541/1000 [00:00<00:00, 1874.89 it/sec, feas=True, obj=4.09]
INFO - 16:19:01: 54%|█████▍ | 542/1000 [00:00<00:00, 1874.88 it/sec, feas=True, obj=0.0786]
INFO - 16:19:01: 54%|█████▍ | 543/1000 [00:00<00:00, 1873.98 it/sec, feas=True, obj=6.9]
INFO - 16:19:01: 54%|█████▍ | 544/1000 [00:00<00:00, 1873.44 it/sec, feas=True, obj=3.77]
INFO - 16:19:01: 55%|█████▍ | 545/1000 [00:00<00:00, 1873.51 it/sec, feas=True, obj=2.68]
INFO - 16:19:01: 55%|█████▍ | 546/1000 [00:00<00:00, 1873.56 it/sec, feas=True, obj=5.03]
INFO - 16:19:01: 55%|█████▍ | 547/1000 [00:00<00:00, 1873.59 it/sec, feas=True, obj=7.02]
INFO - 16:19:01: 55%|█████▍ | 548/1000 [00:00<00:00, 1873.68 it/sec, feas=True, obj=7]
INFO - 16:19:01: 55%|█████▍ | 549/1000 [00:00<00:00, 1873.72 it/sec, feas=True, obj=1.03]
INFO - 16:19:01: 55%|█████▌ | 550/1000 [00:00<00:00, 1873.77 it/sec, feas=True, obj=4.74]
INFO - 16:19:01: 55%|█████▌ | 551/1000 [00:00<00:00, 1873.74 it/sec, feas=True, obj=-0.817]
INFO - 16:19:01: 55%|█████▌ | 552/1000 [00:00<00:00, 1873.60 it/sec, feas=True, obj=2.59]
INFO - 16:19:01: 55%|█████▌ | 553/1000 [00:00<00:00, 1873.84 it/sec, feas=True, obj=3.33]
INFO - 16:19:01: 55%|█████▌ | 554/1000 [00:00<00:00, 1873.66 it/sec, feas=True, obj=2.13]
INFO - 16:19:01: 56%|█████▌ | 555/1000 [00:00<00:00, 1873.91 it/sec, feas=True, obj=-0.076]
INFO - 16:19:01: 56%|█████▌ | 556/1000 [00:00<00:00, 1873.74 it/sec, feas=True, obj=-0.023]
INFO - 16:19:01: 56%|█████▌ | 557/1000 [00:00<00:00, 1874.00 it/sec, feas=True, obj=7.03]
INFO - 16:19:01: 56%|█████▌ | 558/1000 [00:00<00:00, 1873.85 it/sec, feas=True, obj=3.4]
INFO - 16:19:01: 56%|█████▌ | 559/1000 [00:00<00:00, 1874.00 it/sec, feas=True, obj=-1.23]
INFO - 16:19:01: 56%|█████▌ | 560/1000 [00:00<00:00, 1873.91 it/sec, feas=True, obj=7.3]
INFO - 16:19:01: 56%|█████▌ | 561/1000 [00:00<00:00, 1874.15 it/sec, feas=True, obj=4.59]
INFO - 16:19:01: 56%|█████▌ | 562/1000 [00:00<00:00, 1874.18 it/sec, feas=True, obj=-0.53]
INFO - 16:19:01: 56%|█████▋ | 563/1000 [00:00<00:00, 1874.40 it/sec, feas=True, obj=7.24]
INFO - 16:19:01: 56%|█████▋ | 564/1000 [00:00<00:00, 1874.38 it/sec, feas=True, obj=-0.753]
INFO - 16:19:01: 56%|█████▋ | 565/1000 [00:00<00:00, 1874.63 it/sec, feas=True, obj=7.78]
INFO - 16:19:01: 57%|█████▋ | 566/1000 [00:00<00:00, 1874.49 it/sec, feas=True, obj=6.64]
INFO - 16:19:01: 57%|█████▋ | 567/1000 [00:00<00:00, 1874.40 it/sec, feas=True, obj=0.671]
INFO - 16:19:01: 57%|█████▋ | 568/1000 [00:00<00:00, 1874.22 it/sec, feas=True, obj=-2]
INFO - 16:19:01: 57%|█████▋ | 569/1000 [00:00<00:00, 1873.93 it/sec, feas=True, obj=-1.92]
INFO - 16:19:01: 57%|█████▋ | 570/1000 [00:00<00:00, 1874.05 it/sec, feas=True, obj=6.03]
INFO - 16:19:01: 57%|█████▋ | 571/1000 [00:00<00:00, 1873.86 it/sec, feas=True, obj=9.42]
INFO - 16:19:01: 57%|█████▋ | 572/1000 [00:00<00:00, 1874.07 it/sec, feas=True, obj=1.01]
INFO - 16:19:01: 57%|█████▋ | 573/1000 [00:00<00:00, 1873.99 it/sec, feas=True, obj=1.43]
INFO - 16:19:01: 57%|█████▋ | 574/1000 [00:00<00:00, 1874.24 it/sec, feas=True, obj=0.0646]
INFO - 16:19:01: 57%|█████▊ | 575/1000 [00:00<00:00, 1874.25 it/sec, feas=True, obj=5.16]
INFO - 16:19:01: 58%|█████▊ | 576/1000 [00:00<00:00, 1874.49 it/sec, feas=True, obj=1.61]
INFO - 16:19:01: 58%|█████▊ | 577/1000 [00:00<00:00, 1874.48 it/sec, feas=True, obj=0.944]
INFO - 16:19:01: 58%|█████▊ | 578/1000 [00:00<00:00, 1874.61 it/sec, feas=True, obj=0.535]
INFO - 16:19:01: 58%|█████▊ | 579/1000 [00:00<00:00, 1874.63 it/sec, feas=True, obj=1.86]
INFO - 16:19:01: 58%|█████▊ | 580/1000 [00:00<00:00, 1874.68 it/sec, feas=True, obj=2.93]
INFO - 16:19:01: 58%|█████▊ | 581/1000 [00:00<00:00, 1874.76 it/sec, feas=True, obj=2.4]
INFO - 16:19:01: 58%|█████▊ | 582/1000 [00:00<00:00, 1874.81 it/sec, feas=True, obj=6.58]
INFO - 16:19:01: 58%|█████▊ | 583/1000 [00:00<00:00, 1874.93 it/sec, feas=True, obj=-0.0337]
INFO - 16:19:01: 58%|█████▊ | 584/1000 [00:00<00:00, 1874.96 it/sec, feas=True, obj=6.62]
INFO - 16:19:01: 58%|█████▊ | 585/1000 [00:00<00:00, 1875.01 it/sec, feas=True, obj=5.61]
INFO - 16:19:01: 59%|█████▊ | 586/1000 [00:00<00:00, 1875.07 it/sec, feas=True, obj=5.55]
INFO - 16:19:01: 59%|█████▊ | 587/1000 [00:00<00:00, 1874.97 it/sec, feas=True, obj=5.28]
INFO - 16:19:01: 59%|█████▉ | 588/1000 [00:00<00:00, 1874.66 it/sec, feas=True, obj=3.22]
INFO - 16:19:01: 59%|█████▉ | 589/1000 [00:00<00:00, 1874.83 it/sec, feas=True, obj=3.1]
INFO - 16:19:01: 59%|█████▉ | 590/1000 [00:00<00:00, 1874.63 it/sec, feas=True, obj=5.83]
INFO - 16:19:01: 59%|█████▉ | 591/1000 [00:00<00:00, 1874.82 it/sec, feas=True, obj=4.03]
INFO - 16:19:01: 59%|█████▉ | 592/1000 [00:00<00:00, 1874.72 it/sec, feas=True, obj=-3.08]
INFO - 16:19:01: 59%|█████▉ | 593/1000 [00:00<00:00, 1874.78 it/sec, feas=True, obj=3.63]
INFO - 16:19:01: 59%|█████▉ | 594/1000 [00:00<00:00, 1874.68 it/sec, feas=True, obj=0.374]
INFO - 16:19:01: 60%|█████▉ | 595/1000 [00:00<00:00, 1874.91 it/sec, feas=True, obj=7.07]
INFO - 16:19:01: 60%|█████▉ | 596/1000 [00:00<00:00, 1874.80 it/sec, feas=True, obj=0.707]
INFO - 16:19:01: 60%|█████▉ | 597/1000 [00:00<00:00, 1875.05 it/sec, feas=True, obj=5.65]
INFO - 16:19:01: 60%|█████▉ | 598/1000 [00:00<00:00, 1875.16 it/sec, feas=True, obj=5.83]
INFO - 16:19:01: 60%|█████▉ | 599/1000 [00:00<00:00, 1875.25 it/sec, feas=True, obj=4.2]
INFO - 16:19:01: 60%|██████ | 600/1000 [00:00<00:00, 1875.33 it/sec, feas=True, obj=-0.0744]
INFO - 16:19:01: 60%|██████ | 601/1000 [00:00<00:00, 1874.28 it/sec, feas=True, obj=0.391]
INFO - 16:19:01: 60%|██████ | 602/1000 [00:00<00:00, 1873.92 it/sec, feas=True, obj=4.96]
INFO - 16:19:01: 60%|██████ | 603/1000 [00:00<00:00, 1872.83 it/sec, feas=True, obj=2.18]
INFO - 16:19:01: 60%|██████ | 604/1000 [00:00<00:00, 1872.96 it/sec, feas=True, obj=1.55]
INFO - 16:19:01: 60%|██████ | 605/1000 [00:00<00:00, 1872.76 it/sec, feas=True, obj=6.26]
INFO - 16:19:01: 61%|██████ | 606/1000 [00:00<00:00, 1872.73 it/sec, feas=True, obj=5.3]
INFO - 16:19:01: 61%|██████ | 607/1000 [00:00<00:00, 1872.73 it/sec, feas=True, obj=7.18]
INFO - 16:19:01: 61%|██████ | 608/1000 [00:00<00:00, 1872.75 it/sec, feas=True, obj=1.45]
INFO - 16:19:01: 61%|██████ | 609/1000 [00:00<00:00, 1872.02 it/sec, feas=True, obj=8.78]
INFO - 16:19:01: 61%|██████ | 610/1000 [00:00<00:00, 1871.17 it/sec, feas=True, obj=0.233]
INFO - 16:19:01: 61%|██████ | 611/1000 [00:00<00:00, 1870.77 it/sec, feas=True, obj=-1.38]
INFO - 16:19:01: 61%|██████ | 612/1000 [00:00<00:00, 1870.05 it/sec, feas=True, obj=6.09]
INFO - 16:19:01: 61%|██████▏ | 613/1000 [00:00<00:00, 1869.98 it/sec, feas=True, obj=5.58]
INFO - 16:19:01: 61%|██████▏ | 614/1000 [00:00<00:00, 1869.97 it/sec, feas=True, obj=11]
INFO - 16:19:01: 62%|██████▏ | 615/1000 [00:00<00:00, 1869.14 it/sec, feas=True, obj=5.03]
INFO - 16:19:01: 62%|██████▏ | 616/1000 [00:00<00:00, 1868.84 it/sec, feas=True, obj=6.39]
INFO - 16:19:01: 62%|██████▏ | 617/1000 [00:00<00:00, 1868.99 it/sec, feas=True, obj=1.92]
INFO - 16:19:01: 62%|██████▏ | 618/1000 [00:00<00:00, 1868.81 it/sec, feas=True, obj=1.05]
INFO - 16:19:01: 62%|██████▏ | 619/1000 [00:00<00:00, 1869.02 it/sec, feas=True, obj=0.0814]
INFO - 16:19:01: 62%|██████▏ | 620/1000 [00:00<00:00, 1868.96 it/sec, feas=True, obj=5.88]
INFO - 16:19:01: 62%|██████▏ | 621/1000 [00:00<00:00, 1869.14 it/sec, feas=True, obj=14.7]
INFO - 16:19:01: 62%|██████▏ | 622/1000 [00:00<00:00, 1868.34 it/sec, feas=True, obj=4.25]
INFO - 16:19:01: 62%|██████▏ | 623/1000 [00:00<00:00, 1868.02 it/sec, feas=True, obj=-1.9]
INFO - 16:19:01: 62%|██████▏ | 624/1000 [00:00<00:00, 1867.78 it/sec, feas=True, obj=-0.304]
INFO - 16:19:01: 62%|██████▎ | 625/1000 [00:00<00:00, 1867.79 it/sec, feas=True, obj=-0.315]
INFO - 16:19:01: 63%|██████▎ | 626/1000 [00:00<00:00, 1867.77 it/sec, feas=True, obj=-0.772]
INFO - 16:19:01: 63%|██████▎ | 627/1000 [00:00<00:00, 1867.73 it/sec, feas=True, obj=4.47]
INFO - 16:19:01: 63%|██████▎ | 628/1000 [00:00<00:00, 1866.98 it/sec, feas=True, obj=3.87]
INFO - 16:19:01: 63%|██████▎ | 629/1000 [00:00<00:00, 1866.84 it/sec, feas=True, obj=1.69]
INFO - 16:19:01: 63%|██████▎ | 630/1000 [00:00<00:00, 1866.84 it/sec, feas=True, obj=14.2]
INFO - 16:19:01: 63%|██████▎ | 631/1000 [00:00<00:00, 1865.61 it/sec, feas=True, obj=0.467]
INFO - 16:19:01: 63%|██████▎ | 632/1000 [00:00<00:00, 1865.49 it/sec, feas=True, obj=0.13]
INFO - 16:19:01: 63%|██████▎ | 633/1000 [00:00<00:00, 1865.52 it/sec, feas=True, obj=-0.788]
INFO - 16:19:01: 63%|██████▎ | 634/1000 [00:00<00:00, 1865.52 it/sec, feas=True, obj=3.3]
INFO - 16:19:01: 64%|██████▎ | 635/1000 [00:00<00:00, 1865.56 it/sec, feas=True, obj=7.29]
INFO - 16:19:01: 64%|██████▎ | 636/1000 [00:00<00:00, 1864.90 it/sec, feas=True, obj=1.41]
INFO - 16:19:01: 64%|██████▎ | 637/1000 [00:00<00:00, 1864.40 it/sec, feas=True, obj=6.16]
INFO - 16:19:01: 64%|██████▍ | 638/1000 [00:00<00:00, 1864.55 it/sec, feas=True, obj=6.98]
INFO - 16:19:01: 64%|██████▍ | 639/1000 [00:00<00:00, 1864.42 it/sec, feas=True, obj=7.82]
INFO - 16:19:01: 64%|██████▍ | 640/1000 [00:00<00:00, 1864.61 it/sec, feas=True, obj=4.49]
INFO - 16:19:01: 64%|██████▍ | 641/1000 [00:00<00:00, 1864.63 it/sec, feas=True, obj=6.84]
INFO - 16:19:01: 64%|██████▍ | 642/1000 [00:00<00:00, 1864.77 it/sec, feas=True, obj=3.83]
INFO - 16:19:01: 64%|██████▍ | 643/1000 [00:00<00:00, 1864.78 it/sec, feas=True, obj=2.52]
INFO - 16:19:01: 64%|██████▍ | 644/1000 [00:00<00:00, 1865.00 it/sec, feas=True, obj=1]
INFO - 16:19:01: 64%|██████▍ | 645/1000 [00:00<00:00, 1864.88 it/sec, feas=True, obj=3.54]
INFO - 16:19:01: 65%|██████▍ | 646/1000 [00:00<00:00, 1864.91 it/sec, feas=True, obj=5.22]
INFO - 16:19:01: 65%|██████▍ | 647/1000 [00:00<00:00, 1865.05 it/sec, feas=True, obj=7.98]
INFO - 16:19:01: 65%|██████▍ | 648/1000 [00:00<00:00, 1865.08 it/sec, feas=True, obj=3.23]
INFO - 16:19:01: 65%|██████▍ | 649/1000 [00:00<00:00, 1865.22 it/sec, feas=True, obj=2.61]
INFO - 16:19:01: 65%|██████▌ | 650/1000 [00:00<00:00, 1865.27 it/sec, feas=True, obj=4.85]
INFO - 16:19:01: 65%|██████▌ | 651/1000 [00:00<00:00, 1865.32 it/sec, feas=True, obj=1.4]
INFO - 16:19:01: 65%|██████▌ | 652/1000 [00:00<00:00, 1864.90 it/sec, feas=True, obj=-0.857]
INFO - 16:19:01: 65%|██████▌ | 653/1000 [00:00<00:00, 1865.09 it/sec, feas=True, obj=4.01]
INFO - 16:19:01: 65%|██████▌ | 654/1000 [00:00<00:00, 1864.94 it/sec, feas=True, obj=6.3]
INFO - 16:19:01: 66%|██████▌ | 655/1000 [00:00<00:00, 1865.13 it/sec, feas=True, obj=11.4]
INFO - 16:19:01: 66%|██████▌ | 656/1000 [00:00<00:00, 1865.04 it/sec, feas=True, obj=5.47]
INFO - 16:19:01: 66%|██████▌ | 657/1000 [00:00<00:00, 1865.26 it/sec, feas=True, obj=2.72]
INFO - 16:19:01: 66%|██████▌ | 658/1000 [00:00<00:00, 1865.25 it/sec, feas=True, obj=3.85]
INFO - 16:19:01: 66%|██████▌ | 659/1000 [00:00<00:00, 1865.38 it/sec, feas=True, obj=-0.392]
INFO - 16:19:01: 66%|██████▌ | 660/1000 [00:00<00:00, 1865.30 it/sec, feas=True, obj=0.0168]
INFO - 16:19:01: 66%|██████▌ | 661/1000 [00:00<00:00, 1865.39 it/sec, feas=True, obj=2.53]
INFO - 16:19:01: 66%|██████▌ | 662/1000 [00:00<00:00, 1865.42 it/sec, feas=True, obj=1.76]
INFO - 16:19:01: 66%|██████▋ | 663/1000 [00:00<00:00, 1865.63 it/sec, feas=True, obj=4.26]
INFO - 16:19:01: 66%|██████▋ | 664/1000 [00:00<00:00, 1865.60 it/sec, feas=True, obj=5.45]
INFO - 16:19:01: 66%|██████▋ | 665/1000 [00:00<00:00, 1865.82 it/sec, feas=True, obj=6.94]
INFO - 16:19:01: 67%|██████▋ | 666/1000 [00:00<00:00, 1865.74 it/sec, feas=True, obj=5.93]
INFO - 16:19:01: 67%|██████▋ | 667/1000 [00:00<00:00, 1865.96 it/sec, feas=True, obj=5.79]
INFO - 16:19:01: 67%|██████▋ | 668/1000 [00:00<00:00, 1865.80 it/sec, feas=True, obj=2.62]
INFO - 16:19:01: 67%|██████▋ | 669/1000 [00:00<00:00, 1866.00 it/sec, feas=True, obj=6.4]
INFO - 16:19:01: 67%|██████▋ | 670/1000 [00:00<00:00, 1865.89 it/sec, feas=True, obj=-0.703]
INFO - 16:19:01: 67%|██████▋ | 671/1000 [00:00<00:00, 1865.96 it/sec, feas=True, obj=8.61]
INFO - 16:19:01: 67%|██████▋ | 672/1000 [00:00<00:00, 1866.02 it/sec, feas=True, obj=0.91]
INFO - 16:19:01: 67%|██████▋ | 673/1000 [00:00<00:00, 1866.06 it/sec, feas=True, obj=1.05]
INFO - 16:19:01: 67%|██████▋ | 674/1000 [00:00<00:00, 1866.11 it/sec, feas=True, obj=10.1]
INFO - 16:19:01: 68%|██████▊ | 675/1000 [00:00<00:00, 1866.08 it/sec, feas=True, obj=-0.575]
INFO - 16:19:01: 68%|██████▊ | 676/1000 [00:00<00:00, 1866.13 it/sec, feas=True, obj=-2.06]
INFO - 16:19:01: 68%|██████▊ | 677/1000 [00:00<00:00, 1866.18 it/sec, feas=True, obj=7.34]
INFO - 16:19:01: 68%|██████▊ | 678/1000 [00:00<00:00, 1866.25 it/sec, feas=True, obj=2.78]
INFO - 16:19:01: 68%|██████▊ | 679/1000 [00:00<00:00, 1866.35 it/sec, feas=True, obj=1.15]
INFO - 16:19:01: 68%|██████▊ | 680/1000 [00:00<00:00, 1866.44 it/sec, feas=True, obj=-0.227]
INFO - 16:19:01: 68%|██████▊ | 681/1000 [00:00<00:00, 1866.49 it/sec, feas=True, obj=4.3]
INFO - 16:19:01: 68%|██████▊ | 682/1000 [00:00<00:00, 1866.56 it/sec, feas=True, obj=6.14]
INFO - 16:19:01: 68%|██████▊ | 683/1000 [00:00<00:00, 1866.55 it/sec, feas=True, obj=4.76]
INFO - 16:19:01: 68%|██████▊ | 684/1000 [00:00<00:00, 1866.65 it/sec, feas=True, obj=-4.69]
INFO - 16:19:01: 68%|██████▊ | 685/1000 [00:00<00:00, 1866.59 it/sec, feas=True, obj=-0.877]
INFO - 16:19:01: 69%|██████▊ | 686/1000 [00:00<00:00, 1866.80 it/sec, feas=True, obj=3.02]
INFO - 16:19:01: 69%|██████▊ | 687/1000 [00:00<00:00, 1866.67 it/sec, feas=True, obj=6.98]
INFO - 16:19:01: 69%|██████▉ | 688/1000 [00:00<00:00, 1866.90 it/sec, feas=True, obj=4.88]
INFO - 16:19:01: 69%|██████▉ | 689/1000 [00:00<00:00, 1866.76 it/sec, feas=True, obj=4.99]
INFO - 16:19:01: 69%|██████▉ | 690/1000 [00:00<00:00, 1866.72 it/sec, feas=True, obj=9.72]
INFO - 16:19:01: 69%|██████▉ | 691/1000 [00:00<00:00, 1866.72 it/sec, feas=True, obj=1.5]
INFO - 16:19:01: 69%|██████▉ | 692/1000 [00:00<00:00, 1866.74 it/sec, feas=True, obj=5.57]
INFO - 16:19:01: 69%|██████▉ | 693/1000 [00:00<00:00, 1866.76 it/sec, feas=True, obj=6.06]
INFO - 16:19:01: 69%|██████▉ | 694/1000 [00:00<00:00, 1866.82 it/sec, feas=True, obj=1.09]
INFO - 16:19:01: 70%|██████▉ | 695/1000 [00:00<00:00, 1866.88 it/sec, feas=True, obj=-1.97]
INFO - 16:19:01: 70%|██████▉ | 696/1000 [00:00<00:00, 1866.45 it/sec, feas=True, obj=1.88]
INFO - 16:19:01: 70%|██████▉ | 697/1000 [00:00<00:00, 1866.19 it/sec, feas=True, obj=9.32]
INFO - 16:19:01: 70%|██████▉ | 698/1000 [00:00<00:00, 1866.00 it/sec, feas=True, obj=-7.7]
INFO - 16:19:01: 70%|██████▉ | 699/1000 [00:00<00:00, 1865.93 it/sec, feas=True, obj=1.83]
INFO - 16:19:01: 70%|███████ | 700/1000 [00:00<00:00, 1866.06 it/sec, feas=True, obj=0.735]
INFO - 16:19:01: 70%|███████ | 701/1000 [00:00<00:00, 1865.98 it/sec, feas=True, obj=-1.11]
INFO - 16:19:01: 70%|███████ | 702/1000 [00:00<00:00, 1866.15 it/sec, feas=True, obj=1.47]
INFO - 16:19:01: 70%|███████ | 703/1000 [00:00<00:00, 1866.08 it/sec, feas=True, obj=0.283]
INFO - 16:19:01: 70%|███████ | 704/1000 [00:00<00:00, 1866.27 it/sec, feas=True, obj=15.2]
INFO - 16:19:01: 70%|███████ | 705/1000 [00:00<00:00, 1866.21 it/sec, feas=True, obj=3.43]
INFO - 16:19:01: 71%|███████ | 706/1000 [00:00<00:00, 1866.30 it/sec, feas=True, obj=3.17]
INFO - 16:19:01: 71%|███████ | 707/1000 [00:00<00:00, 1865.92 it/sec, feas=True, obj=5.95]
INFO - 16:19:01: 71%|███████ | 708/1000 [00:00<00:00, 1865.55 it/sec, feas=True, obj=-6.33]
INFO - 16:19:01: 71%|███████ | 709/1000 [00:00<00:00, 1865.44 it/sec, feas=True, obj=13.3]
INFO - 16:19:01: 71%|███████ | 710/1000 [00:00<00:00, 1865.29 it/sec, feas=True, obj=1.32]
INFO - 16:19:01: 71%|███████ | 711/1000 [00:00<00:00, 1865.46 it/sec, feas=True, obj=-4.3]
INFO - 16:19:01: 71%|███████ | 712/1000 [00:00<00:00, 1865.22 it/sec, feas=True, obj=1.63]
INFO - 16:19:01: 71%|███████▏ | 713/1000 [00:00<00:00, 1865.39 it/sec, feas=True, obj=1.99]
INFO - 16:19:01: 71%|███████▏ | 714/1000 [00:00<00:00, 1865.38 it/sec, feas=True, obj=0.679]
INFO - 16:19:01: 72%|███████▏ | 715/1000 [00:00<00:00, 1864.70 it/sec, feas=True, obj=-0.377]
INFO - 16:19:01: 72%|███████▏ | 716/1000 [00:00<00:00, 1864.59 it/sec, feas=True, obj=-4.57]
INFO - 16:19:01: 72%|███████▏ | 717/1000 [00:00<00:00, 1864.78 it/sec, feas=True, obj=2.1]
INFO - 16:19:01: 72%|███████▏ | 718/1000 [00:00<00:00, 1864.59 it/sec, feas=True, obj=1.32]
INFO - 16:19:01: 72%|███████▏ | 719/1000 [00:00<00:00, 1864.27 it/sec, feas=True, obj=0.312]
INFO - 16:19:01: 72%|███████▏ | 720/1000 [00:00<00:00, 1863.80 it/sec, feas=True, obj=8.43]
INFO - 16:19:01: 72%|███████▏ | 721/1000 [00:00<00:00, 1863.78 it/sec, feas=True, obj=0.579]
INFO - 16:19:01: 72%|███████▏ | 722/1000 [00:00<00:00, 1863.75 it/sec, feas=True, obj=0.563]
INFO - 16:19:01: 72%|███████▏ | 723/1000 [00:00<00:00, 1863.83 it/sec, feas=True, obj=3.4]
INFO - 16:19:01: 72%|███████▏ | 724/1000 [00:00<00:00, 1863.87 it/sec, feas=True, obj=7.21]
INFO - 16:19:01: 72%|███████▎ | 725/1000 [00:00<00:00, 1863.91 it/sec, feas=True, obj=5.26]
INFO - 16:19:01: 73%|███████▎ | 726/1000 [00:00<00:00, 1863.91 it/sec, feas=True, obj=2.69]
INFO - 16:19:01: 73%|███████▎ | 727/1000 [00:00<00:00, 1863.88 it/sec, feas=True, obj=5.31]
INFO - 16:19:01: 73%|███████▎ | 728/1000 [00:00<00:00, 1863.72 it/sec, feas=True, obj=1.99]
INFO - 16:19:01: 73%|███████▎ | 729/1000 [00:00<00:00, 1863.86 it/sec, feas=True, obj=-6.72]
INFO - 16:19:01: 73%|███████▎ | 730/1000 [00:00<00:00, 1863.70 it/sec, feas=True, obj=0.526]
INFO - 16:19:01: 73%|███████▎ | 731/1000 [00:00<00:00, 1863.86 it/sec, feas=True, obj=4.35]
INFO - 16:19:01: 73%|███████▎ | 732/1000 [00:00<00:00, 1863.81 it/sec, feas=True, obj=8.27]
INFO - 16:19:01: 73%|███████▎ | 733/1000 [00:00<00:00, 1863.23 it/sec, feas=True, obj=0.662]
INFO - 16:19:01: 73%|███████▎ | 734/1000 [00:00<00:00, 1862.99 it/sec, feas=True, obj=8.9]
INFO - 16:19:01: 74%|███████▎ | 735/1000 [00:00<00:00, 1863.15 it/sec, feas=True, obj=6.96]
INFO - 16:19:01: 74%|███████▎ | 736/1000 [00:00<00:00, 1862.99 it/sec, feas=True, obj=1.11]
INFO - 16:19:01: 74%|███████▎ | 737/1000 [00:00<00:00, 1863.00 it/sec, feas=True, obj=-4.5]
INFO - 16:19:01: 74%|███████▍ | 738/1000 [00:00<00:00, 1863.00 it/sec, feas=True, obj=0.0495]
INFO - 16:19:01: 74%|███████▍ | 739/1000 [00:00<00:00, 1863.00 it/sec, feas=True, obj=5.88]
INFO - 16:19:01: 74%|███████▍ | 740/1000 [00:00<00:00, 1863.02 it/sec, feas=True, obj=13.2]
INFO - 16:19:01: 74%|███████▍ | 741/1000 [00:00<00:00, 1863.02 it/sec, feas=True, obj=2.3]
INFO - 16:19:01: 74%|███████▍ | 742/1000 [00:00<00:00, 1863.07 it/sec, feas=True, obj=2.21]
INFO - 16:19:01: 74%|███████▍ | 743/1000 [00:00<00:00, 1863.17 it/sec, feas=True, obj=-4.03]
INFO - 16:19:01: 74%|███████▍ | 744/1000 [00:00<00:00, 1863.23 it/sec, feas=True, obj=4.28]
INFO - 16:19:01: 74%|███████▍ | 745/1000 [00:00<00:00, 1863.26 it/sec, feas=True, obj=6.68]
INFO - 16:19:01: 75%|███████▍ | 746/1000 [00:00<00:00, 1863.29 it/sec, feas=True, obj=7.33]
INFO - 16:19:01: 75%|███████▍ | 747/1000 [00:00<00:00, 1863.37 it/sec, feas=True, obj=-3.91]
INFO - 16:19:01: 75%|███████▍ | 748/1000 [00:00<00:00, 1863.41 it/sec, feas=True, obj=1.16]
INFO - 16:19:01: 75%|███████▍ | 749/1000 [00:00<00:00, 1863.33 it/sec, feas=True, obj=-0.739]
INFO - 16:19:01: 75%|███████▌ | 750/1000 [00:00<00:00, 1863.51 it/sec, feas=True, obj=5.93]
INFO - 16:19:01: 75%|███████▌ | 751/1000 [00:00<00:00, 1863.41 it/sec, feas=True, obj=3.2]
INFO - 16:19:01: 75%|███████▌ | 752/1000 [00:00<00:00, 1863.44 it/sec, feas=True, obj=11.1]
INFO - 16:19:01: 75%|███████▌ | 753/1000 [00:00<00:00, 1863.40 it/sec, feas=True, obj=7.41]
INFO - 16:19:01: 75%|███████▌ | 754/1000 [00:00<00:00, 1863.27 it/sec, feas=True, obj=6.56]
INFO - 16:19:01: 76%|███████▌ | 755/1000 [00:00<00:00, 1863.29 it/sec, feas=True, obj=0.0769]
INFO - 16:19:01: 76%|███████▌ | 756/1000 [00:00<00:00, 1863.22 it/sec, feas=True, obj=-3.24]
INFO - 16:19:01: 76%|███████▌ | 757/1000 [00:00<00:00, 1863.18 it/sec, feas=True, obj=1.73]
INFO - 16:19:01: 76%|███████▌ | 758/1000 [00:00<00:00, 1863.30 it/sec, feas=True, obj=0.263]
INFO - 16:19:01: 76%|███████▌ | 759/1000 [00:00<00:00, 1863.26 it/sec, feas=True, obj=-6.43]
INFO - 16:19:01: 76%|███████▌ | 760/1000 [00:00<00:00, 1863.23 it/sec, feas=True, obj=7.52]
INFO - 16:19:01: 76%|███████▌ | 761/1000 [00:00<00:00, 1863.28 it/sec, feas=True, obj=-2.09]
INFO - 16:19:01: 76%|███████▌ | 762/1000 [00:00<00:00, 1863.02 it/sec, feas=True, obj=-0.0262]
INFO - 16:19:01: 76%|███████▋ | 763/1000 [00:00<00:00, 1863.16 it/sec, feas=True, obj=3.37]
INFO - 16:19:01: 76%|███████▋ | 764/1000 [00:00<00:00, 1862.93 it/sec, feas=True, obj=4.5]
INFO - 16:19:01: 76%|███████▋ | 765/1000 [00:00<00:00, 1862.77 it/sec, feas=True, obj=0.692]
INFO - 16:19:01: 77%|███████▋ | 766/1000 [00:00<00:00, 1862.68 it/sec, feas=True, obj=2.75]
INFO - 16:19:01: 77%|███████▋ | 767/1000 [00:00<00:00, 1862.51 it/sec, feas=True, obj=1.46]
INFO - 16:19:01: 77%|███████▋ | 768/1000 [00:00<00:00, 1862.65 it/sec, feas=True, obj=7.23]
INFO - 16:19:01: 77%|███████▋ | 769/1000 [00:00<00:00, 1862.64 it/sec, feas=True, obj=3.47]
INFO - 16:19:01: 77%|███████▋ | 770/1000 [00:00<00:00, 1862.79 it/sec, feas=True, obj=-0.943]
INFO - 16:19:01: 77%|███████▋ | 771/1000 [00:00<00:00, 1862.74 it/sec, feas=True, obj=0.302]
INFO - 16:19:01: 77%|███████▋ | 772/1000 [00:00<00:00, 1862.75 it/sec, feas=True, obj=6]
INFO - 16:19:01: 77%|███████▋ | 773/1000 [00:00<00:00, 1862.84 it/sec, feas=True, obj=2.71]
INFO - 16:19:01: 77%|███████▋ | 774/1000 [00:00<00:00, 1862.88 it/sec, feas=True, obj=2.8]
INFO - 16:19:01: 78%|███████▊ | 775/1000 [00:00<00:00, 1862.91 it/sec, feas=True, obj=2.67]
INFO - 16:19:01: 78%|███████▊ | 776/1000 [00:00<00:00, 1862.76 it/sec, feas=True, obj=5.44]
INFO - 16:19:01: 78%|███████▊ | 777/1000 [00:00<00:00, 1862.94 it/sec, feas=True, obj=1.65]
INFO - 16:19:01: 78%|███████▊ | 778/1000 [00:00<00:00, 1862.81 it/sec, feas=True, obj=7.13]
INFO - 16:19:01: 78%|███████▊ | 779/1000 [00:00<00:00, 1862.97 it/sec, feas=True, obj=-0.0622]
INFO - 16:19:01: 78%|███████▊ | 780/1000 [00:00<00:00, 1862.81 it/sec, feas=True, obj=5.84]
INFO - 16:19:01: 78%|███████▊ | 781/1000 [00:00<00:00, 1862.94 it/sec, feas=True, obj=2.28]
INFO - 16:19:01: 78%|███████▊ | 782/1000 [00:00<00:00, 1862.92 it/sec, feas=True, obj=6.04]
INFO - 16:19:01: 78%|███████▊ | 783/1000 [00:00<00:00, 1863.04 it/sec, feas=True, obj=7.59]
INFO - 16:19:01: 78%|███████▊ | 784/1000 [00:00<00:00, 1862.98 it/sec, feas=True, obj=-6.19]
INFO - 16:19:01: 78%|███████▊ | 785/1000 [00:00<00:00, 1863.16 it/sec, feas=True, obj=9.25]
INFO - 16:19:01: 79%|███████▊ | 786/1000 [00:00<00:00, 1863.14 it/sec, feas=True, obj=0.676]
INFO - 16:19:01: 79%|███████▊ | 787/1000 [00:00<00:00, 1863.28 it/sec, feas=True, obj=-0.174]
INFO - 16:19:01: 79%|███████▉ | 788/1000 [00:00<00:00, 1863.23 it/sec, feas=True, obj=6.51]
INFO - 16:19:01: 79%|███████▉ | 789/1000 [00:00<00:00, 1863.41 it/sec, feas=True, obj=-0.856]
INFO - 16:19:01: 79%|███████▉ | 790/1000 [00:00<00:00, 1863.13 it/sec, feas=True, obj=5.62]
INFO - 16:19:01: 79%|███████▉ | 791/1000 [00:00<00:00, 1862.73 it/sec, feas=True, obj=5.35]
INFO - 16:19:01: 79%|███████▉ | 792/1000 [00:00<00:00, 1862.63 it/sec, feas=True, obj=0.753]
INFO - 16:19:01: 79%|███████▉ | 793/1000 [00:00<00:00, 1862.57 it/sec, feas=True, obj=4.35]
INFO - 16:19:01: 79%|███████▉ | 794/1000 [00:00<00:00, 1862.69 it/sec, feas=True, obj=3.8]
INFO - 16:19:01: 80%|███████▉ | 795/1000 [00:00<00:00, 1862.78 it/sec, feas=True, obj=7.95]
INFO - 16:19:01: 80%|███████▉ | 796/1000 [00:00<00:00, 1862.79 it/sec, feas=True, obj=5.01]
INFO - 16:19:01: 80%|███████▉ | 797/1000 [00:00<00:00, 1862.73 it/sec, feas=True, obj=6.2]
INFO - 16:19:01: 80%|███████▉ | 798/1000 [00:00<00:00, 1862.32 it/sec, feas=True, obj=-1.82]
INFO - 16:19:01: 80%|███████▉ | 799/1000 [00:00<00:00, 1862.03 it/sec, feas=True, obj=2.4]
INFO - 16:19:01: 80%|████████ | 800/1000 [00:00<00:00, 1861.96 it/sec, feas=True, obj=7.99]
INFO - 16:19:01: 80%|████████ | 801/1000 [00:00<00:00, 1861.98 it/sec, feas=True, obj=2.48]
INFO - 16:19:01: 80%|████████ | 802/1000 [00:00<00:00, 1861.95 it/sec, feas=True, obj=-0.764]
INFO - 16:19:01: 80%|████████ | 803/1000 [00:00<00:00, 1861.96 it/sec, feas=True, obj=3.34]
INFO - 16:19:01: 80%|████████ | 804/1000 [00:00<00:00, 1862.04 it/sec, feas=True, obj=0.787]
INFO - 16:19:01: 80%|████████ | 805/1000 [00:00<00:00, 1862.10 it/sec, feas=True, obj=-1.05]
INFO - 16:19:01: 81%|████████ | 806/1000 [00:00<00:00, 1862.15 it/sec, feas=True, obj=4.98]
INFO - 16:19:01: 81%|████████ | 807/1000 [00:00<00:00, 1862.31 it/sec, feas=True, obj=4.73]
INFO - 16:19:01: 81%|████████ | 808/1000 [00:00<00:00, 1862.29 it/sec, feas=True, obj=-0.742]
INFO - 16:19:01: 81%|████████ | 809/1000 [00:00<00:00, 1862.45 it/sec, feas=True, obj=5.82]
INFO - 16:19:01: 81%|████████ | 810/1000 [00:00<00:00, 1862.43 it/sec, feas=True, obj=10.4]
INFO - 16:19:01: 81%|████████ | 811/1000 [00:00<00:00, 1862.61 it/sec, feas=True, obj=1.86]
INFO - 16:19:01: 81%|████████ | 812/1000 [00:00<00:00, 1862.54 it/sec, feas=True, obj=2.49]
INFO - 16:19:01: 81%|████████▏ | 813/1000 [00:00<00:00, 1862.71 it/sec, feas=True, obj=9.36]
INFO - 16:19:01: 81%|████████▏ | 814/1000 [00:00<00:00, 1862.59 it/sec, feas=True, obj=1.84]
INFO - 16:19:01: 82%|████████▏ | 815/1000 [00:00<00:00, 1862.76 it/sec, feas=True, obj=4.04]
INFO - 16:19:01: 82%|████████▏ | 816/1000 [00:00<00:00, 1862.60 it/sec, feas=True, obj=-4.21]
INFO - 16:19:01: 82%|████████▏ | 817/1000 [00:00<00:00, 1862.14 it/sec, feas=True, obj=3.64]
INFO - 16:19:01: 82%|████████▏ | 818/1000 [00:00<00:00, 1861.72 it/sec, feas=True, obj=4.02]
INFO - 16:19:01: 82%|████████▏ | 819/1000 [00:00<00:00, 1861.64 it/sec, feas=True, obj=6.66]
INFO - 16:19:01: 82%|████████▏ | 820/1000 [00:00<00:00, 1861.80 it/sec, feas=True, obj=-0.0634]
INFO - 16:19:01: 82%|████████▏ | 821/1000 [00:00<00:00, 1861.75 it/sec, feas=True, obj=1.24]
INFO - 16:19:01: 82%|████████▏ | 822/1000 [00:00<00:00, 1861.92 it/sec, feas=True, obj=4.42]
INFO - 16:19:01: 82%|████████▏ | 823/1000 [00:00<00:00, 1861.78 it/sec, feas=True, obj=4.26]
INFO - 16:19:01: 82%|████████▏ | 824/1000 [00:00<00:00, 1861.83 it/sec, feas=True, obj=0.439]
INFO - 16:19:01: 82%|████████▎ | 825/1000 [00:00<00:00, 1861.87 it/sec, feas=True, obj=2.7]
INFO - 16:19:01: 83%|████████▎ | 826/1000 [00:00<00:00, 1861.88 it/sec, feas=True, obj=2.98]
INFO - 16:19:01: 83%|████████▎ | 827/1000 [00:00<00:00, 1861.91 it/sec, feas=True, obj=0.888]
INFO - 16:19:01: 83%|████████▎ | 828/1000 [00:00<00:00, 1861.88 it/sec, feas=True, obj=-0.879]
INFO - 16:19:01: 83%|████████▎ | 829/1000 [00:00<00:00, 1861.13 it/sec, feas=True, obj=0.861]
INFO - 16:19:01: 83%|████████▎ | 830/1000 [00:00<00:00, 1861.12 it/sec, feas=True, obj=3.47]
INFO - 16:19:01: 83%|████████▎ | 831/1000 [00:00<00:00, 1861.13 it/sec, feas=True, obj=7.51]
INFO - 16:19:01: 83%|████████▎ | 832/1000 [00:00<00:00, 1861.05 it/sec, feas=True, obj=4.58]
INFO - 16:19:01: 83%|████████▎ | 833/1000 [00:00<00:00, 1860.38 it/sec, feas=True, obj=5.48]
INFO - 16:19:01: 83%|████████▎ | 834/1000 [00:00<00:00, 1860.27 it/sec, feas=True, obj=-0.412]
INFO - 16:19:01: 84%|████████▎ | 835/1000 [00:00<00:00, 1860.23 it/sec, feas=True, obj=-1.86]
INFO - 16:19:01: 84%|████████▎ | 836/1000 [00:00<00:00, 1860.23 it/sec, feas=True, obj=1.29]
INFO - 16:19:01: 84%|████████▎ | 837/1000 [00:00<00:00, 1860.14 it/sec, feas=True, obj=3.17]
INFO - 16:19:01: 84%|████████▍ | 838/1000 [00:00<00:00, 1860.30 it/sec, feas=True, obj=2.41]
INFO - 16:19:01: 84%|████████▍ | 839/1000 [00:00<00:00, 1860.25 it/sec, feas=True, obj=5.72]
INFO - 16:19:01: 84%|████████▍ | 840/1000 [00:00<00:00, 1860.41 it/sec, feas=True, obj=-1.37]
INFO - 16:19:01: 84%|████████▍ | 841/1000 [00:00<00:00, 1860.31 it/sec, feas=True, obj=6.72]
INFO - 16:19:01: 84%|████████▍ | 842/1000 [00:00<00:00, 1860.34 it/sec, feas=True, obj=3.27]
INFO - 16:19:01: 84%|████████▍ | 843/1000 [00:00<00:00, 1860.38 it/sec, feas=True, obj=-1.46]
INFO - 16:19:01: 84%|████████▍ | 844/1000 [00:00<00:00, 1860.33 it/sec, feas=True, obj=4.38]
INFO - 16:19:01: 84%|████████▍ | 845/1000 [00:00<00:00, 1860.30 it/sec, feas=True, obj=3.82]
INFO - 16:19:01: 85%|████████▍ | 846/1000 [00:00<00:00, 1859.82 it/sec, feas=True, obj=5.89]
INFO - 16:19:01: 85%|████████▍ | 847/1000 [00:00<00:00, 1859.71 it/sec, feas=True, obj=3.11]
INFO - 16:19:01: 85%|████████▍ | 848/1000 [00:00<00:00, 1859.68 it/sec, feas=True, obj=4.37]
INFO - 16:19:01: 85%|████████▍ | 849/1000 [00:00<00:00, 1859.55 it/sec, feas=True, obj=1.84]
INFO - 16:19:01: 85%|████████▌ | 850/1000 [00:00<00:00, 1859.54 it/sec, feas=True, obj=2.82]
INFO - 16:19:01: 85%|████████▌ | 851/1000 [00:00<00:00, 1859.32 it/sec, feas=True, obj=7.38]
INFO - 16:19:01: 85%|████████▌ | 852/1000 [00:00<00:00, 1858.75 it/sec, feas=True, obj=13.8]
INFO - 16:19:01: 85%|████████▌ | 853/1000 [00:00<00:00, 1858.42 it/sec, feas=True, obj=7.76]
INFO - 16:19:01: 85%|████████▌ | 854/1000 [00:00<00:00, 1858.47 it/sec, feas=True, obj=0.998]
INFO - 16:19:01: 86%|████████▌ | 855/1000 [00:00<00:00, 1858.41 it/sec, feas=True, obj=3.88]
INFO - 16:19:01: 86%|████████▌ | 856/1000 [00:00<00:00, 1858.26 it/sec, feas=True, obj=-0.698]
INFO - 16:19:01: 86%|████████▌ | 857/1000 [00:00<00:00, 1858.32 it/sec, feas=True, obj=2.83]
INFO - 16:19:01: 86%|████████▌ | 858/1000 [00:00<00:00, 1858.14 it/sec, feas=True, obj=1.58]
INFO - 16:19:01: 86%|████████▌ | 859/1000 [00:00<00:00, 1858.14 it/sec, feas=True, obj=8.53]
INFO - 16:19:01: 86%|████████▌ | 860/1000 [00:00<00:00, 1857.84 it/sec, feas=True, obj=6.28]
INFO - 16:19:01: 86%|████████▌ | 861/1000 [00:00<00:00, 1857.96 it/sec, feas=True, obj=11.8]
INFO - 16:19:01: 86%|████████▌ | 862/1000 [00:00<00:00, 1857.87 it/sec, feas=True, obj=9.31]
INFO - 16:19:01: 86%|████████▋ | 863/1000 [00:00<00:00, 1858.02 it/sec, feas=True, obj=3.88]
INFO - 16:19:01: 86%|████████▋ | 864/1000 [00:00<00:00, 1857.96 it/sec, feas=True, obj=3.11]
INFO - 16:19:01: 86%|████████▋ | 865/1000 [00:00<00:00, 1858.13 it/sec, feas=True, obj=5.09]
INFO - 16:19:01: 87%|████████▋ | 866/1000 [00:00<00:00, 1857.93 it/sec, feas=True, obj=-0.723]
INFO - 16:19:01: 87%|████████▋ | 867/1000 [00:00<00:00, 1857.93 it/sec, feas=True, obj=1.22]
INFO - 16:19:01: 87%|████████▋ | 868/1000 [00:00<00:00, 1857.95 it/sec, feas=True, obj=7.13]
INFO - 16:19:01: 87%|████████▋ | 869/1000 [00:00<00:00, 1857.93 it/sec, feas=True, obj=12.2]
INFO - 16:19:01: 87%|████████▋ | 870/1000 [00:00<00:00, 1857.92 it/sec, feas=True, obj=1.13]
INFO - 16:19:01: 87%|████████▋ | 871/1000 [00:00<00:00, 1858.01 it/sec, feas=True, obj=0.802]
INFO - 16:19:01: 87%|████████▋ | 872/1000 [00:00<00:00, 1858.07 it/sec, feas=True, obj=2.82]
INFO - 16:19:01: 87%|████████▋ | 873/1000 [00:00<00:00, 1858.11 it/sec, feas=True, obj=-0.932]
INFO - 16:19:01: 87%|████████▋ | 874/1000 [00:00<00:00, 1858.15 it/sec, feas=True, obj=1.6]
INFO - 16:19:01: 88%|████████▊ | 875/1000 [00:00<00:00, 1858.19 it/sec, feas=True, obj=8.68]
INFO - 16:19:01: 88%|████████▊ | 876/1000 [00:00<00:00, 1858.25 it/sec, feas=True, obj=-0.211]
INFO - 16:19:01: 88%|████████▊ | 877/1000 [00:00<00:00, 1858.20 it/sec, feas=True, obj=-3.63]
INFO - 16:19:01: 88%|████████▊ | 878/1000 [00:00<00:00, 1858.29 it/sec, feas=True, obj=4.85]
INFO - 16:19:01: 88%|████████▊ | 879/1000 [00:00<00:00, 1858.19 it/sec, feas=True, obj=4.28]
INFO - 16:19:01: 88%|████████▊ | 880/1000 [00:00<00:00, 1858.05 it/sec, feas=True, obj=-0.285]
INFO - 16:19:01: 88%|████████▊ | 881/1000 [00:00<00:00, 1858.08 it/sec, feas=True, obj=5.96]
INFO - 16:19:01: 88%|████████▊ | 882/1000 [00:00<00:00, 1858.05 it/sec, feas=True, obj=-0.126]
INFO - 16:19:01: 88%|████████▊ | 883/1000 [00:00<00:00, 1858.09 it/sec, feas=True, obj=10.4]
INFO - 16:19:01: 88%|████████▊ | 884/1000 [00:00<00:00, 1858.13 it/sec, feas=True, obj=-1.37]
INFO - 16:19:01: 88%|████████▊ | 885/1000 [00:00<00:00, 1858.16 it/sec, feas=True, obj=4.47]
INFO - 16:19:01: 89%|████████▊ | 886/1000 [00:00<00:00, 1858.25 it/sec, feas=True, obj=1.19]
INFO - 16:19:01: 89%|████████▊ | 887/1000 [00:00<00:00, 1858.27 it/sec, feas=True, obj=6.51]
INFO - 16:19:01: 89%|████████▉ | 888/1000 [00:00<00:00, 1858.26 it/sec, feas=True, obj=-0.5]
INFO - 16:19:01: 89%|████████▉ | 889/1000 [00:00<00:00, 1858.28 it/sec, feas=True, obj=1.33]
INFO - 16:19:01: 89%|████████▉ | 890/1000 [00:00<00:00, 1858.37 it/sec, feas=True, obj=8.1]
INFO - 16:19:01: 89%|████████▉ | 891/1000 [00:00<00:00, 1858.40 it/sec, feas=True, obj=6.34]
INFO - 16:19:01: 89%|████████▉ | 892/1000 [00:00<00:00, 1858.36 it/sec, feas=True, obj=0.425]
INFO - 16:19:01: 89%|████████▉ | 893/1000 [00:00<00:00, 1858.39 it/sec, feas=True, obj=7.99]
INFO - 16:19:01: 89%|████████▉ | 894/1000 [00:00<00:00, 1858.40 it/sec, feas=True, obj=4.73]
INFO - 16:19:01: 90%|████████▉ | 895/1000 [00:00<00:00, 1858.23 it/sec, feas=True, obj=-0.736]
INFO - 16:19:01: 90%|████████▉ | 896/1000 [00:00<00:00, 1858.34 it/sec, feas=True, obj=1.11]
INFO - 16:19:01: 90%|████████▉ | 897/1000 [00:00<00:00, 1858.23 it/sec, feas=True, obj=5.52]
INFO - 16:19:01: 90%|████████▉ | 898/1000 [00:00<00:00, 1858.35 it/sec, feas=True, obj=0.448]
INFO - 16:19:01: 90%|████████▉ | 899/1000 [00:00<00:00, 1858.04 it/sec, feas=True, obj=1.81]
INFO - 16:19:01: 90%|█████████ | 900/1000 [00:00<00:00, 1857.77 it/sec, feas=True, obj=6.25]
INFO - 16:19:01: 90%|█████████ | 901/1000 [00:00<00:00, 1857.62 it/sec, feas=True, obj=-0.151]
INFO - 16:19:01: 90%|█████████ | 902/1000 [00:00<00:00, 1857.55 it/sec, feas=True, obj=7.08]
INFO - 16:19:01: 90%|█████████ | 903/1000 [00:00<00:00, 1857.54 it/sec, feas=True, obj=-0.565]
INFO - 16:19:01: 90%|█████████ | 904/1000 [00:00<00:00, 1857.54 it/sec, feas=True, obj=0.323]
INFO - 16:19:01: 90%|█████████ | 905/1000 [00:00<00:00, 1857.53 it/sec, feas=True, obj=-0.591]
INFO - 16:19:01: 91%|█████████ | 906/1000 [00:00<00:00, 1856.79 it/sec, feas=True, obj=2]
INFO - 16:19:01: 91%|█████████ | 907/1000 [00:00<00:00, 1856.69 it/sec, feas=True, obj=4.54]
INFO - 16:19:01: 91%|█████████ | 908/1000 [00:00<00:00, 1856.59 it/sec, feas=True, obj=2.63]
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INFO - 16:19:01: 91%|█████████ | 910/1000 [00:00<00:00, 1856.55 it/sec, feas=True, obj=5.89]
INFO - 16:19:01: 91%|█████████ | 911/1000 [00:00<00:00, 1856.55 it/sec, feas=True, obj=0.778]
INFO - 16:19:01: 91%|█████████ | 912/1000 [00:00<00:00, 1856.05 it/sec, feas=True, obj=4.03]
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INFO - 16:19:01: 91%|█████████▏| 914/1000 [00:00<00:00, 1856.07 it/sec, feas=True, obj=5.16]
INFO - 16:19:01: 92%|█████████▏| 915/1000 [00:00<00:00, 1856.13 it/sec, feas=True, obj=-0.787]
INFO - 16:19:01: 92%|█████████▏| 916/1000 [00:00<00:00, 1856.16 it/sec, feas=True, obj=5.28]
INFO - 16:19:01: 92%|█████████▏| 917/1000 [00:00<00:00, 1856.15 it/sec, feas=True, obj=2.93]
INFO - 16:19:01: 92%|█████████▏| 918/1000 [00:00<00:00, 1856.06 it/sec, feas=True, obj=0.851]
INFO - 16:19:01: 92%|█████████▏| 919/1000 [00:00<00:00, 1856.21 it/sec, feas=True, obj=6.04]
INFO - 16:19:01: 92%|█████████▏| 920/1000 [00:00<00:00, 1856.13 it/sec, feas=True, obj=5.24]
INFO - 16:19:01: 92%|█████████▏| 921/1000 [00:00<00:00, 1856.28 it/sec, feas=True, obj=0.0179]
INFO - 16:19:01: 92%|█████████▏| 922/1000 [00:00<00:00, 1856.20 it/sec, feas=True, obj=6.08]
INFO - 16:19:01: 92%|█████████▏| 923/1000 [00:00<00:00, 1856.35 it/sec, feas=True, obj=6.95]
INFO - 16:19:01: 92%|█████████▏| 924/1000 [00:00<00:00, 1856.28 it/sec, feas=True, obj=2.64]
INFO - 16:19:01: 92%|█████████▎| 925/1000 [00:00<00:00, 1856.42 it/sec, feas=True, obj=-3.23]
INFO - 16:19:01: 93%|█████████▎| 926/1000 [00:00<00:00, 1856.43 it/sec, feas=True, obj=-7.07]
INFO - 16:19:01: 93%|█████████▎| 927/1000 [00:00<00:00, 1856.52 it/sec, feas=True, obj=1]
INFO - 16:19:01: 93%|█████████▎| 928/1000 [00:00<00:00, 1856.56 it/sec, feas=True, obj=5.49]
INFO - 16:19:01: 93%|█████████▎| 929/1000 [00:00<00:00, 1856.60 it/sec, feas=True, obj=0.827]
INFO - 16:19:01: 93%|█████████▎| 930/1000 [00:00<00:00, 1856.38 it/sec, feas=True, obj=3.6]
INFO - 16:19:01: 93%|█████████▎| 931/1000 [00:00<00:00, 1855.71 it/sec, feas=True, obj=5.72]
INFO - 16:19:01: 93%|█████████▎| 932/1000 [00:00<00:00, 1855.69 it/sec, feas=True, obj=2.44]
INFO - 16:19:01: 93%|█████████▎| 933/1000 [00:00<00:00, 1855.67 it/sec, feas=True, obj=1.38]
INFO - 16:19:01: 93%|█████████▎| 934/1000 [00:00<00:00, 1855.70 it/sec, feas=True, obj=-0.822]
INFO - 16:19:01: 94%|█████████▎| 935/1000 [00:00<00:00, 1855.68 it/sec, feas=True, obj=-3.17]
INFO - 16:19:01: 94%|█████████▎| 936/1000 [00:00<00:00, 1855.81 it/sec, feas=True, obj=6.85]
INFO - 16:19:01: 94%|█████████▎| 937/1000 [00:00<00:00, 1855.80 it/sec, feas=True, obj=3.98]
INFO - 16:19:01: 94%|█████████▍| 938/1000 [00:00<00:00, 1855.81 it/sec, feas=True, obj=-0.244]
INFO - 16:19:01: 94%|█████████▍| 939/1000 [00:00<00:00, 1855.70 it/sec, feas=True, obj=2.77]
INFO - 16:19:01: 94%|█████████▍| 940/1000 [00:00<00:00, 1855.83 it/sec, feas=True, obj=1.68]
INFO - 16:19:01: 94%|█████████▍| 941/1000 [00:00<00:00, 1855.72 it/sec, feas=True, obj=3.7]
INFO - 16:19:01: 94%|█████████▍| 942/1000 [00:00<00:00, 1855.73 it/sec, feas=True, obj=1.83]
INFO - 16:19:01: 94%|█████████▍| 943/1000 [00:00<00:00, 1851.27 it/sec, feas=True, obj=-0.297]
INFO - 16:19:01: 94%|█████████▍| 944/1000 [00:00<00:00, 1851.17 it/sec, feas=True, obj=9.05]
INFO - 16:19:01: 94%|█████████▍| 945/1000 [00:00<00:00, 1851.14 it/sec, feas=True, obj=-7.67]
INFO - 16:19:01: 95%|█████████▍| 946/1000 [00:00<00:00, 1851.04 it/sec, feas=True, obj=6.44]
INFO - 16:19:01: 95%|█████████▍| 947/1000 [00:00<00:00, 1850.96 it/sec, feas=True, obj=7.66]
INFO - 16:19:01: 95%|█████████▍| 948/1000 [00:00<00:00, 1850.98 it/sec, feas=True, obj=5.69]
INFO - 16:19:01: 95%|█████████▍| 949/1000 [00:00<00:00, 1850.82 it/sec, feas=True, obj=4.75]
INFO - 16:19:01: 95%|█████████▌| 950/1000 [00:00<00:00, 1850.87 it/sec, feas=True, obj=0.391]
INFO - 16:19:01: 95%|█████████▌| 951/1000 [00:00<00:00, 1850.83 it/sec, feas=True, obj=7.77]
INFO - 16:19:01: 95%|█████████▌| 952/1000 [00:00<00:00, 1850.55 it/sec, feas=True, obj=-0.712]
INFO - 16:19:01: 95%|█████████▌| 953/1000 [00:00<00:00, 1849.92 it/sec, feas=True, obj=0.439]
INFO - 16:19:01: 95%|█████████▌| 954/1000 [00:00<00:00, 1849.69 it/sec, feas=True, obj=7.43]
INFO - 16:19:01: 96%|█████████▌| 955/1000 [00:00<00:00, 1849.70 it/sec, feas=True, obj=-1.49]
INFO - 16:19:01: 96%|█████████▌| 956/1000 [00:00<00:00, 1849.63 it/sec, feas=True, obj=5.62]
INFO - 16:19:01: 96%|█████████▌| 957/1000 [00:00<00:00, 1849.71 it/sec, feas=True, obj=6.16]
INFO - 16:19:01: 96%|█████████▌| 958/1000 [00:00<00:00, 1849.54 it/sec, feas=True, obj=7.77]
INFO - 16:19:01: 96%|█████████▌| 959/1000 [00:00<00:00, 1849.56 it/sec, feas=True, obj=1.26]
INFO - 16:19:01: 96%|█████████▌| 960/1000 [00:00<00:00, 1849.41 it/sec, feas=True, obj=3.33]
INFO - 16:19:01: 96%|█████████▌| 961/1000 [00:00<00:00, 1849.41 it/sec, feas=True, obj=2.28]
INFO - 16:19:01: 96%|█████████▌| 962/1000 [00:00<00:00, 1849.42 it/sec, feas=True, obj=14.7]
INFO - 16:19:01: 96%|█████████▋| 963/1000 [00:00<00:00, 1849.39 it/sec, feas=True, obj=0.515]
INFO - 16:19:01: 96%|█████████▋| 964/1000 [00:00<00:00, 1849.40 it/sec, feas=True, obj=2.57]
INFO - 16:19:01: 96%|█████████▋| 965/1000 [00:00<00:00, 1849.38 it/sec, feas=True, obj=6.57]
INFO - 16:19:01: 97%|█████████▋| 966/1000 [00:00<00:00, 1849.37 it/sec, feas=True, obj=-0.292]
INFO - 16:19:01: 97%|█████████▋| 967/1000 [00:00<00:00, 1849.42 it/sec, feas=True, obj=-1.65]
INFO - 16:19:01: 97%|█████████▋| 968/1000 [00:00<00:00, 1849.46 it/sec, feas=True, obj=7.01]
INFO - 16:19:01: 97%|█████████▋| 969/1000 [00:00<00:00, 1849.20 it/sec, feas=True, obj=-0.0208]
INFO - 16:19:01: 97%|█████████▋| 970/1000 [00:00<00:00, 1848.66 it/sec, feas=True, obj=2.03]
INFO - 16:19:01: 97%|█████████▋| 971/1000 [00:00<00:00, 1848.68 it/sec, feas=True, obj=0.429]
INFO - 16:19:01: 97%|█████████▋| 972/1000 [00:00<00:00, 1848.57 it/sec, feas=True, obj=-2.02]
INFO - 16:19:01: 97%|█████████▋| 973/1000 [00:00<00:00, 1848.60 it/sec, feas=True, obj=6.01]
INFO - 16:19:01: 97%|█████████▋| 974/1000 [00:00<00:00, 1848.52 it/sec, feas=True, obj=5.07]
INFO - 16:19:01: 98%|█████████▊| 975/1000 [00:00<00:00, 1848.53 it/sec, feas=True, obj=7.26]
INFO - 16:19:01: 98%|█████████▊| 976/1000 [00:00<00:00, 1848.45 it/sec, feas=True, obj=1.83]
INFO - 16:19:01: 98%|█████████▊| 977/1000 [00:00<00:00, 1848.58 it/sec, feas=True, obj=7.93]
INFO - 16:19:01: 98%|█████████▊| 978/1000 [00:00<00:00, 1848.47 it/sec, feas=True, obj=4.96]
INFO - 16:19:01: 98%|█████████▊| 979/1000 [00:00<00:00, 1847.93 it/sec, feas=True, obj=0.739]
INFO - 16:19:01: 98%|█████████▊| 980/1000 [00:00<00:00, 1847.78 it/sec, feas=True, obj=-1.88]
INFO - 16:19:01: 98%|█████████▊| 981/1000 [00:00<00:00, 1847.86 it/sec, feas=True, obj=5.13]
INFO - 16:19:01: 98%|█████████▊| 982/1000 [00:00<00:00, 1847.89 it/sec, feas=True, obj=3.38]
INFO - 16:19:01: 98%|█████████▊| 983/1000 [00:00<00:00, 1847.91 it/sec, feas=True, obj=8.53]
INFO - 16:19:01: 98%|█████████▊| 984/1000 [00:00<00:00, 1847.97 it/sec, feas=True, obj=5.83]
INFO - 16:19:01: 98%|█████████▊| 985/1000 [00:00<00:00, 1848.03 it/sec, feas=True, obj=8.02]
INFO - 16:19:01: 99%|█████████▊| 986/1000 [00:00<00:00, 1847.99 it/sec, feas=True, obj=2.01]
INFO - 16:19:01: 99%|█████████▊| 987/1000 [00:00<00:00, 1848.07 it/sec, feas=True, obj=-0.893]
INFO - 16:19:01: 99%|█████████▉| 988/1000 [00:00<00:00, 1848.03 it/sec, feas=True, obj=4.74]
INFO - 16:19:01: 99%|█████████▉| 989/1000 [00:00<00:00, 1848.11 it/sec, feas=True, obj=1.18]
INFO - 16:19:01: 99%|█████████▉| 990/1000 [00:00<00:00, 1848.03 it/sec, feas=True, obj=6.2]
INFO - 16:19:01: 99%|█████████▉| 991/1000 [00:00<00:00, 1848.15 it/sec, feas=True, obj=4.5]
INFO - 16:19:01: 99%|█████████▉| 992/1000 [00:00<00:00, 1847.99 it/sec, feas=True, obj=-0.907]
INFO - 16:19:01: 99%|█████████▉| 993/1000 [00:00<00:00, 1848.09 it/sec, feas=True, obj=-3.18]
INFO - 16:19:01: 99%|█████████▉| 994/1000 [00:00<00:00, 1848.06 it/sec, feas=True, obj=6.82]
INFO - 16:19:01: 100%|█████████▉| 995/1000 [00:00<00:00, 1848.18 it/sec, feas=True, obj=3.44]
INFO - 16:19:01: 100%|█████████▉| 996/1000 [00:00<00:00, 1848.12 it/sec, feas=True, obj=5.11]
INFO - 16:19:01: 100%|█████████▉| 997/1000 [00:00<00:00, 1848.26 it/sec, feas=True, obj=1.55]
INFO - 16:19:01: 100%|█████████▉| 998/1000 [00:00<00:00, 1848.21 it/sec, feas=True, obj=0.534]
INFO - 16:19:01: 100%|█████████▉| 999/1000 [00:00<00:00, 1848.32 it/sec, feas=True, obj=0.783]
INFO - 16:19:01: 100%|██████████| 1000/1000 [00:00<00:00, 1838.96 it/sec, feas=True, obj=5.65]
INFO - 16:19:01: Optimization result:
INFO - 16:19:01: Optimizer info:
INFO - 16:19:01: Status: None
INFO - 16:19:01: Message: None
INFO - 16:19:01: Solution:
INFO - 16:19:01: Objective: -10.14685071195364
INFO - 16:19:01: Design space:
INFO - 16:19:01: +------+------------------------------------------------------------+
INFO - 16:19:01: | Name | Distribution |
INFO - 16:19:01: +------+------------------------------------------------------------+
INFO - 16:19:01: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:01: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:01: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:19:01: +------+------------------------------------------------------------+
INFO - 16:19:01: *** End Sampling execution ***
Then, we create standard and gradient-enhanced FCEs using an orthonormal polynomial basis (default basis) with a maximum total degree of 7 and different regression techniques from scikit-learn to estimate the coefficients, namely ordinary least squares, ridge (i.e., L2 regularisation), lasso (i.e., L1 regularisation), elasticnet (i.e., L1 and L2 regularisation), least angle regression (LARS) and orthogonal matching pursuit. Note that all these algorithms have been finely tuned using cross-validation, except ordinary least squares regression for which there is no parameter to tune. We also add the SPGL1 algorithm to solve a basis pursuit denoise (BPN) problem, as well as a null space algorithm [GLSS].
r2_learning = []
r2_validation = []
r2_learning_ge = []
r2_validation_ge = []
null_space_settings = NullSpace_Settings()
for linear_model_fitter_settings in [
LinearRegression_Settings(),
RidgeCV_Settings(),
LassoCV_Settings(),
ElasticNetCV_Settings(),
LARSCV_Settings(),
OrthogonalMatchingPursuitCV_Settings(),
SPGL1_Settings(sigma=1e-7),
null_space_settings,
]:
if linear_model_fitter_settings == null_space_settings:
# The null space technique requires gradient observations.
r2_learning.append(0.0)
r2_validation.append(0.0)
else:
# Train an FCE.
fce_settings = FCERegressor_Settings(
degree=7,
linear_model_fitter_settings=linear_model_fitter_settings,
)
fce = FCERegressor(training_dataset, fce_settings)
fce.learn()
# Assess the quality of the FCE.
r2 = R2Measure(fce)
r2_learning.append(r2.compute_learning_measure().round(2)[0])
r2_validation.append(r2.compute_test_measure(validation_dataset).round(2)[0])
# Train a gradient-enhanced FCE.
fce_settings = FCERegressor_Settings(
degree=7,
linear_model_fitter_settings=linear_model_fitter_settings,
learn_jacobian_data=True,
)
fce = FCERegressor(training_dataset, fce_settings)
fce.learn()
# Assess the quality of the gradient-enhanced FCE.
r2 = R2Measure(fce)
r2_learning_ge.append(r2.compute_learning_measure().round(2)[0])
r2_validation_ge.append(r2.compute_test_measure(validation_dataset).round(2)[0])
We create also a PCERegressor
using the LARS algorithm implemented in OpenTURNS:
pce = PCERegressor(training_dataset, PCERegressor_Settings(degree=7, use_lars=True))
pce.learn()
r2 = R2Measure(pce)
r2_learning.append(r2.compute_learning_measure().round(2)[0])
r2_validation.append(r2.compute_test_measure(validation_dataset).round(2)[0])
r2_learning_ge.append(0)
r2_validation_ge.append(0)
From these results, we can plot the quality of the different surrogate models, expressed in terms of coefficient of determination \(R^2\) (the higher, the better):
dataset = Dataset()
dataset.add_group(
"R2",
array([r2_learning, r2_validation, r2_learning_ge, r2_validation_ge]),
("OLS", "L2", "L1", "L1-L2", "LARS", "OMP", "SPGL1", "NullSpace", "OT-LARS"),
)
dataset.index = ["Learning", "Validation", "Learning-GE", "Validation-GE"]
barplot = BarPlot(dataset, annotate=False)
barplot.execute(save=False)
[<Figure size 640x480 with 1 Axes>]
First, let us focus on the standard FCEs that have not learned derivatives ("Learning" and "Validation" in the legend). We can see that the quality of learning is perfect, regardless of the method. That's good, but not enough. But what interests us is the quality of prediction of the validation dataset to see if the surrogate model avoids overfitting. In this regard, ordinary least squares regression and ridge regression are wrong while the other techniques are very good, without really being able to tell them apart. Now, if we have a look to the gradient-enhanced FCEs ("Learning-GE" and "Validation-GE" in the legend). we can see that the quality is significantly better, except for the LARS method.
Lastly, these numerical experiments can be repeated by replacing the polynomial basis with the Fourier series.
r2_learning = []
r2_validation = []
r2_learning_ge = []
r2_validation_ge = []
null_space_settings = NullSpace_Settings()
for linear_model_fitter_settings in [
LinearRegression_Settings(),
RidgeCV_Settings(),
LassoCV_Settings(),
ElasticNetCV_Settings(),
LARSCV_Settings(),
OrthogonalMatchingPursuitCV_Settings(),
SPGL1_Settings(sigma=1e-7),
null_space_settings,
]:
if linear_model_fitter_settings == null_space_settings:
# The null space technique requires gradient observations.
r2_learning.append(0.0)
r2_validation.append(0.0)
else:
# Train an FCE.
fce_settings = FCERegressor_Settings(
degree=7,
linear_model_fitter_settings=linear_model_fitter_settings,
basis=OrthonormalFunctionBasis.FOURIER,
)
fce = FCERegressor(training_dataset, fce_settings)
fce.learn()
# Assess the quality of the FCE.
r2 = R2Measure(fce)
r2_learning.append(r2.compute_learning_measure().round(2)[0])
r2_validation.append(r2.compute_test_measure(validation_dataset).round(2)[0])
# Train a gradient-enhanced FCE.
fce_settings = FCERegressor_Settings(
degree=7,
linear_model_fitter_settings=linear_model_fitter_settings,
basis=OrthonormalFunctionBasis.FOURIER,
learn_jacobian_data=True,
)
fce = FCERegressor(training_dataset, fce_settings)
fce.learn()
# Assess the quality of the gradient-enhanced FCE.
r2 = R2Measure(fce)
r2_learning_ge.append(r2.compute_learning_measure().round(2)[0])
r2_validation_ge.append(r2.compute_test_measure(validation_dataset).round(2)[0])
dataset = Dataset()
dataset.add_group(
"R2",
array([r2_learning, r2_validation, r2_learning_ge, r2_validation_ge]),
("OLS", "L2", "L1", "L1-L2", "LARS", "OMP", "SPGL1", "NullSpace"),
)
dataset.index = ["Learning", "Validation", "Learning-GE", "Validation-GE"]
barplot = BarPlot(dataset, annotate=False)
barplot.execute(save=False)
[<Figure size 640x480 with 1 Axes>]
We then see the same type of ranking, with even better validation qualities. This can be easily explained by the nature of Ishigami's function, in which trigonometric terms are important. Furthermore, learning Jacobian significantly improves the quality of surrogate models in the case of ridge regression and ordinary least squares.
Total running time of the script: (0 minutes 6.924 seconds)