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 - 16:16:02: *** Start Sampling execution ***
INFO - 16:16:02: Sampling
INFO - 16:16:02: Disciplines: IshigamiDiscipline
INFO - 16:16:02: MDO formulation: MDF
INFO - 16:16:02: Optimization problem:
INFO - 16:16:02: minimize y(x1, x2, x3)
INFO - 16:16:02: with respect to x1, x2, x3
INFO - 16:16:02: over the design space:
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: | Name | Distribution |
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: Solving optimization problem with algorithm OT_OPT_LHS:
INFO - 16:16:02: 1%|▏ | 1/70 [00:00<00:00, 372.33 it/sec, feas=True, obj=1.45]
INFO - 16:16:02: 3%|▎ | 2/70 [00:00<00:00, 606.33 it/sec, feas=True, obj=1.01]
INFO - 16:16:02: 4%|▍ | 3/70 [00:00<00:00, 788.30 it/sec, feas=True, obj=6.72]
INFO - 16:16:02: 6%|▌ | 4/70 [00:00<00:00, 927.69 it/sec, feas=True, obj=-0.113]
INFO - 16:16:02: 7%|▋ | 5/70 [00:00<00:00, 1043.83 it/sec, feas=True, obj=7.68]
INFO - 16:16:02: 9%|▊ | 6/70 [00:00<00:00, 1135.44 it/sec, feas=True, obj=1.8]
INFO - 16:16:02: 10%|█ | 7/70 [00:00<00:00, 1216.90 it/sec, feas=True, obj=10.3]
INFO - 16:16:02: 11%|█▏ | 8/70 [00:00<00:00, 1282.51 it/sec, feas=True, obj=5.96]
INFO - 16:16:02: 13%|█▎ | 9/70 [00:00<00:00, 1343.32 it/sec, feas=True, obj=0.0449]
INFO - 16:16:02: 14%|█▍ | 10/70 [00:00<00:00, 1395.26 it/sec, feas=True, obj=4.97]
INFO - 16:16:02: 16%|█▌ | 11/70 [00:00<00:00, 1444.18 it/sec, feas=True, obj=6.94]
INFO - 16:16:02: 17%|█▋ | 12/70 [00:00<00:00, 1487.25 it/sec, feas=True, obj=3.5]
INFO - 16:16:02: 19%|█▊ | 13/70 [00:00<00:00, 1522.14 it/sec, feas=True, obj=4.87]
INFO - 16:16:02: 20%|██ | 14/70 [00:00<00:00, 1556.95 it/sec, feas=True, obj=4.3]
INFO - 16:16:02: 21%|██▏ | 15/70 [00:00<00:00, 1586.27 it/sec, feas=True, obj=2.44]
INFO - 16:16:02: 23%|██▎ | 16/70 [00:00<00:00, 1614.79 it/sec, feas=True, obj=5.7]
INFO - 16:16:02: 24%|██▍ | 17/70 [00:00<00:00, 1638.29 it/sec, feas=True, obj=6.14]
INFO - 16:16:02: 26%|██▌ | 18/70 [00:00<00:00, 1663.71 it/sec, feas=True, obj=5.7]
INFO - 16:16:02: 27%|██▋ | 19/70 [00:00<00:00, 1683.71 it/sec, feas=True, obj=-0.573]
INFO - 16:16:02: 29%|██▊ | 20/70 [00:00<00:00, 1704.07 it/sec, feas=True, obj=5.72]
INFO - 16:16:02: 30%|███ | 21/70 [00:00<00:00, 1720.22 it/sec, feas=True, obj=4.95]
INFO - 16:16:02: 31%|███▏ | 22/70 [00:00<00:00, 1737.59 it/sec, feas=True, obj=1.27]
INFO - 16:16:02: 33%|███▎ | 23/70 [00:00<00:00, 1753.06 it/sec, feas=True, obj=3.54]
INFO - 16:16:02: 34%|███▍ | 24/70 [00:00<00:00, 1765.96 it/sec, feas=True, obj=6.04]
INFO - 16:16:02: 36%|███▌ | 25/70 [00:00<00:00, 1780.72 it/sec, feas=True, obj=7.5]
INFO - 16:16:02: 37%|███▋ | 26/70 [00:00<00:00, 1792.61 it/sec, feas=True, obj=13.2]
INFO - 16:16:02: 39%|███▊ | 27/70 [00:00<00:00, 1806.53 it/sec, feas=True, obj=14.8]
INFO - 16:16:02: 40%|████ | 28/70 [00:00<00:00, 1816.50 it/sec, feas=True, obj=-0.644]
INFO - 16:16:02: 41%|████▏ | 29/70 [00:00<00:00, 1827.80 it/sec, feas=True, obj=4.94]
INFO - 16:16:02: 43%|████▎ | 30/70 [00:00<00:00, 1837.06 it/sec, feas=True, obj=5.5]
INFO - 16:16:02: 44%|████▍ | 31/70 [00:00<00:00, 1847.95 it/sec, feas=True, obj=3.35]
INFO - 16:16:02: 46%|████▌ | 32/70 [00:00<00:00, 1856.27 it/sec, feas=True, obj=4.05]
INFO - 16:16:02: 47%|████▋ | 33/70 [00:00<00:00, 1865.04 it/sec, feas=True, obj=2.43]
INFO - 16:16:02: 49%|████▊ | 34/70 [00:00<00:00, 1872.26 it/sec, feas=True, obj=-0.0246]
INFO - 16:16:02: 50%|█████ | 35/70 [00:00<00:00, 1880.56 it/sec, feas=True, obj=-0.0211]
INFO - 16:16:02: 51%|█████▏ | 36/70 [00:00<00:00, 1887.53 it/sec, feas=True, obj=6.01]
INFO - 16:16:02: 53%|█████▎ | 37/70 [00:00<00:00, 1892.18 it/sec, feas=True, obj=5.03]
INFO - 16:16:02: 54%|█████▍ | 38/70 [00:00<00:00, 1898.94 it/sec, feas=True, obj=0.863]
INFO - 16:16:02: 56%|█████▌ | 39/70 [00:00<00:00, 1903.77 it/sec, feas=True, obj=-0.764]
INFO - 16:16:02: 57%|█████▋ | 40/70 [00:00<00:00, 1910.74 it/sec, feas=True, obj=14.8]
INFO - 16:16:02: 59%|█████▊ | 41/70 [00:00<00:00, 1915.98 it/sec, feas=True, obj=0.87]
INFO - 16:16:02: 60%|██████ | 42/70 [00:00<00:00, 1921.18 it/sec, feas=True, obj=0.829]
INFO - 16:16:02: 61%|██████▏ | 43/70 [00:00<00:00, 1925.08 it/sec, feas=True, obj=5.01]
INFO - 16:16:02: 63%|██████▎ | 44/70 [00:00<00:00, 1930.55 it/sec, feas=True, obj=0.108]
INFO - 16:16:02: 64%|██████▍ | 45/70 [00:00<00:00, 1934.66 it/sec, feas=True, obj=0.948]
INFO - 16:16:02: 66%|██████▌ | 46/70 [00:00<00:00, 1941.24 it/sec, feas=True, obj=1.22]
INFO - 16:16:02: 67%|██████▋ | 47/70 [00:00<00:00, 1946.56 it/sec, feas=True, obj=7.52]
INFO - 16:16:02: 69%|██████▊ | 48/70 [00:00<00:00, 1951.69 it/sec, feas=True, obj=3.97]
INFO - 16:16:02: 70%|███████ | 49/70 [00:00<00:00, 1957.19 it/sec, feas=True, obj=0.768]
INFO - 16:16:02: 71%|███████▏ | 50/70 [00:00<00:00, 1960.63 it/sec, feas=True, obj=-8.26]
INFO - 16:16:02: 73%|███████▎ | 51/70 [00:00<00:00, 1965.25 it/sec, feas=True, obj=-3.5]
INFO - 16:16:02: 74%|███████▍ | 52/70 [00:00<00:00, 1969.03 it/sec, feas=True, obj=7.43]
INFO - 16:16:02: 76%|███████▌ | 53/70 [00:00<00:00, 1973.25 it/sec, feas=True, obj=-2.32]
INFO - 16:16:02: 77%|███████▋ | 54/70 [00:00<00:00, 1975.80 it/sec, feas=True, obj=4.82]
INFO - 16:16:02: 79%|███████▊ | 55/70 [00:00<00:00, 1979.68 it/sec, feas=True, obj=2.5]
INFO - 16:16:02: 80%|████████ | 56/70 [00:00<00:00, 1981.98 it/sec, feas=True, obj=2.58]
INFO - 16:16:02: 81%|████████▏ | 57/70 [00:00<00:00, 1985.94 it/sec, feas=True, obj=-2.55]
INFO - 16:16:02: 83%|████████▎ | 58/70 [00:00<00:00, 1988.11 it/sec, feas=True, obj=2.11]
INFO - 16:16:02: 84%|████████▍ | 59/70 [00:00<00:00, 1991.16 it/sec, feas=True, obj=8.06]
INFO - 16:16:02: 86%|████████▌ | 60/70 [00:00<00:00, 1995.06 it/sec, feas=True, obj=-5.24]
INFO - 16:16:02: 87%|████████▋ | 61/70 [00:00<00:00, 1997.37 it/sec, feas=True, obj=2.4]
INFO - 16:16:02: 89%|████████▊ | 62/70 [00:00<00:00, 2001.59 it/sec, feas=True, obj=3.43]
INFO - 16:16:02: 90%|█████████ | 63/70 [00:00<00:00, 2004.06 it/sec, feas=True, obj=5.99]
INFO - 16:16:02: 91%|█████████▏| 64/70 [00:00<00:00, 2007.28 it/sec, feas=True, obj=0.819]
INFO - 16:16:02: 93%|█████████▎| 65/70 [00:00<00:00, 2009.15 it/sec, feas=True, obj=0.632]
INFO - 16:16:02: 94%|█████████▍| 66/70 [00:00<00:00, 2011.85 it/sec, feas=True, obj=-0.158]
INFO - 16:16:02: 96%|█████████▌| 67/70 [00:00<00:00, 2014.14 it/sec, feas=True, obj=4.05]
INFO - 16:16:02: 97%|█████████▋| 68/70 [00:00<00:00, 2016.79 it/sec, feas=True, obj=7.71]
INFO - 16:16:02: 99%|█████████▊| 69/70 [00:00<00:00, 2018.80 it/sec, feas=True, obj=5.54]
INFO - 16:16:02: 100%|██████████| 70/70 [00:00<00:00, 2009.83 it/sec, feas=True, obj=6.63]
INFO - 16:16:02: Optimization result:
INFO - 16:16:02: Optimizer info:
INFO - 16:16:02: Status: None
INFO - 16:16:02: Message: None
INFO - 16:16:02: Solution:
INFO - 16:16:02: Objective: -8.260663543133736
INFO - 16:16:02: Design space:
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: | Name | Distribution |
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: *** 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 - 16:16:02: *** Start Sampling execution ***
INFO - 16:16:02: Sampling
INFO - 16:16:02: Disciplines: IshigamiDiscipline
INFO - 16:16:02: MDO formulation: MDF
INFO - 16:16:02: Optimization problem:
INFO - 16:16:02: minimize y(x1, x2, x3)
INFO - 16:16:02: with respect to x1, x2, x3
INFO - 16:16:02: over the design space:
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: | Name | Distribution |
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: Solving optimization problem with algorithm MC:
INFO - 16:16:02: 1%| | 6/1000 [00:00<00:00, 4161.02 it/sec, feas=True, obj=3.6]
INFO - 16:16:02: 1%| | 7/1000 [00:00<00:00, 3928.30 it/sec, feas=True, obj=5.41]
INFO - 16:16:02: 1%| | 8/1000 [00:00<00:00, 3888.11 it/sec, feas=True, obj=-9.09]
INFO - 16:16:02: 1%| | 9/1000 [00:00<00:00, 3844.07 it/sec, feas=True, obj=7.06]
INFO - 16:16:02: 1%| | 10/1000 [00:00<00:00, 3827.27 it/sec, feas=True, obj=-3.46]
INFO - 16:16:02: 1%| | 11/1000 [00:00<00:00, 3827.55 it/sec, feas=True, obj=3.2]
INFO - 16:16:02: 1%| | 12/1000 [00:00<00:00, 3837.13 it/sec, feas=True, obj=8.62]
INFO - 16:16:02: 1%|▏ | 13/1000 [00:00<00:00, 3823.97 it/sec, feas=True, obj=-0.0229]
INFO - 16:16:02: 1%|▏ | 14/1000 [00:00<00:00, 3819.70 it/sec, feas=True, obj=2.91]
INFO - 16:16:02: 2%|▏ | 15/1000 [00:00<00:00, 3824.59 it/sec, feas=True, obj=8.67]
INFO - 16:16:02: 2%|▏ | 16/1000 [00:00<00:00, 3836.33 it/sec, feas=True, obj=0.13]
INFO - 16:16:02: 2%|▏ | 17/1000 [00:00<00:00, 3828.77 it/sec, feas=True, obj=4.8]
INFO - 16:16:02: 2%|▏ | 18/1000 [00:00<00:00, 3833.33 it/sec, feas=True, obj=8.34]
INFO - 16:16:02: 2%|▏ | 19/1000 [00:00<00:00, 3840.01 it/sec, feas=True, obj=-1.57]
INFO - 16:16:02: 2%|▏ | 20/1000 [00:00<00:00, 3848.87 it/sec, feas=True, obj=4.2]
INFO - 16:16:02: 2%|▏ | 21/1000 [00:00<00:00, 3842.78 it/sec, feas=True, obj=-1.04]
INFO - 16:16:02: 2%|▏ | 22/1000 [00:00<00:00, 3846.86 it/sec, feas=True, obj=6.49]
INFO - 16:16:02: 2%|▏ | 23/1000 [00:00<00:00, 3846.30 it/sec, feas=True, obj=1.83]
INFO - 16:16:02: 2%|▏ | 24/1000 [00:00<00:00, 3854.76 it/sec, feas=True, obj=4.83]
INFO - 16:16:02: 2%|▎ | 25/1000 [00:00<00:00, 3848.27 it/sec, feas=True, obj=2.15]
INFO - 16:16:02: 3%|▎ | 26/1000 [00:00<00:00, 3854.24 it/sec, feas=True, obj=4.61]
INFO - 16:16:02: 3%|▎ | 27/1000 [00:00<00:00, 3860.45 it/sec, feas=True, obj=4.02]
INFO - 16:16:02: 3%|▎ | 28/1000 [00:00<00:00, 3867.50 it/sec, feas=True, obj=4.83]
INFO - 16:16:02: 3%|▎ | 29/1000 [00:00<00:00, 3858.73 it/sec, feas=True, obj=3.43]
INFO - 16:16:02: 3%|▎ | 30/1000 [00:00<00:00, 3861.80 it/sec, feas=True, obj=2.48]
INFO - 16:16:02: 3%|▎ | 31/1000 [00:00<00:00, 3753.13 it/sec, feas=True, obj=6.63]
INFO - 16:16:02: 3%|▎ | 32/1000 [00:00<00:00, 3739.70 it/sec, feas=True, obj=6.92]
INFO - 16:16:02: 3%|▎ | 33/1000 [00:00<00:00, 3743.70 it/sec, feas=True, obj=3.22]
INFO - 16:16:02: 3%|▎ | 34/1000 [00:00<00:00, 3749.74 it/sec, feas=True, obj=5.73]
INFO - 16:16:02: 4%|▎ | 35/1000 [00:00<00:00, 3748.07 it/sec, feas=True, obj=5.62]
INFO - 16:16:02: 4%|▎ | 36/1000 [00:00<00:00, 3747.89 it/sec, feas=True, obj=-1.44]
INFO - 16:16:02: 4%|▎ | 37/1000 [00:00<00:00, 3748.26 it/sec, feas=True, obj=7.02]
INFO - 16:16:02: 4%|▍ | 38/1000 [00:00<00:00, 3753.38 it/sec, feas=True, obj=6.21]
INFO - 16:16:02: 4%|▍ | 39/1000 [00:00<00:00, 3752.73 it/sec, feas=True, obj=4.64]
INFO - 16:16:02: 4%|▍ | 40/1000 [00:00<00:00, 3756.99 it/sec, feas=True, obj=4.71]
INFO - 16:16:02: 4%|▍ | 41/1000 [00:00<00:00, 3761.54 it/sec, feas=True, obj=5.73]
INFO - 16:16:02: 4%|▍ | 42/1000 [00:00<00:00, 3767.98 it/sec, feas=True, obj=-0.0754]
INFO - 16:16:02: 4%|▍ | 43/1000 [00:00<00:00, 3766.50 it/sec, feas=True, obj=5.56]
INFO - 16:16:02: 4%|▍ | 44/1000 [00:00<00:00, 3771.86 it/sec, feas=True, obj=5.03]
INFO - 16:16:02: 4%|▍ | 45/1000 [00:00<00:00, 3777.14 it/sec, feas=True, obj=7.21]
INFO - 16:16:02: 5%|▍ | 46/1000 [00:00<00:00, 3783.02 it/sec, feas=True, obj=8.03]
INFO - 16:16:02: 5%|▍ | 47/1000 [00:00<00:00, 3782.71 it/sec, feas=True, obj=5.56]
INFO - 16:16:02: 5%|▍ | 48/1000 [00:00<00:00, 3786.97 it/sec, feas=True, obj=6.35]
INFO - 16:16:02: 5%|▍ | 49/1000 [00:00<00:00, 3790.64 it/sec, feas=True, obj=6.71]
INFO - 16:16:02: 5%|▌ | 50/1000 [00:00<00:00, 3796.03 it/sec, feas=True, obj=3.52]
INFO - 16:16:02: 5%|▌ | 51/1000 [00:00<00:00, 3794.27 it/sec, feas=True, obj=2.63]
INFO - 16:16:02: 5%|▌ | 52/1000 [00:00<00:00, 3797.34 it/sec, feas=True, obj=4.68]
INFO - 16:16:02: 5%|▌ | 53/1000 [00:00<00:00, 3797.37 it/sec, feas=True, obj=1.07]
INFO - 16:16:02: 5%|▌ | 54/1000 [00:00<00:00, 3798.87 it/sec, feas=True, obj=10.3]
INFO - 16:16:02: 6%|▌ | 55/1000 [00:00<00:00, 3796.50 it/sec, feas=True, obj=6.87]
INFO - 16:16:02: 6%|▌ | 56/1000 [00:00<00:00, 3799.74 it/sec, feas=True, obj=-3.82]
INFO - 16:16:02: 6%|▌ | 57/1000 [00:00<00:00, 3802.63 it/sec, feas=True, obj=-1.58]
INFO - 16:16:02: 6%|▌ | 58/1000 [00:00<00:00, 3807.63 it/sec, feas=True, obj=3.43]
INFO - 16:16:02: 6%|▌ | 59/1000 [00:00<00:00, 3806.67 it/sec, feas=True, obj=-6.44]
INFO - 16:16:02: 6%|▌ | 60/1000 [00:00<00:00, 3809.94 it/sec, feas=True, obj=0.167]
INFO - 16:16:02: 6%|▌ | 61/1000 [00:00<00:00, 3812.49 it/sec, feas=True, obj=2.98]
INFO - 16:16:02: 6%|▌ | 62/1000 [00:00<00:00, 3816.03 it/sec, feas=True, obj=0.771]
INFO - 16:16:02: 6%|▋ | 63/1000 [00:00<00:00, 3812.95 it/sec, feas=True, obj=6.98]
INFO - 16:16:02: 6%|▋ | 64/1000 [00:00<00:00, 3814.63 it/sec, feas=True, obj=6.81]
INFO - 16:16:02: 6%|▋ | 65/1000 [00:00<00:00, 3816.26 it/sec, feas=True, obj=0.257]
INFO - 16:16:02: 7%|▋ | 66/1000 [00:00<00:00, 3820.37 it/sec, feas=True, obj=3.31]
INFO - 16:16:02: 7%|▋ | 67/1000 [00:00<00:00, 3819.01 it/sec, feas=True, obj=6.07]
INFO - 16:16:02: 7%|▋ | 68/1000 [00:00<00:00, 3821.43 it/sec, feas=True, obj=5.87]
INFO - 16:16:02: 7%|▋ | 69/1000 [00:00<00:00, 3824.24 it/sec, feas=True, obj=7.69]
INFO - 16:16:02: 7%|▋ | 70/1000 [00:00<00:00, 3827.67 it/sec, feas=True, obj=5.16]
INFO - 16:16:02: 7%|▋ | 71/1000 [00:00<00:00, 3826.62 it/sec, feas=True, obj=-0.0811]
INFO - 16:16:02: 7%|▋ | 72/1000 [00:00<00:00, 3830.80 it/sec, feas=True, obj=1.25]
INFO - 16:16:02: 7%|▋ | 73/1000 [00:00<00:00, 3833.72 it/sec, feas=True, obj=5.71]
INFO - 16:16:02: 7%|▋ | 74/1000 [00:00<00:00, 3835.76 it/sec, feas=True, obj=8.15]
INFO - 16:16:02: 8%|▊ | 75/1000 [00:00<00:00, 3831.81 it/sec, feas=True, obj=-2.86]
INFO - 16:16:02: 8%|▊ | 76/1000 [00:00<00:00, 3833.13 it/sec, feas=True, obj=10.5]
INFO - 16:16:02: 8%|▊ | 77/1000 [00:00<00:00, 3835.46 it/sec, feas=True, obj=4.87]
INFO - 16:16:02: 8%|▊ | 78/1000 [00:00<00:00, 3835.89 it/sec, feas=True, obj=2.44]
INFO - 16:16:02: 8%|▊ | 79/1000 [00:00<00:00, 3837.07 it/sec, feas=True, obj=6.74]
INFO - 16:16:02: 8%|▊ | 80/1000 [00:00<00:00, 3839.62 it/sec, feas=True, obj=8.51]
INFO - 16:16:02: 8%|▊ | 81/1000 [00:00<00:00, 3842.37 it/sec, feas=True, obj=0.595]
INFO - 16:16:02: 8%|▊ | 82/1000 [00:00<00:00, 3842.53 it/sec, feas=True, obj=7.84]
INFO - 16:16:02: 8%|▊ | 83/1000 [00:00<00:00, 3843.40 it/sec, feas=True, obj=0.842]
INFO - 16:16:02: 8%|▊ | 84/1000 [00:00<00:00, 3843.87 it/sec, feas=True, obj=9.47]
INFO - 16:16:02: 8%|▊ | 85/1000 [00:00<00:00, 3846.24 it/sec, feas=True, obj=4.54]
INFO - 16:16:02: 9%|▊ | 86/1000 [00:00<00:00, 3844.58 it/sec, feas=True, obj=-3.69]
INFO - 16:16:02: 9%|▊ | 87/1000 [00:00<00:00, 3845.84 it/sec, feas=True, obj=0.849]
INFO - 16:16:02: 9%|▉ | 88/1000 [00:00<00:00, 3848.39 it/sec, feas=True, obj=11.9]
INFO - 16:16:02: 9%|▉ | 89/1000 [00:00<00:00, 3851.12 it/sec, feas=True, obj=5.69]
INFO - 16:16:02: 9%|▉ | 90/1000 [00:00<00:00, 3849.79 it/sec, feas=True, obj=7.25]
INFO - 16:16:02: 9%|▉ | 91/1000 [00:00<00:00, 3851.09 it/sec, feas=True, obj=2.17]
INFO - 16:16:02: 9%|▉ | 92/1000 [00:00<00:00, 3853.63 it/sec, feas=True, obj=5.76]
INFO - 16:16:02: 9%|▉ | 93/1000 [00:00<00:00, 3855.97 it/sec, feas=True, obj=5.32]
INFO - 16:16:02: 9%|▉ | 94/1000 [00:00<00:00, 3854.95 it/sec, feas=True, obj=-3.15]
INFO - 16:16:02: 10%|▉ | 95/1000 [00:00<00:00, 3855.84 it/sec, feas=True, obj=9.36]
INFO - 16:16:02: 10%|▉ | 96/1000 [00:00<00:00, 3857.35 it/sec, feas=True, obj=11.9]
INFO - 16:16:02: 10%|▉ | 97/1000 [00:00<00:00, 3859.12 it/sec, feas=True, obj=8.76]
INFO - 16:16:02: 10%|▉ | 98/1000 [00:00<00:00, 3858.93 it/sec, feas=True, obj=-0.112]
INFO - 16:16:02: 10%|▉ | 99/1000 [00:00<00:00, 3860.58 it/sec, feas=True, obj=1.61]
INFO - 16:16:02: 10%|█ | 100/1000 [00:00<00:00, 3861.45 it/sec, feas=True, obj=4.05]
INFO - 16:16:02: 10%|█ | 101/1000 [00:00<00:00, 3862.83 it/sec, feas=True, obj=-0.31]
INFO - 16:16:02: 10%|█ | 102/1000 [00:00<00:00, 3861.91 it/sec, feas=True, obj=1.46]
INFO - 16:16:02: 10%|█ | 103/1000 [00:00<00:00, 3863.85 it/sec, feas=True, obj=1.1]
INFO - 16:16:02: 10%|█ | 104/1000 [00:00<00:00, 3866.09 it/sec, feas=True, obj=2.69]
INFO - 16:16:02: 10%|█ | 105/1000 [00:00<00:00, 3868.37 it/sec, feas=True, obj=7.7]
INFO - 16:16:02: 11%|█ | 106/1000 [00:00<00:00, 3867.60 it/sec, feas=True, obj=4.86]
INFO - 16:16:02: 11%|█ | 107/1000 [00:00<00:00, 3869.22 it/sec, feas=True, obj=-0.104]
INFO - 16:16:02: 11%|█ | 108/1000 [00:00<00:00, 3871.67 it/sec, feas=True, obj=-7.95]
INFO - 16:16:02: 11%|█ | 109/1000 [00:00<00:00, 3873.84 it/sec, feas=True, obj=0.11]
INFO - 16:16:02: 11%|█ | 110/1000 [00:00<00:00, 3872.82 it/sec, feas=True, obj=-0.471]
INFO - 16:16:02: 11%|█ | 111/1000 [00:00<00:00, 3872.89 it/sec, feas=True, obj=-0.843]
INFO - 16:16:02: 11%|█ | 112/1000 [00:00<00:00, 3873.66 it/sec, feas=True, obj=3.99]
INFO - 16:16:02: 11%|█▏ | 113/1000 [00:00<00:00, 3875.17 it/sec, feas=True, obj=5.95]
INFO - 16:16:02: 11%|█▏ | 114/1000 [00:00<00:00, 3873.05 it/sec, feas=True, obj=6.56]
INFO - 16:16:02: 12%|█▏ | 115/1000 [00:00<00:00, 3873.57 it/sec, feas=True, obj=6.04]
INFO - 16:16:02: 12%|█▏ | 116/1000 [00:00<00:00, 3873.87 it/sec, feas=True, obj=0.26]
INFO - 16:16:02: 12%|█▏ | 117/1000 [00:00<00:00, 3875.09 it/sec, feas=True, obj=5.43]
INFO - 16:16:02: 12%|█▏ | 118/1000 [00:00<00:00, 3874.28 it/sec, feas=True, obj=0.706]
INFO - 16:16:02: 12%|█▏ | 119/1000 [00:00<00:00, 3875.08 it/sec, feas=True, obj=-0.861]
INFO - 16:16:02: 12%|█▏ | 120/1000 [00:00<00:00, 3876.59 it/sec, feas=True, obj=5.7]
INFO - 16:16:02: 12%|█▏ | 121/1000 [00:00<00:00, 3878.63 it/sec, feas=True, obj=3.13]
INFO - 16:16:02: 12%|█▏ | 122/1000 [00:00<00:00, 3878.11 it/sec, feas=True, obj=2.51]
INFO - 16:16:02: 12%|█▏ | 123/1000 [00:00<00:00, 3879.96 it/sec, feas=True, obj=0.0431]
INFO - 16:16:02: 12%|█▏ | 124/1000 [00:00<00:00, 3881.27 it/sec, feas=True, obj=4.08]
INFO - 16:16:02: 12%|█▎ | 125/1000 [00:00<00:00, 3882.90 it/sec, feas=True, obj=3.48]
INFO - 16:16:02: 13%|█▎ | 126/1000 [00:00<00:00, 3881.73 it/sec, feas=True, obj=-0.27]
INFO - 16:16:02: 13%|█▎ | 127/1000 [00:00<00:00, 3882.91 it/sec, feas=True, obj=6.25]
INFO - 16:16:02: 13%|█▎ | 128/1000 [00:00<00:00, 3884.60 it/sec, feas=True, obj=9.11]
INFO - 16:16:02: 13%|█▎ | 129/1000 [00:00<00:00, 3886.29 it/sec, feas=True, obj=-0.828]
INFO - 16:16:02: 13%|█▎ | 130/1000 [00:00<00:00, 3885.61 it/sec, feas=True, obj=10.3]
INFO - 16:16:02: 13%|█▎ | 131/1000 [00:00<00:00, 3886.58 it/sec, feas=True, obj=3.98]
INFO - 16:16:02: 13%|█▎ | 132/1000 [00:00<00:00, 3886.83 it/sec, feas=True, obj=2.27]
INFO - 16:16:02: 13%|█▎ | 133/1000 [00:00<00:00, 3887.51 it/sec, feas=True, obj=1.6]
INFO - 16:16:02: 13%|█▎ | 134/1000 [00:00<00:00, 3885.57 it/sec, feas=True, obj=-7.13]
INFO - 16:16:02: 14%|█▎ | 135/1000 [00:00<00:00, 3886.15 it/sec, feas=True, obj=7.82]
INFO - 16:16:02: 14%|█▎ | 136/1000 [00:00<00:00, 3881.90 it/sec, feas=True, obj=4.68]
INFO - 16:16:02: 14%|█▎ | 137/1000 [00:00<00:00, 3881.36 it/sec, feas=True, obj=-0.627]
INFO - 16:16:02: 14%|█▍ | 138/1000 [00:00<00:00, 3879.06 it/sec, feas=True, obj=-4.07]
INFO - 16:16:02: 14%|█▍ | 139/1000 [00:00<00:00, 3879.38 it/sec, feas=True, obj=1.06]
INFO - 16:16:02: 14%|█▍ | 140/1000 [00:00<00:00, 3880.61 it/sec, feas=True, obj=11.1]
INFO - 16:16:02: 14%|█▍ | 141/1000 [00:00<00:00, 3879.46 it/sec, feas=True, obj=1.87]
INFO - 16:16:02: 14%|█▍ | 142/1000 [00:00<00:00, 3879.52 it/sec, feas=True, obj=7.07]
INFO - 16:16:02: 14%|█▍ | 143/1000 [00:00<00:00, 3880.98 it/sec, feas=True, obj=1.79]
INFO - 16:16:02: 14%|█▍ | 144/1000 [00:00<00:00, 3882.07 it/sec, feas=True, obj=5.97]
INFO - 16:16:02: 14%|█▍ | 145/1000 [00:00<00:00, 3867.76 it/sec, feas=True, obj=6.15]
INFO - 16:16:02: 15%|█▍ | 146/1000 [00:00<00:00, 3866.38 it/sec, feas=True, obj=4.61]
INFO - 16:16:02: 15%|█▍ | 147/1000 [00:00<00:00, 3865.62 it/sec, feas=True, obj=0.433]
INFO - 16:16:02: 15%|█▍ | 148/1000 [00:00<00:00, 3866.83 it/sec, feas=True, obj=2.73]
INFO - 16:16:02: 15%|█▍ | 149/1000 [00:00<00:00, 3864.98 it/sec, feas=True, obj=1.83]
INFO - 16:16:02: 15%|█▌ | 150/1000 [00:00<00:00, 3865.88 it/sec, feas=True, obj=5.3]
INFO - 16:16:02: 15%|█▌ | 151/1000 [00:00<00:00, 3867.30 it/sec, feas=True, obj=-0.935]
INFO - 16:16:02: 15%|█▌ | 152/1000 [00:00<00:00, 3868.53 it/sec, feas=True, obj=7.03]
INFO - 16:16:02: 15%|█▌ | 153/1000 [00:00<00:00, 3866.32 it/sec, feas=True, obj=4.91]
INFO - 16:16:02: 15%|█▌ | 154/1000 [00:00<00:00, 3867.45 it/sec, feas=True, obj=5.89]
INFO - 16:16:02: 16%|█▌ | 155/1000 [00:00<00:00, 3868.82 it/sec, feas=True, obj=-1.07]
INFO - 16:16:02: 16%|█▌ | 156/1000 [00:00<00:00, 3868.53 it/sec, feas=True, obj=2.05]
INFO - 16:16:02: 16%|█▌ | 157/1000 [00:00<00:00, 3868.33 it/sec, feas=True, obj=9.08]
INFO - 16:16:02: 16%|█▌ | 158/1000 [00:00<00:00, 3869.35 it/sec, feas=True, obj=1.28]
INFO - 16:16:02: 16%|█▌ | 159/1000 [00:00<00:00, 3870.09 it/sec, feas=True, obj=5.5]
INFO - 16:16:02: 16%|█▌ | 160/1000 [00:00<00:00, 3869.55 it/sec, feas=True, obj=2.86]
INFO - 16:16:02: 16%|█▌ | 161/1000 [00:00<00:00, 3870.08 it/sec, feas=True, obj=2.58]
INFO - 16:16:02: 16%|█▌ | 162/1000 [00:00<00:00, 3870.58 it/sec, feas=True, obj=6.35]
INFO - 16:16:02: 16%|█▋ | 163/1000 [00:00<00:00, 3871.78 it/sec, feas=True, obj=5.03]
INFO - 16:16:02: 16%|█▋ | 164/1000 [00:00<00:00, 3871.27 it/sec, feas=True, obj=4.89]
INFO - 16:16:02: 16%|█▋ | 165/1000 [00:00<00:00, 3871.64 it/sec, feas=True, obj=-0.862]
INFO - 16:16:02: 17%|█▋ | 166/1000 [00:00<00:00, 3872.66 it/sec, feas=True, obj=5.17]
INFO - 16:16:02: 17%|█▋ | 167/1000 [00:00<00:00, 3873.65 it/sec, feas=True, obj=6.54]
INFO - 16:16:02: 17%|█▋ | 168/1000 [00:00<00:00, 3872.86 it/sec, feas=True, obj=5.04]
INFO - 16:16:02: 17%|█▋ | 169/1000 [00:00<00:00, 3873.39 it/sec, feas=True, obj=5.18]
INFO - 16:16:02: 17%|█▋ | 170/1000 [00:00<00:00, 3874.58 it/sec, feas=True, obj=9.72]
INFO - 16:16:02: 17%|█▋ | 171/1000 [00:00<00:00, 3875.56 it/sec, feas=True, obj=4.51]
INFO - 16:16:02: 17%|█▋ | 172/1000 [00:00<00:00, 3874.94 it/sec, feas=True, obj=5.25]
INFO - 16:16:02: 17%|█▋ | 173/1000 [00:00<00:00, 3875.79 it/sec, feas=True, obj=7.58]
INFO - 16:16:02: 17%|█▋ | 174/1000 [00:00<00:00, 3876.77 it/sec, feas=True, obj=-0.152]
INFO - 16:16:02: 18%|█▊ | 175/1000 [00:00<00:00, 3878.14 it/sec, feas=True, obj=0.707]
INFO - 16:16:02: 18%|█▊ | 176/1000 [00:00<00:00, 3877.78 it/sec, feas=True, obj=1.95]
INFO - 16:16:02: 18%|█▊ | 177/1000 [00:00<00:00, 3878.72 it/sec, feas=True, obj=5.37]
INFO - 16:16:02: 18%|█▊ | 178/1000 [00:00<00:00, 3879.46 it/sec, feas=True, obj=9.3]
INFO - 16:16:02: 18%|█▊ | 179/1000 [00:00<00:00, 3880.30 it/sec, feas=True, obj=-6.59]
INFO - 16:16:02: 18%|█▊ | 180/1000 [00:00<00:00, 3880.20 it/sec, feas=True, obj=0.62]
INFO - 16:16:02: 18%|█▊ | 181/1000 [00:00<00:00, 3881.33 it/sec, feas=True, obj=2.86]
INFO - 16:16:02: 18%|█▊ | 182/1000 [00:00<00:00, 3882.79 it/sec, feas=True, obj=7.64]
INFO - 16:16:02: 18%|█▊ | 183/1000 [00:00<00:00, 3883.79 it/sec, feas=True, obj=1.83]
INFO - 16:16:02: 18%|█▊ | 184/1000 [00:00<00:00, 3882.89 it/sec, feas=True, obj=1.15]
INFO - 16:16:02: 18%|█▊ | 185/1000 [00:00<00:00, 3883.38 it/sec, feas=True, obj=4.53]
INFO - 16:16:02: 19%|█▊ | 186/1000 [00:00<00:00, 3884.27 it/sec, feas=True, obj=5.86]
INFO - 16:16:02: 19%|█▊ | 187/1000 [00:00<00:00, 3885.56 it/sec, feas=True, obj=-4.15]
INFO - 16:16:02: 19%|█▉ | 188/1000 [00:00<00:00, 3885.07 it/sec, feas=True, obj=0.77]
INFO - 16:16:02: 19%|█▉ | 189/1000 [00:00<00:00, 3885.79 it/sec, feas=True, obj=3.77]
INFO - 16:16:02: 19%|█▉ | 190/1000 [00:00<00:00, 3886.74 it/sec, feas=True, obj=0.574]
INFO - 16:16:02: 19%|█▉ | 191/1000 [00:00<00:00, 3887.91 it/sec, feas=True, obj=3.27]
INFO - 16:16:02: 19%|█▉ | 192/1000 [00:00<00:00, 3887.50 it/sec, feas=True, obj=1.31]
INFO - 16:16:02: 19%|█▉ | 193/1000 [00:00<00:00, 3888.30 it/sec, feas=True, obj=2.11]
INFO - 16:16:02: 19%|█▉ | 194/1000 [00:00<00:00, 3888.55 it/sec, feas=True, obj=-0.324]
INFO - 16:16:02: 20%|█▉ | 195/1000 [00:00<00:00, 3889.67 it/sec, feas=True, obj=2.6]
INFO - 16:16:02: 20%|█▉ | 196/1000 [00:00<00:00, 3889.18 it/sec, feas=True, obj=7.25]
INFO - 16:16:02: 20%|█▉ | 197/1000 [00:00<00:00, 3889.83 it/sec, feas=True, obj=12.9]
INFO - 16:16:02: 20%|█▉ | 198/1000 [00:00<00:00, 3890.73 it/sec, feas=True, obj=-1.67]
INFO - 16:16:02: 20%|█▉ | 199/1000 [00:00<00:00, 3891.67 it/sec, feas=True, obj=6.19]
INFO - 16:16:02: 20%|██ | 200/1000 [00:00<00:00, 3890.84 it/sec, feas=True, obj=3.52]
INFO - 16:16:02: 20%|██ | 201/1000 [00:00<00:00, 3891.45 it/sec, feas=True, obj=-1.49]
INFO - 16:16:02: 20%|██ | 202/1000 [00:00<00:00, 3892.36 it/sec, feas=True, obj=7.77]
INFO - 16:16:02: 20%|██ | 203/1000 [00:00<00:00, 3893.28 it/sec, feas=True, obj=0.847]
INFO - 16:16:02: 20%|██ | 204/1000 [00:00<00:00, 3892.91 it/sec, feas=True, obj=-2.82]
INFO - 16:16:02: 20%|██ | 205/1000 [00:00<00:00, 3893.74 it/sec, feas=True, obj=6.59]
INFO - 16:16:02: 21%|██ | 206/1000 [00:00<00:00, 3894.73 it/sec, feas=True, obj=4.35]
INFO - 16:16:02: 21%|██ | 207/1000 [00:00<00:00, 3895.78 it/sec, feas=True, obj=-1.95]
INFO - 16:16:02: 21%|██ | 208/1000 [00:00<00:00, 3895.18 it/sec, feas=True, obj=-0.845]
INFO - 16:16:02: 21%|██ | 209/1000 [00:00<00:00, 3895.77 it/sec, feas=True, obj=7.19]
INFO - 16:16:02: 21%|██ | 210/1000 [00:00<00:00, 3895.98 it/sec, feas=True, obj=-0.108]
INFO - 16:16:02: 21%|██ | 211/1000 [00:00<00:00, 3896.68 it/sec, feas=True, obj=1.42]
INFO - 16:16:02: 21%|██ | 212/1000 [00:00<00:00, 3895.58 it/sec, feas=True, obj=0.785]
INFO - 16:16:02: 21%|██▏ | 213/1000 [00:00<00:00, 3895.72 it/sec, feas=True, obj=-4.97]
INFO - 16:16:02: 21%|██▏ | 214/1000 [00:00<00:00, 3896.28 it/sec, feas=True, obj=7.43]
INFO - 16:16:02: 22%|██▏ | 215/1000 [00:00<00:00, 3897.01 it/sec, feas=True, obj=6.26]
INFO - 16:16:02: 22%|██▏ | 216/1000 [00:00<00:00, 3896.41 it/sec, feas=True, obj=2.43]
INFO - 16:16:02: 22%|██▏ | 217/1000 [00:00<00:00, 3897.00 it/sec, feas=True, obj=11.5]
INFO - 16:16:02: 22%|██▏ | 218/1000 [00:00<00:00, 3897.85 it/sec, feas=True, obj=3.62]
INFO - 16:16:02: 22%|██▏ | 219/1000 [00:00<00:00, 3898.42 it/sec, feas=True, obj=5.45]
INFO - 16:16:02: 22%|██▏ | 220/1000 [00:00<00:00, 3897.90 it/sec, feas=True, obj=8.42]
INFO - 16:16:02: 22%|██▏ | 221/1000 [00:00<00:00, 3898.69 it/sec, feas=True, obj=10.5]
INFO - 16:16:02: 22%|██▏ | 222/1000 [00:00<00:00, 3899.50 it/sec, feas=True, obj=4.04]
INFO - 16:16:02: 22%|██▏ | 223/1000 [00:00<00:00, 3900.69 it/sec, feas=True, obj=-1.33]
INFO - 16:16:02: 22%|██▏ | 224/1000 [00:00<00:00, 3900.01 it/sec, feas=True, obj=5.55]
INFO - 16:16:02: 22%|██▎ | 225/1000 [00:00<00:00, 3899.68 it/sec, feas=True, obj=-0.71]
INFO - 16:16:02: 23%|██▎ | 226/1000 [00:00<00:00, 3899.53 it/sec, feas=True, obj=2.84]
INFO - 16:16:02: 23%|██▎ | 227/1000 [00:00<00:00, 3900.06 it/sec, feas=True, obj=1.75]
INFO - 16:16:02: 23%|██▎ | 228/1000 [00:00<00:00, 3899.32 it/sec, feas=True, obj=1.36]
INFO - 16:16:02: 23%|██▎ | 229/1000 [00:00<00:00, 3899.70 it/sec, feas=True, obj=6.32]
INFO - 16:16:02: 23%|██▎ | 230/1000 [00:00<00:00, 3900.49 it/sec, feas=True, obj=6.66]
INFO - 16:16:02: 23%|██▎ | 231/1000 [00:00<00:00, 3901.18 it/sec, feas=True, obj=5.61]
INFO - 16:16:02: 23%|██▎ | 232/1000 [00:00<00:00, 3900.46 it/sec, feas=True, obj=7.2]
INFO - 16:16:02: 23%|██▎ | 233/1000 [00:00<00:00, 3900.93 it/sec, feas=True, obj=6.4]
INFO - 16:16:02: 23%|██▎ | 234/1000 [00:00<00:00, 3901.57 it/sec, feas=True, obj=0.753]
INFO - 16:16:02: 24%|██▎ | 235/1000 [00:00<00:00, 3902.36 it/sec, feas=True, obj=-0.835]
INFO - 16:16:02: 24%|██▎ | 236/1000 [00:00<00:00, 3901.37 it/sec, feas=True, obj=-0.324]
INFO - 16:16:02: 24%|██▎ | 237/1000 [00:00<00:00, 3901.89 it/sec, feas=True, obj=3.91]
INFO - 16:16:02: 24%|██▍ | 238/1000 [00:00<00:00, 3902.47 it/sec, feas=True, obj=6.01]
INFO - 16:16:02: 24%|██▍ | 239/1000 [00:00<00:00, 3903.03 it/sec, feas=True, obj=0.2]
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INFO - 16:16:02: 24%|██▍ | 241/1000 [00:00<00:00, 3902.28 it/sec, feas=True, obj=5.1]
INFO - 16:16:02: 24%|██▍ | 242/1000 [00:00<00:00, 3902.37 it/sec, feas=True, obj=5.55]
INFO - 16:16:02: 24%|██▍ | 243/1000 [00:00<00:00, 3903.28 it/sec, feas=True, obj=3.32]
INFO - 16:16:02: 24%|██▍ | 244/1000 [00:00<00:00, 3902.45 it/sec, feas=True, obj=8.57]
INFO - 16:16:02: 24%|██▍ | 245/1000 [00:00<00:00, 3902.85 it/sec, feas=True, obj=6.32]
INFO - 16:16:02: 25%|██▍ | 246/1000 [00:00<00:00, 3903.29 it/sec, feas=True, obj=-2.16]
INFO - 16:16:02: 25%|██▍ | 247/1000 [00:00<00:00, 3903.69 it/sec, feas=True, obj=3.75]
INFO - 16:16:02: 25%|██▍ | 248/1000 [00:00<00:00, 3902.21 it/sec, feas=True, obj=2.64]
INFO - 16:16:02: 25%|██▍ | 249/1000 [00:00<00:00, 3902.67 it/sec, feas=True, obj=0.181]
INFO - 16:16:02: 25%|██▌ | 250/1000 [00:00<00:00, 3903.38 it/sec, feas=True, obj=-6.31]
INFO - 16:16:02: 25%|██▌ | 251/1000 [00:00<00:00, 3903.96 it/sec, feas=True, obj=5.01]
INFO - 16:16:02: 25%|██▌ | 252/1000 [00:00<00:00, 3902.73 it/sec, feas=True, obj=8.03]
INFO - 16:16:02: 25%|██▌ | 253/1000 [00:00<00:00, 3903.24 it/sec, feas=True, obj=9.01]
INFO - 16:16:02: 25%|██▌ | 254/1000 [00:00<00:00, 3903.94 it/sec, feas=True, obj=7.08]
INFO - 16:16:02: 26%|██▌ | 255/1000 [00:00<00:00, 3903.50 it/sec, feas=True, obj=-0.461]
INFO - 16:16:02: 26%|██▌ | 256/1000 [00:00<00:00, 3903.04 it/sec, feas=True, obj=-4.41]
INFO - 16:16:02: 26%|██▌ | 257/1000 [00:00<00:00, 3903.15 it/sec, feas=True, obj=-0.98]
INFO - 16:16:02: 26%|██▌ | 258/1000 [00:00<00:00, 3903.10 it/sec, feas=True, obj=7.88]
INFO - 16:16:02: 26%|██▌ | 259/1000 [00:00<00:00, 3895.27 it/sec, feas=True, obj=5.1]
INFO - 16:16:02: 26%|██▌ | 260/1000 [00:00<00:00, 3895.28 it/sec, feas=True, obj=6.42]
INFO - 16:16:02: 26%|██▌ | 261/1000 [00:00<00:00, 3895.47 it/sec, feas=True, obj=2.42]
INFO - 16:16:02: 26%|██▌ | 262/1000 [00:00<00:00, 3895.74 it/sec, feas=True, obj=6.49]
INFO - 16:16:02: 26%|██▋ | 263/1000 [00:00<00:00, 3894.67 it/sec, feas=True, obj=-0.699]
INFO - 16:16:02: 26%|██▋ | 264/1000 [00:00<00:00, 3894.94 it/sec, feas=True, obj=0.137]
INFO - 16:16:02: 26%|██▋ | 265/1000 [00:00<00:00, 3895.55 it/sec, feas=True, obj=9.9]
INFO - 16:16:02: 27%|██▋ | 266/1000 [00:00<00:00, 3896.16 it/sec, feas=True, obj=3.71]
INFO - 16:16:02: 27%|██▋ | 267/1000 [00:00<00:00, 3895.22 it/sec, feas=True, obj=6.84]
INFO - 16:16:02: 27%|██▋ | 268/1000 [00:00<00:00, 3895.89 it/sec, feas=True, obj=1.21]
INFO - 16:16:02: 27%|██▋ | 269/1000 [00:00<00:00, 3896.22 it/sec, feas=True, obj=1.64]
INFO - 16:16:02: 27%|██▋ | 270/1000 [00:00<00:00, 3896.99 it/sec, feas=True, obj=2.2]
INFO - 16:16:02: 27%|██▋ | 271/1000 [00:00<00:00, 3896.21 it/sec, feas=True, obj=14.3]
INFO - 16:16:02: 27%|██▋ | 272/1000 [00:00<00:00, 3896.11 it/sec, feas=True, obj=1.03]
INFO - 16:16:02: 27%|██▋ | 273/1000 [00:00<00:00, 3896.53 it/sec, feas=True, obj=6.25]
INFO - 16:16:02: 27%|██▋ | 274/1000 [00:00<00:00, 3896.20 it/sec, feas=True, obj=3.31]
INFO - 16:16:02: 28%|██▊ | 275/1000 [00:00<00:00, 3895.89 it/sec, feas=True, obj=7.43]
INFO - 16:16:02: 28%|██▊ | 276/1000 [00:00<00:00, 3896.10 it/sec, feas=True, obj=4.7]
INFO - 16:16:02: 28%|██▊ | 277/1000 [00:00<00:00, 3896.69 it/sec, feas=True, obj=-0.0123]
INFO - 16:16:02: 28%|██▊ | 278/1000 [00:00<00:00, 3896.07 it/sec, feas=True, obj=10.9]
INFO - 16:16:02: 28%|██▊ | 279/1000 [00:00<00:00, 3895.94 it/sec, feas=True, obj=2.56]
INFO - 16:16:02: 28%|██▊ | 280/1000 [00:00<00:00, 3896.10 it/sec, feas=True, obj=5.48]
INFO - 16:16:02: 28%|██▊ | 281/1000 [00:00<00:00, 3896.60 it/sec, feas=True, obj=2.51]
INFO - 16:16:02: 28%|██▊ | 282/1000 [00:00<00:00, 3895.96 it/sec, feas=True, obj=5.13]
INFO - 16:16:02: 28%|██▊ | 283/1000 [00:00<00:00, 3895.65 it/sec, feas=True, obj=4.89]
INFO - 16:16:02: 28%|██▊ | 284/1000 [00:00<00:00, 3895.69 it/sec, feas=True, obj=7.69]
INFO - 16:16:02: 28%|██▊ | 285/1000 [00:00<00:00, 3896.16 it/sec, feas=True, obj=3.1]
INFO - 16:16:02: 29%|██▊ | 286/1000 [00:00<00:00, 3895.66 it/sec, feas=True, obj=-5.11]
INFO - 16:16:02: 29%|██▊ | 287/1000 [00:00<00:00, 3895.86 it/sec, feas=True, obj=-0.0286]
INFO - 16:16:02: 29%|██▉ | 288/1000 [00:00<00:00, 3895.53 it/sec, feas=True, obj=1.41]
INFO - 16:16:02: 29%|██▉ | 289/1000 [00:00<00:00, 3895.75 it/sec, feas=True, obj=5.79]
INFO - 16:16:02: 29%|██▉ | 290/1000 [00:00<00:00, 3894.99 it/sec, feas=True, obj=4.71]
INFO - 16:16:02: 29%|██▉ | 291/1000 [00:00<00:00, 3895.38 it/sec, feas=True, obj=6.49]
INFO - 16:16:02: 29%|██▉ | 292/1000 [00:00<00:00, 3895.58 it/sec, feas=True, obj=-7.67]
INFO - 16:16:02: 29%|██▉ | 293/1000 [00:00<00:00, 3896.16 it/sec, feas=True, obj=-0.721]
INFO - 16:16:02: 29%|██▉ | 294/1000 [00:00<00:00, 3895.76 it/sec, feas=True, obj=7.54]
INFO - 16:16:02: 30%|██▉ | 295/1000 [00:00<00:00, 3895.93 it/sec, feas=True, obj=6.14]
INFO - 16:16:02: 30%|██▉ | 296/1000 [00:00<00:00, 3896.12 it/sec, feas=True, obj=-1.73]
INFO - 16:16:02: 30%|██▉ | 297/1000 [00:00<00:00, 3896.59 it/sec, feas=True, obj=8.22]
INFO - 16:16:02: 30%|██▉ | 298/1000 [00:00<00:00, 3896.07 it/sec, feas=True, obj=6.34]
INFO - 16:16:02: 30%|██▉ | 299/1000 [00:00<00:00, 3896.42 it/sec, feas=True, obj=6.14]
INFO - 16:16:02: 30%|███ | 300/1000 [00:00<00:00, 3896.77 it/sec, feas=True, obj=4.71]
INFO - 16:16:02: 30%|███ | 301/1000 [00:00<00:00, 3897.34 it/sec, feas=True, obj=4]
INFO - 16:16:02: 30%|███ | 302/1000 [00:00<00:00, 3896.73 it/sec, feas=True, obj=6.52]
INFO - 16:16:02: 30%|███ | 303/1000 [00:00<00:00, 3897.23 it/sec, feas=True, obj=0.7]
INFO - 16:16:02: 30%|███ | 304/1000 [00:00<00:00, 3897.28 it/sec, feas=True, obj=5.21]
INFO - 16:16:02: 30%|███ | 305/1000 [00:00<00:00, 3897.80 it/sec, feas=True, obj=2.51]
INFO - 16:16:02: 31%|███ | 306/1000 [00:00<00:00, 3896.88 it/sec, feas=True, obj=-0.162]
INFO - 16:16:02: 31%|███ | 307/1000 [00:00<00:00, 3897.17 it/sec, feas=True, obj=2.63]
INFO - 16:16:02: 31%|███ | 308/1000 [00:00<00:00, 3897.61 it/sec, feas=True, obj=4.01]
INFO - 16:16:02: 31%|███ | 309/1000 [00:00<00:00, 3898.09 it/sec, feas=True, obj=2.99]
INFO - 16:16:02: 31%|███ | 310/1000 [00:00<00:00, 3897.35 it/sec, feas=True, obj=2.3]
INFO - 16:16:02: 31%|███ | 311/1000 [00:00<00:00, 3897.85 it/sec, feas=True, obj=3.71]
INFO - 16:16:02: 31%|███ | 312/1000 [00:00<00:00, 3898.57 it/sec, feas=True, obj=5.63]
INFO - 16:16:02: 31%|███▏ | 313/1000 [00:00<00:00, 3899.29 it/sec, feas=True, obj=6.43]
INFO - 16:16:02: 31%|███▏ | 314/1000 [00:00<00:00, 3898.55 it/sec, feas=True, obj=-1.98]
INFO - 16:16:02: 32%|███▏ | 315/1000 [00:00<00:00, 3898.87 it/sec, feas=True, obj=1.03]
INFO - 16:16:02: 32%|███▏ | 316/1000 [00:00<00:00, 3899.39 it/sec, feas=True, obj=-0.511]
INFO - 16:16:02: 32%|███▏ | 317/1000 [00:00<00:00, 3899.71 it/sec, feas=True, obj=-1.34]
INFO - 16:16:02: 32%|███▏ | 318/1000 [00:00<00:00, 3898.64 it/sec, feas=True, obj=6.72]
INFO - 16:16:02: 32%|███▏ | 319/1000 [00:00<00:00, 3898.77 it/sec, feas=True, obj=3.09]
INFO - 16:16:02: 32%|███▏ | 320/1000 [00:00<00:00, 3898.90 it/sec, feas=True, obj=7.12]
INFO - 16:16:02: 32%|███▏ | 321/1000 [00:00<00:00, 3898.59 it/sec, feas=True, obj=5.91]
INFO - 16:16:02: 32%|███▏ | 322/1000 [00:00<00:00, 3898.54 it/sec, feas=True, obj=0.0303]
INFO - 16:16:02: 32%|███▏ | 323/1000 [00:00<00:00, 3898.68 it/sec, feas=True, obj=1.38]
INFO - 16:16:02: 32%|███▏ | 324/1000 [00:00<00:00, 3898.96 it/sec, feas=True, obj=-5.06]
INFO - 16:16:02: 32%|███▎ | 325/1000 [00:00<00:00, 3898.35 it/sec, feas=True, obj=1.18]
INFO - 16:16:02: 33%|███▎ | 326/1000 [00:00<00:00, 3898.49 it/sec, feas=True, obj=0.213]
INFO - 16:16:02: 33%|███▎ | 327/1000 [00:00<00:00, 3898.51 it/sec, feas=True, obj=5.4]
INFO - 16:16:02: 33%|███▎ | 328/1000 [00:00<00:00, 3898.52 it/sec, feas=True, obj=3.09]
INFO - 16:16:02: 33%|███▎ | 329/1000 [00:00<00:00, 3898.04 it/sec, feas=True, obj=1.28]
INFO - 16:16:02: 33%|███▎ | 330/1000 [00:00<00:00, 3898.29 it/sec, feas=True, obj=7.37]
INFO - 16:16:02: 33%|███▎ | 331/1000 [00:00<00:00, 3898.87 it/sec, feas=True, obj=1.31]
INFO - 16:16:02: 33%|███▎ | 332/1000 [00:00<00:00, 3899.53 it/sec, feas=True, obj=2.05]
INFO - 16:16:02: 33%|███▎ | 333/1000 [00:00<00:00, 3899.05 it/sec, feas=True, obj=1.55]
INFO - 16:16:02: 33%|███▎ | 334/1000 [00:00<00:00, 3899.45 it/sec, feas=True, obj=2.46]
INFO - 16:16:02: 34%|███▎ | 335/1000 [00:00<00:00, 3899.79 it/sec, feas=True, obj=1.51]
INFO - 16:16:02: 34%|███▎ | 336/1000 [00:00<00:00, 3900.40 it/sec, feas=True, obj=5.43]
INFO - 16:16:02: 34%|███▎ | 337/1000 [00:00<00:00, 3899.99 it/sec, feas=True, obj=1.14]
INFO - 16:16:02: 34%|███▍ | 338/1000 [00:00<00:00, 3900.01 it/sec, feas=True, obj=7.29]
INFO - 16:16:02: 34%|███▍ | 339/1000 [00:00<00:00, 3900.37 it/sec, feas=True, obj=-0.283]
INFO - 16:16:02: 34%|███▍ | 340/1000 [00:00<00:00, 3900.93 it/sec, feas=True, obj=0.734]
INFO - 16:16:02: 34%|███▍ | 341/1000 [00:00<00:00, 3900.53 it/sec, feas=True, obj=-3.46]
INFO - 16:16:02: 34%|███▍ | 342/1000 [00:00<00:00, 3900.60 it/sec, feas=True, obj=4.12]
INFO - 16:16:02: 34%|███▍ | 343/1000 [00:00<00:00, 3901.14 it/sec, feas=True, obj=3.79]
INFO - 16:16:02: 34%|███▍ | 344/1000 [00:00<00:00, 3901.55 it/sec, feas=True, obj=-3.15]
INFO - 16:16:02: 34%|███▍ | 345/1000 [00:00<00:00, 3901.22 it/sec, feas=True, obj=7.56]
INFO - 16:16:02: 35%|███▍ | 346/1000 [00:00<00:00, 3901.53 it/sec, feas=True, obj=-0.553]
INFO - 16:16:02: 35%|███▍ | 347/1000 [00:00<00:00, 3902.11 it/sec, feas=True, obj=1.43]
INFO - 16:16:02: 35%|███▍ | 348/1000 [00:00<00:00, 3902.84 it/sec, feas=True, obj=-0.851]
INFO - 16:16:02: 35%|███▍ | 349/1000 [00:00<00:00, 3902.54 it/sec, feas=True, obj=8.57]
INFO - 16:16:02: 35%|███▌ | 350/1000 [00:00<00:00, 3902.93 it/sec, feas=True, obj=0.921]
INFO - 16:16:02: 35%|███▌ | 351/1000 [00:00<00:00, 3903.08 it/sec, feas=True, obj=3.01]
INFO - 16:16:02: 35%|███▌ | 352/1000 [00:00<00:00, 3903.46 it/sec, feas=True, obj=4.98]
INFO - 16:16:02: 35%|███▌ | 353/1000 [00:00<00:00, 3903.07 it/sec, feas=True, obj=6.06]
INFO - 16:16:02: 35%|███▌ | 354/1000 [00:00<00:00, 3903.25 it/sec, feas=True, obj=7.26]
INFO - 16:16:02: 36%|███▌ | 355/1000 [00:00<00:00, 3903.36 it/sec, feas=True, obj=5.71]
INFO - 16:16:02: 36%|███▌ | 356/1000 [00:00<00:00, 3903.64 it/sec, feas=True, obj=-5.07]
INFO - 16:16:02: 36%|███▌ | 357/1000 [00:00<00:00, 3903.14 it/sec, feas=True, obj=6.62]
INFO - 16:16:02: 36%|███▌ | 358/1000 [00:00<00:00, 3903.34 it/sec, feas=True, obj=5.86]
INFO - 16:16:02: 36%|███▌ | 359/1000 [00:00<00:00, 3903.86 it/sec, feas=True, obj=6.1]
INFO - 16:16:02: 36%|███▌ | 360/1000 [00:00<00:00, 3904.29 it/sec, feas=True, obj=-0.545]
INFO - 16:16:02: 36%|███▌ | 361/1000 [00:00<00:00, 3903.66 it/sec, feas=True, obj=3.86]
INFO - 16:16:02: 36%|███▌ | 362/1000 [00:00<00:00, 3904.14 it/sec, feas=True, obj=8.51]
INFO - 16:16:02: 36%|███▋ | 363/1000 [00:00<00:00, 3904.61 it/sec, feas=True, obj=5.33]
INFO - 16:16:02: 36%|███▋ | 364/1000 [00:00<00:00, 3905.13 it/sec, feas=True, obj=7.14]
INFO - 16:16:02: 36%|███▋ | 365/1000 [00:00<00:00, 3904.71 it/sec, feas=True, obj=4.01]
INFO - 16:16:02: 37%|███▋ | 366/1000 [00:00<00:00, 3904.85 it/sec, feas=True, obj=2.9]
INFO - 16:16:02: 37%|███▋ | 367/1000 [00:00<00:00, 3904.92 it/sec, feas=True, obj=6.25]
INFO - 16:16:02: 37%|███▋ | 368/1000 [00:00<00:00, 3905.32 it/sec, feas=True, obj=6.85]
INFO - 16:16:02: 37%|███▋ | 369/1000 [00:00<00:00, 3904.58 it/sec, feas=True, obj=4.32]
INFO - 16:16:02: 37%|███▋ | 370/1000 [00:00<00:00, 3904.78 it/sec, feas=True, obj=4.87]
INFO - 16:16:02: 37%|███▋ | 371/1000 [00:00<00:00, 3905.22 it/sec, feas=True, obj=6.43]
INFO - 16:16:02: 37%|███▋ | 372/1000 [00:00<00:00, 3905.40 it/sec, feas=True, obj=2.86]
INFO - 16:16:02: 37%|███▋ | 373/1000 [00:00<00:00, 3899.40 it/sec, feas=True, obj=0.891]
INFO - 16:16:02: 37%|███▋ | 374/1000 [00:00<00:00, 3899.10 it/sec, feas=True, obj=6.47]
INFO - 16:16:02: 38%|███▊ | 375/1000 [00:00<00:00, 3899.39 it/sec, feas=True, obj=-1.87]
INFO - 16:16:02: 38%|███▊ | 376/1000 [00:00<00:00, 3898.91 it/sec, feas=True, obj=-3.28]
INFO - 16:16:02: 38%|███▊ | 377/1000 [00:00<00:00, 3898.90 it/sec, feas=True, obj=0.0745]
INFO - 16:16:02: 38%|███▊ | 378/1000 [00:00<00:00, 3899.30 it/sec, feas=True, obj=5.9]
INFO - 16:16:02: 38%|███▊ | 379/1000 [00:00<00:00, 3899.76 it/sec, feas=True, obj=4.69]
INFO - 16:16:02: 38%|███▊ | 380/1000 [00:00<00:00, 3899.47 it/sec, feas=True, obj=4.66]
INFO - 16:16:02: 38%|███▊ | 381/1000 [00:00<00:00, 3899.85 it/sec, feas=True, obj=6.07]
INFO - 16:16:02: 38%|███▊ | 382/1000 [00:00<00:00, 3899.75 it/sec, feas=True, obj=0.959]
INFO - 16:16:02: 38%|███▊ | 383/1000 [00:00<00:00, 3900.04 it/sec, feas=True, obj=1.9]
INFO - 16:16:02: 38%|███▊ | 384/1000 [00:00<00:00, 3899.49 it/sec, feas=True, obj=7.91]
INFO - 16:16:02: 38%|███▊ | 385/1000 [00:00<00:00, 3899.75 it/sec, feas=True, obj=-0.448]
INFO - 16:16:02: 39%|███▊ | 386/1000 [00:00<00:00, 3900.01 it/sec, feas=True, obj=5.33]
INFO - 16:16:02: 39%|███▊ | 387/1000 [00:00<00:00, 3900.34 it/sec, feas=True, obj=2.88]
INFO - 16:16:02: 39%|███▉ | 388/1000 [00:00<00:00, 3899.81 it/sec, feas=True, obj=0.55]
INFO - 16:16:02: 39%|███▉ | 389/1000 [00:00<00:00, 3900.16 it/sec, feas=True, obj=0.392]
INFO - 16:16:02: 39%|███▉ | 390/1000 [00:00<00:00, 3900.55 it/sec, feas=True, obj=3.32]
INFO - 16:16:02: 39%|███▉ | 391/1000 [00:00<00:00, 3900.90 it/sec, feas=True, obj=7.88]
INFO - 16:16:02: 39%|███▉ | 392/1000 [00:00<00:00, 3900.10 it/sec, feas=True, obj=1.46]
INFO - 16:16:02: 39%|███▉ | 393/1000 [00:00<00:00, 3900.15 it/sec, feas=True, obj=9.49]
INFO - 16:16:02: 39%|███▉ | 394/1000 [00:00<00:00, 3900.37 it/sec, feas=True, obj=-8.6]
INFO - 16:16:02: 40%|███▉ | 395/1000 [00:00<00:00, 3900.66 it/sec, feas=True, obj=6]
INFO - 16:16:02: 40%|███▉ | 396/1000 [00:00<00:00, 3899.83 it/sec, feas=True, obj=6.89]
INFO - 16:16:02: 40%|███▉ | 397/1000 [00:00<00:00, 3899.75 it/sec, feas=True, obj=5.17]
INFO - 16:16:02: 40%|███▉ | 398/1000 [00:00<00:00, 3899.74 it/sec, feas=True, obj=9.21]
INFO - 16:16:02: 40%|███▉ | 399/1000 [00:00<00:00, 3899.39 it/sec, feas=True, obj=8.46]
INFO - 16:16:02: 40%|████ | 400/1000 [00:00<00:00, 3899.29 it/sec, feas=True, obj=9.92]
INFO - 16:16:02: 40%|████ | 401/1000 [00:00<00:00, 3899.29 it/sec, feas=True, obj=2.5]
INFO - 16:16:02: 40%|████ | 402/1000 [00:00<00:00, 3899.63 it/sec, feas=True, obj=2.82]
INFO - 16:16:02: 40%|████ | 403/1000 [00:00<00:00, 3899.40 it/sec, feas=True, obj=9.71]
INFO - 16:16:02: 40%|████ | 404/1000 [00:00<00:00, 3899.44 it/sec, feas=True, obj=-1.54]
INFO - 16:16:02: 40%|████ | 405/1000 [00:00<00:00, 3899.42 it/sec, feas=True, obj=-1.42]
INFO - 16:16:02: 41%|████ | 406/1000 [00:00<00:00, 3899.52 it/sec, feas=True, obj=7.52]
INFO - 16:16:02: 41%|████ | 407/1000 [00:00<00:00, 3899.01 it/sec, feas=True, obj=3.95]
INFO - 16:16:02: 41%|████ | 408/1000 [00:00<00:00, 3899.10 it/sec, feas=True, obj=5.33]
INFO - 16:16:02: 41%|████ | 409/1000 [00:00<00:00, 3899.41 it/sec, feas=True, obj=0.103]
INFO - 16:16:02: 41%|████ | 410/1000 [00:00<00:00, 3899.86 it/sec, feas=True, obj=4.39]
INFO - 16:16:02: 41%|████ | 411/1000 [00:00<00:00, 3899.60 it/sec, feas=True, obj=1.74]
INFO - 16:16:02: 41%|████ | 412/1000 [00:00<00:00, 3899.91 it/sec, feas=True, obj=0.344]
INFO - 16:16:02: 41%|████▏ | 413/1000 [00:00<00:00, 3899.95 it/sec, feas=True, obj=6.49]
INFO - 16:16:02: 41%|████▏ | 414/1000 [00:00<00:00, 3900.27 it/sec, feas=True, obj=4.85]
INFO - 16:16:02: 42%|████▏ | 415/1000 [00:00<00:00, 3899.93 it/sec, feas=True, obj=7.96]
INFO - 16:16:02: 42%|████▏ | 416/1000 [00:00<00:00, 3900.13 it/sec, feas=True, obj=-3.84]
INFO - 16:16:02: 42%|████▏ | 417/1000 [00:00<00:00, 3897.79 it/sec, feas=True, obj=-2.87]
INFO - 16:16:02: 42%|████▏ | 418/1000 [00:00<00:00, 3897.93 it/sec, feas=True, obj=-1.69]
INFO - 16:16:02: 42%|████▏ | 419/1000 [00:00<00:00, 3897.33 it/sec, feas=True, obj=2.32]
INFO - 16:16:02: 42%|████▏ | 420/1000 [00:00<00:00, 3897.71 it/sec, feas=True, obj=8.32]
INFO - 16:16:02: 42%|████▏ | 421/1000 [00:00<00:00, 3898.08 it/sec, feas=True, obj=1.74]
INFO - 16:16:02: 42%|████▏ | 422/1000 [00:00<00:00, 3898.42 it/sec, feas=True, obj=4.05]
INFO - 16:16:02: 42%|████▏ | 423/1000 [00:00<00:00, 3897.84 it/sec, feas=True, obj=2.71]
INFO - 16:16:02: 42%|████▏ | 424/1000 [00:00<00:00, 3898.22 it/sec, feas=True, obj=6.78]
INFO - 16:16:02: 42%|████▎ | 425/1000 [00:00<00:00, 3898.64 it/sec, feas=True, obj=3.95]
INFO - 16:16:02: 43%|████▎ | 426/1000 [00:00<00:00, 3899.10 it/sec, feas=True, obj=-0.0526]
INFO - 16:16:02: 43%|████▎ | 427/1000 [00:00<00:00, 3898.61 it/sec, feas=True, obj=-0.581]
INFO - 16:16:02: 43%|████▎ | 428/1000 [00:00<00:00, 3898.75 it/sec, feas=True, obj=16.2]
INFO - 16:16:02: 43%|████▎ | 429/1000 [00:00<00:00, 3899.01 it/sec, feas=True, obj=1.58]
INFO - 16:16:02: 43%|████▎ | 430/1000 [00:00<00:00, 3899.34 it/sec, feas=True, obj=5.87]
INFO - 16:16:02: 43%|████▎ | 431/1000 [00:00<00:00, 3898.76 it/sec, feas=True, obj=6.51]
INFO - 16:16:02: 43%|████▎ | 432/1000 [00:00<00:00, 3899.03 it/sec, feas=True, obj=4.28]
INFO - 16:16:02: 43%|████▎ | 433/1000 [00:00<00:00, 3899.42 it/sec, feas=True, obj=-6.3]
INFO - 16:16:02: 43%|████▎ | 434/1000 [00:00<00:00, 3899.83 it/sec, feas=True, obj=7.49]
INFO - 16:16:02: 44%|████▎ | 435/1000 [00:00<00:00, 3898.44 it/sec, feas=True, obj=8.03]
INFO - 16:16:02: 44%|████▎ | 436/1000 [00:00<00:00, 3898.58 it/sec, feas=True, obj=1.4]
INFO - 16:16:02: 44%|████▎ | 437/1000 [00:00<00:00, 3898.91 it/sec, feas=True, obj=1.65]
INFO - 16:16:02: 44%|████▍ | 438/1000 [00:00<00:00, 3898.47 it/sec, feas=True, obj=-0.221]
INFO - 16:16:02: 44%|████▍ | 439/1000 [00:00<00:00, 3898.40 it/sec, feas=True, obj=7.25]
INFO - 16:16:02: 44%|████▍ | 440/1000 [00:00<00:00, 3898.72 it/sec, feas=True, obj=5.14]
INFO - 16:16:02: 44%|████▍ | 441/1000 [00:00<00:00, 3899.12 it/sec, feas=True, obj=-0.896]
INFO - 16:16:02: 44%|████▍ | 442/1000 [00:00<00:00, 3898.70 it/sec, feas=True, obj=-0.969]
INFO - 16:16:02: 44%|████▍ | 443/1000 [00:00<00:00, 3898.75 it/sec, feas=True, obj=7.53]
INFO - 16:16:02: 44%|████▍ | 444/1000 [00:00<00:00, 3898.75 it/sec, feas=True, obj=6.62]
INFO - 16:16:02: 44%|████▍ | 445/1000 [00:00<00:00, 3898.80 it/sec, feas=True, obj=3.23]
INFO - 16:16:02: 45%|████▍ | 446/1000 [00:00<00:00, 3898.22 it/sec, feas=True, obj=-10.1]
INFO - 16:16:02: 45%|████▍ | 447/1000 [00:00<00:00, 3898.06 it/sec, feas=True, obj=7.22]
INFO - 16:16:02: 45%|████▍ | 448/1000 [00:00<00:00, 3898.27 it/sec, feas=True, obj=12.9]
INFO - 16:16:02: 45%|████▍ | 449/1000 [00:00<00:00, 3898.55 it/sec, feas=True, obj=7.61]
INFO - 16:16:02: 45%|████▌ | 450/1000 [00:00<00:00, 3898.06 it/sec, feas=True, obj=3.57]
INFO - 16:16:02: 45%|████▌ | 451/1000 [00:00<00:00, 3897.96 it/sec, feas=True, obj=5.91]
INFO - 16:16:02: 45%|████▌ | 452/1000 [00:00<00:00, 3898.04 it/sec, feas=True, obj=-1.97]
INFO - 16:16:02: 45%|████▌ | 453/1000 [00:00<00:00, 3898.28 it/sec, feas=True, obj=7.83]
INFO - 16:16:02: 45%|████▌ | 454/1000 [00:00<00:00, 3897.84 it/sec, feas=True, obj=2.12]
INFO - 16:16:02: 46%|████▌ | 455/1000 [00:00<00:00, 3898.11 it/sec, feas=True, obj=-0.821]
INFO - 16:16:02: 46%|████▌ | 456/1000 [00:00<00:00, 3898.37 it/sec, feas=True, obj=2.27]
INFO - 16:16:02: 46%|████▌ | 457/1000 [00:00<00:00, 3898.70 it/sec, feas=True, obj=7.13]
INFO - 16:16:02: 46%|████▌ | 458/1000 [00:00<00:00, 3898.19 it/sec, feas=True, obj=3.63]
INFO - 16:16:02: 46%|████▌ | 459/1000 [00:00<00:00, 3898.41 it/sec, feas=True, obj=2.21]
INFO - 16:16:02: 46%|████▌ | 460/1000 [00:00<00:00, 3898.14 it/sec, feas=True, obj=3.08]
INFO - 16:16:02: 46%|████▌ | 461/1000 [00:00<00:00, 3898.46 it/sec, feas=True, obj=3.18]
INFO - 16:16:02: 46%|████▌ | 462/1000 [00:00<00:00, 3897.90 it/sec, feas=True, obj=4.64]
INFO - 16:16:02: 46%|████▋ | 463/1000 [00:00<00:00, 3898.00 it/sec, feas=True, obj=0.243]
INFO - 16:16:02: 46%|████▋ | 464/1000 [00:00<00:00, 3898.15 it/sec, feas=True, obj=2.2]
INFO - 16:16:02: 46%|████▋ | 465/1000 [00:00<00:00, 3898.40 it/sec, feas=True, obj=-0.0681]
INFO - 16:16:02: 47%|████▋ | 466/1000 [00:00<00:00, 3897.74 it/sec, feas=True, obj=0.986]
INFO - 16:16:02: 47%|████▋ | 467/1000 [00:00<00:00, 3897.80 it/sec, feas=True, obj=7.39]
INFO - 16:16:02: 47%|████▋ | 468/1000 [00:00<00:00, 3898.10 it/sec, feas=True, obj=6.85]
INFO - 16:16:02: 47%|████▋ | 469/1000 [00:00<00:00, 3897.77 it/sec, feas=True, obj=8.98]
INFO - 16:16:02: 47%|████▋ | 470/1000 [00:00<00:00, 3897.77 it/sec, feas=True, obj=4.98]
INFO - 16:16:02: 47%|████▋ | 471/1000 [00:00<00:00, 3898.10 it/sec, feas=True, obj=0.108]
INFO - 16:16:02: 47%|████▋ | 472/1000 [00:00<00:00, 3898.38 it/sec, feas=True, obj=4.9]
INFO - 16:16:02: 47%|████▋ | 473/1000 [00:00<00:00, 3897.66 it/sec, feas=True, obj=1.98]
INFO - 16:16:02: 47%|████▋ | 474/1000 [00:00<00:00, 3897.42 it/sec, feas=True, obj=-3.79]
INFO - 16:16:02: 48%|████▊ | 475/1000 [00:00<00:00, 3897.33 it/sec, feas=True, obj=13.5]
INFO - 16:16:02: 48%|████▊ | 476/1000 [00:00<00:00, 3897.50 it/sec, feas=True, obj=0.587]
INFO - 16:16:02: 48%|████▊ | 477/1000 [00:00<00:00, 3896.88 it/sec, feas=True, obj=5.28]
INFO - 16:16:02: 48%|████▊ | 478/1000 [00:00<00:00, 3897.07 it/sec, feas=True, obj=6.02]
INFO - 16:16:02: 48%|████▊ | 479/1000 [00:00<00:00, 3897.32 it/sec, feas=True, obj=2.5]
INFO - 16:16:02: 48%|████▊ | 480/1000 [00:00<00:00, 3897.57 it/sec, feas=True, obj=-0.343]
INFO - 16:16:02: 48%|████▊ | 481/1000 [00:00<00:00, 3897.07 it/sec, feas=True, obj=4.72]
INFO - 16:16:02: 48%|████▊ | 482/1000 [00:00<00:00, 3897.15 it/sec, feas=True, obj=6.71]
INFO - 16:16:02: 48%|████▊ | 483/1000 [00:00<00:00, 3897.41 it/sec, feas=True, obj=-2.87]
INFO - 16:16:02: 48%|████▊ | 484/1000 [00:00<00:00, 3897.72 it/sec, feas=True, obj=1]
INFO - 16:16:02: 48%|████▊ | 485/1000 [00:00<00:00, 3897.45 it/sec, feas=True, obj=6.11]
INFO - 16:16:02: 49%|████▊ | 486/1000 [00:00<00:00, 3897.53 it/sec, feas=True, obj=0.946]
INFO - 16:16:02: 49%|████▊ | 487/1000 [00:00<00:00, 3893.74 it/sec, feas=True, obj=2.29]
INFO - 16:16:02: 49%|████▉ | 488/1000 [00:00<00:00, 3893.09 it/sec, feas=True, obj=9.42]
INFO - 16:16:02: 49%|████▉ | 489/1000 [00:00<00:00, 3892.92 it/sec, feas=True, obj=5.01]
INFO - 16:16:02: 49%|████▉ | 490/1000 [00:00<00:00, 3892.77 it/sec, feas=True, obj=1.02]
INFO - 16:16:02: 49%|████▉ | 491/1000 [00:00<00:00, 3893.00 it/sec, feas=True, obj=3.59]
INFO - 16:16:02: 49%|████▉ | 492/1000 [00:00<00:00, 3892.59 it/sec, feas=True, obj=7.01]
INFO - 16:16:02: 49%|████▉ | 493/1000 [00:00<00:00, 3892.82 it/sec, feas=True, obj=8.7]
INFO - 16:16:02: 49%|████▉ | 494/1000 [00:00<00:00, 3893.17 it/sec, feas=True, obj=5.6]
INFO - 16:16:02: 50%|████▉ | 495/1000 [00:00<00:00, 3893.50 it/sec, feas=True, obj=-0.897]
INFO - 16:16:02: 50%|████▉ | 496/1000 [00:00<00:00, 3893.14 it/sec, feas=True, obj=7.82]
INFO - 16:16:02: 50%|████▉ | 497/1000 [00:00<00:00, 3893.42 it/sec, feas=True, obj=6.63]
INFO - 16:16:02: 50%|████▉ | 498/1000 [00:00<00:00, 3893.79 it/sec, feas=True, obj=3.33]
INFO - 16:16:02: 50%|████▉ | 499/1000 [00:00<00:00, 3894.12 it/sec, feas=True, obj=4.36]
INFO - 16:16:02: 50%|█████ | 500/1000 [00:00<00:00, 3893.67 it/sec, feas=True, obj=4.21]
INFO - 16:16:02: 50%|█████ | 501/1000 [00:00<00:00, 3893.71 it/sec, feas=True, obj=3.93]
INFO - 16:16:02: 50%|█████ | 502/1000 [00:00<00:00, 3893.91 it/sec, feas=True, obj=10.4]
INFO - 16:16:02: 50%|█████ | 503/1000 [00:00<00:00, 3894.14 it/sec, feas=True, obj=-1.39]
INFO - 16:16:02: 50%|█████ | 504/1000 [00:00<00:00, 3893.81 it/sec, feas=True, obj=-0.386]
INFO - 16:16:02: 50%|█████ | 505/1000 [00:00<00:00, 3894.02 it/sec, feas=True, obj=4.95]
INFO - 16:16:02: 51%|█████ | 506/1000 [00:00<00:00, 3894.04 it/sec, feas=True, obj=4.8]
INFO - 16:16:02: 51%|█████ | 507/1000 [00:00<00:00, 3894.37 it/sec, feas=True, obj=7.94]
INFO - 16:16:02: 51%|█████ | 508/1000 [00:00<00:00, 3893.91 it/sec, feas=True, obj=1.6]
INFO - 16:16:02: 51%|█████ | 509/1000 [00:00<00:00, 3893.98 it/sec, feas=True, obj=8.91]
INFO - 16:16:02: 51%|█████ | 510/1000 [00:00<00:00, 3894.17 it/sec, feas=True, obj=9.47]
INFO - 16:16:02: 51%|█████ | 511/1000 [00:00<00:00, 3894.21 it/sec, feas=True, obj=-0.535]
INFO - 16:16:02: 51%|█████ | 512/1000 [00:00<00:00, 3893.61 it/sec, feas=True, obj=2.76]
INFO - 16:16:02: 51%|█████▏ | 513/1000 [00:00<00:00, 3893.66 it/sec, feas=True, obj=0.439]
INFO - 16:16:02: 51%|█████▏ | 514/1000 [00:00<00:00, 3893.86 it/sec, feas=True, obj=3.69]
INFO - 16:16:02: 52%|█████▏ | 515/1000 [00:00<00:00, 3894.10 it/sec, feas=True, obj=-1.1]
INFO - 16:16:02: 52%|█████▏ | 516/1000 [00:00<00:00, 3893.68 it/sec, feas=True, obj=2.48]
INFO - 16:16:02: 52%|█████▏ | 517/1000 [00:00<00:00, 3893.86 it/sec, feas=True, obj=2.8]
INFO - 16:16:02: 52%|█████▏ | 518/1000 [00:00<00:00, 3894.13 it/sec, feas=True, obj=13]
INFO - 16:16:02: 52%|█████▏ | 519/1000 [00:00<00:00, 3894.42 it/sec, feas=True, obj=6.01]
INFO - 16:16:02: 52%|█████▏ | 520/1000 [00:00<00:00, 3893.93 it/sec, feas=True, obj=2.49]
INFO - 16:16:02: 52%|█████▏ | 521/1000 [00:00<00:00, 3893.81 it/sec, feas=True, obj=5.92]
INFO - 16:16:02: 52%|█████▏ | 522/1000 [00:00<00:00, 3894.09 it/sec, feas=True, obj=3.4]
INFO - 16:16:02: 52%|█████▏ | 523/1000 [00:00<00:00, 3894.40 it/sec, feas=True, obj=-1.78]
INFO - 16:16:02: 52%|█████▏ | 524/1000 [00:00<00:00, 3893.86 it/sec, feas=True, obj=2.44]
INFO - 16:16:02: 52%|█████▎ | 525/1000 [00:00<00:00, 3894.04 it/sec, feas=True, obj=16]
INFO - 16:16:02: 53%|█████▎ | 526/1000 [00:00<00:00, 3894.28 it/sec, feas=True, obj=6.22]
INFO - 16:16:02: 53%|█████▎ | 527/1000 [00:00<00:00, 3894.06 it/sec, feas=True, obj=7.2]
INFO - 16:16:02: 53%|█████▎ | 528/1000 [00:00<00:00, 3893.93 it/sec, feas=True, obj=4.57]
INFO - 16:16:02: 53%|█████▎ | 529/1000 [00:00<00:00, 3894.22 it/sec, feas=True, obj=6.77]
INFO - 16:16:02: 53%|█████▎ | 530/1000 [00:00<00:00, 3894.48 it/sec, feas=True, obj=13]
INFO - 16:16:02: 53%|█████▎ | 531/1000 [00:00<00:00, 3894.22 it/sec, feas=True, obj=5]
INFO - 16:16:02: 53%|█████▎ | 532/1000 [00:00<00:00, 3894.21 it/sec, feas=True, obj=-0.711]
INFO - 16:16:02: 53%|█████▎ | 533/1000 [00:00<00:00, 3894.44 it/sec, feas=True, obj=-0.543]
INFO - 16:16:02: 53%|█████▎ | 534/1000 [00:00<00:00, 3894.60 it/sec, feas=True, obj=0.469]
INFO - 16:16:02: 54%|█████▎ | 535/1000 [00:00<00:00, 3894.22 it/sec, feas=True, obj=4.16]
INFO - 16:16:02: 54%|█████▎ | 536/1000 [00:00<00:00, 3894.32 it/sec, feas=True, obj=4.73]
INFO - 16:16:02: 54%|█████▎ | 537/1000 [00:00<00:00, 3894.28 it/sec, feas=True, obj=-0.197]
INFO - 16:16:02: 54%|█████▍ | 538/1000 [00:00<00:00, 3894.41 it/sec, feas=True, obj=-2.45]
INFO - 16:16:02: 54%|█████▍ | 539/1000 [00:00<00:00, 3893.91 it/sec, feas=True, obj=2.9]
INFO - 16:16:02: 54%|█████▍ | 540/1000 [00:00<00:00, 3893.86 it/sec, feas=True, obj=4.59]
INFO - 16:16:02: 54%|█████▍ | 541/1000 [00:00<00:00, 3893.96 it/sec, feas=True, obj=4.09]
INFO - 16:16:02: 54%|█████▍ | 542/1000 [00:00<00:00, 3894.10 it/sec, feas=True, obj=0.0786]
INFO - 16:16:02: 54%|█████▍ | 543/1000 [00:00<00:00, 3893.84 it/sec, feas=True, obj=6.9]
INFO - 16:16:02: 54%|█████▍ | 544/1000 [00:00<00:00, 3894.11 it/sec, feas=True, obj=3.77]
INFO - 16:16:02: 55%|█████▍ | 545/1000 [00:00<00:00, 3894.25 it/sec, feas=True, obj=2.68]
INFO - 16:16:02: 55%|█████▍ | 546/1000 [00:00<00:00, 3894.23 it/sec, feas=True, obj=5.03]
INFO - 16:16:02: 55%|█████▍ | 547/1000 [00:00<00:00, 3893.93 it/sec, feas=True, obj=7.02]
INFO - 16:16:02: 55%|█████▍ | 548/1000 [00:00<00:00, 3893.92 it/sec, feas=True, obj=7]
INFO - 16:16:02: 55%|█████▍ | 549/1000 [00:00<00:00, 3894.06 it/sec, feas=True, obj=1.03]
INFO - 16:16:02: 55%|█████▌ | 550/1000 [00:00<00:00, 3894.33 it/sec, feas=True, obj=4.74]
INFO - 16:16:02: 55%|█████▌ | 551/1000 [00:00<00:00, 3893.81 it/sec, feas=True, obj=-0.817]
INFO - 16:16:02: 55%|█████▌ | 552/1000 [00:00<00:00, 3893.98 it/sec, feas=True, obj=2.59]
INFO - 16:16:02: 55%|█████▌ | 553/1000 [00:00<00:00, 3893.85 it/sec, feas=True, obj=3.33]
INFO - 16:16:02: 55%|█████▌ | 554/1000 [00:00<00:00, 3893.98 it/sec, feas=True, obj=2.13]
INFO - 16:16:02: 56%|█████▌ | 555/1000 [00:00<00:00, 3893.48 it/sec, feas=True, obj=-0.076]
INFO - 16:16:02: 56%|█████▌ | 556/1000 [00:00<00:00, 3893.46 it/sec, feas=True, obj=-0.023]
INFO - 16:16:02: 56%|█████▌ | 557/1000 [00:00<00:00, 3893.69 it/sec, feas=True, obj=7.03]
INFO - 16:16:02: 56%|█████▌ | 558/1000 [00:00<00:00, 3893.01 it/sec, feas=True, obj=3.4]
INFO - 16:16:02: 56%|█████▌ | 559/1000 [00:00<00:00, 3892.81 it/sec, feas=True, obj=-1.23]
INFO - 16:16:02: 56%|█████▌ | 560/1000 [00:00<00:00, 3892.88 it/sec, feas=True, obj=7.3]
INFO - 16:16:02: 56%|█████▌ | 561/1000 [00:00<00:00, 3893.02 it/sec, feas=True, obj=4.59]
INFO - 16:16:02: 56%|█████▌ | 562/1000 [00:00<00:00, 3892.76 it/sec, feas=True, obj=-0.53]
INFO - 16:16:02: 56%|█████▋ | 563/1000 [00:00<00:00, 3892.80 it/sec, feas=True, obj=7.24]
INFO - 16:16:02: 56%|█████▋ | 564/1000 [00:00<00:00, 3892.98 it/sec, feas=True, obj=-0.753]
INFO - 16:16:02: 56%|█████▋ | 565/1000 [00:00<00:00, 3893.24 it/sec, feas=True, obj=7.78]
INFO - 16:16:02: 57%|█████▋ | 566/1000 [00:00<00:00, 3892.86 it/sec, feas=True, obj=6.64]
INFO - 16:16:02: 57%|█████▋ | 567/1000 [00:00<00:00, 3893.03 it/sec, feas=True, obj=0.671]
INFO - 16:16:02: 57%|█████▋ | 568/1000 [00:00<00:00, 3893.02 it/sec, feas=True, obj=-2]
INFO - 16:16:02: 57%|█████▋ | 569/1000 [00:00<00:00, 3893.24 it/sec, feas=True, obj=-1.92]
INFO - 16:16:02: 57%|█████▋ | 570/1000 [00:00<00:00, 3892.87 it/sec, feas=True, obj=6.03]
INFO - 16:16:02: 57%|█████▋ | 571/1000 [00:00<00:00, 3893.00 it/sec, feas=True, obj=9.42]
INFO - 16:16:02: 57%|█████▋ | 572/1000 [00:00<00:00, 3893.19 it/sec, feas=True, obj=1.01]
INFO - 16:16:02: 57%|█████▋ | 573/1000 [00:00<00:00, 3893.42 it/sec, feas=True, obj=1.43]
INFO - 16:16:02: 57%|█████▋ | 574/1000 [00:00<00:00, 3892.95 it/sec, feas=True, obj=0.0646]
INFO - 16:16:02: 57%|█████▊ | 575/1000 [00:00<00:00, 3893.02 it/sec, feas=True, obj=5.16]
INFO - 16:16:02: 58%|█████▊ | 576/1000 [00:00<00:00, 3893.36 it/sec, feas=True, obj=1.61]
INFO - 16:16:02: 58%|█████▊ | 577/1000 [00:00<00:00, 3893.62 it/sec, feas=True, obj=0.944]
INFO - 16:16:02: 58%|█████▊ | 578/1000 [00:00<00:00, 3893.25 it/sec, feas=True, obj=0.535]
INFO - 16:16:02: 58%|█████▊ | 579/1000 [00:00<00:00, 3893.36 it/sec, feas=True, obj=1.86]
INFO - 16:16:02: 58%|█████▊ | 580/1000 [00:00<00:00, 3893.53 it/sec, feas=True, obj=2.93]
INFO - 16:16:02: 58%|█████▊ | 581/1000 [00:00<00:00, 3893.65 it/sec, feas=True, obj=2.4]
INFO - 16:16:02: 58%|█████▊ | 582/1000 [00:00<00:00, 3893.41 it/sec, feas=True, obj=6.58]
INFO - 16:16:02: 58%|█████▊ | 583/1000 [00:00<00:00, 3893.55 it/sec, feas=True, obj=-0.0337]
INFO - 16:16:02: 58%|█████▊ | 584/1000 [00:00<00:00, 3893.15 it/sec, feas=True, obj=6.62]
INFO - 16:16:02: 58%|█████▊ | 585/1000 [00:00<00:00, 3893.27 it/sec, feas=True, obj=5.61]
INFO - 16:16:02: 59%|█████▊ | 586/1000 [00:00<00:00, 3892.80 it/sec, feas=True, obj=5.55]
INFO - 16:16:02: 59%|█████▊ | 587/1000 [00:00<00:00, 3892.99 it/sec, feas=True, obj=5.28]
INFO - 16:16:02: 59%|█████▉ | 588/1000 [00:00<00:00, 3893.14 it/sec, feas=True, obj=3.22]
INFO - 16:16:02: 59%|█████▉ | 589/1000 [00:00<00:00, 3893.36 it/sec, feas=True, obj=3.1]
INFO - 16:16:02: 59%|█████▉ | 590/1000 [00:00<00:00, 3892.94 it/sec, feas=True, obj=5.83]
INFO - 16:16:02: 59%|█████▉ | 591/1000 [00:00<00:00, 3893.17 it/sec, feas=True, obj=4.03]
INFO - 16:16:02: 59%|█████▉ | 592/1000 [00:00<00:00, 3893.35 it/sec, feas=True, obj=-3.08]
INFO - 16:16:02: 59%|█████▉ | 593/1000 [00:00<00:00, 3893.62 it/sec, feas=True, obj=3.63]
INFO - 16:16:02: 59%|█████▉ | 594/1000 [00:00<00:00, 3893.26 it/sec, feas=True, obj=0.374]
INFO - 16:16:02: 60%|█████▉ | 595/1000 [00:00<00:00, 3893.44 it/sec, feas=True, obj=7.07]
INFO - 16:16:02: 60%|█████▉ | 596/1000 [00:00<00:00, 3893.60 it/sec, feas=True, obj=0.707]
INFO - 16:16:02: 60%|█████▉ | 597/1000 [00:00<00:00, 3893.49 it/sec, feas=True, obj=5.65]
INFO - 16:16:02: 60%|█████▉ | 598/1000 [00:00<00:00, 3893.55 it/sec, feas=True, obj=5.83]
INFO - 16:16:02: 60%|█████▉ | 599/1000 [00:00<00:00, 3893.52 it/sec, feas=True, obj=4.2]
INFO - 16:16:02: 60%|██████ | 600/1000 [00:00<00:00, 3893.61 it/sec, feas=True, obj=-0.0744]
INFO - 16:16:02: 60%|██████ | 601/1000 [00:00<00:00, 3890.19 it/sec, feas=True, obj=0.391]
INFO - 16:16:02: 60%|██████ | 602/1000 [00:00<00:00, 3890.00 it/sec, feas=True, obj=4.96]
INFO - 16:16:02: 60%|██████ | 603/1000 [00:00<00:00, 3889.98 it/sec, feas=True, obj=2.18]
INFO - 16:16:02: 60%|██████ | 604/1000 [00:00<00:00, 3890.21 it/sec, feas=True, obj=1.55]
INFO - 16:16:02: 60%|██████ | 605/1000 [00:00<00:00, 3889.63 it/sec, feas=True, obj=6.26]
INFO - 16:16:02: 61%|██████ | 606/1000 [00:00<00:00, 3889.68 it/sec, feas=True, obj=5.3]
INFO - 16:16:02: 61%|██████ | 607/1000 [00:00<00:00, 3889.86 it/sec, feas=True, obj=7.18]
INFO - 16:16:02: 61%|██████ | 608/1000 [00:00<00:00, 3889.98 it/sec, feas=True, obj=1.45]
INFO - 16:16:02: 61%|██████ | 609/1000 [00:00<00:00, 3889.34 it/sec, feas=True, obj=8.78]
INFO - 16:16:02: 61%|██████ | 610/1000 [00:00<00:00, 3889.28 it/sec, feas=True, obj=0.233]
INFO - 16:16:02: 61%|██████ | 611/1000 [00:00<00:00, 3889.46 it/sec, feas=True, obj=-1.38]
INFO - 16:16:02: 61%|██████ | 612/1000 [00:00<00:00, 3889.20 it/sec, feas=True, obj=6.09]
INFO - 16:16:02: 61%|██████▏ | 613/1000 [00:00<00:00, 3889.10 it/sec, feas=True, obj=5.58]
INFO - 16:16:02: 61%|██████▏ | 614/1000 [00:00<00:00, 3888.90 it/sec, feas=True, obj=11]
INFO - 16:16:02: 62%|██████▏ | 615/1000 [00:00<00:00, 3888.95 it/sec, feas=True, obj=5.03]
INFO - 16:16:02: 62%|██████▏ | 616/1000 [00:00<00:00, 3888.44 it/sec, feas=True, obj=6.39]
INFO - 16:16:02: 62%|██████▏ | 617/1000 [00:00<00:00, 3888.51 it/sec, feas=True, obj=1.92]
INFO - 16:16:02: 62%|██████▏ | 618/1000 [00:00<00:00, 3888.64 it/sec, feas=True, obj=1.05]
INFO - 16:16:02: 62%|██████▏ | 619/1000 [00:00<00:00, 3888.82 it/sec, feas=True, obj=0.0814]
INFO - 16:16:02: 62%|██████▏ | 620/1000 [00:00<00:00, 3888.46 it/sec, feas=True, obj=5.88]
INFO - 16:16:02: 62%|██████▏ | 621/1000 [00:00<00:00, 3888.35 it/sec, feas=True, obj=14.7]
INFO - 16:16:02: 62%|██████▏ | 622/1000 [00:00<00:00, 3888.36 it/sec, feas=True, obj=4.25]
INFO - 16:16:02: 62%|██████▏ | 623/1000 [00:00<00:00, 3888.51 it/sec, feas=True, obj=-1.9]
INFO - 16:16:02: 62%|██████▏ | 624/1000 [00:00<00:00, 3888.04 it/sec, feas=True, obj=-0.304]
INFO - 16:16:02: 62%|██████▎ | 625/1000 [00:00<00:00, 3888.19 it/sec, feas=True, obj=-0.315]
INFO - 16:16:02: 63%|██████▎ | 626/1000 [00:00<00:00, 3888.38 it/sec, feas=True, obj=-0.772]
INFO - 16:16:02: 63%|██████▎ | 627/1000 [00:00<00:00, 3888.56 it/sec, feas=True, obj=4.47]
INFO - 16:16:02: 63%|██████▎ | 628/1000 [00:00<00:00, 3888.16 it/sec, feas=True, obj=3.87]
INFO - 16:16:02: 63%|██████▎ | 629/1000 [00:00<00:00, 3888.30 it/sec, feas=True, obj=1.69]
INFO - 16:16:02: 63%|██████▎ | 630/1000 [00:00<00:00, 3888.18 it/sec, feas=True, obj=14.2]
INFO - 16:16:02: 63%|██████▎ | 631/1000 [00:00<00:00, 3888.35 it/sec, feas=True, obj=0.467]
INFO - 16:16:02: 63%|██████▎ | 632/1000 [00:00<00:00, 3887.94 it/sec, feas=True, obj=0.13]
INFO - 16:16:02: 63%|██████▎ | 633/1000 [00:00<00:00, 3888.10 it/sec, feas=True, obj=-0.788]
INFO - 16:16:02: 63%|██████▎ | 634/1000 [00:00<00:00, 3888.21 it/sec, feas=True, obj=3.3]
INFO - 16:16:02: 64%|██████▎ | 635/1000 [00:00<00:00, 3888.42 it/sec, feas=True, obj=7.29]
INFO - 16:16:02: 64%|██████▎ | 636/1000 [00:00<00:00, 3887.91 it/sec, feas=True, obj=1.41]
INFO - 16:16:02: 64%|██████▎ | 637/1000 [00:00<00:00, 3888.05 it/sec, feas=True, obj=6.16]
INFO - 16:16:02: 64%|██████▍ | 638/1000 [00:00<00:00, 3888.21 it/sec, feas=True, obj=6.98]
INFO - 16:16:02: 64%|██████▍ | 639/1000 [00:00<00:00, 3887.95 it/sec, feas=True, obj=7.82]
INFO - 16:16:02: 64%|██████▍ | 640/1000 [00:00<00:00, 3887.86 it/sec, feas=True, obj=4.49]
INFO - 16:16:02: 64%|██████▍ | 641/1000 [00:00<00:00, 3888.10 it/sec, feas=True, obj=6.84]
INFO - 16:16:02: 64%|██████▍ | 642/1000 [00:00<00:00, 3888.35 it/sec, feas=True, obj=3.83]
INFO - 16:16:02: 64%|██████▍ | 643/1000 [00:00<00:00, 3888.16 it/sec, feas=True, obj=2.52]
INFO - 16:16:02: 64%|██████▍ | 644/1000 [00:00<00:00, 3888.23 it/sec, feas=True, obj=1]
INFO - 16:16:02: 64%|██████▍ | 645/1000 [00:00<00:00, 3888.06 it/sec, feas=True, obj=3.54]
INFO - 16:16:02: 65%|██████▍ | 646/1000 [00:00<00:00, 3888.25 it/sec, feas=True, obj=5.22]
INFO - 16:16:02: 65%|██████▍ | 647/1000 [00:00<00:00, 3887.98 it/sec, feas=True, obj=7.98]
INFO - 16:16:02: 65%|██████▍ | 648/1000 [00:00<00:00, 3888.03 it/sec, feas=True, obj=3.23]
INFO - 16:16:02: 65%|██████▍ | 649/1000 [00:00<00:00, 3888.09 it/sec, feas=True, obj=2.61]
INFO - 16:16:02: 65%|██████▌ | 650/1000 [00:00<00:00, 3888.31 it/sec, feas=True, obj=4.85]
INFO - 16:16:02: 65%|██████▌ | 651/1000 [00:00<00:00, 3888.02 it/sec, feas=True, obj=1.4]
INFO - 16:16:02: 65%|██████▌ | 652/1000 [00:00<00:00, 3888.04 it/sec, feas=True, obj=-0.857]
INFO - 16:16:02: 65%|██████▌ | 653/1000 [00:00<00:00, 3888.11 it/sec, feas=True, obj=4.01]
INFO - 16:16:02: 65%|██████▌ | 654/1000 [00:00<00:00, 3888.28 it/sec, feas=True, obj=6.3]
INFO - 16:16:02: 66%|██████▌ | 655/1000 [00:00<00:00, 3887.87 it/sec, feas=True, obj=11.4]
INFO - 16:16:02: 66%|██████▌ | 656/1000 [00:00<00:00, 3887.93 it/sec, feas=True, obj=5.47]
INFO - 16:16:02: 66%|██████▌ | 657/1000 [00:00<00:00, 3888.00 it/sec, feas=True, obj=2.72]
INFO - 16:16:02: 66%|██████▌ | 658/1000 [00:00<00:00, 3888.17 it/sec, feas=True, obj=3.85]
INFO - 16:16:02: 66%|██████▌ | 659/1000 [00:00<00:00, 3887.77 it/sec, feas=True, obj=-0.392]
INFO - 16:16:02: 66%|██████▌ | 660/1000 [00:00<00:00, 3887.84 it/sec, feas=True, obj=0.0168]
INFO - 16:16:02: 66%|██████▌ | 661/1000 [00:00<00:00, 3887.74 it/sec, feas=True, obj=2.53]
INFO - 16:16:02: 66%|██████▌ | 662/1000 [00:00<00:00, 3887.87 it/sec, feas=True, obj=1.76]
INFO - 16:16:02: 66%|██████▋ | 663/1000 [00:00<00:00, 3887.30 it/sec, feas=True, obj=4.26]
INFO - 16:16:02: 66%|██████▋ | 664/1000 [00:00<00:00, 3887.40 it/sec, feas=True, obj=5.45]
INFO - 16:16:02: 66%|██████▋ | 665/1000 [00:00<00:00, 3887.61 it/sec, feas=True, obj=6.94]
INFO - 16:16:02: 67%|██████▋ | 666/1000 [00:00<00:00, 3887.17 it/sec, feas=True, obj=5.93]
INFO - 16:16:02: 67%|██████▋ | 667/1000 [00:00<00:00, 3887.04 it/sec, feas=True, obj=5.79]
INFO - 16:16:02: 67%|██████▋ | 668/1000 [00:00<00:00, 3887.15 it/sec, feas=True, obj=2.62]
INFO - 16:16:02: 67%|██████▋ | 669/1000 [00:00<00:00, 3887.35 it/sec, feas=True, obj=6.4]
INFO - 16:16:02: 67%|██████▋ | 670/1000 [00:00<00:00, 3887.13 it/sec, feas=True, obj=-0.703]
INFO - 16:16:02: 67%|██████▋ | 671/1000 [00:00<00:00, 3887.12 it/sec, feas=True, obj=8.61]
INFO - 16:16:02: 67%|██████▋ | 672/1000 [00:00<00:00, 3887.29 it/sec, feas=True, obj=0.91]
INFO - 16:16:02: 67%|██████▋ | 673/1000 [00:00<00:00, 3887.54 it/sec, feas=True, obj=1.05]
INFO - 16:16:02: 67%|██████▋ | 674/1000 [00:00<00:00, 3887.27 it/sec, feas=True, obj=10.1]
INFO - 16:16:02: 68%|██████▊ | 675/1000 [00:00<00:00, 3887.38 it/sec, feas=True, obj=-0.575]
INFO - 16:16:02: 68%|██████▊ | 676/1000 [00:00<00:00, 3887.41 it/sec, feas=True, obj=-2.06]
INFO - 16:16:02: 68%|██████▊ | 677/1000 [00:00<00:00, 3887.57 it/sec, feas=True, obj=7.34]
INFO - 16:16:02: 68%|██████▊ | 678/1000 [00:00<00:00, 3887.33 it/sec, feas=True, obj=2.78]
INFO - 16:16:02: 68%|██████▊ | 679/1000 [00:00<00:00, 3887.41 it/sec, feas=True, obj=1.15]
INFO - 16:16:02: 68%|██████▊ | 680/1000 [00:00<00:00, 3887.61 it/sec, feas=True, obj=-0.227]
INFO - 16:16:02: 68%|██████▊ | 681/1000 [00:00<00:00, 3887.78 it/sec, feas=True, obj=4.3]
INFO - 16:16:02: 68%|██████▊ | 682/1000 [00:00<00:00, 3887.61 it/sec, feas=True, obj=6.14]
INFO - 16:16:02: 68%|██████▊ | 683/1000 [00:00<00:00, 3887.47 it/sec, feas=True, obj=4.76]
INFO - 16:16:02: 68%|██████▊ | 684/1000 [00:00<00:00, 3887.61 it/sec, feas=True, obj=-4.69]
INFO - 16:16:02: 68%|██████▊ | 685/1000 [00:00<00:00, 3887.76 it/sec, feas=True, obj=-0.877]
INFO - 16:16:02: 69%|██████▊ | 686/1000 [00:00<00:00, 3887.42 it/sec, feas=True, obj=3.02]
INFO - 16:16:02: 69%|██████▊ | 687/1000 [00:00<00:00, 3887.52 it/sec, feas=True, obj=6.98]
INFO - 16:16:02: 69%|██████▉ | 688/1000 [00:00<00:00, 3887.77 it/sec, feas=True, obj=4.88]
INFO - 16:16:02: 69%|██████▉ | 689/1000 [00:00<00:00, 3887.98 it/sec, feas=True, obj=4.99]
INFO - 16:16:02: 69%|██████▉ | 690/1000 [00:00<00:00, 3887.69 it/sec, feas=True, obj=9.72]
INFO - 16:16:02: 69%|██████▉ | 691/1000 [00:00<00:00, 3887.75 it/sec, feas=True, obj=1.5]
INFO - 16:16:02: 69%|██████▉ | 692/1000 [00:00<00:00, 3887.70 it/sec, feas=True, obj=5.57]
INFO - 16:16:02: 69%|██████▉ | 693/1000 [00:00<00:00, 3887.84 it/sec, feas=True, obj=6.06]
INFO - 16:16:02: 69%|██████▉ | 694/1000 [00:00<00:00, 3887.36 it/sec, feas=True, obj=1.09]
INFO - 16:16:02: 70%|██████▉ | 695/1000 [00:00<00:00, 3887.46 it/sec, feas=True, obj=-1.97]
INFO - 16:16:02: 70%|██████▉ | 696/1000 [00:00<00:00, 3887.57 it/sec, feas=True, obj=1.88]
INFO - 16:16:02: 70%|██████▉ | 697/1000 [00:00<00:00, 3887.75 it/sec, feas=True, obj=9.32]
INFO - 16:16:02: 70%|██████▉ | 698/1000 [00:00<00:00, 3885.73 it/sec, feas=True, obj=-7.7]
INFO - 16:16:02: 70%|██████▉ | 699/1000 [00:00<00:00, 3885.36 it/sec, feas=True, obj=1.83]
INFO - 16:16:02: 70%|███████ | 700/1000 [00:00<00:00, 3885.40 it/sec, feas=True, obj=0.735]
INFO - 16:16:02: 70%|███████ | 701/1000 [00:00<00:00, 3884.96 it/sec, feas=True, obj=-1.11]
INFO - 16:16:02: 70%|███████ | 702/1000 [00:00<00:00, 3884.82 it/sec, feas=True, obj=1.47]
INFO - 16:16:02: 70%|███████ | 703/1000 [00:00<00:00, 3885.00 it/sec, feas=True, obj=0.283]
INFO - 16:16:02: 70%|███████ | 704/1000 [00:00<00:00, 3885.13 it/sec, feas=True, obj=15.2]
INFO - 16:16:02: 70%|███████ | 705/1000 [00:00<00:00, 3884.81 it/sec, feas=True, obj=3.43]
INFO - 16:16:02: 71%|███████ | 706/1000 [00:00<00:00, 3884.91 it/sec, feas=True, obj=3.17]
INFO - 16:16:02: 71%|███████ | 707/1000 [00:00<00:00, 3884.88 it/sec, feas=True, obj=5.95]
INFO - 16:16:02: 71%|███████ | 708/1000 [00:00<00:00, 3885.03 it/sec, feas=True, obj=-6.33]
INFO - 16:16:02: 71%|███████ | 709/1000 [00:00<00:00, 3884.69 it/sec, feas=True, obj=13.3]
INFO - 16:16:02: 71%|███████ | 710/1000 [00:00<00:00, 3884.81 it/sec, feas=True, obj=1.32]
INFO - 16:16:02: 71%|███████ | 711/1000 [00:00<00:00, 3884.96 it/sec, feas=True, obj=-4.3]
INFO - 16:16:02: 71%|███████ | 712/1000 [00:00<00:00, 3885.11 it/sec, feas=True, obj=1.63]
INFO - 16:16:02: 71%|███████▏ | 713/1000 [00:00<00:00, 3884.66 it/sec, feas=True, obj=1.99]
INFO - 16:16:02: 71%|███████▏ | 714/1000 [00:00<00:00, 3884.61 it/sec, feas=True, obj=0.679]
INFO - 16:16:02: 72%|███████▏ | 715/1000 [00:00<00:00, 3881.95 it/sec, feas=True, obj=-0.377]
INFO - 16:16:02: 72%|███████▏ | 716/1000 [00:00<00:00, 3881.46 it/sec, feas=True, obj=-4.57]
INFO - 16:16:02: 72%|███████▏ | 717/1000 [00:00<00:00, 3881.51 it/sec, feas=True, obj=2.1]
INFO - 16:16:02: 72%|███████▏ | 718/1000 [00:00<00:00, 3881.70 it/sec, feas=True, obj=1.32]
INFO - 16:16:02: 72%|███████▏ | 719/1000 [00:00<00:00, 3881.76 it/sec, feas=True, obj=0.312]
INFO - 16:16:02: 72%|███████▏ | 720/1000 [00:00<00:00, 3881.45 it/sec, feas=True, obj=8.43]
INFO - 16:16:02: 72%|███████▏ | 721/1000 [00:00<00:00, 3881.51 it/sec, feas=True, obj=0.579]
INFO - 16:16:02: 72%|███████▏ | 722/1000 [00:00<00:00, 3881.40 it/sec, feas=True, obj=0.563]
INFO - 16:16:02: 72%|███████▏ | 723/1000 [00:00<00:00, 3880.84 it/sec, feas=True, obj=3.4]
INFO - 16:16:02: 72%|███████▏ | 724/1000 [00:00<00:00, 3880.16 it/sec, feas=True, obj=7.21]
INFO - 16:16:02: 72%|███████▎ | 725/1000 [00:00<00:00, 3880.14 it/sec, feas=True, obj=5.26]
INFO - 16:16:02: 73%|███████▎ | 726/1000 [00:00<00:00, 3880.20 it/sec, feas=True, obj=2.69]
INFO - 16:16:02: 73%|███████▎ | 727/1000 [00:00<00:00, 3879.98 it/sec, feas=True, obj=5.31]
INFO - 16:16:02: 73%|███████▎ | 728/1000 [00:00<00:00, 3879.94 it/sec, feas=True, obj=1.99]
INFO - 16:16:02: 73%|███████▎ | 729/1000 [00:00<00:00, 3880.04 it/sec, feas=True, obj=-6.72]
INFO - 16:16:02: 73%|███████▎ | 730/1000 [00:00<00:00, 3880.15 it/sec, feas=True, obj=0.526]
INFO - 16:16:02: 73%|███████▎ | 731/1000 [00:00<00:00, 3879.94 it/sec, feas=True, obj=4.35]
INFO - 16:16:02: 73%|███████▎ | 732/1000 [00:00<00:00, 3879.95 it/sec, feas=True, obj=8.27]
INFO - 16:16:02: 73%|███████▎ | 733/1000 [00:00<00:00, 3880.00 it/sec, feas=True, obj=0.662]
INFO - 16:16:02: 73%|███████▎ | 734/1000 [00:00<00:00, 3880.22 it/sec, feas=True, obj=8.9]
INFO - 16:16:02: 74%|███████▎ | 735/1000 [00:00<00:00, 3879.94 it/sec, feas=True, obj=6.96]
INFO - 16:16:02: 74%|███████▎ | 736/1000 [00:00<00:00, 3880.11 it/sec, feas=True, obj=1.11]
INFO - 16:16:02: 74%|███████▎ | 737/1000 [00:00<00:00, 3880.14 it/sec, feas=True, obj=-4.5]
INFO - 16:16:02: 74%|███████▍ | 738/1000 [00:00<00:00, 3880.29 it/sec, feas=True, obj=0.0495]
INFO - 16:16:02: 74%|███████▍ | 739/1000 [00:00<00:00, 3879.95 it/sec, feas=True, obj=5.88]
INFO - 16:16:02: 74%|███████▍ | 740/1000 [00:00<00:00, 3879.96 it/sec, feas=True, obj=13.2]
INFO - 16:16:02: 74%|███████▍ | 741/1000 [00:00<00:00, 3880.12 it/sec, feas=True, obj=2.3]
INFO - 16:16:02: 74%|███████▍ | 742/1000 [00:00<00:00, 3880.27 it/sec, feas=True, obj=2.21]
INFO - 16:16:02: 74%|███████▍ | 743/1000 [00:00<00:00, 3880.04 it/sec, feas=True, obj=-4.03]
INFO - 16:16:02: 74%|███████▍ | 744/1000 [00:00<00:00, 3880.18 it/sec, feas=True, obj=4.28]
INFO - 16:16:02: 74%|███████▍ | 745/1000 [00:00<00:00, 3880.44 it/sec, feas=True, obj=6.68]
INFO - 16:16:02: 75%|███████▍ | 746/1000 [00:00<00:00, 3880.60 it/sec, feas=True, obj=7.33]
INFO - 16:16:02: 75%|███████▍ | 747/1000 [00:00<00:00, 3880.23 it/sec, feas=True, obj=-3.91]
INFO - 16:16:02: 75%|███████▍ | 748/1000 [00:00<00:00, 3880.28 it/sec, feas=True, obj=1.16]
INFO - 16:16:02: 75%|███████▍ | 749/1000 [00:00<00:00, 3880.44 it/sec, feas=True, obj=-0.739]
INFO - 16:16:02: 75%|███████▌ | 750/1000 [00:00<00:00, 3880.68 it/sec, feas=True, obj=5.93]
INFO - 16:16:02: 75%|███████▌ | 751/1000 [00:00<00:00, 3880.33 it/sec, feas=True, obj=3.2]
INFO - 16:16:02: 75%|███████▌ | 752/1000 [00:00<00:00, 3880.24 it/sec, feas=True, obj=11.1]
INFO - 16:16:02: 75%|███████▌ | 753/1000 [00:00<00:00, 3880.26 it/sec, feas=True, obj=7.41]
INFO - 16:16:02: 75%|███████▌ | 754/1000 [00:00<00:00, 3879.93 it/sec, feas=True, obj=6.56]
INFO - 16:16:02: 76%|███████▌ | 755/1000 [00:00<00:00, 3879.83 it/sec, feas=True, obj=0.0769]
INFO - 16:16:02: 76%|███████▌ | 756/1000 [00:00<00:00, 3879.97 it/sec, feas=True, obj=-3.24]
INFO - 16:16:02: 76%|███████▌ | 757/1000 [00:00<00:00, 3880.21 it/sec, feas=True, obj=1.73]
INFO - 16:16:02: 76%|███████▌ | 758/1000 [00:00<00:00, 3880.06 it/sec, feas=True, obj=0.263]
INFO - 16:16:02: 76%|███████▌ | 759/1000 [00:00<00:00, 3880.14 it/sec, feas=True, obj=-6.43]
INFO - 16:16:02: 76%|███████▌ | 760/1000 [00:00<00:00, 3880.32 it/sec, feas=True, obj=7.52]
INFO - 16:16:02: 76%|███████▌ | 761/1000 [00:00<00:00, 3880.56 it/sec, feas=True, obj=-2.09]
INFO - 16:16:02: 76%|███████▌ | 762/1000 [00:00<00:00, 3880.20 it/sec, feas=True, obj=-0.0262]
INFO - 16:16:02: 76%|███████▋ | 763/1000 [00:00<00:00, 3880.19 it/sec, feas=True, obj=3.37]
INFO - 16:16:02: 76%|███████▋ | 764/1000 [00:00<00:00, 3880.36 it/sec, feas=True, obj=4.5]
INFO - 16:16:02: 76%|███████▋ | 765/1000 [00:00<00:00, 3880.45 it/sec, feas=True, obj=0.692]
INFO - 16:16:02: 77%|███████▋ | 766/1000 [00:00<00:00, 3880.14 it/sec, feas=True, obj=2.75]
INFO - 16:16:02: 77%|███████▋ | 767/1000 [00:00<00:00, 3880.15 it/sec, feas=True, obj=1.46]
INFO - 16:16:02: 77%|███████▋ | 768/1000 [00:00<00:00, 3880.20 it/sec, feas=True, obj=7.23]
INFO - 16:16:02: 77%|███████▋ | 769/1000 [00:00<00:00, 3880.35 it/sec, feas=True, obj=3.47]
INFO - 16:16:02: 77%|███████▋ | 770/1000 [00:00<00:00, 3880.02 it/sec, feas=True, obj=-0.943]
INFO - 16:16:02: 77%|███████▋ | 771/1000 [00:00<00:00, 3880.14 it/sec, feas=True, obj=0.302]
INFO - 16:16:02: 77%|███████▋ | 772/1000 [00:00<00:00, 3880.39 it/sec, feas=True, obj=6]
INFO - 16:16:02: 77%|███████▋ | 773/1000 [00:00<00:00, 3880.54 it/sec, feas=True, obj=2.71]
INFO - 16:16:02: 77%|███████▋ | 774/1000 [00:00<00:00, 3880.25 it/sec, feas=True, obj=2.8]
INFO - 16:16:02: 78%|███████▊ | 775/1000 [00:00<00:00, 3880.25 it/sec, feas=True, obj=2.67]
INFO - 16:16:02: 78%|███████▊ | 776/1000 [00:00<00:00, 3880.36 it/sec, feas=True, obj=5.44]
INFO - 16:16:02: 78%|███████▊ | 777/1000 [00:00<00:00, 3880.47 it/sec, feas=True, obj=1.65]
INFO - 16:16:02: 78%|███████▊ | 778/1000 [00:00<00:00, 3880.22 it/sec, feas=True, obj=7.13]
INFO - 16:16:02: 78%|███████▊ | 779/1000 [00:00<00:00, 3880.34 it/sec, feas=True, obj=-0.0622]
INFO - 16:16:02: 78%|███████▊ | 780/1000 [00:00<00:00, 3880.53 it/sec, feas=True, obj=5.84]
INFO - 16:16:02: 78%|███████▊ | 781/1000 [00:00<00:00, 3880.68 it/sec, feas=True, obj=2.28]
INFO - 16:16:02: 78%|███████▊ | 782/1000 [00:00<00:00, 3880.23 it/sec, feas=True, obj=6.04]
INFO - 16:16:02: 78%|███████▊ | 783/1000 [00:00<00:00, 3880.16 it/sec, feas=True, obj=7.59]
INFO - 16:16:02: 78%|███████▊ | 784/1000 [00:00<00:00, 3880.21 it/sec, feas=True, obj=-6.19]
INFO - 16:16:02: 78%|███████▊ | 785/1000 [00:00<00:00, 3880.37 it/sec, feas=True, obj=9.25]
INFO - 16:16:02: 79%|███████▊ | 786/1000 [00:00<00:00, 3879.96 it/sec, feas=True, obj=0.676]
INFO - 16:16:02: 79%|███████▊ | 787/1000 [00:00<00:00, 3880.02 it/sec, feas=True, obj=-0.174]
INFO - 16:16:02: 79%|███████▉ | 788/1000 [00:00<00:00, 3880.12 it/sec, feas=True, obj=6.51]
INFO - 16:16:02: 79%|███████▉ | 789/1000 [00:00<00:00, 3879.92 it/sec, feas=True, obj=-0.856]
INFO - 16:16:02: 79%|███████▉ | 790/1000 [00:00<00:00, 3879.76 it/sec, feas=True, obj=5.62]
INFO - 16:16:02: 79%|███████▉ | 791/1000 [00:00<00:00, 3879.84 it/sec, feas=True, obj=5.35]
INFO - 16:16:02: 79%|███████▉ | 792/1000 [00:00<00:00, 3879.95 it/sec, feas=True, obj=0.753]
INFO - 16:16:02: 79%|███████▉ | 793/1000 [00:00<00:00, 3879.66 it/sec, feas=True, obj=4.35]
INFO - 16:16:02: 79%|███████▉ | 794/1000 [00:00<00:00, 3879.69 it/sec, feas=True, obj=3.8]
INFO - 16:16:02: 80%|███████▉ | 795/1000 [00:00<00:00, 3879.76 it/sec, feas=True, obj=7.95]
INFO - 16:16:02: 80%|███████▉ | 796/1000 [00:00<00:00, 3879.81 it/sec, feas=True, obj=5.01]
INFO - 16:16:02: 80%|███████▉ | 797/1000 [00:00<00:00, 3879.47 it/sec, feas=True, obj=6.2]
INFO - 16:16:02: 80%|███████▉ | 798/1000 [00:00<00:00, 3879.62 it/sec, feas=True, obj=-1.82]
INFO - 16:16:02: 80%|███████▉ | 799/1000 [00:00<00:00, 3879.60 it/sec, feas=True, obj=2.4]
INFO - 16:16:02: 80%|████████ | 800/1000 [00:00<00:00, 3879.73 it/sec, feas=True, obj=7.99]
INFO - 16:16:02: 80%|████████ | 801/1000 [00:00<00:00, 3879.47 it/sec, feas=True, obj=2.48]
INFO - 16:16:02: 80%|████████ | 802/1000 [00:00<00:00, 3879.58 it/sec, feas=True, obj=-0.764]
INFO - 16:16:02: 80%|████████ | 803/1000 [00:00<00:00, 3879.58 it/sec, feas=True, obj=3.34]
INFO - 16:16:02: 80%|████████ | 804/1000 [00:00<00:00, 3879.74 it/sec, feas=True, obj=0.787]
INFO - 16:16:02: 80%|████████ | 805/1000 [00:00<00:00, 3879.51 it/sec, feas=True, obj=-1.05]
INFO - 16:16:02: 81%|████████ | 806/1000 [00:00<00:00, 3879.58 it/sec, feas=True, obj=4.98]
INFO - 16:16:02: 81%|████████ | 807/1000 [00:00<00:00, 3879.71 it/sec, feas=True, obj=4.73]
INFO - 16:16:02: 81%|████████ | 808/1000 [00:00<00:00, 3879.85 it/sec, feas=True, obj=-0.742]
INFO - 16:16:02: 81%|████████ | 809/1000 [00:00<00:00, 3879.50 it/sec, feas=True, obj=5.82]
INFO - 16:16:02: 81%|████████ | 810/1000 [00:00<00:00, 3879.55 it/sec, feas=True, obj=10.4]
INFO - 16:16:02: 81%|████████ | 811/1000 [00:00<00:00, 3879.69 it/sec, feas=True, obj=1.86]
INFO - 16:16:02: 81%|████████ | 812/1000 [00:00<00:00, 3879.95 it/sec, feas=True, obj=2.49]
INFO - 16:16:02: 81%|████████▏ | 813/1000 [00:00<00:00, 3879.67 it/sec, feas=True, obj=9.36]
INFO - 16:16:02: 81%|████████▏ | 814/1000 [00:00<00:00, 3879.68 it/sec, feas=True, obj=1.84]
INFO - 16:16:02: 82%|████████▏ | 815/1000 [00:00<00:00, 3879.81 it/sec, feas=True, obj=4.04]
INFO - 16:16:02: 82%|████████▏ | 816/1000 [00:00<00:00, 3879.53 it/sec, feas=True, obj=-4.21]
INFO - 16:16:02: 82%|████████▏ | 817/1000 [00:00<00:00, 3879.46 it/sec, feas=True, obj=3.64]
INFO - 16:16:02: 82%|████████▏ | 818/1000 [00:00<00:00, 3879.55 it/sec, feas=True, obj=4.02]
INFO - 16:16:02: 82%|████████▏ | 819/1000 [00:00<00:00, 3879.67 it/sec, feas=True, obj=6.66]
INFO - 16:16:02: 82%|████████▏ | 820/1000 [00:00<00:00, 3879.46 it/sec, feas=True, obj=-0.0634]
INFO - 16:16:02: 82%|████████▏ | 821/1000 [00:00<00:00, 3879.50 it/sec, feas=True, obj=1.24]
INFO - 16:16:02: 82%|████████▏ | 822/1000 [00:00<00:00, 3879.67 it/sec, feas=True, obj=4.42]
INFO - 16:16:02: 82%|████████▏ | 823/1000 [00:00<00:00, 3879.75 it/sec, feas=True, obj=4.26]
INFO - 16:16:02: 82%|████████▏ | 824/1000 [00:00<00:00, 3879.57 it/sec, feas=True, obj=0.439]
INFO - 16:16:02: 82%|████████▎ | 825/1000 [00:00<00:00, 3879.55 it/sec, feas=True, obj=2.7]
INFO - 16:16:02: 83%|████████▎ | 826/1000 [00:00<00:00, 3879.65 it/sec, feas=True, obj=2.98]
INFO - 16:16:02: 83%|████████▎ | 827/1000 [00:00<00:00, 3879.81 it/sec, feas=True, obj=0.888]
INFO - 16:16:02: 83%|████████▎ | 828/1000 [00:00<00:00, 3879.52 it/sec, feas=True, obj=-0.879]
INFO - 16:16:02: 83%|████████▎ | 829/1000 [00:00<00:00, 3877.23 it/sec, feas=True, obj=0.861]
INFO - 16:16:02: 83%|████████▎ | 830/1000 [00:00<00:00, 3877.26 it/sec, feas=True, obj=3.47]
INFO - 16:16:02: 83%|████████▎ | 831/1000 [00:00<00:00, 3877.04 it/sec, feas=True, obj=7.51]
INFO - 16:16:02: 83%|████████▎ | 832/1000 [00:00<00:00, 3876.95 it/sec, feas=True, obj=4.58]
INFO - 16:16:02: 83%|████████▎ | 833/1000 [00:00<00:00, 3877.11 it/sec, feas=True, obj=5.48]
INFO - 16:16:02: 83%|████████▎ | 834/1000 [00:00<00:00, 3877.25 it/sec, feas=True, obj=-0.412]
INFO - 16:16:02: 84%|████████▎ | 835/1000 [00:00<00:00, 3877.02 it/sec, feas=True, obj=-1.86]
INFO - 16:16:02: 84%|████████▎ | 836/1000 [00:00<00:00, 3876.97 it/sec, feas=True, obj=1.29]
INFO - 16:16:02: 84%|████████▎ | 837/1000 [00:00<00:00, 3877.19 it/sec, feas=True, obj=3.17]
INFO - 16:16:02: 84%|████████▍ | 838/1000 [00:00<00:00, 3877.44 it/sec, feas=True, obj=2.41]
INFO - 16:16:02: 84%|████████▍ | 839/1000 [00:00<00:00, 3877.24 it/sec, feas=True, obj=5.72]
INFO - 16:16:02: 84%|████████▍ | 840/1000 [00:00<00:00, 3877.22 it/sec, feas=True, obj=-1.37]
INFO - 16:16:02: 84%|████████▍ | 841/1000 [00:00<00:00, 3877.22 it/sec, feas=True, obj=6.72]
INFO - 16:16:02: 84%|████████▍ | 842/1000 [00:00<00:00, 3877.29 it/sec, feas=True, obj=3.27]
INFO - 16:16:02: 84%|████████▍ | 843/1000 [00:00<00:00, 3877.06 it/sec, feas=True, obj=-1.46]
INFO - 16:16:02: 84%|████████▍ | 844/1000 [00:00<00:00, 3877.17 it/sec, feas=True, obj=4.38]
INFO - 16:16:02: 84%|████████▍ | 845/1000 [00:00<00:00, 3877.09 it/sec, feas=True, obj=3.82]
INFO - 16:16:02: 85%|████████▍ | 846/1000 [00:00<00:00, 3877.17 it/sec, feas=True, obj=5.89]
INFO - 16:16:02: 85%|████████▍ | 847/1000 [00:00<00:00, 3876.79 it/sec, feas=True, obj=3.11]
INFO - 16:16:02: 85%|████████▍ | 848/1000 [00:00<00:00, 3876.83 it/sec, feas=True, obj=4.37]
INFO - 16:16:02: 85%|████████▍ | 849/1000 [00:00<00:00, 3876.98 it/sec, feas=True, obj=1.84]
INFO - 16:16:02: 85%|████████▌ | 850/1000 [00:00<00:00, 3876.99 it/sec, feas=True, obj=2.82]
INFO - 16:16:02: 85%|████████▌ | 851/1000 [00:00<00:00, 3876.68 it/sec, feas=True, obj=7.38]
INFO - 16:16:02: 85%|████████▌ | 852/1000 [00:00<00:00, 3876.76 it/sec, feas=True, obj=13.8]
INFO - 16:16:02: 85%|████████▌ | 853/1000 [00:00<00:00, 3876.88 it/sec, feas=True, obj=7.76]
INFO - 16:16:02: 85%|████████▌ | 854/1000 [00:00<00:00, 3877.02 it/sec, feas=True, obj=0.998]
INFO - 16:16:02: 86%|████████▌ | 855/1000 [00:00<00:00, 3876.58 it/sec, feas=True, obj=3.88]
INFO - 16:16:02: 86%|████████▌ | 856/1000 [00:00<00:00, 3876.49 it/sec, feas=True, obj=-0.698]
INFO - 16:16:02: 86%|████████▌ | 857/1000 [00:00<00:00, 3876.52 it/sec, feas=True, obj=2.83]
INFO - 16:16:02: 86%|████████▌ | 858/1000 [00:00<00:00, 3876.19 it/sec, feas=True, obj=1.58]
INFO - 16:16:02: 86%|████████▌ | 859/1000 [00:00<00:00, 3876.19 it/sec, feas=True, obj=8.53]
INFO - 16:16:02: 86%|████████▌ | 860/1000 [00:00<00:00, 3876.16 it/sec, feas=True, obj=6.28]
INFO - 16:16:02: 86%|████████▌ | 861/1000 [00:00<00:00, 3876.09 it/sec, feas=True, obj=11.8]
INFO - 16:16:02: 86%|████████▌ | 862/1000 [00:00<00:00, 3875.78 it/sec, feas=True, obj=9.31]
INFO - 16:16:02: 86%|████████▋ | 863/1000 [00:00<00:00, 3875.83 it/sec, feas=True, obj=3.88]
INFO - 16:16:02: 86%|████████▋ | 864/1000 [00:00<00:00, 3875.95 it/sec, feas=True, obj=3.11]
INFO - 16:16:02: 86%|████████▋ | 865/1000 [00:00<00:00, 3876.03 it/sec, feas=True, obj=5.09]
INFO - 16:16:02: 87%|████████▋ | 866/1000 [00:00<00:00, 3875.81 it/sec, feas=True, obj=-0.723]
INFO - 16:16:02: 87%|████████▋ | 867/1000 [00:00<00:00, 3875.92 it/sec, feas=True, obj=1.22]
INFO - 16:16:02: 87%|████████▋ | 868/1000 [00:00<00:00, 3875.92 it/sec, feas=True, obj=7.13]
INFO - 16:16:02: 87%|████████▋ | 869/1000 [00:00<00:00, 3875.98 it/sec, feas=True, obj=12.2]
INFO - 16:16:02: 87%|████████▋ | 870/1000 [00:00<00:00, 3875.71 it/sec, feas=True, obj=1.13]
INFO - 16:16:02: 87%|████████▋ | 871/1000 [00:00<00:00, 3875.67 it/sec, feas=True, obj=0.802]
INFO - 16:16:02: 87%|████████▋ | 872/1000 [00:00<00:00, 3875.79 it/sec, feas=True, obj=2.82]
INFO - 16:16:02: 87%|████████▋ | 873/1000 [00:00<00:00, 3875.94 it/sec, feas=True, obj=-0.932]
INFO - 16:16:02: 87%|████████▋ | 874/1000 [00:00<00:00, 3875.70 it/sec, feas=True, obj=1.6]
INFO - 16:16:02: 88%|████████▊ | 875/1000 [00:00<00:00, 3875.38 it/sec, feas=True, obj=8.68]
INFO - 16:16:02: 88%|████████▊ | 876/1000 [00:00<00:00, 3875.33 it/sec, feas=True, obj=-0.211]
INFO - 16:16:02: 88%|████████▊ | 877/1000 [00:00<00:00, 3875.45 it/sec, feas=True, obj=-3.63]
INFO - 16:16:02: 88%|████████▊ | 878/1000 [00:00<00:00, 3875.01 it/sec, feas=True, obj=4.85]
INFO - 16:16:02: 88%|████████▊ | 879/1000 [00:00<00:00, 3875.08 it/sec, feas=True, obj=4.28]
INFO - 16:16:02: 88%|████████▊ | 880/1000 [00:00<00:00, 3875.05 it/sec, feas=True, obj=-0.285]
INFO - 16:16:02: 88%|████████▊ | 881/1000 [00:00<00:00, 3874.69 it/sec, feas=True, obj=5.96]
INFO - 16:16:02: 88%|████████▊ | 882/1000 [00:00<00:00, 3874.60 it/sec, feas=True, obj=-0.126]
INFO - 16:16:02: 88%|████████▊ | 883/1000 [00:00<00:00, 3874.62 it/sec, feas=True, obj=10.4]
INFO - 16:16:02: 88%|████████▊ | 884/1000 [00:00<00:00, 3874.77 it/sec, feas=True, obj=-1.37]
INFO - 16:16:02: 88%|████████▊ | 885/1000 [00:00<00:00, 3874.58 it/sec, feas=True, obj=4.47]
INFO - 16:16:02: 89%|████████▊ | 886/1000 [00:00<00:00, 3874.68 it/sec, feas=True, obj=1.19]
INFO - 16:16:02: 89%|████████▊ | 887/1000 [00:00<00:00, 3874.61 it/sec, feas=True, obj=6.51]
INFO - 16:16:02: 89%|████████▉ | 888/1000 [00:00<00:00, 3874.75 it/sec, feas=True, obj=-0.5]
INFO - 16:16:02: 89%|████████▉ | 889/1000 [00:00<00:00, 3874.54 it/sec, feas=True, obj=1.33]
INFO - 16:16:02: 89%|████████▉ | 890/1000 [00:00<00:00, 3874.63 it/sec, feas=True, obj=8.1]
INFO - 16:16:02: 89%|████████▉ | 891/1000 [00:00<00:00, 3874.54 it/sec, feas=True, obj=6.34]
INFO - 16:16:02: 89%|████████▉ | 892/1000 [00:00<00:00, 3874.58 it/sec, feas=True, obj=0.425]
INFO - 16:16:02: 89%|████████▉ | 893/1000 [00:00<00:00, 3874.28 it/sec, feas=True, obj=7.99]
INFO - 16:16:02: 89%|████████▉ | 894/1000 [00:00<00:00, 3874.35 it/sec, feas=True, obj=4.73]
INFO - 16:16:02: 90%|████████▉ | 895/1000 [00:00<00:00, 3874.51 it/sec, feas=True, obj=-0.736]
INFO - 16:16:02: 90%|████████▉ | 896/1000 [00:00<00:00, 3874.56 it/sec, feas=True, obj=1.11]
INFO - 16:16:02: 90%|████████▉ | 897/1000 [00:00<00:00, 3874.03 it/sec, feas=True, obj=5.52]
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INFO - 16:16:02: 94%|█████████▍| 944/1000 [00:00<00:00, 3855.09 it/sec, feas=True, obj=9.05]
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INFO - 16:16:02: 95%|█████████▌| 950/1000 [00:00<00:00, 3853.38 it/sec, feas=True, obj=0.391]
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INFO - 16:16:02: 95%|█████████▌| 953/1000 [00:00<00:00, 3853.06 it/sec, feas=True, obj=0.439]
INFO - 16:16:02: 95%|█████████▌| 954/1000 [00:00<00:00, 3852.83 it/sec, feas=True, obj=7.43]
INFO - 16:16:02: 96%|█████████▌| 955/1000 [00:00<00:00, 3852.80 it/sec, feas=True, obj=-1.49]
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INFO - 16:16:02: 96%|█████████▌| 960/1000 [00:00<00:00, 3852.43 it/sec, feas=True, obj=3.33]
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INFO - 16:16:02: 96%|█████████▌| 962/1000 [00:00<00:00, 3852.11 it/sec, feas=True, obj=14.7]
INFO - 16:16:02: 96%|█████████▋| 963/1000 [00:00<00:00, 3851.91 it/sec, feas=True, obj=0.515]
INFO - 16:16:02: 96%|█████████▋| 964/1000 [00:00<00:00, 3851.96 it/sec, feas=True, obj=2.57]
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INFO - 16:16:02: 97%|█████████▋| 966/1000 [00:00<00:00, 3851.68 it/sec, feas=True, obj=-0.292]
INFO - 16:16:02: 97%|█████████▋| 967/1000 [00:00<00:00, 3851.74 it/sec, feas=True, obj=-1.65]
INFO - 16:16:02: 97%|█████████▋| 968/1000 [00:00<00:00, 3851.54 it/sec, feas=True, obj=7.01]
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INFO - 16:16:02: 98%|█████████▊| 975/1000 [00:00<00:00, 3851.31 it/sec, feas=True, obj=7.26]
INFO - 16:16:02: 98%|█████████▊| 976/1000 [00:00<00:00, 3851.16 it/sec, feas=True, obj=1.83]
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INFO - 16:16:02: 98%|█████████▊| 978/1000 [00:00<00:00, 3851.23 it/sec, feas=True, obj=4.96]
INFO - 16:16:02: 98%|█████████▊| 979/1000 [00:00<00:00, 3851.29 it/sec, feas=True, obj=0.739]
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INFO - 16:16:02: 99%|█████████▊| 986/1000 [00:00<00:00, 3850.91 it/sec, feas=True, obj=2.01]
INFO - 16:16:02: 99%|█████████▊| 987/1000 [00:00<00:00, 3851.01 it/sec, feas=True, obj=-0.893]
INFO - 16:16:02: 99%|█████████▉| 988/1000 [00:00<00:00, 3850.68 it/sec, feas=True, obj=4.74]
INFO - 16:16:02: 99%|█████████▉| 989/1000 [00:00<00:00, 3850.77 it/sec, feas=True, obj=1.18]
INFO - 16:16:02: 99%|█████████▉| 990/1000 [00:00<00:00, 3850.85 it/sec, feas=True, obj=6.2]
INFO - 16:16:02: 99%|█████████▉| 991/1000 [00:00<00:00, 3850.64 it/sec, feas=True, obj=4.5]
INFO - 16:16:02: 99%|█████████▉| 992/1000 [00:00<00:00, 3850.55 it/sec, feas=True, obj=-0.907]
INFO - 16:16:02: 99%|█████████▉| 993/1000 [00:00<00:00, 3850.52 it/sec, feas=True, obj=-3.18]
INFO - 16:16:02: 99%|█████████▉| 994/1000 [00:00<00:00, 3850.58 it/sec, feas=True, obj=6.82]
INFO - 16:16:02: 100%|█████████▉| 995/1000 [00:00<00:00, 3850.30 it/sec, feas=True, obj=3.44]
INFO - 16:16:02: 100%|█████████▉| 996/1000 [00:00<00:00, 3850.25 it/sec, feas=True, obj=5.11]
INFO - 16:16:02: 100%|█████████▉| 997/1000 [00:00<00:00, 3850.28 it/sec, feas=True, obj=1.55]
INFO - 16:16:02: 100%|█████████▉| 998/1000 [00:00<00:00, 3850.36 it/sec, feas=True, obj=0.534]
INFO - 16:16:02: 100%|█████████▉| 999/1000 [00:00<00:00, 3850.11 it/sec, feas=True, obj=0.783]
INFO - 16:16:02: 100%|██████████| 1000/1000 [00:00<00:00, 3821.86 it/sec, feas=True, obj=5.65]
INFO - 16:16:02: Optimization result:
INFO - 16:16:02: Optimizer info:
INFO - 16:16:02: Status: None
INFO - 16:16:02: Message: None
INFO - 16:16:02: Solution:
INFO - 16:16:02: Objective: -10.14685071195364
INFO - 16:16:02: Design space:
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: | Name | Distribution |
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:16:02: +------+------------------------------------------------------------+
INFO - 16:16:02: *** 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 4.952 seconds)