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 - 16:22:13: *** Start Sampling execution ***
INFO - 16:22:13: Sampling
INFO - 16:22:13: Disciplines: IshigamiDiscipline
INFO - 16:22:13: MDO formulation: MDF
INFO - 16:22:13: Optimization problem:
INFO - 16:22:13: minimize y(x1, x2, x3)
INFO - 16:22:13: with respect to x1, x2, x3
INFO - 16:22:13: over the design space:
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: | Name | Distribution |
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: Solving optimization problem with algorithm OT_OPT_LHS:
INFO - 16:22:13: 1%|▏ | 1/70 [00:00<00:00, 369.90 it/sec, feas=True, obj=1.45]
INFO - 16:22:13: 3%|▎ | 2/70 [00:00<00:00, 601.51 it/sec, feas=True, obj=1.01]
INFO - 16:22:13: 4%|▍ | 3/70 [00:00<00:00, 782.23 it/sec, feas=True, obj=6.72]
INFO - 16:22:13: 6%|▌ | 4/70 [00:00<00:00, 919.25 it/sec, feas=True, obj=-0.113]
INFO - 16:22:13: 7%|▋ | 5/70 [00:00<00:00, 1034.71 it/sec, feas=True, obj=7.68]
INFO - 16:22:13: 9%|▊ | 6/70 [00:00<00:00, 1125.84 it/sec, feas=True, obj=1.8]
INFO - 16:22:13: 10%|█ | 7/70 [00:00<00:00, 1208.34 it/sec, feas=True, obj=10.3]
INFO - 16:22:13: 11%|█▏ | 8/70 [00:00<00:00, 1273.41 it/sec, feas=True, obj=5.96]
INFO - 16:22:13: 13%|█▎ | 9/70 [00:00<00:00, 1332.42 it/sec, feas=True, obj=0.0449]
INFO - 16:22:13: 14%|█▍ | 10/70 [00:00<00:00, 1382.89 it/sec, feas=True, obj=4.97]
INFO - 16:22:13: 16%|█▌ | 11/70 [00:00<00:00, 1430.39 it/sec, feas=True, obj=6.94]
INFO - 16:22:13: 17%|█▋ | 12/70 [00:00<00:00, 1470.31 it/sec, feas=True, obj=3.5]
INFO - 16:22:13: 19%|█▊ | 13/70 [00:00<00:00, 1507.74 it/sec, feas=True, obj=4.87]
INFO - 16:22:13: 20%|██ | 14/70 [00:00<00:00, 1539.68 it/sec, feas=True, obj=4.3]
INFO - 16:22:13: 21%|██▏ | 15/70 [00:00<00:00, 1570.55 it/sec, feas=True, obj=2.44]
INFO - 16:22:13: 23%|██▎ | 16/70 [00:00<00:00, 1600.65 it/sec, feas=True, obj=5.7]
INFO - 16:22:13: 24%|██▍ | 17/70 [00:00<00:00, 1624.74 it/sec, feas=True, obj=6.14]
INFO - 16:22:13: 26%|██▌ | 18/70 [00:00<00:00, 1650.43 it/sec, feas=True, obj=5.7]
INFO - 16:22:13: 27%|██▋ | 19/70 [00:00<00:00, 1670.23 it/sec, feas=True, obj=-0.573]
INFO - 16:22:13: 29%|██▊ | 20/70 [00:00<00:00, 1690.81 it/sec, feas=True, obj=5.72]
INFO - 16:22:13: 30%|███ | 21/70 [00:00<00:00, 1707.58 it/sec, feas=True, obj=4.95]
INFO - 16:22:13: 31%|███▏ | 22/70 [00:00<00:00, 1724.37 it/sec, feas=True, obj=1.27]
INFO - 16:22:13: 33%|███▎ | 23/70 [00:00<00:00, 1737.99 it/sec, feas=True, obj=3.54]
INFO - 16:22:13: 34%|███▍ | 24/70 [00:00<00:00, 1753.35 it/sec, feas=True, obj=6.04]
INFO - 16:22:13: 36%|███▌ | 25/70 [00:00<00:00, 1766.59 it/sec, feas=True, obj=7.5]
INFO - 16:22:13: 37%|███▋ | 26/70 [00:00<00:00, 1779.86 it/sec, feas=True, obj=13.2]
INFO - 16:22:13: 39%|███▊ | 27/70 [00:00<00:00, 1790.88 it/sec, feas=True, obj=14.8]
INFO - 16:22:13: 40%|████ | 28/70 [00:00<00:00, 1803.59 it/sec, feas=True, obj=-0.644]
INFO - 16:22:13: 41%|████▏ | 29/70 [00:00<00:00, 1814.69 it/sec, feas=True, obj=4.94]
INFO - 16:22:13: 43%|████▎ | 30/70 [00:00<00:00, 1824.80 it/sec, feas=True, obj=5.5]
INFO - 16:22:13: 44%|████▍ | 31/70 [00:00<00:00, 1835.43 it/sec, feas=True, obj=3.35]
INFO - 16:22:13: 46%|████▌ | 32/70 [00:00<00:00, 1844.26 it/sec, feas=True, obj=4.05]
INFO - 16:22:13: 47%|████▋ | 33/70 [00:00<00:00, 1854.72 it/sec, feas=True, obj=2.43]
INFO - 16:22:13: 49%|████▊ | 34/70 [00:00<00:00, 1863.06 it/sec, feas=True, obj=-0.0246]
INFO - 16:22:13: 50%|█████ | 35/70 [00:00<00:00, 1872.10 it/sec, feas=True, obj=-0.0211]
INFO - 16:22:13: 51%|█████▏ | 36/70 [00:00<00:00, 1878.84 it/sec, feas=True, obj=6.01]
INFO - 16:22:13: 53%|█████▎ | 37/70 [00:00<00:00, 1886.48 it/sec, feas=True, obj=5.03]
INFO - 16:22:13: 54%|█████▍ | 38/70 [00:00<00:00, 1893.50 it/sec, feas=True, obj=0.863]
INFO - 16:22:13: 56%|█████▌ | 39/70 [00:00<00:00, 1900.12 it/sec, feas=True, obj=-0.764]
INFO - 16:22:13: 57%|█████▋ | 40/70 [00:00<00:00, 1906.89 it/sec, feas=True, obj=14.8]
INFO - 16:22:13: 59%|█████▊ | 41/70 [00:00<00:00, 1912.18 it/sec, feas=True, obj=0.87]
INFO - 16:22:13: 60%|██████ | 42/70 [00:00<00:00, 1919.49 it/sec, feas=True, obj=0.829]
INFO - 16:22:13: 61%|██████▏ | 43/70 [00:00<00:00, 1923.83 it/sec, feas=True, obj=5.01]
INFO - 16:22:13: 63%|██████▎ | 44/70 [00:00<00:00, 1929.00 it/sec, feas=True, obj=0.108]
INFO - 16:22:13: 64%|██████▍ | 45/70 [00:00<00:00, 1932.52 it/sec, feas=True, obj=0.948]
INFO - 16:22:13: 66%|██████▌ | 46/70 [00:00<00:00, 1937.93 it/sec, feas=True, obj=1.22]
INFO - 16:22:13: 67%|██████▋ | 47/70 [00:00<00:00, 1942.04 it/sec, feas=True, obj=7.52]
INFO - 16:22:13: 69%|██████▊ | 48/70 [00:00<00:00, 1946.50 it/sec, feas=True, obj=3.97]
INFO - 16:22:13: 70%|███████ | 49/70 [00:00<00:00, 1950.30 it/sec, feas=True, obj=0.768]
INFO - 16:22:13: 71%|███████▏ | 50/70 [00:00<00:00, 1955.35 it/sec, feas=True, obj=-8.26]
INFO - 16:22:13: 73%|███████▎ | 51/70 [00:00<00:00, 1960.93 it/sec, feas=True, obj=-3.5]
INFO - 16:22:13: 74%|███████▍ | 52/70 [00:00<00:00, 1964.39 it/sec, feas=True, obj=7.43]
INFO - 16:22:13: 76%|███████▌ | 53/70 [00:00<00:00, 1969.17 it/sec, feas=True, obj=-2.32]
INFO - 16:22:13: 77%|███████▋ | 54/70 [00:00<00:00, 1972.76 it/sec, feas=True, obj=4.82]
INFO - 16:22:13: 79%|███████▊ | 55/70 [00:00<00:00, 1977.82 it/sec, feas=True, obj=2.5]
INFO - 16:22:13: 80%|████████ | 56/70 [00:00<00:00, 1980.65 it/sec, feas=True, obj=2.58]
INFO - 16:22:13: 81%|████████▏ | 57/70 [00:00<00:00, 1984.72 it/sec, feas=True, obj=-2.55]
INFO - 16:22:13: 83%|████████▎ | 58/70 [00:00<00:00, 1987.84 it/sec, feas=True, obj=2.11]
INFO - 16:22:13: 84%|████████▍ | 59/70 [00:00<00:00, 1991.89 it/sec, feas=True, obj=8.06]
INFO - 16:22:13: 86%|████████▌ | 60/70 [00:00<00:00, 1995.23 it/sec, feas=True, obj=-5.24]
INFO - 16:22:13: 87%|████████▋ | 61/70 [00:00<00:00, 1998.96 it/sec, feas=True, obj=2.4]
INFO - 16:22:13: 89%|████████▊ | 62/70 [00:00<00:00, 2003.44 it/sec, feas=True, obj=3.43]
INFO - 16:22:13: 90%|█████████ | 63/70 [00:00<00:00, 2006.43 it/sec, feas=True, obj=5.99]
INFO - 16:22:13: 91%|█████████▏| 64/70 [00:00<00:00, 2010.06 it/sec, feas=True, obj=0.819]
INFO - 16:22:13: 93%|█████████▎| 65/70 [00:00<00:00, 2012.52 it/sec, feas=True, obj=0.632]
INFO - 16:22:13: 94%|█████████▍| 66/70 [00:00<00:00, 2015.24 it/sec, feas=True, obj=-0.158]
INFO - 16:22:13: 96%|█████████▌| 67/70 [00:00<00:00, 2017.56 it/sec, feas=True, obj=4.05]
INFO - 16:22:13: 97%|█████████▋| 68/70 [00:00<00:00, 2020.43 it/sec, feas=True, obj=7.71]
INFO - 16:22:13: 99%|█████████▊| 69/70 [00:00<00:00, 2022.41 it/sec, feas=True, obj=5.54]
INFO - 16:22:13: 100%|██████████| 70/70 [00:00<00:00, 2013.82 it/sec, feas=True, obj=6.63]
INFO - 16:22:13: Optimization result:
INFO - 16:22:13: Optimizer info:
INFO - 16:22:13: Status: None
INFO - 16:22:13: Message: None
INFO - 16:22:13: Solution:
INFO - 16:22:13: Objective: -8.260663543133736
INFO - 16:22:13: Design space:
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: | Name | Distribution |
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: *** 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 - 16:22:13: *** Start Sampling execution ***
INFO - 16:22:13: Sampling
INFO - 16:22:13: Disciplines: IshigamiDiscipline
INFO - 16:22:13: MDO formulation: MDF
INFO - 16:22:13: Optimization problem:
INFO - 16:22:13: minimize y(x1, x2, x3)
INFO - 16:22:13: with respect to x1, x2, x3
INFO - 16:22:13: over the design space:
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: | Name | Distribution |
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: Solving optimization problem with algorithm MC:
INFO - 16:22:13: 1%| | 6/1000 [00:00<00:00, 4288.65 it/sec, feas=True, obj=3.6]
INFO - 16:22:13: 1%| | 7/1000 [00:00<00:00, 4025.80 it/sec, feas=True, obj=5.41]
INFO - 16:22:13: 1%| | 8/1000 [00:00<00:00, 3973.29 it/sec, feas=True, obj=-9.09]
INFO - 16:22:13: 1%| | 9/1000 [00:00<00:00, 3952.75 it/sec, feas=True, obj=7.06]
INFO - 16:22:13: 1%| | 10/1000 [00:00<00:00, 3919.91 it/sec, feas=True, obj=-3.46]
INFO - 16:22:13: 1%| | 11/1000 [00:00<00:00, 3901.68 it/sec, feas=True, obj=3.2]
INFO - 16:22:13: 1%| | 12/1000 [00:00<00:00, 3901.07 it/sec, feas=True, obj=8.62]
INFO - 16:22:13: 1%|▏ | 13/1000 [00:00<00:00, 3900.00 it/sec, feas=True, obj=-0.0229]
INFO - 16:22:13: 1%|▏ | 14/1000 [00:00<00:00, 3883.10 it/sec, feas=True, obj=2.91]
INFO - 16:22:13: 2%|▏ | 15/1000 [00:00<00:00, 3887.21 it/sec, feas=True, obj=8.67]
INFO - 16:22:13: 2%|▏ | 16/1000 [00:00<00:00, 3894.43 it/sec, feas=True, obj=0.13]
INFO - 16:22:13: 2%|▏ | 17/1000 [00:00<00:00, 3896.99 it/sec, feas=True, obj=4.8]
INFO - 16:22:13: 2%|▏ | 18/1000 [00:00<00:00, 3885.21 it/sec, feas=True, obj=8.34]
INFO - 16:22:13: 2%|▏ | 19/1000 [00:00<00:00, 3890.63 it/sec, feas=True, obj=-1.57]
INFO - 16:22:13: 2%|▏ | 20/1000 [00:00<00:00, 3889.92 it/sec, feas=True, obj=4.2]
INFO - 16:22:13: 2%|▏ | 21/1000 [00:00<00:00, 3895.12 it/sec, feas=True, obj=-1.04]
INFO - 16:22:13: 2%|▏ | 22/1000 [00:00<00:00, 3886.72 it/sec, feas=True, obj=6.49]
INFO - 16:22:13: 2%|▏ | 23/1000 [00:00<00:00, 3891.92 it/sec, feas=True, obj=1.83]
INFO - 16:22:13: 2%|▏ | 24/1000 [00:00<00:00, 3891.27 it/sec, feas=True, obj=4.83]
INFO - 16:22:13: 2%|▎ | 25/1000 [00:00<00:00, 3894.87 it/sec, feas=True, obj=2.15]
INFO - 16:22:13: 3%|▎ | 26/1000 [00:00<00:00, 3887.77 it/sec, feas=True, obj=4.61]
INFO - 16:22:13: 3%|▎ | 27/1000 [00:00<00:00, 3891.89 it/sec, feas=True, obj=4.02]
INFO - 16:22:13: 3%|▎ | 28/1000 [00:00<00:00, 3898.05 it/sec, feas=True, obj=4.83]
INFO - 16:22:13: 3%|▎ | 29/1000 [00:00<00:00, 3902.30 it/sec, feas=True, obj=3.43]
INFO - 16:22:13: 3%|▎ | 30/1000 [00:00<00:00, 3895.04 it/sec, feas=True, obj=2.48]
INFO - 16:22:13: 3%|▎ | 31/1000 [00:00<00:00, 3827.71 it/sec, feas=True, obj=6.63]
INFO - 16:22:13: 3%|▎ | 32/1000 [00:00<00:00, 3828.45 it/sec, feas=True, obj=6.92]
INFO - 16:22:13: 3%|▎ | 33/1000 [00:00<00:00, 3823.75 it/sec, feas=True, obj=3.22]
INFO - 16:22:13: 3%|▎ | 34/1000 [00:00<00:00, 3824.05 it/sec, feas=True, obj=5.73]
INFO - 16:22:13: 4%|▎ | 35/1000 [00:00<00:00, 3825.32 it/sec, feas=True, obj=5.62]
INFO - 16:22:13: 4%|▎ | 36/1000 [00:00<00:00, 3828.86 it/sec, feas=True, obj=-1.44]
INFO - 16:22:13: 4%|▎ | 37/1000 [00:00<00:00, 3824.84 it/sec, feas=True, obj=7.02]
INFO - 16:22:13: 4%|▍ | 38/1000 [00:00<00:00, 3826.37 it/sec, feas=True, obj=6.21]
INFO - 16:22:13: 4%|▍ | 39/1000 [00:00<00:00, 3828.98 it/sec, feas=True, obj=4.64]
INFO - 16:22:13: 4%|▍ | 40/1000 [00:00<00:00, 3832.86 it/sec, feas=True, obj=4.71]
INFO - 16:22:13: 4%|▍ | 41/1000 [00:00<00:00, 3830.24 it/sec, feas=True, obj=5.73]
INFO - 16:22:13: 4%|▍ | 42/1000 [00:00<00:00, 3832.75 it/sec, feas=True, obj=-0.0754]
INFO - 16:22:13: 4%|▍ | 43/1000 [00:00<00:00, 3832.61 it/sec, feas=True, obj=5.56]
INFO - 16:22:13: 4%|▍ | 44/1000 [00:00<00:00, 3837.10 it/sec, feas=True, obj=5.03]
INFO - 16:22:13: 4%|▍ | 45/1000 [00:00<00:00, 3834.31 it/sec, feas=True, obj=7.21]
INFO - 16:22:13: 5%|▍ | 46/1000 [00:00<00:00, 3836.89 it/sec, feas=True, obj=8.03]
INFO - 16:22:13: 5%|▍ | 47/1000 [00:00<00:00, 3841.61 it/sec, feas=True, obj=5.56]
INFO - 16:22:13: 5%|▍ | 48/1000 [00:00<00:00, 3847.10 it/sec, feas=True, obj=6.35]
INFO - 16:22:13: 5%|▍ | 49/1000 [00:00<00:00, 3845.11 it/sec, feas=True, obj=6.71]
INFO - 16:22:13: 5%|▌ | 50/1000 [00:00<00:00, 3846.29 it/sec, feas=True, obj=3.52]
INFO - 16:22:13: 5%|▌ | 51/1000 [00:00<00:00, 3845.43 it/sec, feas=True, obj=2.63]
INFO - 16:22:13: 5%|▌ | 52/1000 [00:00<00:00, 3847.51 it/sec, feas=True, obj=4.68]
INFO - 16:22:13: 5%|▌ | 53/1000 [00:00<00:00, 3842.53 it/sec, feas=True, obj=1.07]
INFO - 16:22:13: 5%|▌ | 54/1000 [00:00<00:00, 3845.11 it/sec, feas=True, obj=10.3]
INFO - 16:22:13: 6%|▌ | 55/1000 [00:00<00:00, 3848.76 it/sec, feas=True, obj=6.87]
INFO - 16:22:13: 6%|▌ | 56/1000 [00:00<00:00, 3847.54 it/sec, feas=True, obj=-3.82]
INFO - 16:22:13: 6%|▌ | 57/1000 [00:00<00:00, 3846.69 it/sec, feas=True, obj=-1.58]
INFO - 16:22:13: 6%|▌ | 58/1000 [00:00<00:00, 3849.93 it/sec, feas=True, obj=3.43]
INFO - 16:22:13: 6%|▌ | 59/1000 [00:00<00:00, 3853.26 it/sec, feas=True, obj=-6.44]
INFO - 16:22:13: 6%|▌ | 60/1000 [00:00<00:00, 3852.58 it/sec, feas=True, obj=0.167]
INFO - 16:22:13: 6%|▌ | 61/1000 [00:00<00:00, 3854.71 it/sec, feas=True, obj=2.98]
INFO - 16:22:13: 6%|▌ | 62/1000 [00:00<00:00, 3855.80 it/sec, feas=True, obj=0.771]
INFO - 16:22:13: 6%|▋ | 63/1000 [00:00<00:00, 3854.55 it/sec, feas=True, obj=6.98]
INFO - 16:22:13: 6%|▋ | 64/1000 [00:00<00:00, 3850.86 it/sec, feas=True, obj=6.81]
INFO - 16:22:13: 6%|▋ | 65/1000 [00:00<00:00, 3851.52 it/sec, feas=True, obj=0.257]
INFO - 16:22:13: 7%|▋ | 66/1000 [00:00<00:00, 3851.25 it/sec, feas=True, obj=3.31]
INFO - 16:22:13: 7%|▋ | 67/1000 [00:00<00:00, 3853.68 it/sec, feas=True, obj=6.07]
INFO - 16:22:13: 7%|▋ | 68/1000 [00:00<00:00, 3852.82 it/sec, feas=True, obj=5.87]
INFO - 16:22:13: 7%|▋ | 69/1000 [00:00<00:00, 3853.98 it/sec, feas=True, obj=7.69]
INFO - 16:22:13: 7%|▋ | 70/1000 [00:00<00:00, 3856.98 it/sec, feas=True, obj=5.16]
INFO - 16:22:13: 7%|▋ | 71/1000 [00:00<00:00, 3860.26 it/sec, feas=True, obj=-0.0811]
INFO - 16:22:13: 7%|▋ | 72/1000 [00:00<00:00, 3859.20 it/sec, feas=True, obj=1.25]
INFO - 16:22:13: 7%|▋ | 73/1000 [00:00<00:00, 3861.43 it/sec, feas=True, obj=5.71]
INFO - 16:22:13: 7%|▋ | 74/1000 [00:00<00:00, 3864.08 it/sec, feas=True, obj=8.15]
INFO - 16:22:13: 8%|▊ | 75/1000 [00:00<00:00, 3866.38 it/sec, feas=True, obj=-2.86]
INFO - 16:22:13: 8%|▊ | 76/1000 [00:00<00:00, 3865.01 it/sec, feas=True, obj=10.5]
INFO - 16:22:13: 8%|▊ | 77/1000 [00:00<00:00, 3866.83 it/sec, feas=True, obj=4.87]
INFO - 16:22:13: 8%|▊ | 78/1000 [00:00<00:00, 3870.02 it/sec, feas=True, obj=2.44]
INFO - 16:22:13: 8%|▊ | 79/1000 [00:00<00:00, 3873.26 it/sec, feas=True, obj=6.74]
INFO - 16:22:13: 8%|▊ | 80/1000 [00:00<00:00, 3871.83 it/sec, feas=True, obj=8.51]
INFO - 16:22:13: 8%|▊ | 81/1000 [00:00<00:00, 3873.12 it/sec, feas=True, obj=0.595]
INFO - 16:22:13: 8%|▊ | 82/1000 [00:00<00:00, 3872.94 it/sec, feas=True, obj=7.84]
INFO - 16:22:13: 8%|▊ | 83/1000 [00:00<00:00, 3875.10 it/sec, feas=True, obj=0.842]
INFO - 16:22:13: 8%|▊ | 84/1000 [00:00<00:00, 3873.88 it/sec, feas=True, obj=9.47]
INFO - 16:22:13: 8%|▊ | 85/1000 [00:00<00:00, 3875.55 it/sec, feas=True, obj=4.54]
INFO - 16:22:13: 9%|▊ | 86/1000 [00:00<00:00, 3877.60 it/sec, feas=True, obj=-3.69]
INFO - 16:22:13: 9%|▊ | 87/1000 [00:00<00:00, 3879.73 it/sec, feas=True, obj=0.849]
INFO - 16:22:13: 9%|▉ | 88/1000 [00:00<00:00, 3877.94 it/sec, feas=True, obj=11.9]
INFO - 16:22:13: 9%|▉ | 89/1000 [00:00<00:00, 3879.26 it/sec, feas=True, obj=5.69]
INFO - 16:22:13: 9%|▉ | 90/1000 [00:00<00:00, 3881.18 it/sec, feas=True, obj=7.25]
INFO - 16:22:13: 9%|▉ | 91/1000 [00:00<00:00, 3882.86 it/sec, feas=True, obj=2.17]
INFO - 16:22:13: 9%|▉ | 92/1000 [00:00<00:00, 3881.66 it/sec, feas=True, obj=5.76]
INFO - 16:22:13: 9%|▉ | 93/1000 [00:00<00:00, 3882.80 it/sec, feas=True, obj=5.32]
INFO - 16:22:13: 9%|▉ | 94/1000 [00:00<00:00, 3884.88 it/sec, feas=True, obj=-3.15]
INFO - 16:22:13: 10%|▉ | 95/1000 [00:00<00:00, 3886.80 it/sec, feas=True, obj=9.36]
INFO - 16:22:13: 10%|▉ | 96/1000 [00:00<00:00, 3884.89 it/sec, feas=True, obj=11.9]
INFO - 16:22:13: 10%|▉ | 97/1000 [00:00<00:00, 3886.14 it/sec, feas=True, obj=8.76]
INFO - 16:22:13: 10%|▉ | 98/1000 [00:00<00:00, 3886.37 it/sec, feas=True, obj=-0.112]
INFO - 16:22:13: 10%|▉ | 99/1000 [00:00<00:00, 3887.47 it/sec, feas=True, obj=1.61]
INFO - 16:22:13: 10%|█ | 100/1000 [00:00<00:00, 3885.34 it/sec, feas=True, obj=4.05]
INFO - 16:22:13: 10%|█ | 101/1000 [00:00<00:00, 3886.22 it/sec, feas=True, obj=-0.31]
INFO - 16:22:13: 10%|█ | 102/1000 [00:00<00:00, 3887.89 it/sec, feas=True, obj=1.46]
INFO - 16:22:13: 10%|█ | 103/1000 [00:00<00:00, 3889.70 it/sec, feas=True, obj=1.1]
INFO - 16:22:13: 10%|█ | 104/1000 [00:00<00:00, 3887.56 it/sec, feas=True, obj=2.69]
INFO - 16:22:13: 10%|█ | 105/1000 [00:00<00:00, 3888.52 it/sec, feas=True, obj=7.7]
INFO - 16:22:13: 11%|█ | 106/1000 [00:00<00:00, 3888.88 it/sec, feas=True, obj=4.86]
INFO - 16:22:13: 11%|█ | 107/1000 [00:00<00:00, 3890.11 it/sec, feas=True, obj=-0.104]
INFO - 16:22:13: 11%|█ | 108/1000 [00:00<00:00, 3888.78 it/sec, feas=True, obj=-7.95]
INFO - 16:22:13: 11%|█ | 109/1000 [00:00<00:00, 3890.36 it/sec, feas=True, obj=0.11]
INFO - 16:22:13: 11%|█ | 110/1000 [00:00<00:00, 3892.23 it/sec, feas=True, obj=-0.471]
INFO - 16:22:13: 11%|█ | 111/1000 [00:00<00:00, 3894.04 it/sec, feas=True, obj=-0.843]
INFO - 16:22:13: 11%|█ | 112/1000 [00:00<00:00, 3892.01 it/sec, feas=True, obj=3.99]
INFO - 16:22:13: 11%|█▏ | 113/1000 [00:00<00:00, 3893.82 it/sec, feas=True, obj=5.95]
INFO - 16:22:13: 11%|█▏ | 114/1000 [00:00<00:00, 3893.80 it/sec, feas=True, obj=6.56]
INFO - 16:22:13: 12%|█▏ | 115/1000 [00:00<00:00, 3895.56 it/sec, feas=True, obj=6.04]
INFO - 16:22:13: 12%|█▏ | 116/1000 [00:00<00:00, 3893.62 it/sec, feas=True, obj=0.26]
INFO - 16:22:13: 12%|█▏ | 117/1000 [00:00<00:00, 3895.36 it/sec, feas=True, obj=5.43]
INFO - 16:22:13: 12%|█▏ | 118/1000 [00:00<00:00, 3897.04 it/sec, feas=True, obj=0.706]
INFO - 16:22:13: 12%|█▏ | 119/1000 [00:00<00:00, 3895.47 it/sec, feas=True, obj=-0.861]
INFO - 16:22:13: 12%|█▏ | 120/1000 [00:00<00:00, 3895.07 it/sec, feas=True, obj=5.7]
INFO - 16:22:13: 12%|█▏ | 121/1000 [00:00<00:00, 3896.20 it/sec, feas=True, obj=3.13]
INFO - 16:22:13: 12%|█▏ | 122/1000 [00:00<00:00, 3897.73 it/sec, feas=True, obj=2.51]
INFO - 16:22:13: 12%|█▏ | 123/1000 [00:00<00:00, 3897.05 it/sec, feas=True, obj=0.0431]
INFO - 16:22:13: 12%|█▏ | 124/1000 [00:00<00:00, 3897.15 it/sec, feas=True, obj=4.08]
INFO - 16:22:13: 12%|█▎ | 125/1000 [00:00<00:00, 3898.31 it/sec, feas=True, obj=3.48]
INFO - 16:22:13: 13%|█▎ | 126/1000 [00:00<00:00, 3899.75 it/sec, feas=True, obj=-0.27]
INFO - 16:22:13: 13%|█▎ | 127/1000 [00:00<00:00, 3899.28 it/sec, feas=True, obj=6.25]
INFO - 16:22:13: 13%|█▎ | 128/1000 [00:00<00:00, 3898.45 it/sec, feas=True, obj=9.11]
INFO - 16:22:13: 13%|█▎ | 129/1000 [00:00<00:00, 3897.77 it/sec, feas=True, obj=-0.828]
INFO - 16:22:13: 13%|█▎ | 130/1000 [00:00<00:00, 3898.36 it/sec, feas=True, obj=10.3]
INFO - 16:22:13: 13%|█▎ | 131/1000 [00:00<00:00, 3897.06 it/sec, feas=True, obj=3.98]
INFO - 16:22:13: 13%|█▎ | 132/1000 [00:00<00:00, 3897.26 it/sec, feas=True, obj=2.27]
INFO - 16:22:13: 13%|█▎ | 133/1000 [00:00<00:00, 3897.92 it/sec, feas=True, obj=1.6]
INFO - 16:22:13: 13%|█▎ | 134/1000 [00:00<00:00, 3898.70 it/sec, feas=True, obj=-7.13]
INFO - 16:22:13: 14%|█▎ | 135/1000 [00:00<00:00, 3897.46 it/sec, feas=True, obj=7.82]
INFO - 16:22:13: 14%|█▎ | 136/1000 [00:00<00:00, 3897.97 it/sec, feas=True, obj=4.68]
INFO - 16:22:13: 14%|█▎ | 137/1000 [00:00<00:00, 3898.90 it/sec, feas=True, obj=-0.627]
INFO - 16:22:13: 14%|█▍ | 138/1000 [00:00<00:00, 3899.68 it/sec, feas=True, obj=-4.07]
INFO - 16:22:13: 14%|█▍ | 139/1000 [00:00<00:00, 3898.42 it/sec, feas=True, obj=1.06]
INFO - 16:22:13: 14%|█▍ | 140/1000 [00:00<00:00, 3898.80 it/sec, feas=True, obj=11.1]
INFO - 16:22:13: 14%|█▍ | 141/1000 [00:00<00:00, 3899.70 it/sec, feas=True, obj=1.87]
INFO - 16:22:13: 14%|█▍ | 142/1000 [00:00<00:00, 3900.35 it/sec, feas=True, obj=7.07]
INFO - 16:22:13: 14%|█▍ | 143/1000 [00:00<00:00, 3899.24 it/sec, feas=True, obj=1.79]
INFO - 16:22:13: 14%|█▍ | 144/1000 [00:00<00:00, 3898.98 it/sec, feas=True, obj=5.97]
INFO - 16:22:13: 14%|█▍ | 145/1000 [00:00<00:00, 3886.72 it/sec, feas=True, obj=6.15]
INFO - 16:22:13: 15%|█▍ | 146/1000 [00:00<00:00, 3884.35 it/sec, feas=True, obj=4.61]
INFO - 16:22:13: 15%|█▍ | 147/1000 [00:00<00:00, 3883.86 it/sec, feas=True, obj=0.433]
INFO - 16:22:13: 15%|█▍ | 148/1000 [00:00<00:00, 3884.20 it/sec, feas=True, obj=2.73]
INFO - 16:22:13: 15%|█▍ | 149/1000 [00:00<00:00, 3885.16 it/sec, feas=True, obj=1.83]
INFO - 16:22:13: 15%|█▌ | 150/1000 [00:00<00:00, 3884.12 it/sec, feas=True, obj=5.3]
INFO - 16:22:13: 15%|█▌ | 151/1000 [00:00<00:00, 3884.64 it/sec, feas=True, obj=-0.935]
INFO - 16:22:13: 15%|█▌ | 152/1000 [00:00<00:00, 3884.89 it/sec, feas=True, obj=7.03]
INFO - 16:22:13: 15%|█▌ | 153/1000 [00:00<00:00, 3884.95 it/sec, feas=True, obj=4.91]
INFO - 16:22:13: 15%|█▌ | 154/1000 [00:00<00:00, 3883.90 it/sec, feas=True, obj=5.89]
INFO - 16:22:13: 16%|█▌ | 155/1000 [00:00<00:00, 3884.68 it/sec, feas=True, obj=-1.07]
INFO - 16:22:13: 16%|█▌ | 156/1000 [00:00<00:00, 3884.65 it/sec, feas=True, obj=2.05]
INFO - 16:22:13: 16%|█▌ | 157/1000 [00:00<00:00, 3884.71 it/sec, feas=True, obj=9.08]
INFO - 16:22:13: 16%|█▌ | 158/1000 [00:00<00:00, 3882.86 it/sec, feas=True, obj=1.28]
INFO - 16:22:13: 16%|█▌ | 159/1000 [00:00<00:00, 3883.00 it/sec, feas=True, obj=5.5]
INFO - 16:22:13: 16%|█▌ | 160/1000 [00:00<00:00, 3882.22 it/sec, feas=True, obj=2.86]
INFO - 16:22:13: 16%|█▌ | 161/1000 [00:00<00:00, 3882.30 it/sec, feas=True, obj=2.58]
INFO - 16:22:13: 16%|█▌ | 162/1000 [00:00<00:00, 3881.17 it/sec, feas=True, obj=6.35]
INFO - 16:22:13: 16%|█▋ | 163/1000 [00:00<00:00, 3881.87 it/sec, feas=True, obj=5.03]
INFO - 16:22:13: 16%|█▋ | 164/1000 [00:00<00:00, 3882.72 it/sec, feas=True, obj=4.89]
INFO - 16:22:13: 16%|█▋ | 165/1000 [00:00<00:00, 3883.75 it/sec, feas=True, obj=-0.862]
INFO - 16:22:13: 17%|█▋ | 166/1000 [00:00<00:00, 3882.27 it/sec, feas=True, obj=5.17]
INFO - 16:22:13: 17%|█▋ | 167/1000 [00:00<00:00, 3882.73 it/sec, feas=True, obj=6.54]
INFO - 16:22:13: 17%|█▋ | 168/1000 [00:00<00:00, 3883.14 it/sec, feas=True, obj=5.04]
INFO - 16:22:13: 17%|█▋ | 169/1000 [00:00<00:00, 3883.83 it/sec, feas=True, obj=5.18]
INFO - 16:22:13: 17%|█▋ | 170/1000 [00:00<00:00, 3882.56 it/sec, feas=True, obj=9.72]
INFO - 16:22:13: 17%|█▋ | 171/1000 [00:00<00:00, 3883.40 it/sec, feas=True, obj=4.51]
INFO - 16:22:13: 17%|█▋ | 172/1000 [00:00<00:00, 3884.64 it/sec, feas=True, obj=5.25]
INFO - 16:22:13: 17%|█▋ | 173/1000 [00:00<00:00, 3885.76 it/sec, feas=True, obj=7.58]
INFO - 16:22:13: 17%|█▋ | 174/1000 [00:00<00:00, 3883.84 it/sec, feas=True, obj=-0.152]
INFO - 16:22:13: 18%|█▊ | 175/1000 [00:00<00:00, 3884.54 it/sec, feas=True, obj=0.707]
INFO - 16:22:13: 18%|█▊ | 176/1000 [00:00<00:00, 3884.25 it/sec, feas=True, obj=1.95]
INFO - 16:22:13: 18%|█▊ | 177/1000 [00:00<00:00, 3883.49 it/sec, feas=True, obj=5.37]
INFO - 16:22:13: 18%|█▊ | 178/1000 [00:00<00:00, 3883.92 it/sec, feas=True, obj=9.3]
INFO - 16:22:13: 18%|█▊ | 179/1000 [00:00<00:00, 3884.54 it/sec, feas=True, obj=-6.59]
INFO - 16:22:13: 18%|█▊ | 180/1000 [00:00<00:00, 3885.21 it/sec, feas=True, obj=0.62]
INFO - 16:22:13: 18%|█▊ | 181/1000 [00:00<00:00, 3884.67 it/sec, feas=True, obj=2.86]
INFO - 16:22:13: 18%|█▊ | 182/1000 [00:00<00:00, 3885.12 it/sec, feas=True, obj=7.64]
INFO - 16:22:13: 18%|█▊ | 183/1000 [00:00<00:00, 3886.03 it/sec, feas=True, obj=1.83]
INFO - 16:22:13: 18%|█▊ | 184/1000 [00:00<00:00, 3887.14 it/sec, feas=True, obj=1.15]
INFO - 16:22:13: 18%|█▊ | 185/1000 [00:00<00:00, 3886.71 it/sec, feas=True, obj=4.53]
INFO - 16:22:13: 19%|█▊ | 186/1000 [00:00<00:00, 3887.02 it/sec, feas=True, obj=5.86]
INFO - 16:22:13: 19%|█▊ | 187/1000 [00:00<00:00, 3887.93 it/sec, feas=True, obj=-4.15]
INFO - 16:22:13: 19%|█▉ | 188/1000 [00:00<00:00, 3888.54 it/sec, feas=True, obj=0.77]
INFO - 16:22:13: 19%|█▉ | 189/1000 [00:00<00:00, 3887.58 it/sec, feas=True, obj=3.77]
INFO - 16:22:13: 19%|█▉ | 190/1000 [00:00<00:00, 3888.09 it/sec, feas=True, obj=0.574]
INFO - 16:22:13: 19%|█▉ | 191/1000 [00:00<00:00, 3887.36 it/sec, feas=True, obj=3.27]
INFO - 16:22:13: 19%|█▉ | 192/1000 [00:00<00:00, 3887.95 it/sec, feas=True, obj=1.31]
INFO - 16:22:13: 19%|█▉ | 193/1000 [00:00<00:00, 3887.21 it/sec, feas=True, obj=2.11]
INFO - 16:22:13: 19%|█▉ | 194/1000 [00:00<00:00, 3887.81 it/sec, feas=True, obj=-0.324]
INFO - 16:22:13: 20%|█▉ | 195/1000 [00:00<00:00, 3888.43 it/sec, feas=True, obj=2.6]
INFO - 16:22:13: 20%|█▉ | 196/1000 [00:00<00:00, 3889.51 it/sec, feas=True, obj=7.25]
INFO - 16:22:13: 20%|█▉ | 197/1000 [00:00<00:00, 3889.10 it/sec, feas=True, obj=12.9]
INFO - 16:22:13: 20%|█▉ | 198/1000 [00:00<00:00, 3889.60 it/sec, feas=True, obj=-1.67]
INFO - 16:22:13: 20%|█▉ | 199/1000 [00:00<00:00, 3890.42 it/sec, feas=True, obj=6.19]
INFO - 16:22:13: 20%|██ | 200/1000 [00:00<00:00, 3891.04 it/sec, feas=True, obj=3.52]
INFO - 16:22:13: 20%|██ | 201/1000 [00:00<00:00, 3890.48 it/sec, feas=True, obj=-1.49]
INFO - 16:22:13: 20%|██ | 202/1000 [00:00<00:00, 3890.93 it/sec, feas=True, obj=7.77]
INFO - 16:22:13: 20%|██ | 203/1000 [00:00<00:00, 3891.67 it/sec, feas=True, obj=0.847]
INFO - 16:22:13: 20%|██ | 204/1000 [00:00<00:00, 3892.38 it/sec, feas=True, obj=-2.82]
INFO - 16:22:13: 20%|██ | 205/1000 [00:00<00:00, 3891.74 it/sec, feas=True, obj=6.59]
INFO - 16:22:13: 21%|██ | 206/1000 [00:00<00:00, 3891.96 it/sec, feas=True, obj=4.35]
INFO - 16:22:13: 21%|██ | 207/1000 [00:00<00:00, 3891.92 it/sec, feas=True, obj=-1.95]
INFO - 16:22:13: 21%|██ | 208/1000 [00:00<00:00, 3892.40 it/sec, feas=True, obj=-0.845]
INFO - 16:22:13: 21%|██ | 209/1000 [00:00<00:00, 3891.48 it/sec, feas=True, obj=7.19]
INFO - 16:22:13: 21%|██ | 210/1000 [00:00<00:00, 3891.92 it/sec, feas=True, obj=-0.108]
INFO - 16:22:13: 21%|██ | 211/1000 [00:00<00:00, 3892.63 it/sec, feas=True, obj=1.42]
INFO - 16:22:13: 21%|██ | 212/1000 [00:00<00:00, 3893.41 it/sec, feas=True, obj=0.785]
INFO - 16:22:13: 21%|██▏ | 213/1000 [00:00<00:00, 3892.60 it/sec, feas=True, obj=-4.97]
INFO - 16:22:13: 21%|██▏ | 214/1000 [00:00<00:00, 3893.18 it/sec, feas=True, obj=7.43]
INFO - 16:22:13: 22%|██▏ | 215/1000 [00:00<00:00, 3894.00 it/sec, feas=True, obj=6.26]
INFO - 16:22:13: 22%|██▏ | 216/1000 [00:00<00:00, 3894.87 it/sec, feas=True, obj=2.43]
INFO - 16:22:13: 22%|██▏ | 217/1000 [00:00<00:00, 3894.38 it/sec, feas=True, obj=11.5]
INFO - 16:22:13: 22%|██▏ | 218/1000 [00:00<00:00, 3895.15 it/sec, feas=True, obj=3.62]
INFO - 16:22:13: 22%|██▏ | 219/1000 [00:00<00:00, 3896.02 it/sec, feas=True, obj=5.45]
INFO - 16:22:13: 22%|██▏ | 220/1000 [00:00<00:00, 3896.69 it/sec, feas=True, obj=8.42]
INFO - 16:22:13: 22%|██▏ | 221/1000 [00:00<00:00, 3895.87 it/sec, feas=True, obj=10.5]
INFO - 16:22:13: 22%|██▏ | 222/1000 [00:00<00:00, 3896.50 it/sec, feas=True, obj=4.04]
INFO - 16:22:13: 22%|██▏ | 223/1000 [00:00<00:00, 3896.43 it/sec, feas=True, obj=-1.33]
INFO - 16:22:13: 22%|██▏ | 224/1000 [00:00<00:00, 3897.08 it/sec, feas=True, obj=5.55]
INFO - 16:22:13: 22%|██▎ | 225/1000 [00:00<00:00, 3896.41 it/sec, feas=True, obj=-0.71]
INFO - 16:22:13: 23%|██▎ | 226/1000 [00:00<00:00, 3896.56 it/sec, feas=True, obj=2.84]
INFO - 16:22:13: 23%|██▎ | 227/1000 [00:00<00:00, 3897.30 it/sec, feas=True, obj=1.75]
INFO - 16:22:13: 23%|██▎ | 228/1000 [00:00<00:00, 3897.96 it/sec, feas=True, obj=1.36]
INFO - 16:22:13: 23%|██▎ | 229/1000 [00:00<00:00, 3897.12 it/sec, feas=True, obj=6.32]
INFO - 16:22:13: 23%|██▎ | 230/1000 [00:00<00:00, 3897.78 it/sec, feas=True, obj=6.66]
INFO - 16:22:13: 23%|██▎ | 231/1000 [00:00<00:00, 3897.71 it/sec, feas=True, obj=5.61]
INFO - 16:22:13: 23%|██▎ | 232/1000 [00:00<00:00, 3897.90 it/sec, feas=True, obj=7.2]
INFO - 16:22:13: 23%|██▎ | 233/1000 [00:00<00:00, 3896.84 it/sec, feas=True, obj=6.4]
INFO - 16:22:13: 23%|██▎ | 234/1000 [00:00<00:00, 3897.12 it/sec, feas=True, obj=0.753]
INFO - 16:22:13: 24%|██▎ | 235/1000 [00:00<00:00, 3897.65 it/sec, feas=True, obj=-0.835]
INFO - 16:22:13: 24%|██▎ | 236/1000 [00:00<00:00, 3897.22 it/sec, feas=True, obj=-0.324]
INFO - 16:22:13: 24%|██▎ | 237/1000 [00:00<00:00, 3897.18 it/sec, feas=True, obj=3.91]
INFO - 16:22:13: 24%|██▍ | 238/1000 [00:00<00:00, 3897.70 it/sec, feas=True, obj=6.01]
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INFO - 16:22:13: 26%|██▋ | 265/1000 [00:00<00:00, 3884.99 it/sec, feas=True, obj=9.9]
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INFO - 16:22:13: 27%|██▋ | 271/1000 [00:00<00:00, 3884.12 it/sec, feas=True, obj=14.3]
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INFO - 16:22:13: 28%|██▊ | 278/1000 [00:00<00:00, 3885.04 it/sec, feas=True, obj=10.9]
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INFO - 16:22:13: 29%|██▊ | 286/1000 [00:00<00:00, 3884.78 it/sec, feas=True, obj=-5.11]
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INFO - 16:22:13: 30%|██▉ | 295/1000 [00:00<00:00, 3884.65 it/sec, feas=True, obj=6.14]
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INFO - 16:22:13: 30%|███ | 301/1000 [00:00<00:00, 3885.63 it/sec, feas=True, obj=4]
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INFO - 16:22:13: 31%|███ | 309/1000 [00:00<00:00, 3886.82 it/sec, feas=True, obj=2.99]
INFO - 16:22:13: 31%|███ | 310/1000 [00:00<00:00, 3886.21 it/sec, feas=True, obj=2.3]
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INFO - 16:22:13: 31%|███▏ | 313/1000 [00:00<00:00, 3887.77 it/sec, feas=True, obj=6.43]
INFO - 16:22:13: 31%|███▏ | 314/1000 [00:00<00:00, 3887.11 it/sec, feas=True, obj=-1.98]
INFO - 16:22:13: 32%|███▏ | 315/1000 [00:00<00:00, 3887.57 it/sec, feas=True, obj=1.03]
INFO - 16:22:13: 32%|███▏ | 316/1000 [00:00<00:00, 3887.58 it/sec, feas=True, obj=-0.511]
INFO - 16:22:13: 32%|███▏ | 317/1000 [00:00<00:00, 3888.09 it/sec, feas=True, obj=-1.34]
INFO - 16:22:13: 32%|███▏ | 318/1000 [00:00<00:00, 3887.36 it/sec, feas=True, obj=6.72]
INFO - 16:22:13: 32%|███▏ | 319/1000 [00:00<00:00, 3887.68 it/sec, feas=True, obj=3.09]
INFO - 16:22:13: 32%|███▏ | 320/1000 [00:00<00:00, 3888.17 it/sec, feas=True, obj=7.12]
INFO - 16:22:13: 32%|███▏ | 321/1000 [00:00<00:00, 3888.01 it/sec, feas=True, obj=5.91]
INFO - 16:22:13: 32%|███▏ | 322/1000 [00:00<00:00, 3888.00 it/sec, feas=True, obj=0.0303]
INFO - 16:22:13: 32%|███▏ | 323/1000 [00:00<00:00, 3888.55 it/sec, feas=True, obj=1.38]
INFO - 16:22:13: 32%|███▏ | 324/1000 [00:00<00:00, 3889.06 it/sec, feas=True, obj=-5.06]
INFO - 16:22:13: 32%|███▎ | 325/1000 [00:00<00:00, 3888.96 it/sec, feas=True, obj=1.18]
INFO - 16:22:13: 33%|███▎ | 326/1000 [00:00<00:00, 3889.09 it/sec, feas=True, obj=0.213]
INFO - 16:22:13: 33%|███▎ | 327/1000 [00:00<00:00, 3889.52 it/sec, feas=True, obj=5.4]
INFO - 16:22:13: 33%|███▎ | 328/1000 [00:00<00:00, 3890.04 it/sec, feas=True, obj=3.09]
INFO - 16:22:13: 33%|███▎ | 329/1000 [00:00<00:00, 3889.76 it/sec, feas=True, obj=1.28]
INFO - 16:22:13: 33%|███▎ | 330/1000 [00:00<00:00, 3890.04 it/sec, feas=True, obj=7.37]
INFO - 16:22:13: 33%|███▎ | 331/1000 [00:00<00:00, 3890.13 it/sec, feas=True, obj=1.31]
INFO - 16:22:13: 33%|███▎ | 332/1000 [00:00<00:00, 3890.59 it/sec, feas=True, obj=2.05]
INFO - 16:22:13: 33%|███▎ | 333/1000 [00:00<00:00, 3890.39 it/sec, feas=True, obj=1.55]
INFO - 16:22:13: 33%|███▎ | 334/1000 [00:00<00:00, 3890.67 it/sec, feas=True, obj=2.46]
INFO - 16:22:13: 34%|███▎ | 335/1000 [00:00<00:00, 3891.26 it/sec, feas=True, obj=1.51]
INFO - 16:22:13: 34%|███▎ | 336/1000 [00:00<00:00, 3891.84 it/sec, feas=True, obj=5.43]
INFO - 16:22:13: 34%|███▎ | 337/1000 [00:00<00:00, 3891.74 it/sec, feas=True, obj=1.14]
INFO - 16:22:13: 34%|███▍ | 338/1000 [00:00<00:00, 3891.81 it/sec, feas=True, obj=7.29]
INFO - 16:22:13: 34%|███▍ | 339/1000 [00:00<00:00, 3892.23 it/sec, feas=True, obj=-0.283]
INFO - 16:22:13: 34%|███▍ | 340/1000 [00:00<00:00, 3892.65 it/sec, feas=True, obj=0.734]
INFO - 16:22:13: 34%|███▍ | 341/1000 [00:00<00:00, 3892.25 it/sec, feas=True, obj=-3.46]
INFO - 16:22:13: 34%|███▍ | 342/1000 [00:00<00:00, 3892.22 it/sec, feas=True, obj=4.12]
INFO - 16:22:13: 34%|███▍ | 343/1000 [00:00<00:00, 3892.66 it/sec, feas=True, obj=3.79]
INFO - 16:22:13: 34%|███▍ | 344/1000 [00:00<00:00, 3893.04 it/sec, feas=True, obj=-3.15]
INFO - 16:22:13: 34%|███▍ | 345/1000 [00:00<00:00, 3892.66 it/sec, feas=True, obj=7.56]
INFO - 16:22:13: 35%|███▍ | 346/1000 [00:00<00:00, 3892.77 it/sec, feas=True, obj=-0.553]
INFO - 16:22:13: 35%|███▍ | 347/1000 [00:00<00:00, 3893.00 it/sec, feas=True, obj=1.43]
INFO - 16:22:13: 35%|███▍ | 348/1000 [00:00<00:00, 3893.36 it/sec, feas=True, obj=-0.851]
INFO - 16:22:13: 35%|███▍ | 349/1000 [00:00<00:00, 3892.82 it/sec, feas=True, obj=8.57]
INFO - 16:22:13: 35%|███▌ | 350/1000 [00:00<00:00, 3892.98 it/sec, feas=True, obj=0.921]
INFO - 16:22:13: 35%|███▌ | 351/1000 [00:00<00:00, 3893.41 it/sec, feas=True, obj=3.01]
INFO - 16:22:13: 35%|███▌ | 352/1000 [00:00<00:00, 3893.83 it/sec, feas=True, obj=4.98]
INFO - 16:22:13: 35%|███▌ | 353/1000 [00:00<00:00, 3893.43 it/sec, feas=True, obj=6.06]
INFO - 16:22:13: 35%|███▌ | 354/1000 [00:00<00:00, 3893.56 it/sec, feas=True, obj=7.26]
INFO - 16:22:13: 36%|███▌ | 355/1000 [00:00<00:00, 3893.75 it/sec, feas=True, obj=5.71]
INFO - 16:22:13: 36%|███▌ | 356/1000 [00:00<00:00, 3893.99 it/sec, feas=True, obj=-5.07]
INFO - 16:22:13: 36%|███▌ | 357/1000 [00:00<00:00, 3893.55 it/sec, feas=True, obj=6.62]
INFO - 16:22:13: 36%|███▌ | 358/1000 [00:00<00:00, 3893.70 it/sec, feas=True, obj=5.86]
INFO - 16:22:13: 36%|███▌ | 359/1000 [00:00<00:00, 3894.15 it/sec, feas=True, obj=6.1]
INFO - 16:22:13: 36%|███▌ | 360/1000 [00:00<00:00, 3894.66 it/sec, feas=True, obj=-0.545]
INFO - 16:22:13: 36%|███▌ | 361/1000 [00:00<00:00, 3894.24 it/sec, feas=True, obj=3.86]
INFO - 16:22:13: 36%|███▌ | 362/1000 [00:00<00:00, 3894.45 it/sec, feas=True, obj=8.51]
INFO - 16:22:13: 36%|███▋ | 363/1000 [00:00<00:00, 3894.13 it/sec, feas=True, obj=5.33]
INFO - 16:22:13: 36%|███▋ | 364/1000 [00:00<00:00, 3894.33 it/sec, feas=True, obj=7.14]
INFO - 16:22:13: 36%|███▋ | 365/1000 [00:00<00:00, 3893.73 it/sec, feas=True, obj=4.01]
INFO - 16:22:13: 37%|███▋ | 366/1000 [00:00<00:00, 3893.83 it/sec, feas=True, obj=2.9]
INFO - 16:22:13: 37%|███▋ | 367/1000 [00:00<00:00, 3893.96 it/sec, feas=True, obj=6.25]
INFO - 16:22:13: 37%|███▋ | 368/1000 [00:00<00:00, 3894.18 it/sec, feas=True, obj=6.85]
INFO - 16:22:13: 37%|███▋ | 369/1000 [00:00<00:00, 3893.08 it/sec, feas=True, obj=4.32]
INFO - 16:22:13: 37%|███▋ | 370/1000 [00:00<00:00, 3893.29 it/sec, feas=True, obj=4.87]
INFO - 16:22:13: 37%|███▋ | 371/1000 [00:00<00:00, 3893.68 it/sec, feas=True, obj=6.43]
INFO - 16:22:13: 37%|███▋ | 372/1000 [00:00<00:00, 3893.95 it/sec, feas=True, obj=2.86]
INFO - 16:22:13: 37%|███▋ | 373/1000 [00:00<00:00, 3888.65 it/sec, feas=True, obj=0.891]
INFO - 16:22:13: 37%|███▋ | 374/1000 [00:00<00:00, 3888.60 it/sec, feas=True, obj=6.47]
INFO - 16:22:13: 38%|███▊ | 375/1000 [00:00<00:00, 3888.84 it/sec, feas=True, obj=-1.87]
INFO - 16:22:13: 38%|███▊ | 376/1000 [00:00<00:00, 3888.32 it/sec, feas=True, obj=-3.28]
INFO - 16:22:13: 38%|███▊ | 377/1000 [00:00<00:00, 3888.39 it/sec, feas=True, obj=0.0745]
INFO - 16:22:13: 38%|███▊ | 378/1000 [00:00<00:00, 3888.32 it/sec, feas=True, obj=5.9]
INFO - 16:22:13: 38%|███▊ | 379/1000 [00:00<00:00, 3888.59 it/sec, feas=True, obj=4.69]
INFO - 16:22:13: 38%|███▊ | 380/1000 [00:00<00:00, 3887.69 it/sec, feas=True, obj=4.66]
INFO - 16:22:13: 38%|███▊ | 381/1000 [00:00<00:00, 3887.87 it/sec, feas=True, obj=6.07]
INFO - 16:22:13: 38%|███▊ | 382/1000 [00:00<00:00, 3888.02 it/sec, feas=True, obj=0.959]
INFO - 16:22:13: 38%|███▊ | 383/1000 [00:00<00:00, 3888.32 it/sec, feas=True, obj=1.9]
INFO - 16:22:13: 38%|███▊ | 384/1000 [00:00<00:00, 3887.71 it/sec, feas=True, obj=7.91]
INFO - 16:22:13: 38%|███▊ | 385/1000 [00:00<00:00, 3887.99 it/sec, feas=True, obj=-0.448]
INFO - 16:22:13: 39%|███▊ | 386/1000 [00:00<00:00, 3888.11 it/sec, feas=True, obj=5.33]
INFO - 16:22:13: 39%|███▊ | 387/1000 [00:00<00:00, 3888.48 it/sec, feas=True, obj=2.88]
INFO - 16:22:13: 39%|███▉ | 388/1000 [00:00<00:00, 3887.94 it/sec, feas=True, obj=0.55]
INFO - 16:22:13: 39%|███▉ | 389/1000 [00:00<00:00, 3888.32 it/sec, feas=True, obj=0.392]
INFO - 16:22:13: 39%|███▉ | 390/1000 [00:00<00:00, 3888.64 it/sec, feas=True, obj=3.32]
INFO - 16:22:13: 39%|███▉ | 391/1000 [00:00<00:00, 3888.32 it/sec, feas=True, obj=7.88]
INFO - 16:22:13: 39%|███▉ | 392/1000 [00:00<00:00, 3888.44 it/sec, feas=True, obj=1.46]
INFO - 16:22:13: 39%|███▉ | 393/1000 [00:00<00:00, 3888.89 it/sec, feas=True, obj=9.49]
INFO - 16:22:13: 39%|███▉ | 394/1000 [00:00<00:00, 3888.70 it/sec, feas=True, obj=-8.6]
INFO - 16:22:13: 40%|███▉ | 395/1000 [00:00<00:00, 3888.36 it/sec, feas=True, obj=6]
INFO - 16:22:13: 40%|███▉ | 396/1000 [00:00<00:00, 3888.29 it/sec, feas=True, obj=6.89]
INFO - 16:22:13: 40%|███▉ | 397/1000 [00:00<00:00, 3888.56 it/sec, feas=True, obj=5.17]
INFO - 16:22:13: 40%|███▉ | 398/1000 [00:00<00:00, 3888.44 it/sec, feas=True, obj=9.21]
INFO - 16:22:13: 40%|███▉ | 399/1000 [00:00<00:00, 3887.97 it/sec, feas=True, obj=8.46]
INFO - 16:22:13: 40%|████ | 400/1000 [00:00<00:00, 3888.00 it/sec, feas=True, obj=9.92]
INFO - 16:22:13: 40%|████ | 401/1000 [00:00<00:00, 3888.36 it/sec, feas=True, obj=2.5]
INFO - 16:22:13: 40%|████ | 402/1000 [00:00<00:00, 3888.72 it/sec, feas=True, obj=2.82]
INFO - 16:22:13: 40%|████ | 403/1000 [00:00<00:00, 3888.14 it/sec, feas=True, obj=9.71]
INFO - 16:22:13: 40%|████ | 404/1000 [00:00<00:00, 3888.26 it/sec, feas=True, obj=-1.54]
INFO - 16:22:13: 40%|████ | 405/1000 [00:00<00:00, 3888.49 it/sec, feas=True, obj=-1.42]
INFO - 16:22:13: 41%|████ | 406/1000 [00:00<00:00, 3888.63 it/sec, feas=True, obj=7.52]
INFO - 16:22:13: 41%|████ | 407/1000 [00:00<00:00, 3888.06 it/sec, feas=True, obj=3.95]
INFO - 16:22:13: 41%|████ | 408/1000 [00:00<00:00, 3888.09 it/sec, feas=True, obj=5.33]
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INFO - 16:22:13: 46%|████▌ | 460/1000 [00:00<00:00, 3886.42 it/sec, feas=True, obj=3.08]
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INFO - 16:22:13: 47%|████▋ | 466/1000 [00:00<00:00, 3885.75 it/sec, feas=True, obj=0.986]
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INFO - 16:22:13: 48%|████▊ | 475/1000 [00:00<00:00, 3885.42 it/sec, feas=True, obj=13.5]
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INFO - 16:22:13: 48%|████▊ | 484/1000 [00:00<00:00, 3883.89 it/sec, feas=True, obj=1]
INFO - 16:22:13: 48%|████▊ | 485/1000 [00:00<00:00, 3883.91 it/sec, feas=True, obj=6.11]
INFO - 16:22:13: 49%|████▊ | 486/1000 [00:00<00:00, 3883.65 it/sec, feas=True, obj=0.946]
INFO - 16:22:13: 49%|████▊ | 487/1000 [00:00<00:00, 3880.33 it/sec, feas=True, obj=2.29]
INFO - 16:22:13: 49%|████▉ | 488/1000 [00:00<00:00, 3879.66 it/sec, feas=True, obj=9.42]
INFO - 16:22:13: 49%|████▉ | 489/1000 [00:00<00:00, 3879.90 it/sec, feas=True, obj=5.01]
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INFO - 16:22:13: 49%|████▉ | 491/1000 [00:00<00:00, 3880.47 it/sec, feas=True, obj=3.59]
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INFO - 16:22:13: 49%|████▉ | 493/1000 [00:00<00:00, 3880.18 it/sec, feas=True, obj=8.7]
INFO - 16:22:13: 49%|████▉ | 494/1000 [00:00<00:00, 3880.33 it/sec, feas=True, obj=5.6]
INFO - 16:22:13: 50%|████▉ | 495/1000 [00:00<00:00, 3879.99 it/sec, feas=True, obj=-0.897]
INFO - 16:22:13: 50%|████▉ | 496/1000 [00:00<00:00, 3879.90 it/sec, feas=True, obj=7.82]
INFO - 16:22:13: 50%|████▉ | 497/1000 [00:00<00:00, 3880.16 it/sec, feas=True, obj=6.63]
INFO - 16:22:13: 50%|████▉ | 498/1000 [00:00<00:00, 3880.45 it/sec, feas=True, obj=3.33]
INFO - 16:22:13: 50%|████▉ | 499/1000 [00:00<00:00, 3880.19 it/sec, feas=True, obj=4.36]
INFO - 16:22:13: 50%|█████ | 500/1000 [00:00<00:00, 3880.05 it/sec, feas=True, obj=4.21]
INFO - 16:22:13: 50%|█████ | 501/1000 [00:00<00:00, 3879.86 it/sec, feas=True, obj=3.93]
INFO - 16:22:13: 50%|█████ | 502/1000 [00:00<00:00, 3879.44 it/sec, feas=True, obj=10.4]
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INFO - 16:22:13: 50%|█████ | 504/1000 [00:00<00:00, 3879.10 it/sec, feas=True, obj=-0.386]
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INFO - 16:22:13: 51%|█████ | 506/1000 [00:00<00:00, 3879.36 it/sec, feas=True, obj=4.8]
INFO - 16:22:13: 51%|█████ | 507/1000 [00:00<00:00, 3879.05 it/sec, feas=True, obj=7.94]
INFO - 16:22:13: 51%|█████ | 508/1000 [00:00<00:00, 3879.10 it/sec, feas=True, obj=1.6]
INFO - 16:22:13: 51%|█████ | 509/1000 [00:00<00:00, 3879.27 it/sec, feas=True, obj=8.91]
INFO - 16:22:13: 51%|█████ | 510/1000 [00:00<00:00, 3879.52 it/sec, feas=True, obj=9.47]
INFO - 16:22:13: 51%|█████ | 511/1000 [00:00<00:00, 3879.07 it/sec, feas=True, obj=-0.535]
INFO - 16:22:13: 51%|█████ | 512/1000 [00:00<00:00, 3879.27 it/sec, feas=True, obj=2.76]
INFO - 16:22:13: 51%|█████▏ | 513/1000 [00:00<00:00, 3879.48 it/sec, feas=True, obj=0.439]
INFO - 16:22:13: 51%|█████▏ | 514/1000 [00:00<00:00, 3879.74 it/sec, feas=True, obj=3.69]
INFO - 16:22:13: 52%|█████▏ | 515/1000 [00:00<00:00, 3879.08 it/sec, feas=True, obj=-1.1]
INFO - 16:22:13: 52%|█████▏ | 516/1000 [00:00<00:00, 3879.16 it/sec, feas=True, obj=2.48]
INFO - 16:22:13: 52%|█████▏ | 517/1000 [00:00<00:00, 3879.11 it/sec, feas=True, obj=2.8]
INFO - 16:22:13: 52%|█████▏ | 518/1000 [00:00<00:00, 3879.37 it/sec, feas=True, obj=13]
INFO - 16:22:13: 52%|█████▏ | 519/1000 [00:00<00:00, 3879.01 it/sec, feas=True, obj=6.01]
INFO - 16:22:13: 52%|█████▏ | 520/1000 [00:00<00:00, 3879.21 it/sec, feas=True, obj=2.49]
INFO - 16:22:13: 52%|█████▏ | 521/1000 [00:00<00:00, 3879.40 it/sec, feas=True, obj=5.92]
INFO - 16:22:13: 52%|█████▏ | 522/1000 [00:00<00:00, 3879.25 it/sec, feas=True, obj=3.4]
INFO - 16:22:13: 52%|█████▏ | 523/1000 [00:00<00:00, 3879.24 it/sec, feas=True, obj=-1.78]
INFO - 16:22:13: 52%|█████▏ | 524/1000 [00:00<00:00, 3879.45 it/sec, feas=True, obj=2.44]
INFO - 16:22:13: 52%|█████▎ | 525/1000 [00:00<00:00, 3879.59 it/sec, feas=True, obj=16]
INFO - 16:22:13: 53%|█████▎ | 526/1000 [00:00<00:00, 3879.26 it/sec, feas=True, obj=6.22]
INFO - 16:22:13: 53%|█████▎ | 527/1000 [00:00<00:00, 3879.28 it/sec, feas=True, obj=7.2]
INFO - 16:22:13: 53%|█████▎ | 528/1000 [00:00<00:00, 3879.40 it/sec, feas=True, obj=4.57]
INFO - 16:22:13: 53%|█████▎ | 529/1000 [00:00<00:00, 3879.60 it/sec, feas=True, obj=6.77]
INFO - 16:22:13: 53%|█████▎ | 530/1000 [00:00<00:00, 3879.24 it/sec, feas=True, obj=13]
INFO - 16:22:13: 53%|█████▎ | 531/1000 [00:00<00:00, 3879.31 it/sec, feas=True, obj=5]
INFO - 16:22:13: 53%|█████▎ | 532/1000 [00:00<00:00, 3879.01 it/sec, feas=True, obj=-0.711]
INFO - 16:22:13: 53%|█████▎ | 533/1000 [00:00<00:00, 3879.03 it/sec, feas=True, obj=-0.543]
INFO - 16:22:13: 53%|█████▎ | 534/1000 [00:00<00:00, 3878.52 it/sec, feas=True, obj=0.469]
INFO - 16:22:13: 54%|█████▎ | 535/1000 [00:00<00:00, 3878.41 it/sec, feas=True, obj=4.16]
INFO - 16:22:13: 54%|█████▎ | 536/1000 [00:00<00:00, 3878.47 it/sec, feas=True, obj=4.73]
INFO - 16:22:13: 54%|█████▎ | 537/1000 [00:00<00:00, 3878.61 it/sec, feas=True, obj=-0.197]
INFO - 16:22:13: 54%|█████▍ | 538/1000 [00:00<00:00, 3878.17 it/sec, feas=True, obj=-2.45]
INFO - 16:22:13: 54%|█████▍ | 539/1000 [00:00<00:00, 3878.34 it/sec, feas=True, obj=2.9]
INFO - 16:22:13: 54%|█████▍ | 540/1000 [00:00<00:00, 3878.56 it/sec, feas=True, obj=4.59]
INFO - 16:22:13: 54%|█████▍ | 541/1000 [00:00<00:00, 3878.73 it/sec, feas=True, obj=4.09]
INFO - 16:22:13: 54%|█████▍ | 542/1000 [00:00<00:00, 3878.30 it/sec, feas=True, obj=0.0786]
INFO - 16:22:13: 54%|█████▍ | 543/1000 [00:00<00:00, 3878.43 it/sec, feas=True, obj=6.9]
INFO - 16:22:13: 54%|█████▍ | 544/1000 [00:00<00:00, 3878.57 it/sec, feas=True, obj=3.77]
INFO - 16:22:13: 55%|█████▍ | 545/1000 [00:00<00:00, 3878.78 it/sec, feas=True, obj=2.68]
INFO - 16:22:13: 55%|█████▍ | 546/1000 [00:00<00:00, 3878.32 it/sec, feas=True, obj=5.03]
INFO - 16:22:13: 55%|█████▍ | 547/1000 [00:00<00:00, 3878.56 it/sec, feas=True, obj=7.02]
INFO - 16:22:13: 55%|█████▍ | 548/1000 [00:00<00:00, 3878.57 it/sec, feas=True, obj=7]
INFO - 16:22:13: 55%|█████▍ | 549/1000 [00:00<00:00, 3878.39 it/sec, feas=True, obj=1.03]
INFO - 16:22:13: 55%|█████▌ | 550/1000 [00:00<00:00, 3878.33 it/sec, feas=True, obj=4.74]
INFO - 16:22:13: 55%|█████▌ | 551/1000 [00:00<00:00, 3878.47 it/sec, feas=True, obj=-0.817]
INFO - 16:22:13: 55%|█████▌ | 552/1000 [00:00<00:00, 3878.72 it/sec, feas=True, obj=2.59]
INFO - 16:22:13: 55%|█████▌ | 553/1000 [00:00<00:00, 3878.50 it/sec, feas=True, obj=3.33]
INFO - 16:22:13: 55%|█████▌ | 554/1000 [00:00<00:00, 3878.56 it/sec, feas=True, obj=2.13]
INFO - 16:22:13: 56%|█████▌ | 555/1000 [00:00<00:00, 3878.78 it/sec, feas=True, obj=-0.076]
INFO - 16:22:13: 56%|█████▌ | 556/1000 [00:00<00:00, 3878.96 it/sec, feas=True, obj=-0.023]
INFO - 16:22:13: 56%|█████▌ | 557/1000 [00:00<00:00, 3878.66 it/sec, feas=True, obj=7.03]
INFO - 16:22:13: 56%|█████▌ | 558/1000 [00:00<00:00, 3878.69 it/sec, feas=True, obj=3.4]
INFO - 16:22:13: 56%|█████▌ | 559/1000 [00:00<00:00, 3878.87 it/sec, feas=True, obj=-1.23]
INFO - 16:22:13: 56%|█████▌ | 560/1000 [00:00<00:00, 3879.09 it/sec, feas=True, obj=7.3]
INFO - 16:22:13: 56%|█████▌ | 561/1000 [00:00<00:00, 3878.76 it/sec, feas=True, obj=4.59]
INFO - 16:22:13: 56%|█████▌ | 562/1000 [00:00<00:00, 3878.75 it/sec, feas=True, obj=-0.53]
INFO - 16:22:13: 56%|█████▋ | 563/1000 [00:00<00:00, 3878.66 it/sec, feas=True, obj=7.24]
INFO - 16:22:13: 56%|█████▋ | 564/1000 [00:00<00:00, 3878.88 it/sec, feas=True, obj=-0.753]
INFO - 16:22:13: 56%|█████▋ | 565/1000 [00:00<00:00, 3878.66 it/sec, feas=True, obj=7.78]
INFO - 16:22:13: 57%|█████▋ | 566/1000 [00:00<00:00, 3878.77 it/sec, feas=True, obj=6.64]
INFO - 16:22:13: 57%|█████▋ | 567/1000 [00:00<00:00, 3878.86 it/sec, feas=True, obj=0.671]
INFO - 16:22:13: 57%|█████▋ | 568/1000 [00:00<00:00, 3879.13 it/sec, feas=True, obj=-2]
INFO - 16:22:13: 57%|█████▋ | 569/1000 [00:00<00:00, 3878.77 it/sec, feas=True, obj=-1.92]
INFO - 16:22:13: 57%|█████▋ | 570/1000 [00:00<00:00, 3878.96 it/sec, feas=True, obj=6.03]
INFO - 16:22:13: 57%|█████▋ | 571/1000 [00:00<00:00, 3879.28 it/sec, feas=True, obj=9.42]
INFO - 16:22:13: 57%|█████▋ | 572/1000 [00:00<00:00, 3879.57 it/sec, feas=True, obj=1.01]
INFO - 16:22:13: 57%|█████▋ | 573/1000 [00:00<00:00, 3879.27 it/sec, feas=True, obj=1.43]
INFO - 16:22:13: 57%|█████▋ | 574/1000 [00:00<00:00, 3879.40 it/sec, feas=True, obj=0.0646]
INFO - 16:22:13: 57%|█████▊ | 575/1000 [00:00<00:00, 3879.66 it/sec, feas=True, obj=5.16]
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INFO - 16:22:13: 58%|█████▊ | 577/1000 [00:00<00:00, 3879.44 it/sec, feas=True, obj=0.944]
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INFO - 16:22:13: 58%|█████▊ | 579/1000 [00:00<00:00, 3879.49 it/sec, feas=True, obj=1.86]
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INFO - 16:22:13: 58%|█████▊ | 582/1000 [00:00<00:00, 3879.41 it/sec, feas=True, obj=6.58]
INFO - 16:22:13: 58%|█████▊ | 583/1000 [00:00<00:00, 3879.44 it/sec, feas=True, obj=-0.0337]
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INFO - 16:22:13: 59%|█████▊ | 586/1000 [00:00<00:00, 3879.33 it/sec, feas=True, obj=5.55]
INFO - 16:22:13: 59%|█████▊ | 587/1000 [00:00<00:00, 3879.51 it/sec, feas=True, obj=5.28]
INFO - 16:22:13: 59%|█████▉ | 588/1000 [00:00<00:00, 3879.07 it/sec, feas=True, obj=3.22]
INFO - 16:22:13: 59%|█████▉ | 589/1000 [00:00<00:00, 3878.74 it/sec, feas=True, obj=3.1]
INFO - 16:22:13: 59%|█████▉ | 590/1000 [00:00<00:00, 3878.77 it/sec, feas=True, obj=5.83]
INFO - 16:22:13: 59%|█████▉ | 591/1000 [00:00<00:00, 3878.83 it/sec, feas=True, obj=4.03]
INFO - 16:22:13: 59%|█████▉ | 592/1000 [00:00<00:00, 3878.39 it/sec, feas=True, obj=-3.08]
INFO - 16:22:13: 59%|█████▉ | 593/1000 [00:00<00:00, 3878.33 it/sec, feas=True, obj=3.63]
INFO - 16:22:13: 59%|█████▉ | 594/1000 [00:00<00:00, 3878.20 it/sec, feas=True, obj=0.374]
INFO - 16:22:13: 60%|█████▉ | 595/1000 [00:00<00:00, 3878.27 it/sec, feas=True, obj=7.07]
INFO - 16:22:13: 60%|█████▉ | 596/1000 [00:00<00:00, 3877.98 it/sec, feas=True, obj=0.707]
INFO - 16:22:13: 60%|█████▉ | 597/1000 [00:00<00:00, 3855.27 it/sec, feas=True, obj=5.65]
INFO - 16:22:13: 60%|█████▉ | 598/1000 [00:00<00:00, 3831.20 it/sec, feas=True, obj=5.83]
INFO - 16:22:13: 60%|█████▉ | 599/1000 [00:00<00:00, 3831.20 it/sec, feas=True, obj=4.2]
INFO - 16:22:13: 60%|██████ | 600/1000 [00:00<00:00, 3830.86 it/sec, feas=True, obj=-0.0744]
INFO - 16:22:13: 60%|██████ | 601/1000 [00:00<00:00, 3827.98 it/sec, feas=True, obj=0.391]
INFO - 16:22:13: 60%|██████ | 602/1000 [00:00<00:00, 3827.61 it/sec, feas=True, obj=4.96]
INFO - 16:22:13: 60%|██████ | 603/1000 [00:00<00:00, 3827.20 it/sec, feas=True, obj=2.18]
INFO - 16:22:13: 60%|██████ | 604/1000 [00:00<00:00, 3826.91 it/sec, feas=True, obj=1.55]
INFO - 16:22:13: 60%|██████ | 605/1000 [00:00<00:00, 3826.84 it/sec, feas=True, obj=6.26]
INFO - 16:22:13: 61%|██████ | 606/1000 [00:00<00:00, 3826.96 it/sec, feas=True, obj=5.3]
INFO - 16:22:13: 61%|██████ | 607/1000 [00:00<00:00, 3826.68 it/sec, feas=True, obj=7.18]
INFO - 16:22:13: 61%|██████ | 608/1000 [00:00<00:00, 3826.64 it/sec, feas=True, obj=1.45]
INFO - 16:22:13: 61%|██████ | 609/1000 [00:00<00:00, 3826.84 it/sec, feas=True, obj=8.78]
INFO - 16:22:13: 61%|██████ | 610/1000 [00:00<00:00, 3826.98 it/sec, feas=True, obj=0.233]
INFO - 16:22:13: 61%|██████ | 611/1000 [00:00<00:00, 3826.66 it/sec, feas=True, obj=-1.38]
INFO - 16:22:13: 61%|██████ | 612/1000 [00:00<00:00, 3826.70 it/sec, feas=True, obj=6.09]
INFO - 16:22:13: 61%|██████▏ | 613/1000 [00:00<00:00, 3826.91 it/sec, feas=True, obj=5.58]
INFO - 16:22:13: 61%|██████▏ | 614/1000 [00:00<00:00, 3827.18 it/sec, feas=True, obj=11]
INFO - 16:22:13: 62%|██████▏ | 615/1000 [00:00<00:00, 3826.87 it/sec, feas=True, obj=5.03]
INFO - 16:22:13: 62%|██████▏ | 616/1000 [00:00<00:00, 3826.73 it/sec, feas=True, obj=6.39]
INFO - 16:22:13: 62%|██████▏ | 617/1000 [00:00<00:00, 3826.64 it/sec, feas=True, obj=1.92]
INFO - 16:22:13: 62%|██████▏ | 618/1000 [00:00<00:00, 3826.86 it/sec, feas=True, obj=1.05]
INFO - 16:22:13: 62%|██████▏ | 619/1000 [00:00<00:00, 3826.30 it/sec, feas=True, obj=0.0814]
INFO - 16:22:13: 62%|██████▏ | 620/1000 [00:00<00:00, 3826.41 it/sec, feas=True, obj=5.88]
INFO - 16:22:13: 62%|██████▏ | 621/1000 [00:00<00:00, 3826.62 it/sec, feas=True, obj=14.7]
INFO - 16:22:13: 62%|██████▏ | 622/1000 [00:00<00:00, 3826.49 it/sec, feas=True, obj=4.25]
INFO - 16:22:13: 62%|██████▏ | 623/1000 [00:00<00:00, 3826.52 it/sec, feas=True, obj=-1.9]
INFO - 16:22:13: 62%|██████▏ | 624/1000 [00:00<00:00, 3826.75 it/sec, feas=True, obj=-0.304]
INFO - 16:22:13: 62%|██████▎ | 625/1000 [00:00<00:00, 3827.01 it/sec, feas=True, obj=-0.315]
INFO - 16:22:13: 63%|██████▎ | 626/1000 [00:00<00:00, 3826.74 it/sec, feas=True, obj=-0.772]
INFO - 16:22:13: 63%|██████▎ | 627/1000 [00:00<00:00, 3826.67 it/sec, feas=True, obj=4.47]
INFO - 16:22:13: 63%|██████▎ | 628/1000 [00:00<00:00, 3826.91 it/sec, feas=True, obj=3.87]
INFO - 16:22:13: 63%|██████▎ | 629/1000 [00:00<00:00, 3827.17 it/sec, feas=True, obj=1.69]
INFO - 16:22:13: 63%|██████▎ | 630/1000 [00:00<00:00, 3826.95 it/sec, feas=True, obj=14.2]
INFO - 16:22:13: 63%|██████▎ | 631/1000 [00:00<00:00, 3826.83 it/sec, feas=True, obj=0.467]
INFO - 16:22:13: 63%|██████▎ | 632/1000 [00:00<00:00, 3826.81 it/sec, feas=True, obj=0.13]
INFO - 16:22:13: 63%|██████▎ | 633/1000 [00:00<00:00, 3826.86 it/sec, feas=True, obj=-0.788]
INFO - 16:22:13: 63%|██████▎ | 634/1000 [00:00<00:00, 3826.60 it/sec, feas=True, obj=3.3]
INFO - 16:22:13: 64%|██████▎ | 635/1000 [00:00<00:00, 3826.64 it/sec, feas=True, obj=7.29]
INFO - 16:22:13: 64%|██████▎ | 636/1000 [00:00<00:00, 3826.65 it/sec, feas=True, obj=1.41]
INFO - 16:22:13: 64%|██████▎ | 637/1000 [00:00<00:00, 3826.86 it/sec, feas=True, obj=6.16]
INFO - 16:22:13: 64%|██████▍ | 638/1000 [00:00<00:00, 3826.63 it/sec, feas=True, obj=6.98]
INFO - 16:22:13: 64%|██████▍ | 639/1000 [00:00<00:00, 3826.82 it/sec, feas=True, obj=7.82]
INFO - 16:22:13: 64%|██████▍ | 640/1000 [00:00<00:00, 3827.11 it/sec, feas=True, obj=4.49]
INFO - 16:22:13: 64%|██████▍ | 641/1000 [00:00<00:00, 3827.49 it/sec, feas=True, obj=6.84]
INFO - 16:22:13: 64%|██████▍ | 642/1000 [00:00<00:00, 3827.25 it/sec, feas=True, obj=3.83]
INFO - 16:22:13: 64%|██████▍ | 643/1000 [00:00<00:00, 3827.47 it/sec, feas=True, obj=2.52]
INFO - 16:22:13: 64%|██████▍ | 644/1000 [00:00<00:00, 3827.57 it/sec, feas=True, obj=1]
INFO - 16:22:13: 64%|██████▍ | 645/1000 [00:00<00:00, 3827.73 it/sec, feas=True, obj=3.54]
INFO - 16:22:13: 65%|██████▍ | 646/1000 [00:00<00:00, 3827.44 it/sec, feas=True, obj=5.22]
INFO - 16:22:13: 65%|██████▍ | 647/1000 [00:00<00:00, 3827.70 it/sec, feas=True, obj=7.98]
INFO - 16:22:13: 65%|██████▍ | 648/1000 [00:00<00:00, 3827.73 it/sec, feas=True, obj=3.23]
INFO - 16:22:13: 65%|██████▍ | 649/1000 [00:00<00:00, 3827.59 it/sec, feas=True, obj=2.61]
INFO - 16:22:13: 65%|██████▌ | 650/1000 [00:00<00:00, 3827.66 it/sec, feas=True, obj=4.85]
INFO - 16:22:13: 65%|██████▌ | 651/1000 [00:00<00:00, 3827.81 it/sec, feas=True, obj=1.4]
INFO - 16:22:13: 65%|██████▌ | 652/1000 [00:00<00:00, 3828.05 it/sec, feas=True, obj=-0.857]
INFO - 16:22:13: 65%|██████▌ | 653/1000 [00:00<00:00, 3827.91 it/sec, feas=True, obj=4.01]
INFO - 16:22:13: 65%|██████▌ | 654/1000 [00:00<00:00, 3827.98 it/sec, feas=True, obj=6.3]
INFO - 16:22:13: 66%|██████▌ | 655/1000 [00:00<00:00, 3828.20 it/sec, feas=True, obj=11.4]
INFO - 16:22:13: 66%|██████▌ | 656/1000 [00:00<00:00, 3828.50 it/sec, feas=True, obj=5.47]
INFO - 16:22:13: 66%|██████▌ | 657/1000 [00:00<00:00, 3828.30 it/sec, feas=True, obj=2.72]
INFO - 16:22:13: 66%|██████▌ | 658/1000 [00:00<00:00, 3828.48 it/sec, feas=True, obj=3.85]
INFO - 16:22:13: 66%|██████▌ | 659/1000 [00:00<00:00, 3828.67 it/sec, feas=True, obj=-0.392]
INFO - 16:22:13: 66%|██████▌ | 660/1000 [00:00<00:00, 3828.96 it/sec, feas=True, obj=0.0168]
INFO - 16:22:13: 66%|██████▌ | 661/1000 [00:00<00:00, 3828.84 it/sec, feas=True, obj=2.53]
INFO - 16:22:13: 66%|██████▌ | 662/1000 [00:00<00:00, 3829.01 it/sec, feas=True, obj=1.76]
INFO - 16:22:13: 66%|██████▋ | 663/1000 [00:00<00:00, 3829.01 it/sec, feas=True, obj=4.26]
INFO - 16:22:13: 66%|██████▋ | 664/1000 [00:00<00:00, 3829.18 it/sec, feas=True, obj=5.45]
INFO - 16:22:13: 66%|██████▋ | 665/1000 [00:00<00:00, 3829.14 it/sec, feas=True, obj=6.94]
INFO - 16:22:13: 67%|██████▋ | 666/1000 [00:00<00:00, 3829.34 it/sec, feas=True, obj=5.93]
INFO - 16:22:13: 67%|██████▋ | 667/1000 [00:00<00:00, 3829.53 it/sec, feas=True, obj=5.79]
INFO - 16:22:13: 67%|██████▋ | 668/1000 [00:00<00:00, 3829.71 it/sec, feas=True, obj=2.62]
INFO - 16:22:13: 67%|██████▋ | 669/1000 [00:00<00:00, 3829.53 it/sec, feas=True, obj=6.4]
INFO - 16:22:13: 67%|██████▋ | 670/1000 [00:00<00:00, 3829.67 it/sec, feas=True, obj=-0.703]
INFO - 16:22:13: 67%|██████▋ | 671/1000 [00:00<00:00, 3829.86 it/sec, feas=True, obj=8.61]
INFO - 16:22:13: 67%|██████▋ | 672/1000 [00:00<00:00, 3830.07 it/sec, feas=True, obj=0.91]
INFO - 16:22:13: 67%|██████▋ | 673/1000 [00:00<00:00, 3829.80 it/sec, feas=True, obj=1.05]
INFO - 16:22:13: 67%|██████▋ | 674/1000 [00:00<00:00, 3829.92 it/sec, feas=True, obj=10.1]
INFO - 16:22:13: 68%|██████▊ | 675/1000 [00:00<00:00, 3830.16 it/sec, feas=True, obj=-0.575]
INFO - 16:22:13: 68%|██████▊ | 676/1000 [00:00<00:00, 3830.38 it/sec, feas=True, obj=-2.06]
INFO - 16:22:13: 68%|██████▊ | 677/1000 [00:00<00:00, 3829.97 it/sec, feas=True, obj=7.34]
INFO - 16:22:13: 68%|██████▊ | 678/1000 [00:00<00:00, 3830.07 it/sec, feas=True, obj=2.78]
INFO - 16:22:13: 68%|██████▊ | 679/1000 [00:00<00:00, 3829.97 it/sec, feas=True, obj=1.15]
INFO - 16:22:13: 68%|██████▊ | 680/1000 [00:00<00:00, 3829.82 it/sec, feas=True, obj=-0.227]
INFO - 16:22:13: 68%|██████▊ | 681/1000 [00:00<00:00, 3829.77 it/sec, feas=True, obj=4.3]
INFO - 16:22:13: 68%|██████▊ | 682/1000 [00:00<00:00, 3830.04 it/sec, feas=True, obj=6.14]
INFO - 16:22:13: 68%|██████▊ | 683/1000 [00:00<00:00, 3830.03 it/sec, feas=True, obj=4.76]
INFO - 16:22:13: 68%|██████▊ | 684/1000 [00:00<00:00, 3829.92 it/sec, feas=True, obj=-4.69]
INFO - 16:22:13: 68%|██████▊ | 685/1000 [00:00<00:00, 3830.02 it/sec, feas=True, obj=-0.877]
INFO - 16:22:13: 69%|██████▊ | 686/1000 [00:00<00:00, 3830.22 it/sec, feas=True, obj=3.02]
INFO - 16:22:13: 69%|██████▊ | 687/1000 [00:00<00:00, 3830.51 it/sec, feas=True, obj=6.98]
INFO - 16:22:13: 69%|██████▉ | 688/1000 [00:00<00:00, 3830.40 it/sec, feas=True, obj=4.88]
INFO - 16:22:13: 69%|██████▉ | 689/1000 [00:00<00:00, 3830.56 it/sec, feas=True, obj=4.99]
INFO - 16:22:13: 69%|██████▉ | 690/1000 [00:00<00:00, 3830.75 it/sec, feas=True, obj=9.72]
INFO - 16:22:13: 69%|██████▉ | 691/1000 [00:00<00:00, 3830.99 it/sec, feas=True, obj=1.5]
INFO - 16:22:13: 69%|██████▉ | 692/1000 [00:00<00:00, 3830.84 it/sec, feas=True, obj=5.57]
INFO - 16:22:13: 69%|██████▉ | 693/1000 [00:00<00:00, 3830.92 it/sec, feas=True, obj=6.06]
INFO - 16:22:13: 69%|██████▉ | 694/1000 [00:00<00:00, 3830.92 it/sec, feas=True, obj=1.09]
INFO - 16:22:13: 70%|██████▉ | 695/1000 [00:00<00:00, 3831.16 it/sec, feas=True, obj=-1.97]
INFO - 16:22:13: 70%|██████▉ | 696/1000 [00:00<00:00, 3831.04 it/sec, feas=True, obj=1.88]
INFO - 16:22:13: 70%|██████▉ | 697/1000 [00:00<00:00, 3831.17 it/sec, feas=True, obj=9.32]
INFO - 16:22:13: 70%|██████▉ | 698/1000 [00:00<00:00, 3831.35 it/sec, feas=True, obj=-7.7]
INFO - 16:22:13: 70%|██████▉ | 699/1000 [00:00<00:00, 3831.32 it/sec, feas=True, obj=1.83]
INFO - 16:22:13: 70%|███████ | 700/1000 [00:00<00:00, 3831.10 it/sec, feas=True, obj=0.735]
INFO - 16:22:13: 70%|███████ | 701/1000 [00:00<00:00, 3831.29 it/sec, feas=True, obj=-1.11]
INFO - 16:22:13: 70%|███████ | 702/1000 [00:00<00:00, 3831.54 it/sec, feas=True, obj=1.47]
INFO - 16:22:13: 70%|███████ | 703/1000 [00:00<00:00, 3831.74 it/sec, feas=True, obj=0.283]
INFO - 16:22:13: 70%|███████ | 704/1000 [00:00<00:00, 3831.42 it/sec, feas=True, obj=15.2]
INFO - 16:22:13: 70%|███████ | 705/1000 [00:00<00:00, 3831.49 it/sec, feas=True, obj=3.43]
INFO - 16:22:13: 71%|███████ | 706/1000 [00:00<00:00, 3831.66 it/sec, feas=True, obj=3.17]
INFO - 16:22:13: 71%|███████ | 707/1000 [00:00<00:00, 3831.84 it/sec, feas=True, obj=5.95]
INFO - 16:22:13: 71%|███████ | 708/1000 [00:00<00:00, 3831.57 it/sec, feas=True, obj=-6.33]
INFO - 16:22:13: 71%|███████ | 709/1000 [00:00<00:00, 3831.85 it/sec, feas=True, obj=13.3]
INFO - 16:22:13: 71%|███████ | 710/1000 [00:00<00:00, 3831.93 it/sec, feas=True, obj=1.32]
INFO - 16:22:13: 71%|███████ | 711/1000 [00:00<00:00, 3832.21 it/sec, feas=True, obj=-4.3]
INFO - 16:22:13: 71%|███████ | 712/1000 [00:00<00:00, 3831.97 it/sec, feas=True, obj=1.63]
INFO - 16:22:13: 71%|███████▏ | 713/1000 [00:00<00:00, 3832.12 it/sec, feas=True, obj=1.99]
INFO - 16:22:13: 71%|███████▏ | 714/1000 [00:00<00:00, 3832.39 it/sec, feas=True, obj=0.679]
INFO - 16:22:13: 72%|███████▏ | 715/1000 [00:00<00:00, 3829.68 it/sec, feas=True, obj=-0.377]
INFO - 16:22:13: 72%|███████▏ | 716/1000 [00:00<00:00, 3829.46 it/sec, feas=True, obj=-4.57]
INFO - 16:22:13: 72%|███████▏ | 717/1000 [00:00<00:00, 3829.49 it/sec, feas=True, obj=2.1]
INFO - 16:22:13: 72%|███████▏ | 718/1000 [00:00<00:00, 3829.63 it/sec, feas=True, obj=1.32]
INFO - 16:22:13: 72%|███████▏ | 719/1000 [00:00<00:00, 3829.36 it/sec, feas=True, obj=0.312]
INFO - 16:22:13: 72%|███████▏ | 720/1000 [00:00<00:00, 3829.52 it/sec, feas=True, obj=8.43]
INFO - 16:22:13: 72%|███████▏ | 721/1000 [00:00<00:00, 3829.69 it/sec, feas=True, obj=0.579]
INFO - 16:22:13: 72%|███████▏ | 722/1000 [00:00<00:00, 3829.89 it/sec, feas=True, obj=0.563]
INFO - 16:22:13: 72%|███████▏ | 723/1000 [00:00<00:00, 3829.52 it/sec, feas=True, obj=3.4]
INFO - 16:22:13: 72%|███████▏ | 724/1000 [00:00<00:00, 3829.77 it/sec, feas=True, obj=7.21]
INFO - 16:22:13: 72%|███████▎ | 725/1000 [00:00<00:00, 3829.74 it/sec, feas=True, obj=5.26]
INFO - 16:22:13: 73%|███████▎ | 726/1000 [00:00<00:00, 3829.51 it/sec, feas=True, obj=2.69]
INFO - 16:22:13: 73%|███████▎ | 727/1000 [00:00<00:00, 3829.47 it/sec, feas=True, obj=5.31]
INFO - 16:22:13: 73%|███████▎ | 728/1000 [00:00<00:00, 3829.57 it/sec, feas=True, obj=1.99]
INFO - 16:22:13: 73%|███████▎ | 729/1000 [00:00<00:00, 3829.70 it/sec, feas=True, obj=-6.72]
INFO - 16:22:13: 73%|███████▎ | 730/1000 [00:00<00:00, 3829.35 it/sec, feas=True, obj=0.526]
INFO - 16:22:13: 73%|███████▎ | 731/1000 [00:00<00:00, 3829.40 it/sec, feas=True, obj=4.35]
INFO - 16:22:13: 73%|███████▎ | 732/1000 [00:00<00:00, 3829.54 it/sec, feas=True, obj=8.27]
INFO - 16:22:13: 73%|███████▎ | 733/1000 [00:00<00:00, 3829.75 it/sec, feas=True, obj=0.662]
INFO - 16:22:13: 73%|███████▎ | 734/1000 [00:00<00:00, 3829.59 it/sec, feas=True, obj=8.9]
INFO - 16:22:13: 74%|███████▎ | 735/1000 [00:00<00:00, 3829.65 it/sec, feas=True, obj=6.96]
INFO - 16:22:13: 74%|███████▎ | 736/1000 [00:00<00:00, 3829.77 it/sec, feas=True, obj=1.11]
INFO - 16:22:13: 74%|███████▎ | 737/1000 [00:00<00:00, 3829.98 it/sec, feas=True, obj=-4.5]
INFO - 16:22:13: 74%|███████▍ | 738/1000 [00:00<00:00, 3829.70 it/sec, feas=True, obj=0.0495]
INFO - 16:22:13: 74%|███████▍ | 739/1000 [00:00<00:00, 3829.77 it/sec, feas=True, obj=5.88]
INFO - 16:22:13: 74%|███████▍ | 740/1000 [00:00<00:00, 3829.76 it/sec, feas=True, obj=13.2]
INFO - 16:22:13: 74%|███████▍ | 741/1000 [00:00<00:00, 3829.96 it/sec, feas=True, obj=2.3]
INFO - 16:22:13: 74%|███████▍ | 742/1000 [00:00<00:00, 3829.67 it/sec, feas=True, obj=2.21]
INFO - 16:22:13: 74%|███████▍ | 743/1000 [00:00<00:00, 3829.77 it/sec, feas=True, obj=-4.03]
INFO - 16:22:13: 74%|███████▍ | 744/1000 [00:00<00:00, 3830.03 it/sec, feas=True, obj=4.28]
INFO - 16:22:13: 74%|███████▍ | 745/1000 [00:00<00:00, 3830.31 it/sec, feas=True, obj=6.68]
INFO - 16:22:13: 75%|███████▍ | 746/1000 [00:00<00:00, 3830.03 it/sec, feas=True, obj=7.33]
INFO - 16:22:13: 75%|███████▍ | 747/1000 [00:00<00:00, 3830.10 it/sec, feas=True, obj=-3.91]
INFO - 16:22:13: 75%|███████▍ | 748/1000 [00:00<00:00, 3830.34 it/sec, feas=True, obj=1.16]
INFO - 16:22:13: 75%|███████▍ | 749/1000 [00:00<00:00, 3830.62 it/sec, feas=True, obj=-0.739]
INFO - 16:22:13: 75%|███████▌ | 750/1000 [00:00<00:00, 3830.40 it/sec, feas=True, obj=5.93]
INFO - 16:22:13: 75%|███████▌ | 751/1000 [00:00<00:00, 3830.61 it/sec, feas=True, obj=3.2]
INFO - 16:22:13: 75%|███████▌ | 752/1000 [00:00<00:00, 3830.88 it/sec, feas=True, obj=11.1]
INFO - 16:22:13: 75%|███████▌ | 753/1000 [00:00<00:00, 3830.73 it/sec, feas=True, obj=7.41]
INFO - 16:22:13: 75%|███████▌ | 754/1000 [00:00<00:00, 3830.74 it/sec, feas=True, obj=6.56]
INFO - 16:22:13: 76%|███████▌ | 755/1000 [00:00<00:00, 3830.83 it/sec, feas=True, obj=0.0769]
INFO - 16:22:13: 76%|███████▌ | 756/1000 [00:00<00:00, 3830.97 it/sec, feas=True, obj=-3.24]
INFO - 16:22:13: 76%|███████▌ | 757/1000 [00:00<00:00, 3830.82 it/sec, feas=True, obj=1.73]
INFO - 16:22:13: 76%|███████▌ | 758/1000 [00:00<00:00, 3830.94 it/sec, feas=True, obj=0.263]
INFO - 16:22:13: 76%|███████▌ | 759/1000 [00:00<00:00, 3831.04 it/sec, feas=True, obj=-6.43]
INFO - 16:22:13: 76%|███████▌ | 760/1000 [00:00<00:00, 3831.23 it/sec, feas=True, obj=7.52]
INFO - 16:22:13: 76%|███████▌ | 761/1000 [00:00<00:00, 3831.06 it/sec, feas=True, obj=-2.09]
INFO - 16:22:13: 76%|███████▌ | 762/1000 [00:00<00:00, 3831.17 it/sec, feas=True, obj=-0.0262]
INFO - 16:22:13: 76%|███████▋ | 763/1000 [00:00<00:00, 3831.28 it/sec, feas=True, obj=3.37]
INFO - 16:22:13: 76%|███████▋ | 764/1000 [00:00<00:00, 3831.52 it/sec, feas=True, obj=4.5]
INFO - 16:22:13: 76%|███████▋ | 765/1000 [00:00<00:00, 3831.36 it/sec, feas=True, obj=0.692]
INFO - 16:22:13: 77%|███████▋ | 766/1000 [00:00<00:00, 3831.41 it/sec, feas=True, obj=2.75]
INFO - 16:22:13: 77%|███████▋ | 767/1000 [00:00<00:00, 3831.49 it/sec, feas=True, obj=1.46]
INFO - 16:22:13: 77%|███████▋ | 768/1000 [00:00<00:00, 3831.69 it/sec, feas=True, obj=7.23]
INFO - 16:22:13: 77%|███████▋ | 769/1000 [00:00<00:00, 3831.49 it/sec, feas=True, obj=3.47]
INFO - 16:22:13: 77%|███████▋ | 770/1000 [00:00<00:00, 3831.57 it/sec, feas=True, obj=-0.943]
INFO - 16:22:13: 77%|███████▋ | 771/1000 [00:00<00:00, 3831.60 it/sec, feas=True, obj=0.302]
INFO - 16:22:13: 77%|███████▋ | 772/1000 [00:00<00:00, 3831.75 it/sec, feas=True, obj=6]
INFO - 16:22:13: 77%|███████▋ | 773/1000 [00:00<00:00, 3831.46 it/sec, feas=True, obj=2.71]
INFO - 16:22:13: 77%|███████▋ | 774/1000 [00:00<00:00, 3831.48 it/sec, feas=True, obj=2.8]
INFO - 16:22:13: 78%|███████▊ | 775/1000 [00:00<00:00, 3831.63 it/sec, feas=True, obj=2.67]
INFO - 16:22:13: 78%|███████▊ | 776/1000 [00:00<00:00, 3831.83 it/sec, feas=True, obj=5.44]
INFO - 16:22:13: 78%|███████▊ | 777/1000 [00:00<00:00, 3831.52 it/sec, feas=True, obj=1.65]
INFO - 16:22:13: 78%|███████▊ | 778/1000 [00:00<00:00, 3831.66 it/sec, feas=True, obj=7.13]
INFO - 16:22:13: 78%|███████▊ | 779/1000 [00:00<00:00, 3831.87 it/sec, feas=True, obj=-0.0622]
INFO - 16:22:13: 78%|███████▊ | 780/1000 [00:00<00:00, 3831.81 it/sec, feas=True, obj=5.84]
INFO - 16:22:13: 78%|███████▊ | 781/1000 [00:00<00:00, 3831.75 it/sec, feas=True, obj=2.28]
INFO - 16:22:13: 78%|███████▊ | 782/1000 [00:00<00:00, 3831.90 it/sec, feas=True, obj=6.04]
INFO - 16:22:13: 78%|███████▊ | 783/1000 [00:00<00:00, 3832.13 it/sec, feas=True, obj=7.59]
INFO - 16:22:13: 78%|███████▊ | 784/1000 [00:00<00:00, 3832.06 it/sec, feas=True, obj=-6.19]
INFO - 16:22:13: 78%|███████▊ | 785/1000 [00:00<00:00, 3832.03 it/sec, feas=True, obj=9.25]
INFO - 16:22:13: 79%|███████▊ | 786/1000 [00:00<00:00, 3831.98 it/sec, feas=True, obj=0.676]
INFO - 16:22:13: 79%|███████▊ | 787/1000 [00:00<00:00, 3832.10 it/sec, feas=True, obj=-0.174]
INFO - 16:22:13: 79%|███████▉ | 788/1000 [00:00<00:00, 3831.90 it/sec, feas=True, obj=6.51]
INFO - 16:22:13: 79%|███████▉ | 789/1000 [00:00<00:00, 3832.02 it/sec, feas=True, obj=-0.856]
INFO - 16:22:13: 79%|███████▉ | 790/1000 [00:00<00:00, 3832.17 it/sec, feas=True, obj=5.62]
INFO - 16:22:13: 79%|███████▉ | 791/1000 [00:00<00:00, 3832.30 it/sec, feas=True, obj=5.35]
INFO - 16:22:13: 79%|███████▉ | 792/1000 [00:00<00:00, 3832.15 it/sec, feas=True, obj=0.753]
INFO - 16:22:13: 79%|███████▉ | 793/1000 [00:00<00:00, 3832.17 it/sec, feas=True, obj=4.35]
INFO - 16:22:13: 79%|███████▉ | 794/1000 [00:00<00:00, 3832.25 it/sec, feas=True, obj=3.8]
INFO - 16:22:13: 80%|███████▉ | 795/1000 [00:00<00:00, 3832.40 it/sec, feas=True, obj=7.95]
INFO - 16:22:13: 80%|███████▉ | 796/1000 [00:00<00:00, 3832.19 it/sec, feas=True, obj=5.01]
INFO - 16:22:13: 80%|███████▉ | 797/1000 [00:00<00:00, 3832.31 it/sec, feas=True, obj=6.2]
INFO - 16:22:13: 80%|███████▉ | 798/1000 [00:00<00:00, 3832.50 it/sec, feas=True, obj=-1.82]
INFO - 16:22:13: 80%|███████▉ | 799/1000 [00:00<00:00, 3832.66 it/sec, feas=True, obj=2.4]
INFO - 16:22:13: 80%|████████ | 800/1000 [00:00<00:00, 3832.41 it/sec, feas=True, obj=7.99]
INFO - 16:22:13: 80%|████████ | 801/1000 [00:00<00:00, 3832.57 it/sec, feas=True, obj=2.48]
INFO - 16:22:13: 80%|████████ | 802/1000 [00:00<00:00, 3832.51 it/sec, feas=True, obj=-0.764]
INFO - 16:22:13: 80%|████████ | 803/1000 [00:00<00:00, 3832.64 it/sec, feas=True, obj=3.34]
INFO - 16:22:13: 80%|████████ | 804/1000 [00:00<00:00, 3832.32 it/sec, feas=True, obj=0.787]
INFO - 16:22:13: 80%|████████ | 805/1000 [00:00<00:00, 3832.47 it/sec, feas=True, obj=-1.05]
INFO - 16:22:13: 81%|████████ | 806/1000 [00:00<00:00, 3832.56 it/sec, feas=True, obj=4.98]
INFO - 16:22:13: 81%|████████ | 807/1000 [00:00<00:00, 3832.45 it/sec, feas=True, obj=4.73]
INFO - 16:22:13: 81%|████████ | 808/1000 [00:00<00:00, 3832.43 it/sec, feas=True, obj=-0.742]
INFO - 16:22:13: 81%|████████ | 809/1000 [00:00<00:00, 3832.57 it/sec, feas=True, obj=5.82]
INFO - 16:22:13: 81%|████████ | 810/1000 [00:00<00:00, 3832.83 it/sec, feas=True, obj=10.4]
INFO - 16:22:13: 81%|████████ | 811/1000 [00:00<00:00, 3832.73 it/sec, feas=True, obj=1.86]
INFO - 16:22:13: 81%|████████ | 812/1000 [00:00<00:00, 3832.76 it/sec, feas=True, obj=2.49]
INFO - 16:22:13: 81%|████████▏ | 813/1000 [00:00<00:00, 3832.99 it/sec, feas=True, obj=9.36]
INFO - 16:22:13: 81%|████████▏ | 814/1000 [00:00<00:00, 3833.17 it/sec, feas=True, obj=1.84]
INFO - 16:22:13: 82%|████████▏ | 815/1000 [00:00<00:00, 3833.04 it/sec, feas=True, obj=4.04]
INFO - 16:22:13: 82%|████████▏ | 816/1000 [00:00<00:00, 3833.04 it/sec, feas=True, obj=-4.21]
INFO - 16:22:13: 82%|████████▏ | 817/1000 [00:00<00:00, 3833.01 it/sec, feas=True, obj=3.64]
INFO - 16:22:13: 82%|████████▏ | 818/1000 [00:00<00:00, 3833.11 it/sec, feas=True, obj=4.02]
INFO - 16:22:13: 82%|████████▏ | 819/1000 [00:00<00:00, 3832.98 it/sec, feas=True, obj=6.66]
INFO - 16:22:13: 82%|████████▏ | 820/1000 [00:00<00:00, 3833.13 it/sec, feas=True, obj=-0.0634]
INFO - 16:22:13: 82%|████████▏ | 821/1000 [00:00<00:00, 3833.34 it/sec, feas=True, obj=1.24]
INFO - 16:22:13: 82%|████████▏ | 822/1000 [00:00<00:00, 3833.53 it/sec, feas=True, obj=4.42]
INFO - 16:22:13: 82%|████████▏ | 823/1000 [00:00<00:00, 3833.35 it/sec, feas=True, obj=4.26]
INFO - 16:22:13: 82%|████████▏ | 824/1000 [00:00<00:00, 3833.48 it/sec, feas=True, obj=0.439]
INFO - 16:22:13: 82%|████████▎ | 825/1000 [00:00<00:00, 3833.72 it/sec, feas=True, obj=2.7]
INFO - 16:22:13: 83%|████████▎ | 826/1000 [00:00<00:00, 3833.91 it/sec, feas=True, obj=2.98]
INFO - 16:22:13: 83%|████████▎ | 827/1000 [00:00<00:00, 3833.70 it/sec, feas=True, obj=0.888]
INFO - 16:22:13: 83%|████████▎ | 828/1000 [00:00<00:00, 3833.79 it/sec, feas=True, obj=-0.879]
INFO - 16:22:13: 83%|████████▎ | 829/1000 [00:00<00:00, 3831.85 it/sec, feas=True, obj=0.861]
INFO - 16:22:13: 83%|████████▎ | 830/1000 [00:00<00:00, 3831.09 it/sec, feas=True, obj=3.47]
INFO - 16:22:13: 83%|████████▎ | 831/1000 [00:00<00:00, 3831.07 it/sec, feas=True, obj=7.51]
INFO - 16:22:13: 83%|████████▎ | 832/1000 [00:00<00:00, 3831.04 it/sec, feas=True, obj=4.58]
INFO - 16:22:13: 83%|████████▎ | 833/1000 [00:00<00:00, 3831.10 it/sec, feas=True, obj=5.48]
INFO - 16:22:13: 83%|████████▎ | 834/1000 [00:00<00:00, 3830.85 it/sec, feas=True, obj=-0.412]
INFO - 16:22:13: 84%|████████▎ | 835/1000 [00:00<00:00, 3830.77 it/sec, feas=True, obj=-1.86]
INFO - 16:22:13: 84%|████████▎ | 836/1000 [00:00<00:00, 3830.90 it/sec, feas=True, obj=1.29]
INFO - 16:22:13: 84%|████████▎ | 837/1000 [00:00<00:00, 3831.10 it/sec, feas=True, obj=3.17]
INFO - 16:22:13: 84%|████████▍ | 838/1000 [00:00<00:00, 3830.81 it/sec, feas=True, obj=2.41]
INFO - 16:22:13: 84%|████████▍ | 839/1000 [00:00<00:00, 3830.82 it/sec, feas=True, obj=5.72]
INFO - 16:22:13: 84%|████████▍ | 840/1000 [00:00<00:00, 3830.84 it/sec, feas=True, obj=-1.37]
INFO - 16:22:13: 84%|████████▍ | 841/1000 [00:00<00:00, 3830.93 it/sec, feas=True, obj=6.72]
INFO - 16:22:13: 84%|████████▍ | 842/1000 [00:00<00:00, 3830.62 it/sec, feas=True, obj=3.27]
INFO - 16:22:13: 84%|████████▍ | 843/1000 [00:00<00:00, 3830.79 it/sec, feas=True, obj=-1.46]
INFO - 16:22:13: 84%|████████▍ | 844/1000 [00:00<00:00, 3830.99 it/sec, feas=True, obj=4.38]
INFO - 16:22:13: 84%|████████▍ | 845/1000 [00:00<00:00, 3830.82 it/sec, feas=True, obj=3.82]
INFO - 16:22:13: 85%|████████▍ | 846/1000 [00:00<00:00, 3830.82 it/sec, feas=True, obj=5.89]
INFO - 16:22:13: 85%|████████▍ | 847/1000 [00:00<00:00, 3830.84 it/sec, feas=True, obj=3.11]
INFO - 16:22:13: 85%|████████▍ | 848/1000 [00:00<00:00, 3830.88 it/sec, feas=True, obj=4.37]
INFO - 16:22:13: 85%|████████▍ | 849/1000 [00:00<00:00, 3830.60 it/sec, feas=True, obj=1.84]
INFO - 16:22:13: 85%|████████▌ | 850/1000 [00:00<00:00, 3830.62 it/sec, feas=True, obj=2.82]
INFO - 16:22:13: 85%|████████▌ | 851/1000 [00:00<00:00, 3830.75 it/sec, feas=True, obj=7.38]
INFO - 16:22:13: 85%|████████▌ | 852/1000 [00:00<00:00, 3830.90 it/sec, feas=True, obj=13.8]
INFO - 16:22:13: 85%|████████▌ | 853/1000 [00:00<00:00, 3830.73 it/sec, feas=True, obj=7.76]
INFO - 16:22:13: 85%|████████▌ | 854/1000 [00:00<00:00, 3830.85 it/sec, feas=True, obj=0.998]
INFO - 16:22:13: 86%|████████▌ | 855/1000 [00:00<00:00, 3831.02 it/sec, feas=True, obj=3.88]
INFO - 16:22:13: 86%|████████▌ | 856/1000 [00:00<00:00, 3831.16 it/sec, feas=True, obj=-0.698]
INFO - 16:22:13: 86%|████████▌ | 857/1000 [00:00<00:00, 3830.90 it/sec, feas=True, obj=2.83]
INFO - 16:22:13: 86%|████████▌ | 858/1000 [00:00<00:00, 3830.97 it/sec, feas=True, obj=1.58]
INFO - 16:22:13: 86%|████████▌ | 859/1000 [00:00<00:00, 3831.11 it/sec, feas=True, obj=8.53]
INFO - 16:22:13: 86%|████████▌ | 860/1000 [00:00<00:00, 3831.27 it/sec, feas=True, obj=6.28]
INFO - 16:22:13: 86%|████████▌ | 861/1000 [00:00<00:00, 3831.04 it/sec, feas=True, obj=11.8]
INFO - 16:22:13: 86%|████████▌ | 862/1000 [00:00<00:00, 3831.14 it/sec, feas=True, obj=9.31]
INFO - 16:22:13: 86%|████████▋ | 863/1000 [00:00<00:00, 3831.09 it/sec, feas=True, obj=3.88]
INFO - 16:22:13: 86%|████████▋ | 864/1000 [00:00<00:00, 3831.24 it/sec, feas=True, obj=3.11]
INFO - 16:22:13: 86%|████████▋ | 865/1000 [00:00<00:00, 3830.97 it/sec, feas=True, obj=5.09]
INFO - 16:22:13: 87%|████████▋ | 866/1000 [00:00<00:00, 3831.01 it/sec, feas=True, obj=-0.723]
INFO - 16:22:13: 87%|████████▋ | 867/1000 [00:00<00:00, 3831.10 it/sec, feas=True, obj=1.22]
INFO - 16:22:13: 87%|████████▋ | 868/1000 [00:00<00:00, 3830.93 it/sec, feas=True, obj=7.13]
INFO - 16:22:13: 87%|████████▋ | 869/1000 [00:00<00:00, 3830.92 it/sec, feas=True, obj=12.2]
INFO - 16:22:13: 87%|████████▋ | 870/1000 [00:00<00:00, 3830.97 it/sec, feas=True, obj=1.13]
INFO - 16:22:13: 87%|████████▋ | 871/1000 [00:00<00:00, 3831.11 it/sec, feas=True, obj=0.802]
INFO - 16:22:13: 87%|████████▋ | 872/1000 [00:00<00:00, 3830.92 it/sec, feas=True, obj=2.82]
INFO - 16:22:13: 87%|████████▋ | 873/1000 [00:00<00:00, 3831.02 it/sec, feas=True, obj=-0.932]
INFO - 16:22:13: 87%|████████▋ | 874/1000 [00:00<00:00, 3831.17 it/sec, feas=True, obj=1.6]
INFO - 16:22:13: 88%|████████▊ | 875/1000 [00:00<00:00, 3830.95 it/sec, feas=True, obj=8.68]
INFO - 16:22:13: 88%|████████▊ | 876/1000 [00:00<00:00, 3830.71 it/sec, feas=True, obj=-0.211]
INFO - 16:22:13: 88%|████████▊ | 877/1000 [00:00<00:00, 3830.78 it/sec, feas=True, obj=-3.63]
INFO - 16:22:13: 88%|████████▊ | 878/1000 [00:00<00:00, 3830.78 it/sec, feas=True, obj=4.85]
INFO - 16:22:13: 88%|████████▊ | 879/1000 [00:00<00:00, 3830.79 it/sec, feas=True, obj=4.28]
INFO - 16:22:13: 88%|████████▊ | 880/1000 [00:00<00:00, 3830.45 it/sec, feas=True, obj=-0.285]
INFO - 16:22:13: 88%|████████▊ | 881/1000 [00:00<00:00, 3830.53 it/sec, feas=True, obj=5.96]
INFO - 16:22:13: 88%|████████▊ | 882/1000 [00:00<00:00, 3830.66 it/sec, feas=True, obj=-0.126]
INFO - 16:22:13: 88%|████████▊ | 883/1000 [00:00<00:00, 3830.74 it/sec, feas=True, obj=10.4]
INFO - 16:22:13: 88%|████████▊ | 884/1000 [00:00<00:00, 3830.51 it/sec, feas=True, obj=-1.37]
INFO - 16:22:13: 88%|████████▊ | 885/1000 [00:00<00:00, 3830.68 it/sec, feas=True, obj=4.47]
INFO - 16:22:13: 89%|████████▊ | 886/1000 [00:00<00:00, 3830.74 it/sec, feas=True, obj=1.19]
INFO - 16:22:13: 89%|████████▊ | 887/1000 [00:00<00:00, 3830.59 it/sec, feas=True, obj=6.51]
INFO - 16:22:13: 89%|████████▉ | 888/1000 [00:00<00:00, 3830.60 it/sec, feas=True, obj=-0.5]
INFO - 16:22:13: 89%|████████▉ | 889/1000 [00:00<00:00, 3830.75 it/sec, feas=True, obj=1.33]
INFO - 16:22:13: 89%|████████▉ | 890/1000 [00:00<00:00, 3830.95 it/sec, feas=True, obj=8.1]
INFO - 16:22:13: 89%|████████▉ | 891/1000 [00:00<00:00, 3830.81 it/sec, feas=True, obj=6.34]
INFO - 16:22:13: 89%|████████▉ | 892/1000 [00:00<00:00, 3830.85 it/sec, feas=True, obj=0.425]
INFO - 16:22:13: 89%|████████▉ | 893/1000 [00:00<00:00, 3830.82 it/sec, feas=True, obj=7.99]
INFO - 16:22:13: 89%|████████▉ | 894/1000 [00:00<00:00, 3830.93 it/sec, feas=True, obj=4.73]
INFO - 16:22:13: 90%|████████▉ | 895/1000 [00:00<00:00, 3830.80 it/sec, feas=True, obj=-0.736]
INFO - 16:22:13: 90%|████████▉ | 896/1000 [00:00<00:00, 3830.84 it/sec, feas=True, obj=1.11]
INFO - 16:22:13: 90%|████████▉ | 897/1000 [00:00<00:00, 3831.02 it/sec, feas=True, obj=5.52]
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INFO - 16:22:13: 94%|█████████▍| 944/1000 [00:00<00:00, 3816.90 it/sec, feas=True, obj=9.05]
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INFO - 16:22:13: 95%|█████████▌| 950/1000 [00:00<00:00, 3815.95 it/sec, feas=True, obj=0.391]
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INFO - 16:22:13: 95%|█████████▌| 953/1000 [00:00<00:00, 3815.68 it/sec, feas=True, obj=0.439]
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INFO - 16:22:13: 96%|█████████▌| 955/1000 [00:00<00:00, 3815.71 it/sec, feas=True, obj=-1.49]
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INFO - 16:22:13: 96%|█████████▌| 959/1000 [00:00<00:00, 3815.38 it/sec, feas=True, obj=1.26]
INFO - 16:22:13: 96%|█████████▌| 960/1000 [00:00<00:00, 3815.23 it/sec, feas=True, obj=3.33]
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INFO - 16:22:13: 96%|█████████▌| 962/1000 [00:00<00:00, 3815.42 it/sec, feas=True, obj=14.7]
INFO - 16:22:13: 96%|█████████▋| 963/1000 [00:00<00:00, 3815.62 it/sec, feas=True, obj=0.515]
INFO - 16:22:13: 96%|█████████▋| 964/1000 [00:00<00:00, 3815.54 it/sec, feas=True, obj=2.57]
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INFO - 16:22:13: 97%|█████████▋| 966/1000 [00:00<00:00, 3815.57 it/sec, feas=True, obj=-0.292]
INFO - 16:22:13: 97%|█████████▋| 967/1000 [00:00<00:00, 3815.72 it/sec, feas=True, obj=-1.65]
INFO - 16:22:13: 97%|█████████▋| 968/1000 [00:00<00:00, 3815.61 it/sec, feas=True, obj=7.01]
INFO - 16:22:13: 97%|█████████▋| 969/1000 [00:00<00:00, 3815.71 it/sec, feas=True, obj=-0.0208]
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INFO - 16:22:13: 97%|█████████▋| 974/1000 [00:00<00:00, 3815.89 it/sec, feas=True, obj=5.07]
INFO - 16:22:13: 98%|█████████▊| 975/1000 [00:00<00:00, 3816.04 it/sec, feas=True, obj=7.26]
INFO - 16:22:13: 98%|█████████▊| 976/1000 [00:00<00:00, 3815.82 it/sec, feas=True, obj=1.83]
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INFO - 16:22:13: 98%|█████████▊| 978/1000 [00:00<00:00, 3816.03 it/sec, feas=True, obj=4.96]
INFO - 16:22:13: 98%|█████████▊| 979/1000 [00:00<00:00, 3815.96 it/sec, feas=True, obj=0.739]
INFO - 16:22:13: 98%|█████████▊| 980/1000 [00:00<00:00, 3815.95 it/sec, feas=True, obj=-1.88]
INFO - 16:22:13: 98%|█████████▊| 981/1000 [00:00<00:00, 3816.12 it/sec, feas=True, obj=5.13]
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INFO - 16:22:13: 98%|█████████▊| 983/1000 [00:00<00:00, 3815.91 it/sec, feas=True, obj=8.53]
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INFO - 16:22:13: 98%|█████████▊| 985/1000 [00:00<00:00, 3816.10 it/sec, feas=True, obj=8.02]
INFO - 16:22:13: 99%|█████████▊| 986/1000 [00:00<00:00, 3816.16 it/sec, feas=True, obj=2.01]
INFO - 16:22:13: 99%|█████████▊| 987/1000 [00:00<00:00, 3816.02 it/sec, feas=True, obj=-0.893]
INFO - 16:22:13: 99%|█████████▉| 988/1000 [00:00<00:00, 3816.08 it/sec, feas=True, obj=4.74]
INFO - 16:22:13: 99%|█████████▉| 989/1000 [00:00<00:00, 3816.17 it/sec, feas=True, obj=1.18]
INFO - 16:22:13: 99%|█████████▉| 990/1000 [00:00<00:00, 3816.25 it/sec, feas=True, obj=6.2]
INFO - 16:22:13: 99%|█████████▉| 991/1000 [00:00<00:00, 3816.07 it/sec, feas=True, obj=4.5]
INFO - 16:22:13: 99%|█████████▉| 992/1000 [00:00<00:00, 3816.14 it/sec, feas=True, obj=-0.907]
INFO - 16:22:13: 99%|█████████▉| 993/1000 [00:00<00:00, 3816.25 it/sec, feas=True, obj=-3.18]
INFO - 16:22:13: 99%|█████████▉| 994/1000 [00:00<00:00, 3816.36 it/sec, feas=True, obj=6.82]
INFO - 16:22:13: 100%|█████████▉| 995/1000 [00:00<00:00, 3816.15 it/sec, feas=True, obj=3.44]
INFO - 16:22:13: 100%|█████████▉| 996/1000 [00:00<00:00, 3816.21 it/sec, feas=True, obj=5.11]
INFO - 16:22:13: 100%|█████████▉| 997/1000 [00:00<00:00, 3816.16 it/sec, feas=True, obj=1.55]
INFO - 16:22:13: 100%|█████████▉| 998/1000 [00:00<00:00, 3816.32 it/sec, feas=True, obj=0.534]
INFO - 16:22:13: 100%|█████████▉| 999/1000 [00:00<00:00, 3816.00 it/sec, feas=True, obj=0.783]
INFO - 16:22:13: 100%|██████████| 1000/1000 [00:00<00:00, 3791.09 it/sec, feas=True, obj=5.65]
INFO - 16:22:13: Optimization result:
INFO - 16:22:13: Optimizer info:
INFO - 16:22:13: Status: None
INFO - 16:22:13: Message: None
INFO - 16:22:13: Solution:
INFO - 16:22:13: Objective: -10.14685071195364
INFO - 16:22:13: Design space:
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: | Name | Distribution |
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:22:13: +------+------------------------------------------------------------+
INFO - 16:22:13: *** 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 4.628 seconds)