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 - 16:21:39: *** Start Sampling execution ***
INFO - 16:21:39: Sampling
INFO - 16:21:39: Disciplines: IshigamiDiscipline
INFO - 16:21:39: MDO formulation: MDF
INFO - 16:21:39: Optimization problem:
INFO - 16:21:39: minimize y(x1, x2, x3)
INFO - 16:21:39: with respect to x1, x2, x3
INFO - 16:21:39: over the design space:
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: | Name | Distribution |
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: Solving optimization problem with algorithm OT_OPT_LHS:
INFO - 16:21:39: 1%|▏ | 1/70 [00:00<00:00, 372.43 it/sec, feas=True, obj=1.45]
INFO - 16:21:39: 3%|▎ | 2/70 [00:00<00:00, 599.40 it/sec, feas=True, obj=1.01]
INFO - 16:21:39: 4%|▍ | 3/70 [00:00<00:00, 765.43 it/sec, feas=True, obj=6.72]
INFO - 16:21:39: 6%|▌ | 4/70 [00:00<00:00, 897.90 it/sec, feas=True, obj=-0.113]
INFO - 16:21:39: 7%|▋ | 5/70 [00:00<00:00, 1009.56 it/sec, feas=True, obj=7.68]
INFO - 16:21:39: 9%|▊ | 6/70 [00:00<00:00, 1099.38 it/sec, feas=True, obj=1.8]
INFO - 16:21:39: 10%|█ | 7/70 [00:00<00:00, 1180.07 it/sec, feas=True, obj=10.3]
INFO - 16:21:39: 11%|█▏ | 8/70 [00:00<00:00, 1246.22 it/sec, feas=True, obj=5.96]
INFO - 16:21:39: 13%|█▎ | 9/70 [00:00<00:00, 1304.74 it/sec, feas=True, obj=0.0449]
INFO - 16:21:39: 14%|█▍ | 10/70 [00:00<00:00, 1354.66 it/sec, feas=True, obj=4.97]
INFO - 16:21:39: 16%|█▌ | 11/70 [00:00<00:00, 1401.03 it/sec, feas=True, obj=6.94]
INFO - 16:21:39: 17%|█▋ | 12/70 [00:00<00:00, 1441.34 it/sec, feas=True, obj=3.5]
INFO - 16:21:39: 19%|█▊ | 13/70 [00:00<00:00, 1474.55 it/sec, feas=True, obj=4.87]
INFO - 16:21:39: 20%|██ | 14/70 [00:00<00:00, 1509.48 it/sec, feas=True, obj=4.3]
INFO - 16:21:39: 21%|██▏ | 15/70 [00:00<00:00, 1537.84 it/sec, feas=True, obj=2.44]
INFO - 16:21:39: 23%|██▎ | 16/70 [00:00<00:00, 1566.10 it/sec, feas=True, obj=5.7]
INFO - 16:21:39: 24%|██▍ | 17/70 [00:00<00:00, 1590.13 it/sec, feas=True, obj=6.14]
INFO - 16:21:39: 26%|██▌ | 18/70 [00:00<00:00, 1609.17 it/sec, feas=True, obj=5.7]
INFO - 16:21:39: 27%|██▋ | 19/70 [00:00<00:00, 1621.17 it/sec, feas=True, obj=-0.573]
INFO - 16:21:39: 29%|██▊ | 20/70 [00:00<00:00, 1639.65 it/sec, feas=True, obj=5.72]
INFO - 16:21:39: 30%|███ | 21/70 [00:00<00:00, 1652.08 it/sec, feas=True, obj=4.95]
INFO - 16:21:39: 31%|███▏ | 22/70 [00:00<00:00, 1669.28 it/sec, feas=True, obj=1.27]
INFO - 16:21:39: 33%|███▎ | 23/70 [00:00<00:00, 1684.08 it/sec, feas=True, obj=3.54]
INFO - 16:21:39: 34%|███▍ | 24/70 [00:00<00:00, 1699.47 it/sec, feas=True, obj=6.04]
INFO - 16:21:39: 36%|███▌ | 25/70 [00:00<00:00, 1711.60 it/sec, feas=True, obj=7.5]
INFO - 16:21:39: 37%|███▋ | 26/70 [00:00<00:00, 1724.96 it/sec, feas=True, obj=13.2]
INFO - 16:21:39: 39%|███▊ | 27/70 [00:00<00:00, 1734.51 it/sec, feas=True, obj=14.8]
INFO - 16:21:39: 40%|████ | 28/70 [00:00<00:00, 1744.77 it/sec, feas=True, obj=-0.644]
INFO - 16:21:39: 41%|████▏ | 29/70 [00:00<00:00, 1754.51 it/sec, feas=True, obj=4.94]
INFO - 16:21:39: 43%|████▎ | 30/70 [00:00<00:00, 1766.15 it/sec, feas=True, obj=5.5]
INFO - 16:21:39: 44%|████▍ | 31/70 [00:00<00:00, 1775.11 it/sec, feas=True, obj=3.35]
INFO - 16:21:39: 46%|████▌ | 32/70 [00:00<00:00, 1785.00 it/sec, feas=True, obj=4.05]
INFO - 16:21:39: 47%|████▋ | 33/70 [00:00<00:00, 1793.69 it/sec, feas=True, obj=2.43]
INFO - 16:21:39: 49%|████▊ | 34/70 [00:00<00:00, 1802.52 it/sec, feas=True, obj=-0.0246]
INFO - 16:21:39: 50%|█████ | 35/70 [00:00<00:00, 1811.15 it/sec, feas=True, obj=-0.0211]
INFO - 16:21:39: 51%|█████▏ | 36/70 [00:00<00:00, 1816.46 it/sec, feas=True, obj=6.01]
INFO - 16:21:39: 53%|█████▎ | 37/70 [00:00<00:00, 1823.12 it/sec, feas=True, obj=5.03]
INFO - 16:21:39: 54%|█████▍ | 38/70 [00:00<00:00, 1828.90 it/sec, feas=True, obj=0.863]
INFO - 16:21:39: 56%|█████▌ | 39/70 [00:00<00:00, 1836.06 it/sec, feas=True, obj=-0.764]
INFO - 16:21:39: 57%|█████▋ | 40/70 [00:00<00:00, 1842.03 it/sec, feas=True, obj=14.8]
INFO - 16:21:39: 59%|█████▊ | 41/70 [00:00<00:00, 1848.05 it/sec, feas=True, obj=0.87]
INFO - 16:21:39: 60%|██████ | 42/70 [00:00<00:00, 1853.06 it/sec, feas=True, obj=0.829]
INFO - 16:21:39: 61%|██████▏ | 43/70 [00:00<00:00, 1859.00 it/sec, feas=True, obj=5.01]
INFO - 16:21:39: 63%|██████▎ | 44/70 [00:00<00:00, 1862.87 it/sec, feas=True, obj=0.108]
INFO - 16:21:39: 64%|██████▍ | 45/70 [00:00<00:00, 1867.49 it/sec, feas=True, obj=0.948]
INFO - 16:21:39: 66%|██████▌ | 46/70 [00:00<00:00, 1871.51 it/sec, feas=True, obj=1.22]
INFO - 16:21:39: 67%|██████▋ | 47/70 [00:00<00:00, 1877.34 it/sec, feas=True, obj=7.52]
INFO - 16:21:39: 69%|██████▊ | 48/70 [00:00<00:00, 1880.36 it/sec, feas=True, obj=3.97]
INFO - 16:21:39: 70%|███████ | 49/70 [00:00<00:00, 1884.86 it/sec, feas=True, obj=0.768]
INFO - 16:21:39: 71%|███████▏ | 50/70 [00:00<00:00, 1888.68 it/sec, feas=True, obj=-8.26]
INFO - 16:21:39: 73%|███████▎ | 51/70 [00:00<00:00, 1893.26 it/sec, feas=True, obj=-3.5]
INFO - 16:21:39: 74%|███████▍ | 52/70 [00:00<00:00, 1896.39 it/sec, feas=True, obj=7.43]
INFO - 16:21:39: 76%|███████▌ | 53/70 [00:00<00:00, 1900.63 it/sec, feas=True, obj=-2.32]
INFO - 16:21:39: 77%|███████▋ | 54/70 [00:00<00:00, 1903.55 it/sec, feas=True, obj=4.82]
INFO - 16:21:39: 79%|███████▊ | 55/70 [00:00<00:00, 1907.75 it/sec, feas=True, obj=2.5]
INFO - 16:21:39: 80%|████████ | 56/70 [00:00<00:00, 1912.37 it/sec, feas=True, obj=2.58]
INFO - 16:21:39: 81%|████████▏ | 57/70 [00:00<00:00, 1915.82 it/sec, feas=True, obj=-2.55]
INFO - 16:21:39: 83%|████████▎ | 58/70 [00:00<00:00, 1920.00 it/sec, feas=True, obj=2.11]
INFO - 16:21:39: 84%|████████▍ | 59/70 [00:00<00:00, 1922.74 it/sec, feas=True, obj=8.06]
INFO - 16:21:39: 86%|████████▌ | 60/70 [00:00<00:00, 1926.50 it/sec, feas=True, obj=-5.24]
INFO - 16:21:39: 87%|████████▋ | 61/70 [00:00<00:00, 1928.68 it/sec, feas=True, obj=2.4]
INFO - 16:21:39: 89%|████████▊ | 62/70 [00:00<00:00, 1931.87 it/sec, feas=True, obj=3.43]
INFO - 16:21:39: 90%|█████████ | 63/70 [00:00<00:00, 1934.67 it/sec, feas=True, obj=5.99]
INFO - 16:21:39: 91%|█████████▏| 64/70 [00:00<00:00, 1938.19 it/sec, feas=True, obj=0.819]
INFO - 16:21:39: 93%|█████████▎| 65/70 [00:00<00:00, 1940.80 it/sec, feas=True, obj=0.632]
INFO - 16:21:39: 94%|█████████▍| 66/70 [00:00<00:00, 1944.08 it/sec, feas=True, obj=-0.158]
INFO - 16:21:39: 96%|█████████▌| 67/70 [00:00<00:00, 1946.25 it/sec, feas=True, obj=4.05]
INFO - 16:21:39: 97%|█████████▋| 68/70 [00:00<00:00, 1949.12 it/sec, feas=True, obj=7.71]
INFO - 16:21:39: 99%|█████████▊| 69/70 [00:00<00:00, 1951.93 it/sec, feas=True, obj=5.54]
INFO - 16:21:39: 100%|██████████| 70/70 [00:00<00:00, 1940.08 it/sec, feas=True, obj=6.63]
INFO - 16:21:39: Optimization result:
INFO - 16:21:39: Optimizer info:
INFO - 16:21:39: Status: None
INFO - 16:21:39: Message: None
INFO - 16:21:39: Solution:
INFO - 16:21:39: Objective: -8.260663543133736
INFO - 16:21:39: Design space:
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: | Name | Distribution |
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: *** 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 - 16:21:39: *** Start Sampling execution ***
INFO - 16:21:39: Sampling
INFO - 16:21:39: Disciplines: IshigamiDiscipline
INFO - 16:21:39: MDO formulation: MDF
INFO - 16:21:39: Optimization problem:
INFO - 16:21:39: minimize y(x1, x2, x3)
INFO - 16:21:39: with respect to x1, x2, x3
INFO - 16:21:39: over the design space:
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: | Name | Distribution |
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: Solving optimization problem with algorithm MC:
INFO - 16:21:39: 1%| | 6/1000 [00:00<00:00, 4148.67 it/sec, feas=True, obj=3.6]
INFO - 16:21:39: 1%| | 7/1000 [00:00<00:00, 3934.09 it/sec, feas=True, obj=5.41]
INFO - 16:21:39: 1%| | 8/1000 [00:00<00:00, 3850.19 it/sec, feas=True, obj=-9.09]
INFO - 16:21:39: 1%| | 9/1000 [00:00<00:00, 3823.82 it/sec, feas=True, obj=7.06]
INFO - 16:21:39: 1%| | 10/1000 [00:00<00:00, 3811.62 it/sec, feas=True, obj=-3.46]
INFO - 16:21:39: 1%| | 11/1000 [00:00<00:00, 3805.14 it/sec, feas=True, obj=3.2]
INFO - 16:21:39: 1%| | 12/1000 [00:00<00:00, 3783.48 it/sec, feas=True, obj=8.62]
INFO - 16:21:39: 1%|▏ | 13/1000 [00:00<00:00, 3784.42 it/sec, feas=True, obj=-0.0229]
INFO - 16:21:39: 1%|▏ | 14/1000 [00:00<00:00, 3786.21 it/sec, feas=True, obj=2.91]
INFO - 16:21:39: 2%|▏ | 15/1000 [00:00<00:00, 3766.44 it/sec, feas=True, obj=8.67]
INFO - 16:21:39: 2%|▏ | 16/1000 [00:00<00:00, 3761.71 it/sec, feas=True, obj=0.13]
INFO - 16:21:39: 2%|▏ | 17/1000 [00:00<00:00, 3762.50 it/sec, feas=True, obj=4.8]
INFO - 16:21:39: 2%|▏ | 18/1000 [00:00<00:00, 3763.58 it/sec, feas=True, obj=8.34]
INFO - 16:21:39: 2%|▏ | 19/1000 [00:00<00:00, 3753.38 it/sec, feas=True, obj=-1.57]
INFO - 16:21:39: 2%|▏ | 20/1000 [00:00<00:00, 3757.33 it/sec, feas=True, obj=4.2]
INFO - 16:21:39: 2%|▏ | 21/1000 [00:00<00:00, 3761.23 it/sec, feas=True, obj=-1.04]
INFO - 16:21:39: 2%|▏ | 22/1000 [00:00<00:00, 3770.78 it/sec, feas=True, obj=6.49]
INFO - 16:21:39: 2%|▏ | 23/1000 [00:00<00:00, 3766.55 it/sec, feas=True, obj=1.83]
INFO - 16:21:39: 2%|▏ | 24/1000 [00:00<00:00, 3771.57 it/sec, feas=True, obj=4.83]
INFO - 16:21:39: 2%|▎ | 25/1000 [00:00<00:00, 3776.07 it/sec, feas=True, obj=2.15]
INFO - 16:21:39: 3%|▎ | 26/1000 [00:00<00:00, 3781.01 it/sec, feas=True, obj=4.61]
INFO - 16:21:39: 3%|▎ | 27/1000 [00:00<00:00, 3775.00 it/sec, feas=True, obj=4.02]
INFO - 16:21:39: 3%|▎ | 28/1000 [00:00<00:00, 3781.33 it/sec, feas=True, obj=4.83]
INFO - 16:21:39: 3%|▎ | 29/1000 [00:00<00:00, 3787.71 it/sec, feas=True, obj=3.43]
INFO - 16:21:39: 3%|▎ | 30/1000 [00:00<00:00, 3791.40 it/sec, feas=True, obj=2.48]
INFO - 16:21:39: 3%|▎ | 31/1000 [00:00<00:00, 3712.20 it/sec, feas=True, obj=6.63]
INFO - 16:21:39: 3%|▎ | 32/1000 [00:00<00:00, 3708.80 it/sec, feas=True, obj=6.92]
INFO - 16:21:39: 3%|▎ | 33/1000 [00:00<00:00, 3708.59 it/sec, feas=True, obj=3.22]
INFO - 16:21:39: 3%|▎ | 34/1000 [00:00<00:00, 3701.85 it/sec, feas=True, obj=5.73]
INFO - 16:21:39: 4%|▎ | 35/1000 [00:00<00:00, 3703.25 it/sec, feas=True, obj=5.62]
INFO - 16:21:39: 4%|▎ | 36/1000 [00:00<00:00, 3708.04 it/sec, feas=True, obj=-1.44]
INFO - 16:21:39: 4%|▎ | 37/1000 [00:00<00:00, 3705.30 it/sec, feas=True, obj=7.02]
INFO - 16:21:39: 4%|▍ | 38/1000 [00:00<00:00, 3706.42 it/sec, feas=True, obj=6.21]
INFO - 16:21:39: 4%|▍ | 39/1000 [00:00<00:00, 3710.59 it/sec, feas=True, obj=4.64]
INFO - 16:21:39: 4%|▍ | 40/1000 [00:00<00:00, 3711.45 it/sec, feas=True, obj=4.71]
INFO - 16:21:39: 4%|▍ | 41/1000 [00:00<00:00, 3707.69 it/sec, feas=True, obj=5.73]
INFO - 16:21:39: 4%|▍ | 42/1000 [00:00<00:00, 3708.02 it/sec, feas=True, obj=-0.0754]
INFO - 16:21:39: 4%|▍ | 43/1000 [00:00<00:00, 3709.79 it/sec, feas=True, obj=5.56]
INFO - 16:21:39: 4%|▍ | 44/1000 [00:00<00:00, 3714.01 it/sec, feas=True, obj=5.03]
INFO - 16:21:39: 4%|▍ | 45/1000 [00:00<00:00, 3712.21 it/sec, feas=True, obj=7.21]
INFO - 16:21:39: 5%|▍ | 46/1000 [00:00<00:00, 3715.06 it/sec, feas=True, obj=8.03]
INFO - 16:21:39: 5%|▍ | 47/1000 [00:00<00:00, 3717.51 it/sec, feas=True, obj=5.56]
INFO - 16:21:39: 5%|▍ | 48/1000 [00:00<00:00, 3721.52 it/sec, feas=True, obj=6.35]
INFO - 16:21:39: 5%|▍ | 49/1000 [00:00<00:00, 3718.89 it/sec, feas=True, obj=6.71]
INFO - 16:21:39: 5%|▌ | 50/1000 [00:00<00:00, 3721.92 it/sec, feas=True, obj=3.52]
INFO - 16:21:39: 5%|▌ | 51/1000 [00:00<00:00, 3725.54 it/sec, feas=True, obj=2.63]
INFO - 16:21:39: 5%|▌ | 52/1000 [00:00<00:00, 3731.08 it/sec, feas=True, obj=4.68]
INFO - 16:21:39: 5%|▌ | 53/1000 [00:00<00:00, 3728.52 it/sec, feas=True, obj=1.07]
INFO - 16:21:39: 5%|▌ | 54/1000 [00:00<00:00, 3733.00 it/sec, feas=True, obj=10.3]
INFO - 16:21:39: 6%|▌ | 55/1000 [00:00<00:00, 3738.06 it/sec, feas=True, obj=6.87]
INFO - 16:21:39: 6%|▌ | 56/1000 [00:00<00:00, 3737.23 it/sec, feas=True, obj=-3.82]
INFO - 16:21:39: 6%|▌ | 57/1000 [00:00<00:00, 3738.30 it/sec, feas=True, obj=-1.58]
INFO - 16:21:39: 6%|▌ | 58/1000 [00:00<00:00, 3742.09 it/sec, feas=True, obj=3.43]
INFO - 16:21:39: 6%|▌ | 59/1000 [00:00<00:00, 3747.30 it/sec, feas=True, obj=-6.44]
INFO - 16:21:39: 6%|▌ | 60/1000 [00:00<00:00, 3746.98 it/sec, feas=True, obj=0.167]
INFO - 16:21:39: 6%|▌ | 61/1000 [00:00<00:00, 3747.93 it/sec, feas=True, obj=2.98]
INFO - 16:21:39: 6%|▌ | 62/1000 [00:00<00:00, 3749.45 it/sec, feas=True, obj=0.771]
INFO - 16:21:39: 6%|▋ | 63/1000 [00:00<00:00, 3752.36 it/sec, feas=True, obj=6.98]
INFO - 16:21:39: 6%|▋ | 64/1000 [00:00<00:00, 3751.30 it/sec, feas=True, obj=6.81]
INFO - 16:21:39: 6%|▋ | 65/1000 [00:00<00:00, 3752.44 it/sec, feas=True, obj=0.257]
INFO - 16:21:39: 7%|▋ | 66/1000 [00:00<00:00, 3756.04 it/sec, feas=True, obj=3.31]
INFO - 16:21:39: 7%|▋ | 67/1000 [00:00<00:00, 3759.54 it/sec, feas=True, obj=6.07]
INFO - 16:21:39: 7%|▋ | 68/1000 [00:00<00:00, 3759.48 it/sec, feas=True, obj=5.87]
INFO - 16:21:39: 7%|▋ | 69/1000 [00:00<00:00, 3760.68 it/sec, feas=True, obj=7.69]
INFO - 16:21:39: 7%|▋ | 70/1000 [00:00<00:00, 3763.20 it/sec, feas=True, obj=5.16]
INFO - 16:21:39: 7%|▋ | 71/1000 [00:00<00:00, 3766.23 it/sec, feas=True, obj=-0.0811]
INFO - 16:21:39: 7%|▋ | 72/1000 [00:00<00:00, 3765.60 it/sec, feas=True, obj=1.25]
INFO - 16:21:39: 7%|▋ | 73/1000 [00:00<00:00, 3768.10 it/sec, feas=True, obj=5.71]
INFO - 16:21:39: 7%|▋ | 74/1000 [00:00<00:00, 3771.17 it/sec, feas=True, obj=8.15]
INFO - 16:21:39: 8%|▊ | 75/1000 [00:00<00:00, 3774.66 it/sec, feas=True, obj=-2.86]
INFO - 16:21:39: 8%|▊ | 76/1000 [00:00<00:00, 3773.55 it/sec, feas=True, obj=10.5]
INFO - 16:21:39: 8%|▊ | 77/1000 [00:00<00:00, 3775.25 it/sec, feas=True, obj=4.87]
INFO - 16:21:39: 8%|▊ | 78/1000 [00:00<00:00, 3776.51 it/sec, feas=True, obj=2.44]
INFO - 16:21:39: 8%|▊ | 79/1000 [00:00<00:00, 3778.61 it/sec, feas=True, obj=6.74]
INFO - 16:21:39: 8%|▊ | 80/1000 [00:00<00:00, 3775.85 it/sec, feas=True, obj=8.51]
INFO - 16:21:39: 8%|▊ | 81/1000 [00:00<00:00, 3776.43 it/sec, feas=True, obj=0.595]
INFO - 16:21:39: 8%|▊ | 82/1000 [00:00<00:00, 3777.99 it/sec, feas=True, obj=7.84]
INFO - 16:21:39: 8%|▊ | 83/1000 [00:00<00:00, 3777.09 it/sec, feas=True, obj=0.842]
INFO - 16:21:39: 8%|▊ | 84/1000 [00:00<00:00, 3777.56 it/sec, feas=True, obj=9.47]
INFO - 16:21:39: 8%|▊ | 85/1000 [00:00<00:00, 3780.21 it/sec, feas=True, obj=4.54]
INFO - 16:21:39: 9%|▊ | 86/1000 [00:00<00:00, 3780.79 it/sec, feas=True, obj=-3.69]
INFO - 16:21:39: 9%|▊ | 87/1000 [00:00<00:00, 3778.93 it/sec, feas=True, obj=0.849]
INFO - 16:21:39: 9%|▉ | 88/1000 [00:00<00:00, 3778.85 it/sec, feas=True, obj=11.9]
INFO - 16:21:39: 9%|▉ | 89/1000 [00:00<00:00, 3780.60 it/sec, feas=True, obj=5.69]
INFO - 16:21:39: 9%|▉ | 90/1000 [00:00<00:00, 3782.93 it/sec, feas=True, obj=7.25]
INFO - 16:21:39: 9%|▉ | 91/1000 [00:00<00:00, 3781.16 it/sec, feas=True, obj=2.17]
INFO - 16:21:39: 9%|▉ | 92/1000 [00:00<00:00, 3782.02 it/sec, feas=True, obj=5.76]
INFO - 16:21:39: 9%|▉ | 93/1000 [00:00<00:00, 3782.02 it/sec, feas=True, obj=5.32]
INFO - 16:21:39: 9%|▉ | 94/1000 [00:00<00:00, 3783.77 it/sec, feas=True, obj=-3.15]
INFO - 16:21:39: 10%|▉ | 95/1000 [00:00<00:00, 3782.10 it/sec, feas=True, obj=9.36]
INFO - 16:21:39: 10%|▉ | 96/1000 [00:00<00:00, 3783.09 it/sec, feas=True, obj=11.9]
INFO - 16:21:39: 10%|▉ | 97/1000 [00:00<00:00, 3782.87 it/sec, feas=True, obj=8.76]
INFO - 16:21:39: 10%|▉ | 98/1000 [00:00<00:00, 3783.35 it/sec, feas=True, obj=-0.112]
INFO - 16:21:39: 10%|▉ | 99/1000 [00:00<00:00, 3780.48 it/sec, feas=True, obj=1.61]
INFO - 16:21:39: 10%|█ | 100/1000 [00:00<00:00, 3781.41 it/sec, feas=True, obj=4.05]
INFO - 16:21:39: 10%|█ | 101/1000 [00:00<00:00, 3782.87 it/sec, feas=True, obj=-0.31]
INFO - 16:21:39: 10%|█ | 102/1000 [00:00<00:00, 3781.39 it/sec, feas=True, obj=1.46]
INFO - 16:21:39: 10%|█ | 103/1000 [00:00<00:00, 3781.17 it/sec, feas=True, obj=1.1]
INFO - 16:21:39: 10%|█ | 104/1000 [00:00<00:00, 3782.49 it/sec, feas=True, obj=2.69]
INFO - 16:21:39: 10%|█ | 105/1000 [00:00<00:00, 3782.84 it/sec, feas=True, obj=7.7]
INFO - 16:21:39: 11%|█ | 106/1000 [00:00<00:00, 3782.03 it/sec, feas=True, obj=4.86]
INFO - 16:21:39: 11%|█ | 107/1000 [00:00<00:00, 3782.67 it/sec, feas=True, obj=-0.104]
INFO - 16:21:39: 11%|█ | 108/1000 [00:00<00:00, 3783.35 it/sec, feas=True, obj=-7.95]
INFO - 16:21:39: 11%|█ | 109/1000 [00:00<00:00, 3784.63 it/sec, feas=True, obj=0.11]
INFO - 16:21:39: 11%|█ | 110/1000 [00:00<00:00, 3783.33 it/sec, feas=True, obj=-0.471]
INFO - 16:21:39: 11%|█ | 111/1000 [00:00<00:00, 3784.12 it/sec, feas=True, obj=-0.843]
INFO - 16:21:39: 11%|█ | 112/1000 [00:00<00:00, 3784.83 it/sec, feas=True, obj=3.99]
INFO - 16:21:39: 11%|█▏ | 113/1000 [00:00<00:00, 3786.32 it/sec, feas=True, obj=5.95]
INFO - 16:21:39: 11%|█▏ | 114/1000 [00:00<00:00, 3784.39 it/sec, feas=True, obj=6.56]
INFO - 16:21:39: 12%|█▏ | 115/1000 [00:00<00:00, 3785.26 it/sec, feas=True, obj=6.04]
INFO - 16:21:39: 12%|█▏ | 116/1000 [00:00<00:00, 3786.71 it/sec, feas=True, obj=0.26]
INFO - 16:21:39: 12%|█▏ | 117/1000 [00:00<00:00, 3788.22 it/sec, feas=True, obj=5.43]
INFO - 16:21:39: 12%|█▏ | 118/1000 [00:00<00:00, 3786.60 it/sec, feas=True, obj=0.706]
INFO - 16:21:39: 12%|█▏ | 119/1000 [00:00<00:00, 3787.63 it/sec, feas=True, obj=-0.861]
INFO - 16:21:39: 12%|█▏ | 120/1000 [00:00<00:00, 3788.95 it/sec, feas=True, obj=5.7]
INFO - 16:21:39: 12%|█▏ | 121/1000 [00:00<00:00, 3790.51 it/sec, feas=True, obj=3.13]
INFO - 16:21:39: 12%|█▏ | 122/1000 [00:00<00:00, 3788.95 it/sec, feas=True, obj=2.51]
INFO - 16:21:39: 12%|█▏ | 123/1000 [00:00<00:00, 3790.28 it/sec, feas=True, obj=0.0431]
INFO - 16:21:39: 12%|█▏ | 124/1000 [00:00<00:00, 3790.27 it/sec, feas=True, obj=4.08]
INFO - 16:21:39: 12%|█▎ | 125/1000 [00:00<00:00, 3790.07 it/sec, feas=True, obj=3.48]
INFO - 16:21:39: 13%|█▎ | 126/1000 [00:00<00:00, 3790.25 it/sec, feas=True, obj=-0.27]
INFO - 16:21:39: 13%|█▎ | 127/1000 [00:00<00:00, 3791.67 it/sec, feas=True, obj=6.25]
INFO - 16:21:39: 13%|█▎ | 128/1000 [00:00<00:00, 3793.18 it/sec, feas=True, obj=9.11]
INFO - 16:21:39: 13%|█▎ | 129/1000 [00:00<00:00, 3792.58 it/sec, feas=True, obj=-0.828]
INFO - 16:21:39: 13%|█▎ | 130/1000 [00:00<00:00, 3793.00 it/sec, feas=True, obj=10.3]
INFO - 16:21:39: 13%|█▎ | 131/1000 [00:00<00:00, 3794.20 it/sec, feas=True, obj=3.98]
INFO - 16:21:39: 13%|█▎ | 132/1000 [00:00<00:00, 3795.57 it/sec, feas=True, obj=2.27]
INFO - 16:21:39: 13%|█▎ | 133/1000 [00:00<00:00, 3794.90 it/sec, feas=True, obj=1.6]
INFO - 16:21:39: 13%|█▎ | 134/1000 [00:00<00:00, 3794.90 it/sec, feas=True, obj=-7.13]
INFO - 16:21:39: 14%|█▎ | 135/1000 [00:00<00:00, 3795.83 it/sec, feas=True, obj=7.82]
INFO - 16:21:39: 14%|█▎ | 136/1000 [00:00<00:00, 3796.28 it/sec, feas=True, obj=4.68]
INFO - 16:21:39: 14%|█▎ | 137/1000 [00:00<00:00, 3795.52 it/sec, feas=True, obj=-0.627]
INFO - 16:21:39: 14%|█▍ | 138/1000 [00:00<00:00, 3795.68 it/sec, feas=True, obj=-4.07]
INFO - 16:21:39: 14%|█▍ | 139/1000 [00:00<00:00, 3795.58 it/sec, feas=True, obj=1.06]
INFO - 16:21:39: 14%|█▍ | 140/1000 [00:00<00:00, 3797.08 it/sec, feas=True, obj=11.1]
INFO - 16:21:39: 14%|█▍ | 141/1000 [00:00<00:00, 3796.38 it/sec, feas=True, obj=1.87]
INFO - 16:21:39: 14%|█▍ | 142/1000 [00:00<00:00, 3797.18 it/sec, feas=True, obj=7.07]
INFO - 16:21:39: 14%|█▍ | 143/1000 [00:00<00:00, 3798.20 it/sec, feas=True, obj=1.79]
INFO - 16:21:39: 14%|█▍ | 144/1000 [00:00<00:00, 3799.74 it/sec, feas=True, obj=5.97]
INFO - 16:21:39: 14%|█▍ | 145/1000 [00:00<00:00, 3787.36 it/sec, feas=True, obj=6.15]
INFO - 16:21:39: 15%|█▍ | 146/1000 [00:00<00:00, 3786.92 it/sec, feas=True, obj=4.61]
INFO - 16:21:39: 15%|█▍ | 147/1000 [00:00<00:00, 3787.43 it/sec, feas=True, obj=0.433]
INFO - 16:21:39: 15%|█▍ | 148/1000 [00:00<00:00, 3786.67 it/sec, feas=True, obj=2.73]
INFO - 16:21:39: 15%|█▍ | 149/1000 [00:00<00:00, 3787.65 it/sec, feas=True, obj=1.83]
INFO - 16:21:39: 15%|█▌ | 150/1000 [00:00<00:00, 3788.82 it/sec, feas=True, obj=5.3]
INFO - 16:21:39: 15%|█▌ | 151/1000 [00:00<00:00, 3789.50 it/sec, feas=True, obj=-0.935]
INFO - 16:21:39: 15%|█▌ | 152/1000 [00:00<00:00, 3788.71 it/sec, feas=True, obj=7.03]
INFO - 16:21:39: 15%|█▌ | 153/1000 [00:00<00:00, 3789.25 it/sec, feas=True, obj=4.91]
INFO - 16:21:39: 15%|█▌ | 154/1000 [00:00<00:00, 3789.07 it/sec, feas=True, obj=5.89]
INFO - 16:21:39: 16%|█▌ | 155/1000 [00:00<00:00, 3789.31 it/sec, feas=True, obj=-1.07]
INFO - 16:21:39: 16%|█▌ | 156/1000 [00:00<00:00, 3785.54 it/sec, feas=True, obj=2.05]
INFO - 16:21:39: 16%|█▌ | 157/1000 [00:00<00:00, 3785.49 it/sec, feas=True, obj=9.08]
INFO - 16:21:39: 16%|█▌ | 158/1000 [00:00<00:00, 3786.14 it/sec, feas=True, obj=1.28]
INFO - 16:21:39: 16%|█▌ | 159/1000 [00:00<00:00, 3785.75 it/sec, feas=True, obj=5.5]
INFO - 16:21:39: 16%|█▌ | 160/1000 [00:00<00:00, 3786.11 it/sec, feas=True, obj=2.86]
INFO - 16:21:39: 16%|█▌ | 161/1000 [00:00<00:00, 3786.75 it/sec, feas=True, obj=2.58]
INFO - 16:21:39: 16%|█▌ | 162/1000 [00:00<00:00, 3787.71 it/sec, feas=True, obj=6.35]
INFO - 16:21:39: 16%|█▋ | 163/1000 [00:00<00:00, 3786.56 it/sec, feas=True, obj=5.03]
INFO - 16:21:39: 16%|█▋ | 164/1000 [00:00<00:00, 3787.20 it/sec, feas=True, obj=4.89]
INFO - 16:21:39: 16%|█▋ | 165/1000 [00:00<00:00, 3787.81 it/sec, feas=True, obj=-0.862]
INFO - 16:21:39: 17%|█▋ | 166/1000 [00:00<00:00, 3788.91 it/sec, feas=True, obj=5.17]
INFO - 16:21:39: 17%|█▋ | 167/1000 [00:00<00:00, 3788.26 it/sec, feas=True, obj=6.54]
INFO - 16:21:39: 17%|█▋ | 168/1000 [00:00<00:00, 3789.18 it/sec, feas=True, obj=5.04]
INFO - 16:21:39: 17%|█▋ | 169/1000 [00:00<00:00, 3789.26 it/sec, feas=True, obj=5.18]
INFO - 16:21:39: 17%|█▋ | 170/1000 [00:00<00:00, 3790.32 it/sec, feas=True, obj=9.72]
INFO - 16:21:39: 17%|█▋ | 171/1000 [00:00<00:00, 3789.51 it/sec, feas=True, obj=4.51]
INFO - 16:21:39: 17%|█▋ | 172/1000 [00:00<00:00, 3790.47 it/sec, feas=True, obj=5.25]
INFO - 16:21:39: 17%|█▋ | 173/1000 [00:00<00:00, 3791.27 it/sec, feas=True, obj=7.58]
INFO - 16:21:39: 17%|█▋ | 174/1000 [00:00<00:00, 3792.20 it/sec, feas=True, obj=-0.152]
INFO - 16:21:39: 18%|█▊ | 175/1000 [00:00<00:00, 3790.87 it/sec, feas=True, obj=0.707]
INFO - 16:21:39: 18%|█▊ | 176/1000 [00:00<00:00, 3791.19 it/sec, feas=True, obj=1.95]
INFO - 16:21:39: 18%|█▊ | 177/1000 [00:00<00:00, 3791.91 it/sec, feas=True, obj=5.37]
INFO - 16:21:39: 18%|█▊ | 178/1000 [00:00<00:00, 3792.93 it/sec, feas=True, obj=9.3]
INFO - 16:21:39: 18%|█▊ | 179/1000 [00:00<00:00, 3790.31 it/sec, feas=True, obj=-6.59]
INFO - 16:21:39: 18%|█▊ | 180/1000 [00:00<00:00, 3790.89 it/sec, feas=True, obj=0.62]
INFO - 16:21:39: 18%|█▊ | 181/1000 [00:00<00:00, 3791.47 it/sec, feas=True, obj=2.86]
INFO - 16:21:39: 18%|█▊ | 182/1000 [00:00<00:00, 3791.15 it/sec, feas=True, obj=7.64]
INFO - 16:21:39: 18%|█▊ | 183/1000 [00:00<00:00, 3791.44 it/sec, feas=True, obj=1.83]
INFO - 16:21:39: 18%|█▊ | 184/1000 [00:00<00:00, 3791.68 it/sec, feas=True, obj=1.15]
INFO - 16:21:39: 18%|█▊ | 185/1000 [00:00<00:00, 3792.00 it/sec, feas=True, obj=4.53]
INFO - 16:21:39: 19%|█▊ | 186/1000 [00:00<00:00, 3790.72 it/sec, feas=True, obj=5.86]
INFO - 16:21:39: 19%|█▊ | 187/1000 [00:00<00:00, 3791.47 it/sec, feas=True, obj=-4.15]
INFO - 16:21:39: 19%|█▉ | 188/1000 [00:00<00:00, 3792.19 it/sec, feas=True, obj=0.77]
INFO - 16:21:39: 19%|█▉ | 189/1000 [00:00<00:00, 3792.99 it/sec, feas=True, obj=3.77]
INFO - 16:21:39: 19%|█▉ | 190/1000 [00:00<00:00, 3792.59 it/sec, feas=True, obj=0.574]
INFO - 16:21:39: 19%|█▉ | 191/1000 [00:00<00:00, 3793.14 it/sec, feas=True, obj=3.27]
INFO - 16:21:39: 19%|█▉ | 192/1000 [00:00<00:00, 3794.02 it/sec, feas=True, obj=1.31]
INFO - 16:21:39: 19%|█▉ | 193/1000 [00:00<00:00, 3795.09 it/sec, feas=True, obj=2.11]
INFO - 16:21:39: 19%|█▉ | 194/1000 [00:00<00:00, 3794.51 it/sec, feas=True, obj=-0.324]
INFO - 16:21:39: 20%|█▉ | 195/1000 [00:00<00:00, 3794.99 it/sec, feas=True, obj=2.6]
INFO - 16:21:39: 20%|█▉ | 196/1000 [00:00<00:00, 3795.89 it/sec, feas=True, obj=7.25]
INFO - 16:21:39: 20%|█▉ | 197/1000 [00:00<00:00, 3796.73 it/sec, feas=True, obj=12.9]
INFO - 16:21:39: 20%|█▉ | 198/1000 [00:00<00:00, 3795.96 it/sec, feas=True, obj=-1.67]
INFO - 16:21:39: 20%|█▉ | 199/1000 [00:00<00:00, 3796.54 it/sec, feas=True, obj=6.19]
INFO - 16:21:39: 20%|██ | 200/1000 [00:00<00:00, 3796.47 it/sec, feas=True, obj=3.52]
INFO - 16:21:39: 20%|██ | 201/1000 [00:00<00:00, 3797.20 it/sec, feas=True, obj=-1.49]
INFO - 16:21:39: 20%|██ | 202/1000 [00:00<00:00, 3795.75 it/sec, feas=True, obj=7.77]
INFO - 16:21:39: 20%|██ | 203/1000 [00:00<00:00, 3796.55 it/sec, feas=True, obj=0.847]
INFO - 16:21:39: 20%|██ | 204/1000 [00:00<00:00, 3797.42 it/sec, feas=True, obj=-2.82]
INFO - 16:21:39: 20%|██ | 205/1000 [00:00<00:00, 3797.04 it/sec, feas=True, obj=6.59]
INFO - 16:21:39: 21%|██ | 206/1000 [00:00<00:00, 3797.27 it/sec, feas=True, obj=4.35]
INFO - 16:21:39: 21%|██ | 207/1000 [00:00<00:00, 3797.38 it/sec, feas=True, obj=-1.95]
INFO - 16:21:39: 21%|██ | 208/1000 [00:00<00:00, 3797.82 it/sec, feas=True, obj=-0.845]
INFO - 16:21:39: 21%|██ | 209/1000 [00:00<00:00, 3796.75 it/sec, feas=True, obj=7.19]
INFO - 16:21:39: 21%|██ | 210/1000 [00:00<00:00, 3797.17 it/sec, feas=True, obj=-0.108]
INFO - 16:21:39: 21%|██ | 211/1000 [00:00<00:00, 3797.51 it/sec, feas=True, obj=1.42]
INFO - 16:21:39: 21%|██ | 212/1000 [00:00<00:00, 3797.84 it/sec, feas=True, obj=0.785]
INFO - 16:21:39: 21%|██▏ | 213/1000 [00:00<00:00, 3797.33 it/sec, feas=True, obj=-4.97]
INFO - 16:21:39: 21%|██▏ | 214/1000 [00:00<00:00, 3797.73 it/sec, feas=True, obj=7.43]
INFO - 16:21:39: 22%|██▏ | 215/1000 [00:00<00:00, 3797.97 it/sec, feas=True, obj=6.26]
INFO - 16:21:39: 22%|██▏ | 216/1000 [00:00<00:00, 3798.22 it/sec, feas=True, obj=2.43]
INFO - 16:21:39: 22%|██▏ | 217/1000 [00:00<00:00, 3797.48 it/sec, feas=True, obj=11.5]
INFO - 16:21:39: 22%|██▏ | 218/1000 [00:00<00:00, 3798.26 it/sec, feas=True, obj=3.62]
INFO - 16:21:39: 22%|██▏ | 219/1000 [00:00<00:00, 3799.22 it/sec, feas=True, obj=5.45]
INFO - 16:21:39: 22%|██▏ | 220/1000 [00:00<00:00, 3800.06 it/sec, feas=True, obj=8.42]
INFO - 16:21:39: 22%|██▏ | 221/1000 [00:00<00:00, 3799.17 it/sec, feas=True, obj=10.5]
INFO - 16:21:39: 22%|██▏ | 222/1000 [00:00<00:00, 3799.96 it/sec, feas=True, obj=4.04]
INFO - 16:21:39: 22%|██▏ | 223/1000 [00:00<00:00, 3801.04 it/sec, feas=True, obj=-1.33]
INFO - 16:21:39: 22%|██▏ | 224/1000 [00:00<00:00, 3801.60 it/sec, feas=True, obj=5.55]
INFO - 16:21:39: 22%|██▎ | 225/1000 [00:00<00:00, 3800.55 it/sec, feas=True, obj=-0.71]
INFO - 16:21:39: 23%|██▎ | 226/1000 [00:00<00:00, 3801.18 it/sec, feas=True, obj=2.84]
INFO - 16:21:39: 23%|██▎ | 227/1000 [00:00<00:00, 3801.92 it/sec, feas=True, obj=1.75]
INFO - 16:21:39: 23%|██▎ | 228/1000 [00:00<00:00, 3801.64 it/sec, feas=True, obj=1.36]
INFO - 16:21:39: 23%|██▎ | 229/1000 [00:00<00:00, 3801.61 it/sec, feas=True, obj=6.32]
INFO - 16:21:39: 23%|██▎ | 230/1000 [00:00<00:00, 3802.32 it/sec, feas=True, obj=6.66]
INFO - 16:21:39: 23%|██▎ | 231/1000 [00:00<00:00, 3802.29 it/sec, feas=True, obj=5.61]
INFO - 16:21:39: 23%|██▎ | 232/1000 [00:00<00:00, 3801.79 it/sec, feas=True, obj=7.2]
INFO - 16:21:39: 23%|██▎ | 233/1000 [00:00<00:00, 3802.19 it/sec, feas=True, obj=6.4]
INFO - 16:21:39: 23%|██▎ | 234/1000 [00:00<00:00, 3802.43 it/sec, feas=True, obj=0.753]
INFO - 16:21:39: 24%|██▎ | 235/1000 [00:00<00:00, 3803.04 it/sec, feas=True, obj=-0.835]
INFO - 16:21:39: 24%|██▎ | 236/1000 [00:00<00:00, 3802.33 it/sec, feas=True, obj=-0.324]
INFO - 16:21:39: 24%|██▎ | 237/1000 [00:00<00:00, 3802.68 it/sec, feas=True, obj=3.91]
INFO - 16:21:39: 24%|██▍ | 238/1000 [00:00<00:00, 3802.88 it/sec, feas=True, obj=6.01]
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INFO - 16:21:39: 25%|██▌ | 253/1000 [00:00<00:00, 3801.42 it/sec, feas=True, obj=9.01]
INFO - 16:21:39: 25%|██▌ | 254/1000 [00:00<00:00, 3802.08 it/sec, feas=True, obj=7.08]
INFO - 16:21:39: 26%|██▌ | 255/1000 [00:00<00:00, 3801.17 it/sec, feas=True, obj=-0.461]
INFO - 16:21:39: 26%|██▌ | 256/1000 [00:00<00:00, 3801.33 it/sec, feas=True, obj=-4.41]
INFO - 16:21:39: 26%|██▌ | 257/1000 [00:00<00:00, 3801.84 it/sec, feas=True, obj=-0.98]
INFO - 16:21:39: 26%|██▌ | 258/1000 [00:00<00:00, 3802.18 it/sec, feas=True, obj=7.88]
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INFO - 16:21:39: 26%|██▌ | 260/1000 [00:00<00:00, 3793.70 it/sec, feas=True, obj=6.42]
INFO - 16:21:39: 26%|██▌ | 261/1000 [00:00<00:00, 3793.82 it/sec, feas=True, obj=2.42]
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INFO - 16:21:39: 26%|██▋ | 263/1000 [00:00<00:00, 3793.01 it/sec, feas=True, obj=-0.699]
INFO - 16:21:39: 26%|██▋ | 264/1000 [00:00<00:00, 3793.32 it/sec, feas=True, obj=0.137]
INFO - 16:21:39: 26%|██▋ | 265/1000 [00:00<00:00, 3793.64 it/sec, feas=True, obj=9.9]
INFO - 16:21:39: 27%|██▋ | 266/1000 [00:00<00:00, 3792.60 it/sec, feas=True, obj=3.71]
INFO - 16:21:39: 27%|██▋ | 267/1000 [00:00<00:00, 3792.70 it/sec, feas=True, obj=6.84]
INFO - 16:21:39: 27%|██▋ | 268/1000 [00:00<00:00, 3793.14 it/sec, feas=True, obj=1.21]
INFO - 16:21:39: 27%|██▋ | 269/1000 [00:00<00:00, 3793.64 it/sec, feas=True, obj=1.64]
INFO - 16:21:39: 27%|██▋ | 270/1000 [00:00<00:00, 3792.67 it/sec, feas=True, obj=2.2]
INFO - 16:21:39: 27%|██▋ | 271/1000 [00:00<00:00, 3792.93 it/sec, feas=True, obj=14.3]
INFO - 16:21:39: 27%|██▋ | 272/1000 [00:00<00:00, 3793.11 it/sec, feas=True, obj=1.03]
INFO - 16:21:39: 27%|██▋ | 273/1000 [00:00<00:00, 3792.75 it/sec, feas=True, obj=6.25]
INFO - 16:21:39: 27%|██▋ | 274/1000 [00:00<00:00, 3792.56 it/sec, feas=True, obj=3.31]
INFO - 16:21:39: 28%|██▊ | 275/1000 [00:00<00:00, 3792.58 it/sec, feas=True, obj=7.43]
INFO - 16:21:39: 28%|██▊ | 276/1000 [00:00<00:00, 3792.69 it/sec, feas=True, obj=4.7]
INFO - 16:21:39: 28%|██▊ | 277/1000 [00:00<00:00, 3791.98 it/sec, feas=True, obj=-0.0123]
INFO - 16:21:39: 28%|██▊ | 278/1000 [00:00<00:00, 3792.12 it/sec, feas=True, obj=10.9]
INFO - 16:21:39: 28%|██▊ | 279/1000 [00:00<00:00, 3792.31 it/sec, feas=True, obj=2.56]
INFO - 16:21:39: 28%|██▊ | 280/1000 [00:00<00:00, 3792.45 it/sec, feas=True, obj=5.48]
INFO - 16:21:39: 28%|██▊ | 281/1000 [00:00<00:00, 3792.03 it/sec, feas=True, obj=2.51]
INFO - 16:21:39: 28%|██▊ | 282/1000 [00:00<00:00, 3792.18 it/sec, feas=True, obj=5.13]
INFO - 16:21:39: 28%|██▊ | 283/1000 [00:00<00:00, 3792.33 it/sec, feas=True, obj=4.89]
INFO - 16:21:39: 28%|██▊ | 284/1000 [00:00<00:00, 3792.68 it/sec, feas=True, obj=7.69]
INFO - 16:21:39: 28%|██▊ | 285/1000 [00:00<00:00, 3791.91 it/sec, feas=True, obj=3.1]
INFO - 16:21:39: 29%|██▊ | 286/1000 [00:00<00:00, 3792.16 it/sec, feas=True, obj=-5.11]
INFO - 16:21:39: 29%|██▊ | 287/1000 [00:00<00:00, 3792.71 it/sec, feas=True, obj=-0.0286]
INFO - 16:21:39: 29%|██▉ | 288/1000 [00:00<00:00, 3793.10 it/sec, feas=True, obj=1.41]
INFO - 16:21:39: 29%|██▉ | 289/1000 [00:00<00:00, 3792.24 it/sec, feas=True, obj=5.79]
INFO - 16:21:39: 29%|██▉ | 290/1000 [00:00<00:00, 3792.63 it/sec, feas=True, obj=4.71]
INFO - 16:21:39: 29%|██▉ | 291/1000 [00:00<00:00, 3792.46 it/sec, feas=True, obj=6.49]
INFO - 16:21:39: 29%|██▉ | 292/1000 [00:00<00:00, 3792.14 it/sec, feas=True, obj=-7.67]
INFO - 16:21:39: 29%|██▉ | 293/1000 [00:00<00:00, 3792.11 it/sec, feas=True, obj=-0.721]
INFO - 16:21:39: 29%|██▉ | 294/1000 [00:00<00:00, 3792.68 it/sec, feas=True, obj=7.54]
INFO - 16:21:39: 30%|██▉ | 295/1000 [00:00<00:00, 3793.27 it/sec, feas=True, obj=6.14]
INFO - 16:21:39: 30%|██▉ | 296/1000 [00:00<00:00, 3792.94 it/sec, feas=True, obj=-1.73]
INFO - 16:21:39: 30%|██▉ | 297/1000 [00:00<00:00, 3792.99 it/sec, feas=True, obj=8.22]
INFO - 16:21:39: 30%|██▉ | 298/1000 [00:00<00:00, 3793.45 it/sec, feas=True, obj=6.34]
INFO - 16:21:39: 30%|██▉ | 299/1000 [00:00<00:00, 3793.88 it/sec, feas=True, obj=6.14]
INFO - 16:21:39: 30%|███ | 300/1000 [00:00<00:00, 3793.26 it/sec, feas=True, obj=4.71]
INFO - 16:21:39: 30%|███ | 301/1000 [00:00<00:00, 3793.38 it/sec, feas=True, obj=4]
INFO - 16:21:39: 30%|███ | 302/1000 [00:00<00:00, 3793.48 it/sec, feas=True, obj=6.52]
INFO - 16:21:39: 30%|███ | 303/1000 [00:00<00:00, 3793.93 it/sec, feas=True, obj=0.7]
INFO - 16:21:39: 30%|███ | 304/1000 [00:00<00:00, 3793.42 it/sec, feas=True, obj=5.21]
INFO - 16:21:39: 30%|███ | 305/1000 [00:00<00:00, 3793.68 it/sec, feas=True, obj=2.51]
INFO - 16:21:39: 31%|███ | 306/1000 [00:00<00:00, 3793.71 it/sec, feas=True, obj=-0.162]
INFO - 16:21:39: 31%|███ | 307/1000 [00:00<00:00, 3794.05 it/sec, feas=True, obj=2.63]
INFO - 16:21:39: 31%|███ | 308/1000 [00:00<00:00, 3793.14 it/sec, feas=True, obj=4.01]
INFO - 16:21:39: 31%|███ | 309/1000 [00:00<00:00, 3793.59 it/sec, feas=True, obj=2.99]
INFO - 16:21:39: 31%|███ | 310/1000 [00:00<00:00, 3793.89 it/sec, feas=True, obj=2.3]
INFO - 16:21:39: 31%|███ | 311/1000 [00:00<00:00, 3793.61 it/sec, feas=True, obj=3.71]
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INFO - 16:21:39: 31%|███▏ | 313/1000 [00:00<00:00, 3794.08 it/sec, feas=True, obj=6.43]
INFO - 16:21:39: 31%|███▏ | 314/1000 [00:00<00:00, 3794.47 it/sec, feas=True, obj=-1.98]
INFO - 16:21:39: 32%|███▏ | 315/1000 [00:00<00:00, 3793.88 it/sec, feas=True, obj=1.03]
INFO - 16:21:39: 32%|███▏ | 316/1000 [00:00<00:00, 3793.89 it/sec, feas=True, obj=-0.511]
INFO - 16:21:39: 32%|███▏ | 317/1000 [00:00<00:00, 3794.23 it/sec, feas=True, obj=-1.34]
INFO - 16:21:39: 32%|███▏ | 318/1000 [00:00<00:00, 3794.74 it/sec, feas=True, obj=6.72]
INFO - 16:21:39: 32%|███▏ | 319/1000 [00:00<00:00, 3794.24 it/sec, feas=True, obj=3.09]
INFO - 16:21:39: 32%|███▏ | 320/1000 [00:00<00:00, 3794.48 it/sec, feas=True, obj=7.12]
INFO - 16:21:39: 32%|███▏ | 321/1000 [00:00<00:00, 3794.53 it/sec, feas=True, obj=5.91]
INFO - 16:21:39: 32%|███▏ | 322/1000 [00:00<00:00, 3794.93 it/sec, feas=True, obj=0.0303]
INFO - 16:21:39: 32%|███▏ | 323/1000 [00:00<00:00, 3794.70 it/sec, feas=True, obj=1.38]
INFO - 16:21:39: 32%|███▏ | 324/1000 [00:00<00:00, 3795.08 it/sec, feas=True, obj=-5.06]
INFO - 16:21:39: 32%|███▎ | 325/1000 [00:00<00:00, 3795.69 it/sec, feas=True, obj=1.18]
INFO - 16:21:39: 33%|███▎ | 326/1000 [00:00<00:00, 3796.15 it/sec, feas=True, obj=0.213]
INFO - 16:21:39: 33%|███▎ | 327/1000 [00:00<00:00, 3795.63 it/sec, feas=True, obj=5.4]
INFO - 16:21:39: 33%|███▎ | 328/1000 [00:00<00:00, 3795.89 it/sec, feas=True, obj=3.09]
INFO - 16:21:39: 33%|███▎ | 329/1000 [00:00<00:00, 3796.37 it/sec, feas=True, obj=1.28]
INFO - 16:21:39: 33%|███▎ | 330/1000 [00:00<00:00, 3796.96 it/sec, feas=True, obj=7.37]
INFO - 16:21:39: 33%|███▎ | 331/1000 [00:00<00:00, 3796.27 it/sec, feas=True, obj=1.31]
INFO - 16:21:39: 33%|███▎ | 332/1000 [00:00<00:00, 3796.32 it/sec, feas=True, obj=2.05]
INFO - 16:21:39: 33%|███▎ | 333/1000 [00:00<00:00, 3796.53 it/sec, feas=True, obj=1.55]
INFO - 16:21:39: 33%|███▎ | 334/1000 [00:00<00:00, 3796.02 it/sec, feas=True, obj=2.46]
INFO - 16:21:39: 34%|███▎ | 335/1000 [00:00<00:00, 3796.08 it/sec, feas=True, obj=1.51]
INFO - 16:21:39: 34%|███▎ | 336/1000 [00:00<00:00, 3796.28 it/sec, feas=True, obj=5.43]
INFO - 16:21:39: 34%|███▎ | 337/1000 [00:00<00:00, 3796.48 it/sec, feas=True, obj=1.14]
INFO - 16:21:39: 34%|███▍ | 338/1000 [00:00<00:00, 3796.03 it/sec, feas=True, obj=7.29]
INFO - 16:21:39: 34%|███▍ | 339/1000 [00:00<00:00, 3796.25 it/sec, feas=True, obj=-0.283]
INFO - 16:21:39: 34%|███▍ | 340/1000 [00:00<00:00, 3796.59 it/sec, feas=True, obj=0.734]
INFO - 16:21:39: 34%|███▍ | 341/1000 [00:00<00:00, 3797.12 it/sec, feas=True, obj=-3.46]
INFO - 16:21:39: 34%|███▍ | 342/1000 [00:00<00:00, 3796.56 it/sec, feas=True, obj=4.12]
INFO - 16:21:39: 34%|███▍ | 343/1000 [00:00<00:00, 3796.92 it/sec, feas=True, obj=3.79]
INFO - 16:21:39: 34%|███▍ | 344/1000 [00:00<00:00, 3797.30 it/sec, feas=True, obj=-3.15]
INFO - 16:21:39: 34%|███▍ | 345/1000 [00:00<00:00, 3797.76 it/sec, feas=True, obj=7.56]
INFO - 16:21:39: 35%|███▍ | 346/1000 [00:00<00:00, 3797.16 it/sec, feas=True, obj=-0.553]
INFO - 16:21:39: 35%|███▍ | 347/1000 [00:00<00:00, 3797.46 it/sec, feas=True, obj=1.43]
INFO - 16:21:39: 35%|███▍ | 348/1000 [00:00<00:00, 3797.86 it/sec, feas=True, obj=-0.851]
INFO - 16:21:39: 35%|███▍ | 349/1000 [00:00<00:00, 3798.29 it/sec, feas=True, obj=8.57]
INFO - 16:21:39: 35%|███▌ | 350/1000 [00:00<00:00, 3797.94 it/sec, feas=True, obj=0.921]
INFO - 16:21:39: 35%|███▌ | 351/1000 [00:00<00:00, 3798.47 it/sec, feas=True, obj=3.01]
INFO - 16:21:39: 35%|███▌ | 352/1000 [00:00<00:00, 3798.40 it/sec, feas=True, obj=4.98]
INFO - 16:21:39: 35%|███▌ | 353/1000 [00:00<00:00, 3798.60 it/sec, feas=True, obj=6.06]
INFO - 16:21:39: 35%|███▌ | 354/1000 [00:00<00:00, 3797.88 it/sec, feas=True, obj=7.26]
INFO - 16:21:39: 36%|███▌ | 355/1000 [00:00<00:00, 3797.85 it/sec, feas=True, obj=5.71]
INFO - 16:21:39: 36%|███▌ | 356/1000 [00:00<00:00, 3798.07 it/sec, feas=True, obj=-5.07]
INFO - 16:21:39: 36%|███▌ | 357/1000 [00:00<00:00, 3797.60 it/sec, feas=True, obj=6.62]
INFO - 16:21:39: 36%|███▌ | 358/1000 [00:00<00:00, 3797.70 it/sec, feas=True, obj=5.86]
INFO - 16:21:39: 36%|███▌ | 359/1000 [00:00<00:00, 3798.06 it/sec, feas=True, obj=6.1]
INFO - 16:21:39: 36%|███▌ | 360/1000 [00:00<00:00, 3798.37 it/sec, feas=True, obj=-0.545]
INFO - 16:21:39: 36%|███▌ | 361/1000 [00:00<00:00, 3797.90 it/sec, feas=True, obj=3.86]
INFO - 16:21:39: 36%|███▌ | 362/1000 [00:00<00:00, 3798.10 it/sec, feas=True, obj=8.51]
INFO - 16:21:39: 36%|███▋ | 363/1000 [00:00<00:00, 3798.49 it/sec, feas=True, obj=5.33]
INFO - 16:21:39: 36%|███▋ | 364/1000 [00:00<00:00, 3798.96 it/sec, feas=True, obj=7.14]
INFO - 16:21:39: 36%|███▋ | 365/1000 [00:00<00:00, 3798.70 it/sec, feas=True, obj=4.01]
INFO - 16:21:39: 37%|███▋ | 366/1000 [00:00<00:00, 3798.94 it/sec, feas=True, obj=2.9]
INFO - 16:21:39: 37%|███▋ | 367/1000 [00:00<00:00, 3799.03 it/sec, feas=True, obj=6.25]
INFO - 16:21:39: 37%|███▋ | 368/1000 [00:00<00:00, 3799.36 it/sec, feas=True, obj=6.85]
INFO - 16:21:39: 37%|███▋ | 369/1000 [00:00<00:00, 3799.05 it/sec, feas=True, obj=4.32]
INFO - 16:21:39: 37%|███▋ | 370/1000 [00:00<00:00, 3799.23 it/sec, feas=True, obj=4.87]
INFO - 16:21:39: 37%|███▋ | 371/1000 [00:00<00:00, 3799.73 it/sec, feas=True, obj=6.43]
INFO - 16:21:39: 37%|███▋ | 372/1000 [00:00<00:00, 3800.15 it/sec, feas=True, obj=2.86]
INFO - 16:21:39: 37%|███▋ | 373/1000 [00:00<00:00, 3794.92 it/sec, feas=True, obj=0.891]
INFO - 16:21:39: 37%|███▋ | 374/1000 [00:00<00:00, 3794.91 it/sec, feas=True, obj=6.47]
INFO - 16:21:39: 38%|███▊ | 375/1000 [00:00<00:00, 3795.16 it/sec, feas=True, obj=-1.87]
INFO - 16:21:39: 38%|███▊ | 376/1000 [00:00<00:00, 3789.82 it/sec, feas=True, obj=-3.28]
INFO - 16:21:39: 38%|███▊ | 377/1000 [00:00<00:00, 3788.32 it/sec, feas=True, obj=0.0745]
INFO - 16:21:39: 38%|███▊ | 378/1000 [00:00<00:00, 3788.29 it/sec, feas=True, obj=5.9]
INFO - 16:21:39: 38%|███▊ | 379/1000 [00:00<00:00, 3787.48 it/sec, feas=True, obj=4.69]
INFO - 16:21:39: 38%|███▊ | 380/1000 [00:00<00:00, 3787.42 it/sec, feas=True, obj=4.66]
INFO - 16:21:39: 38%|███▊ | 381/1000 [00:00<00:00, 3787.40 it/sec, feas=True, obj=6.07]
INFO - 16:21:39: 38%|███▊ | 382/1000 [00:00<00:00, 3787.48 it/sec, feas=True, obj=0.959]
INFO - 16:21:39: 38%|███▊ | 383/1000 [00:00<00:00, 3786.87 it/sec, feas=True, obj=1.9]
INFO - 16:21:39: 38%|███▊ | 384/1000 [00:00<00:00, 3786.99 it/sec, feas=True, obj=7.91]
INFO - 16:21:39: 38%|███▊ | 385/1000 [00:00<00:00, 3787.31 it/sec, feas=True, obj=-0.448]
INFO - 16:21:39: 39%|███▊ | 386/1000 [00:00<00:00, 3787.52 it/sec, feas=True, obj=5.33]
INFO - 16:21:39: 39%|███▊ | 387/1000 [00:00<00:00, 3786.91 it/sec, feas=True, obj=2.88]
INFO - 16:21:39: 39%|███▉ | 388/1000 [00:00<00:00, 3787.08 it/sec, feas=True, obj=0.55]
INFO - 16:21:39: 39%|███▉ | 389/1000 [00:00<00:00, 3787.30 it/sec, feas=True, obj=0.392]
INFO - 16:21:39: 39%|███▉ | 390/1000 [00:00<00:00, 3786.86 it/sec, feas=True, obj=3.32]
INFO - 16:21:39: 39%|███▉ | 391/1000 [00:00<00:00, 3786.64 it/sec, feas=True, obj=7.88]
INFO - 16:21:39: 39%|███▉ | 392/1000 [00:00<00:00, 3786.83 it/sec, feas=True, obj=1.46]
INFO - 16:21:39: 39%|███▉ | 393/1000 [00:00<00:00, 3787.09 it/sec, feas=True, obj=9.49]
INFO - 16:21:39: 39%|███▉ | 394/1000 [00:00<00:00, 3786.69 it/sec, feas=True, obj=-8.6]
INFO - 16:21:39: 40%|███▉ | 395/1000 [00:00<00:00, 3786.71 it/sec, feas=True, obj=6]
INFO - 16:21:39: 40%|███▉ | 396/1000 [00:00<00:00, 3786.53 it/sec, feas=True, obj=6.89]
INFO - 16:21:39: 40%|███▉ | 397/1000 [00:00<00:00, 3786.76 it/sec, feas=True, obj=5.17]
INFO - 16:21:39: 40%|███▉ | 398/1000 [00:00<00:00, 3786.10 it/sec, feas=True, obj=9.21]
INFO - 16:21:39: 40%|███▉ | 399/1000 [00:00<00:00, 3786.23 it/sec, feas=True, obj=8.46]
INFO - 16:21:39: 40%|████ | 400/1000 [00:00<00:00, 3786.69 it/sec, feas=True, obj=9.92]
INFO - 16:21:39: 40%|████ | 401/1000 [00:00<00:00, 3786.95 it/sec, feas=True, obj=2.5]
INFO - 16:21:39: 40%|████ | 402/1000 [00:00<00:00, 3786.31 it/sec, feas=True, obj=2.82]
INFO - 16:21:39: 40%|████ | 403/1000 [00:00<00:00, 3786.50 it/sec, feas=True, obj=9.71]
INFO - 16:21:39: 40%|████ | 404/1000 [00:00<00:00, 3786.70 it/sec, feas=True, obj=-1.54]
INFO - 16:21:39: 40%|████ | 405/1000 [00:00<00:00, 3786.89 it/sec, feas=True, obj=-1.42]
INFO - 16:21:39: 41%|████ | 406/1000 [00:00<00:00, 3785.97 it/sec, feas=True, obj=7.52]
INFO - 16:21:39: 41%|████ | 407/1000 [00:00<00:00, 3786.30 it/sec, feas=True, obj=3.95]
INFO - 16:21:39: 41%|████ | 408/1000 [00:00<00:00, 3786.71 it/sec, feas=True, obj=5.33]
INFO - 16:21:39: 41%|████ | 409/1000 [00:00<00:00, 3786.32 it/sec, feas=True, obj=0.103]
INFO - 16:21:39: 41%|████ | 410/1000 [00:00<00:00, 3786.11 it/sec, feas=True, obj=4.39]
INFO - 16:21:39: 41%|████ | 411/1000 [00:00<00:00, 3786.12 it/sec, feas=True, obj=1.74]
INFO - 16:21:39: 41%|████ | 412/1000 [00:00<00:00, 3786.24 it/sec, feas=True, obj=0.344]
INFO - 16:21:39: 41%|████▏ | 413/1000 [00:00<00:00, 3785.75 it/sec, feas=True, obj=6.49]
INFO - 16:21:39: 41%|████▏ | 414/1000 [00:00<00:00, 3785.79 it/sec, feas=True, obj=4.85]
INFO - 16:21:39: 42%|████▏ | 415/1000 [00:00<00:00, 3786.03 it/sec, feas=True, obj=7.96]
INFO - 16:21:39: 42%|████▏ | 416/1000 [00:00<00:00, 3786.11 it/sec, feas=True, obj=-3.84]
INFO - 16:21:39: 42%|████▏ | 417/1000 [00:00<00:00, 3785.62 it/sec, feas=True, obj=-2.87]
INFO - 16:21:39: 42%|████▏ | 418/1000 [00:00<00:00, 3785.88 it/sec, feas=True, obj=-1.69]
INFO - 16:21:39: 42%|████▏ | 419/1000 [00:00<00:00, 3786.08 it/sec, feas=True, obj=2.32]
INFO - 16:21:39: 42%|████▏ | 420/1000 [00:00<00:00, 3786.36 it/sec, feas=True, obj=8.32]
INFO - 16:21:39: 42%|████▏ | 421/1000 [00:00<00:00, 3785.83 it/sec, feas=True, obj=1.74]
INFO - 16:21:39: 42%|████▏ | 422/1000 [00:00<00:00, 3785.90 it/sec, feas=True, obj=4.05]
INFO - 16:21:39: 42%|████▏ | 423/1000 [00:00<00:00, 3786.08 it/sec, feas=True, obj=2.71]
INFO - 16:21:39: 42%|████▏ | 424/1000 [00:00<00:00, 3785.80 it/sec, feas=True, obj=6.78]
INFO - 16:21:39: 42%|████▎ | 425/1000 [00:00<00:00, 3785.69 it/sec, feas=True, obj=3.95]
INFO - 16:21:39: 43%|████▎ | 426/1000 [00:00<00:00, 3785.58 it/sec, feas=True, obj=-0.0526]
INFO - 16:21:39: 43%|████▎ | 427/1000 [00:00<00:00, 3785.61 it/sec, feas=True, obj=-0.581]
INFO - 16:21:39: 43%|████▎ | 428/1000 [00:00<00:00, 3785.26 it/sec, feas=True, obj=16.2]
INFO - 16:21:39: 43%|████▎ | 429/1000 [00:00<00:00, 3785.39 it/sec, feas=True, obj=1.58]
INFO - 16:21:39: 43%|████▎ | 430/1000 [00:00<00:00, 3785.49 it/sec, feas=True, obj=5.87]
INFO - 16:21:39: 43%|████▎ | 431/1000 [00:00<00:00, 3785.62 it/sec, feas=True, obj=6.51]
INFO - 16:21:39: 43%|████▎ | 432/1000 [00:00<00:00, 3784.96 it/sec, feas=True, obj=4.28]
INFO - 16:21:39: 43%|████▎ | 433/1000 [00:00<00:00, 3784.92 it/sec, feas=True, obj=-6.3]
INFO - 16:21:39: 43%|████▎ | 434/1000 [00:00<00:00, 3785.17 it/sec, feas=True, obj=7.49]
INFO - 16:21:39: 44%|████▎ | 435/1000 [00:00<00:00, 3785.44 it/sec, feas=True, obj=8.03]
INFO - 16:21:39: 44%|████▎ | 436/1000 [00:00<00:00, 3784.71 it/sec, feas=True, obj=1.4]
INFO - 16:21:39: 44%|████▎ | 437/1000 [00:00<00:00, 3784.86 it/sec, feas=True, obj=1.65]
INFO - 16:21:39: 44%|████▍ | 438/1000 [00:00<00:00, 3785.06 it/sec, feas=True, obj=-0.221]
INFO - 16:21:39: 44%|████▍ | 439/1000 [00:00<00:00, 3784.62 it/sec, feas=True, obj=7.25]
INFO - 16:21:39: 44%|████▍ | 440/1000 [00:00<00:00, 3784.55 it/sec, feas=True, obj=5.14]
INFO - 16:21:39: 44%|████▍ | 441/1000 [00:00<00:00, 3784.61 it/sec, feas=True, obj=-0.896]
INFO - 16:21:39: 44%|████▍ | 442/1000 [00:00<00:00, 3784.71 it/sec, feas=True, obj=-0.969]
INFO - 16:21:39: 44%|████▍ | 443/1000 [00:00<00:00, 3784.25 it/sec, feas=True, obj=7.53]
INFO - 16:21:39: 44%|████▍ | 444/1000 [00:00<00:00, 3784.31 it/sec, feas=True, obj=6.62]
INFO - 16:21:39: 44%|████▍ | 445/1000 [00:00<00:00, 3784.19 it/sec, feas=True, obj=3.23]
INFO - 16:21:39: 45%|████▍ | 446/1000 [00:00<00:00, 3784.37 it/sec, feas=True, obj=-10.1]
INFO - 16:21:39: 45%|████▍ | 447/1000 [00:00<00:00, 3783.96 it/sec, feas=True, obj=7.22]
INFO - 16:21:39: 45%|████▍ | 448/1000 [00:00<00:00, 3783.96 it/sec, feas=True, obj=12.9]
INFO - 16:21:39: 45%|████▍ | 449/1000 [00:00<00:00, 3784.34 it/sec, feas=True, obj=7.61]
INFO - 16:21:39: 45%|████▌ | 450/1000 [00:00<00:00, 3784.76 it/sec, feas=True, obj=3.57]
INFO - 16:21:39: 45%|████▌ | 451/1000 [00:00<00:00, 3784.25 it/sec, feas=True, obj=5.91]
INFO - 16:21:39: 45%|████▌ | 452/1000 [00:00<00:00, 3784.31 it/sec, feas=True, obj=-1.97]
INFO - 16:21:39: 45%|████▌ | 453/1000 [00:00<00:00, 3784.44 it/sec, feas=True, obj=7.83]
INFO - 16:21:39: 45%|████▌ | 454/1000 [00:00<00:00, 3784.90 it/sec, feas=True, obj=2.12]
INFO - 16:21:39: 46%|████▌ | 455/1000 [00:00<00:00, 3784.65 it/sec, feas=True, obj=-0.821]
INFO - 16:21:39: 46%|████▌ | 456/1000 [00:00<00:00, 3785.05 it/sec, feas=True, obj=2.27]
INFO - 16:21:39: 46%|████▌ | 457/1000 [00:00<00:00, 3785.11 it/sec, feas=True, obj=7.13]
INFO - 16:21:39: 46%|████▌ | 458/1000 [00:00<00:00, 3785.03 it/sec, feas=True, obj=3.63]
INFO - 16:21:39: 46%|████▌ | 459/1000 [00:00<00:00, 3785.15 it/sec, feas=True, obj=2.21]
INFO - 16:21:39: 46%|████▌ | 460/1000 [00:00<00:00, 3785.55 it/sec, feas=True, obj=3.08]
INFO - 16:21:39: 46%|████▌ | 461/1000 [00:00<00:00, 3785.92 it/sec, feas=True, obj=3.18]
INFO - 16:21:39: 46%|████▌ | 462/1000 [00:00<00:00, 3785.68 it/sec, feas=True, obj=4.64]
INFO - 16:21:39: 46%|████▋ | 463/1000 [00:00<00:00, 3785.80 it/sec, feas=True, obj=0.243]
INFO - 16:21:39: 46%|████▋ | 464/1000 [00:00<00:00, 3786.19 it/sec, feas=True, obj=2.2]
INFO - 16:21:39: 46%|████▋ | 465/1000 [00:00<00:00, 3786.64 it/sec, feas=True, obj=-0.0681]
INFO - 16:21:39: 47%|████▋ | 466/1000 [00:00<00:00, 3786.30 it/sec, feas=True, obj=0.986]
INFO - 16:21:39: 47%|████▋ | 467/1000 [00:00<00:00, 3786.44 it/sec, feas=True, obj=7.39]
INFO - 16:21:39: 47%|████▋ | 468/1000 [00:00<00:00, 3786.50 it/sec, feas=True, obj=6.85]
INFO - 16:21:39: 47%|████▋ | 469/1000 [00:00<00:00, 3786.63 it/sec, feas=True, obj=8.98]
INFO - 16:21:39: 47%|████▋ | 470/1000 [00:00<00:00, 3786.21 it/sec, feas=True, obj=4.98]
INFO - 16:21:39: 47%|████▋ | 471/1000 [00:00<00:00, 3786.30 it/sec, feas=True, obj=0.108]
INFO - 16:21:39: 47%|████▋ | 472/1000 [00:00<00:00, 3786.10 it/sec, feas=True, obj=4.9]
INFO - 16:21:39: 47%|████▋ | 473/1000 [00:00<00:00, 3786.16 it/sec, feas=True, obj=1.98]
INFO - 16:21:39: 47%|████▋ | 474/1000 [00:00<00:00, 3785.39 it/sec, feas=True, obj=-3.79]
INFO - 16:21:39: 48%|████▊ | 475/1000 [00:00<00:00, 3785.54 it/sec, feas=True, obj=13.5]
INFO - 16:21:39: 48%|████▊ | 476/1000 [00:00<00:00, 3785.74 it/sec, feas=True, obj=0.587]
INFO - 16:21:39: 48%|████▊ | 477/1000 [00:00<00:00, 3785.42 it/sec, feas=True, obj=5.28]
INFO - 16:21:39: 48%|████▊ | 478/1000 [00:00<00:00, 3785.44 it/sec, feas=True, obj=6.02]
INFO - 16:21:39: 48%|████▊ | 479/1000 [00:00<00:00, 3785.66 it/sec, feas=True, obj=2.5]
INFO - 16:21:39: 48%|████▊ | 480/1000 [00:00<00:00, 3785.96 it/sec, feas=True, obj=-0.343]
INFO - 16:21:39: 48%|████▊ | 481/1000 [00:00<00:00, 3785.61 it/sec, feas=True, obj=4.72]
INFO - 16:21:39: 48%|████▊ | 482/1000 [00:00<00:00, 3785.74 it/sec, feas=True, obj=6.71]
INFO - 16:21:39: 48%|████▊ | 483/1000 [00:00<00:00, 3786.07 it/sec, feas=True, obj=-2.87]
INFO - 16:21:39: 48%|████▊ | 484/1000 [00:00<00:00, 3786.31 it/sec, feas=True, obj=1]
INFO - 16:21:39: 48%|████▊ | 485/1000 [00:00<00:00, 3786.06 it/sec, feas=True, obj=6.11]
INFO - 16:21:39: 49%|████▊ | 486/1000 [00:00<00:00, 3786.20 it/sec, feas=True, obj=0.946]
INFO - 16:21:39: 49%|████▊ | 487/1000 [00:00<00:00, 3782.54 it/sec, feas=True, obj=2.29]
INFO - 16:21:39: 49%|████▉ | 488/1000 [00:00<00:00, 3782.09 it/sec, feas=True, obj=9.42]
INFO - 16:21:39: 49%|████▉ | 489/1000 [00:00<00:00, 3782.12 it/sec, feas=True, obj=5.01]
INFO - 16:21:39: 49%|████▉ | 490/1000 [00:00<00:00, 3782.24 it/sec, feas=True, obj=1.02]
INFO - 16:21:39: 49%|████▉ | 491/1000 [00:00<00:00, 3782.52 it/sec, feas=True, obj=3.59]
INFO - 16:21:39: 49%|████▉ | 492/1000 [00:00<00:00, 3782.18 it/sec, feas=True, obj=7.01]
INFO - 16:21:39: 49%|████▉ | 493/1000 [00:00<00:00, 3782.31 it/sec, feas=True, obj=8.7]
INFO - 16:21:39: 49%|████▉ | 494/1000 [00:00<00:00, 3782.52 it/sec, feas=True, obj=5.6]
INFO - 16:21:39: 50%|████▉ | 495/1000 [00:00<00:00, 3782.81 it/sec, feas=True, obj=-0.897]
INFO - 16:21:39: 50%|████▉ | 496/1000 [00:00<00:00, 3782.36 it/sec, feas=True, obj=7.82]
INFO - 16:21:39: 50%|████▉ | 497/1000 [00:00<00:00, 3782.39 it/sec, feas=True, obj=6.63]
INFO - 16:21:39: 50%|████▉ | 498/1000 [00:00<00:00, 3782.57 it/sec, feas=True, obj=3.33]
INFO - 16:21:39: 50%|████▉ | 499/1000 [00:00<00:00, 3782.82 it/sec, feas=True, obj=4.36]
INFO - 16:21:39: 50%|█████ | 500/1000 [00:00<00:00, 3782.31 it/sec, feas=True, obj=4.21]
INFO - 16:21:39: 50%|█████ | 501/1000 [00:00<00:00, 3782.48 it/sec, feas=True, obj=3.93]
INFO - 16:21:39: 50%|█████ | 502/1000 [00:00<00:00, 3782.41 it/sec, feas=True, obj=10.4]
INFO - 16:21:39: 50%|█████ | 503/1000 [00:00<00:00, 3782.24 it/sec, feas=True, obj=-1.39]
INFO - 16:21:39: 50%|█████ | 504/1000 [00:00<00:00, 3782.18 it/sec, feas=True, obj=-0.386]
INFO - 16:21:39: 50%|█████ | 505/1000 [00:00<00:00, 3782.28 it/sec, feas=True, obj=4.95]
INFO - 16:21:39: 51%|█████ | 506/1000 [00:00<00:00, 3782.48 it/sec, feas=True, obj=4.8]
INFO - 16:21:39: 51%|█████ | 507/1000 [00:00<00:00, 3782.23 it/sec, feas=True, obj=7.94]
INFO - 16:21:39: 51%|█████ | 508/1000 [00:00<00:00, 3782.26 it/sec, feas=True, obj=1.6]
INFO - 16:21:39: 51%|█████ | 509/1000 [00:00<00:00, 3782.49 it/sec, feas=True, obj=8.91]
INFO - 16:21:39: 51%|█████ | 510/1000 [00:00<00:00, 3782.56 it/sec, feas=True, obj=9.47]
INFO - 16:21:39: 51%|█████ | 511/1000 [00:00<00:00, 3782.14 it/sec, feas=True, obj=-0.535]
INFO - 16:21:39: 51%|█████ | 512/1000 [00:00<00:00, 3782.23 it/sec, feas=True, obj=2.76]
INFO - 16:21:39: 51%|█████▏ | 513/1000 [00:00<00:00, 3782.48 it/sec, feas=True, obj=0.439]
INFO - 16:21:39: 51%|█████▏ | 514/1000 [00:00<00:00, 3782.72 it/sec, feas=True, obj=3.69]
INFO - 16:21:39: 52%|█████▏ | 515/1000 [00:00<00:00, 3782.29 it/sec, feas=True, obj=-1.1]
INFO - 16:21:39: 52%|█████▏ | 516/1000 [00:00<00:00, 3782.44 it/sec, feas=True, obj=2.48]
INFO - 16:21:39: 52%|█████▏ | 517/1000 [00:00<00:00, 3782.40 it/sec, feas=True, obj=2.8]
INFO - 16:21:39: 52%|█████▏ | 518/1000 [00:00<00:00, 3782.57 it/sec, feas=True, obj=13]
INFO - 16:21:39: 52%|█████▏ | 519/1000 [00:00<00:00, 3782.18 it/sec, feas=True, obj=6.01]
INFO - 16:21:39: 52%|█████▏ | 520/1000 [00:00<00:00, 3782.37 it/sec, feas=True, obj=2.49]
INFO - 16:21:39: 52%|█████▏ | 521/1000 [00:00<00:00, 3782.63 it/sec, feas=True, obj=5.92]
INFO - 16:21:39: 52%|█████▏ | 522/1000 [00:00<00:00, 3782.46 it/sec, feas=True, obj=3.4]
INFO - 16:21:39: 52%|█████▏ | 523/1000 [00:00<00:00, 3782.48 it/sec, feas=True, obj=-1.78]
INFO - 16:21:39: 52%|█████▏ | 524/1000 [00:00<00:00, 3782.73 it/sec, feas=True, obj=2.44]
INFO - 16:21:39: 52%|█████▎ | 525/1000 [00:00<00:00, 3782.72 it/sec, feas=True, obj=16]
INFO - 16:21:39: 53%|█████▎ | 526/1000 [00:00<00:00, 3782.29 it/sec, feas=True, obj=6.22]
INFO - 16:21:39: 53%|█████▎ | 527/1000 [00:00<00:00, 3782.36 it/sec, feas=True, obj=7.2]
INFO - 16:21:39: 53%|█████▎ | 528/1000 [00:00<00:00, 3782.54 it/sec, feas=True, obj=4.57]
INFO - 16:21:39: 53%|█████▎ | 529/1000 [00:00<00:00, 3782.79 it/sec, feas=True, obj=6.77]
INFO - 16:21:39: 53%|█████▎ | 530/1000 [00:00<00:00, 3782.32 it/sec, feas=True, obj=13]
INFO - 16:21:39: 53%|█████▎ | 531/1000 [00:00<00:00, 3782.42 it/sec, feas=True, obj=5]
INFO - 16:21:39: 53%|█████▎ | 532/1000 [00:00<00:00, 3782.27 it/sec, feas=True, obj=-0.711]
INFO - 16:21:39: 53%|█████▎ | 533/1000 [00:00<00:00, 3782.48 it/sec, feas=True, obj=-0.543]
INFO - 16:21:39: 53%|█████▎ | 534/1000 [00:00<00:00, 3782.03 it/sec, feas=True, obj=0.469]
INFO - 16:21:39: 54%|█████▎ | 535/1000 [00:00<00:00, 3782.25 it/sec, feas=True, obj=4.16]
INFO - 16:21:39: 54%|█████▎ | 536/1000 [00:00<00:00, 3782.50 it/sec, feas=True, obj=4.73]
INFO - 16:21:39: 54%|█████▎ | 537/1000 [00:00<00:00, 3782.40 it/sec, feas=True, obj=-0.197]
INFO - 16:21:39: 54%|█████▍ | 538/1000 [00:00<00:00, 3782.31 it/sec, feas=True, obj=-2.45]
INFO - 16:21:39: 54%|█████▍ | 539/1000 [00:00<00:00, 3782.49 it/sec, feas=True, obj=2.9]
INFO - 16:21:39: 54%|█████▍ | 540/1000 [00:00<00:00, 3782.60 it/sec, feas=True, obj=4.59]
INFO - 16:21:39: 54%|█████▍ | 541/1000 [00:00<00:00, 3782.17 it/sec, feas=True, obj=4.09]
INFO - 16:21:39: 54%|█████▍ | 542/1000 [00:00<00:00, 3782.18 it/sec, feas=True, obj=0.0786]
INFO - 16:21:39: 54%|█████▍ | 543/1000 [00:00<00:00, 3782.26 it/sec, feas=True, obj=6.9]
INFO - 16:21:39: 54%|█████▍ | 544/1000 [00:00<00:00, 3782.45 it/sec, feas=True, obj=3.77]
INFO - 16:21:39: 55%|█████▍ | 545/1000 [00:00<00:00, 3782.17 it/sec, feas=True, obj=2.68]
INFO - 16:21:39: 55%|█████▍ | 546/1000 [00:00<00:00, 3782.23 it/sec, feas=True, obj=5.03]
INFO - 16:21:39: 55%|█████▍ | 547/1000 [00:00<00:00, 3782.27 it/sec, feas=True, obj=7.02]
INFO - 16:21:39: 55%|█████▍ | 548/1000 [00:00<00:00, 3782.48 it/sec, feas=True, obj=7]
INFO - 16:21:39: 55%|█████▍ | 549/1000 [00:00<00:00, 3782.08 it/sec, feas=True, obj=1.03]
INFO - 16:21:39: 55%|█████▌ | 550/1000 [00:00<00:00, 3782.22 it/sec, feas=True, obj=4.74]
INFO - 16:21:39: 55%|█████▌ | 551/1000 [00:00<00:00, 3782.44 it/sec, feas=True, obj=-0.817]
INFO - 16:21:39: 55%|█████▌ | 552/1000 [00:00<00:00, 3782.65 it/sec, feas=True, obj=2.59]
INFO - 16:21:39: 55%|█████▌ | 553/1000 [00:00<00:00, 3782.17 it/sec, feas=True, obj=3.33]
INFO - 16:21:39: 55%|█████▌ | 554/1000 [00:00<00:00, 3782.26 it/sec, feas=True, obj=2.13]
INFO - 16:21:39: 56%|█████▌ | 555/1000 [00:00<00:00, 3782.27 it/sec, feas=True, obj=-0.076]
INFO - 16:21:39: 56%|█████▌ | 556/1000 [00:00<00:00, 3781.78 it/sec, feas=True, obj=-0.023]
INFO - 16:21:39: 56%|█████▌ | 557/1000 [00:00<00:00, 3781.77 it/sec, feas=True, obj=7.03]
INFO - 16:21:39: 56%|█████▌ | 558/1000 [00:00<00:00, 3781.82 it/sec, feas=True, obj=3.4]
INFO - 16:21:39: 56%|█████▌ | 559/1000 [00:00<00:00, 3781.93 it/sec, feas=True, obj=-1.23]
INFO - 16:21:39: 56%|█████▌ | 560/1000 [00:00<00:00, 3781.43 it/sec, feas=True, obj=7.3]
INFO - 16:21:39: 56%|█████▌ | 561/1000 [00:00<00:00, 3781.45 it/sec, feas=True, obj=4.59]
INFO - 16:21:39: 56%|█████▌ | 562/1000 [00:00<00:00, 3781.33 it/sec, feas=True, obj=-0.53]
INFO - 16:21:39: 56%|█████▋ | 563/1000 [00:00<00:00, 3781.39 it/sec, feas=True, obj=7.24]
INFO - 16:21:39: 56%|█████▋ | 564/1000 [00:00<00:00, 3780.92 it/sec, feas=True, obj=-0.753]
INFO - 16:21:39: 56%|█████▋ | 565/1000 [00:00<00:00, 3781.06 it/sec, feas=True, obj=7.78]
INFO - 16:21:39: 57%|█████▋ | 566/1000 [00:00<00:00, 3781.21 it/sec, feas=True, obj=6.64]
INFO - 16:21:39: 57%|█████▋ | 567/1000 [00:00<00:00, 3781.43 it/sec, feas=True, obj=0.671]
INFO - 16:21:39: 57%|█████▋ | 568/1000 [00:00<00:00, 3780.93 it/sec, feas=True, obj=-2]
INFO - 16:21:39: 57%|█████▋ | 569/1000 [00:00<00:00, 3780.90 it/sec, feas=True, obj=-1.92]
INFO - 16:21:39: 57%|█████▋ | 570/1000 [00:00<00:00, 3781.11 it/sec, feas=True, obj=6.03]
INFO - 16:21:39: 57%|█████▋ | 571/1000 [00:00<00:00, 3780.85 it/sec, feas=True, obj=9.42]
INFO - 16:21:39: 57%|█████▋ | 572/1000 [00:00<00:00, 3780.99 it/sec, feas=True, obj=1.01]
INFO - 16:21:39: 57%|█████▋ | 573/1000 [00:00<00:00, 3781.04 it/sec, feas=True, obj=1.43]
INFO - 16:21:39: 57%|█████▋ | 574/1000 [00:00<00:00, 3781.28 it/sec, feas=True, obj=0.0646]
INFO - 16:21:39: 57%|█████▊ | 575/1000 [00:00<00:00, 3780.96 it/sec, feas=True, obj=5.16]
INFO - 16:21:39: 58%|█████▊ | 576/1000 [00:00<00:00, 3780.98 it/sec, feas=True, obj=1.61]
INFO - 16:21:39: 58%|█████▊ | 577/1000 [00:00<00:00, 3780.85 it/sec, feas=True, obj=0.944]
INFO - 16:21:39: 58%|█████▊ | 578/1000 [00:00<00:00, 3780.90 it/sec, feas=True, obj=0.535]
INFO - 16:21:39: 58%|█████▊ | 579/1000 [00:00<00:00, 3780.51 it/sec, feas=True, obj=1.86]
INFO - 16:21:39: 58%|█████▊ | 580/1000 [00:00<00:00, 3780.60 it/sec, feas=True, obj=2.93]
INFO - 16:21:39: 58%|█████▊ | 581/1000 [00:00<00:00, 3780.72 it/sec, feas=True, obj=2.4]
INFO - 16:21:39: 58%|█████▊ | 582/1000 [00:00<00:00, 3780.92 it/sec, feas=True, obj=6.58]
INFO - 16:21:39: 58%|█████▊ | 583/1000 [00:00<00:00, 3780.48 it/sec, feas=True, obj=-0.0337]
INFO - 16:21:39: 58%|█████▊ | 584/1000 [00:00<00:00, 3780.59 it/sec, feas=True, obj=6.62]
INFO - 16:21:39: 58%|█████▊ | 585/1000 [00:00<00:00, 3780.53 it/sec, feas=True, obj=5.61]
INFO - 16:21:39: 59%|█████▊ | 586/1000 [00:00<00:00, 3780.30 it/sec, feas=True, obj=5.55]
INFO - 16:21:39: 59%|█████▊ | 587/1000 [00:00<00:00, 3780.35 it/sec, feas=True, obj=5.28]
INFO - 16:21:39: 59%|█████▉ | 588/1000 [00:00<00:00, 3780.54 it/sec, feas=True, obj=3.22]
INFO - 16:21:39: 59%|█████▉ | 589/1000 [00:00<00:00, 3780.66 it/sec, feas=True, obj=3.1]
INFO - 16:21:39: 59%|█████▉ | 590/1000 [00:00<00:00, 3780.40 it/sec, feas=True, obj=5.83]
INFO - 16:21:39: 59%|█████▉ | 591/1000 [00:00<00:00, 3780.42 it/sec, feas=True, obj=4.03]
INFO - 16:21:39: 59%|█████▉ | 592/1000 [00:00<00:00, 3780.29 it/sec, feas=True, obj=-3.08]
INFO - 16:21:39: 59%|█████▉ | 593/1000 [00:00<00:00, 3780.36 it/sec, feas=True, obj=3.63]
INFO - 16:21:39: 59%|█████▉ | 594/1000 [00:00<00:00, 3779.96 it/sec, feas=True, obj=0.374]
INFO - 16:21:39: 60%|█████▉ | 595/1000 [00:00<00:00, 3780.08 it/sec, feas=True, obj=7.07]
INFO - 16:21:39: 60%|█████▉ | 596/1000 [00:00<00:00, 3780.28 it/sec, feas=True, obj=0.707]
INFO - 16:21:39: 60%|█████▉ | 597/1000 [00:00<00:00, 3780.57 it/sec, feas=True, obj=5.65]
INFO - 16:21:39: 60%|█████▉ | 598/1000 [00:00<00:00, 3780.04 it/sec, feas=True, obj=5.83]
INFO - 16:21:39: 60%|█████▉ | 599/1000 [00:00<00:00, 3780.11 it/sec, feas=True, obj=4.2]
INFO - 16:21:39: 60%|██████ | 600/1000 [00:00<00:00, 3780.20 it/sec, feas=True, obj=-0.0744]
INFO - 16:21:39: 60%|██████ | 601/1000 [00:00<00:00, 3776.89 it/sec, feas=True, obj=0.391]
INFO - 16:21:39: 60%|██████ | 602/1000 [00:00<00:00, 3776.81 it/sec, feas=True, obj=4.96]
INFO - 16:21:39: 60%|██████ | 603/1000 [00:00<00:00, 3776.69 it/sec, feas=True, obj=2.18]
INFO - 16:21:39: 60%|██████ | 604/1000 [00:00<00:00, 3776.60 it/sec, feas=True, obj=1.55]
INFO - 16:21:39: 60%|██████ | 605/1000 [00:00<00:00, 3776.10 it/sec, feas=True, obj=6.26]
INFO - 16:21:39: 61%|██████ | 606/1000 [00:00<00:00, 3776.09 it/sec, feas=True, obj=5.3]
INFO - 16:21:39: 61%|██████ | 607/1000 [00:00<00:00, 3775.77 it/sec, feas=True, obj=7.18]
INFO - 16:21:39: 61%|██████ | 608/1000 [00:00<00:00, 3775.32 it/sec, feas=True, obj=1.45]
INFO - 16:21:39: 61%|██████ | 609/1000 [00:00<00:00, 3775.18 it/sec, feas=True, obj=8.78]
INFO - 16:21:39: 61%|██████ | 610/1000 [00:00<00:00, 3775.23 it/sec, feas=True, obj=0.233]
INFO - 16:21:39: 61%|██████ | 611/1000 [00:00<00:00, 3775.25 it/sec, feas=True, obj=-1.38]
INFO - 16:21:39: 61%|██████ | 612/1000 [00:00<00:00, 3774.88 it/sec, feas=True, obj=6.09]
INFO - 16:21:39: 61%|██████▏ | 613/1000 [00:00<00:00, 3774.91 it/sec, feas=True, obj=5.58]
INFO - 16:21:39: 61%|██████▏ | 614/1000 [00:00<00:00, 3775.10 it/sec, feas=True, obj=11]
INFO - 16:21:39: 62%|██████▏ | 615/1000 [00:00<00:00, 3775.17 it/sec, feas=True, obj=5.03]
INFO - 16:21:39: 62%|██████▏ | 616/1000 [00:00<00:00, 3774.48 it/sec, feas=True, obj=6.39]
INFO - 16:21:39: 62%|██████▏ | 617/1000 [00:00<00:00, 3774.53 it/sec, feas=True, obj=1.92]
INFO - 16:21:39: 62%|██████▏ | 618/1000 [00:00<00:00, 3774.70 it/sec, feas=True, obj=1.05]
INFO - 16:21:39: 62%|██████▏ | 619/1000 [00:00<00:00, 3774.39 it/sec, feas=True, obj=0.0814]
INFO - 16:21:39: 62%|██████▏ | 620/1000 [00:00<00:00, 3774.28 it/sec, feas=True, obj=5.88]
INFO - 16:21:39: 62%|██████▏ | 621/1000 [00:00<00:00, 3774.12 it/sec, feas=True, obj=14.7]
INFO - 16:21:39: 62%|██████▏ | 622/1000 [00:00<00:00, 3774.09 it/sec, feas=True, obj=4.25]
INFO - 16:21:39: 62%|██████▏ | 623/1000 [00:00<00:00, 3773.89 it/sec, feas=True, obj=-1.9]
INFO - 16:21:39: 62%|██████▏ | 624/1000 [00:00<00:00, 3774.00 it/sec, feas=True, obj=-0.304]
INFO - 16:21:39: 62%|██████▎ | 625/1000 [00:00<00:00, 3774.16 it/sec, feas=True, obj=-0.315]
INFO - 16:21:39: 63%|██████▎ | 626/1000 [00:00<00:00, 3774.33 it/sec, feas=True, obj=-0.772]
INFO - 16:21:39: 63%|██████▎ | 627/1000 [00:00<00:00, 3774.06 it/sec, feas=True, obj=4.47]
INFO - 16:21:39: 63%|██████▎ | 628/1000 [00:00<00:00, 3774.21 it/sec, feas=True, obj=3.87]
INFO - 16:21:39: 63%|██████▎ | 629/1000 [00:00<00:00, 3774.37 it/sec, feas=True, obj=1.69]
INFO - 16:21:39: 63%|██████▎ | 630/1000 [00:00<00:00, 3774.57 it/sec, feas=True, obj=14.2]
INFO - 16:21:39: 63%|██████▎ | 631/1000 [00:00<00:00, 3774.05 it/sec, feas=True, obj=0.467]
INFO - 16:21:39: 63%|██████▎ | 632/1000 [00:00<00:00, 3774.04 it/sec, feas=True, obj=0.13]
INFO - 16:21:39: 63%|██████▎ | 633/1000 [00:00<00:00, 3774.10 it/sec, feas=True, obj=-0.788]
INFO - 16:21:39: 63%|██████▎ | 634/1000 [00:00<00:00, 3773.73 it/sec, feas=True, obj=3.3]
INFO - 16:21:39: 64%|██████▎ | 635/1000 [00:00<00:00, 3773.53 it/sec, feas=True, obj=7.29]
INFO - 16:21:39: 64%|██████▎ | 636/1000 [00:00<00:00, 3773.44 it/sec, feas=True, obj=1.41]
INFO - 16:21:39: 64%|██████▎ | 637/1000 [00:00<00:00, 3773.37 it/sec, feas=True, obj=6.16]
INFO - 16:21:39: 64%|██████▍ | 638/1000 [00:00<00:00, 3773.00 it/sec, feas=True, obj=6.98]
INFO - 16:21:39: 64%|██████▍ | 639/1000 [00:00<00:00, 3772.92 it/sec, feas=True, obj=7.82]
INFO - 16:21:39: 64%|██████▍ | 640/1000 [00:00<00:00, 3773.01 it/sec, feas=True, obj=4.49]
INFO - 16:21:39: 64%|██████▍ | 641/1000 [00:00<00:00, 3772.95 it/sec, feas=True, obj=6.84]
INFO - 16:21:39: 64%|██████▍ | 642/1000 [00:00<00:00, 3772.56 it/sec, feas=True, obj=3.83]
INFO - 16:21:39: 64%|██████▍ | 643/1000 [00:00<00:00, 3772.65 it/sec, feas=True, obj=2.52]
INFO - 16:21:39: 64%|██████▍ | 644/1000 [00:00<00:00, 3772.81 it/sec, feas=True, obj=1]
INFO - 16:21:39: 64%|██████▍ | 645/1000 [00:00<00:00, 3772.40 it/sec, feas=True, obj=3.54]
INFO - 16:21:39: 65%|██████▍ | 646/1000 [00:00<00:00, 3772.19 it/sec, feas=True, obj=5.22]
INFO - 16:21:39: 65%|██████▍ | 647/1000 [00:00<00:00, 3772.34 it/sec, feas=True, obj=7.98]
INFO - 16:21:39: 65%|██████▍ | 648/1000 [00:00<00:00, 3772.42 it/sec, feas=True, obj=3.23]
INFO - 16:21:39: 65%|██████▍ | 649/1000 [00:00<00:00, 3772.05 it/sec, feas=True, obj=2.61]
INFO - 16:21:39: 65%|██████▌ | 650/1000 [00:00<00:00, 3771.95 it/sec, feas=True, obj=4.85]
INFO - 16:21:39: 65%|██████▌ | 651/1000 [00:00<00:00, 3771.80 it/sec, feas=True, obj=1.4]
INFO - 16:21:39: 65%|██████▌ | 652/1000 [00:00<00:00, 3771.89 it/sec, feas=True, obj=-0.857]
INFO - 16:21:39: 65%|██████▌ | 653/1000 [00:00<00:00, 3771.58 it/sec, feas=True, obj=4.01]
INFO - 16:21:39: 65%|██████▌ | 654/1000 [00:00<00:00, 3771.51 it/sec, feas=True, obj=6.3]
INFO - 16:21:39: 66%|██████▌ | 655/1000 [00:00<00:00, 3771.57 it/sec, feas=True, obj=11.4]
INFO - 16:21:39: 66%|██████▌ | 656/1000 [00:00<00:00, 3771.61 it/sec, feas=True, obj=5.47]
INFO - 16:21:39: 66%|██████▌ | 657/1000 [00:00<00:00, 3771.13 it/sec, feas=True, obj=2.72]
INFO - 16:21:39: 66%|██████▌ | 658/1000 [00:00<00:00, 3771.17 it/sec, feas=True, obj=3.85]
INFO - 16:21:39: 66%|██████▌ | 659/1000 [00:00<00:00, 3771.26 it/sec, feas=True, obj=-0.392]
INFO - 16:21:39: 66%|██████▌ | 660/1000 [00:00<00:00, 3770.97 it/sec, feas=True, obj=0.0168]
INFO - 16:21:39: 66%|██████▌ | 661/1000 [00:00<00:00, 3770.88 it/sec, feas=True, obj=2.53]
INFO - 16:21:39: 66%|██████▌ | 662/1000 [00:00<00:00, 3771.02 it/sec, feas=True, obj=1.76]
INFO - 16:21:39: 66%|██████▋ | 663/1000 [00:00<00:00, 3770.95 it/sec, feas=True, obj=4.26]
INFO - 16:21:39: 66%|██████▋ | 664/1000 [00:00<00:00, 3770.57 it/sec, feas=True, obj=5.45]
INFO - 16:21:39: 66%|██████▋ | 665/1000 [00:00<00:00, 3770.63 it/sec, feas=True, obj=6.94]
INFO - 16:21:39: 67%|██████▋ | 666/1000 [00:00<00:00, 3770.47 it/sec, feas=True, obj=5.93]
INFO - 16:21:39: 67%|██████▋ | 667/1000 [00:00<00:00, 3770.59 it/sec, feas=True, obj=5.79]
INFO - 16:21:39: 67%|██████▋ | 668/1000 [00:00<00:00, 3770.15 it/sec, feas=True, obj=2.62]
INFO - 16:21:39: 67%|██████▋ | 669/1000 [00:00<00:00, 3770.22 it/sec, feas=True, obj=6.4]
INFO - 16:21:39: 67%|██████▋ | 670/1000 [00:00<00:00, 3770.36 it/sec, feas=True, obj=-0.703]
INFO - 16:21:39: 67%|██████▋ | 671/1000 [00:00<00:00, 3770.15 it/sec, feas=True, obj=8.61]
INFO - 16:21:39: 67%|██████▋ | 672/1000 [00:00<00:00, 3770.05 it/sec, feas=True, obj=0.91]
INFO - 16:21:39: 67%|██████▋ | 673/1000 [00:00<00:00, 3770.12 it/sec, feas=True, obj=1.05]
INFO - 16:21:39: 67%|██████▋ | 674/1000 [00:00<00:00, 3770.32 it/sec, feas=True, obj=10.1]
INFO - 16:21:39: 68%|██████▊ | 675/1000 [00:00<00:00, 3770.10 it/sec, feas=True, obj=-0.575]
INFO - 16:21:39: 68%|██████▊ | 676/1000 [00:00<00:00, 3770.10 it/sec, feas=True, obj=-2.06]
INFO - 16:21:39: 68%|██████▊ | 677/1000 [00:00<00:00, 3770.21 it/sec, feas=True, obj=7.34]
INFO - 16:21:39: 68%|██████▊ | 678/1000 [00:00<00:00, 3770.40 it/sec, feas=True, obj=2.78]
INFO - 16:21:39: 68%|██████▊ | 679/1000 [00:00<00:00, 3770.18 it/sec, feas=True, obj=1.15]
INFO - 16:21:39: 68%|██████▊ | 680/1000 [00:00<00:00, 3770.18 it/sec, feas=True, obj=-0.227]
INFO - 16:21:39: 68%|██████▊ | 681/1000 [00:00<00:00, 3770.19 it/sec, feas=True, obj=4.3]
INFO - 16:21:39: 68%|██████▊ | 682/1000 [00:00<00:00, 3770.39 it/sec, feas=True, obj=6.14]
INFO - 16:21:39: 68%|██████▊ | 683/1000 [00:00<00:00, 3768.34 it/sec, feas=True, obj=4.76]
INFO - 16:21:39: 68%|██████▊ | 684/1000 [00:00<00:00, 3768.10 it/sec, feas=True, obj=-4.69]
INFO - 16:21:39: 68%|██████▊ | 685/1000 [00:00<00:00, 3768.17 it/sec, feas=True, obj=-0.877]
INFO - 16:21:39: 69%|██████▊ | 686/1000 [00:00<00:00, 3767.62 it/sec, feas=True, obj=3.02]
INFO - 16:21:39: 69%|██████▊ | 687/1000 [00:00<00:00, 3767.57 it/sec, feas=True, obj=6.98]
INFO - 16:21:39: 69%|██████▉ | 688/1000 [00:00<00:00, 3767.67 it/sec, feas=True, obj=4.88]
INFO - 16:21:39: 69%|██████▉ | 689/1000 [00:00<00:00, 3767.84 it/sec, feas=True, obj=4.99]
INFO - 16:21:39: 69%|██████▉ | 690/1000 [00:00<00:00, 3767.48 it/sec, feas=True, obj=9.72]
INFO - 16:21:39: 69%|██████▉ | 691/1000 [00:00<00:00, 3767.39 it/sec, feas=True, obj=1.5]
INFO - 16:21:39: 69%|██████▉ | 692/1000 [00:00<00:00, 3767.40 it/sec, feas=True, obj=5.57]
INFO - 16:21:39: 69%|██████▉ | 693/1000 [00:00<00:00, 3767.49 it/sec, feas=True, obj=6.06]
INFO - 16:21:39: 69%|██████▉ | 694/1000 [00:00<00:00, 3767.03 it/sec, feas=True, obj=1.09]
INFO - 16:21:39: 70%|██████▉ | 695/1000 [00:00<00:00, 3767.13 it/sec, feas=True, obj=-1.97]
INFO - 16:21:39: 70%|██████▉ | 696/1000 [00:00<00:00, 3766.98 it/sec, feas=True, obj=1.88]
INFO - 16:21:39: 70%|██████▉ | 697/1000 [00:00<00:00, 3766.72 it/sec, feas=True, obj=9.32]
INFO - 16:21:39: 70%|██████▉ | 698/1000 [00:00<00:00, 3766.76 it/sec, feas=True, obj=-7.7]
INFO - 16:21:39: 70%|██████▉ | 699/1000 [00:00<00:00, 3766.49 it/sec, feas=True, obj=1.83]
INFO - 16:21:39: 70%|███████ | 700/1000 [00:00<00:00, 3766.49 it/sec, feas=True, obj=0.735]
INFO - 16:21:39: 70%|███████ | 701/1000 [00:00<00:00, 3766.29 it/sec, feas=True, obj=-1.11]
INFO - 16:21:39: 70%|███████ | 702/1000 [00:00<00:00, 3766.17 it/sec, feas=True, obj=1.47]
INFO - 16:21:39: 70%|███████ | 703/1000 [00:00<00:00, 3766.33 it/sec, feas=True, obj=0.283]
INFO - 16:21:39: 70%|███████ | 704/1000 [00:00<00:00, 3766.51 it/sec, feas=True, obj=15.2]
INFO - 16:21:39: 70%|███████ | 705/1000 [00:00<00:00, 3766.11 it/sec, feas=True, obj=3.43]
INFO - 16:21:39: 71%|███████ | 706/1000 [00:00<00:00, 3766.09 it/sec, feas=True, obj=3.17]
INFO - 16:21:39: 71%|███████ | 707/1000 [00:00<00:00, 3766.29 it/sec, feas=True, obj=5.95]
INFO - 16:21:39: 71%|███████ | 708/1000 [00:00<00:00, 3766.20 it/sec, feas=True, obj=-6.33]
INFO - 16:21:39: 71%|███████ | 709/1000 [00:00<00:00, 3765.50 it/sec, feas=True, obj=13.3]
INFO - 16:21:39: 71%|███████ | 710/1000 [00:00<00:00, 3765.23 it/sec, feas=True, obj=1.32]
INFO - 16:21:39: 71%|███████ | 711/1000 [00:00<00:00, 3765.26 it/sec, feas=True, obj=-4.3]
INFO - 16:21:39: 71%|███████ | 712/1000 [00:00<00:00, 3765.03 it/sec, feas=True, obj=1.63]
INFO - 16:21:39: 71%|███████▏ | 713/1000 [00:00<00:00, 3765.10 it/sec, feas=True, obj=1.99]
INFO - 16:21:39: 71%|███████▏ | 714/1000 [00:00<00:00, 3765.17 it/sec, feas=True, obj=0.679]
INFO - 16:21:39: 72%|███████▏ | 715/1000 [00:00<00:00, 3762.27 it/sec, feas=True, obj=-0.377]
INFO - 16:21:39: 72%|███████▏ | 716/1000 [00:00<00:00, 3762.06 it/sec, feas=True, obj=-4.57]
INFO - 16:21:39: 72%|███████▏ | 717/1000 [00:00<00:00, 3761.91 it/sec, feas=True, obj=2.1]
INFO - 16:21:39: 72%|███████▏ | 718/1000 [00:00<00:00, 3761.81 it/sec, feas=True, obj=1.32]
INFO - 16:21:39: 72%|███████▏ | 719/1000 [00:00<00:00, 3761.52 it/sec, feas=True, obj=0.312]
INFO - 16:21:39: 72%|███████▏ | 720/1000 [00:00<00:00, 3761.53 it/sec, feas=True, obj=8.43]
INFO - 16:21:39: 72%|███████▏ | 721/1000 [00:00<00:00, 3761.60 it/sec, feas=True, obj=0.579]
INFO - 16:21:39: 72%|███████▏ | 722/1000 [00:00<00:00, 3761.58 it/sec, feas=True, obj=0.563]
INFO - 16:21:39: 72%|███████▏ | 723/1000 [00:00<00:00, 3761.16 it/sec, feas=True, obj=3.4]
INFO - 16:21:39: 72%|███████▏ | 724/1000 [00:00<00:00, 3761.31 it/sec, feas=True, obj=7.21]
INFO - 16:21:39: 72%|███████▎ | 725/1000 [00:00<00:00, 3761.27 it/sec, feas=True, obj=5.26]
INFO - 16:21:39: 73%|███████▎ | 726/1000 [00:00<00:00, 3761.01 it/sec, feas=True, obj=2.69]
INFO - 16:21:39: 73%|███████▎ | 727/1000 [00:00<00:00, 3761.08 it/sec, feas=True, obj=5.31]
INFO - 16:21:39: 73%|███████▎ | 728/1000 [00:00<00:00, 3761.23 it/sec, feas=True, obj=1.99]
INFO - 16:21:39: 73%|███████▎ | 729/1000 [00:00<00:00, 3761.35 it/sec, feas=True, obj=-6.72]
INFO - 16:21:39: 73%|███████▎ | 730/1000 [00:00<00:00, 3761.16 it/sec, feas=True, obj=0.526]
INFO - 16:21:39: 73%|███████▎ | 731/1000 [00:00<00:00, 3761.31 it/sec, feas=True, obj=4.35]
INFO - 16:21:39: 73%|███████▎ | 732/1000 [00:00<00:00, 3761.42 it/sec, feas=True, obj=8.27]
INFO - 16:21:39: 73%|███████▎ | 733/1000 [00:00<00:00, 3761.56 it/sec, feas=True, obj=0.662]
INFO - 16:21:39: 73%|███████▎ | 734/1000 [00:00<00:00, 3761.36 it/sec, feas=True, obj=8.9]
INFO - 16:21:39: 74%|███████▎ | 735/1000 [00:00<00:00, 3761.39 it/sec, feas=True, obj=6.96]
INFO - 16:21:39: 74%|███████▎ | 736/1000 [00:00<00:00, 3761.48 it/sec, feas=True, obj=1.11]
INFO - 16:21:39: 74%|███████▎ | 737/1000 [00:00<00:00, 3761.63 it/sec, feas=True, obj=-4.5]
INFO - 16:21:39: 74%|███████▍ | 738/1000 [00:00<00:00, 3761.15 it/sec, feas=True, obj=0.0495]
INFO - 16:21:39: 74%|███████▍ | 739/1000 [00:00<00:00, 3761.08 it/sec, feas=True, obj=5.88]
INFO - 16:21:39: 74%|███████▍ | 740/1000 [00:00<00:00, 3760.26 it/sec, feas=True, obj=13.2]
INFO - 16:21:39: 74%|███████▍ | 741/1000 [00:00<00:00, 3759.67 it/sec, feas=True, obj=2.3]
INFO - 16:21:39: 74%|███████▍ | 742/1000 [00:00<00:00, 3759.57 it/sec, feas=True, obj=2.21]
INFO - 16:21:39: 74%|███████▍ | 743/1000 [00:00<00:00, 3759.51 it/sec, feas=True, obj=-4.03]
INFO - 16:21:39: 74%|███████▍ | 744/1000 [00:00<00:00, 3759.63 it/sec, feas=True, obj=4.28]
INFO - 16:21:39: 74%|███████▍ | 745/1000 [00:00<00:00, 3759.18 it/sec, feas=True, obj=6.68]
INFO - 16:21:39: 75%|███████▍ | 746/1000 [00:00<00:00, 3759.17 it/sec, feas=True, obj=7.33]
INFO - 16:21:39: 75%|███████▍ | 747/1000 [00:00<00:00, 3759.13 it/sec, feas=True, obj=-3.91]
INFO - 16:21:39: 75%|███████▍ | 748/1000 [00:00<00:00, 3758.83 it/sec, feas=True, obj=1.16]
INFO - 16:21:39: 75%|███████▍ | 749/1000 [00:00<00:00, 3758.72 it/sec, feas=True, obj=-0.739]
INFO - 16:21:39: 75%|███████▌ | 750/1000 [00:00<00:00, 3758.81 it/sec, feas=True, obj=5.93]
INFO - 16:21:39: 75%|███████▌ | 751/1000 [00:00<00:00, 3758.84 it/sec, feas=True, obj=3.2]
INFO - 16:21:39: 75%|███████▌ | 752/1000 [00:00<00:00, 3758.61 it/sec, feas=True, obj=11.1]
INFO - 16:21:39: 75%|███████▌ | 753/1000 [00:00<00:00, 3758.59 it/sec, feas=True, obj=7.41]
INFO - 16:21:39: 75%|███████▌ | 754/1000 [00:00<00:00, 3758.39 it/sec, feas=True, obj=6.56]
INFO - 16:21:39: 76%|███████▌ | 755/1000 [00:00<00:00, 3758.26 it/sec, feas=True, obj=0.0769]
INFO - 16:21:39: 76%|███████▌ | 756/1000 [00:00<00:00, 3757.88 it/sec, feas=True, obj=-3.24]
INFO - 16:21:39: 76%|███████▌ | 757/1000 [00:00<00:00, 3757.98 it/sec, feas=True, obj=1.73]
INFO - 16:21:39: 76%|███████▌ | 758/1000 [00:00<00:00, 3758.04 it/sec, feas=True, obj=0.263]
INFO - 16:21:39: 76%|███████▌ | 759/1000 [00:00<00:00, 3757.72 it/sec, feas=True, obj=-6.43]
INFO - 16:21:39: 76%|███████▌ | 760/1000 [00:00<00:00, 3757.50 it/sec, feas=True, obj=7.52]
INFO - 16:21:39: 76%|███████▌ | 761/1000 [00:00<00:00, 3757.57 it/sec, feas=True, obj=-2.09]
INFO - 16:21:39: 76%|███████▌ | 762/1000 [00:00<00:00, 3757.64 it/sec, feas=True, obj=-0.0262]
INFO - 16:21:39: 76%|███████▋ | 763/1000 [00:00<00:00, 3757.34 it/sec, feas=True, obj=3.37]
INFO - 16:21:39: 76%|███████▋ | 764/1000 [00:00<00:00, 3756.53 it/sec, feas=True, obj=4.5]
INFO - 16:21:39: 76%|███████▋ | 765/1000 [00:00<00:00, 3756.28 it/sec, feas=True, obj=0.692]
INFO - 16:21:39: 77%|███████▋ | 766/1000 [00:00<00:00, 3756.24 it/sec, feas=True, obj=2.75]
INFO - 16:21:39: 77%|███████▋ | 767/1000 [00:00<00:00, 3755.76 it/sec, feas=True, obj=1.46]
INFO - 16:21:39: 77%|███████▋ | 768/1000 [00:00<00:00, 3755.86 it/sec, feas=True, obj=7.23]
INFO - 16:21:39: 77%|███████▋ | 769/1000 [00:00<00:00, 3755.83 it/sec, feas=True, obj=3.47]
INFO - 16:21:39: 77%|███████▋ | 770/1000 [00:00<00:00, 3755.63 it/sec, feas=True, obj=-0.943]
INFO - 16:21:39: 77%|███████▋ | 771/1000 [00:00<00:00, 3755.67 it/sec, feas=True, obj=0.302]
INFO - 16:21:39: 77%|███████▋ | 772/1000 [00:00<00:00, 3755.82 it/sec, feas=True, obj=6]
INFO - 16:21:39: 77%|███████▋ | 773/1000 [00:00<00:00, 3755.77 it/sec, feas=True, obj=2.71]
INFO - 16:21:39: 77%|███████▋ | 774/1000 [00:00<00:00, 3755.51 it/sec, feas=True, obj=2.8]
INFO - 16:21:39: 78%|███████▊ | 775/1000 [00:00<00:00, 3755.54 it/sec, feas=True, obj=2.67]
INFO - 16:21:39: 78%|███████▊ | 776/1000 [00:00<00:00, 3755.64 it/sec, feas=True, obj=5.44]
INFO - 16:21:39: 78%|███████▊ | 777/1000 [00:00<00:00, 3755.60 it/sec, feas=True, obj=1.65]
INFO - 16:21:39: 78%|███████▊ | 778/1000 [00:00<00:00, 3755.37 it/sec, feas=True, obj=7.13]
INFO - 16:21:39: 78%|███████▊ | 779/1000 [00:00<00:00, 3755.51 it/sec, feas=True, obj=-0.0622]
INFO - 16:21:39: 78%|███████▊ | 780/1000 [00:00<00:00, 3755.66 it/sec, feas=True, obj=5.84]
INFO - 16:21:39: 78%|███████▊ | 781/1000 [00:00<00:00, 3755.76 it/sec, feas=True, obj=2.28]
INFO - 16:21:39: 78%|███████▊ | 782/1000 [00:00<00:00, 3755.35 it/sec, feas=True, obj=6.04]
INFO - 16:21:39: 78%|███████▊ | 783/1000 [00:00<00:00, 3755.28 it/sec, feas=True, obj=7.59]
INFO - 16:21:39: 78%|███████▊ | 784/1000 [00:00<00:00, 3755.35 it/sec, feas=True, obj=-6.19]
INFO - 16:21:39: 78%|███████▊ | 785/1000 [00:00<00:00, 3755.04 it/sec, feas=True, obj=9.25]
INFO - 16:21:39: 79%|███████▊ | 786/1000 [00:00<00:00, 3754.96 it/sec, feas=True, obj=0.676]
INFO - 16:21:39: 79%|███████▊ | 787/1000 [00:00<00:00, 3754.93 it/sec, feas=True, obj=-0.174]
INFO - 16:21:39: 79%|███████▉ | 788/1000 [00:00<00:00, 3754.86 it/sec, feas=True, obj=6.51]
INFO - 16:21:39: 79%|███████▉ | 789/1000 [00:00<00:00, 3754.60 it/sec, feas=True, obj=-0.856]
INFO - 16:21:39: 79%|███████▉ | 790/1000 [00:00<00:00, 3754.68 it/sec, feas=True, obj=5.62]
INFO - 16:21:39: 79%|███████▉ | 791/1000 [00:00<00:00, 3754.67 it/sec, feas=True, obj=5.35]
INFO - 16:21:39: 79%|███████▉ | 792/1000 [00:00<00:00, 3754.73 it/sec, feas=True, obj=0.753]
INFO - 16:21:39: 79%|███████▉ | 793/1000 [00:00<00:00, 3754.29 it/sec, feas=True, obj=4.35]
INFO - 16:21:39: 79%|███████▉ | 794/1000 [00:00<00:00, 3754.32 it/sec, feas=True, obj=3.8]
INFO - 16:21:39: 80%|███████▉ | 795/1000 [00:00<00:00, 3754.30 it/sec, feas=True, obj=7.95]
INFO - 16:21:39: 80%|███████▉ | 796/1000 [00:00<00:00, 3754.10 it/sec, feas=True, obj=5.01]
INFO - 16:21:39: 80%|███████▉ | 797/1000 [00:00<00:00, 3754.04 it/sec, feas=True, obj=6.2]
INFO - 16:21:39: 80%|███████▉ | 798/1000 [00:00<00:00, 3753.63 it/sec, feas=True, obj=-1.82]
INFO - 16:21:39: 80%|███████▉ | 799/1000 [00:00<00:00, 3753.30 it/sec, feas=True, obj=2.4]
INFO - 16:21:39: 80%|████████ | 800/1000 [00:00<00:00, 3752.95 it/sec, feas=True, obj=7.99]
INFO - 16:21:39: 80%|████████ | 801/1000 [00:00<00:00, 3753.01 it/sec, feas=True, obj=2.48]
INFO - 16:21:39: 80%|████████ | 802/1000 [00:00<00:00, 3753.09 it/sec, feas=True, obj=-0.764]
INFO - 16:21:39: 80%|████████ | 803/1000 [00:00<00:00, 3752.70 it/sec, feas=True, obj=3.34]
INFO - 16:21:39: 80%|████████ | 804/1000 [00:00<00:00, 3752.59 it/sec, feas=True, obj=0.787]
INFO - 16:21:39: 80%|████████ | 805/1000 [00:00<00:00, 3752.73 it/sec, feas=True, obj=-1.05]
INFO - 16:21:39: 81%|████████ | 806/1000 [00:00<00:00, 3752.81 it/sec, feas=True, obj=4.98]
INFO - 16:21:39: 81%|████████ | 807/1000 [00:00<00:00, 3752.60 it/sec, feas=True, obj=4.73]
INFO - 16:21:39: 81%|████████ | 808/1000 [00:00<00:00, 3752.51 it/sec, feas=True, obj=-0.742]
INFO - 16:21:39: 81%|████████ | 809/1000 [00:00<00:00, 3752.51 it/sec, feas=True, obj=5.82]
INFO - 16:21:39: 81%|████████ | 810/1000 [00:00<00:00, 3752.68 it/sec, feas=True, obj=10.4]
INFO - 16:21:39: 81%|████████ | 811/1000 [00:00<00:00, 3752.45 it/sec, feas=True, obj=1.86]
INFO - 16:21:39: 81%|████████ | 812/1000 [00:00<00:00, 3752.44 it/sec, feas=True, obj=2.49]
INFO - 16:21:39: 81%|████████▏ | 813/1000 [00:00<00:00, 3752.28 it/sec, feas=True, obj=9.36]
INFO - 16:21:39: 81%|████████▏ | 814/1000 [00:00<00:00, 3752.35 it/sec, feas=True, obj=1.84]
INFO - 16:21:39: 82%|████████▏ | 815/1000 [00:00<00:00, 3751.93 it/sec, feas=True, obj=4.04]
INFO - 16:21:39: 82%|████████▏ | 816/1000 [00:00<00:00, 3751.92 it/sec, feas=True, obj=-4.21]
INFO - 16:21:39: 82%|████████▏ | 817/1000 [00:00<00:00, 3751.92 it/sec, feas=True, obj=3.64]
INFO - 16:21:39: 82%|████████▏ | 818/1000 [00:00<00:00, 3751.65 it/sec, feas=True, obj=4.02]
INFO - 16:21:39: 82%|████████▏ | 819/1000 [00:00<00:00, 3751.62 it/sec, feas=True, obj=6.66]
INFO - 16:21:39: 82%|████████▏ | 820/1000 [00:00<00:00, 3751.72 it/sec, feas=True, obj=-0.0634]
INFO - 16:21:39: 82%|████████▏ | 821/1000 [00:00<00:00, 3751.70 it/sec, feas=True, obj=1.24]
INFO - 16:21:39: 82%|████████▏ | 822/1000 [00:00<00:00, 3751.47 it/sec, feas=True, obj=4.42]
INFO - 16:21:39: 82%|████████▏ | 823/1000 [00:00<00:00, 3751.50 it/sec, feas=True, obj=4.26]
INFO - 16:21:39: 82%|████████▏ | 824/1000 [00:00<00:00, 3751.67 it/sec, feas=True, obj=0.439]
INFO - 16:21:39: 82%|████████▎ | 825/1000 [00:00<00:00, 3751.17 it/sec, feas=True, obj=2.7]
INFO - 16:21:39: 83%|████████▎ | 826/1000 [00:00<00:00, 3750.72 it/sec, feas=True, obj=2.98]
INFO - 16:21:39: 83%|████████▎ | 827/1000 [00:00<00:00, 3750.59 it/sec, feas=True, obj=0.888]
INFO - 16:21:39: 83%|████████▎ | 828/1000 [00:00<00:00, 3750.58 it/sec, feas=True, obj=-0.879]
INFO - 16:21:39: 83%|████████▎ | 829/1000 [00:00<00:00, 3748.28 it/sec, feas=True, obj=0.861]
INFO - 16:21:39: 83%|████████▎ | 830/1000 [00:00<00:00, 3748.13 it/sec, feas=True, obj=3.47]
INFO - 16:21:39: 83%|████████▎ | 831/1000 [00:00<00:00, 3748.10 it/sec, feas=True, obj=7.51]
INFO - 16:21:39: 83%|████████▎ | 832/1000 [00:00<00:00, 3747.90 it/sec, feas=True, obj=4.58]
INFO - 16:21:39: 83%|████████▎ | 833/1000 [00:00<00:00, 3747.87 it/sec, feas=True, obj=5.48]
INFO - 16:21:39: 83%|████████▎ | 834/1000 [00:00<00:00, 3747.86 it/sec, feas=True, obj=-0.412]
INFO - 16:21:39: 84%|████████▎ | 835/1000 [00:00<00:00, 3747.85 it/sec, feas=True, obj=-1.86]
INFO - 16:21:39: 84%|████████▎ | 836/1000 [00:00<00:00, 3747.59 it/sec, feas=True, obj=1.29]
INFO - 16:21:39: 84%|████████▎ | 837/1000 [00:00<00:00, 3747.58 it/sec, feas=True, obj=3.17]
INFO - 16:21:39: 84%|████████▍ | 838/1000 [00:00<00:00, 3747.64 it/sec, feas=True, obj=2.41]
INFO - 16:21:39: 84%|████████▍ | 839/1000 [00:00<00:00, 3747.73 it/sec, feas=True, obj=5.72]
INFO - 16:21:39: 84%|████████▍ | 840/1000 [00:00<00:00, 3747.45 it/sec, feas=True, obj=-1.37]
INFO - 16:21:39: 84%|████████▍ | 841/1000 [00:00<00:00, 3747.41 it/sec, feas=True, obj=6.72]
INFO - 16:21:39: 84%|████████▍ | 842/1000 [00:00<00:00, 3747.12 it/sec, feas=True, obj=3.27]
INFO - 16:21:39: 84%|████████▍ | 843/1000 [00:00<00:00, 3746.93 it/sec, feas=True, obj=-1.46]
INFO - 16:21:39: 84%|████████▍ | 844/1000 [00:00<00:00, 3746.81 it/sec, feas=True, obj=4.38]
INFO - 16:21:39: 84%|████████▍ | 845/1000 [00:00<00:00, 3746.75 it/sec, feas=True, obj=3.82]
INFO - 16:21:39: 85%|████████▍ | 846/1000 [00:00<00:00, 3746.83 it/sec, feas=True, obj=5.89]
INFO - 16:21:39: 85%|████████▍ | 847/1000 [00:00<00:00, 3746.60 it/sec, feas=True, obj=3.11]
INFO - 16:21:39: 85%|████████▍ | 848/1000 [00:00<00:00, 3746.60 it/sec, feas=True, obj=4.37]
INFO - 16:21:39: 85%|████████▍ | 849/1000 [00:00<00:00, 3746.56 it/sec, feas=True, obj=1.84]
INFO - 16:21:39: 85%|████████▌ | 850/1000 [00:00<00:00, 3746.57 it/sec, feas=True, obj=2.82]
INFO - 16:21:39: 85%|████████▌ | 851/1000 [00:00<00:00, 3746.27 it/sec, feas=True, obj=7.38]
INFO - 16:21:39: 85%|████████▌ | 852/1000 [00:00<00:00, 3746.24 it/sec, feas=True, obj=13.8]
INFO - 16:21:39: 85%|████████▌ | 853/1000 [00:00<00:00, 3746.22 it/sec, feas=True, obj=7.76]
INFO - 16:21:39: 85%|████████▌ | 854/1000 [00:00<00:00, 3746.06 it/sec, feas=True, obj=0.998]
INFO - 16:21:39: 86%|████████▌ | 855/1000 [00:00<00:00, 3745.96 it/sec, feas=True, obj=3.88]
INFO - 16:21:39: 86%|████████▌ | 856/1000 [00:00<00:00, 3745.86 it/sec, feas=True, obj=-0.698]
INFO - 16:21:39: 86%|████████▌ | 857/1000 [00:00<00:00, 3745.50 it/sec, feas=True, obj=2.83]
INFO - 16:21:39: 86%|████████▌ | 858/1000 [00:00<00:00, 3744.87 it/sec, feas=True, obj=1.58]
INFO - 16:21:39: 86%|████████▌ | 859/1000 [00:00<00:00, 3744.91 it/sec, feas=True, obj=8.53]
INFO - 16:21:39: 86%|████████▌ | 860/1000 [00:00<00:00, 3745.10 it/sec, feas=True, obj=6.28]
INFO - 16:21:39: 86%|████████▌ | 861/1000 [00:00<00:00, 3745.26 it/sec, feas=True, obj=11.8]
INFO - 16:21:39: 86%|████████▌ | 862/1000 [00:00<00:00, 3745.02 it/sec, feas=True, obj=9.31]
INFO - 16:21:39: 86%|████████▋ | 863/1000 [00:00<00:00, 3745.00 it/sec, feas=True, obj=3.88]
INFO - 16:21:39: 86%|████████▋ | 864/1000 [00:00<00:00, 3745.17 it/sec, feas=True, obj=3.11]
INFO - 16:21:39: 86%|████████▋ | 865/1000 [00:00<00:00, 3745.38 it/sec, feas=True, obj=5.09]
INFO - 16:21:39: 87%|████████▋ | 866/1000 [00:00<00:00, 3745.05 it/sec, feas=True, obj=-0.723]
INFO - 16:21:39: 87%|████████▋ | 867/1000 [00:00<00:00, 3745.06 it/sec, feas=True, obj=1.22]
INFO - 16:21:39: 87%|████████▋ | 868/1000 [00:00<00:00, 3745.17 it/sec, feas=True, obj=7.13]
INFO - 16:21:39: 87%|████████▋ | 869/1000 [00:00<00:00, 3745.01 it/sec, feas=True, obj=12.2]
INFO - 16:21:39: 87%|████████▋ | 870/1000 [00:00<00:00, 3745.03 it/sec, feas=True, obj=1.13]
INFO - 16:21:39: 87%|████████▋ | 871/1000 [00:00<00:00, 3745.07 it/sec, feas=True, obj=0.802]
INFO - 16:21:39: 87%|████████▋ | 872/1000 [00:00<00:00, 3745.07 it/sec, feas=True, obj=2.82]
INFO - 16:21:39: 87%|████████▋ | 873/1000 [00:00<00:00, 3744.81 it/sec, feas=True, obj=-0.932]
INFO - 16:21:39: 87%|████████▋ | 874/1000 [00:00<00:00, 3744.88 it/sec, feas=True, obj=1.6]
INFO - 16:21:39: 88%|████████▊ | 875/1000 [00:00<00:00, 3744.70 it/sec, feas=True, obj=8.68]
INFO - 16:21:39: 88%|████████▊ | 876/1000 [00:00<00:00, 3744.75 it/sec, feas=True, obj=-0.211]
INFO - 16:21:39: 88%|████████▊ | 877/1000 [00:00<00:00, 3744.41 it/sec, feas=True, obj=-3.63]
INFO - 16:21:39: 88%|████████▊ | 878/1000 [00:00<00:00, 3744.55 it/sec, feas=True, obj=4.85]
INFO - 16:21:39: 88%|████████▊ | 879/1000 [00:00<00:00, 3744.64 it/sec, feas=True, obj=4.28]
INFO - 16:21:39: 88%|████████▊ | 880/1000 [00:00<00:00, 3744.42 it/sec, feas=True, obj=-0.285]
INFO - 16:21:39: 88%|████████▊ | 881/1000 [00:00<00:00, 3744.35 it/sec, feas=True, obj=5.96]
INFO - 16:21:39: 88%|████████▊ | 882/1000 [00:00<00:00, 3744.39 it/sec, feas=True, obj=-0.126]
INFO - 16:21:39: 88%|████████▊ | 883/1000 [00:00<00:00, 3744.51 it/sec, feas=True, obj=10.4]
INFO - 16:21:39: 88%|████████▊ | 884/1000 [00:00<00:00, 3744.29 it/sec, feas=True, obj=-1.37]
INFO - 16:21:39: 88%|████████▊ | 885/1000 [00:00<00:00, 3744.29 it/sec, feas=True, obj=4.47]
INFO - 16:21:39: 89%|████████▊ | 886/1000 [00:00<00:00, 3744.10 it/sec, feas=True, obj=1.19]
INFO - 16:21:39: 89%|████████▊ | 887/1000 [00:00<00:00, 3744.10 it/sec, feas=True, obj=6.51]
INFO - 16:21:39: 89%|████████▉ | 888/1000 [00:00<00:00, 3743.66 it/sec, feas=True, obj=-0.5]
INFO - 16:21:39: 89%|████████▉ | 889/1000 [00:00<00:00, 3743.62 it/sec, feas=True, obj=1.33]
INFO - 16:21:39: 89%|████████▉ | 890/1000 [00:00<00:00, 3743.78 it/sec, feas=True, obj=8.1]
INFO - 16:21:39: 89%|████████▉ | 891/1000 [00:00<00:00, 3743.67 it/sec, feas=True, obj=6.34]
INFO - 16:21:39: 89%|████████▉ | 892/1000 [00:00<00:00, 3743.60 it/sec, feas=True, obj=0.425]
INFO - 16:21:39: 89%|████████▉ | 893/1000 [00:00<00:00, 3743.64 it/sec, feas=True, obj=7.99]
INFO - 16:21:39: 89%|████████▉ | 894/1000 [00:00<00:00, 3743.69 it/sec, feas=True, obj=4.73]
INFO - 16:21:39: 90%|████████▉ | 895/1000 [00:00<00:00, 3743.43 it/sec, feas=True, obj=-0.736]
INFO - 16:21:39: 90%|████████▉ | 896/1000 [00:00<00:00, 3743.36 it/sec, feas=True, obj=1.11]
INFO - 16:21:39: 90%|████████▉ | 897/1000 [00:00<00:00, 3743.25 it/sec, feas=True, obj=5.52]
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INFO - 16:21:39: 90%|█████████ | 900/1000 [00:00<00:00, 3743.16 it/sec, feas=True, obj=6.25]
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INFO - 16:21:39: 91%|█████████ | 911/1000 [00:00<00:00, 3741.96 it/sec, feas=True, obj=0.778]
INFO - 16:21:39: 91%|█████████ | 912/1000 [00:00<00:00, 3741.82 it/sec, feas=True, obj=4.03]
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INFO - 16:21:39: 92%|█████████▏| 922/1000 [00:00<00:00, 3741.27 it/sec, feas=True, obj=6.08]
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INFO - 16:21:39: 94%|█████████▍| 944/1000 [00:00<00:00, 3724.68 it/sec, feas=True, obj=9.05]
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INFO - 16:21:39: 95%|█████████▌| 950/1000 [00:00<00:00, 3723.72 it/sec, feas=True, obj=0.391]
INFO - 16:21:39: 95%|█████████▌| 951/1000 [00:00<00:00, 3723.63 it/sec, feas=True, obj=7.77]
INFO - 16:21:39: 95%|█████████▌| 952/1000 [00:00<00:00, 3723.73 it/sec, feas=True, obj=-0.712]
INFO - 16:21:39: 95%|█████████▌| 953/1000 [00:00<00:00, 3723.81 it/sec, feas=True, obj=0.439]
INFO - 16:21:39: 95%|█████████▌| 954/1000 [00:00<00:00, 3723.28 it/sec, feas=True, obj=7.43]
INFO - 16:21:39: 96%|█████████▌| 955/1000 [00:00<00:00, 3723.15 it/sec, feas=True, obj=-1.49]
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INFO - 16:21:39: 96%|█████████▌| 959/1000 [00:00<00:00, 3722.80 it/sec, feas=True, obj=1.26]
INFO - 16:21:39: 96%|█████████▌| 960/1000 [00:00<00:00, 3722.88 it/sec, feas=True, obj=3.33]
INFO - 16:21:39: 96%|█████████▌| 961/1000 [00:00<00:00, 3722.74 it/sec, feas=True, obj=2.28]
INFO - 16:21:39: 96%|█████████▌| 962/1000 [00:00<00:00, 3722.76 it/sec, feas=True, obj=14.7]
INFO - 16:21:39: 96%|█████████▋| 963/1000 [00:00<00:00, 3722.73 it/sec, feas=True, obj=0.515]
INFO - 16:21:39: 96%|█████████▋| 964/1000 [00:00<00:00, 3722.81 it/sec, feas=True, obj=2.57]
INFO - 16:21:39: 96%|█████████▋| 965/1000 [00:00<00:00, 3722.51 it/sec, feas=True, obj=6.57]
INFO - 16:21:39: 97%|█████████▋| 966/1000 [00:00<00:00, 3722.61 it/sec, feas=True, obj=-0.292]
INFO - 16:21:39: 97%|█████████▋| 967/1000 [00:00<00:00, 3722.64 it/sec, feas=True, obj=-1.65]
INFO - 16:21:39: 97%|█████████▋| 968/1000 [00:00<00:00, 3722.53 it/sec, feas=True, obj=7.01]
INFO - 16:21:39: 97%|█████████▋| 969/1000 [00:00<00:00, 3722.52 it/sec, feas=True, obj=-0.0208]
INFO - 16:21:39: 97%|█████████▋| 970/1000 [00:00<00:00, 3722.48 it/sec, feas=True, obj=2.03]
INFO - 16:21:39: 97%|█████████▋| 971/1000 [00:00<00:00, 3722.56 it/sec, feas=True, obj=0.429]
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INFO - 16:21:39: 97%|█████████▋| 973/1000 [00:00<00:00, 3721.81 it/sec, feas=True, obj=6.01]
INFO - 16:21:39: 97%|█████████▋| 974/1000 [00:00<00:00, 3721.95 it/sec, feas=True, obj=5.07]
INFO - 16:21:39: 98%|█████████▊| 975/1000 [00:00<00:00, 3722.09 it/sec, feas=True, obj=7.26]
INFO - 16:21:39: 98%|█████████▊| 976/1000 [00:00<00:00, 3721.73 it/sec, feas=True, obj=1.83]
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INFO - 16:21:39: 98%|█████████▊| 978/1000 [00:00<00:00, 3722.04 it/sec, feas=True, obj=4.96]
INFO - 16:21:39: 98%|█████████▊| 979/1000 [00:00<00:00, 3721.90 it/sec, feas=True, obj=0.739]
INFO - 16:21:39: 98%|█████████▊| 980/1000 [00:00<00:00, 3721.89 it/sec, feas=True, obj=-1.88]
INFO - 16:21:39: 98%|█████████▊| 981/1000 [00:00<00:00, 3722.04 it/sec, feas=True, obj=5.13]
INFO - 16:21:39: 98%|█████████▊| 982/1000 [00:00<00:00, 3722.06 it/sec, feas=True, obj=3.38]
INFO - 16:21:39: 98%|█████████▊| 983/1000 [00:00<00:00, 3721.97 it/sec, feas=True, obj=8.53]
INFO - 16:21:39: 98%|█████████▊| 984/1000 [00:00<00:00, 3722.02 it/sec, feas=True, obj=5.83]
INFO - 16:21:39: 98%|█████████▊| 985/1000 [00:00<00:00, 3721.93 it/sec, feas=True, obj=8.02]
INFO - 16:21:39: 99%|█████████▊| 986/1000 [00:00<00:00, 3721.96 it/sec, feas=True, obj=2.01]
INFO - 16:21:39: 99%|█████████▊| 987/1000 [00:00<00:00, 3721.73 it/sec, feas=True, obj=-0.893]
INFO - 16:21:39: 99%|█████████▉| 988/1000 [00:00<00:00, 3721.83 it/sec, feas=True, obj=4.74]
INFO - 16:21:39: 99%|█████████▉| 989/1000 [00:00<00:00, 3721.99 it/sec, feas=True, obj=1.18]
INFO - 16:21:39: 99%|█████████▉| 990/1000 [00:00<00:00, 3721.84 it/sec, feas=True, obj=6.2]
INFO - 16:21:39: 99%|█████████▉| 991/1000 [00:00<00:00, 3721.86 it/sec, feas=True, obj=4.5]
INFO - 16:21:39: 99%|█████████▉| 992/1000 [00:00<00:00, 3722.01 it/sec, feas=True, obj=-0.907]
INFO - 16:21:39: 99%|█████████▉| 993/1000 [00:00<00:00, 3722.16 it/sec, feas=True, obj=-3.18]
INFO - 16:21:39: 99%|█████████▉| 994/1000 [00:00<00:00, 3722.02 it/sec, feas=True, obj=6.82]
INFO - 16:21:39: 100%|█████████▉| 995/1000 [00:00<00:00, 3722.03 it/sec, feas=True, obj=3.44]
INFO - 16:21:39: 100%|█████████▉| 996/1000 [00:00<00:00, 3722.01 it/sec, feas=True, obj=5.11]
INFO - 16:21:39: 100%|█████████▉| 997/1000 [00:00<00:00, 3721.79 it/sec, feas=True, obj=1.55]
INFO - 16:21:39: 100%|█████████▉| 998/1000 [00:00<00:00, 3721.32 it/sec, feas=True, obj=0.534]
INFO - 16:21:39: 100%|█████████▉| 999/1000 [00:00<00:00, 3721.42 it/sec, feas=True, obj=0.783]
INFO - 16:21:39: 100%|██████████| 1000/1000 [00:00<00:00, 3695.50 it/sec, feas=True, obj=5.65]
INFO - 16:21:39: Optimization result:
INFO - 16:21:39: Optimizer info:
INFO - 16:21:39: Status: None
INFO - 16:21:39: Message: None
INFO - 16:21:39: Solution:
INFO - 16:21:39: Objective: -10.14685071195364
INFO - 16:21:39: Design space:
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: | Name | Distribution |
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) |
INFO - 16:21:39: +------+------------------------------------------------------------+
INFO - 16:21:39: *** 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 4.702 seconds)