.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/mlearning/regression_model/plot_fce_regression.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_mlearning_regression_model_plot_fce_regression.py: Function chaos expansion ======================== Given a training dataset whose input samples are generated from OpenTURNS probability distributions, the :class:`.FCERegressor` can use any linear model fitting algorithm, including sparse techniques, to fit a functional chaos expansion (FCE) model of the form .. math:: Y = \sum_{i\in\mathcal{I}\subset\mathbb{N}^d} w_i\Psi_i(X) where :math:`\Psi_i(X)=\prod_{j=1}^d\psi_{i,j}(X_j)` and :math:`\mathbb{E}[\Psi_i(X)\Psi_j(X)]=\delta_{ij}` with :math:`\delta` the Kronecker delta and :math:`X` a random vector. A particular version of FCE is the polynomial chaos expansion (PCE) for which the class :class:`.PCERegressor` interfaces the OpenTURNS algorithm :class:`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 :class:`.FCERegressor` to approximate the Ishigami function .. math:: f(X) = \sin(X_1) + 7\sin(X_2)^2 + 0.1X_3^4\sin(X_1) where :math:`X_1`, :math:`X_2` and :math:`X_3` are independent and uniformly distributed over the interval :math:`[-\pi,\pi]`. .. GENERATED FROM PYTHON SOURCE LINES 53-86 .. code-block:: Python 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 .. GENERATED FROM PYTHON SOURCE LINES 87-89 First, we define the Ishigami discipline and its uncertain space: .. GENERATED FROM PYTHON SOURCE LINES 89-92 .. code-block:: Python discipline = IshigamiDiscipline() uncertain_space = IshigamiSpace(IshigamiSpace.UniformDistribution.OPENTURNS) .. GENERATED FROM PYTHON SOURCE LINES 93-94 and create a training dataset using an optimized latin hypercube sampling: .. GENERATED FROM PYTHON SOURCE LINES 94-101 .. code-block:: Python training_dataset = sample_disciplines( [discipline], uncertain_space, "y", algo_settings_model=OT_OPT_LHS_Settings(n_samples=70, eval_jac=True), ) .. rst-class:: sphx-glr-script-out .. code-block:: none INFO - 16:22:13: *** Start Sampling execution *** INFO - 16:22:13: Sampling INFO - 16:22:13: Disciplines: IshigamiDiscipline INFO - 16:22:13: MDO formulation: MDF INFO - 16:22:13: Optimization problem: INFO - 16:22:13: minimize y(x1, x2, x3) INFO - 16:22:13: with respect to x1, x2, x3 INFO - 16:22:13: over the design space: INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: | Name | Distribution | INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: Solving optimization problem with algorithm OT_OPT_LHS: INFO - 16:22:13: 1%|▏ | 1/70 [00:00<00:00, 369.90 it/sec, feas=True, obj=1.45] INFO - 16:22:13: 3%|▎ | 2/70 [00:00<00:00, 601.51 it/sec, feas=True, obj=1.01] INFO - 16:22:13: 4%|▍ | 3/70 [00:00<00:00, 782.23 it/sec, feas=True, obj=6.72] INFO - 16:22:13: 6%|▌ | 4/70 [00:00<00:00, 919.25 it/sec, feas=True, obj=-0.113] INFO - 16:22:13: 7%|▋ | 5/70 [00:00<00:00, 1034.71 it/sec, feas=True, obj=7.68] INFO - 16:22:13: 9%|▊ | 6/70 [00:00<00:00, 1125.84 it/sec, feas=True, obj=1.8] INFO - 16:22:13: 10%|█ | 7/70 [00:00<00:00, 1208.34 it/sec, feas=True, obj=10.3] INFO - 16:22:13: 11%|█▏ | 8/70 [00:00<00:00, 1273.41 it/sec, feas=True, obj=5.96] INFO - 16:22:13: 13%|█▎ | 9/70 [00:00<00:00, 1332.42 it/sec, feas=True, obj=0.0449] INFO - 16:22:13: 14%|█▍ | 10/70 [00:00<00:00, 1382.89 it/sec, feas=True, obj=4.97] INFO - 16:22:13: 16%|█▌ | 11/70 [00:00<00:00, 1430.39 it/sec, feas=True, obj=6.94] INFO - 16:22:13: 17%|█▋ | 12/70 [00:00<00:00, 1470.31 it/sec, feas=True, obj=3.5] INFO - 16:22:13: 19%|█▊ | 13/70 [00:00<00:00, 1507.74 it/sec, feas=True, obj=4.87] INFO - 16:22:13: 20%|██ | 14/70 [00:00<00:00, 1539.68 it/sec, feas=True, obj=4.3] INFO - 16:22:13: 21%|██▏ | 15/70 [00:00<00:00, 1570.55 it/sec, feas=True, obj=2.44] INFO - 16:22:13: 23%|██▎ | 16/70 [00:00<00:00, 1600.65 it/sec, feas=True, obj=5.7] INFO - 16:22:13: 24%|██▍ | 17/70 [00:00<00:00, 1624.74 it/sec, feas=True, obj=6.14] INFO - 16:22:13: 26%|██▌ | 18/70 [00:00<00:00, 1650.43 it/sec, feas=True, obj=5.7] INFO - 16:22:13: 27%|██▋ | 19/70 [00:00<00:00, 1670.23 it/sec, feas=True, obj=-0.573] INFO - 16:22:13: 29%|██▊ | 20/70 [00:00<00:00, 1690.81 it/sec, feas=True, obj=5.72] INFO - 16:22:13: 30%|███ | 21/70 [00:00<00:00, 1707.58 it/sec, feas=True, obj=4.95] INFO - 16:22:13: 31%|███▏ | 22/70 [00:00<00:00, 1724.37 it/sec, feas=True, obj=1.27] INFO - 16:22:13: 33%|███▎ | 23/70 [00:00<00:00, 1737.99 it/sec, feas=True, obj=3.54] INFO - 16:22:13: 34%|███▍ | 24/70 [00:00<00:00, 1753.35 it/sec, feas=True, obj=6.04] INFO - 16:22:13: 36%|███▌ | 25/70 [00:00<00:00, 1766.59 it/sec, feas=True, obj=7.5] INFO - 16:22:13: 37%|███▋ | 26/70 [00:00<00:00, 1779.86 it/sec, feas=True, obj=13.2] INFO - 16:22:13: 39%|███▊ | 27/70 [00:00<00:00, 1790.88 it/sec, feas=True, obj=14.8] INFO - 16:22:13: 40%|████ | 28/70 [00:00<00:00, 1803.59 it/sec, feas=True, obj=-0.644] INFO - 16:22:13: 41%|████▏ | 29/70 [00:00<00:00, 1814.69 it/sec, feas=True, obj=4.94] INFO - 16:22:13: 43%|████▎ | 30/70 [00:00<00:00, 1824.80 it/sec, feas=True, obj=5.5] INFO - 16:22:13: 44%|████▍ | 31/70 [00:00<00:00, 1835.43 it/sec, feas=True, obj=3.35] INFO - 16:22:13: 46%|████▌ | 32/70 [00:00<00:00, 1844.26 it/sec, feas=True, obj=4.05] INFO - 16:22:13: 47%|████▋ | 33/70 [00:00<00:00, 1854.72 it/sec, feas=True, obj=2.43] INFO - 16:22:13: 49%|████▊ | 34/70 [00:00<00:00, 1863.06 it/sec, feas=True, obj=-0.0246] INFO - 16:22:13: 50%|█████ | 35/70 [00:00<00:00, 1872.10 it/sec, feas=True, obj=-0.0211] INFO - 16:22:13: 51%|█████▏ | 36/70 [00:00<00:00, 1878.84 it/sec, feas=True, obj=6.01] INFO - 16:22:13: 53%|█████▎ | 37/70 [00:00<00:00, 1886.48 it/sec, feas=True, obj=5.03] INFO - 16:22:13: 54%|█████▍ | 38/70 [00:00<00:00, 1893.50 it/sec, feas=True, obj=0.863] INFO - 16:22:13: 56%|█████▌ | 39/70 [00:00<00:00, 1900.12 it/sec, feas=True, obj=-0.764] INFO - 16:22:13: 57%|█████▋ | 40/70 [00:00<00:00, 1906.89 it/sec, feas=True, obj=14.8] INFO - 16:22:13: 59%|█████▊ | 41/70 [00:00<00:00, 1912.18 it/sec, feas=True, obj=0.87] INFO - 16:22:13: 60%|██████ | 42/70 [00:00<00:00, 1919.49 it/sec, feas=True, obj=0.829] INFO - 16:22:13: 61%|██████▏ | 43/70 [00:00<00:00, 1923.83 it/sec, feas=True, obj=5.01] INFO - 16:22:13: 63%|██████▎ | 44/70 [00:00<00:00, 1929.00 it/sec, feas=True, obj=0.108] INFO - 16:22:13: 64%|██████▍ | 45/70 [00:00<00:00, 1932.52 it/sec, feas=True, obj=0.948] INFO - 16:22:13: 66%|██████▌ | 46/70 [00:00<00:00, 1937.93 it/sec, feas=True, obj=1.22] INFO - 16:22:13: 67%|██████▋ | 47/70 [00:00<00:00, 1942.04 it/sec, feas=True, obj=7.52] INFO - 16:22:13: 69%|██████▊ | 48/70 [00:00<00:00, 1946.50 it/sec, feas=True, obj=3.97] INFO - 16:22:13: 70%|███████ | 49/70 [00:00<00:00, 1950.30 it/sec, feas=True, obj=0.768] INFO - 16:22:13: 71%|███████▏ | 50/70 [00:00<00:00, 1955.35 it/sec, feas=True, obj=-8.26] INFO - 16:22:13: 73%|███████▎ | 51/70 [00:00<00:00, 1960.93 it/sec, feas=True, obj=-3.5] INFO - 16:22:13: 74%|███████▍ | 52/70 [00:00<00:00, 1964.39 it/sec, feas=True, obj=7.43] INFO - 16:22:13: 76%|███████▌ | 53/70 [00:00<00:00, 1969.17 it/sec, feas=True, obj=-2.32] INFO - 16:22:13: 77%|███████▋ | 54/70 [00:00<00:00, 1972.76 it/sec, feas=True, obj=4.82] INFO - 16:22:13: 79%|███████▊ | 55/70 [00:00<00:00, 1977.82 it/sec, feas=True, obj=2.5] INFO - 16:22:13: 80%|████████ | 56/70 [00:00<00:00, 1980.65 it/sec, feas=True, obj=2.58] INFO - 16:22:13: 81%|████████▏ | 57/70 [00:00<00:00, 1984.72 it/sec, feas=True, obj=-2.55] INFO - 16:22:13: 83%|████████▎ | 58/70 [00:00<00:00, 1987.84 it/sec, feas=True, obj=2.11] INFO - 16:22:13: 84%|████████▍ | 59/70 [00:00<00:00, 1991.89 it/sec, feas=True, obj=8.06] INFO - 16:22:13: 86%|████████▌ | 60/70 [00:00<00:00, 1995.23 it/sec, feas=True, obj=-5.24] INFO - 16:22:13: 87%|████████▋ | 61/70 [00:00<00:00, 1998.96 it/sec, feas=True, obj=2.4] INFO - 16:22:13: 89%|████████▊ | 62/70 [00:00<00:00, 2003.44 it/sec, feas=True, obj=3.43] INFO - 16:22:13: 90%|█████████ | 63/70 [00:00<00:00, 2006.43 it/sec, feas=True, obj=5.99] INFO - 16:22:13: 91%|█████████▏| 64/70 [00:00<00:00, 2010.06 it/sec, feas=True, obj=0.819] INFO - 16:22:13: 93%|█████████▎| 65/70 [00:00<00:00, 2012.52 it/sec, feas=True, obj=0.632] INFO - 16:22:13: 94%|█████████▍| 66/70 [00:00<00:00, 2015.24 it/sec, feas=True, obj=-0.158] INFO - 16:22:13: 96%|█████████▌| 67/70 [00:00<00:00, 2017.56 it/sec, feas=True, obj=4.05] INFO - 16:22:13: 97%|█████████▋| 68/70 [00:00<00:00, 2020.43 it/sec, feas=True, obj=7.71] INFO - 16:22:13: 99%|█████████▊| 69/70 [00:00<00:00, 2022.41 it/sec, feas=True, obj=5.54] INFO - 16:22:13: 100%|██████████| 70/70 [00:00<00:00, 2013.82 it/sec, feas=True, obj=6.63] INFO - 16:22:13: Optimization result: INFO - 16:22:13: Optimizer info: INFO - 16:22:13: Status: None INFO - 16:22:13: Message: None INFO - 16:22:13: Solution: INFO - 16:22:13: Objective: -8.260663543133736 INFO - 16:22:13: Design space: INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: | Name | Distribution | INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: *** End Sampling execution *** .. GENERATED FROM PYTHON SOURCE LINES 102-103 as well as a validation dataset using Monte Carlo sampling: .. GENERATED FROM PYTHON SOURCE LINES 103-110 .. code-block:: Python validation_dataset = sample_disciplines( [discipline], uncertain_space, "y", algo_settings_model=MC_Settings(n_samples=1000), ) .. rst-class:: sphx-glr-script-out .. code-block:: none INFO - 16:22:13: *** Start Sampling execution *** INFO - 16:22:13: Sampling INFO - 16:22:13: Disciplines: IshigamiDiscipline INFO - 16:22:13: MDO formulation: MDF INFO - 16:22:13: Optimization problem: INFO - 16:22:13: minimize y(x1, x2, x3) INFO - 16:22:13: with respect to x1, x2, x3 INFO - 16:22:13: over the design space: INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: | Name | Distribution | INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: Solving optimization problem with algorithm MC: INFO - 16:22:13: 1%| | 6/1000 [00:00<00:00, 4288.65 it/sec, feas=True, obj=3.6] INFO - 16:22:13: 1%| | 7/1000 [00:00<00:00, 4025.80 it/sec, feas=True, obj=5.41] INFO - 16:22:13: 1%| | 8/1000 [00:00<00:00, 3973.29 it/sec, feas=True, obj=-9.09] INFO - 16:22:13: 1%| | 9/1000 [00:00<00:00, 3952.75 it/sec, feas=True, obj=7.06] INFO - 16:22:13: 1%| | 10/1000 [00:00<00:00, 3919.91 it/sec, feas=True, obj=-3.46] INFO - 16:22:13: 1%| | 11/1000 [00:00<00:00, 3901.68 it/sec, feas=True, obj=3.2] INFO - 16:22:13: 1%| | 12/1000 [00:00<00:00, 3901.07 it/sec, feas=True, obj=8.62] INFO - 16:22:13: 1%|▏ | 13/1000 [00:00<00:00, 3900.00 it/sec, feas=True, obj=-0.0229] INFO - 16:22:13: 1%|▏ | 14/1000 [00:00<00:00, 3883.10 it/sec, feas=True, obj=2.91] INFO - 16:22:13: 2%|▏ | 15/1000 [00:00<00:00, 3887.21 it/sec, feas=True, obj=8.67] INFO - 16:22:13: 2%|▏ | 16/1000 [00:00<00:00, 3894.43 it/sec, feas=True, obj=0.13] INFO - 16:22:13: 2%|▏ | 17/1000 [00:00<00:00, 3896.99 it/sec, feas=True, obj=4.8] INFO - 16:22:13: 2%|▏ | 18/1000 [00:00<00:00, 3885.21 it/sec, feas=True, obj=8.34] INFO - 16:22:13: 2%|▏ | 19/1000 [00:00<00:00, 3890.63 it/sec, feas=True, obj=-1.57] INFO - 16:22:13: 2%|▏ | 20/1000 [00:00<00:00, 3889.92 it/sec, feas=True, obj=4.2] INFO - 16:22:13: 2%|▏ | 21/1000 [00:00<00:00, 3895.12 it/sec, feas=True, obj=-1.04] INFO - 16:22:13: 2%|▏ | 22/1000 [00:00<00:00, 3886.72 it/sec, feas=True, obj=6.49] INFO - 16:22:13: 2%|▏ | 23/1000 [00:00<00:00, 3891.92 it/sec, feas=True, obj=1.83] INFO - 16:22:13: 2%|▏ | 24/1000 [00:00<00:00, 3891.27 it/sec, feas=True, obj=4.83] INFO - 16:22:13: 2%|▎ | 25/1000 [00:00<00:00, 3894.87 it/sec, feas=True, obj=2.15] INFO - 16:22:13: 3%|▎ | 26/1000 [00:00<00:00, 3887.77 it/sec, feas=True, obj=4.61] INFO - 16:22:13: 3%|▎ | 27/1000 [00:00<00:00, 3891.89 it/sec, feas=True, obj=4.02] INFO - 16:22:13: 3%|▎ | 28/1000 [00:00<00:00, 3898.05 it/sec, feas=True, obj=4.83] INFO - 16:22:13: 3%|▎ | 29/1000 [00:00<00:00, 3902.30 it/sec, feas=True, obj=3.43] INFO - 16:22:13: 3%|▎ | 30/1000 [00:00<00:00, 3895.04 it/sec, feas=True, obj=2.48] INFO - 16:22:13: 3%|▎ | 31/1000 [00:00<00:00, 3827.71 it/sec, feas=True, obj=6.63] INFO - 16:22:13: 3%|▎ | 32/1000 [00:00<00:00, 3828.45 it/sec, feas=True, obj=6.92] INFO - 16:22:13: 3%|▎ | 33/1000 [00:00<00:00, 3823.75 it/sec, feas=True, obj=3.22] INFO - 16:22:13: 3%|▎ | 34/1000 [00:00<00:00, 3824.05 it/sec, feas=True, obj=5.73] INFO - 16:22:13: 4%|▎ | 35/1000 [00:00<00:00, 3825.32 it/sec, feas=True, obj=5.62] INFO - 16:22:13: 4%|▎ | 36/1000 [00:00<00:00, 3828.86 it/sec, feas=True, obj=-1.44] INFO - 16:22:13: 4%|▎ | 37/1000 [00:00<00:00, 3824.84 it/sec, feas=True, obj=7.02] INFO - 16:22:13: 4%|▍ | 38/1000 [00:00<00:00, 3826.37 it/sec, feas=True, obj=6.21] INFO - 16:22:13: 4%|▍ | 39/1000 [00:00<00:00, 3828.98 it/sec, feas=True, obj=4.64] INFO - 16:22:13: 4%|▍ | 40/1000 [00:00<00:00, 3832.86 it/sec, feas=True, obj=4.71] INFO - 16:22:13: 4%|▍ | 41/1000 [00:00<00:00, 3830.24 it/sec, feas=True, obj=5.73] INFO - 16:22:13: 4%|▍ | 42/1000 [00:00<00:00, 3832.75 it/sec, feas=True, obj=-0.0754] INFO - 16:22:13: 4%|▍ | 43/1000 [00:00<00:00, 3832.61 it/sec, feas=True, obj=5.56] INFO - 16:22:13: 4%|▍ | 44/1000 [00:00<00:00, 3837.10 it/sec, feas=True, obj=5.03] INFO - 16:22:13: 4%|▍ | 45/1000 [00:00<00:00, 3834.31 it/sec, feas=True, obj=7.21] INFO - 16:22:13: 5%|▍ | 46/1000 [00:00<00:00, 3836.89 it/sec, feas=True, obj=8.03] INFO - 16:22:13: 5%|▍ | 47/1000 [00:00<00:00, 3841.61 it/sec, feas=True, obj=5.56] INFO - 16:22:13: 5%|▍ | 48/1000 [00:00<00:00, 3847.10 it/sec, feas=True, obj=6.35] INFO - 16:22:13: 5%|▍ | 49/1000 [00:00<00:00, 3845.11 it/sec, feas=True, obj=6.71] INFO - 16:22:13: 5%|▌ | 50/1000 [00:00<00:00, 3846.29 it/sec, feas=True, obj=3.52] INFO - 16:22:13: 5%|▌ | 51/1000 [00:00<00:00, 3845.43 it/sec, feas=True, obj=2.63] INFO - 16:22:13: 5%|▌ | 52/1000 [00:00<00:00, 3847.51 it/sec, feas=True, obj=4.68] INFO - 16:22:13: 5%|▌ | 53/1000 [00:00<00:00, 3842.53 it/sec, feas=True, obj=1.07] INFO - 16:22:13: 5%|▌ | 54/1000 [00:00<00:00, 3845.11 it/sec, feas=True, obj=10.3] INFO - 16:22:13: 6%|▌ | 55/1000 [00:00<00:00, 3848.76 it/sec, feas=True, obj=6.87] INFO - 16:22:13: 6%|▌ | 56/1000 [00:00<00:00, 3847.54 it/sec, feas=True, obj=-3.82] INFO - 16:22:13: 6%|▌ | 57/1000 [00:00<00:00, 3846.69 it/sec, feas=True, obj=-1.58] INFO - 16:22:13: 6%|▌ | 58/1000 [00:00<00:00, 3849.93 it/sec, feas=True, obj=3.43] INFO - 16:22:13: 6%|▌ | 59/1000 [00:00<00:00, 3853.26 it/sec, feas=True, obj=-6.44] INFO - 16:22:13: 6%|▌ | 60/1000 [00:00<00:00, 3852.58 it/sec, feas=True, obj=0.167] INFO - 16:22:13: 6%|▌ | 61/1000 [00:00<00:00, 3854.71 it/sec, feas=True, obj=2.98] INFO - 16:22:13: 6%|▌ | 62/1000 [00:00<00:00, 3855.80 it/sec, feas=True, obj=0.771] INFO - 16:22:13: 6%|▋ | 63/1000 [00:00<00:00, 3854.55 it/sec, feas=True, obj=6.98] INFO - 16:22:13: 6%|▋ | 64/1000 [00:00<00:00, 3850.86 it/sec, feas=True, obj=6.81] INFO - 16:22:13: 6%|▋ | 65/1000 [00:00<00:00, 3851.52 it/sec, feas=True, obj=0.257] INFO - 16:22:13: 7%|▋ | 66/1000 [00:00<00:00, 3851.25 it/sec, feas=True, obj=3.31] INFO - 16:22:13: 7%|▋ | 67/1000 [00:00<00:00, 3853.68 it/sec, feas=True, obj=6.07] INFO - 16:22:13: 7%|▋ | 68/1000 [00:00<00:00, 3852.82 it/sec, feas=True, obj=5.87] INFO - 16:22:13: 7%|▋ | 69/1000 [00:00<00:00, 3853.98 it/sec, feas=True, obj=7.69] INFO - 16:22:13: 7%|▋ | 70/1000 [00:00<00:00, 3856.98 it/sec, feas=True, obj=5.16] INFO - 16:22:13: 7%|▋ | 71/1000 [00:00<00:00, 3860.26 it/sec, feas=True, obj=-0.0811] INFO - 16:22:13: 7%|▋ | 72/1000 [00:00<00:00, 3859.20 it/sec, feas=True, obj=1.25] INFO - 16:22:13: 7%|▋ | 73/1000 [00:00<00:00, 3861.43 it/sec, feas=True, obj=5.71] INFO - 16:22:13: 7%|▋ | 74/1000 [00:00<00:00, 3864.08 it/sec, feas=True, obj=8.15] INFO - 16:22:13: 8%|▊ | 75/1000 [00:00<00:00, 3866.38 it/sec, feas=True, obj=-2.86] INFO - 16:22:13: 8%|▊ | 76/1000 [00:00<00:00, 3865.01 it/sec, feas=True, obj=10.5] INFO - 16:22:13: 8%|▊ | 77/1000 [00:00<00:00, 3866.83 it/sec, feas=True, obj=4.87] INFO - 16:22:13: 8%|▊ | 78/1000 [00:00<00:00, 3870.02 it/sec, feas=True, obj=2.44] INFO - 16:22:13: 8%|▊ | 79/1000 [00:00<00:00, 3873.26 it/sec, feas=True, obj=6.74] INFO - 16:22:13: 8%|▊ | 80/1000 [00:00<00:00, 3871.83 it/sec, feas=True, obj=8.51] INFO - 16:22:13: 8%|▊ | 81/1000 [00:00<00:00, 3873.12 it/sec, feas=True, obj=0.595] INFO - 16:22:13: 8%|▊ | 82/1000 [00:00<00:00, 3872.94 it/sec, feas=True, obj=7.84] INFO - 16:22:13: 8%|▊ | 83/1000 [00:00<00:00, 3875.10 it/sec, feas=True, obj=0.842] INFO - 16:22:13: 8%|▊ | 84/1000 [00:00<00:00, 3873.88 it/sec, feas=True, obj=9.47] INFO - 16:22:13: 8%|▊ | 85/1000 [00:00<00:00, 3875.55 it/sec, feas=True, obj=4.54] INFO - 16:22:13: 9%|▊ | 86/1000 [00:00<00:00, 3877.60 it/sec, feas=True, obj=-3.69] INFO - 16:22:13: 9%|▊ | 87/1000 [00:00<00:00, 3879.73 it/sec, feas=True, obj=0.849] INFO - 16:22:13: 9%|▉ | 88/1000 [00:00<00:00, 3877.94 it/sec, feas=True, obj=11.9] INFO - 16:22:13: 9%|▉ | 89/1000 [00:00<00:00, 3879.26 it/sec, feas=True, obj=5.69] INFO - 16:22:13: 9%|▉ | 90/1000 [00:00<00:00, 3881.18 it/sec, feas=True, obj=7.25] INFO - 16:22:13: 9%|▉ | 91/1000 [00:00<00:00, 3882.86 it/sec, feas=True, obj=2.17] INFO - 16:22:13: 9%|▉ | 92/1000 [00:00<00:00, 3881.66 it/sec, feas=True, obj=5.76] INFO - 16:22:13: 9%|▉ | 93/1000 [00:00<00:00, 3882.80 it/sec, feas=True, obj=5.32] INFO - 16:22:13: 9%|▉ | 94/1000 [00:00<00:00, 3884.88 it/sec, feas=True, obj=-3.15] INFO - 16:22:13: 10%|▉ | 95/1000 [00:00<00:00, 3886.80 it/sec, feas=True, obj=9.36] INFO - 16:22:13: 10%|▉ | 96/1000 [00:00<00:00, 3884.89 it/sec, feas=True, obj=11.9] INFO - 16:22:13: 10%|▉ | 97/1000 [00:00<00:00, 3886.14 it/sec, feas=True, obj=8.76] INFO - 16:22:13: 10%|▉ | 98/1000 [00:00<00:00, 3886.37 it/sec, feas=True, obj=-0.112] INFO - 16:22:13: 10%|▉ | 99/1000 [00:00<00:00, 3887.47 it/sec, feas=True, obj=1.61] INFO - 16:22:13: 10%|█ | 100/1000 [00:00<00:00, 3885.34 it/sec, feas=True, obj=4.05] INFO - 16:22:13: 10%|█ | 101/1000 [00:00<00:00, 3886.22 it/sec, feas=True, obj=-0.31] INFO - 16:22:13: 10%|█ | 102/1000 [00:00<00:00, 3887.89 it/sec, feas=True, obj=1.46] INFO - 16:22:13: 10%|█ | 103/1000 [00:00<00:00, 3889.70 it/sec, feas=True, obj=1.1] INFO - 16:22:13: 10%|█ | 104/1000 [00:00<00:00, 3887.56 it/sec, feas=True, obj=2.69] INFO - 16:22:13: 10%|█ | 105/1000 [00:00<00:00, 3888.52 it/sec, feas=True, obj=7.7] INFO - 16:22:13: 11%|█ | 106/1000 [00:00<00:00, 3888.88 it/sec, feas=True, obj=4.86] INFO - 16:22:13: 11%|█ | 107/1000 [00:00<00:00, 3890.11 it/sec, feas=True, obj=-0.104] INFO - 16:22:13: 11%|█ | 108/1000 [00:00<00:00, 3888.78 it/sec, feas=True, obj=-7.95] INFO - 16:22:13: 11%|█ | 109/1000 [00:00<00:00, 3890.36 it/sec, feas=True, obj=0.11] INFO - 16:22:13: 11%|█ | 110/1000 [00:00<00:00, 3892.23 it/sec, feas=True, obj=-0.471] INFO - 16:22:13: 11%|█ | 111/1000 [00:00<00:00, 3894.04 it/sec, feas=True, obj=-0.843] INFO - 16:22:13: 11%|█ | 112/1000 [00:00<00:00, 3892.01 it/sec, feas=True, obj=3.99] INFO - 16:22:13: 11%|█▏ | 113/1000 [00:00<00:00, 3893.82 it/sec, feas=True, obj=5.95] INFO - 16:22:13: 11%|█▏ | 114/1000 [00:00<00:00, 3893.80 it/sec, feas=True, obj=6.56] INFO - 16:22:13: 12%|█▏ | 115/1000 [00:00<00:00, 3895.56 it/sec, feas=True, obj=6.04] INFO - 16:22:13: 12%|█▏ | 116/1000 [00:00<00:00, 3893.62 it/sec, feas=True, obj=0.26] INFO - 16:22:13: 12%|█▏ | 117/1000 [00:00<00:00, 3895.36 it/sec, feas=True, obj=5.43] INFO - 16:22:13: 12%|█▏ | 118/1000 [00:00<00:00, 3897.04 it/sec, feas=True, obj=0.706] INFO - 16:22:13: 12%|█▏ | 119/1000 [00:00<00:00, 3895.47 it/sec, feas=True, obj=-0.861] INFO - 16:22:13: 12%|█▏ | 120/1000 [00:00<00:00, 3895.07 it/sec, feas=True, obj=5.7] INFO - 16:22:13: 12%|█▏ | 121/1000 [00:00<00:00, 3896.20 it/sec, feas=True, obj=3.13] INFO - 16:22:13: 12%|█▏ | 122/1000 [00:00<00:00, 3897.73 it/sec, feas=True, obj=2.51] INFO - 16:22:13: 12%|█▏ | 123/1000 [00:00<00:00, 3897.05 it/sec, feas=True, obj=0.0431] INFO - 16:22:13: 12%|█▏ | 124/1000 [00:00<00:00, 3897.15 it/sec, feas=True, obj=4.08] INFO - 16:22:13: 12%|█▎ | 125/1000 [00:00<00:00, 3898.31 it/sec, feas=True, obj=3.48] INFO - 16:22:13: 13%|█▎ | 126/1000 [00:00<00:00, 3899.75 it/sec, feas=True, obj=-0.27] INFO - 16:22:13: 13%|█▎ | 127/1000 [00:00<00:00, 3899.28 it/sec, feas=True, obj=6.25] INFO - 16:22:13: 13%|█▎ | 128/1000 [00:00<00:00, 3898.45 it/sec, feas=True, obj=9.11] INFO - 16:22:13: 13%|█▎ | 129/1000 [00:00<00:00, 3897.77 it/sec, feas=True, obj=-0.828] INFO - 16:22:13: 13%|█▎ | 130/1000 [00:00<00:00, 3898.36 it/sec, feas=True, obj=10.3] INFO - 16:22:13: 13%|█▎ | 131/1000 [00:00<00:00, 3897.06 it/sec, feas=True, obj=3.98] INFO - 16:22:13: 13%|█▎ | 132/1000 [00:00<00:00, 3897.26 it/sec, feas=True, obj=2.27] INFO - 16:22:13: 13%|█▎ | 133/1000 [00:00<00:00, 3897.92 it/sec, feas=True, obj=1.6] INFO - 16:22:13: 13%|█▎ | 134/1000 [00:00<00:00, 3898.70 it/sec, feas=True, obj=-7.13] INFO - 16:22:13: 14%|█▎ | 135/1000 [00:00<00:00, 3897.46 it/sec, feas=True, obj=7.82] INFO - 16:22:13: 14%|█▎ | 136/1000 [00:00<00:00, 3897.97 it/sec, feas=True, obj=4.68] INFO - 16:22:13: 14%|█▎ | 137/1000 [00:00<00:00, 3898.90 it/sec, feas=True, obj=-0.627] INFO - 16:22:13: 14%|█▍ | 138/1000 [00:00<00:00, 3899.68 it/sec, feas=True, obj=-4.07] INFO - 16:22:13: 14%|█▍ | 139/1000 [00:00<00:00, 3898.42 it/sec, feas=True, obj=1.06] INFO - 16:22:13: 14%|█▍ | 140/1000 [00:00<00:00, 3898.80 it/sec, feas=True, obj=11.1] INFO - 16:22:13: 14%|█▍ | 141/1000 [00:00<00:00, 3899.70 it/sec, feas=True, obj=1.87] INFO - 16:22:13: 14%|█▍ | 142/1000 [00:00<00:00, 3900.35 it/sec, feas=True, obj=7.07] INFO - 16:22:13: 14%|█▍ | 143/1000 [00:00<00:00, 3899.24 it/sec, feas=True, obj=1.79] INFO - 16:22:13: 14%|█▍ | 144/1000 [00:00<00:00, 3898.98 it/sec, feas=True, obj=5.97] INFO - 16:22:13: 14%|█▍ | 145/1000 [00:00<00:00, 3886.72 it/sec, feas=True, obj=6.15] INFO - 16:22:13: 15%|█▍ | 146/1000 [00:00<00:00, 3884.35 it/sec, feas=True, obj=4.61] INFO - 16:22:13: 15%|█▍ | 147/1000 [00:00<00:00, 3883.86 it/sec, feas=True, obj=0.433] INFO - 16:22:13: 15%|█▍ | 148/1000 [00:00<00:00, 3884.20 it/sec, feas=True, obj=2.73] INFO - 16:22:13: 15%|█▍ | 149/1000 [00:00<00:00, 3885.16 it/sec, feas=True, obj=1.83] INFO - 16:22:13: 15%|█▌ | 150/1000 [00:00<00:00, 3884.12 it/sec, feas=True, obj=5.3] INFO - 16:22:13: 15%|█▌ | 151/1000 [00:00<00:00, 3884.64 it/sec, feas=True, obj=-0.935] INFO - 16:22:13: 15%|█▌ | 152/1000 [00:00<00:00, 3884.89 it/sec, feas=True, obj=7.03] INFO - 16:22:13: 15%|█▌ | 153/1000 [00:00<00:00, 3884.95 it/sec, feas=True, obj=4.91] INFO - 16:22:13: 15%|█▌ | 154/1000 [00:00<00:00, 3883.90 it/sec, feas=True, obj=5.89] INFO - 16:22:13: 16%|█▌ | 155/1000 [00:00<00:00, 3884.68 it/sec, feas=True, obj=-1.07] INFO - 16:22:13: 16%|█▌ | 156/1000 [00:00<00:00, 3884.65 it/sec, feas=True, obj=2.05] INFO - 16:22:13: 16%|█▌ | 157/1000 [00:00<00:00, 3884.71 it/sec, feas=True, obj=9.08] INFO - 16:22:13: 16%|█▌ | 158/1000 [00:00<00:00, 3882.86 it/sec, feas=True, obj=1.28] INFO - 16:22:13: 16%|█▌ | 159/1000 [00:00<00:00, 3883.00 it/sec, feas=True, obj=5.5] INFO - 16:22:13: 16%|█▌ | 160/1000 [00:00<00:00, 3882.22 it/sec, feas=True, obj=2.86] INFO - 16:22:13: 16%|█▌ | 161/1000 [00:00<00:00, 3882.30 it/sec, feas=True, obj=2.58] INFO - 16:22:13: 16%|█▌ | 162/1000 [00:00<00:00, 3881.17 it/sec, feas=True, obj=6.35] INFO - 16:22:13: 16%|█▋ | 163/1000 [00:00<00:00, 3881.87 it/sec, feas=True, obj=5.03] INFO - 16:22:13: 16%|█▋ | 164/1000 [00:00<00:00, 3882.72 it/sec, feas=True, obj=4.89] INFO - 16:22:13: 16%|█▋ | 165/1000 [00:00<00:00, 3883.75 it/sec, feas=True, obj=-0.862] INFO - 16:22:13: 17%|█▋ | 166/1000 [00:00<00:00, 3882.27 it/sec, feas=True, obj=5.17] INFO - 16:22:13: 17%|█▋ | 167/1000 [00:00<00:00, 3882.73 it/sec, feas=True, obj=6.54] INFO - 16:22:13: 17%|█▋ | 168/1000 [00:00<00:00, 3883.14 it/sec, feas=True, obj=5.04] INFO - 16:22:13: 17%|█▋ | 169/1000 [00:00<00:00, 3883.83 it/sec, feas=True, obj=5.18] INFO - 16:22:13: 17%|█▋ | 170/1000 [00:00<00:00, 3882.56 it/sec, feas=True, obj=9.72] INFO - 16:22:13: 17%|█▋ | 171/1000 [00:00<00:00, 3883.40 it/sec, feas=True, obj=4.51] INFO - 16:22:13: 17%|█▋ | 172/1000 [00:00<00:00, 3884.64 it/sec, feas=True, obj=5.25] INFO - 16:22:13: 17%|█▋ | 173/1000 [00:00<00:00, 3885.76 it/sec, feas=True, obj=7.58] INFO - 16:22:13: 17%|█▋ | 174/1000 [00:00<00:00, 3883.84 it/sec, feas=True, obj=-0.152] INFO - 16:22:13: 18%|█▊ | 175/1000 [00:00<00:00, 3884.54 it/sec, feas=True, obj=0.707] INFO - 16:22:13: 18%|█▊ | 176/1000 [00:00<00:00, 3884.25 it/sec, feas=True, obj=1.95] INFO - 16:22:13: 18%|█▊ | 177/1000 [00:00<00:00, 3883.49 it/sec, feas=True, obj=5.37] INFO - 16:22:13: 18%|█▊ | 178/1000 [00:00<00:00, 3883.92 it/sec, feas=True, obj=9.3] INFO - 16:22:13: 18%|█▊ | 179/1000 [00:00<00:00, 3884.54 it/sec, feas=True, obj=-6.59] INFO - 16:22:13: 18%|█▊ | 180/1000 [00:00<00:00, 3885.21 it/sec, feas=True, obj=0.62] INFO - 16:22:13: 18%|█▊ | 181/1000 [00:00<00:00, 3884.67 it/sec, feas=True, obj=2.86] INFO - 16:22:13: 18%|█▊ | 182/1000 [00:00<00:00, 3885.12 it/sec, feas=True, obj=7.64] INFO - 16:22:13: 18%|█▊ | 183/1000 [00:00<00:00, 3886.03 it/sec, feas=True, obj=1.83] INFO - 16:22:13: 18%|█▊ | 184/1000 [00:00<00:00, 3887.14 it/sec, feas=True, obj=1.15] INFO - 16:22:13: 18%|█▊ | 185/1000 [00:00<00:00, 3886.71 it/sec, feas=True, obj=4.53] INFO - 16:22:13: 19%|█▊ | 186/1000 [00:00<00:00, 3887.02 it/sec, feas=True, obj=5.86] INFO - 16:22:13: 19%|█▊ | 187/1000 [00:00<00:00, 3887.93 it/sec, feas=True, obj=-4.15] INFO - 16:22:13: 19%|█▉ | 188/1000 [00:00<00:00, 3888.54 it/sec, feas=True, obj=0.77] INFO - 16:22:13: 19%|█▉ | 189/1000 [00:00<00:00, 3887.58 it/sec, feas=True, obj=3.77] INFO - 16:22:13: 19%|█▉ | 190/1000 [00:00<00:00, 3888.09 it/sec, feas=True, obj=0.574] INFO - 16:22:13: 19%|█▉ | 191/1000 [00:00<00:00, 3887.36 it/sec, feas=True, obj=3.27] INFO - 16:22:13: 19%|█▉ | 192/1000 [00:00<00:00, 3887.95 it/sec, feas=True, obj=1.31] INFO - 16:22:13: 19%|█▉ | 193/1000 [00:00<00:00, 3887.21 it/sec, feas=True, obj=2.11] INFO - 16:22:13: 19%|█▉ | 194/1000 [00:00<00:00, 3887.81 it/sec, feas=True, obj=-0.324] INFO - 16:22:13: 20%|█▉ | 195/1000 [00:00<00:00, 3888.43 it/sec, feas=True, obj=2.6] INFO - 16:22:13: 20%|█▉ | 196/1000 [00:00<00:00, 3889.51 it/sec, feas=True, obj=7.25] INFO - 16:22:13: 20%|█▉ | 197/1000 [00:00<00:00, 3889.10 it/sec, feas=True, obj=12.9] INFO - 16:22:13: 20%|█▉ | 198/1000 [00:00<00:00, 3889.60 it/sec, feas=True, obj=-1.67] INFO - 16:22:13: 20%|█▉ | 199/1000 [00:00<00:00, 3890.42 it/sec, feas=True, obj=6.19] INFO - 16:22:13: 20%|██ | 200/1000 [00:00<00:00, 3891.04 it/sec, feas=True, obj=3.52] INFO - 16:22:13: 20%|██ | 201/1000 [00:00<00:00, 3890.48 it/sec, feas=True, obj=-1.49] INFO - 16:22:13: 20%|██ | 202/1000 [00:00<00:00, 3890.93 it/sec, feas=True, obj=7.77] INFO - 16:22:13: 20%|██ | 203/1000 [00:00<00:00, 3891.67 it/sec, feas=True, obj=0.847] INFO - 16:22:13: 20%|██ | 204/1000 [00:00<00:00, 3892.38 it/sec, feas=True, obj=-2.82] INFO - 16:22:13: 20%|██ | 205/1000 [00:00<00:00, 3891.74 it/sec, feas=True, obj=6.59] INFO - 16:22:13: 21%|██ | 206/1000 [00:00<00:00, 3891.96 it/sec, feas=True, obj=4.35] INFO - 16:22:13: 21%|██ | 207/1000 [00:00<00:00, 3891.92 it/sec, feas=True, obj=-1.95] INFO - 16:22:13: 21%|██ | 208/1000 [00:00<00:00, 3892.40 it/sec, feas=True, obj=-0.845] INFO - 16:22:13: 21%|██ | 209/1000 [00:00<00:00, 3891.48 it/sec, feas=True, obj=7.19] INFO - 16:22:13: 21%|██ | 210/1000 [00:00<00:00, 3891.92 it/sec, feas=True, obj=-0.108] INFO - 16:22:13: 21%|██ | 211/1000 [00:00<00:00, 3892.63 it/sec, feas=True, obj=1.42] INFO - 16:22:13: 21%|██ | 212/1000 [00:00<00:00, 3893.41 it/sec, feas=True, obj=0.785] INFO - 16:22:13: 21%|██▏ | 213/1000 [00:00<00:00, 3892.60 it/sec, feas=True, obj=-4.97] INFO - 16:22:13: 21%|██▏ | 214/1000 [00:00<00:00, 3893.18 it/sec, feas=True, obj=7.43] INFO - 16:22:13: 22%|██▏ | 215/1000 [00:00<00:00, 3894.00 it/sec, feas=True, obj=6.26] INFO - 16:22:13: 22%|██▏ | 216/1000 [00:00<00:00, 3894.87 it/sec, feas=True, obj=2.43] INFO - 16:22:13: 22%|██▏ | 217/1000 [00:00<00:00, 3894.38 it/sec, feas=True, obj=11.5] INFO - 16:22:13: 22%|██▏ | 218/1000 [00:00<00:00, 3895.15 it/sec, feas=True, obj=3.62] INFO - 16:22:13: 22%|██▏ | 219/1000 [00:00<00:00, 3896.02 it/sec, feas=True, obj=5.45] INFO - 16:22:13: 22%|██▏ | 220/1000 [00:00<00:00, 3896.69 it/sec, feas=True, obj=8.42] INFO - 16:22:13: 22%|██▏ | 221/1000 [00:00<00:00, 3895.87 it/sec, feas=True, obj=10.5] INFO - 16:22:13: 22%|██▏ | 222/1000 [00:00<00:00, 3896.50 it/sec, feas=True, obj=4.04] INFO - 16:22:13: 22%|██▏ | 223/1000 [00:00<00:00, 3896.43 it/sec, feas=True, obj=-1.33] INFO - 16:22:13: 22%|██▏ | 224/1000 [00:00<00:00, 3897.08 it/sec, feas=True, obj=5.55] INFO - 16:22:13: 22%|██▎ | 225/1000 [00:00<00:00, 3896.41 it/sec, feas=True, obj=-0.71] INFO - 16:22:13: 23%|██▎ | 226/1000 [00:00<00:00, 3896.56 it/sec, feas=True, obj=2.84] INFO - 16:22:13: 23%|██▎ | 227/1000 [00:00<00:00, 3897.30 it/sec, feas=True, obj=1.75] INFO - 16:22:13: 23%|██▎ | 228/1000 [00:00<00:00, 3897.96 it/sec, feas=True, obj=1.36] INFO - 16:22:13: 23%|██▎ | 229/1000 [00:00<00:00, 3897.12 it/sec, feas=True, obj=6.32] INFO - 16:22:13: 23%|██▎ | 230/1000 [00:00<00:00, 3897.78 it/sec, feas=True, obj=6.66] INFO - 16:22:13: 23%|██▎ | 231/1000 [00:00<00:00, 3897.71 it/sec, feas=True, obj=5.61] INFO - 16:22:13: 23%|██▎ | 232/1000 [00:00<00:00, 3897.90 it/sec, feas=True, obj=7.2] INFO - 16:22:13: 23%|██▎ | 233/1000 [00:00<00:00, 3896.84 it/sec, feas=True, obj=6.4] INFO - 16:22:13: 23%|██▎ | 234/1000 [00:00<00:00, 3897.12 it/sec, feas=True, obj=0.753] INFO - 16:22:13: 24%|██▎ | 235/1000 [00:00<00:00, 3897.65 it/sec, feas=True, obj=-0.835] INFO - 16:22:13: 24%|██▎ | 236/1000 [00:00<00:00, 3897.22 it/sec, feas=True, obj=-0.324] INFO - 16:22:13: 24%|██▎ | 237/1000 [00:00<00:00, 3897.18 it/sec, feas=True, obj=3.91] INFO - 16:22:13: 24%|██▍ | 238/1000 [00:00<00:00, 3897.70 it/sec, feas=True, obj=6.01] INFO - 16:22:13: 24%|██▍ | 239/1000 [00:00<00:00, 3897.20 it/sec, feas=True, obj=0.2] INFO - 16:22:13: 24%|██▍ | 240/1000 [00:00<00:00, 3896.48 it/sec, feas=True, obj=1.91] INFO - 16:22:13: 24%|██▍ | 241/1000 [00:00<00:00, 3896.55 it/sec, feas=True, obj=5.1] INFO - 16:22:13: 24%|██▍ | 242/1000 [00:00<00:00, 3896.99 it/sec, feas=True, obj=5.55] INFO - 16:22:13: 24%|██▍ | 243/1000 [00:00<00:00, 3897.22 it/sec, feas=True, obj=3.32] INFO - 16:22:13: 24%|██▍ | 244/1000 [00:00<00:00, 3896.38 it/sec, feas=True, obj=8.57] INFO - 16:22:13: 24%|██▍ | 245/1000 [00:00<00:00, 3896.53 it/sec, feas=True, obj=6.32] INFO - 16:22:13: 25%|██▍ | 246/1000 [00:00<00:00, 3897.15 it/sec, feas=True, obj=-2.16] INFO - 16:22:13: 25%|██▍ | 247/1000 [00:00<00:00, 3897.13 it/sec, feas=True, obj=3.75] INFO - 16:22:13: 25%|██▍ | 248/1000 [00:00<00:00, 3894.53 it/sec, feas=True, obj=2.64] INFO - 16:22:13: 25%|██▍ | 249/1000 [00:00<00:00, 3893.55 it/sec, feas=True, obj=0.181] INFO - 16:22:13: 25%|██▌ | 250/1000 [00:00<00:00, 3893.58 it/sec, feas=True, obj=-6.31] INFO - 16:22:13: 25%|██▌ | 251/1000 [00:00<00:00, 3893.60 it/sec, feas=True, obj=5.01] INFO - 16:22:13: 25%|██▌ | 252/1000 [00:00<00:00, 3892.50 it/sec, feas=True, obj=8.03] INFO - 16:22:13: 25%|██▌ | 253/1000 [00:00<00:00, 3892.50 it/sec, feas=True, obj=9.01] INFO - 16:22:13: 25%|██▌ | 254/1000 [00:00<00:00, 3892.41 it/sec, feas=True, obj=7.08] INFO - 16:22:13: 26%|██▌ | 255/1000 [00:00<00:00, 3893.06 it/sec, feas=True, obj=-0.461] INFO - 16:22:13: 26%|██▌ | 256/1000 [00:00<00:00, 3891.86 it/sec, feas=True, obj=-4.41] INFO - 16:22:13: 26%|██▌ | 257/1000 [00:00<00:00, 3892.21 it/sec, feas=True, obj=-0.98] INFO - 16:22:13: 26%|██▌ | 258/1000 [00:00<00:00, 3892.26 it/sec, feas=True, obj=7.88] INFO - 16:22:13: 26%|██▌ | 259/1000 [00:00<00:00, 3885.24 it/sec, feas=True, obj=5.1] INFO - 16:22:13: 26%|██▌ | 260/1000 [00:00<00:00, 3884.98 it/sec, feas=True, obj=6.42] INFO - 16:22:13: 26%|██▌ | 261/1000 [00:00<00:00, 3884.99 it/sec, feas=True, obj=2.42] INFO - 16:22:13: 26%|██▌ | 262/1000 [00:00<00:00, 3885.29 it/sec, feas=True, obj=6.49] INFO - 16:22:13: 26%|██▋ | 263/1000 [00:00<00:00, 3884.72 it/sec, feas=True, obj=-0.699] INFO - 16:22:13: 26%|██▋ | 264/1000 [00:00<00:00, 3884.77 it/sec, feas=True, obj=0.137] INFO - 16:22:13: 26%|██▋ | 265/1000 [00:00<00:00, 3884.99 it/sec, feas=True, obj=9.9] INFO - 16:22:13: 27%|██▋ | 266/1000 [00:00<00:00, 3885.29 it/sec, feas=True, obj=3.71] INFO - 16:22:13: 27%|██▋ | 267/1000 [00:00<00:00, 3884.46 it/sec, feas=True, obj=6.84] INFO - 16:22:13: 27%|██▋ | 268/1000 [00:00<00:00, 3884.86 it/sec, feas=True, obj=1.21] INFO - 16:22:13: 27%|██▋ | 269/1000 [00:00<00:00, 3885.01 it/sec, feas=True, obj=1.64] INFO - 16:22:13: 27%|██▋ | 270/1000 [00:00<00:00, 3885.31 it/sec, feas=True, obj=2.2] INFO - 16:22:13: 27%|██▋ | 271/1000 [00:00<00:00, 3884.12 it/sec, feas=True, obj=14.3] INFO - 16:22:13: 27%|██▋ | 272/1000 [00:00<00:00, 3884.42 it/sec, feas=True, obj=1.03] INFO - 16:22:13: 27%|██▋ | 273/1000 [00:00<00:00, 3884.73 it/sec, feas=True, obj=6.25] INFO - 16:22:13: 27%|██▋ | 274/1000 [00:00<00:00, 3885.26 it/sec, feas=True, obj=3.31] INFO - 16:22:13: 28%|██▊ | 275/1000 [00:00<00:00, 3884.26 it/sec, feas=True, obj=7.43] INFO - 16:22:13: 28%|██▊ | 276/1000 [00:00<00:00, 3884.62 it/sec, feas=True, obj=4.7] INFO - 16:22:13: 28%|██▊ | 277/1000 [00:00<00:00, 3885.25 it/sec, feas=True, obj=-0.0123] INFO - 16:22:13: 28%|██▊ | 278/1000 [00:00<00:00, 3885.04 it/sec, feas=True, obj=10.9] INFO - 16:22:13: 28%|██▊ | 279/1000 [00:00<00:00, 3885.16 it/sec, feas=True, obj=2.56] INFO - 16:22:13: 28%|██▊ | 280/1000 [00:00<00:00, 3885.41 it/sec, feas=True, obj=5.48] INFO - 16:22:13: 28%|██▊ | 281/1000 [00:00<00:00, 3885.79 it/sec, feas=True, obj=2.51] INFO - 16:22:13: 28%|██▊ | 282/1000 [00:00<00:00, 3885.17 it/sec, feas=True, obj=5.13] INFO - 16:22:13: 28%|██▊ | 283/1000 [00:00<00:00, 3885.03 it/sec, feas=True, obj=4.89] INFO - 16:22:13: 28%|██▊ | 284/1000 [00:00<00:00, 3884.92 it/sec, feas=True, obj=7.69] INFO - 16:22:13: 28%|██▊ | 285/1000 [00:00<00:00, 3885.29 it/sec, feas=True, obj=3.1] INFO - 16:22:13: 29%|██▊ | 286/1000 [00:00<00:00, 3884.78 it/sec, feas=True, obj=-5.11] INFO - 16:22:13: 29%|██▊ | 287/1000 [00:00<00:00, 3884.83 it/sec, feas=True, obj=-0.0286] INFO - 16:22:13: 29%|██▉ | 288/1000 [00:00<00:00, 3885.06 it/sec, feas=True, obj=1.41] INFO - 16:22:13: 29%|██▉ | 289/1000 [00:00<00:00, 3885.57 it/sec, feas=True, obj=5.79] INFO - 16:22:13: 29%|██▉ | 290/1000 [00:00<00:00, 3884.69 it/sec, feas=True, obj=4.71] INFO - 16:22:13: 29%|██▉ | 291/1000 [00:00<00:00, 3884.55 it/sec, feas=True, obj=6.49] INFO - 16:22:13: 29%|██▉ | 292/1000 [00:00<00:00, 3884.86 it/sec, feas=True, obj=-7.67] INFO - 16:22:13: 29%|██▉ | 293/1000 [00:00<00:00, 3885.21 it/sec, feas=True, obj=-0.721] INFO - 16:22:13: 29%|██▉ | 294/1000 [00:00<00:00, 3884.43 it/sec, feas=True, obj=7.54] INFO - 16:22:13: 30%|██▉ | 295/1000 [00:00<00:00, 3884.65 it/sec, feas=True, obj=6.14] INFO - 16:22:13: 30%|██▉ | 296/1000 [00:00<00:00, 3884.98 it/sec, feas=True, obj=-1.73] INFO - 16:22:13: 30%|██▉ | 297/1000 [00:00<00:00, 3885.38 it/sec, feas=True, obj=8.22] INFO - 16:22:13: 30%|██▉ | 298/1000 [00:00<00:00, 3884.79 it/sec, feas=True, obj=6.34] INFO - 16:22:13: 30%|██▉ | 299/1000 [00:00<00:00, 3885.26 it/sec, feas=True, obj=6.14] INFO - 16:22:13: 30%|███ | 300/1000 [00:00<00:00, 3885.21 it/sec, feas=True, obj=4.71] INFO - 16:22:13: 30%|███ | 301/1000 [00:00<00:00, 3885.63 it/sec, feas=True, obj=4] INFO - 16:22:13: 30%|███ | 302/1000 [00:00<00:00, 3885.03 it/sec, feas=True, obj=6.52] INFO - 16:22:13: 30%|███ | 303/1000 [00:00<00:00, 3885.40 it/sec, feas=True, obj=0.7] INFO - 16:22:13: 30%|███ | 304/1000 [00:00<00:00, 3885.88 it/sec, feas=True, obj=5.21] INFO - 16:22:13: 30%|███ | 305/1000 [00:00<00:00, 3886.53 it/sec, feas=True, obj=2.51] INFO - 16:22:13: 31%|███ | 306/1000 [00:00<00:00, 3885.64 it/sec, feas=True, obj=-0.162] INFO - 16:22:13: 31%|███ | 307/1000 [00:00<00:00, 3885.84 it/sec, feas=True, obj=2.63] INFO - 16:22:13: 31%|███ | 308/1000 [00:00<00:00, 3886.21 it/sec, feas=True, obj=4.01] INFO - 16:22:13: 31%|███ | 309/1000 [00:00<00:00, 3886.82 it/sec, feas=True, obj=2.99] INFO - 16:22:13: 31%|███ | 310/1000 [00:00<00:00, 3886.21 it/sec, feas=True, obj=2.3] INFO - 16:22:13: 31%|███ | 311/1000 [00:00<00:00, 3886.52 it/sec, feas=True, obj=3.71] INFO - 16:22:13: 31%|███ | 312/1000 [00:00<00:00, 3887.09 it/sec, feas=True, obj=5.63] INFO - 16:22:13: 31%|███▏ | 313/1000 [00:00<00:00, 3887.77 it/sec, feas=True, obj=6.43] INFO - 16:22:13: 31%|███▏ | 314/1000 [00:00<00:00, 3887.11 it/sec, feas=True, obj=-1.98] INFO - 16:22:13: 32%|███▏ | 315/1000 [00:00<00:00, 3887.57 it/sec, feas=True, obj=1.03] INFO - 16:22:13: 32%|███▏ | 316/1000 [00:00<00:00, 3887.58 it/sec, feas=True, obj=-0.511] INFO - 16:22:13: 32%|███▏ | 317/1000 [00:00<00:00, 3888.09 it/sec, feas=True, obj=-1.34] INFO - 16:22:13: 32%|███▏ | 318/1000 [00:00<00:00, 3887.36 it/sec, feas=True, obj=6.72] INFO - 16:22:13: 32%|███▏ | 319/1000 [00:00<00:00, 3887.68 it/sec, feas=True, obj=3.09] INFO - 16:22:13: 32%|███▏ | 320/1000 [00:00<00:00, 3888.17 it/sec, feas=True, obj=7.12] INFO - 16:22:13: 32%|███▏ | 321/1000 [00:00<00:00, 3888.01 it/sec, feas=True, obj=5.91] INFO - 16:22:13: 32%|███▏ | 322/1000 [00:00<00:00, 3888.00 it/sec, feas=True, obj=0.0303] INFO - 16:22:13: 32%|███▏ | 323/1000 [00:00<00:00, 3888.55 it/sec, feas=True, obj=1.38] INFO - 16:22:13: 32%|███▏ | 324/1000 [00:00<00:00, 3889.06 it/sec, feas=True, obj=-5.06] INFO - 16:22:13: 32%|███▎ | 325/1000 [00:00<00:00, 3888.96 it/sec, feas=True, obj=1.18] INFO - 16:22:13: 33%|███▎ | 326/1000 [00:00<00:00, 3889.09 it/sec, feas=True, obj=0.213] INFO - 16:22:13: 33%|███▎ | 327/1000 [00:00<00:00, 3889.52 it/sec, feas=True, obj=5.4] INFO - 16:22:13: 33%|███▎ | 328/1000 [00:00<00:00, 3890.04 it/sec, feas=True, obj=3.09] INFO - 16:22:13: 33%|███▎ | 329/1000 [00:00<00:00, 3889.76 it/sec, feas=True, obj=1.28] INFO - 16:22:13: 33%|███▎ | 330/1000 [00:00<00:00, 3890.04 it/sec, feas=True, obj=7.37] INFO - 16:22:13: 33%|███▎ | 331/1000 [00:00<00:00, 3890.13 it/sec, feas=True, obj=1.31] INFO - 16:22:13: 33%|███▎ | 332/1000 [00:00<00:00, 3890.59 it/sec, feas=True, obj=2.05] INFO - 16:22:13: 33%|███▎ | 333/1000 [00:00<00:00, 3890.39 it/sec, feas=True, obj=1.55] INFO - 16:22:13: 33%|███▎ | 334/1000 [00:00<00:00, 3890.67 it/sec, feas=True, obj=2.46] INFO - 16:22:13: 34%|███▎ | 335/1000 [00:00<00:00, 3891.26 it/sec, feas=True, obj=1.51] INFO - 16:22:13: 34%|███▎ | 336/1000 [00:00<00:00, 3891.84 it/sec, feas=True, obj=5.43] INFO - 16:22:13: 34%|███▎ | 337/1000 [00:00<00:00, 3891.74 it/sec, feas=True, obj=1.14] INFO - 16:22:13: 34%|███▍ | 338/1000 [00:00<00:00, 3891.81 it/sec, feas=True, obj=7.29] INFO - 16:22:13: 34%|███▍ | 339/1000 [00:00<00:00, 3892.23 it/sec, feas=True, obj=-0.283] INFO - 16:22:13: 34%|███▍ | 340/1000 [00:00<00:00, 3892.65 it/sec, feas=True, obj=0.734] INFO - 16:22:13: 34%|███▍ | 341/1000 [00:00<00:00, 3892.25 it/sec, feas=True, obj=-3.46] INFO - 16:22:13: 34%|███▍ | 342/1000 [00:00<00:00, 3892.22 it/sec, feas=True, obj=4.12] INFO - 16:22:13: 34%|███▍ | 343/1000 [00:00<00:00, 3892.66 it/sec, feas=True, obj=3.79] INFO - 16:22:13: 34%|███▍ | 344/1000 [00:00<00:00, 3893.04 it/sec, feas=True, obj=-3.15] INFO - 16:22:13: 34%|███▍ | 345/1000 [00:00<00:00, 3892.66 it/sec, feas=True, obj=7.56] INFO - 16:22:13: 35%|███▍ | 346/1000 [00:00<00:00, 3892.77 it/sec, feas=True, obj=-0.553] INFO - 16:22:13: 35%|███▍ | 347/1000 [00:00<00:00, 3893.00 it/sec, feas=True, obj=1.43] INFO - 16:22:13: 35%|███▍ | 348/1000 [00:00<00:00, 3893.36 it/sec, feas=True, obj=-0.851] INFO - 16:22:13: 35%|███▍ | 349/1000 [00:00<00:00, 3892.82 it/sec, feas=True, obj=8.57] INFO - 16:22:13: 35%|███▌ | 350/1000 [00:00<00:00, 3892.98 it/sec, feas=True, obj=0.921] INFO - 16:22:13: 35%|███▌ | 351/1000 [00:00<00:00, 3893.41 it/sec, feas=True, obj=3.01] INFO - 16:22:13: 35%|███▌ | 352/1000 [00:00<00:00, 3893.83 it/sec, feas=True, obj=4.98] INFO - 16:22:13: 35%|███▌ | 353/1000 [00:00<00:00, 3893.43 it/sec, feas=True, obj=6.06] INFO - 16:22:13: 35%|███▌ | 354/1000 [00:00<00:00, 3893.56 it/sec, feas=True, obj=7.26] INFO - 16:22:13: 36%|███▌ | 355/1000 [00:00<00:00, 3893.75 it/sec, feas=True, obj=5.71] INFO - 16:22:13: 36%|███▌ | 356/1000 [00:00<00:00, 3893.99 it/sec, feas=True, obj=-5.07] INFO - 16:22:13: 36%|███▌ | 357/1000 [00:00<00:00, 3893.55 it/sec, feas=True, obj=6.62] INFO - 16:22:13: 36%|███▌ | 358/1000 [00:00<00:00, 3893.70 it/sec, feas=True, obj=5.86] INFO - 16:22:13: 36%|███▌ | 359/1000 [00:00<00:00, 3894.15 it/sec, feas=True, obj=6.1] INFO - 16:22:13: 36%|███▌ | 360/1000 [00:00<00:00, 3894.66 it/sec, feas=True, obj=-0.545] INFO - 16:22:13: 36%|███▌ | 361/1000 [00:00<00:00, 3894.24 it/sec, feas=True, obj=3.86] INFO - 16:22:13: 36%|███▌ | 362/1000 [00:00<00:00, 3894.45 it/sec, feas=True, obj=8.51] INFO - 16:22:13: 36%|███▋ | 363/1000 [00:00<00:00, 3894.13 it/sec, feas=True, obj=5.33] INFO - 16:22:13: 36%|███▋ | 364/1000 [00:00<00:00, 3894.33 it/sec, feas=True, obj=7.14] INFO - 16:22:13: 36%|███▋ | 365/1000 [00:00<00:00, 3893.73 it/sec, feas=True, obj=4.01] INFO - 16:22:13: 37%|███▋ | 366/1000 [00:00<00:00, 3893.83 it/sec, feas=True, obj=2.9] INFO - 16:22:13: 37%|███▋ | 367/1000 [00:00<00:00, 3893.96 it/sec, feas=True, obj=6.25] INFO - 16:22:13: 37%|███▋ | 368/1000 [00:00<00:00, 3894.18 it/sec, feas=True, obj=6.85] INFO - 16:22:13: 37%|███▋ | 369/1000 [00:00<00:00, 3893.08 it/sec, feas=True, obj=4.32] INFO - 16:22:13: 37%|███▋ | 370/1000 [00:00<00:00, 3893.29 it/sec, feas=True, obj=4.87] INFO - 16:22:13: 37%|███▋ | 371/1000 [00:00<00:00, 3893.68 it/sec, feas=True, obj=6.43] INFO - 16:22:13: 37%|███▋ | 372/1000 [00:00<00:00, 3893.95 it/sec, feas=True, obj=2.86] INFO - 16:22:13: 37%|███▋ | 373/1000 [00:00<00:00, 3888.65 it/sec, feas=True, obj=0.891] INFO - 16:22:13: 37%|███▋ | 374/1000 [00:00<00:00, 3888.60 it/sec, feas=True, obj=6.47] INFO - 16:22:13: 38%|███▊ | 375/1000 [00:00<00:00, 3888.84 it/sec, feas=True, obj=-1.87] INFO - 16:22:13: 38%|███▊ | 376/1000 [00:00<00:00, 3888.32 it/sec, feas=True, obj=-3.28] INFO - 16:22:13: 38%|███▊ | 377/1000 [00:00<00:00, 3888.39 it/sec, feas=True, obj=0.0745] INFO - 16:22:13: 38%|███▊ | 378/1000 [00:00<00:00, 3888.32 it/sec, feas=True, obj=5.9] INFO - 16:22:13: 38%|███▊ | 379/1000 [00:00<00:00, 3888.59 it/sec, feas=True, obj=4.69] INFO - 16:22:13: 38%|███▊ | 380/1000 [00:00<00:00, 3887.69 it/sec, feas=True, obj=4.66] INFO - 16:22:13: 38%|███▊ | 381/1000 [00:00<00:00, 3887.87 it/sec, feas=True, obj=6.07] INFO - 16:22:13: 38%|███▊ | 382/1000 [00:00<00:00, 3888.02 it/sec, feas=True, obj=0.959] INFO - 16:22:13: 38%|███▊ | 383/1000 [00:00<00:00, 3888.32 it/sec, feas=True, obj=1.9] INFO - 16:22:13: 38%|███▊ | 384/1000 [00:00<00:00, 3887.71 it/sec, feas=True, obj=7.91] INFO - 16:22:13: 38%|███▊ | 385/1000 [00:00<00:00, 3887.99 it/sec, feas=True, obj=-0.448] INFO - 16:22:13: 39%|███▊ | 386/1000 [00:00<00:00, 3888.11 it/sec, feas=True, obj=5.33] INFO - 16:22:13: 39%|███▊ | 387/1000 [00:00<00:00, 3888.48 it/sec, feas=True, obj=2.88] INFO - 16:22:13: 39%|███▉ | 388/1000 [00:00<00:00, 3887.94 it/sec, feas=True, obj=0.55] INFO - 16:22:13: 39%|███▉ | 389/1000 [00:00<00:00, 3888.32 it/sec, feas=True, obj=0.392] INFO - 16:22:13: 39%|███▉ | 390/1000 [00:00<00:00, 3888.64 it/sec, feas=True, obj=3.32] INFO - 16:22:13: 39%|███▉ | 391/1000 [00:00<00:00, 3888.32 it/sec, feas=True, obj=7.88] INFO - 16:22:13: 39%|███▉ | 392/1000 [00:00<00:00, 3888.44 it/sec, feas=True, obj=1.46] INFO - 16:22:13: 39%|███▉ | 393/1000 [00:00<00:00, 3888.89 it/sec, feas=True, obj=9.49] INFO - 16:22:13: 39%|███▉ | 394/1000 [00:00<00:00, 3888.70 it/sec, feas=True, obj=-8.6] INFO - 16:22:13: 40%|███▉ | 395/1000 [00:00<00:00, 3888.36 it/sec, feas=True, obj=6] INFO - 16:22:13: 40%|███▉ | 396/1000 [00:00<00:00, 3888.29 it/sec, feas=True, obj=6.89] INFO - 16:22:13: 40%|███▉ | 397/1000 [00:00<00:00, 3888.56 it/sec, feas=True, obj=5.17] INFO - 16:22:13: 40%|███▉ | 398/1000 [00:00<00:00, 3888.44 it/sec, feas=True, obj=9.21] INFO - 16:22:13: 40%|███▉ | 399/1000 [00:00<00:00, 3887.97 it/sec, feas=True, obj=8.46] INFO - 16:22:13: 40%|████ | 400/1000 [00:00<00:00, 3888.00 it/sec, feas=True, obj=9.92] INFO - 16:22:13: 40%|████ | 401/1000 [00:00<00:00, 3888.36 it/sec, feas=True, obj=2.5] INFO - 16:22:13: 40%|████ | 402/1000 [00:00<00:00, 3888.72 it/sec, feas=True, obj=2.82] INFO - 16:22:13: 40%|████ | 403/1000 [00:00<00:00, 3888.14 it/sec, feas=True, obj=9.71] INFO - 16:22:13: 40%|████ | 404/1000 [00:00<00:00, 3888.26 it/sec, feas=True, obj=-1.54] INFO - 16:22:13: 40%|████ | 405/1000 [00:00<00:00, 3888.49 it/sec, feas=True, obj=-1.42] INFO - 16:22:13: 41%|████ | 406/1000 [00:00<00:00, 3888.63 it/sec, feas=True, obj=7.52] INFO - 16:22:13: 41%|████ | 407/1000 [00:00<00:00, 3888.06 it/sec, feas=True, obj=3.95] INFO - 16:22:13: 41%|████ | 408/1000 [00:00<00:00, 3888.09 it/sec, feas=True, obj=5.33] INFO - 16:22:13: 41%|████ | 409/1000 [00:00<00:00, 3887.99 it/sec, feas=True, obj=0.103] INFO - 16:22:13: 41%|████ | 410/1000 [00:00<00:00, 3888.24 it/sec, feas=True, obj=4.39] INFO - 16:22:13: 41%|████ | 411/1000 [00:00<00:00, 3886.81 it/sec, feas=True, obj=1.74] INFO - 16:22:13: 41%|████ | 412/1000 [00:00<00:00, 3885.60 it/sec, feas=True, obj=0.344] INFO - 16:22:13: 41%|████▏ | 413/1000 [00:00<00:00, 3885.65 it/sec, feas=True, obj=6.49] INFO - 16:22:13: 41%|████▏ | 414/1000 [00:00<00:00, 3885.30 it/sec, feas=True, obj=4.85] INFO - 16:22:13: 42%|████▏ | 415/1000 [00:00<00:00, 3885.21 it/sec, feas=True, obj=7.96] INFO - 16:22:13: 42%|████▏ | 416/1000 [00:00<00:00, 3885.42 it/sec, feas=True, obj=-3.84] INFO - 16:22:13: 42%|████▏ | 417/1000 [00:00<00:00, 3885.60 it/sec, feas=True, obj=-2.87] INFO - 16:22:13: 42%|████▏ | 418/1000 [00:00<00:00, 3885.24 it/sec, feas=True, obj=-1.69] INFO - 16:22:13: 42%|████▏ | 419/1000 [00:00<00:00, 3885.13 it/sec, feas=True, obj=2.32] INFO - 16:22:13: 42%|████▏ | 420/1000 [00:00<00:00, 3885.45 it/sec, feas=True, obj=8.32] INFO - 16:22:13: 42%|████▏ | 421/1000 [00:00<00:00, 3885.82 it/sec, feas=True, obj=1.74] INFO - 16:22:13: 42%|████▏ | 422/1000 [00:00<00:00, 3885.31 it/sec, feas=True, obj=4.05] INFO - 16:22:13: 42%|████▏ | 423/1000 [00:00<00:00, 3885.32 it/sec, feas=True, obj=2.71] INFO - 16:22:13: 42%|████▏ | 424/1000 [00:00<00:00, 3885.33 it/sec, feas=True, obj=6.78] INFO - 16:22:13: 42%|████▎ | 425/1000 [00:00<00:00, 3885.51 it/sec, feas=True, obj=3.95] INFO - 16:22:13: 43%|████▎ | 426/1000 [00:00<00:00, 3885.09 it/sec, feas=True, obj=-0.0526] INFO - 16:22:13: 43%|████▎ | 427/1000 [00:00<00:00, 3885.23 it/sec, feas=True, obj=-0.581] INFO - 16:22:13: 43%|████▎ | 428/1000 [00:00<00:00, 3885.59 it/sec, feas=True, obj=16.2] INFO - 16:22:13: 43%|████▎ | 429/1000 [00:00<00:00, 3886.01 it/sec, feas=True, obj=1.58] INFO - 16:22:13: 43%|████▎ | 430/1000 [00:00<00:00, 3885.67 it/sec, feas=True, obj=5.87] INFO - 16:22:13: 43%|████▎ | 431/1000 [00:00<00:00, 3885.93 it/sec, feas=True, obj=6.51] INFO - 16:22:13: 43%|████▎ | 432/1000 [00:00<00:00, 3886.28 it/sec, feas=True, obj=4.28] INFO - 16:22:13: 43%|████▎ | 433/1000 [00:00<00:00, 3886.60 it/sec, feas=True, obj=-6.3] INFO - 16:22:13: 43%|████▎ | 434/1000 [00:00<00:00, 3886.24 it/sec, feas=True, obj=7.49] INFO - 16:22:13: 44%|████▎ | 435/1000 [00:00<00:00, 3886.51 it/sec, feas=True, obj=8.03] INFO - 16:22:13: 44%|████▎ | 436/1000 [00:00<00:00, 3886.79 it/sec, feas=True, obj=1.4] INFO - 16:22:13: 44%|████▎ | 437/1000 [00:00<00:00, 3887.17 it/sec, feas=True, obj=1.65] INFO - 16:22:13: 44%|████▍ | 438/1000 [00:00<00:00, 3886.93 it/sec, feas=True, obj=-0.221] INFO - 16:22:13: 44%|████▍ | 439/1000 [00:00<00:00, 3887.19 it/sec, feas=True, obj=7.25] INFO - 16:22:13: 44%|████▍ | 440/1000 [00:00<00:00, 3887.20 it/sec, feas=True, obj=5.14] INFO - 16:22:13: 44%|████▍ | 441/1000 [00:00<00:00, 3887.54 it/sec, feas=True, obj=-0.896] INFO - 16:22:13: 44%|████▍ | 442/1000 [00:00<00:00, 3887.20 it/sec, feas=True, obj=-0.969] INFO - 16:22:13: 44%|████▍ | 443/1000 [00:00<00:00, 3887.44 it/sec, feas=True, obj=7.53] INFO - 16:22:13: 44%|████▍ | 444/1000 [00:00<00:00, 3887.87 it/sec, feas=True, obj=6.62] INFO - 16:22:13: 44%|████▍ | 445/1000 [00:00<00:00, 3887.98 it/sec, feas=True, obj=3.23] INFO - 16:22:13: 45%|████▍ | 446/1000 [00:00<00:00, 3887.33 it/sec, feas=True, obj=-10.1] INFO - 16:22:13: 45%|████▍ | 447/1000 [00:00<00:00, 3887.34 it/sec, feas=True, obj=7.22] INFO - 16:22:13: 45%|████▍ | 448/1000 [00:00<00:00, 3887.60 it/sec, feas=True, obj=12.9] INFO - 16:22:13: 45%|████▍ | 449/1000 [00:00<00:00, 3887.90 it/sec, feas=True, obj=7.61] INFO - 16:22:13: 45%|████▌ | 450/1000 [00:00<00:00, 3887.27 it/sec, feas=True, obj=3.57] INFO - 16:22:13: 45%|████▌ | 451/1000 [00:00<00:00, 3887.28 it/sec, feas=True, obj=5.91] INFO - 16:22:13: 45%|████▌ | 452/1000 [00:00<00:00, 3887.41 it/sec, feas=True, obj=-1.97] INFO - 16:22:13: 45%|████▌ | 453/1000 [00:00<00:00, 3887.74 it/sec, feas=True, obj=7.83] INFO - 16:22:13: 45%|████▌ | 454/1000 [00:00<00:00, 3887.25 it/sec, feas=True, obj=2.12] INFO - 16:22:13: 46%|████▌ | 455/1000 [00:00<00:00, 3887.56 it/sec, feas=True, obj=-0.821] INFO - 16:22:13: 46%|████▌ | 456/1000 [00:00<00:00, 3887.51 it/sec, feas=True, obj=2.27] INFO - 16:22:13: 46%|████▌ | 457/1000 [00:00<00:00, 3887.25 it/sec, feas=True, obj=7.13] INFO - 16:22:13: 46%|████▌ | 458/1000 [00:00<00:00, 3886.75 it/sec, feas=True, obj=3.63] INFO - 16:22:13: 46%|████▌ | 459/1000 [00:00<00:00, 3886.45 it/sec, feas=True, obj=2.21] INFO - 16:22:13: 46%|████▌ | 460/1000 [00:00<00:00, 3886.42 it/sec, feas=True, obj=3.08] INFO - 16:22:13: 46%|████▌ | 461/1000 [00:00<00:00, 3885.96 it/sec, feas=True, obj=3.18] INFO - 16:22:13: 46%|████▌ | 462/1000 [00:00<00:00, 3885.95 it/sec, feas=True, obj=4.64] INFO - 16:22:13: 46%|████▋ | 463/1000 [00:00<00:00, 3886.09 it/sec, feas=True, obj=0.243] INFO - 16:22:13: 46%|████▋ | 464/1000 [00:00<00:00, 3886.04 it/sec, feas=True, obj=2.2] INFO - 16:22:13: 46%|████▋ | 465/1000 [00:00<00:00, 3885.68 it/sec, feas=True, obj=-0.0681] INFO - 16:22:13: 47%|████▋ | 466/1000 [00:00<00:00, 3885.75 it/sec, feas=True, obj=0.986] INFO - 16:22:13: 47%|████▋ | 467/1000 [00:00<00:00, 3885.95 it/sec, feas=True, obj=7.39] INFO - 16:22:13: 47%|████▋ | 468/1000 [00:00<00:00, 3886.25 it/sec, feas=True, obj=6.85] INFO - 16:22:13: 47%|████▋ | 469/1000 [00:00<00:00, 3885.92 it/sec, feas=True, obj=8.98] INFO - 16:22:13: 47%|████▋ | 470/1000 [00:00<00:00, 3885.96 it/sec, feas=True, obj=4.98] INFO - 16:22:13: 47%|████▋ | 471/1000 [00:00<00:00, 3885.83 it/sec, feas=True, obj=0.108] INFO - 16:22:13: 47%|████▋ | 472/1000 [00:00<00:00, 3886.01 it/sec, feas=True, obj=4.9] INFO - 16:22:13: 47%|████▋ | 473/1000 [00:00<00:00, 3885.34 it/sec, feas=True, obj=1.98] INFO - 16:22:13: 47%|████▋ | 474/1000 [00:00<00:00, 3885.31 it/sec, feas=True, obj=-3.79] INFO - 16:22:13: 48%|████▊ | 475/1000 [00:00<00:00, 3885.42 it/sec, feas=True, obj=13.5] INFO - 16:22:13: 48%|████▊ | 476/1000 [00:00<00:00, 3885.43 it/sec, feas=True, obj=0.587] INFO - 16:22:13: 48%|████▊ | 477/1000 [00:00<00:00, 3884.62 it/sec, feas=True, obj=5.28] INFO - 16:22:13: 48%|████▊ | 478/1000 [00:00<00:00, 3884.56 it/sec, feas=True, obj=6.02] INFO - 16:22:13: 48%|████▊ | 479/1000 [00:00<00:00, 3884.60 it/sec, feas=True, obj=2.5] INFO - 16:22:13: 48%|████▊ | 480/1000 [00:00<00:00, 3884.24 it/sec, feas=True, obj=-0.343] INFO - 16:22:13: 48%|████▊ | 481/1000 [00:00<00:00, 3884.06 it/sec, feas=True, obj=4.72] INFO - 16:22:13: 48%|████▊ | 482/1000 [00:00<00:00, 3884.22 it/sec, feas=True, obj=6.71] INFO - 16:22:13: 48%|████▊ | 483/1000 [00:00<00:00, 3884.26 it/sec, feas=True, obj=-2.87] INFO - 16:22:13: 48%|████▊ | 484/1000 [00:00<00:00, 3883.89 it/sec, feas=True, obj=1] INFO - 16:22:13: 48%|████▊ | 485/1000 [00:00<00:00, 3883.91 it/sec, feas=True, obj=6.11] INFO - 16:22:13: 49%|████▊ | 486/1000 [00:00<00:00, 3883.65 it/sec, feas=True, obj=0.946] INFO - 16:22:13: 49%|████▊ | 487/1000 [00:00<00:00, 3880.33 it/sec, feas=True, obj=2.29] INFO - 16:22:13: 49%|████▉ | 488/1000 [00:00<00:00, 3879.66 it/sec, feas=True, obj=9.42] INFO - 16:22:13: 49%|████▉ | 489/1000 [00:00<00:00, 3879.90 it/sec, feas=True, obj=5.01] INFO - 16:22:13: 49%|████▉ | 490/1000 [00:00<00:00, 3880.12 it/sec, feas=True, obj=1.02] INFO - 16:22:13: 49%|████▉ | 491/1000 [00:00<00:00, 3880.47 it/sec, feas=True, obj=3.59] INFO - 16:22:13: 49%|████▉ | 492/1000 [00:00<00:00, 3879.92 it/sec, feas=True, obj=7.01] INFO - 16:22:13: 49%|████▉ | 493/1000 [00:00<00:00, 3880.18 it/sec, feas=True, obj=8.7] INFO - 16:22:13: 49%|████▉ | 494/1000 [00:00<00:00, 3880.33 it/sec, feas=True, obj=5.6] INFO - 16:22:13: 50%|████▉ | 495/1000 [00:00<00:00, 3879.99 it/sec, feas=True, obj=-0.897] INFO - 16:22:13: 50%|████▉ | 496/1000 [00:00<00:00, 3879.90 it/sec, feas=True, obj=7.82] INFO - 16:22:13: 50%|████▉ | 497/1000 [00:00<00:00, 3880.16 it/sec, feas=True, obj=6.63] INFO - 16:22:13: 50%|████▉ | 498/1000 [00:00<00:00, 3880.45 it/sec, feas=True, obj=3.33] INFO - 16:22:13: 50%|████▉ | 499/1000 [00:00<00:00, 3880.19 it/sec, feas=True, obj=4.36] INFO - 16:22:13: 50%|█████ | 500/1000 [00:00<00:00, 3880.05 it/sec, feas=True, obj=4.21] INFO - 16:22:13: 50%|█████ | 501/1000 [00:00<00:00, 3879.86 it/sec, feas=True, obj=3.93] INFO - 16:22:13: 50%|█████ | 502/1000 [00:00<00:00, 3879.44 it/sec, feas=True, obj=10.4] INFO - 16:22:13: 50%|█████ | 503/1000 [00:00<00:00, 3879.02 it/sec, feas=True, obj=-1.39] INFO - 16:22:13: 50%|█████ | 504/1000 [00:00<00:00, 3879.10 it/sec, feas=True, obj=-0.386] INFO - 16:22:13: 50%|█████ | 505/1000 [00:00<00:00, 3879.27 it/sec, feas=True, obj=4.95] INFO - 16:22:13: 51%|█████ | 506/1000 [00:00<00:00, 3879.36 it/sec, feas=True, obj=4.8] INFO - 16:22:13: 51%|█████ | 507/1000 [00:00<00:00, 3879.05 it/sec, feas=True, obj=7.94] INFO - 16:22:13: 51%|█████ | 508/1000 [00:00<00:00, 3879.10 it/sec, feas=True, obj=1.6] INFO - 16:22:13: 51%|█████ | 509/1000 [00:00<00:00, 3879.27 it/sec, feas=True, obj=8.91] INFO - 16:22:13: 51%|█████ | 510/1000 [00:00<00:00, 3879.52 it/sec, feas=True, obj=9.47] INFO - 16:22:13: 51%|█████ | 511/1000 [00:00<00:00, 3879.07 it/sec, feas=True, obj=-0.535] INFO - 16:22:13: 51%|█████ | 512/1000 [00:00<00:00, 3879.27 it/sec, feas=True, obj=2.76] INFO - 16:22:13: 51%|█████▏ | 513/1000 [00:00<00:00, 3879.48 it/sec, feas=True, obj=0.439] INFO - 16:22:13: 51%|█████▏ | 514/1000 [00:00<00:00, 3879.74 it/sec, feas=True, obj=3.69] INFO - 16:22:13: 52%|█████▏ | 515/1000 [00:00<00:00, 3879.08 it/sec, feas=True, obj=-1.1] INFO - 16:22:13: 52%|█████▏ | 516/1000 [00:00<00:00, 3879.16 it/sec, feas=True, obj=2.48] INFO - 16:22:13: 52%|█████▏ | 517/1000 [00:00<00:00, 3879.11 it/sec, feas=True, obj=2.8] INFO - 16:22:13: 52%|█████▏ | 518/1000 [00:00<00:00, 3879.37 it/sec, feas=True, obj=13] INFO - 16:22:13: 52%|█████▏ | 519/1000 [00:00<00:00, 3879.01 it/sec, feas=True, obj=6.01] INFO - 16:22:13: 52%|█████▏ | 520/1000 [00:00<00:00, 3879.21 it/sec, feas=True, obj=2.49] INFO - 16:22:13: 52%|█████▏ | 521/1000 [00:00<00:00, 3879.40 it/sec, feas=True, obj=5.92] INFO - 16:22:13: 52%|█████▏ | 522/1000 [00:00<00:00, 3879.25 it/sec, feas=True, obj=3.4] INFO - 16:22:13: 52%|█████▏ | 523/1000 [00:00<00:00, 3879.24 it/sec, feas=True, obj=-1.78] INFO - 16:22:13: 52%|█████▏ | 524/1000 [00:00<00:00, 3879.45 it/sec, feas=True, obj=2.44] INFO - 16:22:13: 52%|█████▎ | 525/1000 [00:00<00:00, 3879.59 it/sec, feas=True, obj=16] INFO - 16:22:13: 53%|█████▎ | 526/1000 [00:00<00:00, 3879.26 it/sec, feas=True, obj=6.22] INFO - 16:22:13: 53%|█████▎ | 527/1000 [00:00<00:00, 3879.28 it/sec, feas=True, obj=7.2] INFO - 16:22:13: 53%|█████▎ | 528/1000 [00:00<00:00, 3879.40 it/sec, feas=True, obj=4.57] INFO - 16:22:13: 53%|█████▎ | 529/1000 [00:00<00:00, 3879.60 it/sec, feas=True, obj=6.77] INFO - 16:22:13: 53%|█████▎ | 530/1000 [00:00<00:00, 3879.24 it/sec, feas=True, obj=13] INFO - 16:22:13: 53%|█████▎ | 531/1000 [00:00<00:00, 3879.31 it/sec, feas=True, obj=5] INFO - 16:22:13: 53%|█████▎ | 532/1000 [00:00<00:00, 3879.01 it/sec, feas=True, obj=-0.711] INFO - 16:22:13: 53%|█████▎ | 533/1000 [00:00<00:00, 3879.03 it/sec, feas=True, obj=-0.543] INFO - 16:22:13: 53%|█████▎ | 534/1000 [00:00<00:00, 3878.52 it/sec, feas=True, obj=0.469] INFO - 16:22:13: 54%|█████▎ | 535/1000 [00:00<00:00, 3878.41 it/sec, feas=True, obj=4.16] INFO - 16:22:13: 54%|█████▎ | 536/1000 [00:00<00:00, 3878.47 it/sec, feas=True, obj=4.73] INFO - 16:22:13: 54%|█████▎ | 537/1000 [00:00<00:00, 3878.61 it/sec, feas=True, obj=-0.197] INFO - 16:22:13: 54%|█████▍ | 538/1000 [00:00<00:00, 3878.17 it/sec, feas=True, obj=-2.45] INFO - 16:22:13: 54%|█████▍ | 539/1000 [00:00<00:00, 3878.34 it/sec, feas=True, obj=2.9] INFO - 16:22:13: 54%|█████▍ | 540/1000 [00:00<00:00, 3878.56 it/sec, feas=True, obj=4.59] INFO - 16:22:13: 54%|█████▍ | 541/1000 [00:00<00:00, 3878.73 it/sec, feas=True, obj=4.09] INFO - 16:22:13: 54%|█████▍ | 542/1000 [00:00<00:00, 3878.30 it/sec, feas=True, obj=0.0786] INFO - 16:22:13: 54%|█████▍ | 543/1000 [00:00<00:00, 3878.43 it/sec, feas=True, obj=6.9] INFO - 16:22:13: 54%|█████▍ | 544/1000 [00:00<00:00, 3878.57 it/sec, feas=True, obj=3.77] INFO - 16:22:13: 55%|█████▍ | 545/1000 [00:00<00:00, 3878.78 it/sec, feas=True, obj=2.68] INFO - 16:22:13: 55%|█████▍ | 546/1000 [00:00<00:00, 3878.32 it/sec, feas=True, obj=5.03] INFO - 16:22:13: 55%|█████▍ | 547/1000 [00:00<00:00, 3878.56 it/sec, feas=True, obj=7.02] INFO - 16:22:13: 55%|█████▍ | 548/1000 [00:00<00:00, 3878.57 it/sec, feas=True, obj=7] INFO - 16:22:13: 55%|█████▍ | 549/1000 [00:00<00:00, 3878.39 it/sec, feas=True, obj=1.03] INFO - 16:22:13: 55%|█████▌ | 550/1000 [00:00<00:00, 3878.33 it/sec, feas=True, obj=4.74] INFO - 16:22:13: 55%|█████▌ | 551/1000 [00:00<00:00, 3878.47 it/sec, feas=True, obj=-0.817] INFO - 16:22:13: 55%|█████▌ | 552/1000 [00:00<00:00, 3878.72 it/sec, feas=True, obj=2.59] INFO - 16:22:13: 55%|█████▌ | 553/1000 [00:00<00:00, 3878.50 it/sec, feas=True, obj=3.33] INFO - 16:22:13: 55%|█████▌ | 554/1000 [00:00<00:00, 3878.56 it/sec, feas=True, obj=2.13] INFO - 16:22:13: 56%|█████▌ | 555/1000 [00:00<00:00, 3878.78 it/sec, feas=True, obj=-0.076] INFO - 16:22:13: 56%|█████▌ | 556/1000 [00:00<00:00, 3878.96 it/sec, feas=True, obj=-0.023] INFO - 16:22:13: 56%|█████▌ | 557/1000 [00:00<00:00, 3878.66 it/sec, feas=True, obj=7.03] INFO - 16:22:13: 56%|█████▌ | 558/1000 [00:00<00:00, 3878.69 it/sec, feas=True, obj=3.4] INFO - 16:22:13: 56%|█████▌ | 559/1000 [00:00<00:00, 3878.87 it/sec, feas=True, obj=-1.23] INFO - 16:22:13: 56%|█████▌ | 560/1000 [00:00<00:00, 3879.09 it/sec, feas=True, obj=7.3] INFO - 16:22:13: 56%|█████▌ | 561/1000 [00:00<00:00, 3878.76 it/sec, feas=True, obj=4.59] INFO - 16:22:13: 56%|█████▌ | 562/1000 [00:00<00:00, 3878.75 it/sec, feas=True, obj=-0.53] INFO - 16:22:13: 56%|█████▋ | 563/1000 [00:00<00:00, 3878.66 it/sec, feas=True, obj=7.24] INFO - 16:22:13: 56%|█████▋ | 564/1000 [00:00<00:00, 3878.88 it/sec, feas=True, obj=-0.753] INFO - 16:22:13: 56%|█████▋ | 565/1000 [00:00<00:00, 3878.66 it/sec, feas=True, obj=7.78] INFO - 16:22:13: 57%|█████▋ | 566/1000 [00:00<00:00, 3878.77 it/sec, feas=True, obj=6.64] INFO - 16:22:13: 57%|█████▋ | 567/1000 [00:00<00:00, 3878.86 it/sec, feas=True, obj=0.671] INFO - 16:22:13: 57%|█████▋ | 568/1000 [00:00<00:00, 3879.13 it/sec, feas=True, obj=-2] INFO - 16:22:13: 57%|█████▋ | 569/1000 [00:00<00:00, 3878.77 it/sec, feas=True, obj=-1.92] INFO - 16:22:13: 57%|█████▋ | 570/1000 [00:00<00:00, 3878.96 it/sec, feas=True, obj=6.03] INFO - 16:22:13: 57%|█████▋ | 571/1000 [00:00<00:00, 3879.28 it/sec, feas=True, obj=9.42] INFO - 16:22:13: 57%|█████▋ | 572/1000 [00:00<00:00, 3879.57 it/sec, feas=True, obj=1.01] INFO - 16:22:13: 57%|█████▋ | 573/1000 [00:00<00:00, 3879.27 it/sec, feas=True, obj=1.43] INFO - 16:22:13: 57%|█████▋ | 574/1000 [00:00<00:00, 3879.40 it/sec, feas=True, obj=0.0646] INFO - 16:22:13: 57%|█████▊ | 575/1000 [00:00<00:00, 3879.66 it/sec, feas=True, obj=5.16] INFO - 16:22:13: 58%|█████▊ | 576/1000 [00:00<00:00, 3879.90 it/sec, feas=True, obj=1.61] INFO - 16:22:13: 58%|█████▊ | 577/1000 [00:00<00:00, 3879.44 it/sec, feas=True, obj=0.944] INFO - 16:22:13: 58%|█████▊ | 578/1000 [00:00<00:00, 3879.54 it/sec, feas=True, obj=0.535] INFO - 16:22:13: 58%|█████▊ | 579/1000 [00:00<00:00, 3879.49 it/sec, feas=True, obj=1.86] INFO - 16:22:13: 58%|█████▊ | 580/1000 [00:00<00:00, 3879.64 it/sec, feas=True, obj=2.93] INFO - 16:22:13: 58%|█████▊ | 581/1000 [00:00<00:00, 3879.23 it/sec, feas=True, obj=2.4] INFO - 16:22:13: 58%|█████▊ | 582/1000 [00:00<00:00, 3879.41 it/sec, feas=True, obj=6.58] INFO - 16:22:13: 58%|█████▊ | 583/1000 [00:00<00:00, 3879.44 it/sec, feas=True, obj=-0.0337] INFO - 16:22:13: 58%|█████▊ | 584/1000 [00:00<00:00, 3879.62 it/sec, feas=True, obj=6.62] INFO - 16:22:13: 58%|█████▊ | 585/1000 [00:00<00:00, 3879.20 it/sec, feas=True, obj=5.61] INFO - 16:22:13: 59%|█████▊ | 586/1000 [00:00<00:00, 3879.33 it/sec, feas=True, obj=5.55] INFO - 16:22:13: 59%|█████▊ | 587/1000 [00:00<00:00, 3879.51 it/sec, feas=True, obj=5.28] INFO - 16:22:13: 59%|█████▉ | 588/1000 [00:00<00:00, 3879.07 it/sec, feas=True, obj=3.22] INFO - 16:22:13: 59%|█████▉ | 589/1000 [00:00<00:00, 3878.74 it/sec, feas=True, obj=3.1] INFO - 16:22:13: 59%|█████▉ | 590/1000 [00:00<00:00, 3878.77 it/sec, feas=True, obj=5.83] INFO - 16:22:13: 59%|█████▉ | 591/1000 [00:00<00:00, 3878.83 it/sec, feas=True, obj=4.03] INFO - 16:22:13: 59%|█████▉ | 592/1000 [00:00<00:00, 3878.39 it/sec, feas=True, obj=-3.08] INFO - 16:22:13: 59%|█████▉ | 593/1000 [00:00<00:00, 3878.33 it/sec, feas=True, obj=3.63] INFO - 16:22:13: 59%|█████▉ | 594/1000 [00:00<00:00, 3878.20 it/sec, feas=True, obj=0.374] INFO - 16:22:13: 60%|█████▉ | 595/1000 [00:00<00:00, 3878.27 it/sec, feas=True, obj=7.07] INFO - 16:22:13: 60%|█████▉ | 596/1000 [00:00<00:00, 3877.98 it/sec, feas=True, obj=0.707] INFO - 16:22:13: 60%|█████▉ | 597/1000 [00:00<00:00, 3855.27 it/sec, feas=True, obj=5.65] INFO - 16:22:13: 60%|█████▉ | 598/1000 [00:00<00:00, 3831.20 it/sec, feas=True, obj=5.83] INFO - 16:22:13: 60%|█████▉ | 599/1000 [00:00<00:00, 3831.20 it/sec, feas=True, obj=4.2] INFO - 16:22:13: 60%|██████ | 600/1000 [00:00<00:00, 3830.86 it/sec, feas=True, obj=-0.0744] INFO - 16:22:13: 60%|██████ | 601/1000 [00:00<00:00, 3827.98 it/sec, feas=True, obj=0.391] INFO - 16:22:13: 60%|██████ | 602/1000 [00:00<00:00, 3827.61 it/sec, feas=True, obj=4.96] INFO - 16:22:13: 60%|██████ | 603/1000 [00:00<00:00, 3827.20 it/sec, feas=True, obj=2.18] INFO - 16:22:13: 60%|██████ | 604/1000 [00:00<00:00, 3826.91 it/sec, feas=True, obj=1.55] INFO - 16:22:13: 60%|██████ | 605/1000 [00:00<00:00, 3826.84 it/sec, feas=True, obj=6.26] INFO - 16:22:13: 61%|██████ | 606/1000 [00:00<00:00, 3826.96 it/sec, feas=True, obj=5.3] INFO - 16:22:13: 61%|██████ | 607/1000 [00:00<00:00, 3826.68 it/sec, feas=True, obj=7.18] INFO - 16:22:13: 61%|██████ | 608/1000 [00:00<00:00, 3826.64 it/sec, feas=True, obj=1.45] INFO - 16:22:13: 61%|██████ | 609/1000 [00:00<00:00, 3826.84 it/sec, feas=True, obj=8.78] INFO - 16:22:13: 61%|██████ | 610/1000 [00:00<00:00, 3826.98 it/sec, feas=True, obj=0.233] INFO - 16:22:13: 61%|██████ | 611/1000 [00:00<00:00, 3826.66 it/sec, feas=True, obj=-1.38] INFO - 16:22:13: 61%|██████ | 612/1000 [00:00<00:00, 3826.70 it/sec, feas=True, obj=6.09] INFO - 16:22:13: 61%|██████▏ | 613/1000 [00:00<00:00, 3826.91 it/sec, feas=True, obj=5.58] INFO - 16:22:13: 61%|██████▏ | 614/1000 [00:00<00:00, 3827.18 it/sec, feas=True, obj=11] INFO - 16:22:13: 62%|██████▏ | 615/1000 [00:00<00:00, 3826.87 it/sec, feas=True, obj=5.03] INFO - 16:22:13: 62%|██████▏ | 616/1000 [00:00<00:00, 3826.73 it/sec, feas=True, obj=6.39] INFO - 16:22:13: 62%|██████▏ | 617/1000 [00:00<00:00, 3826.64 it/sec, feas=True, obj=1.92] INFO - 16:22:13: 62%|██████▏ | 618/1000 [00:00<00:00, 3826.86 it/sec, feas=True, obj=1.05] INFO - 16:22:13: 62%|██████▏ | 619/1000 [00:00<00:00, 3826.30 it/sec, feas=True, obj=0.0814] INFO - 16:22:13: 62%|██████▏ | 620/1000 [00:00<00:00, 3826.41 it/sec, feas=True, obj=5.88] INFO - 16:22:13: 62%|██████▏ | 621/1000 [00:00<00:00, 3826.62 it/sec, feas=True, obj=14.7] INFO - 16:22:13: 62%|██████▏ | 622/1000 [00:00<00:00, 3826.49 it/sec, feas=True, obj=4.25] INFO - 16:22:13: 62%|██████▏ | 623/1000 [00:00<00:00, 3826.52 it/sec, feas=True, obj=-1.9] INFO - 16:22:13: 62%|██████▏ | 624/1000 [00:00<00:00, 3826.75 it/sec, feas=True, obj=-0.304] INFO - 16:22:13: 62%|██████▎ | 625/1000 [00:00<00:00, 3827.01 it/sec, feas=True, obj=-0.315] INFO - 16:22:13: 63%|██████▎ | 626/1000 [00:00<00:00, 3826.74 it/sec, feas=True, obj=-0.772] INFO - 16:22:13: 63%|██████▎ | 627/1000 [00:00<00:00, 3826.67 it/sec, feas=True, obj=4.47] INFO - 16:22:13: 63%|██████▎ | 628/1000 [00:00<00:00, 3826.91 it/sec, feas=True, obj=3.87] INFO - 16:22:13: 63%|██████▎ | 629/1000 [00:00<00:00, 3827.17 it/sec, feas=True, obj=1.69] INFO - 16:22:13: 63%|██████▎ | 630/1000 [00:00<00:00, 3826.95 it/sec, feas=True, obj=14.2] INFO - 16:22:13: 63%|██████▎ | 631/1000 [00:00<00:00, 3826.83 it/sec, feas=True, obj=0.467] INFO - 16:22:13: 63%|██████▎ | 632/1000 [00:00<00:00, 3826.81 it/sec, feas=True, obj=0.13] INFO - 16:22:13: 63%|██████▎ | 633/1000 [00:00<00:00, 3826.86 it/sec, feas=True, obj=-0.788] INFO - 16:22:13: 63%|██████▎ | 634/1000 [00:00<00:00, 3826.60 it/sec, feas=True, obj=3.3] INFO - 16:22:13: 64%|██████▎ | 635/1000 [00:00<00:00, 3826.64 it/sec, feas=True, obj=7.29] INFO - 16:22:13: 64%|██████▎ | 636/1000 [00:00<00:00, 3826.65 it/sec, feas=True, obj=1.41] INFO - 16:22:13: 64%|██████▎ | 637/1000 [00:00<00:00, 3826.86 it/sec, feas=True, obj=6.16] INFO - 16:22:13: 64%|██████▍ | 638/1000 [00:00<00:00, 3826.63 it/sec, feas=True, obj=6.98] INFO - 16:22:13: 64%|██████▍ | 639/1000 [00:00<00:00, 3826.82 it/sec, feas=True, obj=7.82] INFO - 16:22:13: 64%|██████▍ | 640/1000 [00:00<00:00, 3827.11 it/sec, feas=True, obj=4.49] INFO - 16:22:13: 64%|██████▍ | 641/1000 [00:00<00:00, 3827.49 it/sec, feas=True, obj=6.84] INFO - 16:22:13: 64%|██████▍ | 642/1000 [00:00<00:00, 3827.25 it/sec, feas=True, obj=3.83] INFO - 16:22:13: 64%|██████▍ | 643/1000 [00:00<00:00, 3827.47 it/sec, feas=True, obj=2.52] INFO - 16:22:13: 64%|██████▍ | 644/1000 [00:00<00:00, 3827.57 it/sec, feas=True, obj=1] INFO - 16:22:13: 64%|██████▍ | 645/1000 [00:00<00:00, 3827.73 it/sec, feas=True, obj=3.54] INFO - 16:22:13: 65%|██████▍ | 646/1000 [00:00<00:00, 3827.44 it/sec, feas=True, obj=5.22] INFO - 16:22:13: 65%|██████▍ | 647/1000 [00:00<00:00, 3827.70 it/sec, feas=True, obj=7.98] INFO - 16:22:13: 65%|██████▍ | 648/1000 [00:00<00:00, 3827.73 it/sec, feas=True, obj=3.23] INFO - 16:22:13: 65%|██████▍ | 649/1000 [00:00<00:00, 3827.59 it/sec, feas=True, obj=2.61] INFO - 16:22:13: 65%|██████▌ | 650/1000 [00:00<00:00, 3827.66 it/sec, feas=True, obj=4.85] INFO - 16:22:13: 65%|██████▌ | 651/1000 [00:00<00:00, 3827.81 it/sec, feas=True, obj=1.4] INFO - 16:22:13: 65%|██████▌ | 652/1000 [00:00<00:00, 3828.05 it/sec, feas=True, obj=-0.857] INFO - 16:22:13: 65%|██████▌ | 653/1000 [00:00<00:00, 3827.91 it/sec, feas=True, obj=4.01] INFO - 16:22:13: 65%|██████▌ | 654/1000 [00:00<00:00, 3827.98 it/sec, feas=True, obj=6.3] INFO - 16:22:13: 66%|██████▌ | 655/1000 [00:00<00:00, 3828.20 it/sec, feas=True, obj=11.4] INFO - 16:22:13: 66%|██████▌ | 656/1000 [00:00<00:00, 3828.50 it/sec, feas=True, obj=5.47] INFO - 16:22:13: 66%|██████▌ | 657/1000 [00:00<00:00, 3828.30 it/sec, feas=True, obj=2.72] INFO - 16:22:13: 66%|██████▌ | 658/1000 [00:00<00:00, 3828.48 it/sec, feas=True, obj=3.85] INFO - 16:22:13: 66%|██████▌ | 659/1000 [00:00<00:00, 3828.67 it/sec, feas=True, obj=-0.392] INFO - 16:22:13: 66%|██████▌ | 660/1000 [00:00<00:00, 3828.96 it/sec, feas=True, obj=0.0168] INFO - 16:22:13: 66%|██████▌ | 661/1000 [00:00<00:00, 3828.84 it/sec, feas=True, obj=2.53] INFO - 16:22:13: 66%|██████▌ | 662/1000 [00:00<00:00, 3829.01 it/sec, feas=True, obj=1.76] INFO - 16:22:13: 66%|██████▋ | 663/1000 [00:00<00:00, 3829.01 it/sec, feas=True, obj=4.26] INFO - 16:22:13: 66%|██████▋ | 664/1000 [00:00<00:00, 3829.18 it/sec, feas=True, obj=5.45] INFO - 16:22:13: 66%|██████▋ | 665/1000 [00:00<00:00, 3829.14 it/sec, feas=True, obj=6.94] INFO - 16:22:13: 67%|██████▋ | 666/1000 [00:00<00:00, 3829.34 it/sec, feas=True, obj=5.93] INFO - 16:22:13: 67%|██████▋ | 667/1000 [00:00<00:00, 3829.53 it/sec, feas=True, obj=5.79] INFO - 16:22:13: 67%|██████▋ | 668/1000 [00:00<00:00, 3829.71 it/sec, feas=True, obj=2.62] INFO - 16:22:13: 67%|██████▋ | 669/1000 [00:00<00:00, 3829.53 it/sec, feas=True, obj=6.4] INFO - 16:22:13: 67%|██████▋ | 670/1000 [00:00<00:00, 3829.67 it/sec, feas=True, obj=-0.703] INFO - 16:22:13: 67%|██████▋ | 671/1000 [00:00<00:00, 3829.86 it/sec, feas=True, obj=8.61] INFO - 16:22:13: 67%|██████▋ | 672/1000 [00:00<00:00, 3830.07 it/sec, feas=True, obj=0.91] INFO - 16:22:13: 67%|██████▋ | 673/1000 [00:00<00:00, 3829.80 it/sec, feas=True, obj=1.05] INFO - 16:22:13: 67%|██████▋ | 674/1000 [00:00<00:00, 3829.92 it/sec, feas=True, obj=10.1] INFO - 16:22:13: 68%|██████▊ | 675/1000 [00:00<00:00, 3830.16 it/sec, feas=True, obj=-0.575] INFO - 16:22:13: 68%|██████▊ | 676/1000 [00:00<00:00, 3830.38 it/sec, feas=True, obj=-2.06] INFO - 16:22:13: 68%|██████▊ | 677/1000 [00:00<00:00, 3829.97 it/sec, feas=True, obj=7.34] INFO - 16:22:13: 68%|██████▊ | 678/1000 [00:00<00:00, 3830.07 it/sec, feas=True, obj=2.78] INFO - 16:22:13: 68%|██████▊ | 679/1000 [00:00<00:00, 3829.97 it/sec, feas=True, obj=1.15] INFO - 16:22:13: 68%|██████▊ | 680/1000 [00:00<00:00, 3829.82 it/sec, feas=True, obj=-0.227] INFO - 16:22:13: 68%|██████▊ | 681/1000 [00:00<00:00, 3829.77 it/sec, feas=True, obj=4.3] INFO - 16:22:13: 68%|██████▊ | 682/1000 [00:00<00:00, 3830.04 it/sec, feas=True, obj=6.14] INFO - 16:22:13: 68%|██████▊ | 683/1000 [00:00<00:00, 3830.03 it/sec, feas=True, obj=4.76] INFO - 16:22:13: 68%|██████▊ | 684/1000 [00:00<00:00, 3829.92 it/sec, feas=True, obj=-4.69] INFO - 16:22:13: 68%|██████▊ | 685/1000 [00:00<00:00, 3830.02 it/sec, feas=True, obj=-0.877] INFO - 16:22:13: 69%|██████▊ | 686/1000 [00:00<00:00, 3830.22 it/sec, feas=True, obj=3.02] INFO - 16:22:13: 69%|██████▊ | 687/1000 [00:00<00:00, 3830.51 it/sec, feas=True, obj=6.98] INFO - 16:22:13: 69%|██████▉ | 688/1000 [00:00<00:00, 3830.40 it/sec, feas=True, obj=4.88] INFO - 16:22:13: 69%|██████▉ | 689/1000 [00:00<00:00, 3830.56 it/sec, feas=True, obj=4.99] INFO - 16:22:13: 69%|██████▉ | 690/1000 [00:00<00:00, 3830.75 it/sec, feas=True, obj=9.72] INFO - 16:22:13: 69%|██████▉ | 691/1000 [00:00<00:00, 3830.99 it/sec, feas=True, obj=1.5] INFO - 16:22:13: 69%|██████▉ | 692/1000 [00:00<00:00, 3830.84 it/sec, feas=True, obj=5.57] INFO - 16:22:13: 69%|██████▉ | 693/1000 [00:00<00:00, 3830.92 it/sec, feas=True, obj=6.06] INFO - 16:22:13: 69%|██████▉ | 694/1000 [00:00<00:00, 3830.92 it/sec, feas=True, obj=1.09] INFO - 16:22:13: 70%|██████▉ | 695/1000 [00:00<00:00, 3831.16 it/sec, feas=True, obj=-1.97] INFO - 16:22:13: 70%|██████▉ | 696/1000 [00:00<00:00, 3831.04 it/sec, feas=True, obj=1.88] INFO - 16:22:13: 70%|██████▉ | 697/1000 [00:00<00:00, 3831.17 it/sec, feas=True, obj=9.32] INFO - 16:22:13: 70%|██████▉ | 698/1000 [00:00<00:00, 3831.35 it/sec, feas=True, obj=-7.7] INFO - 16:22:13: 70%|██████▉ | 699/1000 [00:00<00:00, 3831.32 it/sec, feas=True, obj=1.83] INFO - 16:22:13: 70%|███████ | 700/1000 [00:00<00:00, 3831.10 it/sec, feas=True, obj=0.735] INFO - 16:22:13: 70%|███████ | 701/1000 [00:00<00:00, 3831.29 it/sec, feas=True, obj=-1.11] INFO - 16:22:13: 70%|███████ | 702/1000 [00:00<00:00, 3831.54 it/sec, feas=True, obj=1.47] INFO - 16:22:13: 70%|███████ | 703/1000 [00:00<00:00, 3831.74 it/sec, feas=True, obj=0.283] INFO - 16:22:13: 70%|███████ | 704/1000 [00:00<00:00, 3831.42 it/sec, feas=True, obj=15.2] INFO - 16:22:13: 70%|███████ | 705/1000 [00:00<00:00, 3831.49 it/sec, feas=True, obj=3.43] INFO - 16:22:13: 71%|███████ | 706/1000 [00:00<00:00, 3831.66 it/sec, feas=True, obj=3.17] INFO - 16:22:13: 71%|███████ | 707/1000 [00:00<00:00, 3831.84 it/sec, feas=True, obj=5.95] INFO - 16:22:13: 71%|███████ | 708/1000 [00:00<00:00, 3831.57 it/sec, feas=True, obj=-6.33] INFO - 16:22:13: 71%|███████ | 709/1000 [00:00<00:00, 3831.85 it/sec, feas=True, obj=13.3] INFO - 16:22:13: 71%|███████ | 710/1000 [00:00<00:00, 3831.93 it/sec, feas=True, obj=1.32] INFO - 16:22:13: 71%|███████ | 711/1000 [00:00<00:00, 3832.21 it/sec, feas=True, obj=-4.3] INFO - 16:22:13: 71%|███████ | 712/1000 [00:00<00:00, 3831.97 it/sec, feas=True, obj=1.63] INFO - 16:22:13: 71%|███████▏ | 713/1000 [00:00<00:00, 3832.12 it/sec, feas=True, obj=1.99] INFO - 16:22:13: 71%|███████▏ | 714/1000 [00:00<00:00, 3832.39 it/sec, feas=True, obj=0.679] INFO - 16:22:13: 72%|███████▏ | 715/1000 [00:00<00:00, 3829.68 it/sec, feas=True, obj=-0.377] INFO - 16:22:13: 72%|███████▏ | 716/1000 [00:00<00:00, 3829.46 it/sec, feas=True, obj=-4.57] INFO - 16:22:13: 72%|███████▏ | 717/1000 [00:00<00:00, 3829.49 it/sec, feas=True, obj=2.1] INFO - 16:22:13: 72%|███████▏ | 718/1000 [00:00<00:00, 3829.63 it/sec, feas=True, obj=1.32] INFO - 16:22:13: 72%|███████▏ | 719/1000 [00:00<00:00, 3829.36 it/sec, feas=True, obj=0.312] INFO - 16:22:13: 72%|███████▏ | 720/1000 [00:00<00:00, 3829.52 it/sec, feas=True, obj=8.43] INFO - 16:22:13: 72%|███████▏ | 721/1000 [00:00<00:00, 3829.69 it/sec, feas=True, obj=0.579] INFO - 16:22:13: 72%|███████▏ | 722/1000 [00:00<00:00, 3829.89 it/sec, feas=True, obj=0.563] INFO - 16:22:13: 72%|███████▏ | 723/1000 [00:00<00:00, 3829.52 it/sec, feas=True, obj=3.4] INFO - 16:22:13: 72%|███████▏ | 724/1000 [00:00<00:00, 3829.77 it/sec, feas=True, obj=7.21] INFO - 16:22:13: 72%|███████▎ | 725/1000 [00:00<00:00, 3829.74 it/sec, feas=True, obj=5.26] INFO - 16:22:13: 73%|███████▎ | 726/1000 [00:00<00:00, 3829.51 it/sec, feas=True, obj=2.69] INFO - 16:22:13: 73%|███████▎ | 727/1000 [00:00<00:00, 3829.47 it/sec, feas=True, obj=5.31] INFO - 16:22:13: 73%|███████▎ | 728/1000 [00:00<00:00, 3829.57 it/sec, feas=True, obj=1.99] INFO - 16:22:13: 73%|███████▎ | 729/1000 [00:00<00:00, 3829.70 it/sec, feas=True, obj=-6.72] INFO - 16:22:13: 73%|███████▎ | 730/1000 [00:00<00:00, 3829.35 it/sec, feas=True, obj=0.526] INFO - 16:22:13: 73%|███████▎ | 731/1000 [00:00<00:00, 3829.40 it/sec, feas=True, obj=4.35] INFO - 16:22:13: 73%|███████▎ | 732/1000 [00:00<00:00, 3829.54 it/sec, feas=True, obj=8.27] INFO - 16:22:13: 73%|███████▎ | 733/1000 [00:00<00:00, 3829.75 it/sec, feas=True, obj=0.662] INFO - 16:22:13: 73%|███████▎ | 734/1000 [00:00<00:00, 3829.59 it/sec, feas=True, obj=8.9] INFO - 16:22:13: 74%|███████▎ | 735/1000 [00:00<00:00, 3829.65 it/sec, feas=True, obj=6.96] INFO - 16:22:13: 74%|███████▎ | 736/1000 [00:00<00:00, 3829.77 it/sec, feas=True, obj=1.11] INFO - 16:22:13: 74%|███████▎ | 737/1000 [00:00<00:00, 3829.98 it/sec, feas=True, obj=-4.5] INFO - 16:22:13: 74%|███████▍ | 738/1000 [00:00<00:00, 3829.70 it/sec, feas=True, obj=0.0495] INFO - 16:22:13: 74%|███████▍ | 739/1000 [00:00<00:00, 3829.77 it/sec, feas=True, obj=5.88] INFO - 16:22:13: 74%|███████▍ | 740/1000 [00:00<00:00, 3829.76 it/sec, feas=True, obj=13.2] INFO - 16:22:13: 74%|███████▍ | 741/1000 [00:00<00:00, 3829.96 it/sec, feas=True, obj=2.3] INFO - 16:22:13: 74%|███████▍ | 742/1000 [00:00<00:00, 3829.67 it/sec, feas=True, obj=2.21] INFO - 16:22:13: 74%|███████▍ | 743/1000 [00:00<00:00, 3829.77 it/sec, feas=True, obj=-4.03] INFO - 16:22:13: 74%|███████▍ | 744/1000 [00:00<00:00, 3830.03 it/sec, feas=True, obj=4.28] INFO - 16:22:13: 74%|███████▍ | 745/1000 [00:00<00:00, 3830.31 it/sec, feas=True, obj=6.68] INFO - 16:22:13: 75%|███████▍ | 746/1000 [00:00<00:00, 3830.03 it/sec, feas=True, obj=7.33] INFO - 16:22:13: 75%|███████▍ | 747/1000 [00:00<00:00, 3830.10 it/sec, feas=True, obj=-3.91] INFO - 16:22:13: 75%|███████▍ | 748/1000 [00:00<00:00, 3830.34 it/sec, feas=True, obj=1.16] INFO - 16:22:13: 75%|███████▍ | 749/1000 [00:00<00:00, 3830.62 it/sec, feas=True, obj=-0.739] INFO - 16:22:13: 75%|███████▌ | 750/1000 [00:00<00:00, 3830.40 it/sec, feas=True, obj=5.93] INFO - 16:22:13: 75%|███████▌ | 751/1000 [00:00<00:00, 3830.61 it/sec, feas=True, obj=3.2] INFO - 16:22:13: 75%|███████▌ | 752/1000 [00:00<00:00, 3830.88 it/sec, feas=True, obj=11.1] INFO - 16:22:13: 75%|███████▌ | 753/1000 [00:00<00:00, 3830.73 it/sec, feas=True, obj=7.41] INFO - 16:22:13: 75%|███████▌ | 754/1000 [00:00<00:00, 3830.74 it/sec, feas=True, obj=6.56] INFO - 16:22:13: 76%|███████▌ | 755/1000 [00:00<00:00, 3830.83 it/sec, feas=True, obj=0.0769] INFO - 16:22:13: 76%|███████▌ | 756/1000 [00:00<00:00, 3830.97 it/sec, feas=True, obj=-3.24] INFO - 16:22:13: 76%|███████▌ | 757/1000 [00:00<00:00, 3830.82 it/sec, feas=True, obj=1.73] INFO - 16:22:13: 76%|███████▌ | 758/1000 [00:00<00:00, 3830.94 it/sec, feas=True, obj=0.263] INFO - 16:22:13: 76%|███████▌ | 759/1000 [00:00<00:00, 3831.04 it/sec, feas=True, obj=-6.43] INFO - 16:22:13: 76%|███████▌ | 760/1000 [00:00<00:00, 3831.23 it/sec, feas=True, obj=7.52] INFO - 16:22:13: 76%|███████▌ | 761/1000 [00:00<00:00, 3831.06 it/sec, feas=True, obj=-2.09] INFO - 16:22:13: 76%|███████▌ | 762/1000 [00:00<00:00, 3831.17 it/sec, feas=True, obj=-0.0262] INFO - 16:22:13: 76%|███████▋ | 763/1000 [00:00<00:00, 3831.28 it/sec, feas=True, obj=3.37] INFO - 16:22:13: 76%|███████▋ | 764/1000 [00:00<00:00, 3831.52 it/sec, feas=True, obj=4.5] INFO - 16:22:13: 76%|███████▋ | 765/1000 [00:00<00:00, 3831.36 it/sec, feas=True, obj=0.692] INFO - 16:22:13: 77%|███████▋ | 766/1000 [00:00<00:00, 3831.41 it/sec, feas=True, obj=2.75] INFO - 16:22:13: 77%|███████▋ | 767/1000 [00:00<00:00, 3831.49 it/sec, feas=True, obj=1.46] INFO - 16:22:13: 77%|███████▋ | 768/1000 [00:00<00:00, 3831.69 it/sec, feas=True, obj=7.23] INFO - 16:22:13: 77%|███████▋ | 769/1000 [00:00<00:00, 3831.49 it/sec, feas=True, obj=3.47] INFO - 16:22:13: 77%|███████▋ | 770/1000 [00:00<00:00, 3831.57 it/sec, feas=True, obj=-0.943] INFO - 16:22:13: 77%|███████▋ | 771/1000 [00:00<00:00, 3831.60 it/sec, feas=True, obj=0.302] INFO - 16:22:13: 77%|███████▋ | 772/1000 [00:00<00:00, 3831.75 it/sec, feas=True, obj=6] INFO - 16:22:13: 77%|███████▋ | 773/1000 [00:00<00:00, 3831.46 it/sec, feas=True, obj=2.71] INFO - 16:22:13: 77%|███████▋ | 774/1000 [00:00<00:00, 3831.48 it/sec, feas=True, obj=2.8] INFO - 16:22:13: 78%|███████▊ | 775/1000 [00:00<00:00, 3831.63 it/sec, feas=True, obj=2.67] INFO - 16:22:13: 78%|███████▊ | 776/1000 [00:00<00:00, 3831.83 it/sec, feas=True, obj=5.44] INFO - 16:22:13: 78%|███████▊ | 777/1000 [00:00<00:00, 3831.52 it/sec, feas=True, obj=1.65] INFO - 16:22:13: 78%|███████▊ | 778/1000 [00:00<00:00, 3831.66 it/sec, feas=True, obj=7.13] INFO - 16:22:13: 78%|███████▊ | 779/1000 [00:00<00:00, 3831.87 it/sec, feas=True, obj=-0.0622] INFO - 16:22:13: 78%|███████▊ | 780/1000 [00:00<00:00, 3831.81 it/sec, feas=True, obj=5.84] INFO - 16:22:13: 78%|███████▊ | 781/1000 [00:00<00:00, 3831.75 it/sec, feas=True, obj=2.28] INFO - 16:22:13: 78%|███████▊ | 782/1000 [00:00<00:00, 3831.90 it/sec, feas=True, obj=6.04] INFO - 16:22:13: 78%|███████▊ | 783/1000 [00:00<00:00, 3832.13 it/sec, feas=True, obj=7.59] INFO - 16:22:13: 78%|███████▊ | 784/1000 [00:00<00:00, 3832.06 it/sec, feas=True, obj=-6.19] INFO - 16:22:13: 78%|███████▊ | 785/1000 [00:00<00:00, 3832.03 it/sec, feas=True, obj=9.25] INFO - 16:22:13: 79%|███████▊ | 786/1000 [00:00<00:00, 3831.98 it/sec, feas=True, obj=0.676] INFO - 16:22:13: 79%|███████▊ | 787/1000 [00:00<00:00, 3832.10 it/sec, feas=True, obj=-0.174] INFO - 16:22:13: 79%|███████▉ | 788/1000 [00:00<00:00, 3831.90 it/sec, feas=True, obj=6.51] INFO - 16:22:13: 79%|███████▉ | 789/1000 [00:00<00:00, 3832.02 it/sec, feas=True, obj=-0.856] INFO - 16:22:13: 79%|███████▉ | 790/1000 [00:00<00:00, 3832.17 it/sec, feas=True, obj=5.62] INFO - 16:22:13: 79%|███████▉ | 791/1000 [00:00<00:00, 3832.30 it/sec, feas=True, obj=5.35] INFO - 16:22:13: 79%|███████▉ | 792/1000 [00:00<00:00, 3832.15 it/sec, feas=True, obj=0.753] INFO - 16:22:13: 79%|███████▉ | 793/1000 [00:00<00:00, 3832.17 it/sec, feas=True, obj=4.35] INFO - 16:22:13: 79%|███████▉ | 794/1000 [00:00<00:00, 3832.25 it/sec, feas=True, obj=3.8] INFO - 16:22:13: 80%|███████▉ | 795/1000 [00:00<00:00, 3832.40 it/sec, feas=True, obj=7.95] INFO - 16:22:13: 80%|███████▉ | 796/1000 [00:00<00:00, 3832.19 it/sec, feas=True, obj=5.01] INFO - 16:22:13: 80%|███████▉ | 797/1000 [00:00<00:00, 3832.31 it/sec, feas=True, obj=6.2] INFO - 16:22:13: 80%|███████▉ | 798/1000 [00:00<00:00, 3832.50 it/sec, feas=True, obj=-1.82] INFO - 16:22:13: 80%|███████▉ | 799/1000 [00:00<00:00, 3832.66 it/sec, feas=True, obj=2.4] INFO - 16:22:13: 80%|████████ | 800/1000 [00:00<00:00, 3832.41 it/sec, feas=True, obj=7.99] INFO - 16:22:13: 80%|████████ | 801/1000 [00:00<00:00, 3832.57 it/sec, feas=True, obj=2.48] INFO - 16:22:13: 80%|████████ | 802/1000 [00:00<00:00, 3832.51 it/sec, feas=True, obj=-0.764] INFO - 16:22:13: 80%|████████ | 803/1000 [00:00<00:00, 3832.64 it/sec, feas=True, obj=3.34] INFO - 16:22:13: 80%|████████ | 804/1000 [00:00<00:00, 3832.32 it/sec, feas=True, obj=0.787] INFO - 16:22:13: 80%|████████ | 805/1000 [00:00<00:00, 3832.47 it/sec, feas=True, obj=-1.05] INFO - 16:22:13: 81%|████████ | 806/1000 [00:00<00:00, 3832.56 it/sec, feas=True, obj=4.98] INFO - 16:22:13: 81%|████████ | 807/1000 [00:00<00:00, 3832.45 it/sec, feas=True, obj=4.73] INFO - 16:22:13: 81%|████████ | 808/1000 [00:00<00:00, 3832.43 it/sec, feas=True, obj=-0.742] INFO - 16:22:13: 81%|████████ | 809/1000 [00:00<00:00, 3832.57 it/sec, feas=True, obj=5.82] INFO - 16:22:13: 81%|████████ | 810/1000 [00:00<00:00, 3832.83 it/sec, feas=True, obj=10.4] INFO - 16:22:13: 81%|████████ | 811/1000 [00:00<00:00, 3832.73 it/sec, feas=True, obj=1.86] INFO - 16:22:13: 81%|████████ | 812/1000 [00:00<00:00, 3832.76 it/sec, feas=True, obj=2.49] INFO - 16:22:13: 81%|████████▏ | 813/1000 [00:00<00:00, 3832.99 it/sec, feas=True, obj=9.36] INFO - 16:22:13: 81%|████████▏ | 814/1000 [00:00<00:00, 3833.17 it/sec, feas=True, obj=1.84] INFO - 16:22:13: 82%|████████▏ | 815/1000 [00:00<00:00, 3833.04 it/sec, feas=True, obj=4.04] INFO - 16:22:13: 82%|████████▏ | 816/1000 [00:00<00:00, 3833.04 it/sec, feas=True, obj=-4.21] INFO - 16:22:13: 82%|████████▏ | 817/1000 [00:00<00:00, 3833.01 it/sec, feas=True, obj=3.64] INFO - 16:22:13: 82%|████████▏ | 818/1000 [00:00<00:00, 3833.11 it/sec, feas=True, obj=4.02] INFO - 16:22:13: 82%|████████▏ | 819/1000 [00:00<00:00, 3832.98 it/sec, feas=True, obj=6.66] INFO - 16:22:13: 82%|████████▏ | 820/1000 [00:00<00:00, 3833.13 it/sec, feas=True, obj=-0.0634] INFO - 16:22:13: 82%|████████▏ | 821/1000 [00:00<00:00, 3833.34 it/sec, feas=True, obj=1.24] INFO - 16:22:13: 82%|████████▏ | 822/1000 [00:00<00:00, 3833.53 it/sec, feas=True, obj=4.42] INFO - 16:22:13: 82%|████████▏ | 823/1000 [00:00<00:00, 3833.35 it/sec, feas=True, obj=4.26] INFO - 16:22:13: 82%|████████▏ | 824/1000 [00:00<00:00, 3833.48 it/sec, feas=True, obj=0.439] INFO - 16:22:13: 82%|████████▎ | 825/1000 [00:00<00:00, 3833.72 it/sec, feas=True, obj=2.7] INFO - 16:22:13: 83%|████████▎ | 826/1000 [00:00<00:00, 3833.91 it/sec, feas=True, obj=2.98] INFO - 16:22:13: 83%|████████▎ | 827/1000 [00:00<00:00, 3833.70 it/sec, feas=True, obj=0.888] INFO - 16:22:13: 83%|████████▎ | 828/1000 [00:00<00:00, 3833.79 it/sec, feas=True, obj=-0.879] INFO - 16:22:13: 83%|████████▎ | 829/1000 [00:00<00:00, 3831.85 it/sec, feas=True, obj=0.861] INFO - 16:22:13: 83%|████████▎ | 830/1000 [00:00<00:00, 3831.09 it/sec, feas=True, obj=3.47] INFO - 16:22:13: 83%|████████▎ | 831/1000 [00:00<00:00, 3831.07 it/sec, feas=True, obj=7.51] INFO - 16:22:13: 83%|████████▎ | 832/1000 [00:00<00:00, 3831.04 it/sec, feas=True, obj=4.58] INFO - 16:22:13: 83%|████████▎ | 833/1000 [00:00<00:00, 3831.10 it/sec, feas=True, obj=5.48] INFO - 16:22:13: 83%|████████▎ | 834/1000 [00:00<00:00, 3830.85 it/sec, feas=True, obj=-0.412] INFO - 16:22:13: 84%|████████▎ | 835/1000 [00:00<00:00, 3830.77 it/sec, feas=True, obj=-1.86] INFO - 16:22:13: 84%|████████▎ | 836/1000 [00:00<00:00, 3830.90 it/sec, feas=True, obj=1.29] INFO - 16:22:13: 84%|████████▎ | 837/1000 [00:00<00:00, 3831.10 it/sec, feas=True, obj=3.17] INFO - 16:22:13: 84%|████████▍ | 838/1000 [00:00<00:00, 3830.81 it/sec, feas=True, obj=2.41] INFO - 16:22:13: 84%|████████▍ | 839/1000 [00:00<00:00, 3830.82 it/sec, feas=True, obj=5.72] INFO - 16:22:13: 84%|████████▍ | 840/1000 [00:00<00:00, 3830.84 it/sec, feas=True, obj=-1.37] INFO - 16:22:13: 84%|████████▍ | 841/1000 [00:00<00:00, 3830.93 it/sec, feas=True, obj=6.72] INFO - 16:22:13: 84%|████████▍ | 842/1000 [00:00<00:00, 3830.62 it/sec, feas=True, obj=3.27] INFO - 16:22:13: 84%|████████▍ | 843/1000 [00:00<00:00, 3830.79 it/sec, feas=True, obj=-1.46] INFO - 16:22:13: 84%|████████▍ | 844/1000 [00:00<00:00, 3830.99 it/sec, feas=True, obj=4.38] INFO - 16:22:13: 84%|████████▍ | 845/1000 [00:00<00:00, 3830.82 it/sec, feas=True, obj=3.82] INFO - 16:22:13: 85%|████████▍ | 846/1000 [00:00<00:00, 3830.82 it/sec, feas=True, obj=5.89] INFO - 16:22:13: 85%|████████▍ | 847/1000 [00:00<00:00, 3830.84 it/sec, feas=True, obj=3.11] INFO - 16:22:13: 85%|████████▍ | 848/1000 [00:00<00:00, 3830.88 it/sec, feas=True, obj=4.37] INFO - 16:22:13: 85%|████████▍ | 849/1000 [00:00<00:00, 3830.60 it/sec, feas=True, obj=1.84] INFO - 16:22:13: 85%|████████▌ | 850/1000 [00:00<00:00, 3830.62 it/sec, feas=True, obj=2.82] INFO - 16:22:13: 85%|████████▌ | 851/1000 [00:00<00:00, 3830.75 it/sec, feas=True, obj=7.38] INFO - 16:22:13: 85%|████████▌ | 852/1000 [00:00<00:00, 3830.90 it/sec, feas=True, obj=13.8] INFO - 16:22:13: 85%|████████▌ | 853/1000 [00:00<00:00, 3830.73 it/sec, feas=True, obj=7.76] INFO - 16:22:13: 85%|████████▌ | 854/1000 [00:00<00:00, 3830.85 it/sec, feas=True, obj=0.998] INFO - 16:22:13: 86%|████████▌ | 855/1000 [00:00<00:00, 3831.02 it/sec, feas=True, obj=3.88] INFO - 16:22:13: 86%|████████▌ | 856/1000 [00:00<00:00, 3831.16 it/sec, feas=True, obj=-0.698] INFO - 16:22:13: 86%|████████▌ | 857/1000 [00:00<00:00, 3830.90 it/sec, feas=True, obj=2.83] INFO - 16:22:13: 86%|████████▌ | 858/1000 [00:00<00:00, 3830.97 it/sec, feas=True, obj=1.58] INFO - 16:22:13: 86%|████████▌ | 859/1000 [00:00<00:00, 3831.11 it/sec, feas=True, obj=8.53] INFO - 16:22:13: 86%|████████▌ | 860/1000 [00:00<00:00, 3831.27 it/sec, feas=True, obj=6.28] INFO - 16:22:13: 86%|████████▌ | 861/1000 [00:00<00:00, 3831.04 it/sec, feas=True, obj=11.8] INFO - 16:22:13: 86%|████████▌ | 862/1000 [00:00<00:00, 3831.14 it/sec, feas=True, obj=9.31] INFO - 16:22:13: 86%|████████▋ | 863/1000 [00:00<00:00, 3831.09 it/sec, feas=True, obj=3.88] INFO - 16:22:13: 86%|████████▋ | 864/1000 [00:00<00:00, 3831.24 it/sec, feas=True, obj=3.11] INFO - 16:22:13: 86%|████████▋ | 865/1000 [00:00<00:00, 3830.97 it/sec, feas=True, obj=5.09] INFO - 16:22:13: 87%|████████▋ | 866/1000 [00:00<00:00, 3831.01 it/sec, feas=True, obj=-0.723] INFO - 16:22:13: 87%|████████▋ | 867/1000 [00:00<00:00, 3831.10 it/sec, feas=True, obj=1.22] INFO - 16:22:13: 87%|████████▋ | 868/1000 [00:00<00:00, 3830.93 it/sec, feas=True, obj=7.13] INFO - 16:22:13: 87%|████████▋ | 869/1000 [00:00<00:00, 3830.92 it/sec, feas=True, obj=12.2] INFO - 16:22:13: 87%|████████▋ | 870/1000 [00:00<00:00, 3830.97 it/sec, feas=True, obj=1.13] INFO - 16:22:13: 87%|████████▋ | 871/1000 [00:00<00:00, 3831.11 it/sec, feas=True, obj=0.802] INFO - 16:22:13: 87%|████████▋ | 872/1000 [00:00<00:00, 3830.92 it/sec, feas=True, obj=2.82] INFO - 16:22:13: 87%|████████▋ | 873/1000 [00:00<00:00, 3831.02 it/sec, feas=True, obj=-0.932] INFO - 16:22:13: 87%|████████▋ | 874/1000 [00:00<00:00, 3831.17 it/sec, feas=True, obj=1.6] INFO - 16:22:13: 88%|████████▊ | 875/1000 [00:00<00:00, 3830.95 it/sec, feas=True, obj=8.68] INFO - 16:22:13: 88%|████████▊ | 876/1000 [00:00<00:00, 3830.71 it/sec, feas=True, obj=-0.211] INFO - 16:22:13: 88%|████████▊ | 877/1000 [00:00<00:00, 3830.78 it/sec, feas=True, obj=-3.63] INFO - 16:22:13: 88%|████████▊ | 878/1000 [00:00<00:00, 3830.78 it/sec, feas=True, obj=4.85] INFO - 16:22:13: 88%|████████▊ | 879/1000 [00:00<00:00, 3830.79 it/sec, feas=True, obj=4.28] INFO - 16:22:13: 88%|████████▊ | 880/1000 [00:00<00:00, 3830.45 it/sec, feas=True, obj=-0.285] INFO - 16:22:13: 88%|████████▊ | 881/1000 [00:00<00:00, 3830.53 it/sec, feas=True, obj=5.96] INFO - 16:22:13: 88%|████████▊ | 882/1000 [00:00<00:00, 3830.66 it/sec, feas=True, obj=-0.126] INFO - 16:22:13: 88%|████████▊ | 883/1000 [00:00<00:00, 3830.74 it/sec, feas=True, obj=10.4] INFO - 16:22:13: 88%|████████▊ | 884/1000 [00:00<00:00, 3830.51 it/sec, feas=True, obj=-1.37] INFO - 16:22:13: 88%|████████▊ | 885/1000 [00:00<00:00, 3830.68 it/sec, feas=True, obj=4.47] INFO - 16:22:13: 89%|████████▊ | 886/1000 [00:00<00:00, 3830.74 it/sec, feas=True, obj=1.19] INFO - 16:22:13: 89%|████████▊ | 887/1000 [00:00<00:00, 3830.59 it/sec, feas=True, obj=6.51] INFO - 16:22:13: 89%|████████▉ | 888/1000 [00:00<00:00, 3830.60 it/sec, feas=True, obj=-0.5] INFO - 16:22:13: 89%|████████▉ | 889/1000 [00:00<00:00, 3830.75 it/sec, feas=True, obj=1.33] INFO - 16:22:13: 89%|████████▉ | 890/1000 [00:00<00:00, 3830.95 it/sec, feas=True, obj=8.1] INFO - 16:22:13: 89%|████████▉ | 891/1000 [00:00<00:00, 3830.81 it/sec, feas=True, obj=6.34] INFO - 16:22:13: 89%|████████▉ | 892/1000 [00:00<00:00, 3830.85 it/sec, feas=True, obj=0.425] INFO - 16:22:13: 89%|████████▉ | 893/1000 [00:00<00:00, 3830.82 it/sec, feas=True, obj=7.99] INFO - 16:22:13: 89%|████████▉ | 894/1000 [00:00<00:00, 3830.93 it/sec, feas=True, obj=4.73] INFO - 16:22:13: 90%|████████▉ | 895/1000 [00:00<00:00, 3830.80 it/sec, feas=True, obj=-0.736] INFO - 16:22:13: 90%|████████▉ | 896/1000 [00:00<00:00, 3830.84 it/sec, feas=True, obj=1.11] INFO - 16:22:13: 90%|████████▉ | 897/1000 [00:00<00:00, 3831.02 it/sec, feas=True, obj=5.52] INFO - 16:22:13: 90%|████████▉ | 898/1000 [00:00<00:00, 3831.18 it/sec, feas=True, obj=0.448] INFO - 16:22:13: 90%|████████▉ | 899/1000 [00:00<00:00, 3831.04 it/sec, feas=True, obj=1.81] INFO - 16:22:13: 90%|█████████ | 900/1000 [00:00<00:00, 3831.16 it/sec, feas=True, obj=6.25] INFO - 16:22:13: 90%|█████████ | 901/1000 [00:00<00:00, 3831.35 it/sec, feas=True, obj=-0.151] INFO - 16:22:13: 90%|█████████ | 902/1000 [00:00<00:00, 3831.50 it/sec, feas=True, obj=7.08] INFO - 16:22:13: 90%|█████████ | 903/1000 [00:00<00:00, 3831.33 it/sec, feas=True, obj=-0.565] INFO - 16:22:13: 90%|█████████ | 904/1000 [00:00<00:00, 3831.42 it/sec, feas=True, obj=0.323] INFO - 16:22:13: 90%|█████████ | 905/1000 [00:00<00:00, 3831.60 it/sec, feas=True, obj=-0.591] INFO - 16:22:13: 91%|█████████ | 906/1000 [00:00<00:00, 3831.80 it/sec, feas=True, obj=2] INFO - 16:22:13: 91%|█████████ | 907/1000 [00:00<00:00, 3831.64 it/sec, feas=True, obj=4.54] INFO - 16:22:13: 91%|█████████ | 908/1000 [00:00<00:00, 3831.75 it/sec, feas=True, obj=2.63] INFO - 16:22:13: 91%|█████████ | 909/1000 [00:00<00:00, 3831.74 it/sec, feas=True, obj=1.07] INFO - 16:22:13: 91%|█████████ | 910/1000 [00:00<00:00, 3831.90 it/sec, feas=True, obj=5.89] INFO - 16:22:13: 91%|█████████ | 911/1000 [00:00<00:00, 3831.71 it/sec, feas=True, obj=0.778] INFO - 16:22:13: 91%|█████████ | 912/1000 [00:00<00:00, 3831.90 it/sec, feas=True, obj=4.03] INFO - 16:22:13: 91%|█████████▏| 913/1000 [00:00<00:00, 3832.09 it/sec, feas=True, obj=1.89] INFO - 16:22:13: 91%|█████████▏| 914/1000 [00:00<00:00, 3832.24 it/sec, feas=True, obj=5.16] INFO - 16:22:13: 92%|█████████▏| 915/1000 [00:00<00:00, 3831.96 it/sec, feas=True, obj=-0.787] INFO - 16:22:13: 92%|█████████▏| 916/1000 [00:00<00:00, 3831.99 it/sec, feas=True, obj=5.28] INFO - 16:22:13: 92%|█████████▏| 917/1000 [00:00<00:00, 3832.14 it/sec, feas=True, obj=2.93] INFO - 16:22:13: 92%|█████████▏| 918/1000 [00:00<00:00, 3832.00 it/sec, feas=True, obj=0.851] INFO - 16:22:13: 92%|█████████▏| 919/1000 [00:00<00:00, 3831.99 it/sec, feas=True, obj=6.04] INFO - 16:22:13: 92%|█████████▏| 920/1000 [00:00<00:00, 3832.02 it/sec, feas=True, obj=5.24] INFO - 16:22:13: 92%|█████████▏| 921/1000 [00:00<00:00, 3832.15 it/sec, feas=True, obj=0.0179] INFO - 16:22:13: 92%|█████████▏| 922/1000 [00:00<00:00, 3832.00 it/sec, feas=True, obj=6.08] INFO - 16:22:13: 92%|█████████▏| 923/1000 [00:00<00:00, 3832.03 it/sec, feas=True, obj=6.95] INFO - 16:22:13: 92%|█████████▏| 924/1000 [00:00<00:00, 3831.96 it/sec, feas=True, obj=2.64] INFO - 16:22:13: 92%|█████████▎| 925/1000 [00:00<00:00, 3832.03 it/sec, feas=True, obj=-3.23] INFO - 16:22:13: 93%|█████████▎| 926/1000 [00:00<00:00, 3831.85 it/sec, feas=True, obj=-7.07] INFO - 16:22:13: 93%|█████████▎| 927/1000 [00:00<00:00, 3831.95 it/sec, feas=True, obj=1] INFO - 16:22:13: 93%|█████████▎| 928/1000 [00:00<00:00, 3831.99 it/sec, feas=True, obj=5.49] INFO - 16:22:13: 93%|█████████▎| 929/1000 [00:00<00:00, 3832.15 it/sec, feas=True, obj=0.827] INFO - 16:22:13: 93%|█████████▎| 930/1000 [00:00<00:00, 3832.02 it/sec, feas=True, obj=3.6] INFO - 16:22:13: 93%|█████████▎| 931/1000 [00:00<00:00, 3832.12 it/sec, feas=True, obj=5.72] INFO - 16:22:13: 93%|█████████▎| 932/1000 [00:00<00:00, 3832.15 it/sec, feas=True, obj=2.44] INFO - 16:22:13: 93%|█████████▎| 933/1000 [00:00<00:00, 3832.30 it/sec, feas=True, obj=1.38] INFO - 16:22:13: 93%|█████████▎| 934/1000 [00:00<00:00, 3832.05 it/sec, feas=True, obj=-0.822] INFO - 16:22:13: 94%|█████████▎| 935/1000 [00:00<00:00, 3832.14 it/sec, feas=True, obj=-3.17] INFO - 16:22:13: 94%|█████████▎| 936/1000 [00:00<00:00, 3832.25 it/sec, feas=True, obj=6.85] INFO - 16:22:13: 94%|█████████▎| 937/1000 [00:00<00:00, 3832.41 it/sec, feas=True, obj=3.98] INFO - 16:22:13: 94%|█████████▍| 938/1000 [00:00<00:00, 3832.16 it/sec, feas=True, obj=-0.244] INFO - 16:22:13: 94%|█████████▍| 939/1000 [00:00<00:00, 3832.27 it/sec, feas=True, obj=2.77] INFO - 16:22:13: 94%|█████████▍| 940/1000 [00:00<00:00, 3832.25 it/sec, feas=True, obj=1.68] INFO - 16:22:13: 94%|█████████▍| 941/1000 [00:00<00:00, 3832.18 it/sec, feas=True, obj=3.7] INFO - 16:22:13: 94%|█████████▍| 942/1000 [00:00<00:00, 3832.19 it/sec, feas=True, obj=1.83] INFO - 16:22:13: 94%|█████████▍| 943/1000 [00:00<00:00, 3817.48 it/sec, feas=True, obj=-0.297] INFO - 16:22:13: 94%|█████████▍| 944/1000 [00:00<00:00, 3816.90 it/sec, feas=True, obj=9.05] INFO - 16:22:13: 94%|█████████▍| 945/1000 [00:00<00:00, 3816.33 it/sec, feas=True, obj=-7.67] INFO - 16:22:13: 95%|█████████▍| 946/1000 [00:00<00:00, 3816.19 it/sec, feas=True, obj=6.44] INFO - 16:22:13: 95%|█████████▍| 947/1000 [00:00<00:00, 3816.22 it/sec, feas=True, obj=7.66] INFO - 16:22:13: 95%|█████████▍| 948/1000 [00:00<00:00, 3816.29 it/sec, feas=True, obj=5.69] INFO - 16:22:13: 95%|█████████▍| 949/1000 [00:00<00:00, 3815.98 it/sec, feas=True, obj=4.75] INFO - 16:22:13: 95%|█████████▌| 950/1000 [00:00<00:00, 3815.95 it/sec, feas=True, obj=0.391] INFO - 16:22:13: 95%|█████████▌| 951/1000 [00:00<00:00, 3815.93 it/sec, feas=True, obj=7.77] INFO - 16:22:13: 95%|█████████▌| 952/1000 [00:00<00:00, 3816.00 it/sec, feas=True, obj=-0.712] INFO - 16:22:13: 95%|█████████▌| 953/1000 [00:00<00:00, 3815.68 it/sec, feas=True, obj=0.439] INFO - 16:22:13: 95%|█████████▌| 954/1000 [00:00<00:00, 3815.63 it/sec, feas=True, obj=7.43] INFO - 16:22:13: 96%|█████████▌| 955/1000 [00:00<00:00, 3815.71 it/sec, feas=True, obj=-1.49] INFO - 16:22:13: 96%|█████████▌| 956/1000 [00:00<00:00, 3815.44 it/sec, feas=True, obj=5.62] INFO - 16:22:13: 96%|█████████▌| 957/1000 [00:00<00:00, 3815.34 it/sec, feas=True, obj=6.16] INFO - 16:22:13: 96%|█████████▌| 958/1000 [00:00<00:00, 3815.32 it/sec, feas=True, obj=7.77] INFO - 16:22:13: 96%|█████████▌| 959/1000 [00:00<00:00, 3815.38 it/sec, feas=True, obj=1.26] INFO - 16:22:13: 96%|█████████▌| 960/1000 [00:00<00:00, 3815.23 it/sec, feas=True, obj=3.33] INFO - 16:22:13: 96%|█████████▌| 961/1000 [00:00<00:00, 3815.33 it/sec, feas=True, obj=2.28] INFO - 16:22:13: 96%|█████████▌| 962/1000 [00:00<00:00, 3815.42 it/sec, feas=True, obj=14.7] INFO - 16:22:13: 96%|█████████▋| 963/1000 [00:00<00:00, 3815.62 it/sec, feas=True, obj=0.515] INFO - 16:22:13: 96%|█████████▋| 964/1000 [00:00<00:00, 3815.54 it/sec, feas=True, obj=2.57] INFO - 16:22:13: 96%|█████████▋| 965/1000 [00:00<00:00, 3815.63 it/sec, feas=True, obj=6.57] INFO - 16:22:13: 97%|█████████▋| 966/1000 [00:00<00:00, 3815.57 it/sec, feas=True, obj=-0.292] INFO - 16:22:13: 97%|█████████▋| 967/1000 [00:00<00:00, 3815.72 it/sec, feas=True, obj=-1.65] INFO - 16:22:13: 97%|█████████▋| 968/1000 [00:00<00:00, 3815.61 it/sec, feas=True, obj=7.01] INFO - 16:22:13: 97%|█████████▋| 969/1000 [00:00<00:00, 3815.71 it/sec, feas=True, obj=-0.0208] INFO - 16:22:13: 97%|█████████▋| 970/1000 [00:00<00:00, 3815.79 it/sec, feas=True, obj=2.03] INFO - 16:22:13: 97%|█████████▋| 971/1000 [00:00<00:00, 3815.97 it/sec, feas=True, obj=0.429] INFO - 16:22:13: 97%|█████████▋| 972/1000 [00:00<00:00, 3815.78 it/sec, feas=True, obj=-2.02] INFO - 16:22:13: 97%|█████████▋| 973/1000 [00:00<00:00, 3815.82 it/sec, feas=True, obj=6.01] INFO - 16:22:13: 97%|█████████▋| 974/1000 [00:00<00:00, 3815.89 it/sec, feas=True, obj=5.07] INFO - 16:22:13: 98%|█████████▊| 975/1000 [00:00<00:00, 3816.04 it/sec, feas=True, obj=7.26] INFO - 16:22:13: 98%|█████████▊| 976/1000 [00:00<00:00, 3815.82 it/sec, feas=True, obj=1.83] INFO - 16:22:13: 98%|█████████▊| 977/1000 [00:00<00:00, 3815.95 it/sec, feas=True, obj=7.93] INFO - 16:22:13: 98%|█████████▊| 978/1000 [00:00<00:00, 3816.03 it/sec, feas=True, obj=4.96] INFO - 16:22:13: 98%|█████████▊| 979/1000 [00:00<00:00, 3815.96 it/sec, feas=True, obj=0.739] INFO - 16:22:13: 98%|█████████▊| 980/1000 [00:00<00:00, 3815.95 it/sec, feas=True, obj=-1.88] INFO - 16:22:13: 98%|█████████▊| 981/1000 [00:00<00:00, 3816.12 it/sec, feas=True, obj=5.13] INFO - 16:22:13: 98%|█████████▊| 982/1000 [00:00<00:00, 3816.03 it/sec, feas=True, obj=3.38] INFO - 16:22:13: 98%|█████████▊| 983/1000 [00:00<00:00, 3815.91 it/sec, feas=True, obj=8.53] INFO - 16:22:13: 98%|█████████▊| 984/1000 [00:00<00:00, 3815.97 it/sec, feas=True, obj=5.83] INFO - 16:22:13: 98%|█████████▊| 985/1000 [00:00<00:00, 3816.10 it/sec, feas=True, obj=8.02] INFO - 16:22:13: 99%|█████████▊| 986/1000 [00:00<00:00, 3816.16 it/sec, feas=True, obj=2.01] INFO - 16:22:13: 99%|█████████▊| 987/1000 [00:00<00:00, 3816.02 it/sec, feas=True, obj=-0.893] INFO - 16:22:13: 99%|█████████▉| 988/1000 [00:00<00:00, 3816.08 it/sec, feas=True, obj=4.74] INFO - 16:22:13: 99%|█████████▉| 989/1000 [00:00<00:00, 3816.17 it/sec, feas=True, obj=1.18] INFO - 16:22:13: 99%|█████████▉| 990/1000 [00:00<00:00, 3816.25 it/sec, feas=True, obj=6.2] INFO - 16:22:13: 99%|█████████▉| 991/1000 [00:00<00:00, 3816.07 it/sec, feas=True, obj=4.5] INFO - 16:22:13: 99%|█████████▉| 992/1000 [00:00<00:00, 3816.14 it/sec, feas=True, obj=-0.907] INFO - 16:22:13: 99%|█████████▉| 993/1000 [00:00<00:00, 3816.25 it/sec, feas=True, obj=-3.18] INFO - 16:22:13: 99%|█████████▉| 994/1000 [00:00<00:00, 3816.36 it/sec, feas=True, obj=6.82] INFO - 16:22:13: 100%|█████████▉| 995/1000 [00:00<00:00, 3816.15 it/sec, feas=True, obj=3.44] INFO - 16:22:13: 100%|█████████▉| 996/1000 [00:00<00:00, 3816.21 it/sec, feas=True, obj=5.11] INFO - 16:22:13: 100%|█████████▉| 997/1000 [00:00<00:00, 3816.16 it/sec, feas=True, obj=1.55] INFO - 16:22:13: 100%|█████████▉| 998/1000 [00:00<00:00, 3816.32 it/sec, feas=True, obj=0.534] INFO - 16:22:13: 100%|█████████▉| 999/1000 [00:00<00:00, 3816.00 it/sec, feas=True, obj=0.783] INFO - 16:22:13: 100%|██████████| 1000/1000 [00:00<00:00, 3791.09 it/sec, feas=True, obj=5.65] INFO - 16:22:13: Optimization result: INFO - 16:22:13: Optimizer info: INFO - 16:22:13: Status: None INFO - 16:22:13: Message: None INFO - 16:22:13: Solution: INFO - 16:22:13: Objective: -10.14685071195364 INFO - 16:22:13: Design space: INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: | Name | Distribution | INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: | x1 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: | x2 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: | x3 | Uniform(lower=-3.141592653589793, upper=3.141592653589793) | INFO - 16:22:13: +------+------------------------------------------------------------+ INFO - 16:22:13: *** End Sampling execution *** .. GENERATED FROM PYTHON SOURCE LINES 111-127 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 :cite:`ghisu2021`. .. GENERATED FROM PYTHON SOURCE LINES 127-174 .. code-block:: Python 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]) .. GENERATED FROM PYTHON SOURCE LINES 175-177 We create also a :class:`.PCERegressor` using the LARS algorithm implemented in OpenTURNS: .. GENERATED FROM PYTHON SOURCE LINES 177-185 .. code-block:: Python 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) .. GENERATED FROM PYTHON SOURCE LINES 186-190 From these results, we can plot the quality of the different surrogate models, expressed in terms of coefficient of determination :math:`R^2` (the higher, the better): .. GENERATED FROM PYTHON SOURCE LINES 190-201 .. code-block:: Python 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) .. image-sg:: /examples/mlearning/regression_model/images/sphx_glr_plot_fce_regression_001.png :alt: plot fce regression :srcset: /examples/mlearning/regression_model/images/sphx_glr_plot_fce_regression_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [
] .. GENERATED FROM PYTHON SOURCE LINES 202-222 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. .. GENERATED FROM PYTHON SOURCE LINES 222-282 .. code-block:: Python 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) .. image-sg:: /examples/mlearning/regression_model/images/sphx_glr_plot_fce_regression_002.png :alt: plot fce regression :srcset: /examples/mlearning/regression_model/images/sphx_glr_plot_fce_regression_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [
] .. GENERATED FROM PYTHON SOURCE LINES 283-289 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. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 4.628 seconds) .. _sphx_glr_download_examples_mlearning_regression_model_plot_fce_regression.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_fce_regression.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_fce_regression.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_fce_regression.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_