.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/swissmetro/plot_b01logit_bis.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_swissmetro_plot_b01logit_bis.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_swissmetro_plot_b01logit_bis.py:


Illustration of additional features of Biogeme
==============================================

Same model as b01logit, using bioLinearUtility, segmentations
 and features.

:author: Michel Bierlaire, EPFL
:date: Sun Apr  9 17:03:31 2023

.. GENERATED FROM PYTHON SOURCE LINES 13-19

.. code-block:: default

    import biogeme.biogeme_logging as blog
    import biogeme.biogeme as bio
    from biogeme import models
    import biogeme.segmentation as seg
    from biogeme.expressions import Beta, bioLinearUtility








.. GENERATED FROM PYTHON SOURCE LINES 20-21

See the data processing script: :ref:`swissmetro_data`.

.. GENERATED FROM PYTHON SOURCE LINES 21-40

.. code-block:: default

    from swissmetro_data import (
        database,
        CHOICE,
        GA,
        CAR_AV_SP,
        TRAIN_AV_SP,
        TRAIN_TT_SCALED,
        TRAIN_COST_SCALED,
        SM_TT_SCALED,
        SM_COST_SCALED,
        CAR_TT_SCALED,
        CAR_CO_SCALED,
        MALE,
        SM_AV,
    )

    logger = blog.get_screen_logger(level=blog.INFO)
    logger.info('Example b01logit_bis.py')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Example b01logit_bis.py 




.. GENERATED FROM PYTHON SOURCE LINES 41-42

Parameters to be estimated.

.. GENERATED FROM PYTHON SOURCE LINES 42-45

.. code-block:: default

    ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
    ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)








.. GENERATED FROM PYTHON SOURCE LINES 46-48

Starting value.
We use starting values estimated from a previous run

.. GENERATED FROM PYTHON SOURCE LINES 48-51

.. code-block:: default

    B_TIME = Beta('B_TIME', -1.28, None, None, 0)
    B_COST = Beta('B_COST', -1.08, None, None, 0)








.. GENERATED FROM PYTHON SOURCE LINES 52-53

Define segmentations.

.. GENERATED FROM PYTHON SOURCE LINES 53-66

.. code-block:: default

    gender_segmentation = database.generate_segmentation(
        variable=MALE, mapping={0: 'female', 1: 'male'}
    )

    GA_segmentation = database.generate_segmentation(
        variable=GA, mapping={0: 'without_ga', 1: 'with_ga'}
    )

    segmentations_for_asc = [
        gender_segmentation,
        GA_segmentation,
    ]








.. GENERATED FROM PYTHON SOURCE LINES 67-68

Segmentation of the constants.

.. GENERATED FROM PYTHON SOURCE LINES 68-73

.. code-block:: default

    ASC_TRAIN_segmentation = seg.Segmentation(ASC_TRAIN, segmentations_for_asc)
    segmented_ASC_TRAIN = ASC_TRAIN_segmentation.segmented_beta()
    ASC_CAR_segmentation = seg.Segmentation(ASC_CAR, segmentations_for_asc)
    segmented_ASC_CAR = ASC_CAR_segmentation.segmented_beta()








.. GENERATED FROM PYTHON SOURCE LINES 74-75

Definition of the utility functions.

.. GENERATED FROM PYTHON SOURCE LINES 75-84

.. code-block:: default

    terms1 = [(B_TIME, TRAIN_TT_SCALED), (B_COST, TRAIN_COST_SCALED)]
    V1 = segmented_ASC_TRAIN + bioLinearUtility(terms1)

    terms2 = [(B_TIME, SM_TT_SCALED), (B_COST, SM_COST_SCALED)]
    V2 = bioLinearUtility(terms2)

    terms3 = [(B_TIME, CAR_TT_SCALED), (B_COST, CAR_CO_SCALED)]
    V3 = segmented_ASC_CAR + bioLinearUtility(terms3)








.. GENERATED FROM PYTHON SOURCE LINES 85-86

Associate utility functions with the numbering of alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 86-88

.. code-block:: default

    V = {1: V1, 2: V2, 3: V3}








.. GENERATED FROM PYTHON SOURCE LINES 89-90

Associate the availability conditions with the alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 90-92

.. code-block:: default

    av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}








.. GENERATED FROM PYTHON SOURCE LINES 93-97

Definition of the model.

This is the contribution of each observation to the log likelihood
function.

.. GENERATED FROM PYTHON SOURCE LINES 97-99

.. code-block:: default

    logprob = models.loglogit(V, av, CHOICE)








.. GENERATED FROM PYTHON SOURCE LINES 100-103

User notes.

These notes will be included as such in the report file.

.. GENERATED FROM PYTHON SOURCE LINES 103-110

.. code-block:: default

    USER_NOTES = (
        'Example of a logit model with three alternatives: Train, Car and'
        ' Swissmetro. Same as 01logit and '
        'introducing some options and features. In particular, bioLinearUtility,'
        ' and automatic segmentation of parameters.'
    )








.. GENERATED FROM PYTHON SOURCE LINES 111-112

Create the Biogeme object.

.. GENERATED FROM PYTHON SOURCE LINES 112-116

.. code-block:: default

    the_biogeme = bio.BIOGEME(
        database, logprob, userNotes=USER_NOTES, parameter_file='b01logit_bis.toml'
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    File b01logit_bis.toml has been parsed. 




.. GENERATED FROM PYTHON SOURCE LINES 117-121

Calculate the null log likelihood for reporting.

As we have used starting values different from 0, the initial model
is not the equal probability model.

.. GENERATED FROM PYTHON SOURCE LINES 121-124

.. code-block:: default

    the_biogeme.calculateNullLoglikelihood(av)
    the_biogeme.modelName = 'b01logit_bis'








.. GENERATED FROM PYTHON SOURCE LINES 125-127

Turn off saving iterations.


.. GENERATED FROM PYTHON SOURCE LINES 127-129

.. code-block:: default

    the_biogeme.saveIterations = False








.. GENERATED FROM PYTHON SOURCE LINES 130-132

Estimate the parameters.


.. GENERATED FROM PYTHON SOURCE LINES 132-135

.. code-block:: default

    the_biogeme.bootstrap_samples = 100
    results = the_biogeme.estimate(run_bootstrap=True)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.38          -0.046            -0.2           -0.21           -0.78               1              -1            -1.3      5.1e+03      0.053       10      1.1   ++ 
        1           -0.61            0.42           -0.42           -0.53              -1             1.8            -1.1            -1.1      4.9e+03     0.0091    1e+02        1   ++ 
        2           -0.61            0.41           -0.41           -0.53            -1.1             1.9            -1.1            -1.2      4.9e+03    0.00031    1e+03        1   ++ 
        3           -0.61            0.41           -0.41           -0.53            -1.1             1.9            -1.1            -1.2      4.9e+03    6.6e-07    1e+03        1   ++ 
    Re-estimate the model 100 times for bootstrapping 

      0%|          | 0/100 [00:00<?, ?it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.52             0.3           -0.08           -0.56            -1.1               2            -1.1            -1.2        5e+03    0.00026       10     0.96   ++ 
        1           -0.52             0.3           -0.08           -0.56            -1.1               2            -1.1            -1.2        5e+03    2.1e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.59             0.5           -0.51           -0.48           -0.94             1.8            -1.2            -1.4      4.9e+03    0.00073       10        1   ++ 
        1           -0.59             0.5           -0.51           -0.48           -0.94             1.8            -1.2            -1.4      4.9e+03    1.9e-06       10        1   ++ 

      2%|▏         | 2/100 [00:00<00:06, 15.35it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57            0.41           -0.52           -0.55            -1.2               2           -0.94            -1.2      4.9e+03    0.00056       10        1   ++ 
        1           -0.57            0.41           -0.52           -0.55            -1.2               2           -0.94            -1.2      4.9e+03    2.1e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.58            0.38          -0.062           -0.49            -1.1             1.9            -1.1            -1.2        5e+03    0.00036       10     0.92   ++ 
        1           -0.58            0.38          -0.062           -0.49            -1.1             1.9            -1.1            -1.2        5e+03    5.2e-06       10        1   ++ 

      4%|▍         | 4/100 [00:00<00:06, 14.67it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.86            0.62           -0.56           -0.53            -1.2               2            -1.1            -1.2      4.9e+03    0.00051       10        1   ++ 
        1           -0.86            0.62           -0.56           -0.53            -1.2               2            -1.1            -1.2      4.9e+03    3.5e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.48            0.26           -0.31           -0.64           -0.97             1.8              -1            -1.2        5e+03    0.00021       10     0.99   ++ 
        1           -0.48            0.26           -0.31           -0.64           -0.97             1.8              -1            -1.2        5e+03    6.7e-07       10        1   ++ 

      6%|▌         | 6/100 [00:00<00:06, 14.75it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.74            0.51           -0.55           -0.64              -1             1.9            -1.1            -1.2      4.9e+03    0.00013       10        1   ++ 
        1           -0.74            0.51           -0.55           -0.64              -1             1.9            -1.1            -1.2      4.9e+03    3.9e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.71            0.61           -0.73           -0.43            -1.1               2              -1            -1.4      4.8e+03      0.002       10        1   ++ 
        1           -0.71            0.63           -0.76           -0.42            -1.1               2            -1.1            -1.4      4.8e+03      1e-05    1e+02        1   ++ 
        2           -0.71            0.63           -0.76           -0.42            -1.1               2            -1.1            -1.4      4.8e+03    8.2e-10    1e+02        1   ++ 

      8%|▊         | 8/100 [00:00<00:06, 13.98it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.55            0.29           -0.41           -0.62            -1.1             1.9              -1              -1        5e+03    0.00065       10     0.97   ++ 
        1           -0.55            0.29           -0.41           -0.62            -1.1             1.9              -1              -1        5e+03    1.7e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.66            0.52           -0.44           -0.48              -1             1.7              -1            -1.2        5e+03    0.00016       10        1   ++ 
        1           -0.66            0.52           -0.44           -0.48              -1             1.7              -1            -1.2        5e+03    1.2e-07       10        1   ++ 

     10%|█         | 10/100 [00:00<00:06, 14.33it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.66            0.48           -0.59           -0.53           -0.99             1.8              -1            -1.1        5e+03    0.00043       10     0.98   ++ 
        1           -0.66            0.48           -0.59           -0.53           -0.99             1.8              -1            -1.1        5e+03      1e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57             0.4           -0.62           -0.52            -1.2             1.9              -1            -1.2      4.9e+03    0.00028       10        1   ++ 
        1           -0.57             0.4           -0.62           -0.52            -1.2             1.9              -1            -1.2      4.9e+03    4.8e-07       10        1   ++ 

     12%|█▏        | 12/100 [00:00<00:06, 14.56it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.71            0.46           -0.07            -0.6            -1.1             1.9            -1.2            -1.1        5e+03    0.00042       10     0.99   ++ 
        1           -0.71            0.46           -0.07            -0.6            -1.1             1.9            -1.2            -1.1        5e+03    1.3e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.64            0.46           -0.53           -0.46            -1.1             1.8            -1.1            -1.3      4.8e+03    0.00073       10        1   ++ 
        1           -0.64            0.46           -0.53           -0.46            -1.1             1.8            -1.1            -1.3      4.8e+03    1.3e-06       10        1   ++ 

     14%|█▍        | 14/100 [00:00<00:05, 14.94it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.77            0.59           -0.43           -0.62           -0.91             1.9            -1.1            -1.2        5e+03    0.00026       10     0.99   ++ 
        1           -0.77            0.59           -0.43           -0.62           -0.91             1.9            -1.1            -1.2        5e+03    3.4e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.73            0.47           -0.12           -0.72              -1             2.1            -1.1            -1.1        5e+03     0.0003       10     0.99   ++ 
        1           -0.73            0.47           -0.12           -0.72              -1             2.1            -1.1            -1.1        5e+03    6.5e-07       10        1   ++ 

     16%|█▌        | 16/100 [00:01<00:05, 14.98it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57             0.4           -0.35           -0.52           -0.98             1.9            -1.1            -1.3      4.9e+03    0.00033       10        1   ++ 
        1           -0.57             0.4           -0.35           -0.52           -0.98             1.9            -1.1            -1.3      4.9e+03    2.4e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.65            0.34           -0.29           -0.63            -1.1             2.1            -1.1            -1.2      4.8e+03    0.00022       10        1   ++ 
        1           -0.65            0.34           -0.29           -0.63            -1.1             2.1            -1.1            -1.2      4.8e+03    1.3e-07       10        1   ++ 

     18%|█▊        | 18/100 [00:01<00:05, 15.20it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.75            0.58           -0.81           -0.58              -1             1.8           -0.97            -1.1        5e+03    0.00054       10     0.99   ++ 
        1           -0.75            0.58           -0.81           -0.58              -1             1.8           -0.97            -1.1        5e+03    4.2e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.62            0.34           -0.44           -0.55            -1.1             1.9              -1            -1.1        5e+03    0.00011       10        1   ++ 
        1           -0.62            0.34           -0.44           -0.55            -1.1             1.9              -1            -1.1        5e+03    5.3e-08       10        1   ++ 

     20%|██        | 20/100 [00:01<00:05, 15.20it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.64            0.49           -0.22           -0.55            -1.1             1.9            -1.1            -1.2      4.9e+03    0.00014       10     0.98   ++ 
        1           -0.64            0.49           -0.22           -0.55            -1.1             1.9            -1.1            -1.2      4.9e+03    6.8e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57            0.39           -0.22           -0.61           -0.96             1.9            -1.1            -1.1        5e+03    0.00028       10     0.98   ++ 
        1           -0.57            0.39           -0.22           -0.61           -0.96             1.9            -1.1            -1.1        5e+03      9e-07       10        1   ++ 

     22%|██▏       | 22/100 [00:01<00:05, 15.18it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.51            0.26           -0.33            -0.6            -1.1             2.1           -0.95            -1.1        5e+03    0.00043       10     0.98   ++ 
        1           -0.51            0.26           -0.33            -0.6            -1.1             2.1           -0.95            -1.1        5e+03    9.4e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.61             0.4           -0.29            -0.5              -1             1.8            -1.2            -1.3      4.9e+03    0.00038       10        1   ++ 
        1           -0.61             0.4           -0.29            -0.5              -1             1.8            -1.2            -1.3      4.9e+03      7e-07       10        1   ++ 

     24%|██▍       | 24/100 [00:01<00:04, 15.46it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.36           0.037           -0.17           -0.53            -1.2             1.9              -1           -0.94      5.1e+03     0.0015       10     0.96   ++ 
        1           -0.38           0.059           -0.19           -0.52            -1.2             1.9              -1           -0.96      5.1e+03    8.6e-06    1e+02        1   ++ 
        2           -0.38           0.059           -0.19           -0.52            -1.2             1.9              -1           -0.96      5.1e+03    3.6e-10    1e+02        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.29            0.21           -0.56           -0.57           -0.98             1.9           -0.91            -1.2        5e+03     0.0009       10     0.97   ++ 
        1           -0.29            0.21           -0.56           -0.57           -0.98             1.9           -0.91            -1.2        5e+03    3.6e-06       10        1   ++ 

     26%|██▌       | 26/100 [00:01<00:04, 14.95it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.72            0.58           -0.57           -0.53              -1               2              -1            -1.3      4.9e+03    0.00027       10        1   ++ 
        1           -0.72            0.58           -0.57           -0.53              -1               2              -1            -1.3      4.9e+03    3.6e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.31            0.16           -0.15           -0.31            -1.2             1.9            -1.1            -1.3      4.9e+03    0.00057       10        1   ++ 
        1           -0.31            0.16           -0.15           -0.31            -1.2             1.9            -1.1            -1.3      4.9e+03      3e-06       10        1   ++ 

     28%|██▊       | 28/100 [00:01<00:04, 15.24it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.56            0.35           -0.34           -0.66              -1             1.9            -1.2            -1.2      4.9e+03    0.00017       10        1   ++ 
        1           -0.56            0.35           -0.34           -0.66              -1             1.9            -1.2            -1.2      4.9e+03    1.8e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57            0.37           -0.76           -0.56            -1.1             1.8           -0.97            -1.1        5e+03    0.00031       10     0.99   ++ 
        1           -0.57            0.37           -0.76           -0.56            -1.1             1.8           -0.97            -1.1        5e+03    1.5e-06       10        1   ++ 

     30%|███       | 30/100 [00:02<00:04, 15.24it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.44            0.22           -0.72           -0.47            -1.1             1.8            -1.1            -1.2        5e+03    0.00028       10        1   ++ 
        1           -0.44            0.22           -0.72           -0.47            -1.1             1.8            -1.1            -1.2        5e+03    2.6e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0            -0.6            0.43           -0.44           -0.52            -1.1             1.8            -1.1            -1.2        5e+03    4.7e-05       10        1   ++ 
        1            -0.6            0.43           -0.44           -0.52            -1.1             1.8            -1.1            -1.2        5e+03    8.2e-09       10        1   ++ 

     32%|███▏      | 32/100 [00:02<00:04, 15.46it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.69            0.53           -0.39           -0.67              -1               2            -1.1            -1.1        5e+03    0.00045       10     0.99   ++ 
        1           -0.69            0.53           -0.39           -0.67              -1               2            -1.1            -1.1        5e+03    5.1e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.66            0.57           -0.44           -0.49            -1.1             1.9           -0.97            -1.2        5e+03    0.00037       10     0.98   ++ 
        1           -0.66            0.57           -0.44           -0.49            -1.1             1.9           -0.97            -1.2        5e+03    5.9e-07       10        1   ++ 

     34%|███▍      | 34/100 [00:02<00:04, 15.57it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.69            0.41           -0.39           -0.53            -1.2             1.9            -1.1              -1        5e+03    0.00046       10     0.98   ++ 
        1           -0.69            0.41           -0.39           -0.53            -1.2             1.9            -1.1              -1        5e+03    5.5e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.78            0.54           -0.72           -0.57            -1.1               2              -1            -1.1        5e+03    0.00037       10        1   ++ 
        1           -0.78            0.54           -0.72           -0.57            -1.1               2              -1            -1.1        5e+03    5.5e-06       10        1   ++ 

     36%|███▌      | 36/100 [00:02<00:04, 15.78it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.72            0.54           -0.39           -0.44            -1.1             1.9            -1.1            -1.2      4.9e+03    0.00016       10        1   ++ 
        1           -0.72            0.54           -0.39           -0.44            -1.1             1.9            -1.1            -1.2      4.9e+03    1.5e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.59            0.34           -0.13           -0.69            -1.1               2            -1.1            -1.2      4.8e+03    0.00055       10        1   ++ 
        1           -0.59            0.34           -0.13           -0.69            -1.1               2            -1.1            -1.2      4.8e+03    1.5e-06       10        1   ++ 

     38%|███▊      | 38/100 [00:02<00:03, 15.90it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.72            0.48           -0.55           -0.61            -1.1             1.7           -0.97              -1        5e+03    0.00038       10     0.98   ++ 
        1           -0.72            0.48           -0.55           -0.61            -1.1             1.7           -0.97              -1        5e+03      9e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.59            0.37           -0.34           -0.48            -1.2             1.8            -1.1            -1.1        5e+03    0.00033       10     0.99   ++ 
        1           -0.59            0.37           -0.34           -0.48            -1.2             1.8            -1.1            -1.1        5e+03    2.8e-07       10        1   ++ 

     40%|████      | 40/100 [00:02<00:03, 16.02it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.58            0.44           -0.67           -0.41            -1.1             1.7           -0.92            -1.3      4.9e+03    0.00045       10        1   ++ 
        1           -0.58            0.44           -0.67           -0.41            -1.1             1.7           -0.92            -1.3      4.9e+03    9.7e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57            0.13          -0.038           -0.86           -0.95             1.9            -1.1           -0.88        5e+03     0.0014       10     0.97   ++ 
        1           -0.58            0.14          -0.064           -0.86           -0.95             1.9            -1.1            -0.9        5e+03    7.2e-06    1e+02        1   ++ 
        2           -0.58            0.14          -0.064           -0.86           -0.95             1.9            -1.1            -0.9        5e+03    6.5e-10    1e+02        1   ++ 

     42%|████▏     | 42/100 [00:02<00:03, 15.33it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0            -0.6            0.45           -0.57           -0.58            -1.1               2              -1            -1.2      4.9e+03    0.00022       10        1   ++ 
        1            -0.6            0.45           -0.57           -0.58            -1.1               2              -1            -1.2      4.9e+03    2.3e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.65            0.41           -0.44           -0.61            -1.1             1.9            -1.2            -1.2      4.9e+03    0.00023       10        1   ++ 
        1           -0.65            0.41           -0.44           -0.61            -1.1             1.9            -1.2            -1.2      4.9e+03    2.8e-07       10        1   ++ 

     44%|████▍     | 44/100 [00:02<00:03, 15.48it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57            0.38           -0.52           -0.45            -1.3               2              -1            -1.1        5e+03    0.00023       10        1   ++ 
        1           -0.57            0.38           -0.52           -0.45            -1.3               2              -1            -1.1        5e+03    2.2e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.82            0.51           -0.28           -0.76              -1               2            -1.1            -1.1      4.9e+03    0.00028       10        1   ++ 
        1           -0.82            0.51           -0.28           -0.76              -1               2            -1.1            -1.1      4.9e+03    3.2e-07       10        1   ++ 

     46%|████▌     | 46/100 [00:03<00:03, 15.64it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0            -0.6            0.43           -0.41           -0.43            -1.1             1.9            -1.1            -1.3      4.9e+03    0.00027       10        1   ++ 
        1            -0.6            0.43           -0.41           -0.43            -1.1             1.9            -1.1            -1.3      4.9e+03    1.7e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.58            0.31           -0.19           -0.77           -0.83             1.8           -0.97           -0.97      5.1e+03     0.0015       10     0.95   ++ 
        1           -0.59            0.32           -0.22           -0.77           -0.84             1.8           -0.98           -0.98      5.1e+03    8.1e-06    1e+02        1   ++ 
        2           -0.59            0.32           -0.22           -0.77           -0.84             1.8           -0.98           -0.98      5.1e+03    3.8e-10    1e+02        1   ++ 

     48%|████▊     | 48/100 [00:03<00:03, 15.13it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.59            0.36           -0.59           -0.61              -1               2            -1.1            -1.2      4.9e+03    0.00024       10        1   ++ 
        1           -0.59            0.36           -0.59           -0.61              -1               2            -1.1            -1.2      4.9e+03    1.6e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.55            0.37            -0.5           -0.57              -1             1.8              -1            -1.2      4.9e+03    8.7e-05       10        1   ++ 
        1           -0.55            0.37            -0.5           -0.57              -1             1.8              -1            -1.2      4.9e+03    3.6e-08       10        1   ++ 

     50%|█████     | 50/100 [00:03<00:03, 14.86it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.56            0.36           -0.29           -0.41            -1.1             1.7            -1.2            -1.2        5e+03    0.00024       10        1   ++ 
        1           -0.56            0.36           -0.29           -0.41            -1.1             1.7            -1.2            -1.2        5e+03    2.8e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.68             0.5           -0.36           -0.64           -0.98             1.9            -1.2            -1.3      4.9e+03    0.00043       10        1   ++ 
        1           -0.68             0.5           -0.36           -0.64           -0.98             1.9            -1.2            -1.3      4.9e+03    8.4e-07       10        1   ++ 

     52%|█████▏    | 52/100 [00:03<00:03, 15.17it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57            0.36           -0.32           -0.51            -1.1             1.9            -1.2            -1.3      4.9e+03    0.00046       10        1   ++ 
        1           -0.57            0.36           -0.32           -0.51            -1.1             1.9            -1.2            -1.3      4.9e+03    1.1e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.66            0.45           -0.38            -0.4            -1.2             1.8              -1            -1.2        5e+03    0.00023       10        1   ++ 
        1           -0.66            0.45           -0.38            -0.4            -1.2             1.8              -1            -1.2        5e+03    1.4e-07       10        1   ++ 

     54%|█████▍    | 54/100 [00:03<00:02, 15.41it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.63            0.37          -0.025            -0.6            -1.1               2            -1.3            -1.1      4.9e+03    0.00041       10        1   ++ 
        1           -0.63            0.37          -0.025            -0.6            -1.1               2            -1.3            -1.1      4.9e+03    1.2e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.68            0.54           -0.76           -0.41            -1.2             1.7            -1.1            -1.3      4.9e+03    0.00025       10        1   ++ 
        1           -0.68            0.54           -0.76           -0.41            -1.2             1.7            -1.1            -1.3      4.9e+03    1.4e-06       10        1   ++ 

     56%|█████▌    | 56/100 [00:03<00:02, 15.63it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.49            0.25           -0.32           -0.57            -1.1               2            -1.1            -1.2      4.9e+03     0.0001       10     0.99   ++ 
        1           -0.49            0.25           -0.32           -0.57            -1.1               2            -1.1            -1.2      4.9e+03    9.1e-08       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.75            0.45           -0.35           -0.74            -1.1               2            -1.1           -0.96        5e+03    0.00094       10     0.97   ++ 
        1           -0.75            0.45           -0.35           -0.74            -1.1               2            -1.1           -0.96        5e+03    2.4e-06       10        1   ++ 

     58%|█████▊    | 58/100 [00:03<00:02, 15.77it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.48            0.37           -0.42           -0.49              -1             1.9           -0.96            -1.2        5e+03    0.00023       10     0.99   ++ 
        1           -0.48            0.37           -0.42           -0.49              -1             1.9           -0.96            -1.2        5e+03    3.5e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0            -0.5            0.32           -0.31           -0.44            -1.1             1.9            -1.1            -1.3      4.9e+03    0.00033       10        1   ++ 
        1            -0.5            0.32           -0.31           -0.44            -1.1             1.9            -1.1            -1.3      4.9e+03    2.9e-07       10        1   ++ 

     60%|██████    | 60/100 [00:03<00:02, 15.84it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.75            0.49           -0.28           -0.65            -1.1             1.9            -1.1            -1.1      4.9e+03    0.00017       10        1   ++ 
        1           -0.75            0.49           -0.28           -0.65            -1.1             1.9            -1.1            -1.1      4.9e+03    7.9e-08       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.46            0.28           -0.28           -0.42            -1.1               2            -1.1            -1.3      4.9e+03    0.00049       10        1   ++ 
        1           -0.46            0.28           -0.28           -0.42            -1.1               2            -1.1            -1.3      4.9e+03      8e-07       10        1   ++ 

     62%|██████▏   | 62/100 [00:04<00:02, 15.90it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.68             0.4           -0.45           -0.51            -1.1             1.8            -1.1            -1.1      4.9e+03     0.0001       10        1   ++ 
        1           -0.68             0.4           -0.45           -0.51            -1.1             1.8            -1.1            -1.1      4.9e+03    3.9e-08       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.69            0.45           -0.33           -0.58            -1.2             1.9            -1.2            -1.1        5e+03    0.00029       10        1   ++ 
        1           -0.69            0.45           -0.33           -0.58            -1.2             1.9            -1.2            -1.1        5e+03    2.6e-07       10        1   ++ 

     64%|██████▍   | 64/100 [00:04<00:02, 16.06it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.59            0.45            -0.5           -0.49            -1.1             1.9            -1.1            -1.2      4.9e+03    0.00012       10        1   ++ 
        1           -0.59            0.45            -0.5           -0.49            -1.1             1.9            -1.1            -1.2      4.9e+03    4.5e-08       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.66            0.46           -0.26           -0.64            -1.1             1.9            -1.2            -1.2      4.9e+03    0.00019       10        1   ++ 
        1           -0.66            0.46           -0.26           -0.64            -1.1             1.9            -1.2            -1.2      4.9e+03      2e-07       10        1   ++ 

     66%|██████▌   | 66/100 [00:04<00:02, 16.06it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0            -0.5            0.26           -0.21           -0.56              -1             1.8            -1.2            -1.2        5e+03    0.00034       10        1   ++ 
        1            -0.5            0.26           -0.21           -0.56              -1             1.8            -1.2            -1.2        5e+03    5.2e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.65            0.44           -0.63           -0.52            -1.3             2.1            -1.2            -1.2      4.8e+03    0.00073       10        1   ++ 
        1           -0.66            0.45           -0.69           -0.52            -1.3             2.1            -1.2            -1.2      4.8e+03    6.6e-06    1e+02        1   ++ 
        2           -0.66            0.45           -0.69           -0.52            -1.3             2.1            -1.2            -1.2      4.8e+03    3.5e-09    1e+02        1   ++ 

     68%|██████▊   | 68/100 [00:04<00:02, 15.41it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57            0.32           -0.33           -0.62            -1.1             1.8            -1.1            -1.1        5e+03    0.00034       10     0.98   ++ 
        1           -0.57            0.32           -0.33           -0.62            -1.1             1.8            -1.1            -1.1        5e+03    3.9e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.46            0.29          -0.098           -0.47            -1.1             1.9            -1.2            -1.2        5e+03    0.00058       10        1   ++ 
        1           -0.46            0.29          -0.098           -0.47            -1.1             1.9            -1.2            -1.2        5e+03    1.9e-06       10        1   ++ 

     70%|███████   | 70/100 [00:04<00:01, 15.52it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.61            0.47           -0.34           -0.38            -1.2             1.8            -1.1            -1.3      4.9e+03    0.00032       10        1   ++ 
        1           -0.61            0.47           -0.34           -0.38            -1.2             1.8            -1.1            -1.3      4.9e+03    1.7e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.52             0.3           -0.48           -0.53            -1.2             1.9              -1            -1.2      4.9e+03    0.00043       10        1   ++ 
        1           -0.52             0.3           -0.48           -0.53            -1.2             1.9              -1            -1.2      4.9e+03    1.1e-06       10        1   ++ 

     72%|███████▏  | 72/100 [00:04<00:01, 15.53it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.59            0.46           -0.31           -0.39            -1.1             1.8            -1.1            -1.3      4.9e+03    0.00049       10        1   ++ 
        1           -0.59            0.46           -0.31           -0.39            -1.1             1.8            -1.1            -1.3      4.9e+03    5.4e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.56            0.21           -0.59           -0.61            -1.2             1.9              -1              -1      4.9e+03    0.00031       10     0.99   ++ 
        1           -0.56            0.21           -0.59           -0.61            -1.2             1.9              -1              -1      4.9e+03    1.4e-06       10        1   ++ 

     74%|███████▍  | 74/100 [00:04<00:01, 15.74it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.62             0.5              -1           -0.41            -1.1             1.8           -0.95            -1.3        5e+03    0.00055       10        1   ++ 
        1           -0.62             0.5            -1.1           -0.41            -1.1             1.8           -0.95            -1.3        5e+03    2.2e-05    1e+02        1   ++ 
        2           -0.62             0.5            -1.1           -0.41            -1.1             1.8           -0.95            -1.3        5e+03    4.4e-08    1e+02        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.58            0.37           -0.41           -0.59            -1.1               2            -1.1            -1.2      4.9e+03    0.00011       10        1   ++ 
        1           -0.58            0.37           -0.41           -0.59            -1.1               2            -1.1            -1.2      4.9e+03    5.2e-08       10        1   ++ 

     76%|███████▌  | 76/100 [00:04<00:01, 15.28it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57             0.3           -0.13           -0.65            -1.1               2            -1.2            -1.1      4.9e+03    0.00028       10     0.99   ++ 
        1           -0.57             0.3           -0.13           -0.65            -1.1               2            -1.2            -1.1      4.9e+03    4.7e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.72            0.43           -0.47           -0.55            -1.2               2            -1.1            -1.1      4.9e+03    0.00011       10        1   ++ 
        1           -0.72            0.43           -0.47           -0.55            -1.2               2            -1.1            -1.1      4.9e+03    1.8e-07       10        1   ++ 

     78%|███████▊  | 78/100 [00:05<00:01, 15.70it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.66            0.53           -0.62            -0.5            -1.1             1.8              -1            -1.2        5e+03    0.00013       10        1   ++ 
        1           -0.66            0.53           -0.62            -0.5            -1.1             1.8              -1            -1.2        5e+03    1.3e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.67            0.51           -0.49           -0.44            -1.1               2            -1.1            -1.3      4.9e+03    0.00038       10        1   ++ 
        1           -0.67            0.51           -0.49           -0.44            -1.1               2            -1.1            -1.3      4.9e+03    5.2e-07       10        1   ++ 

     80%|████████  | 80/100 [00:05<00:01, 15.79it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.75            0.62           -0.39           -0.55              -1             1.9           -0.95            -1.1        5e+03    0.00043       10     0.98   ++ 
        1           -0.75            0.62           -0.39           -0.55              -1             1.9           -0.95            -1.1        5e+03    9.5e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.45            0.23           -0.33           -0.55              -1             1.8            -1.1            -1.2        5e+03    0.00018       10     0.97   ++ 
        1           -0.45            0.23           -0.33           -0.55              -1             1.8            -1.1            -1.2        5e+03      4e-07       10        1   ++ 

     82%|████████▏ | 82/100 [00:05<00:01, 15.91it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.62            0.48           -0.76           -0.42            -1.1             1.9           -0.98            -1.2        5e+03    0.00022       10        1   ++ 
        1           -0.62            0.48           -0.76           -0.42            -1.1             1.9           -0.98            -1.2        5e+03    1.6e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.63            0.29           -0.24           -0.53            -1.2               2            -1.2              -1        5e+03     0.0006       10     0.99   ++ 
        1           -0.63            0.29           -0.24           -0.53            -1.2               2            -1.2              -1        5e+03      1e-06       10        1   ++ 

     84%|████████▍ | 84/100 [00:05<00:01, 15.95it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0            -0.6            0.26          -0.019           -0.65            -1.1               2            -1.2            -1.1      4.9e+03    0.00042       10        1   ++ 
        1            -0.6            0.26          -0.019           -0.65            -1.1               2            -1.2            -1.1      4.9e+03    1.2e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.57            0.31           -0.51           -0.63            -1.1             1.8           -0.95              -1        5e+03    0.00058       10     0.96   ++ 
        1           -0.57            0.31           -0.51           -0.63            -1.1             1.8           -0.95              -1        5e+03      2e-06       10        1   ++ 

     86%|████████▌ | 86/100 [00:05<00:00, 16.21it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.52            0.35           -0.56            -0.6           -0.97               2            -1.1            -1.2      4.9e+03    0.00024       10        1   ++ 
        1           -0.52            0.35           -0.56            -0.6           -0.97               2            -1.1            -1.2      4.9e+03    1.1e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.56            0.46           -0.15           -0.33            -1.2             1.9            -1.2            -1.4      4.9e+03      0.001       10        1   ++ 
        1           -0.56            0.46           -0.15           -0.33            -1.2             1.9            -1.2            -1.4      4.9e+03    3.6e-06       10        1   ++ 

     88%|████████▊ | 88/100 [00:05<00:00, 16.30it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.62             0.6           -0.56           -0.42            -1.1             1.8              -1            -1.4      4.8e+03     0.0016       10        1   ++ 
        1           -0.62            0.61           -0.56           -0.42            -1.2             1.8              -1            -1.4      4.8e+03    6.2e-06    1e+02        1   ++ 
        2           -0.62            0.61           -0.56           -0.42            -1.2             1.8              -1            -1.4      4.8e+03    9.2e-11    1e+02        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.53            0.38            -0.4           -0.37            -1.1             1.9            -1.2            -1.3      4.9e+03    0.00033       10        1   ++ 
        1           -0.53            0.38            -0.4           -0.37            -1.1             1.9            -1.2            -1.3      4.9e+03    4.7e-07       10        1   ++ 

     90%|█████████ | 90/100 [00:05<00:00, 15.59it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.61            0.39           -0.33           -0.46            -1.1             1.9            -1.1            -1.2        5e+03    0.00011       10        1   ++ 
        1           -0.61            0.39           -0.33           -0.46            -1.1             1.9            -1.1            -1.2        5e+03    5.3e-08       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.54            0.39           -0.25           -0.49            -1.2               2            -1.1            -1.2        5e+03    0.00023       10     0.99   ++ 
        1           -0.54            0.39           -0.25           -0.49            -1.2               2            -1.1            -1.2        5e+03    5.8e-07       10        1   ++ 

     92%|█████████▏| 92/100 [00:05<00:00, 15.33it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.48            0.37              -1           -0.44            -1.1               2              -1            -1.3      4.9e+03    0.00083       10      1.1   ++ 
        1           -0.48            0.37            -1.2           -0.44            -1.1               2              -1            -1.3      4.9e+03    5.1e-05    1e+02        1   ++ 
        2           -0.48            0.37            -1.2           -0.44            -1.1               2              -1            -1.3      4.9e+03    2.4e-07    1e+02        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.59            0.39           -0.57           -0.51            -1.2             1.9              -1            -1.2      4.9e+03    9.5e-05       10        1   ++ 
        1           -0.59            0.39           -0.57           -0.51            -1.2             1.9              -1            -1.2      4.9e+03    8.4e-08       10        1   ++ 

     94%|█████████▍| 94/100 [00:06<00:00, 14.93it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.52            0.32           -0.51            -0.5            -1.1             1.9            -1.1            -1.2      4.9e+03    9.4e-05       10        1   ++ 
        1           -0.52            0.32           -0.51            -0.5            -1.1             1.9            -1.1            -1.2      4.9e+03    4.1e-08       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.64            0.42          -0.073           -0.47            -1.1               2              -1            -1.1        5e+03    0.00031       10     0.99   ++ 
        1           -0.64            0.42          -0.073           -0.47            -1.1               2              -1            -1.1        5e+03    1.6e-06       10        1   ++ 

     96%|█████████▌| 96/100 [00:06<00:00, 15.28it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.71             0.4           -0.16           -0.56            -1.2             1.8            -1.1              -1        5e+03    0.00033       10     0.98   ++ 
        1           -0.71             0.4           -0.16           -0.56            -1.2             1.8            -1.1              -1        5e+03    8.6e-07       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.49            0.32           -0.37           -0.41            -1.1             1.7            -1.1            -1.2        5e+03    0.00015       10     0.99   ++ 
        1           -0.49            0.32           -0.37           -0.41            -1.1             1.7            -1.1            -1.2        5e+03    1.1e-07       10        1   ++ 

     98%|█████████▊| 98/100 [00:06<00:00, 15.47it/s]Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0           -0.75            0.54           -0.62           -0.51              -1             1.9            -1.1            -1.3      4.9e+03    0.00071       10        1   ++ 
        1           -0.75            0.54           -0.62           -0.51              -1             1.9            -1.1            -1.3      4.9e+03    3.1e-06       10        1   ++ 
    Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] 
    ** Optimization: Newton with trust region for simple bounds 
    Iter.         ASC_CAR    ASC_CAR_male ASC_CAR_with_ga       ASC_TRAIN  ASC_TRAIN_male ASC_TRAIN_with_          B_COST          B_TIME     Function    Relgrad   Radius      Rho      
        0            -0.3            0.16           -0.23           -0.59              -1             1.8            -1.1            -1.2        5e+03    0.00072       10     0.96   ++ 
        1            -0.3            0.16           -0.23           -0.59              -1             1.8            -1.1            -1.2        5e+03    4.4e-06       10        1   ++ 

    100%|██████████| 100/100 [00:06<00:00, 15.79it/s]
    100%|██████████| 100/100 [00:06<00:00, 15.45it/s]
    Results saved in file b01logit_bis.html 
    Results saved in file b01logit_bis.pickle 




.. GENERATED FROM PYTHON SOURCE LINES 136-138

Get the results in a pandas table.


.. GENERATED FROM PYTHON SOURCE LINES 138-143

.. code-block:: default

    print('Parameters')
    print('----------')
    pandas_results = results.getEstimatedParameters()
    pandas_results





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Parameters
    ----------


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Value</th>
          <th>Rob. Std err</th>
          <th>Rob. t-test</th>
          <th>Rob. p-value</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>ASC_CAR</th>
          <td>-0.612868</td>
          <td>0.097955</td>
          <td>-6.256612</td>
          <td>3.934308e-10</td>
        </tr>
        <tr>
          <th>ASC_CAR_male</th>
          <td>0.408127</td>
          <td>0.102408</td>
          <td>3.985298</td>
          <td>6.739540e-05</td>
        </tr>
        <tr>
          <th>ASC_CAR_with_ga</th>
          <td>-0.414517</td>
          <td>0.198807</td>
          <td>-2.085019</td>
          <td>3.706757e-02</td>
        </tr>
        <tr>
          <th>ASC_TRAIN</th>
          <td>-0.534244</td>
          <td>0.102923</td>
          <td>-5.190713</td>
          <td>2.094900e-07</td>
        </tr>
        <tr>
          <th>ASC_TRAIN_male</th>
          <td>-1.103399</td>
          <td>0.085401</td>
          <td>-12.920189</td>
          <td>0.000000e+00</td>
        </tr>
        <tr>
          <th>ASC_TRAIN_with_ga</th>
          <td>1.889379</td>
          <td>0.091601</td>
          <td>20.626287</td>
          <td>0.000000e+00</td>
        </tr>
        <tr>
          <th>B_COST</th>
          <td>-1.089785</td>
          <td>0.072442</td>
          <td>-15.043632</td>
          <td>0.000000e+00</td>
        </tr>
        <tr>
          <th>B_TIME</th>
          <td>-1.173068</td>
          <td>0.106327</td>
          <td>-11.032621</td>
          <td>0.000000e+00</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 144-146

Get general statistics.


.. GENERATED FROM PYTHON SOURCE LINES 146-152

.. code-block:: default

    print('General statistics')
    print('------------------')
    stats = results.getGeneralStatistics()
    for description, (value, formatting) in stats.items():
        print(f'{description}: {value:{formatting}}')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    General statistics
    ------------------
    Number of estimated parameters: 8
    Sample size: 6768
    Excluded observations: 3960
    Null log likelihood: -6964.663
    Init log likelihood: -5533.155
    Final log likelihood: -4943.895
    Likelihood ratio test for the null model: 4041.535
    Rho-square for the null model: 0.29
    Rho-square-bar for the null model: 0.289
    Likelihood ratio test for the init. model: 1178.519
    Rho-square for the init. model: 0.106
    Rho-square-bar for the init. model: 0.105
    Akaike Information Criterion: 9903.791
    Bayesian Information Criterion: 9958.351
    Final gradient norm: 5.3376E-03
    Bootstrapping time: 0:00:06.475044
    Nbr of threads: 12




.. GENERATED FROM PYTHON SOURCE LINES 153-155

Messages from the optimization algorithm.


.. GENERATED FROM PYTHON SOURCE LINES 155-160

.. code-block:: default

    print('Optimization algorithm')
    print('----------------------')
    for description, message in results.data.optimizationMessages.items():
        print(f'{description}:\t{message}')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Optimization algorithm
    ----------------------
    Relative gradient:      6.556090729381411e-07
    Cause of termination:   Relative gradient = 6.6e-07 <= 6.1e-06
    Number of function evaluations: 5
    Number of gradient evaluations: 5
    Number of hessian evaluations:  4
    Algorithm:      Newton with trust region for simple bound constraints
    Number of iterations:   4
    Proportion of Hessian calculation:      4/4 = 100.0%
    Optimization time:      0:00:00.080396




.. GENERATED FROM PYTHON SOURCE LINES 161-163

Generate the file in Alogit format.


.. GENERATED FROM PYTHON SOURCE LINES 163-166

.. code-block:: default

    results.writeF12(robustStdErr=True)
    print(f'Estimation results in ALogit format generated: {results.data.F12FileName}')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Results saved in file b01logit_bis.F12 
    Estimation results in ALogit format generated: b01logit_bis.F12




.. GENERATED FROM PYTHON SOURCE LINES 167-169

Generate LaTeX code with the results.


.. GENERATED FROM PYTHON SOURCE LINES 169-171

.. code-block:: default

    results.writeLaTeX()
    print(f'Estimation results in LaTeX format generated: {results.data.latexFileName}')




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Results saved in file b01logit_bis.tex 
    Estimation results in LaTeX format generated: b01logit_bis.tex





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 6.800 seconds)


.. _sphx_glr_download_auto_examples_swissmetro_plot_b01logit_bis.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_b01logit_bis.py <plot_b01logit_bis.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_b01logit_bis.ipynb <plot_b01logit_bis.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_