Note
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Assisted specification¶
Example of the estimation of several versions of the model using assisted specification algorithm. The catalog of specifications is defined in Specification of a catalog of models . Compared to 21a. Assisted specification, the number of specifications exceeds the maximum limit, so a heuristic is applied. See Bierlaire and Ortelli, 2023 for a detailed description of the use of the assisted specification algorithm.
Michel Bierlaire, EPFL Sat Jun 28 2025, 12:25:12
import biogeme.biogeme_logging as blog
from biogeme.assisted import AssistedSpecification
from biogeme.catalog import count_number_of_specifications
from biogeme.multiobjectives import aic_bic_dimension
from biogeme.results_processing import compile_estimation_results
from plot_b22b_multiple_models_spec import PARETO_FILE_NAME, the_biogeme
logger = blog.get_screen_logger(blog.INFO)
logger.info('Example b22multiple_models')
Example b22multiple_models
nbr = count_number_of_specifications(the_biogeme.log_like)
if nbr is None:
print('There are too many possible specifications to be enumerated')
else:
print(f'There are {nbr} possible specifications')
There are 504 possible specifications
Creation of the object capturing the assisted specification algorithm. Its constructor takes three arguments:
the biogeme object containing the specifications and the database,
an object defining the objectives to minimize. Here, we use three objectives: AIC, BIC and number of parameters.
the name of the file where the estimated are saved, and organized into a Pareto set.
assisted_specification = AssistedSpecification(
biogeme_object=the_biogeme,
multi_objectives=aic_bic_dimension,
pareto_file_name=PARETO_FILE_NAME,
)
Unable to read file b22_multiple_models.pareto. Pareto set empty.
The algorithm is run.
non_dominated_models = assisted_specification.run()
Biogeme parameters read from biogeme.toml.
Model with 4 unknown parameters [max: 50]
*** Estimate b07everything_000185
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost asc_car Function Relgrad Radius Rho
0 -0.92 -0.67 -0.88 -0.49 5.4e+03 0.041 10 1.1 ++
1 -0.73 -1.2 -1 -0.18 5.3e+03 0.0072 1e+02 1.1 ++
2 -0.7 -1.3 -1.1 -0.16 5.3e+03 0.00018 1e+03 1 ++
3 -0.7 -1.3 -1.1 -0.16 5.3e+03 1.1e-07 1e+03 1 ++
default_specification=asc:no_seg;train_cost_catalog:linear;train_headway_catalog:without_headway;train_tt_catalog:linear
The number of possible specifications [504] exceeds the maximum number [100]. A heuristic algorithm is applied.
*** VNS ***
asc:no_seg;train_cost_catalog:linear;train_headway_catalog:without_headway;train_tt_catalog:linear [10670.504013832326, np.float64(10697.78385743747), 4]
Initial pareto: 1
Attempt 0/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000186
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -1 -1 0 0 0 0 0 -0.079 0 0 5.7e+03 0.036 10 1.1 ++
1 -0.53 -2.7 0 0 0 0 0 0.14 0 0 5.6e+03 0.012 1e+02 1.1 ++
2 -0.42 -3 0 0 0 0 0 0.19 0 0 5.6e+03 0.00049 1e+03 1 ++
3 -0.42 -3 0 0 0 0 0 0.19 0 0 5.6e+03 6.5e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b07everything_000187
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.88 0.29 -0.0088 -0.77 -1 -0.19 -0.18 -0.056 5.4e+03 0.038 10 1.1 ++
1 -1.4 0.9 0.75 -1.1 -2.2 -0.24 0.19 -0.068 5.2e+03 0.009 1e+02 1.1 ++
2 -1.6 1.1 0.95 -1.2 -2.4 -0.24 0.2 -0.19 5.2e+03 0.00085 1e+03 1 ++
3 -1.6 1.2 0.98 -1.2 -2.4 -0.24 0.2 -0.2 5.2e+03 9.5e-06 1e+04 1 ++
4 -1.6 1.2 0.98 -1.2 -2.4 -0.24 0.2 -0.2 5.2e+03 1.2e-09 1e+04 1 ++
Considering neighbor 1/20 for current solution
*** New pareto solution:
asc:LUGGAGE;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:linear [10415.149498177643, np.float64(10469.709185387932), 8]
Attempt 1/100
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000188
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.93 0.5 -1 0 0 0 -0.0031 0 0 -0.65 -0.25 0 0 5.6e+03 2.7 10 1 ++
1 -0.6 1.8 -2.8 0 0 0 -0.005 0 0 0.11 -1.1 0 0 5.2e+03 0.68 1e+02 1 ++
2 -0.63 1.8 -3 0 0 0 -0.0059 0 0 0.094 -1.4 0 0 5.2e+03 0.036 1e+03 1 ++
3 -0.63 1.8 -3 0 0 0 -0.006 0 0 0.094 -1.4 0 0 5.2e+03 0.00017 1e+04 1 ++
4 -0.63 1.8 -3 0 0 0 -0.006 0 0 0.094 -1.4 0 0 5.2e+03 3.4e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b07everything_000189
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost lambda_cost b_headway asc_car Function Relgrad Radius Rho
0 -0.93 -1 -0.55 1 -0.0032 -0.77 5.6e+03 2.4 10 1 ++
1 -0.35 -2.9 -1.1 0.23 -0.0053 -0.23 5.4e+03 0.17 1e+02 0.99 ++
2 -0.33 -3.1 -1.1 0.64 -0.0053 -0.23 5.3e+03 0.005 1e+02 0.85 +
3 -0.32 -3.1 -1.1 0.59 -0.0054 -0.22 5.3e+03 0.00053 1e+03 1 ++
4 -0.32 -3.1 -1.1 0.59 -0.0054 -0.22 5.3e+03 1.7e-05 1e+04 1 ++
5 -0.32 -3.1 -1.1 0.59 -0.0054 -0.22 5.3e+03 1.1e-06 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000190
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost lambda_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 0 0 0 -0.3 2 0 0 -0.13 0 0 6e+03 0.08 10 1 ++
1 -1 0 0 0 -0.3 2 0 0 -0.13 0 0 6e+03 0.08 5 -8.3e+05 -
2 -1 0 0 0 -0.3 2 0 0 -0.13 0 0 6e+03 0.08 2.5 -42 -
3 -1.5 0 0 0 -1.7 -0.5 0 0 -1.3 0 0 5.9e+03 0.082 2.5 0.2 +
4 -1.5 0 0 0 -1.7 -0.5 0 0 -1.3 0 0 5.9e+03 0.082 1 -0.66 -
5 -1.4 0 0 0 -0.71 -0.45 0 0 -0.58 0 0 5.7e+03 0.026 1 0.82 +
6 -1.7 0 0 0 -1.4 0.59 0 0 -0.93 0 0 5.7e+03 0.026 1 0.38 +
7 -1.7 0 0 0 -1.1 0.38 0 0 -0.88 0 0 5.7e+03 0.0015 10 1.1 ++
8 -1.7 0 0 0 -1.1 0.17 0 0 -0.88 0 0 5.7e+03 0.0012 1e+02 0.98 ++
9 -1.7 0 0 0 -1.1 0.17 0 0 -0.88 0 0 5.7e+03 3.3e-06 1e+02 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000191
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 3 10 1.1 ++
1 5.3e+03 0.43 1e+02 1.1 ++
2 5.3e+03 0.038 1e+03 1 ++
3 5.3e+03 0.00034 1e+04 1 ++
4 5.3e+03 3.1e-08 1e+04 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 2/100
Considering neighbor 0/20 for current solution
Attempt 3/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000192
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.5e+03 0.039 10 1 ++
1 5.4e+03 0.0087 1e+02 1.1 ++
2 5.4e+03 0.00053 1e+03 1.1 ++
3 5.4e+03 4.5e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000193
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.54 -0.84 0.24 -0.0055 -1 -0.91 1 -0.0017 -0.49 -0.18 -0.22 -0.058 5.4e+03 2.6 10 1.1 ++
1 -0.22 -1.1 0.75 0.34 -2.7 -1.3 0.24 -0.0049 -0.56 0.25 -0.02 -0.39 5.1e+03 0.48 1e+02 1 ++
2 -0.23 -1.1 0.93 0.49 -3 -1.2 0.46 -0.0058 -0.51 0.29 -0.051 -0.49 5.1e+03 0.05 1e+03 1.1 ++
3 -0.24 -1.1 0.96 0.51 -3 -1.1 0.56 -0.0059 -0.51 0.29 -0.051 -0.5 5.1e+03 0.00073 1e+04 1 ++
4 -0.24 -1.1 0.96 0.51 -3 -1.1 0.56 -0.0059 -0.51 0.29 -0.051 -0.5 5.1e+03 3.6e-06 1e+04 1 ++
Considering neighbor 1/20 for current solution
*** New pareto solution:
asc:MALE-LUGGAGE;train_cost_catalog:boxcox;train_headway_catalog:with_headway;train_tt_catalog:sqrt [10279.062298118724, np.float64(10360.901828934157), 12]
Attempt 4/100
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000194
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -0.86 0.79 -0.82 -1 1 -0.48 -0.37 5.3e+03 0.04 10 1.1 ++
1 -1.4 2 -1 -1.9 -0.36 -0.45 -1.5 5.1e+03 0.054 10 0.67 +
2 -1.5 2.1 -0.98 -1.3 -0.3 -0.34 -1.8 5e+03 0.0023 1e+02 1 ++
3 -1.5 2.1 -1 -1.6 0.14 -0.38 -1.8 5e+03 0.0071 1e+02 0.82 +
4 -1.5 2.1 -1.1 -1.5 0.12 -0.35 -1.8 5e+03 0.00014 1e+03 0.99 ++
5 -1.5 2.1 -1.1 -1.5 0.12 -0.35 -1.8 5e+03 1.3e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
asc:GA;train_cost_catalog:boxcox;train_headway_catalog:without_headway;train_tt_catalog:linear [10019.113148838624, np.float64(10066.852875147626), 7]
Attempt 5/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000195
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 0.038 10 1.1 ++
1 5.3e+03 0.013 1e+02 1.1 ++
2 5.3e+03 0.0006 1e+03 1 ++
3 5.3e+03 3.1e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 6/100
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b07everything_000196
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.66 -0.088 -0.015 -1 -0.3 1 -0.35 -0.27 -0.022 5.6e+03 0.057 10 1 ++
1 -0.66 -0.088 -0.015 -1 -0.3 1 -0.35 -0.27 -0.022 5.6e+03 0.057 4.5 -2.9e+05 -
2 -0.66 -0.088 -0.015 -1 -0.3 1 -0.35 -0.27 -0.022 5.6e+03 0.057 2.2 -1.3e+02 -
3 -0.66 -0.088 -0.015 -1 -0.3 1 -0.35 -0.27 -0.022 5.6e+03 0.057 1.1 -4.7 -
4 -1.1 0.83 0.97 -1.6 -0.86 2.1 0.0079 -0.16 -0.75 5.6e+03 0.089 1.1 0.13 +
5 -1.1 0.83 0.97 -1.6 -0.86 2.1 0.0079 -0.16 -0.75 5.6e+03 0.089 0.56 -1.2 -
6 -1.1 1.1 0.92 -1.6 -0.3 2.1 0.12 -0.074 -0.72 5.4e+03 0.012 0.56 0.89 +
7 -1.3 1.1 0.91 -1.7 -0.56 1.5 0.0027 -0.18 -0.7 5.4e+03 0.031 5.6 0.96 ++
8 -1.3 1.1 0.91 -1.7 -0.56 1.5 0.0027 -0.18 -0.7 5.4e+03 0.031 1.1 -1.5 -
9 -1.3 1.1 0.92 -1.6 -0.9 0.41 0.02 0.017 -0.68 5.3e+03 0.0093 11 0.95 ++
10 -1.4 1.2 1 -1.5 -1.1 0.41 -0.084 -0.068 -0.65 5.3e+03 0.00049 1.1e+02 1 ++
11 -1.4 1.2 1 -1.5 -1.1 0.41 -0.084 -0.068 -0.65 5.3e+03 5.2e-06 1.1e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b07everything_000197
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -1 0.26 0.015 -0.7 -0.83 1 -0.4 -0.16 -0.087 5.4e+03 0.043 10 1.1 ++
1 -1.4 0.9 0.74 -1.1 -1.3 0.3 -0.34 -0.016 -0.48 5.3e+03 0.011 1e+02 0.98 ++
2 -1.6 1.1 0.94 -1.1 -1.1 0.52 -0.28 -0.028 -0.55 5.3e+03 0.0015 1e+03 1.1 ++
3 -1.6 1.2 0.97 -1.1 -1.1 0.6 -0.27 -0.028 -0.54 5.3e+03 0.00012 1e+04 1 ++
4 -1.6 1.2 0.97 -1.1 -1.1 0.6 -0.27 -0.028 -0.54 5.3e+03 3.6e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000198
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -1 0.4 0.04 -0.65 0 0 0 0 0 -0.29 -0.071 -0.12 0 0 5.6e+03 0.037 10 1.1 ++
1 -1.2 0.89 0.71 -1.1 0 0 0 0 0 0.045 -0.044 -0.64 0 0 5.5e+03 0.0076 1e+02 1.1 ++
2 -1.4 1.1 0.89 -1.1 0 0 0 0 0 0.057 -0.046 -0.68 0 0 5.5e+03 0.00067 1e+03 1.1 ++
3 -1.4 1.1 0.91 -1.1 0 0 0 0 0 0.058 -0.046 -0.69 0 0 5.5e+03 6.1e-06 1e+04 1 ++
4 -1.4 1.1 0.91 -1.1 0 0 0 0 0 0.058 -0.046 -0.69 0 0 5.5e+03 5.2e-10 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000199
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost lambda_cost asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.61 0.03 -0.014 -1 1.5 -0.47 1 -0.44 -0.34 -0.03 5.7e+03 0.076 1 0.86 +
1 -1.1 1 0.08 -1.8 0.52 -1.2 1.2 -0.076 -0.037 -0.16 5.3e+03 0.019 10 0.9 ++
2 -1.4 1.1 1.2 -1.6 0.46 -1.2 0.73 -0.1 -0.063 -0.55 5.3e+03 0.0066 1e+02 1.1 ++
3 -1.4 1.2 0.97 -1.6 0.44 -1.1 0.58 -0.089 -0.064 -0.62 5.3e+03 0.00077 1e+03 1.1 ++
4 -1.4 1.2 0.95 -1.5 0.44 -1.1 0.55 -0.087 -0.065 -0.63 5.3e+03 3.6e-05 1e+04 1 ++
5 -1.4 1.2 0.95 -1.5 0.44 -1.1 0.55 -0.087 -0.065 -0.63 5.3e+03 3.4e-08 1e+04 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000200
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.69 0.53 -1 1.8 0 0 0 0 0 -0.57 -0.28 0 0 6e+03 0.1 1 0.6 +
1 -1.1 1.5 -0.78 1.5 0 0 0 0 0 0.15 -0.67 0 0 5.4e+03 0.034 10 0.95 ++
2 -1.1 1.5 -0.78 1.5 0 0 0 0 0 0.15 -0.67 0 0 5.4e+03 0.034 1.3 -3.9 -
3 -0.93 2.1 -1.8 0.15 0 0 0 0 0 0.33 -1.1 0 0 5.2e+03 0.017 1.3 0.82 +
4 -0.84 1.8 -1.6 0.31 0 0 0 0 0 0.25 -1.3 0 0 5.2e+03 0.0014 13 1 ++
5 -0.87 1.8 -1.6 0.34 0 0 0 0 0 0.24 -1.4 0 0 5.2e+03 3.9e-05 1.3e+02 1 ++
6 -0.87 1.8 -1.6 0.34 0 0 0 0 0 0.24 -1.4 0 0 5.2e+03 2.2e-08 1.3e+02 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b07everything_000201
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -0.53 0.2 -1 -0.23 1 -0.0043 -0.53 -0.13 5.5e+03 2.5 10 1 ++
1 -0.53 0.2 -1 -0.23 1 -0.0043 -0.53 -0.13 5.5e+03 2.5 4.5 -3e+05 -
2 -0.53 0.2 -1 -0.23 1 -0.0043 -0.53 -0.13 5.5e+03 2.5 2.2 -1.1e+02 -
3 -0.53 0.2 -1 -0.23 1 -0.0043 -0.53 -0.13 5.5e+03 2.5 1.1 -3.7 -
4 -0.75 1.3 -1.6 -1.2 1.1 -0.0031 -0.22 -0.49 5.1e+03 0.22 11 1 ++
5 -0.75 1.3 -1.6 -1.2 1.1 -0.0031 -0.22 -0.49 5.1e+03 0.22 1.1 -6.5 -
6 -0.73 2.4 -1.7 -1.4 0.48 -0.007 -0.27 -0.99 5e+03 0.27 11 1 ++
7 -0.94 2.2 -1.6 -1.7 -0.12 -0.0061 -0.31 -1.8 4.9e+03 0.021 11 0.79 +
8 -0.93 2.2 -1.6 -1.5 -0.069 -0.0061 -0.28 -1.8 4.9e+03 0.00089 1.1e+02 1 ++
9 -0.92 2.2 -1.6 -1.5 -0.038 -0.0061 -0.28 -1.9 4.9e+03 4.3e-05 1.1e+03 1 ++
10 -0.92 2.2 -1.6 -1.5 -0.038 -0.0061 -0.28 -1.9 4.9e+03 3.9e-07 1.1e+03 1 ++
Considering neighbor 5/20 for current solution
*** New pareto solution:
asc:GA;train_cost_catalog:boxcox;train_headway_catalog:with_headway;train_tt_catalog:log [9880.76215679441, np.float64(9935.321844004699), 8]
Attempt 7/100
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000202
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.9 0.16 -0.013 0.76 -0.85 -1 1 -0.26 -0.21 -0.05 -0.33 5.2e+03 0.046 10 1.1 ++
1 -1.7 0.52 0.36 1.8 -1 -1.9 -0.33 -0.52 0.13 -0.15 -1.5 5.1e+03 0.052 10 0.72 +
2 -1.9 0.66 0.5 1.9 -0.97 -1.3 -0.27 -0.41 0.11 -0.47 -1.8 5e+03 0.0033 1e+02 1 ++
3 -1.9 0.71 0.57 1.9 -1 -1.6 0.12 -0.44 0.12 -0.4 -1.9 5e+03 0.0057 1e+02 0.87 +
4 -1.9 0.71 0.57 1.9 -1 -1.5 0.11 -0.42 0.11 -0.39 -1.8 5e+03 7.3e-05 1e+03 0.99 ++
5 -1.9 0.71 0.57 1.9 -1 -1.5 0.11 -0.42 0.11 -0.39 -1.8 5e+03 1.8e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000203
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.9 1 -0.68 0 0 0 0 0 -0.38 -0.45 0 0 5.5e+03 0.041 10 1.1 ++
1 -1 1.7 -1.1 0 0 0 0 0 0.053 -1.2 0 0 5.3e+03 0.0097 1e+02 1.1 ++
2 -1.1 1.8 -1.1 0 0 0 0 0 0.057 -1.4 0 0 5.3e+03 0.00044 1e+03 1 ++
3 -1.1 1.8 -1.1 0 0 0 0 0 0.057 -1.4 0 0 5.3e+03 1.2e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000204
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 0.059 10 1 ++
1 5.2e+03 0.025 1e+02 0.97 ++
2 5.2e+03 0.0014 1e+03 1 ++
3 5.2e+03 3.2e-05 1e+04 1 ++
4 5.2e+03 1.6e-08 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000205
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 6.1e+03 2 1 0.51 +
1 5.7e+03 0.32 10 0.92 ++
2 5.7e+03 0.32 5 -2.1e+03 -
3 5.7e+03 0.32 2.5 -18 -
4 5.7e+03 0.32 1.2 -0.14 -
5 5.5e+03 0.16 1.2 0.86 +
6 5.4e+03 0.053 12 1 ++
7 5.4e+03 0.00091 1.2e+02 0.97 ++
8 5.4e+03 2.4e-06 1.2e+02 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000206
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -1 0.37 0.049 -0.63 -0.85 1.1 -0.0029 -0.6 -0.071 -0.13 5.4e+03 2.3 10 1.1 ++
1 -1.2 0.92 0.75 -1.1 -1.3 0.23 -0.005 -0.45 -0.019 -0.51 5.3e+03 0.45 1e+02 0.93 ++
2 -1.4 1.1 0.94 -1.1 -1.1 0.47 -0.0055 -0.39 -0.028 -0.56 5.3e+03 0.045 1e+03 1.1 ++
3 -1.4 1.2 0.98 -1.1 -1.1 0.59 -0.0056 -0.38 -0.025 -0.54 5.3e+03 0.00073 1e+04 1 ++
4 -1.4 1.2 0.97 -1.1 -1.1 0.6 -0.0056 -0.38 -0.026 -0.54 5.3e+03 7.7e-06 1e+05 1 ++
5 -1.4 1.2 0.97 -1.1 -1.1 0.6 -0.0056 -0.38 -0.026 -0.54 5.3e+03 1.3e-06 1e+05 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000207
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.83 1 -0.71 0 0 0 -0.00098 0 0 -0.26 -0.43 0 0 5.4e+03 2.9 10 1.1 ++
1 -0.83 1.7 -1.1 0 0 0 -0.0046 0 0 -0.054 -1.1 0 0 5.3e+03 0.58 1e+02 1.1 ++
2 -0.85 1.8 -1.1 0 0 0 -0.006 0 0 -0.065 -1.3 0 0 5.3e+03 0.035 1e+03 1 ++
3 -0.86 1.8 -1.1 0 0 0 -0.0061 0 0 -0.066 -1.3 0 0 5.3e+03 0.00014 1e+04 1 ++
4 -0.86 1.8 -1.1 0 0 0 -0.0061 0 0 -0.066 -1.3 0 0 5.3e+03 2.2e-09 1e+04 1 ++
Considering neighbor 5/20 for current solution
Considering neighbor 6/20 for current solution
Attempt 8/100
Considering neighbor 0/20 for current solution
Attempt 9/100
Considering neighbor 0/20 for current solution
Attempt 10/100
Considering neighbor 0/20 for current solution
Attempt 11/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000208
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 0.055 10 1 ++
1 5.2e+03 0.027 1e+02 0.99 ++
2 5.2e+03 0.0017 1e+03 1.1 ++
3 5.2e+03 4.1e-05 1e+04 1 ++
4 5.2e+03 2.5e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000209
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -1 0.12 -0.012 0.86 -0.75 -0.92 -0.34 -0.1 -0.05 -0.22 5.2e+03 0.047 10 1.1 ++
1 -1.5 0.49 0.36 1.7 -1.1 -1.1 -0.32 0.08 -0.21 -0.28 5e+03 0.016 1e+02 1.1 ++
2 -1.7 0.68 0.56 1.8 -1.2 -1.1 -0.29 0.075 -0.25 -0.29 5e+03 0.0014 1e+03 1.1 ++
3 -1.7 0.71 0.59 1.8 -1.2 -1.1 -0.29 0.074 -0.25 -0.29 5e+03 1.8e-05 1e+04 1 ++
4 -1.7 0.71 0.59 1.8 -1.2 -1.1 -0.29 0.074 -0.25 -0.29 5e+03 3.4e-09 1e+04 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 12/100
Biogeme parameters read from biogeme.toml.
Model with 4 unknown parameters [max: 50]
*** Estimate b07everything_000210
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost asc_car Function Relgrad Radius Rho
0 -1 -1 -0.38 -0.11 5.6e+03 0.041 10 1.1 ++
1 -0.65 -2.8 -0.89 -0.037 5.3e+03 0.016 1e+02 1.1 ++
2 -0.49 -3.3 -1.1 -0.0039 5.3e+03 0.0015 1e+03 1.1 ++
3 -0.48 -3.4 -1.1 -0.0026 5.3e+03 9.8e-06 1e+04 1 ++
4 -0.48 -3.4 -1.1 -0.0026 5.3e+03 4.4e-10 1e+04 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
asc:no_seg;train_cost_catalog:linear;train_headway_catalog:without_headway;train_tt_catalog:sqrt [10592.228471637549, np.float64(10619.508315242692), 4]
Attempt 13/100
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000211
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -0.73 0.39 -1 1.6 -0.73 -0.45 -0.21 5.5e+03 0.049 1 0.83 +
1 -1.1 1.4 -1.3 0.9 -1.1 -0.2 -0.35 5.1e+03 0.022 10 1.1 ++
2 -0.87 2 -1.9 0.26 -1.1 0.037 -0.36 5e+03 0.012 10 0.82 +
3 -1 2 -1.7 0.36 -1.1 -0.061 -0.31 5e+03 0.0012 1e+02 1 ++
4 -1 2 -1.7 0.38 -1.1 -0.064 -0.31 5e+03 3.1e-05 1e+03 1 ++
5 -1 2 -1.7 0.38 -1.1 -0.064 -0.31 5e+03 3.6e-09 1e+03 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
asc:GA;train_cost_catalog:linear;train_headway_catalog:without_headway;train_tt_catalog:boxcox [10005.510774519118, np.float64(10053.25050082812), 7]
Attempt 14/100
Considering neighbor 0/20 for current solution
Attempt 15/100
Considering neighbor 0/20 for current solution
Attempt 16/100
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b07everything_000212
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -0.73 0.22 -1 -0.27 -0.29 -0.13 5.5e+03 0.039 10 1 ++
1 -1.1 2.3 -1.5 -2.5 -0.11 1.3 5e+03 0.028 1e+02 0.96 ++
2 -1.1 2.2 -1.6 -2.8 -0.13 1.2 5e+03 0.00085 1e+03 1 ++
3 -1.1 2.2 -1.6 -2.8 -0.13 1.2 5e+03 2.8e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
asc:GA;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:log [9958.803287791457, np.float64(9999.723053199174), 6]
Attempt 17/100
Biogeme parameters read from biogeme.toml.
Model with 22 unknown parameters [max: 50]
*** Estimate b07everything_000213
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 0.039 10 1 ++
1 5.6e+03 0.0083 1e+02 1.1 ++
2 5.6e+03 0.00067 1e+03 1 ++
3 5.6e+03 5.4e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 18/100
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000214
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 2.7 10 1 ++
1 5.2e+03 0.77 1e+02 1 ++
2 5.2e+03 0.072 1e+03 1.1 ++
3 5.2e+03 0.0013 1e+04 1 ++
4 5.2e+03 5.2e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b07everything_000215
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 2.9 1 0.66 +
1 5.7e+03 0.71 1 0.43 +
2 5.7e+03 0.71 0.5 -1.9 -
3 5.7e+03 0.71 0.25 -0.21 -
4 5.7e+03 0.065 0.25 0.31 +
5 5.6e+03 0.012 2.5 1.1 ++
6 5.6e+03 0.012 1.2 -0.013 -
7 5.4e+03 0.27 1.2 0.41 +
8 5.3e+03 0.014 12 1 ++
9 5.3e+03 0.011 12 0.74 +
10 5.3e+03 0.00051 1.2e+02 1 ++
11 5.3e+03 2.2e-06 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 19/100
Considering neighbor 0/20 for current solution
Attempt 20/100
Biogeme parameters read from biogeme.toml.
Model with 22 unknown parameters [max: 50]
*** Estimate b07everything_000216
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.041 10 1 ++
1 5.5e+03 0.053 1e+02 0.97 ++
2 5.5e+03 0.0043 1e+03 1 ++
3 5.5e+03 0.00016 1e+04 1 ++
4 5.5e+03 2.1e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000217
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.22 -0.6 -1 0 0 0 -0.0001 0 0 -0.25 0.0035 0 0 5.5e+03 2.7 10 1 ++
1 0.67 -1.2 -1.5 0 0 0 -0.0046 0 0 -0.15 0.27 0 0 5.4e+03 0.46 1e+02 1 ++
2 0.72 -1.3 -1.6 0 0 0 -0.0056 0 0 -0.17 0.27 0 0 5.4e+03 0.021 1e+03 1 ++
3 0.72 -1.3 -1.6 0 0 0 -0.0057 0 0 -0.17 0.27 0 0 5.4e+03 4.4e-05 1e+04 1 ++
4 0.72 -1.3 -1.6 0 0 0 -0.0057 0 0 -0.17 0.27 0 0 5.4e+03 2e-10 1e+04 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 21/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000218
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.91 0.18 -0.013 0.8 -0.85 -1 -0.25 -0.22 -0.054 -0.36 5.2e+03 0.044 10 1.1 ++
1 -1.6 0.51 0.34 1.8 -0.98 -1.4 -0.42 0.12 -0.22 -1.4 5e+03 0.02 1e+02 1.1 ++
2 -1.9 0.68 0.53 1.9 -1 -1.5 -0.42 0.11 -0.4 -1.8 5e+03 0.0015 1e+03 1.1 ++
3 -1.9 0.71 0.56 1.9 -1 -1.5 -0.42 0.11 -0.42 -1.9 5e+03 1.7e-05 1e+04 1 ++
4 -1.9 0.71 0.56 1.9 -1 -1.5 -0.42 0.11 -0.42 -1.9 5e+03 3.3e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 22/100
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000219
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -0.71 0.47 -1 1.7 -0.56 -0.51 -0.24 5.7e+03 0.08 1 0.7 +
1 -1.1 1.5 -1 1.3 -1.6 0.04 -0.42 5.1e+03 0.025 10 1 ++
2 -1.1 1.5 -1 1.3 -1.6 0.04 -0.42 5.1e+03 0.025 0.81 -0.21 -
3 -1.2 1.8 -1.5 0.44 -2 -0.059 -0.32 5e+03 0.018 8.1 1.1 ++
4 -1.1 2.1 -1.6 0.33 -2.7 -0.12 1.3 5e+03 0.0031 81 0.98 ++
5 -1.1 2.1 -1.6 0.34 -2.8 -0.12 1.2 5e+03 0.00014 8.1e+02 1 ++
6 -1.1 2.1 -1.6 0.34 -2.8 -0.12 1.2 5e+03 5.9e-07 8.1e+02 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
asc:GA;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:boxcox [9922.718145551291, np.float64(9970.457871860293), 7]
Attempt 23/100
Considering neighbor 0/20 for current solution
Attempt 24/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000220
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.72 0.11 -0.0034 -0.97 2 -0.8 -0.0012 -0.29 -0.15 -0.05 5.9e+03 1.9 1 0.55 +
1 -0.93 0.75 0.059 -0.68 1.7 -1.8 -0.008 -0.26 0.0067 -0.11 5.4e+03 0.021 10 1 ++
2 -0.93 0.75 0.059 -0.68 1.7 -1.8 -0.008 -0.26 0.0067 -0.11 5.4e+03 0.021 4.1 -6.6e+02 -
3 -0.93 0.75 0.059 -0.68 1.7 -1.8 -0.008 -0.26 0.0067 -0.11 5.4e+03 0.021 2 -12 -
4 -0.93 0.75 0.059 -0.68 1.7 -1.8 -0.008 -0.26 0.0067 -0.11 5.4e+03 0.021 1 0.069 -
5 -1.3 0.83 0.16 -1.5 0.72 -2.1 -0.00037 -0.24 0.33 -0.15 5.2e+03 0.19 10 0.97 ++
6 -1 1.1 1.1 -1.7 0.43 -2.3 -0.0057 -0.14 0.16 -0.31 5.1e+03 0.12 1e+02 0.93 ++
7 -1.1 1.2 0.95 -1.6 0.46 -2.4 -0.0055 -0.17 0.16 -0.23 5.1e+03 0.0019 1e+03 1 ++
8 -1.1 1.2 0.95 -1.6 0.46 -2.4 -0.0055 -0.17 0.16 -0.23 5.1e+03 1.6e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 5 unknown parameters [max: 50]
*** Estimate b07everything_000221
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time lambda_tt b_cost asc_car Function Relgrad Radius Rho
0 -0.68 -1 1.9 -0.64 -0.59 6e+03 0.11 1 0.59 +
1 -0.92 -0.75 1.6 -1.6 -0.024 5.4e+03 0.031 10 0.96 ++
2 -0.92 -0.75 1.6 -1.6 -0.024 5.4e+03 0.031 1.2 -1.7 -
3 -0.68 -1.8 0.39 -2.3 0.03 5.3e+03 0.04 1.2 0.88 +
4 -0.48 -1.7 0.47 -2.3 0.054 5.2e+03 0.0029 12 0.95 ++
5 -0.5 -1.7 0.48 -2.4 0.057 5.2e+03 2.1e-05 1.2e+02 1 ++
6 -0.5 -1.7 0.48 -2.4 0.057 5.2e+03 1.2e-09 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
*** New pareto solution:
asc:no_seg;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:boxcox [10501.31230191711, np.float64(10535.41210642354), 5]
Attempt 25/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000222
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.78 -0.65 0 0 0 0 0 -0.35 0 0 5.7e+03 0.035 10 1.1 ++
1 -0.64 -1.1 0 0 0 0 0 0.032 0 0 5.6e+03 0.0056 1e+02 1 ++
2 -0.63 -1.1 0 0 0 0 0 0.044 0 0 5.6e+03 8.2e-05 1e+03 1 ++
3 -0.63 -1.1 0 0 0 0 0 0.044 0 0 5.6e+03 1.9e-08 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000223
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.97 0.51 0.33 -0.63 0 0 0 -0.0026 0 0 -0.47 0.1 -0.43 0 0 5.6e+03 2.7 10 1.1 ++
1 -1 0.91 0.71 -1.1 0 0 0 -0.0047 0 0 -0.042 -0.054 -0.68 0 0 5.5e+03 0.35 1e+02 1.1 ++
2 -1.2 1.1 0.89 -1.1 0 0 0 -0.0053 0 0 -0.05 -0.044 -0.68 0 0 5.5e+03 0.026 1e+03 1 ++
3 -1.2 1.1 0.91 -1.1 0 0 0 -0.0054 0 0 -0.05 -0.044 -0.68 0 0 5.5e+03 0.00018 1e+04 1 ++
4 -1.2 1.1 0.91 -1.1 0 0 0 -0.0054 0 0 -0.05 -0.044 -0.68 0 0 5.5e+03 1.1e-08 1e+04 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 26/100
Biogeme parameters read from biogeme.toml.
Model with 5 unknown parameters [max: 50]
*** Estimate b07everything_000224
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time lambda_tt b_cost asc_car Function Relgrad Radius Rho
0 -0.69 -1 1.8 -0.64 -0.56 5.8e+03 0.086 1 0.66 +
1 -0.41 -1.8 0.78 -1.6 -0.11 5.5e+03 0.034 1 0.87 +
2 -0.65 -1.4 0.6 -0.95 -0.11 5.4e+03 0.0035 10 0.98 ++
3 -0.59 -1.5 0.39 -1 -0.097 5.4e+03 0.0015 1e+02 0.95 ++
4 -0.61 -1.5 0.41 -1 -0.11 5.4e+03 2e-05 1e+03 1 ++
5 -0.61 -1.5 0.41 -1 -0.11 5.4e+03 8.8e-09 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 4 unknown parameters [max: 50]
*** Estimate b07everything_000225
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost asc_car Function Relgrad Radius Rho
0 -0.79 -0.72 -1 -0.42 5.5e+03 0.037 10 1.1 ++
1 -0.75 -1.2 -2.2 -0.1 5.3e+03 0.0076 1e+02 1.1 ++
2 -0.73 -1.2 -2.3 -0.11 5.3e+03 0.00023 1e+03 1 ++
3 -0.73 -1.2 -2.3 -0.11 5.3e+03 2.7e-07 1e+03 1 ++
Considering neighbor 1/20 for current solution
*** New pareto solution:
asc:no_seg;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:linear [10585.801045811912, np.float64(10613.080889417055), 4]
Attempt 27/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000226
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.53 -0.053 -0.0067 0.32 -1 -0.41 1 -0.004 -0.39 -0.22 -0.028 -0.15 5.4e+03 2.4 10 1 ++
1 -0.53 -0.053 -0.0067 0.32 -1 -0.41 1 -0.004 -0.39 -0.22 -0.028 -0.15 5.4e+03 2.4 1.8 -24 -
2 -0.97 0.41 0.11 2.2 -1.6 -0.96 0.7 -0.004 -0.11 0.036 -0.15 -0.86 5e+03 1.1 18 1 ++
3 -0.97 0.41 0.11 2.2 -1.6 -0.96 0.7 -0.004 -0.11 0.036 -0.15 -0.86 5e+03 1.1 1.1 -13 -
4 -1.2 0.68 0.16 2 -1.7 -1.7 -0.36 -0.0061 -0.26 -0.04 -0.25 -1.2 5e+03 0.13 1.1 0.26 +
5 -1.4 0.7 0.4 2.1 -1.6 -1.5 -0.24 -0.0061 -0.31 0.049 -0.45 -1.7 4.9e+03 0.0045 11 1.1 ++
6 -1.4 0.71 0.42 2.1 -1.6 -1.5 -0.054 -0.0061 -0.32 0.048 -0.46 -1.8 4.9e+03 0.0019 1.1e+02 1.1 ++
7 -1.4 0.71 0.42 2.1 -1.6 -1.5 -0.034 -0.0061 -0.31 0.045 -0.45 -1.8 4.9e+03 6.4e-05 1.1e+03 1 ++
8 -1.4 0.71 0.42 2.1 -1.6 -1.5 -0.034 -0.0061 -0.31 0.045 -0.45 -1.8 4.9e+03 3.2e-07 1.1e+03 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
asc:LUGGAGE-GA;train_cost_catalog:boxcox;train_headway_catalog:with_headway;train_tt_catalog:log [9830.542339359698, np.float64(9912.38187017513), 12]
Attempt 28/100
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000227
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 -0.33 -0.026 0 0 0 -0.22 0 0 -0.38 -0.27 -0.02 0 0 5.9e+03 0.04 10 1 ++
1 -2.1 0.8 0.74 0 0 0 -1.8 0 0 -0.85 0.28 0.22 0 0 5.5e+03 0.021 1e+02 1.1 ++
2 -2.5 1.2 1.1 0 0 0 -2.2 0 0 -0.9 0.24 -0.01 0 0 5.5e+03 0.0037 1e+03 1.1 ++
3 -2.6 1.2 1.2 0 0 0 -2.2 0 0 -0.91 0.23 -0.038 0 0 5.5e+03 0.00012 1e+04 1 ++
4 -2.6 1.2 1.2 0 0 0 -2.2 0 0 -0.91 0.23 -0.038 0 0 5.5e+03 1.1e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000228
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 -0.15 -0.015 0 0 0 -0.53 -0.0043 0 0 -0.67 -0.32 -0.045 0 0 5.8e+03 2.5 10 1 ++
1 -1.9 0.85 0.79 0 0 0 -2 -0.0051 0 0 -0.97 0.27 0.16 0 0 5.5e+03 0.35 1e+02 1.1 ++
2 -2.2 1.2 1.1 0 0 0 -2.2 -0.0055 0 0 -1 0.24 -0.022 0 0 5.5e+03 0.063 1e+03 1.1 ++
3 -2.3 1.2 1.2 0 0 0 -2.2 -0.0055 0 0 -1 0.24 -0.035 0 0 5.5e+03 0.0015 1e+04 1 ++
4 -2.3 1.2 1.2 0 0 0 -2.2 -0.0055 0 0 -1 0.24 -0.035 0 0 5.5e+03 8.7e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000229
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 -0.17 -0.015 0 0 0 -0.53 -0.0047 0 0 -0.73 -0.37 -0.047 0 0 5.8e+03 2.4 10 1 ++
1 -1.9 0.83 0.76 0 0 0 -1.1 -0.0052 0 0 -1 0.05 -0.39 0 0 5.5e+03 0.53 1e+02 1.1 ++
2 -2.3 1.2 1.1 0 0 0 -1.1 -0.0055 0 0 -1 0.028 -0.47 0 0 5.5e+03 0.073 1e+03 1.1 ++
3 -2.3 1.2 1.2 0 0 0 -1.1 -0.0055 0 0 -1 0.028 -0.48 0 0 5.5e+03 0.0018 1e+04 1 ++
4 -2.3 1.2 1.2 0 0 0 -1.1 -0.0055 0 0 -1 0.028 -0.48 0 0 5.5e+03 1.2e-06 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b07everything_000230
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.61 0.037 -0.014 -1 1.5 -0.44 -0.45 -0.34 -0.03 5.7e+03 0.08 1 0.84 +
1 -1.1 1 0.12 -1.8 0.54 -1.2 -0.022 0.043 -0.23 5.3e+03 0.012 10 0.95 ++
2 -1.4 1.1 1.1 -1.5 0.43 -1.1 -0.081 -0.081 -0.67 5.3e+03 0.0015 1e+02 0.98 ++
3 -1.4 1.1 0.95 -1.5 0.38 -1.1 -0.081 -0.077 -0.73 5.3e+03 8.8e-05 1e+03 1 ++
4 -1.4 1.1 0.95 -1.5 0.38 -1.1 -0.081 -0.077 -0.73 5.3e+03 1.1e-07 1e+03 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 29/100
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b07everything_000231
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 2.5 10 1 ++
1 5.7e+03 2.5 3.3 -1.1e+04 -
2 5.7e+03 2.5 1.7 -13 -
3 5.3e+03 1.2 17 1.1 ++
4 5.3e+03 1.2 1.2 -29 -
5 5.3e+03 1.2 0.62 -0.081 -
6 5.2e+03 0.011 6.2 0.98 ++
7 5.2e+03 0.0076 62 0.96 ++
8 5.2e+03 0.0018 6.2e+02 1.1 ++
9 5.2e+03 1.2e-05 6.2e+03 1 ++
10 5.2e+03 1.1e-09 6.2e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000232
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 -0.065 0 0 0 -0.27 0 0 -0.047 -0.85 0 0 6e+03 0.075 10 1.1 ++
1 -2 2.2 0 0 0 -0.88 0 0 -0.84 0.038 0 0 5.3e+03 0.038 1e+02 1 ++
2 -2.2 2.1 0 0 0 -1 0 0 -0.88 -0.067 0 0 5.3e+03 0.0012 1e+03 1 ++
3 -2.2 2.1 0 0 0 -1 0 0 -0.88 -0.067 0 0 5.3e+03 5.4e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000233
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.91 0 0 0 -0.3 -0.0074 0 0 -1 0 0 5.9e+03 2.6 10 1 ++
1 -1.4 0 0 0 -2 -0.0057 0 0 -0.78 0 0 5.6e+03 0.22 1e+02 0.99 ++
2 -1.4 0 0 0 -2.2 -0.0055 0 0 -0.87 0 0 5.6e+03 0.0089 1e+03 1 ++
3 -1.4 0 0 0 -2.2 -0.0055 0 0 -0.88 0 0 5.6e+03 1.2e-05 1e+04 1 ++
4 -1.4 0 0 0 -2.2 -0.0055 0 0 -0.88 0 0 5.6e+03 4.4e-11 1e+04 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 30/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000234
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.67 0.14 -0.011 -1 1.8 0 0 0 0 0 -0.37 -0.3 -0.04 0 0 6e+03 0.092 1 0.6 +
1 -1.3 1.1 0.11 -1.3 1.1 0 0 0 0 0 0.15 -0.018 -0.23 0 0 5.5e+03 0.018 10 1.1 ++
2 -0.74 1.1 1.1 -2.2 0.1 0 0 0 0 0 0.56 -0.13 -0.84 0 0 5.5e+03 0.042 10 0.29 +
3 -1.2 1.1 0.9 -1.6 0.28 0 0 0 0 0 0.24 -0.079 -0.78 0 0 5.5e+03 0.0026 1e+02 1 ++
4 -1.2 1.1 0.88 -1.6 0.43 0 0 0 0 0 0.25 -0.081 -0.78 0 0 5.5e+03 0.0013 1e+03 0.95 ++
5 -1.2 1.1 0.89 -1.5 0.42 0 0 0 0 0 0.24 -0.079 -0.77 0 0 5.5e+03 7.2e-06 1e+04 1 ++
6 -1.2 1.1 0.89 -1.5 0.42 0 0 0 0 0 0.24 -0.079 -0.77 0 0 5.5e+03 1.1e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 31/100
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000235
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.5e+03 2.6 10 1 ++
1 5.2e+03 0.87 1e+02 1 ++
2 5.2e+03 0.069 1e+03 1.1 ++
3 5.2e+03 0.0012 1e+04 1 ++
4 5.2e+03 4.5e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000236
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 0.059 10 1 ++
1 5.2e+03 0.025 1e+02 0.97 ++
2 5.2e+03 0.0014 1e+03 1 ++
3 5.2e+03 3.2e-05 1e+04 1 ++
4 5.2e+03 1.6e-08 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000237
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.22 -0.77 -0.62 0 0 0 0 0 -0.42 0.022 0 0 5.6e+03 0.038 10 1.1 ++
1 0.1 -1.1 -1.1 0 0 0 0 0 -0.29 0.33 0 0 5.5e+03 0.0078 1e+02 1.1 ++
2 0.13 -1.2 -1.1 0 0 0 0 0 -0.28 0.33 0 0 5.5e+03 0.00024 1e+03 1 ++
3 0.13 -1.2 -1.1 0 0 0 0 0 -0.28 0.33 0 0 5.5e+03 3.2e-07 1e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000238
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.5e+03 2.6 10 1 ++
1 5.2e+03 0.87 1e+02 1 ++
2 5.2e+03 0.069 1e+03 1.1 ++
3 5.2e+03 0.0012 1e+04 1 ++
4 5.2e+03 4.5e-07 1e+04 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 32/100
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000239
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 -0.33 -0.026 0 0 0 -0.19 0 0 -0.38 -0.27 -0.021 0 0 5.9e+03 0.04 10 1 ++
1 -2.1 0.8 0.71 0 0 0 -1 0 0 -0.87 0.072 -0.35 0 0 5.6e+03 0.024 1e+02 1.1 ++
2 -2.5 1.2 1.1 0 0 0 -1.1 0 0 -0.9 0.028 -0.48 0 0 5.5e+03 0.004 1e+03 1.1 ++
3 -2.6 1.2 1.2 0 0 0 -1.1 0 0 -0.9 0.027 -0.48 0 0 5.5e+03 0.00013 1e+04 1 ++
4 -2.6 1.2 1.2 0 0 0 -1.1 0 0 -0.9 0.027 -0.48 0 0 5.5e+03 1.2e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000240
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 0.15 0 0 0 -0.35 0 0 -0.045 -1 0 0 5.9e+03 0.076 10 1.1 ++
1 -2.1 2.2 0 0 0 -1.2 0 0 -0.86 -1.1 0 0 5.2e+03 0.031 1e+02 1 ++
2 -2.3 2.2 0 0 0 -1.5 0 0 -0.95 -1.5 0 0 5.2e+03 0.0012 1e+03 1 ++
3 -2.3 2.2 0 0 0 -1.5 0 0 -0.95 -1.5 0 0 5.2e+03 3.9e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000241
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost lambda_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.91 0.23 0 0 0 -0.22 1 -0.0077 0 0 -1 -0.2 0 0 5.8e+03 2.6 10 1 ++
1 -0.91 0.23 0 0 0 -0.22 1 -0.0077 0 0 -1 -0.2 0 0 5.8e+03 2.6 4.5 -2.4e+05 -
2 -0.91 0.23 0 0 0 -0.22 1 -0.0077 0 0 -1 -0.2 0 0 5.8e+03 2.6 2.2 -98 -
3 -0.91 0.23 0 0 0 -0.22 1 -0.0077 0 0 -1 -0.2 0 0 5.8e+03 2.6 1.1 -3.4 -
4 -1.6 1.4 0 0 0 -1.1 1 -0.0053 0 0 -0.96 -0.53 0 0 5.4e+03 0.29 11 1 ++
5 -1.6 1.4 0 0 0 -1.1 1 -0.0053 0 0 -0.96 -0.53 0 0 5.4e+03 0.29 3.2 -2e+04 -
6 -1.6 1.4 0 0 0 -1.1 1 -0.0053 0 0 -0.96 -0.53 0 0 5.4e+03 0.29 1.6 -43 -
7 -2.2 3 0 0 0 -2 -0.32 -0.0068 0 0 -1.3 -1.1 0 0 5.3e+03 0.44 1.6 0.46 +
8 -2 2.2 0 0 0 -1.3 -0.31 -0.0062 0 0 -1 -1.6 0 0 5.2e+03 0.049 16 0.93 ++
9 -2 2.3 0 0 0 -1.5 -0.13 -0.0064 0 0 -1.1 -1.7 0 0 5.2e+03 0.0049 1.6e+02 0.92 ++
10 -2 2.3 0 0 0 -1.5 -0.15 -0.0064 0 0 -1.1 -1.7 0 0 5.2e+03 5.1e-05 1.6e+03 1 ++
11 -2 2.3 0 0 0 -1.5 -0.15 -0.0064 0 0 -1.1 -1.7 0 0 5.2e+03 5.9e-09 1.6e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000242
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 0 0 0 -0.38 0 0 -0.11 0 0 6e+03 0.074 10 1.1 ++
1 -1.6 0 0 0 -0.83 0 0 -0.75 0 0 5.6e+03 0.0058 1e+02 1 ++
2 -1.7 0 0 0 -0.94 0 0 -0.8 0 0 5.6e+03 0.00016 1e+03 1 ++
3 -1.7 0 0 0 -0.94 0 0 -0.8 0 0 5.6e+03 1.4e-07 1e+03 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000243
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 0 0 0 -0.28 0 0 -0.11 0 0 6e+03 0.073 10 1.1 ++
1 -1.6 0 0 0 -0.94 0 0 -0.79 0 0 5.7e+03 0.006 1e+02 1.1 ++
2 -1.7 0 0 0 -1.1 0 0 -0.87 0 0 5.7e+03 0.00022 1e+03 1 ++
3 -1.7 0 0 0 -1.1 0 0 -0.87 0 0 5.7e+03 4e-07 1e+03 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000244
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 -0.18 -0.017 0 0 0 -0.67 -0.005 0 0 -0.74 -0.36 -0.043 0 0 5.7e+03 2.3 10 1 ++
1 -1.9 0.85 0.8 0 0 0 -0.94 -0.0052 0 0 -1 0.18 -0.054 0 0 5.5e+03 0.56 1e+02 1.1 ++
2 -2.2 1.2 1.1 0 0 0 -0.96 -0.0055 0 0 -1 0.16 -0.097 0 0 5.5e+03 0.071 1e+03 1.1 ++
3 -2.3 1.2 1.2 0 0 0 -0.96 -0.0056 0 0 -1 0.16 -0.097 0 0 5.5e+03 0.0017 1e+04 1 ++
4 -2.3 1.2 1.2 0 0 0 -0.96 -0.0056 0 0 -1 0.16 -0.097 0 0 5.5e+03 1.1e-06 1e+04 1 ++
Considering neighbor 5/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000245
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost lambda_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 0 0 0 -0.3 2 0 0 -0.13 0 0 6e+03 0.08 10 1 ++
1 -1 0 0 0 -0.3 2 0 0 -0.13 0 0 6e+03 0.08 5 -8.3e+05 -
2 -1 0 0 0 -0.3 2 0 0 -0.13 0 0 6e+03 0.08 2.5 -42 -
3 -1.5 0 0 0 -1.7 -0.5 0 0 -1.3 0 0 5.9e+03 0.082 2.5 0.2 +
4 -1.5 0 0 0 -1.7 -0.5 0 0 -1.3 0 0 5.9e+03 0.082 1 -0.66 -
5 -1.4 0 0 0 -0.71 -0.45 0 0 -0.58 0 0 5.7e+03 0.026 1 0.82 +
6 -1.7 0 0 0 -1.4 0.59 0 0 -0.93 0 0 5.7e+03 0.026 1 0.38 +
7 -1.7 0 0 0 -1.1 0.38 0 0 -0.88 0 0 5.7e+03 0.0015 10 1.1 ++
8 -1.7 0 0 0 -1.1 0.17 0 0 -0.88 0 0 5.7e+03 0.0012 1e+02 0.98 ++
9 -1.7 0 0 0 -1.1 0.17 0 0 -0.88 0 0 5.7e+03 3.3e-06 1e+02 1 ++
Considering neighbor 6/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000246
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 0 0 0 -0.38 0 0 -0.11 0 0 6e+03 0.074 10 1.1 ++
1 -1.6 0 0 0 -0.83 0 0 -0.75 0 0 5.6e+03 0.0058 1e+02 1 ++
2 -1.7 0 0 0 -0.94 0 0 -0.8 0 0 5.6e+03 0.00016 1e+03 1 ++
3 -1.7 0 0 0 -0.94 0 0 -0.8 0 0 5.6e+03 1.4e-07 1e+03 1 ++
Considering neighbor 7/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000247
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.91 0.23 0 0 0 -0.22 -0.0078 0 0 -1 -0.2 0 0 5.8e+03 2.6 10 1 ++
1 -1.8 2.3 0 0 0 -1.4 -0.0061 0 0 -0.98 -1.2 0 0 5.2e+03 0.85 1e+02 0.99 ++
2 -2 2.3 0 0 0 -1.5 -0.0063 0 0 -1.1 -1.6 0 0 5.2e+03 0.044 1e+03 1 ++
3 -2 2.3 0 0 0 -1.5 -0.0064 0 0 -1.1 -1.6 0 0 5.2e+03 0.00035 1e+04 1 ++
4 -2 2.3 0 0 0 -1.5 -0.0064 0 0 -1.1 -1.6 0 0 5.2e+03 1.5e-08 1e+04 1 ++
Considering neighbor 8/20 for current solution
Considering neighbor 9/20 for current solution
Attempt 33/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000248
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.47 -0.7 0.44 -1 -0.64 1 0.0022 -0.28 -0.067 -0.19 5.3e+03 2.7 10 1.1 ++
1 -0.47 -0.7 0.44 -1 -0.64 1 0.0022 -0.28 -0.067 -0.19 5.3e+03 2.7 1.3 -3.2 -
2 -0.12 -1 1.7 -1.4 -1.2 0.84 -0.0024 -0.33 0.21 -0.62 4.9e+03 0.7 13 1 ++
3 -0.12 -1 1.7 -1.4 -1.2 0.84 -0.0024 -0.33 0.21 -0.62 4.9e+03 0.7 0.64 0.017 -
4 0.019 -1.2 2.2 -1.6 -1.4 0.2 -0.0063 -0.52 0.31 -1.1 4.8e+03 0.15 6.4 1.1 ++
5 -0.071 -1.2 2.2 -1.6 -1.5 -0.045 -0.0066 -0.68 0.45 -1.8 4.8e+03 0.03 64 1 ++
6 -0.071 -1.2 2.2 -1.6 -1.5 -0.036 -0.0066 -0.69 0.46 -2 4.8e+03 0.0014 6.4e+02 1 ++
7 -0.071 -1.2 2.2 -1.6 -1.5 -0.036 -0.0066 -0.69 0.46 -2 4.8e+03 4.6e-06 6.4e+02 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
asc:MALE-GA;train_cost_catalog:boxcox;train_headway_catalog:with_headway;train_tt_catalog:log [9640.412331069461, np.float64(9708.611940082323), 10]
Attempt 34/100
Considering neighbor 0/20 for current solution
Attempt 35/100
Considering neighbor 0/20 for current solution
Attempt 36/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000249
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.4e+03 0.045 10 1.1 ++
1 5.3e+03 0.012 1e+02 1.1 ++
2 5.3e+03 0.0009 1e+03 1 ++
3 5.3e+03 7.2e-06 1e+04 1 ++
4 5.3e+03 6.1e-10 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000250
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.04 10 1.1 ++
1 5.9e+03 0.04 5 -1.1e+07 -
2 5.9e+03 0.04 2.5 -1.8e+02 -
3 5.9e+03 0.04 1.2 0.051 -
4 5.4e+03 0.043 12 1.1 ++
5 5.4e+03 0.043 2.7 -3.1e+03 -
6 5.4e+03 0.043 1.4 -17 -
7 5.3e+03 0.057 1.4 0.48 +
8 5.2e+03 0.0044 14 0.91 ++
9 5.2e+03 0.0019 14 0.89 +
10 5.2e+03 3.7e-05 1.4e+02 1 ++
11 5.2e+03 5e-08 1.4e+02 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000251
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 0.038 10 1 ++
1 5.6e+03 0.038 0.95 -1.8 -
2 5.4e+03 0.021 9.5 0.92 ++
3 5.4e+03 0.00087 95 0.97 ++
4 5.4e+03 2.2e-05 9.5e+02 0.99 ++
5 5.4e+03 3.2e-08 9.5e+02 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000252
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -1 -0.24 -0.026 0.25 -0.81 -0.28 -0.52 -0.37 -0.027 -0.17 5.7e+03 0.052 10 1 ++
1 -1.3 0.35 0.16 2.2 -2.5 -1.3 -0.23 0.13 -0.18 -1.5 5e+03 0.032 1e+02 0.97 ++
2 -1.6 0.63 0.43 1.9 -2.9 -1.5 -0.24 0.083 -0.43 -1.9 4.9e+03 0.002 1e+03 1 ++
3 -1.7 0.7 0.49 1.9 -2.9 -1.5 -0.23 0.078 -0.46 -1.9 4.9e+03 6.6e-05 1e+04 1 ++
4 -1.7 0.7 0.49 1.9 -2.9 -1.5 -0.23 0.078 -0.46 -1.9 4.9e+03 5.2e-08 1e+04 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000253
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho
0 -0.37 -0.65 -1 -0.83 0.0008 -0.3 -0.073 5.3e+03 2.4 10 1.1 ++
1 0.6 -1.3 -1.6 -1 -0.0046 -0.37 0.25 5.2e+03 0.49 1e+02 1.1 ++
2 0.68 -1.4 -1.7 -1 -0.0057 -0.36 0.25 5.2e+03 0.021 1e+03 1 ++
3 0.68 -1.4 -1.7 -1 -0.0057 -0.35 0.24 5.2e+03 2.6e-05 1e+04 1 ++
4 0.68 -1.4 -1.7 -1 -0.0057 -0.35 0.24 5.2e+03 1.1e-08 1e+04 1 ++
Considering neighbor 4/20 for current solution
Considering neighbor 5/20 for current solution
Attempt 37/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000254
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.83 0.17 0.0037 1 -0.76 -0.99 1 -0.0043 -0.48 -0.17 -0.071 -0.42 5.2e+03 2.5 10 1.1 ++
1 -0.83 0.17 0.0037 1 -0.76 -0.99 1 -0.0043 -0.48 -0.17 -0.071 -0.42 5.2e+03 2.5 0.82 0.069 -
2 -1.1 0.57 0.066 1.8 -1.2 -1.2 0.6 -0.0075 -0.4 -0.018 -0.14 -0.81 5e+03 0.69 8.2 1.1 ++
3 -1.6 0.69 0.52 1.9 -1 -1.6 -0.016 -0.0064 -0.56 0.11 -0.33 -1.7 5e+03 0.033 8.2 0.9 +
4 -1.6 0.71 0.55 1.9 -1 -1.5 0.081 -0.0063 -0.55 0.12 -0.39 -1.8 5e+03 0.00094 82 1.1 ++
5 -1.6 0.71 0.55 1.9 -1 -1.5 0.11 -0.0063 -0.54 0.11 -0.39 -1.8 5e+03 4.2e-05 8.2e+02 1 ++
6 -1.6 0.71 0.55 1.9 -1 -1.5 0.11 -0.0063 -0.54 0.11 -0.39 -1.8 5e+03 1.3e-07 8.2e+02 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 38/100
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000255
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.41 -0.74 -1 0 0 0 -0.00051 0 0 -0.44 -0.088 0 0 5.6e+03 2.8 10 1 ++
1 0.54 -1.2 -2.8 0 0 0 -0.0045 0 0 -0.22 0.3 0 0 5.4e+03 0.37 1e+02 1.1 ++
2 0.65 -1.2 -3 0 0 0 -0.0056 0 0 -0.23 0.32 0 0 5.4e+03 0.019 1e+03 1 ++
3 0.65 -1.2 -3 0 0 0 -0.0057 0 0 -0.23 0.32 0 0 5.4e+03 4.8e-05 1e+04 1 ++
4 0.65 -1.2 -3 0 0 0 -0.0057 0 0 -0.23 0.32 0 0 5.4e+03 2.5e-10 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 19 unknown parameters [max: 50]
*** Estimate b07everything_000256
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.8e+03 2.9 10 1 ++
1 5.7e+03 0.32 1e+02 1.1 ++
2 5.7e+03 0.014 1e+03 1 ++
3 5.7e+03 2.1e-05 1e+04 1 ++
4 5.7e+03 4.6e-11 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000257
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.91 0.23 0 0 0 -0.3 -0.0079 0 0 -1 -0.2 0 0 5.8e+03 2.6 10 1 ++
1 -1.8 2.3 0 0 0 -2.7 -0.0061 0 0 -0.98 1.5 0 0 5.2e+03 0.78 1e+02 0.97 ++
2 -2 2.2 0 0 0 -2.7 -0.0064 0 0 -1.1 1.4 0 0 5.2e+03 0.046 1e+03 1 ++
3 -2 2.2 0 0 0 -2.7 -0.0064 0 0 -1.1 1.4 0 0 5.2e+03 0.00027 1e+04 1 ++
4 -2 2.2 0 0 0 -2.7 -0.0064 0 0 -1.1 1.4 0 0 5.2e+03 8.8e-09 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 19 unknown parameters [max: 50]
*** Estimate b07everything_000258
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 2.7 10 1 ++
1 5.5e+03 0.93 1e+02 0.97 ++
2 5.5e+03 0.053 1e+03 1 ++
3 5.5e+03 0.00034 1e+04 1 ++
4 5.5e+03 1.4e-08 1e+04 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000259
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho
0 -0.34 -0.75 -0.64 -1 0.00013 -0.33 -0.036 5.3e+03 2.7 10 1.1 ++
1 0.23 -1.2 -1.1 -2.1 -0.0045 -0.47 0.27 5.1e+03 0.4 1e+02 1.1 ++
2 0.33 -1.2 -1.2 -2.3 -0.0057 -0.54 0.32 5.1e+03 0.019 1e+03 1 ++
3 0.34 -1.2 -1.2 -2.4 -0.0058 -0.55 0.33 5.1e+03 5.5e-05 1e+04 1 ++
4 0.34 -1.2 -1.2 -2.4 -0.0058 -0.55 0.33 5.1e+03 6e-08 1e+04 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000260
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -0.95 0.38 -1 -0.61 -0.0045 -0.85 -0.21 5.4e+03 2.4 10 1 ++
1 -0.76 2.1 -2.9 -1 -0.0053 -0.22 -0.29 5e+03 0.91 1e+02 0.99 ++
2 -0.77 2 -3.2 -1.1 -0.0061 -0.21 -0.29 5e+03 0.045 1e+03 1 ++
3 -0.77 2 -3.2 -1.1 -0.0062 -0.21 -0.3 5e+03 0.00024 1e+04 1 ++
4 -0.77 2 -3.2 -1.1 -0.0062 -0.21 -0.3 5e+03 9.4e-07 1e+04 1 ++
Considering neighbor 5/20 for current solution
Considering neighbor 6/20 for current solution
Attempt 39/100
Biogeme parameters read from biogeme.toml.
Model with 21 unknown parameters [max: 50]
*** Estimate b07everything_000261
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 2.5 10 1 ++
1 5.8e+03 0.56 1e+02 1.1 ++
2 5.7e+03 0.07 1e+03 1.1 ++
3 5.7e+03 0.0016 1e+04 1 ++
4 5.7e+03 9.4e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000262
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 2.5 10 1 ++
1 5.2e+03 1 1e+02 1 ++
2 5.2e+03 0.088 1e+03 1.1 ++
3 5.2e+03 0.0024 1e+04 1 ++
4 5.2e+03 2e-06 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000263
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -1 -0.065 0 0 0 -0.72 0 0 -0.048 -0.46 0 0 5.9e+03 0.076 10 1.1 ++
1 -2.1 2.3 0 0 0 -2.3 0 0 -0.87 1.3 0 0 5.3e+03 0.038 1e+02 1 ++
2 -2.3 2.2 0 0 0 -2.7 0 0 -0.93 1.4 0 0 5.3e+03 0.0012 1e+03 1 ++
3 -2.3 2.2 0 0 0 -2.7 0 0 -0.93 1.4 0 0 5.3e+03 5.7e-06 1e+03 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 40/100
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b07everything_000264
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -1 0.34 0.05 -0.62 -0.97 -0.003 -0.59 -0.089 -0.14 5.4e+03 2.3 10 1.1 ++
1 -1.2 0.91 0.7 -0.99 -1.1 -0.0051 -0.4 -0.024 -0.61 5.3e+03 0.33 1e+02 1.1 ++
2 -1.4 1.1 0.93 -1 -1.1 -0.0055 -0.39 -0.036 -0.66 5.3e+03 0.034 1e+03 1.1 ++
3 -1.4 1.2 0.96 -1 -1.1 -0.0055 -0.39 -0.036 -0.65 5.3e+03 0.00019 1e+04 1 ++
4 -1.4 1.2 0.96 -1 -1.1 -0.0055 -0.39 -0.036 -0.65 5.3e+03 3.1e-06 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b07everything_000265
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost lambda_cost b_headway asc_car Function Relgrad Radius Rho
0 -0.8 -0.64 -0.82 1.1 -0.0026 -0.61 5.5e+03 2.2 10 1.1 ++
1 -0.49 -1.1 -1.2 0.56 -0.0056 -0.41 5.4e+03 0.26 1e+02 1 ++
2 -0.53 -1.2 -1.1 0.62 -0.0054 -0.37 5.4e+03 0.0047 1e+03 1 ++
3 -0.53 -1.2 -1.1 0.62 -0.0054 -0.37 5.4e+03 4.7e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b07everything_000266
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho
0 -0.22 -0.78 -0.62 -0.87 2 -0.0029 -0.76 0.094 5.6e+03 2.2 10 0.94 ++
1 -0.22 -0.78 -0.62 -0.87 2 -0.0029 -0.76 0.094 5.6e+03 2.2 4.2 -1.5e+05 -
2 -0.22 -0.78 -0.62 -0.87 2 -0.0029 -0.76 0.094 5.6e+03 2.2 2.1 -1.3e+02 -
3 -0.22 -0.78 -0.62 -0.87 2 -0.0029 -0.76 0.094 5.6e+03 2.2 1.1 -10 -
4 -0.22 -0.78 -0.62 -0.87 2 -0.0029 -0.76 0.094 5.6e+03 2.2 0.53 -0.41 -
5 0.11 -0.97 -1.1 -0.59 1.8 -0.0034 -0.59 0.36 5.3e+03 0.54 5.3 1.1 ++
6 0.11 -0.97 -1.1 -0.59 1.8 -0.0034 -0.59 0.36 5.3e+03 0.54 2.6 -5.9e+02 -
7 0.11 -0.97 -1.1 -0.59 1.8 -0.0034 -0.59 0.36 5.3e+03 0.54 1.3 -5.2 -
8 0.56 -1.5 -1.2 -1.2 0.46 -0.0063 -0.66 0.32 5.2e+03 0.23 1.3 0.61 +
9 0.28 -1.3 -1.1 -1.1 0.58 -0.0058 -0.71 0.32 5.2e+03 0.015 13 1 ++
10 0.29 -1.2 -1.1 -1.1 0.61 -0.0059 -0.7 0.33 5.2e+03 5.9e-05 1.3e+02 1 ++
11 0.29 -1.2 -1.1 -1.1 0.61 -0.0059 -0.7 0.33 5.2e+03 8e-08 1.3e+02 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 41/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000267
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.65 0.046 -0.0052 -0.98 1.8 -1 -0.0022 -0.35 -0.18 -0.042 5.6e+03 1.6 1 0.76 +
1 -0.98 1 0.075 -1.7 0.81 -1.1 -0.0048 -0.34 0.15 -0.11 5.2e+03 0.56 10 1 ++
2 -1 1.1 1.1 -1.7 0.53 -1.1 -0.0055 -0.13 0.061 -0.41 5.2e+03 0.029 1e+02 0.99 ++
3 -1.1 1.2 0.96 -1.6 0.5 -1.1 -0.0055 -0.18 0.071 -0.3 5.2e+03 0.0026 1e+03 1 ++
4 -1.1 1.2 0.96 -1.6 0.5 -1.1 -0.0055 -0.18 0.071 -0.3 5.2e+03 6.7e-06 1e+04 1 ++
5 -1.1 1.2 0.96 -1.6 0.5 -1.1 -0.0055 -0.18 0.071 -0.3 5.2e+03 1.6e-06 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 42/100
Considering neighbor 0/20 for current solution
Attempt 43/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000268
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.71 0.23 -1 0 0 0 0 0 -0.31 -0.14 0 0 5.6e+03 0.044 10 1 ++
1 -0.77 2.1 -1.6 0 0 0 0 0 0.24 -1 0 0 5.3e+03 0.023 1e+02 0.93 ++
2 -0.88 1.9 -1.6 0 0 0 0 0 0.23 -1.3 0 0 5.2e+03 0.00045 1e+03 1 ++
3 -0.88 1.9 -1.6 0 0 0 0 0 0.23 -1.3 0 0 5.2e+03 1.6e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000269
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.72 0.65 -0.95 2 0 0 0 -0.0015 0 0 -0.41 -0.3 0 0 6e+03 2.3 1 0.54 +
1 -0.93 1.6 -0.38 1.9 0 0 0 -0.0073 0 0 -0.15 -0.73 0 0 5.5e+03 0.17 1 0.86 +
2 -1.2 1.8 -0.83 0.89 0 0 0 -0.0047 0 0 -0.24 -0.85 0 0 5.3e+03 0.026 10 1 ++
3 -0.51 1.8 -1.7 -0.05 0 0 0 -0.0059 0 0 0.19 -1.3 0 0 5.2e+03 0.12 10 0.49 +
4 -0.51 1.9 -1.7 0.32 0 0 0 -0.006 0 0 0.18 -1.4 0 0 5.2e+03 0.015 1e+02 1 ++
5 -0.6 1.9 -1.6 0.34 0 0 0 -0.006 0 0 0.12 -1.3 0 0 5.2e+03 0.00078 1e+03 0.98 ++
6 -0.6 1.9 -1.6 0.34 0 0 0 -0.006 0 0 0.12 -1.3 0 0 5.2e+03 8.4e-07 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000270
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.9 1 -0.68 0 0 0 0 0 -0.38 -0.45 0 0 5.5e+03 0.041 10 1.1 ++
1 -1 1.7 -1.1 0 0 0 0 0 0.053 -1.2 0 0 5.3e+03 0.0097 1e+02 1.1 ++
2 -1.1 1.8 -1.1 0 0 0 0 0 0.057 -1.4 0 0 5.3e+03 0.00044 1e+03 1 ++
3 -1.1 1.8 -1.1 0 0 0 0 0 0.057 -1.4 0 0 5.3e+03 1.2e-06 1e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b07everything_000271
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost lambda_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -0.66 0.5 -1 1.7 -0.73 1 -0.0022 -0.46 -0.25 5.5e+03 2 1 0.76 +
1 -0.9 1.5 -1.1 1.1 -1 0.9 -0.0058 -0.52 -0.65 5.1e+03 0.38 10 1.1 ++
2 -0.45 2.1 -2.3 -0.071 -1.7 -0.25 -0.0061 -0.02 -1.7 5e+03 0.034 10 0.45 +
3 -0.92 2.2 -1.6 0.1 -1.5 -0.11 -0.006 -0.28 -1.8 4.9e+03 0.03 1e+02 1 ++
4 -0.88 2.2 -1.6 0.29 -1.5 0.035 -0.0062 -0.27 -1.9 4.9e+03 0.0066 1e+03 0.97 ++
5 -0.9 2.2 -1.6 0.28 -1.5 0.043 -0.0062 -0.28 -1.8 4.9e+03 9e-05 1e+04 1 ++
6 -0.9 2.2 -1.6 0.28 -1.5 0.043 -0.0062 -0.28 -1.8 4.9e+03 3.2e-06 1e+04 1 ++
Considering neighbor 3/20 for current solution
*** New pareto solution:
asc:GA;train_cost_catalog:boxcox;train_headway_catalog:with_headway;train_tt_catalog:boxcox [9859.55511216498, np.float64(9920.934760276556), 9]
Attempt 44/100
Considering neighbor 0/20 for current solution
Attempt 45/100
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b07everything_000272
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 6e+03 2.3 1 0.54 +
1 5.4e+03 0.19 10 1 ++
2 5.4e+03 0.19 5 -3e+03 -
3 5.4e+03 0.19 2.5 -24 -
4 5.4e+03 0.19 1.2 -0.64 -
5 5.2e+03 0.1 1.2 0.83 +
6 5.2e+03 0.079 12 0.96 ++
7 5.2e+03 0.0015 1.2e+02 1 ++
8 5.2e+03 1.6e-07 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000273
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.36 -0.61 -0.83 2 0 0 0 0 0 -0.4 -0.18 0 0 6e+03 0.13 1 0.55 +
1 -0.099 -1.6 -0.16 2 0 0 0 0 0 -0.64 0.41 0 0 5.7e+03 0.061 1 0.72 +
2 -0.42 -1.3 -0.4 0.97 0 0 0 0 0 -0.67 0.2 0 0 5.6e+03 0.024 10 0.99 ++
3 0.22 -1.2 -1.3 0.08 0 0 0 0 0 -0.2 0.31 0 0 5.4e+03 0.018 1e+02 0.99 ++
4 0.49 -1.2 -1.7 0.63 0 0 0 0 0 -0.057 0.34 0 0 5.4e+03 0.019 1e+02 0.25 +
5 0.43 -1.2 -1.6 0.43 0 0 0 0 0 -0.09 0.33 0 0 5.4e+03 0.0028 1e+03 1.1 ++
6 0.42 -1.2 -1.6 0.39 0 0 0 0 0 -0.096 0.32 0 0 5.4e+03 0.00012 1e+04 1 ++
7 0.42 -1.2 -1.6 0.39 0 0 0 0 0 -0.096 0.32 0 0 5.4e+03 2.7e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000274
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.5e+03 2.9 10 1.1 ++
1 5.5e+03 2.9 2 -98 -
2 5.5e+03 2.9 1 0.016 -
3 5.2e+03 0.54 10 1 ++
4 5.2e+03 0.54 1 -6.2 -
5 5.1e+03 0.22 10 0.92 ++
6 5.1e+03 0.034 1e+02 1 ++
7 5.1e+03 0.00087 1e+03 1 ++
8 5.1e+03 2.2e-06 1e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b07everything_000275
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.8e+03 0.037 10 1 ++
1 5.7e+03 0.0061 1e+02 1.1 ++
2 5.7e+03 0.00019 1e+03 1 ++
3 5.7e+03 1.9e-07 1e+03 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b07everything_000276
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.8e+03 0.037 10 1 ++
1 5.7e+03 0.0061 1e+02 1.1 ++
2 5.7e+03 0.00019 1e+03 1 ++
3 5.7e+03 1.9e-07 1e+03 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000277
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.55 -0.94 -0.82 2 0 0 0 0.0077 0 0 -0.18 0.11 0 0 6e+03 2.7 1 0.65 +
1 -0.55 -0.94 -0.82 2 0 0 0 0.0077 0 0 -0.18 0.11 0 0 6e+03 2.7 0.5 -0.64 -
2 -0.047 -0.9 -0.46 1.8 0 0 0 -0.006 0 0 -0.43 0.11 0 0 5.6e+03 0.32 5 1 ++
3 -0.047 -0.9 -0.46 1.8 0 0 0 -0.006 0 0 -0.43 0.11 0 0 5.6e+03 0.32 2.5 -22 -
4 -0.047 -0.9 -0.46 1.8 0 0 0 -0.006 0 0 -0.43 0.11 0 0 5.6e+03 0.32 1.2 -0.83 -
5 -0.078 -1.4 -1.2 0.54 0 0 0 0.004 0 0 -0.27 0.54 0 0 5.5e+03 0.41 1.2 0.78 +
6 0.72 -1.2 -1.5 0.34 0 0 0 -0.0061 0 0 -0.23 0.32 0 0 5.4e+03 0.19 12 0.95 ++
7 0.69 -1.3 -1.6 0.39 0 0 0 -0.0057 0 0 -0.21 0.31 0 0 5.4e+03 0.0054 1.2e+02 1 ++
8 0.69 -1.3 -1.6 0.39 0 0 0 -0.0057 0 0 -0.21 0.31 0 0 5.4e+03 3.1e-06 1.2e+02 1 ++
Considering neighbor 5/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 19 unknown parameters [max: 50]
*** Estimate b07everything_000278
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.8e+03 2.9 10 1 ++
1 5.7e+03 0.32 1e+02 1.1 ++
2 5.7e+03 0.014 1e+03 1 ++
3 5.7e+03 2.1e-05 1e+04 1 ++
4 5.7e+03 4.6e-11 1e+04 1 ++
Considering neighbor 6/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 21 unknown parameters [max: 50]
*** Estimate b07everything_000279
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 3.3 10 1.1 ++
1 5.4e+03 0.55 1e+02 1.1 ++
2 5.4e+03 0.048 1e+03 1 ++
3 5.4e+03 0.00034 1e+04 1 ++
4 5.4e+03 1.7e-08 1e+04 1 ++
Considering neighbor 7/20 for current solution
Considering neighbor 8/20 for current solution
Attempt 46/100
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b07everything_000280
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time lambda_tt b_cost b_headway asc_car Function Relgrad Radius Rho
0 -0.68 -0.99 1.8 -1 -0.0017 -0.46 5.7e+03 1.6 1 0.71 +
1 -0.7 -1.7 0.83 -1.3 0.00048 -0.15 5.4e+03 0.19 10 0.92 ++
2 -0.15 -1.7 0.59 -1.1 -0.0062 -0.13 5.3e+03 0.076 1e+02 0.98 ++
3 -0.23 -1.7 0.51 -1.1 -0.0054 -0.11 5.3e+03 0.0013 1e+03 1 ++
4 -0.23 -1.7 0.51 -1.1 -0.0054 -0.11 5.3e+03 6.9e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b07everything_000281
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -0.68 0.55 -1 1.8 -0.74 -0.0021 -0.46 -0.27 5.6e+03 2 1 0.69 +
1 -0.93 1.6 -0.93 1.3 -1.2 -0.0064 -0.47 -0.74 5.1e+03 0.24 10 1.1 ++
2 -0.93 1.6 -0.93 1.3 -1.2 -0.0064 -0.47 -0.74 5.1e+03 0.24 4.9 -2.4e+03 -
3 -0.93 1.6 -0.93 1.3 -1.2 -0.0064 -0.47 -0.74 5.1e+03 0.24 2.5 -34 -
4 -0.93 1.6 -0.93 1.3 -1.2 -0.0064 -0.47 -0.74 5.1e+03 0.24 1.2 -2.9 -
5 -1.1 2.5 -1.9 0.12 -1.6 -0.0027 -0.15 -1.4 5e+03 0.081 1.2 0.78 +
6 -0.87 2.2 -1.6 0.24 -1.5 -0.0064 -0.28 -1.8 4.9e+03 0.0087 12 0.98 ++
7 -0.9 2.2 -1.6 0.27 -1.5 -0.0061 -0.28 -1.9 4.9e+03 0.0002 1.2e+02 1 ++
8 -0.9 2.2 -1.6 0.27 -1.5 -0.0061 -0.28 -1.9 4.9e+03 5.2e-07 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
*** New pareto solution:
asc:GA;train_cost_catalog:log;train_headway_catalog:with_headway;train_tt_catalog:boxcox [9857.857591110185, np.float64(9912.417278320474), 8]
Attempt 47/100
Considering neighbor 0/20 for current solution
Attempt 48/100
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000282
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.91 0 0 0 -0.3 -0.0074 0 0 -1 0 0 5.9e+03 2.6 10 1 ++
1 -1.4 0 0 0 -2 -0.0057 0 0 -0.78 0 0 5.6e+03 0.22 1e+02 0.99 ++
2 -1.4 0 0 0 -2.2 -0.0055 0 0 -0.87 0 0 5.6e+03 0.0089 1e+03 1 ++
3 -1.4 0 0 0 -2.2 -0.0055 0 0 -0.88 0 0 5.6e+03 1.2e-05 1e+04 1 ++
4 -1.4 0 0 0 -2.2 -0.0055 0 0 -0.88 0 0 5.6e+03 4.4e-11 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000283
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.91 0 0 0 -0.22 -0.0073 0 0 -1 0 0 5.9e+03 2.6 10 1 ++
1 -1.4 0 0 0 -1.1 -0.0057 0 0 -0.93 0 0 5.6e+03 0.042 1e+02 0.97 ++
2 -1.4 0 0 0 -1.1 -0.0055 0 0 -0.98 0 0 5.6e+03 0.00046 1e+03 1 ++
3 -1.4 0 0 0 -1.1 -0.0055 0 0 -0.98 0 0 5.6e+03 4.8e-08 1e+03 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 49/100
Considering neighbor 0/20 for current solution
Attempt 50/100
Considering neighbor 0/20 for current solution
Attempt 51/100
Considering neighbor 0/20 for current solution
Attempt 52/100
Considering neighbor 0/20 for current solution
Attempt 53/100
Considering neighbor 0/20 for current solution
Attempt 54/100
Considering neighbor 0/20 for current solution
Attempt 55/100
Considering neighbor 0/20 for current solution
Attempt 56/100
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000284
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho
0 -0.36 -0.57 -1 -0.74 1 -0.35 -0.15 5.4e+03 0.037 10 1 ++
1 0.28 -1.3 -1.5 -1.3 -0.089 -0.39 0.24 5.3e+03 0.016 10 0.8 +
2 0.27 -1.3 -1.6 -1.2 0.22 -0.41 0.29 5.2e+03 0.0039 1e+02 1.2 ++
3 0.3 -1.3 -1.6 -1.1 0.4 -0.39 0.28 5.2e+03 0.00075 1e+03 1.1 ++
4 0.3 -1.3 -1.6 -1.1 0.43 -0.38 0.28 5.2e+03 1.7e-05 1e+04 1 ++
5 0.3 -1.3 -1.6 -1.1 0.43 -0.38 0.28 5.2e+03 2.4e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 57/100
Considering neighbor 0/20 for current solution
Attempt 58/100
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000285
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.5e+03 2.8 10 1 ++
1 5.4e+03 0.53 1e+02 1.1 ++
2 5.3e+03 0.044 1e+03 1.1 ++
3 5.3e+03 0.00041 1e+04 1 ++
4 5.3e+03 4.5e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000286
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.52 -1 0 0 0 -0.0026 0 0 -0.34 0 0 5.7e+03 2.5 10 1 ++
1 -0.18 -1.5 0 0 0 -0.0048 0 0 0.1 0 0 5.6e+03 0.13 1e+02 1 ++
2 -0.17 -1.6 0 0 0 -0.0053 0 0 0.091 0 0 5.6e+03 0.0022 1e+03 1 ++
3 -0.17 -1.6 0 0 0 -0.0053 0 0 0.091 0 0 5.6e+03 5e-07 1e+03 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 59/100
Considering neighbor 0/20 for current solution
Attempt 60/100
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b07everything_000287
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.43 -0.58 0.47 -1 -0.62 1 -0.37 -0.19 -0.21 5.3e+03 0.05 10 1 ++
1 -0.43 -0.58 0.47 -1 -0.62 1 -0.37 -0.19 -0.21 5.3e+03 0.05 1.3 -3 -
2 -0.32 -1.1 1.8 -1.6 -1.1 0.81 -0.38 0.15 -0.66 4.9e+03 0.019 13 1.1 ++
3 -0.32 -1.1 1.8 -1.6 -1.1 0.81 -0.38 0.15 -0.66 4.9e+03 0.019 0.82 -2.4 -
4 -0.26 -1.2 2.2 -1.7 -1.3 -0.011 -0.37 0.38 -1.1 4.8e+03 0.0087 8.2 0.99 ++
5 -0.38 -1.2 2.1 -1.6 -1.5 -0.026 -0.55 0.46 -1.8 4.8e+03 0.0014 82 1.1 ++
6 -0.38 -1.2 2.1 -1.6 -1.5 -0.04 -0.56 0.46 -2 4.8e+03 8.5e-05 8.2e+02 1 ++
7 -0.38 -1.2 2.1 -1.6 -1.5 -0.04 -0.56 0.46 -2 4.8e+03 2.8e-07 8.2e+02 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
asc:MALE-GA;train_cost_catalog:boxcox;train_headway_catalog:without_headway;train_tt_catalog:log [9678.269631854551, np.float64(9739.649279966126), 9]
Attempt 61/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000288
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.52 0.052 -0.0022 -1 0 0 0 -0.0028 0 0 -0.25 -0.14 -0.034 0 0 5.6e+03 2.5 10 1 ++
1 -0.7 0.81 0.63 -1.5 0 0 0 -0.0047 0 0 0.15 -0.089 -0.71 0 0 5.5e+03 0.44 1e+02 1.1 ++
2 -0.94 1.1 0.9 -1.6 0 0 0 -0.0053 0 0 0.13 -0.081 -0.76 0 0 5.5e+03 0.05 1e+03 1.1 ++
3 -0.98 1.1 0.94 -1.6 0 0 0 -0.0053 0 0 0.13 -0.082 -0.76 0 0 5.5e+03 0.00074 1e+04 1 ++
4 -0.98 1.1 0.94 -1.6 0 0 0 -0.0053 0 0 0.13 -0.082 -0.76 0 0 5.5e+03 2e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000289
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.22 -0.6 -1 0 0 0 -0.0001 0 0 -0.25 0.0035 0 0 5.5e+03 2.7 10 1 ++
1 0.67 -1.2 -1.5 0 0 0 -0.0046 0 0 -0.15 0.27 0 0 5.4e+03 0.46 1e+02 1 ++
2 0.72 -1.3 -1.6 0 0 0 -0.0056 0 0 -0.17 0.27 0 0 5.4e+03 0.021 1e+03 1 ++
3 0.72 -1.3 -1.6 0 0 0 -0.0057 0 0 -0.17 0.27 0 0 5.4e+03 4.4e-05 1e+04 1 ++
4 0.72 -1.3 -1.6 0 0 0 -0.0057 0 0 -0.17 0.27 0 0 5.4e+03 2e-10 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000290
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -1 0.066 -0.017 -1 0 0 0 0 0 -0.39 -0.27 -0.046 0 0 5.7e+03 0.041 10 1 ++
1 -0.97 0.8 0.62 -2.8 0 0 0 0 0 0.19 -0.051 -0.68 0 0 5.5e+03 0.014 1e+02 1.1 ++
2 -1.2 1.1 0.85 -3 0 0 0 0 0 0.22 -0.075 -0.76 0 0 5.5e+03 0.00099 1e+03 1.1 ++
3 -1.2 1.1 0.88 -3 0 0 0 0 0 0.22 -0.076 -0.77 0 0 5.5e+03 1.6e-05 1e+04 1 ++
4 -1.2 1.1 0.88 -3 0 0 0 0 0 0.22 -0.076 -0.77 0 0 5.5e+03 4.3e-09 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000291
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 0.055 10 1 ++
1 5.2e+03 0.027 1e+02 0.99 ++
2 5.2e+03 0.0017 1e+03 1.1 ++
3 5.2e+03 4.1e-05 1e+04 1 ++
4 5.2e+03 2.5e-08 1e+04 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000292
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.24 -0.56 -1 0 0 0 0 0 -0.26 -0.026 0 0 5.6e+03 0.04 10 1 ++
1 0.45 -1.2 -1.5 0 0 0 0 0 -0.062 0.28 0 0 5.4e+03 0.0081 1e+02 1 ++
2 0.45 -1.3 -1.6 0 0 0 0 0 -0.062 0.27 0 0 5.4e+03 0.00022 1e+03 1 ++
3 0.45 -1.3 -1.6 0 0 0 0 0 -0.062 0.27 0 0 5.4e+03 2.4e-07 1e+03 1 ++
Considering neighbor 4/20 for current solution
Considering neighbor 5/20 for current solution
Attempt 62/100
Biogeme parameters read from biogeme.toml.
Model with 5 unknown parameters [max: 50]
*** Estimate b07everything_000293
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost b_headway asc_car Function Relgrad Radius Rho
0 -0.72 -0.66 -0.95 -0.0037 -0.61 5.5e+03 2.2 10 1.1 ++
1 -0.62 -1 -1.1 -0.0051 -0.41 5.4e+03 0.2 1e+02 1.1 ++
2 -0.6 -1.1 -1 -0.0053 -0.38 5.4e+03 0.0056 1e+03 1 ++
3 -0.6 -1.1 -1 -0.0054 -0.38 5.4e+03 0.00051 1e+04 1 ++
4 -0.6 -1.1 -1 -0.0054 -0.38 5.4e+03 1e-05 1e+05 1 ++
5 -0.6 -1.1 -1 -0.0054 -0.38 5.4e+03 4e-07 1e+05 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b07everything_000294
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time lambda_tt b_cost b_headway asc_car Function Relgrad Radius Rho
0 -0.72 -1 2 -0.85 -0.00082 -0.46 5.9e+03 1.6 1 0.56 +
1 -1 0 2.1 -1.8 -0.0076 -0.84 5.8e+03 0.42 1 0.24 +
2 -1 0 2.1 -1.8 -0.0076 -0.84 5.8e+03 0.42 0.5 -0.05 -
3 -1.4 -0.039 2.3 -1.3 -0.0067 -1.1 5.6e+03 0.13 5 0.95 ++
4 -1.4 -0.039 2.3 -1.3 -0.0067 -1.1 5.6e+03 0.13 2.5 -3.9 -
5 -1.4 -0.039 2.3 -1.3 -0.0067 -1.1 5.6e+03 0.13 1.2 -0.59 -
6 -1.4 -0.26 1 -0.7 -0.0003 -0.44 5.6e+03 0.31 1.2 0.38 +
7 -0.76 -0.82 1 -0.99 -0.0051 -0.46 5.4e+03 0.041 12 1.1 ++
8 -0.25 -1.7 0.064 -1 -0.0054 -0.14 5.4e+03 0.068 12 0.53 +
9 -0.3 -1.6 0.37 -1 -0.0053 -0.18 5.4e+03 0.0066 1.2e+02 1 ++
10 -0.37 -1.5 0.4 -1 -0.0053 -0.22 5.4e+03 0.00033 1.2e+03 1 ++
11 -0.37 -1.5 0.4 -1 -0.0053 -0.22 5.4e+03 1.5e-06 1.2e+03 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 63/100
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000295
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.83 1 -0.71 0 0 0 -0.00098 0 0 -0.26 -0.43 0 0 5.4e+03 2.9 10 1.1 ++
1 -0.83 1.7 -1.1 0 0 0 -0.0046 0 0 -0.054 -1.1 0 0 5.3e+03 0.58 1e+02 1.1 ++
2 -0.85 1.8 -1.1 0 0 0 -0.006 0 0 -0.065 -1.3 0 0 5.3e+03 0.035 1e+03 1 ++
3 -0.86 1.8 -1.1 0 0 0 -0.0061 0 0 -0.066 -1.3 0 0 5.3e+03 0.00014 1e+04 1 ++
4 -0.86 1.8 -1.1 0 0 0 -0.0061 0 0 -0.066 -1.3 0 0 5.3e+03 2.2e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 64/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000296
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 6.1e+03 0.07 10 1.1 ++
1 5.9e+03 0.0023 1e+02 1 ++
2 5.9e+03 1.1e-05 1e+03 1 ++
3 5.9e+03 2.6e-10 1e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 65/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000297
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.78 -0.65 0 0 0 0 0 -0.35 0 0 5.7e+03 0.035 10 1.1 ++
1 -0.64 -1.1 0 0 0 0 0 0.032 0 0 5.6e+03 0.0056 1e+02 1 ++
2 -0.63 -1.1 0 0 0 0 0 0.044 0 0 5.6e+03 8.2e-05 1e+03 1 ++
3 -0.63 -1.1 0 0 0 0 0 0.044 0 0 5.6e+03 1.9e-08 1e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 66/100
Considering neighbor 0/20 for current solution
Attempt 67/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000298
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.52 0.052 -0.0022 -1 0 0 0 -0.0028 0 0 -0.25 -0.14 -0.034 0 0 5.6e+03 2.5 10 1 ++
1 -0.7 0.81 0.63 -1.5 0 0 0 -0.0047 0 0 0.15 -0.089 -0.71 0 0 5.5e+03 0.44 1e+02 1.1 ++
2 -0.94 1.1 0.9 -1.6 0 0 0 -0.0053 0 0 0.13 -0.081 -0.76 0 0 5.5e+03 0.05 1e+03 1.1 ++
3 -0.98 1.1 0.94 -1.6 0 0 0 -0.0053 0 0 0.13 -0.082 -0.76 0 0 5.5e+03 0.00074 1e+04 1 ++
4 -0.98 1.1 0.94 -1.6 0 0 0 -0.0053 0 0 0.13 -0.082 -0.76 0 0 5.5e+03 2e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000299
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -1 0.11 -1 -0.27 1.1 -0.047 -1 5.6e+03 0.042 10 1.1 ++
1 -1 0.11 -1 -0.27 1.1 -0.047 -1 5.6e+03 0.042 5 -1.4e+07 -
2 -1 0.11 -1 -0.27 1.1 -0.047 -1 5.6e+03 0.042 2.5 -2.1e+02 -
3 -1 0.11 -1 -0.27 1.1 -0.047 -1 5.6e+03 0.042 1.2 -0.26 -
4 -1.4 1.4 -1.9 -1.1 0.99 -0.38 -1.1 5.1e+03 0.023 12 1.1 ++
5 -1.4 1.4 -1.9 -1.1 0.99 -0.38 -1.1 5.1e+03 0.023 1.2 -12 -
6 -1.3 2.5 -3 -2 -0.19 -0.31 -1.4 5e+03 0.041 1.2 0.5 +
7 -1.3 2.1 -2.9 -1.4 -0.13 -0.18 -1.8 5e+03 0.0015 12 0.98 ++
8 -1.2 2.1 -3 -1.5 0.1 -0.19 -1.9 4.9e+03 0.002 1.2e+02 0.91 ++
9 -1.2 2.1 -3 -1.5 0.091 -0.19 -1.9 4.9e+03 1.9e-05 1.2e+03 1 ++
10 -1.2 2.1 -3 -1.5 0.091 -0.19 -1.9 4.9e+03 2.3e-08 1.2e+03 1 ++
Considering neighbor 1/20 for current solution
*** New pareto solution:
asc:GA;train_cost_catalog:boxcox;train_headway_catalog:without_headway;train_tt_catalog:sqrt [9906.475472994323, np.float64(9954.215199303326), 7]
Attempt 68/100
Considering neighbor 0/20 for current solution
Attempt 69/100
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000300
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.68 -0.11 -0.016 0.24 -1 -0.29 1 -0.34 -0.26 -0.021 -0.14 5.5e+03 0.055 10 1 ++
1 -0.68 -0.11 -0.016 0.24 -1 -0.29 1 -0.34 -0.26 -0.021 -0.14 5.5e+03 0.055 4.5 -2.2e+05 -
2 -0.68 -0.11 -0.016 0.24 -1 -0.29 1 -0.34 -0.26 -0.021 -0.14 5.5e+03 0.055 2.2 -88 -
3 -0.68 -0.11 -0.016 0.24 -1 -0.29 1 -0.34 -0.26 -0.021 -0.14 5.5e+03 0.055 1.1 -2.8 -
4 -1 0.49 0.0064 1.4 -1.6 -1.2 1 -0.036 -0.14 -0.077 -0.49 5.1e+03 0.018 11 1 ++
5 -1 0.49 0.0064 1.4 -1.6 -1.2 1 -0.036 -0.14 -0.077 -0.49 5.1e+03 0.018 0.99 -4.4 -
6 -1.5 0.62 0.15 2.4 -1.7 -1.7 0.25 -0.099 -0.032 -0.22 -1.1 4.9e+03 0.013 9.9 1 ++
7 -1.7 0.71 0.47 2 -1.6 -1.5 0.016 -0.19 0.046 -0.41 -1.8 4.9e+03 0.0019 99 1 ++
8 -1.7 0.71 0.45 2 -1.6 -1.5 -0.035 -0.18 0.044 -0.45 -1.8 4.9e+03 9.4e-05 9.9e+02 1 ++
9 -1.7 0.71 0.45 2 -1.6 -1.5 -0.035 -0.18 0.044 -0.45 -1.8 4.9e+03 9.2e-08 9.9e+02 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 70/100
Considering neighbor 0/20 for current solution
Attempt 71/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000301
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.53 -0.81 0.78 -1 0 0 0 0.00034 0 0 -0.42 -0.079 -0.32 0 0 5.4e+03 3.2 10 1.1 ++
1 0.04 -0.98 1.6 -2.8 0 0 0 -0.0045 0 0 -0.25 0.38 -1.2 0 0 5.1e+03 0.64 1e+02 1.1 ++
2 0.16 -1.1 1.7 -3 0 0 0 -0.0063 0 0 -0.29 0.42 -1.5 0 0 5.1e+03 0.059 1e+03 1.1 ++
3 0.17 -1.1 1.8 -3 0 0 0 -0.0065 0 0 -0.29 0.43 -1.5 0 0 5.1e+03 0.00056 1e+04 1 ++
4 0.17 -1.1 1.8 -3 0 0 0 -0.0065 0 0 -0.29 0.43 -1.5 0 0 5.1e+03 4.8e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 72/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000302
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.53 -0.81 0.78 -1 0 0 0 0.00034 0 0 -0.42 -0.079 -0.32 0 0 5.4e+03 3.2 10 1.1 ++
1 0.04 -0.98 1.6 -2.8 0 0 0 -0.0045 0 0 -0.25 0.38 -1.2 0 0 5.1e+03 0.64 1e+02 1.1 ++
2 0.16 -1.1 1.7 -3 0 0 0 -0.0063 0 0 -0.29 0.42 -1.5 0 0 5.1e+03 0.059 1e+03 1.1 ++
3 0.17 -1.1 1.8 -3 0 0 0 -0.0065 0 0 -0.29 0.43 -1.5 0 0 5.1e+03 0.00056 1e+04 1 ++
4 0.17 -1.1 1.8 -3 0 0 0 -0.0065 0 0 -0.29 0.43 -1.5 0 0 5.1e+03 4.8e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b07everything_000303
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 3 10 1 ++
1 5.6e+03 0.41 1e+02 1.1 ++
2 5.6e+03 0.034 1e+03 1.1 ++
3 5.6e+03 0.00026 1e+04 1 ++
4 5.6e+03 2.1e-08 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b07everything_000304
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.59 -0.77 0.75 -1 -0.86 -0.46 -0.18 -0.28 5.3e+03 0.047 10 1.1 ++
1 -0.43 -0.97 1.8 -2.7 -2.5 -0.5 0.37 0.88 4.9e+03 0.019 1e+02 1.1 ++
2 -0.37 -1.1 2 -3.1 -2.7 -0.54 0.44 1 4.8e+03 0.0014 1e+03 1 ++
3 -0.36 -1.1 2 -3.1 -2.8 -0.54 0.44 1 4.8e+03 7.5e-06 1e+04 1 ++
4 -0.36 -1.1 2 -3.1 -2.8 -0.54 0.44 1 4.8e+03 2.5e-10 1e+04 1 ++
Considering neighbor 2/20 for current solution
*** New pareto solution:
asc:MALE-GA;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:sqrt [9708.681955643766, np.float64(9763.241642854055), 8]
Attempt 73/100
Biogeme parameters read from biogeme.toml.
Model with 21 unknown parameters [max: 50]
*** Estimate b07everything_000305
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 2.5 10 1 ++
1 5.8e+03 0.56 1e+02 1.1 ++
2 5.7e+03 0.07 1e+03 1.1 ++
3 5.7e+03 0.0016 1e+04 1 ++
4 5.7e+03 9.4e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000306
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_ma asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.87 -0.95 0.75 0 0 0 -1 0 0 -0.69 -0.37 -0.35 0 0 5.4e+03 0.041 10 1 ++
1 -1.4 -0.99 2.1 0 0 0 -1.4 0 0 -1.2 0.3 -1.3 0 0 5.1e+03 0.017 1e+02 1.1 ++
2 -1.5 -1.2 2.2 0 0 0 -1.5 0 0 -1.3 0.34 -1.6 0 0 5.1e+03 0.001 1e+03 1 ++
3 -1.5 -1.2 2.2 0 0 0 -1.5 0 0 -1.3 0.35 -1.7 0 0 5.1e+03 6.4e-06 1e+04 1 ++
4 -1.5 -1.2 2.2 0 0 0 -1.5 0 0 -1.3 0.35 -1.7 0 0 5.1e+03 8.5e-10 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000307
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_ma asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.83 -1 0.49 0 0 0 -0.66 0.00091 0 0 -0.59 -0.29 -0.24 0 0 5.5e+03 3 10 1.1 ++
1 -1.2 -0.99 2.2 0 0 0 -1.4 -0.0048 0 0 -1.3 0.29 -1.2 0 0 5.1e+03 0.89 1e+02 1 ++
2 -1.2 -1.2 2.2 0 0 0 -1.5 -0.0067 0 0 -1.4 0.34 -1.6 0 0 5.1e+03 0.066 1e+03 1 ++
3 -1.2 -1.2 2.2 0 0 0 -1.5 -0.0069 0 0 -1.4 0.35 -1.7 0 0 5.1e+03 0.00066 1e+04 1 ++
4 -1.2 -1.2 2.2 0 0 0 -1.5 -0.0069 0 0 -1.4 0.35 -1.7 0 0 5.1e+03 6.1e-08 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b07everything_000308
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.66 -0.081 -0.015 -1 -0.29 -0.35 -0.27 -0.023 5.6e+03 0.058 10 1 ++
1 -1.1 0.8 0.64 -1.5 -1.1 -0.072 -0.072 -0.68 5.3e+03 0.013 1e+02 1 ++
2 -1.4 1.1 0.94 -1.5 -1.1 -0.083 -0.081 -0.72 5.3e+03 0.0014 1e+03 1.1 ++
3 -1.5 1.2 0.99 -1.5 -1.1 -0.082 -0.083 -0.73 5.3e+03 2.9e-05 1e+04 1 ++
4 -1.5 1.2 0.99 -1.5 -1.1 -0.082 -0.083 -0.73 5.3e+03 1.1e-08 1e+04 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000309
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.43 -0.71 0.12 -0.0019 -1 -0.67 0.002 -0.21 -0.0063 -0.14 -0.037 5.3e+03 2.6 10 1.1 ++
1 -0.12 -1.1 0.73 0.33 -1.5 -1.1 -0.0043 -0.41 0.24 -0.069 -0.51 5.2e+03 0.64 1e+02 1.1 ++
2 -0.22 -1.2 0.94 0.5 -1.5 -1.1 -0.0057 -0.45 0.27 -0.079 -0.62 5.2e+03 0.055 1e+03 1.1 ++
3 -0.24 -1.2 0.97 0.52 -1.5 -1.1 -0.0058 -0.45 0.27 -0.08 -0.63 5.2e+03 0.00059 1e+04 1 ++
4 -0.24 -1.2 0.97 0.52 -1.5 -1.1 -0.0058 -0.45 0.27 -0.08 -0.63 5.2e+03 1.2e-07 1e+04 1 ++
Considering neighbor 4/20 for current solution
Considering neighbor 5/20 for current solution
Attempt 74/100
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000310
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho
0 -0.2 -0.82 -0.6 -0.93 -0.0028 -0.63 -0.038 5.4e+03 2.6 10 1.1 ++
1 0.15 -1.2 -0.98 -1 -0.0051 -0.71 0.32 5.3e+03 0.36 1e+02 1.1 ++
2 0.21 -1.2 -1 -1.1 -0.0058 -0.74 0.35 5.3e+03 0.015 1e+03 1 ++
3 0.21 -1.2 -1 -1.1 -0.0058 -0.74 0.36 5.3e+03 2.9e-05 1e+04 1 ++
4 0.21 -1.2 -1 -1.1 -0.0058 -0.74 0.36 5.3e+03 1.1e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000311
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.62 0.017 -0.015 0.36 -1 1.5 -0.41 -0.42 -0.32 -0.029 -0.19 5.7e+03 0.088 1 0.85 +
1 -1.2 0.38 0.0051 1.3 -1.3 0.9 -1.4 0.16 0.092 -0.078 -0.39 5.1e+03 0.032 10 0.95 ++
2 -1.4 0.67 0.49 1.9 -1.9 0.22 -2.6 -0.002 0.048 -0.35 0.95 4.9e+03 0.0098 1e+02 0.97 ++
3 -1.6 0.71 0.49 1.9 -1.6 0.31 -2.8 -0.15 0.048 -0.36 1.2 4.9e+03 0.001 1e+03 1 ++
4 -1.6 0.71 0.49 1.9 -1.6 0.33 -2.8 -0.15 0.049 -0.36 1.2 4.9e+03 2.6e-05 1e+04 1 ++
5 -1.6 0.71 0.49 1.9 -1.6 0.33 -2.8 -0.15 0.049 -0.36 1.2 4.9e+03 2.7e-09 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000312
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.53 -0.046 -0.0064 0.33 -1 -0.4 -0.004 -0.38 -0.22 -0.029 -0.16 5.4e+03 2.5 10 1 ++
1 -1.1 0.41 0.16 2.2 -1.5 -1.3 -0.0052 -0.27 0.056 -0.23 -1.3 4.9e+03 1 1e+02 1 ++
2 -1.3 0.65 0.35 2 -1.6 -1.5 -0.006 -0.31 0.047 -0.42 -1.8 4.9e+03 0.07 1e+03 1 ++
3 -1.4 0.71 0.42 2.1 -1.6 -1.5 -0.0061 -0.31 0.045 -0.45 -1.8 4.9e+03 0.0018 1e+04 1 ++
4 -1.4 0.71 0.42 2.1 -1.6 -1.5 -0.0061 -0.31 0.045 -0.45 -1.8 4.9e+03 1.2e-06 1e+04 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 75/100
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b07everything_000313
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.44 -0.59 0.47 -1 -0.76 -0.34 -0.17 -0.18 5.2e+03 0.044 10 1 ++
1 -0.21 -1 2 -1.6 -1 -0.36 0.33 -0.35 4.9e+03 0.018 1e+02 1 ++
2 -0.2 -1.2 2 -1.7 -1.1 -0.39 0.37 -0.42 4.9e+03 0.0011 1e+03 1 ++
3 -0.2 -1.2 2 -1.7 -1.1 -0.39 0.38 -0.42 4.9e+03 8.5e-06 1e+04 1 ++
4 -0.2 -1.2 2 -1.7 -1.1 -0.39 0.38 -0.42 4.9e+03 5.5e-10 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 76/100
Considering neighbor 0/20 for current solution
Attempt 77/100
Biogeme parameters read from biogeme.toml.
Model with 20 unknown parameters [max: 50]
*** Estimate b07everything_000314
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 6e+03 0.041 10 1 ++
1 5.8e+03 0.023 1e+02 1.1 ++
2 5.8e+03 0.0038 1e+03 1.1 ++
3 5.8e+03 0.00012 1e+04 1 ++
4 5.8e+03 1.3e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 20 unknown parameters [max: 50]
*** Estimate b07everything_000315
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 6e+03 0.041 10 1 ++
1 5.8e+03 0.023 1e+02 1.1 ++
2 5.8e+03 0.0038 1e+03 1.1 ++
3 5.8e+03 0.00012 1e+04 1 ++
4 5.8e+03 1.3e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b07everything_000316
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 6e+03 0.073 10 1.1 ++
1 5.5e+03 0.031 1e+02 0.98 ++
2 5.5e+03 0.0011 1e+03 1 ++
3 5.5e+03 4.7e-06 1e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b07everything_000317
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.92 -0.039 -0.011 -1 -0.64 -0.0019 -0.46 -0.2 -0.047 5.6e+03 2.5 10 1 ++
1 -0.89 0.82 0.5 -2.9 -2 -0.0046 -0.16 0.17 -0.14 5.2e+03 0.39 1e+02 1.1 ++
2 -1.1 1.1 0.9 -3.2 -2.3 -0.0055 -0.17 0.17 -0.24 5.1e+03 0.02 1e+03 1.1 ++
3 -1.1 1.2 0.95 -3.2 -2.4 -0.0055 -0.18 0.17 -0.23 5.1e+03 0.00054 1e+04 1 ++
4 -1.1 1.2 0.95 -3.2 -2.4 -0.0055 -0.18 0.17 -0.22 5.1e+03 0.0001 1e+05 1 ++
5 -1.1 1.2 0.95 -3.2 -2.4 -0.0055 -0.18 0.17 -0.22 5.1e+03 6e-09 1e+05 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 19 unknown parameters [max: 50]
*** Estimate b07everything_000318
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 2.7 10 1 ++
1 5.5e+03 0.93 1e+02 0.97 ++
2 5.5e+03 0.053 1e+03 1 ++
3 5.5e+03 0.00034 1e+04 1 ++
4 5.5e+03 1.4e-08 1e+04 1 ++
Considering neighbor 4/20 for current solution
Considering neighbor 5/20 for current solution
Attempt 78/100
Considering neighbor 0/20 for current solution
Attempt 79/100
Considering neighbor 0/20 for current solution
Attempt 80/100
Considering neighbor 0/20 for current solution
Attempt 81/100
Considering neighbor 0/20 for current solution
Attempt 82/100
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000319
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.4e+03 3 10 1.1 ++
1 5.2e+03 0.64 1e+02 1.1 ++
2 5.2e+03 0.05 1e+03 1.1 ++
3 5.2e+03 0.00049 1e+04 1 ++
4 5.2e+03 6.3e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000320
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.8e+03 0.087 1 0.74 +
1 5.3e+03 0.024 10 0.98 ++
2 5.3e+03 0.043 10 0.36 +
3 5.2e+03 0.0014 1e+02 1.1 ++
4 5.2e+03 0.0017 1e+03 0.99 ++
5 5.2e+03 9.9e-06 1e+04 1 ++
6 5.2e+03 3.1e-10 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000321
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.59 -0.97 0.73 -0.88 2 -0.87 0.0066 -0.2 0.088 -0.3 5.6e+03 2.9 1 0.7 +
1 -0.29 -1.1 1.7 -0.49 1.8 -1.9 -0.008 -0.61 0.12 -0.39 5.1e+03 0.61 10 1 ++
2 -0.29 -1.1 1.7 -0.49 1.8 -1.9 -0.008 -0.61 0.12 -0.39 5.1e+03 0.61 5 -1.7e+03 -
3 -0.29 -1.1 1.7 -0.49 1.8 -1.9 -0.008 -0.61 0.12 -0.39 5.1e+03 0.61 2.5 -14 -
4 -0.29 -1.1 1.7 -0.49 1.8 -1.9 -0.008 -0.61 0.12 -0.39 5.1e+03 0.61 1.2 -0.42 -
5 -0.59 -1.3 1.8 -1.2 0.54 -2.2 0.0012 -0.52 0.48 -0.23 4.9e+03 0.19 1.2 0.87 +
6 0.11 -1.2 1.9 -1.7 0.2 -2.7 -0.0069 -0.55 0.38 1 4.8e+03 0.035 12 0.93 ++
7 0.028 -1.2 2 -1.7 0.29 -2.8 -0.0067 -0.63 0.43 1 4.8e+03 0.0025 1.2e+02 1 ++
8 0.028 -1.2 2 -1.7 0.29 -2.8 -0.0067 -0.63 0.43 1 4.8e+03 3.9e-06 1.2e+02 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000322
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.74 0.083 -0.0053 0.69 -1 2 -0.78 -0.0018 -0.3 -0.15 -0.049 -0.28 5.8e+03 2.2 1 0.58 +
1 -1.2 0.49 0.044 1.6 -0.68 1.7 -1.8 -0.008 -0.31 0.042 -0.11 -0.41 5.2e+03 0.1 10 1 ++
2 -1.2 0.49 0.044 1.6 -0.68 1.7 -1.8 -0.008 -0.31 0.042 -0.11 -0.41 5.2e+03 0.1 5 -3.3e+03 -
3 -1.2 0.49 0.044 1.6 -0.68 1.7 -1.8 -0.008 -0.31 0.042 -0.11 -0.41 5.2e+03 0.1 2.5 -29 -
4 -1.2 0.49 0.044 1.6 -0.68 1.7 -1.8 -0.008 -0.31 0.042 -0.11 -0.41 5.2e+03 0.1 1.2 -0.75 -
5 -1.5 0.54 0.12 1.8 -1.6 0.47 -2.2 -0.00014 -0.22 0.39 -0.15 -0.23 5e+03 0.059 1.2 0.88 +
6 -1.1 0.66 0.53 1.9 -1.7 0.35 -2.7 -0.0071 -0.26 0.081 -0.32 1 4.9e+03 0.046 12 0.96 ++
7 -1.3 0.71 0.47 1.9 -1.6 0.34 -2.8 -0.0062 -0.28 0.051 -0.36 1.2 4.9e+03 0.0038 1.2e+02 1 ++
8 -1.3 0.71 0.47 1.9 -1.6 0.34 -2.8 -0.0062 -0.28 0.051 -0.36 1.2 4.9e+03 6.6e-06 1.2e+03 1 ++
9 -1.3 0.71 0.47 1.9 -1.6 0.34 -2.8 -0.0062 -0.28 0.051 -0.36 1.2 4.9e+03 1.6e-08 1.2e+03 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 83/100
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000323
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.94 -0.088 -0.013 0.54 -1 -0.59 -0.0026 -0.5 -0.22 -0.044 -0.22 5.5e+03 2.6 10 1 ++
1 -1.1 0.41 0.23 1.9 -2.6 -2.5 -0.0049 -0.31 0.083 -0.2 1 4.9e+03 0.75 1e+02 1 ++
2 -1.3 0.66 0.45 1.9 -3 -2.8 -0.0061 -0.32 0.066 -0.34 1.2 4.9e+03 0.071 1e+03 1.1 ++
3 -1.3 0.71 0.49 1.9 -3.1 -2.8 -0.0063 -0.32 0.064 -0.36 1.2 4.9e+03 0.0013 1e+04 1 ++
4 -1.3 0.71 0.49 1.9 -3.1 -2.8 -0.0063 -0.32 0.064 -0.36 1.2 4.9e+03 4.5e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000324
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.65 -0.65 0 0 0 -0.0026 0 0 -0.4 0 0 5.7e+03 2.5 10 1.1 ++
1 -0.41 -1.1 0 0 0 -0.0047 0 0 -0.064 0 0 5.6e+03 0.22 1e+02 1.1 ++
2 -0.39 -1.1 0 0 0 -0.0052 0 0 -0.062 0 0 5.6e+03 0.0049 1e+03 1 ++
3 -0.39 -1.1 0 0 0 -0.0052 0 0 -0.062 0 0 5.6e+03 2.4e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b07everything_000325
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 3 10 1 ++
1 5.6e+03 0.41 1e+02 1.1 ++
2 5.6e+03 0.034 1e+03 1.1 ++
3 5.6e+03 0.00026 1e+04 1 ++
4 5.6e+03 2.1e-08 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000326
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.8 0.19 -0.0025 0.9 -0.84 -1 -0.0018 -0.16 -0.055 -0.057 -0.33 5.2e+03 2.7 10 1.1 ++
1 -1.4 0.52 0.38 1.7 -1 -2.5 -0.0049 -0.46 0.093 -0.22 0.99 5e+03 0.63 1e+02 1.1 ++
2 -1.5 0.69 0.54 1.9 -1.1 -2.8 -0.0062 -0.5 0.1 -0.31 1.2 5e+03 0.054 1e+03 1.1 ++
3 -1.5 0.72 0.56 1.9 -1.1 -2.8 -0.0063 -0.5 0.1 -0.31 1.2 5e+03 0.00056 1e+04 1 ++
4 -1.5 0.72 0.56 1.9 -1.1 -2.8 -0.0063 -0.5 0.1 -0.31 1.2 5e+03 9.2e-08 1e+04 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b07everything_000327
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.8e+03 2.6 10 1 ++
1 5.5e+03 0.94 1e+02 1 ++
2 5.5e+03 0.089 1e+03 1.1 ++
3 5.5e+03 0.0022 1e+04 1 ++
4 5.5e+03 1.6e-06 1e+04 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000328
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.57 -0.89 0.24 -0.0075 -1 -0.88 0.00074 -0.36 -0.062 -0.15 -0.054 5.4e+03 2.9 10 1.1 ++
1 -0.19 -1.1 0.75 0.35 -2.8 -2.1 -0.0043 -0.46 0.29 0.18 0.021 5e+03 0.43 1e+02 1.1 ++
2 -0.18 -1.1 0.94 0.52 -3.2 -2.4 -0.0057 -0.53 0.36 0.19 -0.0019 5e+03 0.037 1e+03 1 ++
3 -0.19 -1.1 0.96 0.55 -3.2 -2.4 -0.0059 -0.54 0.37 0.19 0.0044 5e+03 0.00036 1e+04 1 ++
4 -0.19 -1.1 0.96 0.55 -3.2 -2.4 -0.0059 -0.54 0.37 0.19 0.0044 5e+03 3.5e-07 1e+04 1 ++
Considering neighbor 5/20 for current solution
Considering neighbor 6/20 for current solution
Attempt 84/100
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000329
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 2.5 10 1 ++
1 5.2e+03 0.86 1e+02 1 ++
2 5.2e+03 0.085 1e+03 1.1 ++
3 5.2e+03 0.002 1e+04 1 ++
4 5.2e+03 1.4e-06 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 85/100
Considering neighbor 0/20 for current solution
Attempt 86/100
Considering neighbor 0/20 for current solution
Attempt 87/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000330
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.67 -0.1 -0.016 0.24 -1 -0.28 -0.34 -0.26 -0.021 -0.14 5.6e+03 0.065 10 1 ++
1 -1.3 0.39 0.2 2.2 -1.5 -2.7 -0.14 0.07 -0.18 1.3 5e+03 0.031 1e+02 0.95 ++
2 -1.5 0.66 0.42 2 -1.6 -2.8 -0.15 0.033 -0.34 1.2 4.9e+03 0.0017 1e+03 1 ++
3 -1.6 0.71 0.47 2 -1.6 -2.8 -0.15 0.029 -0.35 1.2 4.9e+03 3.9e-05 1e+04 1 ++
4 -1.6 0.71 0.47 2 -1.6 -2.8 -0.15 0.029 -0.35 1.2 4.9e+03 1.9e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000331
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.94 -0.12 -0.015 0.51 -1 -0.79 -0.0032 -0.59 -0.27 -0.042 -0.21 5.4e+03 2.3 10 1 ++
1 -1 0.41 0.22 1.9 -2.8 -1 -0.005 -0.27 0.066 -0.21 -0.36 5e+03 0.97 1e+02 1 ++
2 -1.2 0.66 0.45 1.9 -3.2 -1.1 -0.0061 -0.24 0.04 -0.3 -0.27 5e+03 0.084 1e+03 1.1 ++
3 -1.2 0.71 0.5 1.9 -3.2 -1.1 -0.0062 -0.24 0.039 -0.3 -0.27 5e+03 0.0016 1e+04 1 ++
4 -1.2 0.71 0.5 1.9 -3.2 -1.1 -0.0062 -0.24 0.039 -0.3 -0.27 5e+03 7.4e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000332
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho
0 -0.51 -0.8 -1 -0.93 -0.0005 -0.54 -0.19 5.4e+03 2.6 10 1.1 ++
1 0.29 -1.1 -2.6 -1 -0.0046 -0.59 0.34 5.2e+03 0.36 1e+02 1.1 ++
2 0.44 -1.3 -2.9 -1.1 -0.0057 -0.6 0.36 5.2e+03 0.021 1e+03 1 ++
3 0.45 -1.3 -2.9 -1.1 -0.0058 -0.59 0.35 5.2e+03 5.6e-05 1e+04 1 ++
4 0.45 -1.3 -2.9 -1.1 -0.0058 -0.59 0.35 5.2e+03 2.1e-07 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000333
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.04 10 1.1 ++
1 5.2e+03 0.059 1e+02 1 ++
2 5.2e+03 0.0037 1e+03 1 ++
3 5.2e+03 0.00017 1e+04 1 ++
4 5.2e+03 2e-07 1e+04 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 88/100
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000334
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.083 -0.77 -0.62 0 0 0 -0.0027 0 0 -0.47 0.023 0 0 5.6e+03 2.9 10 1.1 ++
1 0.34 -1.1 -1.1 0 0 0 -0.005 0 0 -0.38 0.33 0 0 5.4e+03 0.37 1e+02 1.1 ++
2 0.41 -1.2 -1.1 0 0 0 -0.0057 0 0 -0.39 0.32 0 0 5.4e+03 0.018 1e+03 1 ++
3 0.41 -1.2 -1.1 0 0 0 -0.0057 0 0 -0.39 0.32 0 0 5.4e+03 3.6e-05 1e+04 1 ++
4 0.41 -1.2 -1.1 0 0 0 -0.0057 0 0 -0.39 0.32 0 0 5.4e+03 1.4e-10 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 89/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000335
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -1 0.036 -1 0 0 0 0 0 -0.0065 -1 0 0 5.7e+03 0.038 10 1.1 ++
1 -0.95 2.1 -2.6 0 0 0 0 0 0.14 -1.2 0 0 5.3e+03 0.027 1e+02 0.92 ++
2 -0.91 1.8 -3 0 0 0 0 0 0.21 -1.4 0 0 5.2e+03 0.00075 1e+03 1 ++
3 -0.91 1.8 -3 0 0 0 0 0 0.21 -1.4 0 0 5.2e+03 1.9e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000336
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.65 0.022 -0.01 -1 0 0 0 0 0 -0.22 -0.2 -0.029 0 0 5.7e+03 0.045 10 1 ++
1 -0.91 0.8 0.63 -1.5 0 0 0 0 0 0.25 -0.088 -0.72 0 0 5.5e+03 0.009 1e+02 1.1 ++
2 -1.2 1.1 0.9 -1.6 0 0 0 0 0 0.24 -0.083 -0.77 0 0 5.5e+03 0.001 1e+03 1.1 ++
3 -1.2 1.1 0.94 -1.6 0 0 0 0 0 0.24 -0.084 -0.77 0 0 5.5e+03 1.8e-05 1e+04 1 ++
4 -1.2 1.1 0.94 -1.6 0 0 0 0 0 0.24 -0.084 -0.77 0 0 5.5e+03 5e-09 1e+04 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 90/100
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b07everything_000337
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho
0 -0.48 -0.77 -1 -0.99 -0.0016 -0.55 -0.21 5.4e+03 2.4 10 1 ++
1 0.49 -1.2 -3 -1 -0.0048 -0.39 0.26 5.1e+03 0.35 1e+02 1.1 ++
2 0.62 -1.3 -3.3 -1.1 -0.0057 -0.42 0.31 5.1e+03 0.017 1e+03 1 ++
3 0.63 -1.3 -3.3 -1.1 -0.0058 -0.41 0.3 5.1e+03 6.3e-05 1e+04 1 ++
4 0.63 -1.3 -3.3 -1.1 -0.0058 -0.41 0.3 5.1e+03 3.6e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b07everything_000338
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho
0 -0.37 -0.59 -1 -0.89 -0.33 -0.13 5.3e+03 0.036 10 1 ++
1 0.4 -1.3 -1.6 -1 -0.22 0.22 5.2e+03 0.0091 1e+02 1 ++
2 0.4 -1.3 -1.7 -1 -0.24 0.25 5.2e+03 0.00022 1e+03 1 ++
3 0.4 -1.3 -1.7 -1 -0.24 0.25 5.2e+03 2.4e-07 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000339
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_ma beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.24 -0.56 -1 0 0 0 0 0 -0.26 -0.026 0 0 5.6e+03 0.04 10 1 ++
1 0.45 -1.2 -1.5 0 0 0 0 0 -0.062 0.28 0 0 5.4e+03 0.0081 1e+02 1 ++
2 0.45 -1.3 -1.6 0 0 0 0 0 -0.062 0.27 0 0 5.4e+03 0.00022 1e+03 1 ++
3 0.45 -1.3 -1.6 0 0 0 0 0 -0.062 0.27 0 0 5.4e+03 2.4e-07 1e+03 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 91/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000340
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time lambda_tt b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_on asc_car_diff_se beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.67 0.14 -0.011 -1 1.8 0 0 0 0 0 -0.37 -0.3 -0.04 0 0 6e+03 0.092 1 0.6 +
1 -1.3 1.1 0.11 -1.3 1.1 0 0 0 0 0 0.15 -0.018 -0.23 0 0 5.5e+03 0.018 10 1.1 ++
2 -0.74 1.1 1.1 -2.2 0.1 0 0 0 0 0 0.56 -0.13 -0.84 0 0 5.5e+03 0.042 10 0.29 +
3 -1.2 1.1 0.9 -1.6 0.28 0 0 0 0 0 0.24 -0.079 -0.78 0 0 5.5e+03 0.0026 1e+02 1 ++
4 -1.2 1.1 0.88 -1.6 0.43 0 0 0 0 0 0.25 -0.081 -0.78 0 0 5.5e+03 0.0013 1e+03 0.95 ++
5 -1.2 1.1 0.89 -1.5 0.42 0 0 0 0 0 0.24 -0.079 -0.77 0 0 5.5e+03 7.2e-06 1e+04 1 ++
6 -1.2 1.1 0.89 -1.5 0.42 0 0 0 0 0 0.24 -0.079 -0.77 0 0 5.5e+03 1.1e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000341
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time lambda_tt b_cost beta_TRAIN_COST beta_TRAIN_COST b_headway beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.72 0.65 -0.95 2 0 0 0 -0.0015 0 0 -0.41 -0.3 0 0 6e+03 2.3 1 0.54 +
1 -0.93 1.6 -0.38 1.9 0 0 0 -0.0073 0 0 -0.15 -0.73 0 0 5.5e+03 0.17 1 0.86 +
2 -1.2 1.8 -0.83 0.89 0 0 0 -0.0047 0 0 -0.24 -0.85 0 0 5.3e+03 0.026 10 1 ++
3 -0.51 1.8 -1.7 -0.05 0 0 0 -0.0059 0 0 0.19 -1.3 0 0 5.2e+03 0.12 10 0.49 +
4 -0.51 1.9 -1.7 0.32 0 0 0 -0.006 0 0 0.18 -1.4 0 0 5.2e+03 0.015 1e+02 1 ++
5 -0.6 1.9 -1.6 0.34 0 0 0 -0.006 0 0 0.12 -1.3 0 0 5.2e+03 0.00078 1e+03 0.98 ++
6 -0.6 1.9 -1.6 0.34 0 0 0 -0.006 0 0 0.12 -1.3 0 0 5.2e+03 8.4e-07 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b07everything_000342
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.8e+03 2.6 10 1 ++
1 5.5e+03 0.94 1e+02 1 ++
2 5.5e+03 0.089 1e+03 1.1 ++
3 5.5e+03 0.0022 1e+04 1 ++
4 5.5e+03 1.6e-06 1e+04 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 92/100
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000343
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_ma beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.67 -0.94 0 0 0 -1 -0.00071 0 0 -0.61 -0.21 0 0 5.6e+03 2.8 10 1.1 ++
1 -0.53 -1.3 0 0 0 -2.1 -0.0048 0 0 -0.97 0.13 0 0 5.4e+03 0.29 1e+02 1.1 ++
2 -0.49 -1.4 0 0 0 -2.2 -0.0058 0 0 -1 0.16 0 0 5.4e+03 0.012 1e+03 1 ++
3 -0.49 -1.4 0 0 0 -2.2 -0.0059 0 0 -1.1 0.16 0 0 5.4e+03 1.9e-05 1e+04 1 ++
4 -0.49 -1.4 0 0 0 -2.2 -0.0059 0 0 -1.1 0.16 0 0 5.4e+03 3.9e-11 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 93/100
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b07everything_000344
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 3 10 1 ++
1 5.6e+03 0.41 1e+02 1.1 ++
2 5.6e+03 0.034 1e+03 1.1 ++
3 5.6e+03 0.00026 1e+04 1 ++
4 5.6e+03 2.1e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 94/100
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b07everything_000345
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time beta_TRAIN_TT_S beta_TRAIN_TT_S b_cost b_headway beta_SM_TT_SCAL beta_SM_TT_SCAL asc_car_ref asc_car_diff_wi beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.91 0.23 0 0 0 -0.42 -0.008 0 0 -1 -0.2 0 0 5.7e+03 2.5 10 1 ++
1 -1.7 2.2 0 0 0 -0.97 -0.0062 0 0 -0.97 0.0081 0 0 5.3e+03 1.1 1e+02 1 ++
2 -1.9 2.2 0 0 0 -1 -0.0064 0 0 -1 -0.049 0 0 5.3e+03 0.062 1e+03 1 ++
3 -1.9 2.2 0 0 0 -1 -0.0064 0 0 -1 -0.042 0 0 5.3e+03 0.00046 1e+04 1 ++
4 -1.9 2.2 0 0 0 -1 -0.0064 0 0 -1 -0.042 0 0 5.3e+03 2.6e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 95/100
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b07everything_000346
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 6e+03 0.073 10 1.1 ++
1 5.5e+03 0.031 1e+02 0.98 ++
2 5.5e+03 0.0011 1e+03 1 ++
3 5.5e+03 4.7e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000347
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_on asc_car_diff_se asc_car_diff_wi Function Relgrad Radius Rho
0 -0.96 0.24 -0.014 0.89 -0.79 -1 -0.16 -0.16 -0.054 -0.33 5.2e+03 0.043 10 1.1 ++
1 -1.6 0.54 0.39 1.7 -1 -2.5 -0.36 0.088 -0.25 0.99 5e+03 0.015 1e+02 1.1 ++
2 -1.8 0.69 0.56 1.8 -1.1 -2.8 -0.38 0.1 -0.31 1.2 5e+03 0.0012 1e+03 1 ++
3 -1.8 0.71 0.58 1.8 -1.1 -2.8 -0.37 0.1 -0.31 1.2 5e+03 1.1e-05 1e+04 1 ++
4 -1.8 0.71 0.58 1.8 -1.1 -2.8 -0.37 0.1 -0.31 1.2 5e+03 1.2e-09 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000348
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost beta_TRAIN_COST beta_TRAIN_COST beta_SM_COST_SC beta_SM_COST_SC asc_car_ref asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -1 0.036 -1 0 0 0 0 0 -0.0065 -1 0 0 5.7e+03 0.038 10 1.1 ++
1 -0.95 2.1 -2.6 0 0 0 0 0 0.14 -1.2 0 0 5.3e+03 0.027 1e+02 0.92 ++
2 -0.91 1.8 -3 0 0 0 0 0 0.21 -1.4 0 0 5.2e+03 0.00075 1e+03 1 ++
3 -0.91 1.8 -3 0 0 0 0 0 0.21 -1.4 0 0 5.2e+03 1.9e-06 1e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b07everything_000349
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -1 -0.065 -1 -0.72 -0.048 -0.46 5.6e+03 0.042 10 1.1 ++
1 -1.2 2.4 -2.5 -2.2 -0.17 0.87 5e+03 0.039 1e+02 0.94 ++
2 -1.1 2.1 -3 -2.7 -0.16 1.1 5e+03 0.0016 1e+03 1 ++
3 -1.1 2.1 -3.1 -2.8 -0.15 1.2 5e+03 1.6e-05 1e+04 1 ++
4 -1.1 2.1 -3.1 -2.8 -0.15 1.2 5e+03 1.3e-09 1e+04 1 ++
Considering neighbor 3/20 for current solution
*** New pareto solution:
asc:GA;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:sqrt [9929.037358349122, np.float64(9969.957123756838), 6]
Attempt 96/100
Considering neighbor 0/20 for current solution
Attempt 97/100
Considering neighbor 0/20 for current solution
Attempt 98/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000350
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Analytical Hessian method: full
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.04 10 1 ++
1 5.3e+03 0.056 1e+02 1 ++
2 5.2e+03 0.004 1e+03 1 ++
3 5.2e+03 0.00015 1e+04 1 ++
4 5.2e+03 1.7e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 99/100
Considering neighbor 0/20 for current solution
Pareto file has been updated: b22_multiple_models.pareto
Before the algorithm: 1 models, with 1 Pareto.
After the algorithm: 166 models, with 7 Pareto.
VNS algorithm completed. Postprocessing of the Pareto optimal solutions
Pareto set initialized from file with 166 elements [7 Pareto] and 0 invalid elements.
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22_multiple_models_000000.iter
Cannot read file __b22_multiple_models_000000.iter. Statement is ignored.
Starting values for the algorithm: {}
Analytical Hessian method: full
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -1 -0.065 -1 -0.72 -0.048 -0.46 5.6e+03 0.042 10 1.1 ++
1 -1.2 2.4 -2.5 -2.2 -0.17 0.87 5e+03 0.039 1e+02 0.94 ++
2 -1.1 2.1 -3 -2.7 -0.16 1.1 5e+03 0.0016 1e+03 1 ++
3 -1.1 2.1 -3.1 -2.8 -0.15 1.2 5e+03 1.6e-05 1e+04 1 ++
4 -1.1 2.1 -3.1 -2.8 -0.15 1.2 5e+03 1.3e-09 1e+04 1 ++
Optimization algorithm has converged.
Relative gradient: 1.3349627072966304e-09
Cause of termination: Relative gradient = 1.3e-09 <= 6.1e-06
Number of function evaluations: 16
Number of gradient evaluations: 11
Number of hessian evaluations: 5
Algorithm: Newton with trust region for simple bound constraints
Number of iterations: 5
Proportion of Hessian calculation: 5/5 = 100.0%
Optimization time: 0:00:02.813950
Calculate final gradient and BHHH
Calculate second derivatives
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22_multiple_models_000001.iter
Cannot read file __b22_multiple_models_000001.iter. Statement is ignored.
Starting values for the algorithm: {}
Analytical Hessian method: full
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.59 -0.77 0.75 -1 -0.86 -0.46 -0.18 -0.28 5.3e+03 0.047 10 1.1 ++
1 -0.43 -0.97 1.8 -2.7 -2.5 -0.5 0.37 0.88 4.9e+03 0.019 1e+02 1.1 ++
2 -0.37 -1.1 2 -3.1 -2.7 -0.54 0.44 1 4.8e+03 0.0014 1e+03 1 ++
3 -0.36 -1.1 2 -3.1 -2.8 -0.54 0.44 1 4.8e+03 7.5e-06 1e+04 1 ++
4 -0.36 -1.1 2 -3.1 -2.8 -0.54 0.44 1 4.8e+03 2.5e-10 1e+04 1 ++
Optimization algorithm has converged.
Relative gradient: 2.54955549725972e-10
Cause of termination: Relative gradient = 2.5e-10 <= 6.1e-06
Number of function evaluations: 16
Number of gradient evaluations: 11
Number of hessian evaluations: 5
Algorithm: Newton with trust region for simple bound constraints
Number of iterations: 5
Proportion of Hessian calculation: 5/5 = 100.0%
Optimization time: 0:00:02.052012
Calculate final gradient and BHHH
Calculate second derivatives
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22_multiple_models_000002.iter
Cannot read file __b22_multiple_models_000002.iter. Statement is ignored.
Starting values for the algorithm: {}
Analytical Hessian method: full
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_wi Function Relgrad Radius Rho
0 -1 0.11 -1 -0.27 1.1 -0.047 -1 5.6e+03 0.042 10 1.1 ++
1 -1 0.11 -1 -0.27 1.1 -0.047 -1 5.6e+03 0.042 5 -1.4e+07 -
2 -1 0.11 -1 -0.27 1.1 -0.047 -1 5.6e+03 0.042 2.5 -2.1e+02 -
3 -1 0.11 -1 -0.27 1.1 -0.047 -1 5.6e+03 0.042 1.2 -0.26 -
4 -1.4 1.4 -1.9 -1.1 0.99 -0.38 -1.1 5.1e+03 0.023 12 1.1 ++
5 -1.4 1.4 -1.9 -1.1 0.99 -0.38 -1.1 5.1e+03 0.023 1.2 -12 -
6 -1.3 2.5 -3 -2 -0.19 -0.31 -1.4 5e+03 0.041 1.2 0.5 +
7 -1.3 2.1 -2.9 -1.4 -0.13 -0.18 -1.8 5e+03 0.0015 12 0.98 ++
8 -1.2 2.1 -3 -1.5 0.1 -0.19 -1.9 4.9e+03 0.002 1.2e+02 0.91 ++
9 -1.2 2.1 -3 -1.5 0.091 -0.19 -1.9 4.9e+03 1.9e-05 1.2e+03 1 ++
10 -1.2 2.1 -3 -1.5 0.091 -0.19 -1.9 4.9e+03 2.3e-08 1.2e+03 1 ++
Optimization algorithm has converged.
Relative gradient: 2.3131979598718067e-08
Cause of termination: Relative gradient = 2.3e-08 <= 6.1e-06
Number of function evaluations: 26
Number of gradient evaluations: 15
Number of hessian evaluations: 7
Algorithm: Newton with trust region for simple bound constraints
Number of iterations: 11
Proportion of Hessian calculation: 7/7 = 100.0%
Optimization time: 0:00:02.428096
Calculate final gradient and BHHH
Calculate second derivatives
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22_multiple_models_000003.iter
Cannot read file __b22_multiple_models_000003.iter. Statement is ignored.
Starting values for the algorithm: {}
Analytical Hessian method: full
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time b_cost asc_car Function Relgrad Radius Rho
0 -0.79 -0.72 -1 -0.42 5.5e+03 0.037 10 1.1 ++
1 -0.75 -1.2 -2.2 -0.1 5.3e+03 0.0076 1e+02 1.1 ++
2 -0.73 -1.2 -2.3 -0.11 5.3e+03 0.00023 1e+03 1 ++
3 -0.73 -1.2 -2.3 -0.11 5.3e+03 2.7e-07 1e+03 1 ++
Optimization algorithm has converged.
Relative gradient: 2.7148828825437547e-07
Cause of termination: Relative gradient = 2.7e-07 <= 6.1e-06
Number of function evaluations: 13
Number of gradient evaluations: 9
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: 0:00:01.447006
Calculate final gradient and BHHH
Calculate second derivatives
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22_multiple_models_000004.iter
Cannot read file __b22_multiple_models_000004.iter. Statement is ignored.
Starting values for the algorithm: {}
Analytical Hessian method: full
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.47 -0.7 0.44 -1 -0.64 1 0.0022 -0.28 -0.067 -0.19 5.3e+03 2.7 10 1.1 ++
1 -0.47 -0.7 0.44 -1 -0.64 1 0.0022 -0.28 -0.067 -0.19 5.3e+03 2.7 1.3 -3.2 -
2 -0.12 -1 1.7 -1.4 -1.2 0.84 -0.0024 -0.33 0.21 -0.62 4.9e+03 0.7 13 1 ++
3 -0.12 -1 1.7 -1.4 -1.2 0.84 -0.0024 -0.33 0.21 -0.62 4.9e+03 0.7 0.64 0.017 -
4 0.019 -1.2 2.2 -1.6 -1.4 0.2 -0.0063 -0.52 0.31 -1.1 4.8e+03 0.15 6.4 1.1 ++
5 -0.071 -1.2 2.2 -1.6 -1.5 -0.045 -0.0066 -0.68 0.45 -1.8 4.8e+03 0.03 64 1 ++
6 -0.071 -1.2 2.2 -1.6 -1.5 -0.036 -0.0066 -0.69 0.46 -2 4.8e+03 0.0014 6.4e+02 1 ++
7 -0.071 -1.2 2.2 -1.6 -1.5 -0.036 -0.0066 -0.69 0.46 -2 4.8e+03 4.6e-06 6.4e+02 1 ++
Optimization algorithm has converged.
Relative gradient: 4.589403692686364e-06
Cause of termination: Relative gradient = 4.6e-06 <= 6.1e-06
Number of function evaluations: 21
Number of gradient evaluations: 13
Number of hessian evaluations: 6
Algorithm: Newton with trust region for simple bound constraints
Number of iterations: 8
Proportion of Hessian calculation: 6/6 = 100.0%
Optimization time: 0:00:03.526943
Calculate final gradient and BHHH
Calculate second derivatives
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22_multiple_models_000005.iter
Cannot read file __b22_multiple_models_000005.iter. Statement is ignored.
Starting values for the algorithm: {}
Analytical Hessian method: full
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train b_time lambda_tt b_cost asc_car Function Relgrad Radius Rho
0 -0.68 -1 1.9 -0.64 -0.59 6e+03 0.11 1 0.59 +
1 -0.92 -0.75 1.6 -1.6 -0.024 5.4e+03 0.031 10 0.96 ++
2 -0.92 -0.75 1.6 -1.6 -0.024 5.4e+03 0.031 1.2 -1.7 -
3 -0.68 -1.8 0.39 -2.3 0.03 5.3e+03 0.04 1.2 0.88 +
4 -0.48 -1.7 0.47 -2.3 0.054 5.2e+03 0.0029 12 0.95 ++
5 -0.5 -1.7 0.48 -2.4 0.057 5.2e+03 2.1e-05 1.2e+02 1 ++
6 -0.5 -1.7 0.48 -2.4 0.057 5.2e+03 1.2e-09 1.2e+02 1 ++
Optimization algorithm has converged.
Relative gradient: 1.240651099909684e-09
Cause of termination: Relative gradient = 1.2e-09 <= 6.1e-06
Number of function evaluations: 20
Number of gradient evaluations: 13
Number of hessian evaluations: 6
Algorithm: Newton with trust region for simple bound constraints
Number of iterations: 7
Proportion of Hessian calculation: 6/6 = 100.0%
Optimization time: 0:00:02.718280
Calculate final gradient and BHHH
Calculate second derivatives
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22_multiple_models_000006.iter
Cannot read file __b22_multiple_models_000006.iter. Statement is ignored.
Starting values for the algorithm: {}
Analytical Hessian method: full
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. asc_train_ref asc_train_diff_ asc_train_diff_ b_time b_cost lambda_cost asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.43 -0.58 0.47 -1 -0.62 1 -0.37 -0.19 -0.21 5.3e+03 0.05 10 1 ++
1 -0.43 -0.58 0.47 -1 -0.62 1 -0.37 -0.19 -0.21 5.3e+03 0.05 1.3 -3 -
2 -0.32 -1.1 1.8 -1.6 -1.1 0.81 -0.38 0.15 -0.66 4.9e+03 0.019 13 1.1 ++
3 -0.32 -1.1 1.8 -1.6 -1.1 0.81 -0.38 0.15 -0.66 4.9e+03 0.019 0.82 -2.4 -
4 -0.26 -1.2 2.2 -1.7 -1.3 -0.011 -0.37 0.38 -1.1 4.8e+03 0.0087 8.2 0.99 ++
5 -0.38 -1.2 2.1 -1.6 -1.5 -0.026 -0.55 0.46 -1.8 4.8e+03 0.0014 82 1.1 ++
6 -0.38 -1.2 2.1 -1.6 -1.5 -0.04 -0.56 0.46 -2 4.8e+03 8.5e-05 8.2e+02 1 ++
7 -0.38 -1.2 2.1 -1.6 -1.5 -0.04 -0.56 0.46 -2 4.8e+03 2.8e-07 8.2e+02 1 ++
Optimization algorithm has converged.
Relative gradient: 2.8139970693199444e-07
Cause of termination: Relative gradient = 2.8e-07 <= 6.1e-06
Number of function evaluations: 21
Number of gradient evaluations: 13
Number of hessian evaluations: 6
Algorithm: Newton with trust region for simple bound constraints
Number of iterations: 8
Proportion of Hessian calculation: 6/6 = 100.0%
Optimization time: 0:00:02.997571
Calculate final gradient and BHHH
Calculate second derivatives
Pareto: 7
Considered: 166
Removed: 12
summary, description = compile_estimation_results(
non_dominated_models, use_short_names=True
)
print(summary)
Model_000000 ... Model_000006
Number of estimated parameters 6 ... 9
Sample size 6768 ... 6768
Final log likelihood -4958.519 ... -4830.135
Akaike Information Criterion 9929.037 ... 9678.27
Bayesian Information Criterion 9969.957 ... 9739.649
asc_train_ref (t-test) -1.14 (-14) ... -0.382 (-4.12)
asc_train_diff_with_ga (t-test) 2.07 (23.7) ... 2.13 (23.5)
b_time (t-test) -3.11 (-17) ... -1.62 (-20.1)
b_cost (t-test) -2.79 (-17.2) ... -1.5 (-18.3)
asc_car_ref (t-test) -0.153 (-2.64) ... -0.557 (-5.27)
asc_car_diff_with_ga (t-test) 1.15 (5.02) ... -2.02 (-9.06)
asc_train_diff_male (t-test) ... -1.17 (-13.8)
asc_car_diff_male (t-test) ... 0.465 (4.28)
lambda_cost (t-test) ... -0.0401 (-0.444)
asc_train (t-test) ...
asc_car (t-test) ...
b_headway (t-test) ...
lambda_tt (t-test) ...
[18 rows x 7 columns]
Explanation of the short names of the model.
for k, v in description.items():
if k != v:
print(f'{k}: {v}')
Model_000000: asc:GA;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:sqrt
Model_000001: asc:MALE-GA;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:sqrt
Model_000002: asc:GA;train_cost_catalog:boxcox;train_headway_catalog:without_headway;train_tt_catalog:sqrt
Model_000003: asc:no_seg;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:linear
Model_000004: asc:MALE-GA;train_cost_catalog:boxcox;train_headway_catalog:with_headway;train_tt_catalog:log
Model_000005: asc:no_seg;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:boxcox
Model_000006: asc:MALE-GA;train_cost_catalog:boxcox;train_headway_catalog:without_headway;train_tt_catalog:log
Total running time of the script: (7 minutes 37.310 seconds)