Note
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Assisted specificationΒΆ
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 Assisted specification, the number fo 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_b22multiple_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,
)
Biogeme parameters read from biogeme.toml.
Pareto set initialized from file with 465 elements [7 Pareto] and 0 invalid elements.
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_000161
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 -0.92 -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: 7
Attempt 0/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b07everything_000162
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 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 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000163
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 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 0.0025 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 5.5e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Attempt 1/100
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000164
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 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 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b07everything_000165
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 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 1/20 for current solution
Attempt 2/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b07everything_000166
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. 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
Attempt 3/100
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b07everything_000167
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. Function Relgrad Radius Rho
0 5.5e+03 2.7 10 1.1 ++
1 5.4e+03 0.42 1e+02 1.1 ++
2 5.4e+03 0.038 1e+03 1.1 ++
3 5.4e+03 0.0004 1e+04 1 ++
4 5.4e+03 5.3e-08 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_000168
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 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 -0.72 -0.9 1 0 0 0 -0.94 -0.0035 0 0 -0.78 -0.29 -0.18 0 0 5.3e+03 2.9 10 1.1 ++
1 -1 -1.1 1.9 0 0 0 -0.99 -0.0058 0 0 -1.2 0.25 -0.14 0 0 5.2e+03 0.61 1e+02 1.1 ++
2 -1 -1.2 2.1 0 0 0 -1 -0.0068 0 0 -1.2 0.25 -0.14 0 0 5.2e+03 0.05 1e+03 1.1 ++
3 -1 -1.2 2.1 0 0 0 -1 -0.007 0 0 -1.2 0.25 -0.14 0 0 5.2e+03 0.00038 1e+04 1 ++
4 -1 -1.2 2.1 0 0 0 -1 -0.007 0 0 -1.2 0.25 -0.14 0 0 5.2e+03 2.2e-08 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_000169
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_ 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 -0.52 -0.82 0.23 -0.0093 -1 -1 -0.0023 -0.48 -0.18 -0.18 -0.049 5.3e+03 2.5 10 1.1 ++
1 -0.091 -1.1 0.73 0.32 -2.9 -1 -0.0049 -0.48 0.27 0.11 -0.11 5.1e+03 0.42 1e+02 1.1 ++
2 -0.13 -1.1 0.93 0.5 -3.3 -1.1 -0.0058 -0.48 0.31 0.082 -0.14 5.1e+03 0.04 1e+03 1 ++
3 -0.14 -1.1 0.96 0.53 -3.3 -1.1 -0.0059 -0.49 0.31 0.082 -0.14 5.1e+03 0.00046 1e+04 1 ++
4 -0.14 -1.1 0.96 0.53 -3.3 -1.1 -0.0059 -0.49 0.31 0.082 -0.14 5.1e+03 6.7e-07 1e+04 1 ++
Considering neighbor 2/20 for current solution
Attempt 4/100
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b07everything_000170
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 lambda_tt b_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho
0 -0.57 -0.95 -0.86 2 -0.88 0.0074 -0.2 0.092 5.8e+03 2.6 1 0.68 +
1 0.056 -0.95 -0.49 1.8 -1.9 -0.0081 -0.56 0.079 5.3e+03 0.43 10 0.99 ++
2 0.056 -0.95 -0.49 1.8 -1.9 -0.0081 -0.56 0.079 5.3e+03 0.43 5 -2.7e+03 -
3 0.056 -0.95 -0.49 1.8 -1.9 -0.0081 -0.56 0.079 5.3e+03 0.43 2.5 -16 -
4 0.056 -0.95 -0.49 1.8 -1.9 -0.0081 -0.56 0.079 5.3e+03 0.43 1.2 -0.53 -
5 -0.035 -1.3 -1.3 0.56 -2.2 0.0026 -0.35 0.51 5.1e+03 0.48 1.2 0.83 +
6 0.63 -1.3 -1.7 0.38 -2.3 -0.0057 -0.37 0.34 5.1e+03 0.14 12 0.99 ++
7 0.63 -1.3 -1.7 0.42 -2.4 -0.0057 -0.36 0.33 5.1e+03 0.0024 1.2e+02 0.99 ++
8 0.63 -1.3 -1.7 0.42 -2.4 -0.0057 -0.36 0.33 5.1e+03 2.3e-07 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 19 unknown parameters [max: 50]
*** Estimate b07everything_000171
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. 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
Attempt 5/100
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000172
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 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_ma beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 10 1 ++
1 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 2.1 -2.1e+02 -
2 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 1 -2.6 -
3 -0.39 -1.5 0 0 0 -1.3 0.24 -0.0064 0 0 -1.3 0.078 0 0 5.5e+03 0.58 10 1 ++
4 -0.51 -1.4 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.22 0 0 5.5e+03 0.022 1e+02 0.97 ++
5 -0.52 -1.3 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.21 0 0 5.5e+03 5.2e-05 1e+03 1 ++
6 -0.52 -1.3 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.21 0 0 5.5e+03 1.4e-09 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 6/100
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b07everything_000173
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 lambda_tt b_cost lambda_cost b_headway asc_car_ref asc_car_diff_ma Function Relgrad Radius Rho
0 -0.55 -0.88 -0.9 1.8 -1 1 0.006 -0.27 -0.004 5.6e+03 2.2 1 0.78 +
1 0.45 -1.1 -1.1 1.3 -0.97 0.49 -0.0078 -0.68 0.34 5.3e+03 0.47 10 1.1 ++
2 0.45 -1.1 -1.1 1.3 -0.97 0.49 -0.0078 -0.68 0.34 5.3e+03 0.47 1 -2.4 -
3 0.52 -1.1 -1.9 0.27 -1.3 0.69 -0.0043 -0.54 0.46 5.2e+03 0.039 1 0.68 +
4 0.57 -1.3 -1.6 0.36 -1.1 0.62 -0.0058 -0.51 0.31 5.2e+03 0.014 10 0.99 ++
5 0.56 -1.3 -1.6 0.39 -1.1 0.56 -0.0058 -0.53 0.32 5.2e+03 0.00011 1e+02 0.98 ++
6 0.56 -1.3 -1.6 0.39 -1.1 0.56 -0.0058 -0.53 0.32 5.2e+03 8.6e-07 1e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000174
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 lambda_tt b_cost lambda_cost b_headway asc_car_ref asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.68 0.065 -0.004 -1 1.8 -0.88 1 -0.0015 -0.35 -0.19 -0.046 5.7e+03 1.6 1 0.7 +
1 -0.96 1.1 0.085 -1.4 1.1 -1 0.73 -0.0064 -0.33 -0.04 -0.16 5.3e+03 0.57 10 1.1 ++
2 -0.74 1.1 1 -2.1 0.28 -1.1 0.7 -0.0056 0.05 -0.1 -0.7 5.3e+03 0.069 10 0.69 +
3 -1.2 1.2 0.95 -1.5 0.38 -1.1 0.55 -0.0055 -0.2 -0.059 -0.63 5.2e+03 0.0064 1e+02 0.97 ++
4 -1.1 1.2 0.95 -1.6 0.44 -1.1 0.55 -0.0055 -0.2 -0.062 -0.62 5.2e+03 0.00054 1e+03 0.96 ++
5 -1.1 1.2 0.95 -1.6 0.44 -1.1 0.55 -0.0055 -0.2 -0.062 -0.62 5.2e+03 1.1e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Attempt 7/100
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b07everything_000175
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 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 0/20 for current solution
Attempt 8/100
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b07everything_000176
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. 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
Attempt 9/100
Biogeme parameters read from biogeme.toml.
Model with 5 unknown parameters [max: 50]
*** Estimate b07everything_000177
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 b_headway asc_car Function Relgrad Radius Rho
0 -0.74 -0.73 -1 -0.0011 -0.3 5.4e+03 2.3 10 1.1 ++
1 -0.54 -1.2 -2.1 -0.0044 -0.19 5.3e+03 0.25 1e+02 1.1 ++
2 -0.49 -1.2 -2.3 -0.0053 -0.21 5.3e+03 0.0056 1e+03 1 ++
3 -0.48 -1.2 -2.3 -0.0053 -0.21 5.3e+03 0.0011 1e+04 1 ++
4 -0.48 -1.2 -2.3 -0.0053 -0.21 5.3e+03 0.00014 1e+05 1 ++
5 -0.48 -1.2 -2.3 -0.0053 -0.21 5.3e+03 1.2e-05 1e+06 1 ++
6 -0.48 -1.2 -2.3 -0.0053 -0.21 5.3e+03 1.4e-08 1e+06 1 ++
Considering neighbor 0/20 for current solution
Attempt 10/100
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b07everything_000178
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 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 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 11/100
Biogeme parameters read from biogeme.toml.
Model with 20 unknown parameters [max: 50]
*** Estimate b07everything_000179
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. Function Relgrad Radius Rho
0 5.6e+03 0.044 10 1.1 ++
1 5.4e+03 0.01 1e+02 1.1 ++
2 5.4e+03 0.00066 1e+03 1 ++
3 5.4e+03 3.5e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b07everything_000180
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. 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 1/20 for current solution
Attempt 12/100
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b07everything_000181
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_ma Function Relgrad Radius Rho
0 -0.33 -0.81 -0.63 -0.89 -0.52 -0.039 5.3e+03 0.043 10 1.1 ++
1 0.038 -1.2 -1.2 -1 -0.47 0.28 5.2e+03 0.0093 1e+02 1.1 ++
2 0.089 -1.2 -1.2 -1.1 -0.46 0.31 5.2e+03 0.00033 1e+03 1 ++
3 0.089 -1.2 -1.2 -1.1 -0.46 0.31 5.2e+03 4.1e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 13/100
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b07everything_000182
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 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 -0.42 -0.0075 0 0 -1 0 0 5.8e+03 2.5 10 1 ++
1 -1.3 0 0 0 -0.91 -0.0058 0 0 -0.89 0 0 5.6e+03 0.098 1e+02 1 ++
2 -1.4 0 0 0 -0.94 -0.0055 0 0 -0.91 0 0 5.6e+03 0.0014 1e+03 1 ++
3 -1.4 0 0 0 -0.94 -0.0055 0 0 -0.91 0 0 5.6e+03 2.2e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 14/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000183
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 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 -0.00036 -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
Attempt 15/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b07everything_000184
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 lambda_tt b_cost lambda_cost asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.45 -0.67 0.68 -0.96 2 -0.96 1 -0.51 -0.28 -0.32 5.7e+03 0.13 1 0.6 +
1 -0.68 -1.3 1.7 -0.62 1.7 -0.84 0.66 -0.49 0.16 -0.78 5.1e+03 0.015 10 1.1 ++
2 -0.68 -1.3 1.7 -0.62 1.7 -0.84 0.66 -0.49 0.16 -0.78 5.1e+03 0.015 5 -3.1e+03 -
3 -0.68 -1.3 1.7 -0.62 1.7 -0.84 0.66 -0.49 0.16 -0.78 5.1e+03 0.015 2.5 -28 -
4 -0.68 -1.3 1.7 -0.62 1.7 -0.84 0.66 -0.49 0.16 -0.78 5.1e+03 0.015 1.2 -0.62 -
5 -0.85 -1.2 1.9 -1.3 0.4 -1.6 0.41 -0.6 0.3 -1.1 4.9e+03 0.033 1.2 0.9 +
6 -0.36 -1.1 2.1 -1.6 0.22 -1.5 0.12 -0.55 0.46 -1.8 4.8e+03 0.0056 12 0.99 ++
7 -0.38 -1.1 2.1 -1.6 0.22 -1.5 0.032 -0.56 0.48 -2 4.8e+03 0.00028 1.2e+02 1 ++
8 -0.38 -1.1 2.1 -1.6 0.22 -1.5 0.032 -0.56 0.48 -2 4.8e+03 2.1e-06 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 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.
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 -0.52 -0.79 0.53 -0.88 1.7 -1 0.003 -0.29 -0.051 -0.22 5.3e+03 2.3 1 0.88 +
1 -0.29 -1 1.5 -1.1 1 -1 -0.0043 -0.51 0.087 -0.35 4.9e+03 0.6 10 1.1 ++
2 0.43 -1.1 1.9 -2.2 -0.078 -1.1 -0.0066 -0.37 0.46 -0.44 4.9e+03 0.14 10 0.21 +
3 0.15 -1.2 2 -1.8 0.17 -1.1 -0.0066 -0.51 0.4 -0.44 4.9e+03 0.0078 1e+02 1.1 ++
4 0.11 -1.2 2 -1.7 0.33 -1.1 -0.0067 -0.54 0.41 -0.43 4.9e+03 0.0043 1e+03 1 ++
5 0.098 -1.2 2 -1.7 0.34 -1.1 -0.0067 -0.55 0.41 -0.43 4.9e+03 0.00011 1e+04 1 ++
6 0.098 -1.2 2 -1.7 0.34 -1.1 -0.0067 -0.55 0.41 -0.43 4.9e+03 2.2e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
Attempt 16/100
Biogeme parameters read from biogeme.toml.
Model with 16 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.
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 ++
1 5.4e+03 0.009 1e+02 1.1 ++
2 5.4e+03 0.00054 1e+03 1.1 ++
3 5.4e+03 4.8e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 17/100
Biogeme parameters read from biogeme.toml.
Model with 6 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.
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 lambda_cost asc_car Function Relgrad Radius Rho
0 -0.69 -1 1.7 -0.67 1 -0.55 5.8e+03 0.073 1 0.71 +
1 -0.69 -1.7 0.75 -1.3 0.87 -0.33 5.4e+03 0.053 10 0.98 ++
2 -0.55 -1.5 0.55 -1.1 0.68 -0.09 5.4e+03 0.0023 1e+02 1 ++
3 -0.56 -1.6 0.46 -1.1 0.58 -0.1 5.4e+03 0.0003 1e+03 1 ++
4 -0.56 -1.6 0.46 -1.1 0.58 -0.1 5.4e+03 5.4e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 18/100
Considering neighbor 0/20 for current solution
Attempt 19/100
Biogeme parameters read from biogeme.toml.
Model with 5 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.
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 asc_car Function Relgrad Radius Rho
0 -0.93 -0.64 -0.85 2 -0.56 5.7e+03 0.067 10 0.92 ++
1 -0.93 -0.64 -0.85 2 -0.56 5.7e+03 0.067 4.1 -1.3e+05 -
2 -0.93 -0.64 -0.85 2 -0.56 5.7e+03 0.067 2.1 -1.2e+02 -
3 -0.93 -0.64 -0.85 2 -0.56 5.7e+03 0.067 1 -9.9 -
4 -0.93 -0.64 -0.85 2 -0.56 5.7e+03 0.067 0.52 -0.53 -
5 -0.7 -1.2 -0.62 1.7 -0.18 5.5e+03 0.0091 5.2 1.1 ++
6 -0.7 -1.2 -0.62 1.7 -0.18 5.5e+03 0.0091 2.5 -5.6e+02 -
7 -0.7 -1.2 -0.62 1.7 -0.18 5.5e+03 0.0091 1.3 -5.7 -
8 -0.56 -1.3 -1.2 0.46 -0.14 5.4e+03 0.012 1.3 0.57 +
9 -0.78 -1.2 -1.1 0.6 -0.26 5.4e+03 0.00052 13 1 ++
10 -0.78 -1.2 -1.1 0.63 -0.26 5.4e+03 2.1e-05 1.3e+02 1 ++
11 -0.78 -1.2 -1.1 0.63 -0.26 5.4e+03 1e-08 1.3e+02 1 ++
Considering neighbor 0/20 for current solution
Attempt 20/100
Biogeme parameters read from biogeme.toml.
Model with 8 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.
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_ma Function Relgrad Radius Rho
0 -0.5 -0.77 -0.86 1.7 -1 0.0035 -0.28 -0.05 5.4e+03 2.1 1 0.87 +
1 0.27 -1.3 -1.6 0.68 -1.2 -0.0017 -0.54 0.39 5.1e+03 0.41 10 0.92 ++
2 0.69 -1.3 -1.7 0.44 -1.1 -0.0059 -0.4 0.32 5.1e+03 0.022 1e+02 0.97 ++
3 0.65 -1.3 -1.7 0.45 -1.1 -0.0058 -0.4 0.3 5.1e+03 2.5e-05 1e+03 1 ++
4 0.65 -1.3 -1.7 0.45 -1.1 -0.0058 -0.4 0.3 5.1e+03 4.8e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 21/100
Biogeme parameters read from biogeme.toml.
Model with 6 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.
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 -0.69 -0.63 -1 -0.37 -0.11 5.4e+03 0.038 10 1.1 ++
1 0.00095 -1.1 -1.1 -2.1 -0.39 0.28 5.2e+03 0.011 1e+02 1.1 ++
2 0.052 -1.2 -1.2 -2.3 -0.43 0.33 5.1e+03 0.00047 1e+03 1 ++
3 0.052 -1.2 -1.2 -2.3 -0.43 0.33 5.1e+03 9e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 22/100
Biogeme parameters read from biogeme.toml.
Model with 7 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.
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 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 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.
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_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.58 -0.83 0.7 -1 -0.96 -0.00091 -0.53 -0.2 -0.22 5.2e+03 2.6 10 1.1 ++
1 -0.099 -0.98 1.8 -2.8 -1 -0.005 -0.54 0.34 -0.38 4.9e+03 0.69 1e+02 1.1 ++
2 0.04 -1.1 1.9 -3.2 -1.1 -0.0066 -0.58 0.41 -0.42 4.9e+03 0.057 1e+03 1.1 ++
3 0.049 -1.2 2 -3.2 -1.1 -0.0068 -0.59 0.41 -0.43 4.9e+03 0.00035 1e+04 1 ++
4 0.049 -1.2 2 -3.2 -1.1 -0.0068 -0.59 0.41 -0.43 4.9e+03 9.9e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
Attempt 23/100
Biogeme parameters read from biogeme.toml.
Model with 7 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.
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 -0.38 -0.76 -0.61 -0.87 2 -0.72 0.11 5.6e+03 0.068 10 0.94 ++
1 -0.38 -0.76 -0.61 -0.87 2 -0.72 0.11 5.6e+03 0.068 4.2 -1.4e+05 -
2 -0.38 -0.76 -0.61 -0.87 2 -0.72 0.11 5.6e+03 0.068 2.1 -1.3e+02 -
3 -0.38 -0.76 -0.61 -0.87 2 -0.72 0.11 5.6e+03 0.068 1.1 -9.9 -
4 -0.38 -0.76 -0.61 -0.87 2 -0.72 0.11 5.6e+03 0.068 0.53 -0.41 -
5 -0.077 -0.94 -1.1 -0.59 1.8 -0.53 0.38 5.3e+03 0.0089 5.3 1.1 ++
6 -0.077 -0.94 -1.1 -0.59 1.8 -0.53 0.38 5.3e+03 0.0089 2.6 -6e+02 -
7 -0.077 -0.94 -1.1 -0.59 1.8 -0.53 0.38 5.3e+03 0.0089 1.3 -5.5 -
8 0.33 -1.4 -1.1 -1.1 0.44 -0.54 0.33 5.3e+03 0.021 1.3 0.67 +
9 0.014 -1.2 -1.1 -1.1 0.61 -0.6 0.33 5.3e+03 0.00094 13 1 ++
10 0.014 -1.2 -1.1 -1.1 0.61 -0.6 0.33 5.3e+03 4.5e-06 13 1 ++
Considering neighbor 0/20 for current solution
Attempt 24/100
Biogeme parameters read from biogeme.toml.
Model with 13 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.
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 0/20 for current solution
Attempt 25/100
Biogeme parameters read from biogeme.toml.
Model with 17 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.
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
Biogeme parameters read from biogeme.toml.
Model with 18 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.
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 1/20 for current solution
Attempt 26/100
Considering neighbor 0/20 for current solution
Attempt 27/100
Biogeme parameters read from biogeme.toml.
Model with 14 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.
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_ma beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 10 1 ++
1 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 2.1 -2.1e+02 -
2 -0.57 -0.94 0 0 0 -0.84 1.3 -0.0031 0 0 -1 -0.046 0 0 5.6e+03 2.6 1 -2.6 -
3 -0.39 -1.5 0 0 0 -1.3 0.24 -0.0064 0 0 -1.3 0.078 0 0 5.5e+03 0.58 10 1 ++
4 -0.51 -1.4 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.22 0 0 5.5e+03 0.022 1e+02 0.97 ++
5 -0.52 -1.3 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.21 0 0 5.5e+03 5.2e-05 1e+03 1 ++
6 -0.52 -1.3 0 0 0 -1.1 0.19 -0.0059 0 0 -1.2 0.21 0 0 5.5e+03 1.4e-09 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 28/100
Biogeme parameters read from biogeme.toml.
Model with 17 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.
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
Attempt 29/100
Biogeme parameters read from biogeme.toml.
Model with 11 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.
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 -0.11 -0.014 0.52 -1 -0.65 -0.0031 -0.59 -0.29 -0.046 -0.24 5.4e+03 2.5 10 1 ++
1 -1.2 0.4 0.18 2 -2.6 -1.3 -0.005 -0.36 0.097 -0.23 -1.4 4.9e+03 0.96 1e+02 1 ++
2 -1.4 0.65 0.41 1.9 -2.9 -1.5 -0.0061 -0.36 0.082 -0.43 -1.8 4.9e+03 0.076 1e+03 1.1 ++
3 -1.4 0.7 0.47 2 -2.9 -1.5 -0.0062 -0.36 0.08 -0.45 -1.9 4.9e+03 0.0016 1e+04 1 ++
4 -1.4 0.7 0.47 2 -2.9 -1.5 -0.0062 -0.36 0.08 -0.45 -1.9 4.9e+03 8.6e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 30/100
Biogeme parameters read from biogeme.toml.
Model with 16 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.
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 0.5 10 0.92 ++
2 5.7e+03 0.5 5 -2.2e+03 -
3 5.7e+03 0.5 2.5 -18 -
4 5.7e+03 0.5 1.2 -0.14 -
5 5.5e+03 0.19 1.2 0.86 +
6 5.4e+03 0.053 12 1 ++
7 5.4e+03 0.00089 1.2e+02 0.97 ++
8 5.4e+03 2.4e-06 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 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.
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 1/20 for current solution
Attempt 31/100
Biogeme parameters read from biogeme.toml.
Model with 16 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.
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
Biogeme parameters read from biogeme.toml.
Model with 5 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.
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 -0.92 -1 -0.58 -0.0028 -0.66 5.6e+03 2.5 10 1 ++
1 -0.53 -2.4 -2.3 -0.0062 -0.22 5.3e+03 0.4 1e+02 1 ++
2 -0.27 -3.3 -2.3 -0.0053 -0.059 5.2e+03 0.0023 1e+03 1 ++
3 -0.26 -3.3 -2.4 -0.0053 -0.055 5.2e+03 0.0058 1e+04 1 ++
4 -0.26 -3.3 -2.4 -0.0053 -0.055 5.2e+03 3e-06 1e+04 1 ++
Considering neighbor 1/20 for current solution
Attempt 32/100
Considering neighbor 0/20 for current solution
Attempt 33/100
Biogeme parameters read from biogeme.toml.
Model with 15 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.
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 0/20 for current solution
Attempt 34/100
Biogeme parameters read from biogeme.toml.
Model with 13 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.
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 -0.63 -0.9 0 0 0 -0.97 -0.003 0 0 -1 -0.032 0 0 5.6e+03 2.7 10 1.1 ++
1 -0.55 -1.3 0 0 0 -1.1 -0.0052 0 0 -1.2 0.23 0 0 5.5e+03 0.33 1e+02 1.1 ++
2 -0.53 -1.3 0 0 0 -1.1 -0.0059 0 0 -1.2 0.23 0 0 5.5e+03 0.013 1e+03 1 ++
3 -0.53 -1.3 0 0 0 -1.1 -0.0059 0 0 -1.2 0.23 0 0 5.5e+03 1.8e-05 1e+04 1 ++
4 -0.53 -1.3 0 0 0 -1.1 -0.0059 0 0 -1.2 0.23 0 0 5.5e+03 3.7e-11 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 35/100
Biogeme parameters read from biogeme.toml.
Model with 20 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.
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
Attempt 36/100
Biogeme parameters read from biogeme.toml.
Model with 11 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.
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_ma asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.43 -0.72 0.31 -0.011 -0.9 2 -0.84 -0.31 -0.1 -0.23 -0.046 5.8e+03 0.12 1 0.6 +
1 -0.69 -1.3 0.68 0.032 -0.7 1.6 -1.8 -0.41 0.11 0.024 -0.11 5.2e+03 0.016 10 1.1 ++
2 -0.69 -1.3 0.68 0.032 -0.7 1.6 -1.8 -0.41 0.11 0.024 -0.11 5.2e+03 0.016 5 -4e+03 -
3 -0.69 -1.3 0.68 0.032 -0.7 1.6 -1.8 -0.41 0.11 0.024 -0.11 5.2e+03 0.016 2.5 -24 -
4 -0.69 -1.3 0.68 0.032 -0.7 1.6 -1.8 -0.41 0.11 0.024 -0.11 5.2e+03 0.016 1.2 -1.4 -
5 -0.77 -1.2 0.78 0.11 -1.4 0.35 -2.2 -0.45 0.24 0.3 -0.13 5.1e+03 0.033 1.2 0.78 +
6 -0.45 -1.1 1 0.71 -1.7 0.43 -2.4 -0.4 0.36 0.18 -0.019 5e+03 0.0049 12 0.91 ++
7 -0.44 -1.1 0.96 0.56 -1.7 0.41 -2.4 -0.4 0.37 0.18 -0.0041 5e+03 0.0001 1.2e+02 1 ++
8 -0.44 -1.1 0.96 0.56 -1.7 0.41 -2.4 -0.4 0.37 0.18 -0.0041 5e+03 9.7e-08 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Attempt 37/100
Biogeme parameters read from biogeme.toml.
Model with 22 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.
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
Attempt 38/100
Biogeme parameters read from biogeme.toml.
Model with 13 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.
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
Attempt 39/100
Biogeme parameters read from biogeme.toml.
Model with 8 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.
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 -0.003 -0.15 -1.4 5e+03 0.032 1.2 0.78 +
6 -0.87 2.2 -1.6 0.24 -1.5 -0.0064 -0.28 -1.8 4.9e+03 0.013 12 0.98 ++
7 -0.9 2.2 -1.6 0.27 -1.5 -0.0061 -0.28 -1.9 4.9e+03 0.00024 1.2e+02 1 ++
8 -0.9 2.2 -1.6 0.27 -1.5 -0.0061 -0.28 -1.9 4.9e+03 6.2e-07 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 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.
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 1/20 for current solution
Attempt 40/100
Considering neighbor 0/20 for current solution
Attempt 41/100
Biogeme parameters read from biogeme.toml.
Model with 9 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.
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 -0.068 -1.7 -0.26 -0.0061 -0.023 -1.7 5e+03 0.033 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 0.0062 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 0.00011 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 8.1e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 42/100
Considering neighbor 0/20 for current solution
Attempt 43/100
Biogeme parameters read from biogeme.toml.
Model with 17 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.
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
Biogeme parameters read from biogeme.toml.
Model with 22 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.
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 1/20 for current solution
Attempt 44/100
Biogeme parameters read from biogeme.toml.
Model with 7 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.
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 -0.72 0.44 -1 1.7 -0.56 -0.49 -0.24 5.6e+03 0.064 1 0.75 +
1 -1.2 1.4 -1.1 1.1 -1.2 -0.19 -0.7 5.1e+03 0.024 10 1.1 ++
2 -1.2 1.4 -1.1 1.1 -1.2 -0.19 -0.7 5.1e+03 0.024 0.73 -0.33 -
3 -1.3 2.1 -1.5 0.34 -1.3 -0.26 -1.1 5e+03 0.016 7.3 1.1 ++
4 -1.2 2.2 -1.6 0.27 -1.5 -0.15 -1.7 4.9e+03 0.0012 73 1 ++
5 -1.2 2.2 -1.6 0.27 -1.5 -0.15 -1.9 4.9e+03 5.7e-05 7.3e+02 1 ++
6 -1.2 2.2 -1.6 0.27 -1.5 -0.15 -1.9 4.9e+03 1.4e-07 7.3e+02 1 ++
Considering neighbor 0/20 for current solution
Attempt 45/100
Biogeme parameters read from biogeme.toml.
Model with 10 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.
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 -1 0 0 -0.079 0 0 5.9e+03 0.076 10 1.1 ++
1 -1.6 0 0 0 -1.8 0 0 -0.7 0 0 5.6e+03 0.0079 1e+02 1 ++
2 -1.7 0 0 0 -2.2 0 0 -0.76 0 0 5.6e+03 0.0005 1e+03 1 ++
3 -1.7 0 0 0 -2.2 0 0 -0.76 0 0 5.6e+03 1.8e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 46/100
Biogeme parameters read from biogeme.toml.
Model with 11 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.
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 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 47/100
Biogeme parameters read from biogeme.toml.
Model with 7 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.
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 -0.94 0.44 -1 -0.52 -0.0037 -0.72 -0.22 5.5e+03 2.6 10 1 ++
1 -0.84 2 -2.7 -2.6 -0.0052 -0.24 0.95 5e+03 0.58 1e+02 1 ++
2 -0.84 2.1 -3.1 -2.7 -0.0061 -0.27 1.1 4.9e+03 0.036 1e+03 1 ++
3 -0.85 2.1 -3.1 -2.8 -0.0062 -0.28 1.2 4.9e+03 0.00012 1e+04 1 ++
4 -0.85 2.1 -3.1 -2.8 -0.0062 -0.28 1.2 4.9e+03 2.6e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 48/100
Biogeme parameters read from biogeme.toml.
Model with 21 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.
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 0/20 for current solution
Attempt 49/100
Biogeme parameters read from biogeme.toml.
Model with 21 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.
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
Attempt 50/100
Biogeme parameters read from biogeme.toml.
Model with 11 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.
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 0/20 for current solution
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.
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 -0.92 -0.072 -0.011 -1 -0.7 1 -0.0024 -0.56 -0.27 -0.048 5.5e+03 2.3 10 1 ++
1 -0.99 0.83 0.65 -2.7 -1.3 -0.15 -0.0047 -0.31 -0.031 -0.53 5.3e+03 0.65 10 0.88 +
2 -1.1 1.1 0.89 -3 -1.2 0.25 -0.0055 -0.25 -0.063 -0.64 5.3e+03 0.097 1e+02 1.2 ++
3 -1.2 1.2 0.94 -3 -1.1 0.49 -0.0055 -0.22 -0.059 -0.63 5.2e+03 0.0061 1e+03 1.1 ++
4 -1.2 1.2 0.95 -3 -1.1 0.56 -0.0055 -0.21 -0.059 -0.62 5.2e+03 0.00029 1e+04 1 ++
5 -1.2 1.2 0.95 -3 -1.1 0.56 -0.0055 -0.21 -0.059 -0.62 5.2e+03 1.3e-06 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_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.
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 -0.95 0.39 -1 -0.47 1 -0.0042 -0.84 -0.24 5.5e+03 2.5 10 1 ++
1 -0.95 0.39 -1 -0.47 1 -0.0042 -0.84 -0.24 5.5e+03 2.5 1.6 -7.6 -
2 -1.3 2 -2 -1.2 0.85 -0.0031 -0.5 -0.7 5e+03 0.72 16 1 ++
3 -1.3 2 -2 -1.2 0.85 -0.0031 -0.5 -0.7 5e+03 0.72 0.63 -0.076 -
4 -1 2.2 -2.4 -1.3 0.23 -0.0065 -0.36 -0.92 5e+03 0.035 6.3 1.1 ++
5 -0.95 2.1 -2.9 -1.5 0.065 -0.0062 -0.32 -1.7 4.9e+03 0.034 63 1 ++
6 -0.94 2.1 -3 -1.5 0.094 -0.0062 -0.31 -1.8 4.9e+03 0.0017 6.3e+02 1 ++
7 -0.94 2.1 -3 -1.5 0.093 -0.0062 -0.31 -1.8 4.9e+03 7.4e-06 6.3e+03 1 ++
8 -0.94 2.1 -3 -1.5 0.093 -0.0062 -0.31 -1.8 4.9e+03 7.1e-08 6.3e+03 1 ++
Considering neighbor 2/20 for current solution
Attempt 51/100
Biogeme parameters read from biogeme.toml.
Model with 16 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.
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 13 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.
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 -0.53 0.32 -1 0 0 0 -0.0036 0 0 -0.41 -0.16 0 0 5.6e+03 2.6 10 1 ++
1 -0.53 2 -1.6 0 0 0 -0.0051 0 0 0.14 -1 0 0 5.2e+03 0.87 1e+02 0.97 ++
2 -0.61 1.9 -1.6 0 0 0 -0.0059 0 0 0.11 -1.3 0 0 5.2e+03 0.039 1e+03 1 ++
3 -0.62 1.9 -1.6 0 0 0 -0.006 0 0 0.11 -1.3 0 0 5.2e+03 0.00019 1e+04 1 ++
4 -0.62 1.9 -1.6 0 0 0 -0.006 0 0 0.11 -1.3 0 0 5.2e+03 4.2e-09 1e+04 1 ++
Considering neighbor 1/20 for current solution
Attempt 52/100
Biogeme parameters read from biogeme.toml.
Model with 8 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.
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 -1 -0.098 -0.023 -0.9 -0.46 -0.47 -0.33 -0.035 5.7e+03 0.05 10 1 ++
1 -1.1 0.79 0.64 -2.9 -2.1 -0.076 0.2 -0.041 5.2e+03 0.02 1e+02 1 ++
2 -1.3 1.1 0.91 -3.2 -2.4 -0.072 0.17 -0.21 5.2e+03 0.0017 1e+03 1.1 ++
3 -1.4 1.1 0.95 -3.2 -2.4 -0.07 0.16 -0.23 5.2e+03 2.6e-05 1e+04 1 ++
4 -1.4 1.1 0.95 -3.2 -2.4 -0.07 0.16 -0.23 5.2e+03 1e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 53/100
Biogeme parameters read from biogeme.toml.
Model with 16 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.
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 0.039 10 1.1 ++
1 5.3e+03 0.058 1e+02 0.98 ++
2 5.3e+03 0.0041 1e+03 1 ++
3 5.3e+03 0.00015 1e+04 1 ++
4 5.3e+03 1.7e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 54/100
Biogeme parameters read from biogeme.toml.
Model with 12 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.
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 -0.45 -0.7 -1 0 0 0 0 0 -0.43 -0.12 0 0 5.6e+03 0.044 10 1 ++
1 0.32 -1.1 -2.8 0 0 0 0 0 -0.13 0.31 0 0 5.4e+03 0.013 1e+02 1 ++
2 0.38 -1.2 -3 0 0 0 0 0 -0.12 0.32 0 0 5.4e+03 0.00038 1e+03 1 ++
3 0.38 -1.2 -3 0 0 0 0 0 -0.12 0.32 0 0 5.4e+03 3.7e-07 1e+03 1 ++
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 11 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.
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 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 18 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.
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 1/20 for current solution
Attempt 57/100
Biogeme parameters read from biogeme.toml.
Model with 15 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.
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 0/20 for current solution
Attempt 58/100
Biogeme parameters read from biogeme.toml.
Model with 17 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.
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 2.7 10 1.1 ++
1 5.4e+03 0.39 1e+02 1.1 ++
2 5.4e+03 0.032 1e+03 1.1 ++
3 5.4e+03 0.00027 1e+04 1 ++
4 5.4e+03 2.3e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 59/100
Biogeme parameters read from biogeme.toml.
Model with 12 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.
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_ma asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.5 -0.79 0.093 -0.0049 -0.88 1.6 -1 0.0035 -0.21 0.00042 -0.13 -0.038 5.4e+03 2.2 1 0.9 +
1 -0.18 -1.2 1.1 0.056 -1.5 0.76 -1.1 -0.0049 -0.46 0.21 0.093 -0.099 5.1e+03 0.88 10 1 ++
2 -0.051 -1.1 0.96 0.57 -1.8 0.39 -1.1 -0.0058 -0.41 0.3 0.068 -0.17 5.1e+03 0.034 1e+02 0.91 ++
3 -0.12 -1.1 0.96 0.53 -1.7 0.44 -1.1 -0.0059 -0.47 0.31 0.077 -0.14 5.1e+03 0.00022 1e+03 1 ++
4 -0.12 -1.1 0.96 0.53 -1.7 0.44 -1.1 -0.0059 -0.47 0.31 0.077 -0.14 5.1e+03 7.1e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 60/100
Biogeme parameters read from biogeme.toml.
Model with 10 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.
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_ma asc_car_diff_on asc_car_diff_se Function Relgrad Radius Rho
0 -0.65 -0.72 0.42 0.13 -0.63 -0.9 -0.69 0.0066 0.24 0.016 5.3e+03 0.043 10 1.1 ++
1 -0.58 -1 0.76 0.4 -1.1 -1.1 -0.56 0.3 0.12 -0.07 5.1e+03 0.01 1e+02 1.1 ++
2 -0.68 -1.1 0.94 0.56 -1.2 -1.1 -0.56 0.33 0.12 -0.069 5.1e+03 0.00059 1e+03 1 ++
3 -0.68 -1.1 0.94 0.56 -1.2 -1.1 -0.56 0.33 0.12 -0.069 5.1e+03 4.5e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 61/100
Biogeme parameters read from biogeme.toml.
Model with 19 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.
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 0/20 for current solution
Attempt 62/100
Biogeme parameters read from biogeme.toml.
Model with 12 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.
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 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 63/100
Biogeme parameters read from biogeme.toml.
Model with 17 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.
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 2.3 10 1 ++
1 5.3e+03 1.1 1e+02 1 ++
2 5.3e+03 0.095 1e+03 1.1 ++
3 5.3e+03 0.0024 1e+04 1 ++
4 5.3e+03 1.9e-06 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_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.
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 -0.34 -0.026 0 0 0 -0.29 0 0 -0.35 -0.24 -0.019 0 0 5.9e+03 0.039 10 1.1 ++
1 -2.1 0.81 0.76 0 0 0 -0.86 0 0 -0.88 0.18 0.015 0 0 5.6e+03 0.023 1e+02 1.1 ++
2 -2.5 1.2 1.1 0 0 0 -0.95 0 0 -0.9 0.16 -0.097 0 0 5.5e+03 0.0038 1e+03 1.1 ++
3 -2.5 1.2 1.2 0 0 0 -0.95 0 0 -0.9 0.16 -0.1 0 0 5.5e+03 0.00011 1e+04 1 ++
4 -2.5 1.2 1.2 0 0 0 -0.95 0 0 -0.9 0.16 -0.1 0 0 5.5e+03 1e-07 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_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.
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 -0.53 -0.019 -0.005 -1 -0.46 1 -0.0034 -0.36 -0.21 -0.03 5.5e+03 2.4 10 1 ++
1 -0.87 0.83 0.79 -1.5 -1.1 -0.38 -0.005 -0.15 -0.079 -0.65 5.3e+03 0.63 10 0.88 +
2 -1.2 1.1 0.97 -1.5 -1.4 0.29 -0.0055 -0.29 -0.069 -0.7 5.3e+03 0.046 1e+02 0.91 ++
3 -1.2 1.2 0.99 -1.5 -1.1 0.36 -0.0055 -0.19 -0.072 -0.66 5.3e+03 0.0008 1e+03 0.97 ++
4 -1.2 1.2 1 -1.6 -1.1 0.4 -0.0055 -0.2 -0.069 -0.65 5.3e+03 0.00023 1e+04 0.97 ++
5 -1.2 1.2 1 -1.6 -1.1 0.4 -0.0055 -0.2 -0.069 -0.65 5.3e+03 6.3e-07 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_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.
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 -0.061 -0.0074 0.31 -1 -0.5 -0.0042 -0.4 -0.22 -0.027 -0.14 5.4e+03 2.4 10 1 ++
1 -0.93 0.41 0.2 2.1 -1.6 -0.99 -0.0052 -0.19 0.017 -0.2 -0.21 5e+03 1.1 1e+02 0.99 ++
2 -1.2 0.67 0.4 2 -1.7 -1.1 -0.006 -0.2 0.0097 -0.3 -0.22 5e+03 0.082 1e+03 1 ++
3 -1.2 0.72 0.45 2 -1.7 -1.1 -0.0062 -0.2 0.0086 -0.3 -0.2 5e+03 0.0016 1e+04 1 ++
4 -1.2 0.72 0.45 2 -1.7 -1.1 -0.0062 -0.2 0.0086 -0.3 -0.2 5e+03 8.9e-07 1e+04 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 64/100
Biogeme parameters read from biogeme.toml.
Model with 17 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.
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 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 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.
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 -0.64 -0.021 -0.016 0.32 -1 1.4 -0.58 -0.4 -0.3 -0.026 -0.17 5.5e+03 0.068 10 0.91 ++
1 -0.64 -0.021 -0.016 0.32 -1 1.4 -0.58 -0.4 -0.3 -0.026 -0.17 5.5e+03 0.068 5 -7.7e+02 -
2 -0.64 -0.021 -0.016 0.32 -1 1.4 -0.58 -0.4 -0.3 -0.026 -0.17 5.5e+03 0.068 2.5 -17 -
3 -0.64 -0.021 -0.016 0.32 -1 1.4 -0.58 -0.4 -0.3 -0.026 -0.17 5.5e+03 0.068 1.2 -1 -
4 -1.3 0.52 0.0085 1.6 -1.6 0.6 -1.2 -0.13 0.0032 -0.078 -0.38 5e+03 0.0097 12 1 ++
5 -1.4 0.68 0.52 1.8 -1.7 0.37 -1.1 -0.067 0.027 -0.3 -0.29 5e+03 0.0013 1.2e+02 0.99 ++
6 -1.5 0.71 0.51 1.9 -1.7 0.38 -1.1 -0.083 0.027 -0.31 -0.28 5e+03 1.5e-05 1.2e+03 1 ++
7 -1.5 0.71 0.51 1.9 -1.7 0.38 -1.1 -0.083 0.027 -0.31 -0.28 5e+03 1.8e-09 1.2e+03 1 ++
Considering neighbor 1/20 for current solution
Attempt 65/100
Biogeme parameters read from biogeme.toml.
Model with 8 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.
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 -0.044 -0.014 -1 -0.35 -0.29 -0.23 -0.024 5.6e+03 0.055 10 1 ++
1 -1.1 0.82 0.68 -1.5 -2.1 -0.02 0.17 -0.0051 5.2e+03 0.015 1e+02 1 ++
2 -1.3 1.1 0.97 -1.6 -2.3 -0.052 0.15 -0.18 5.2e+03 0.0012 1e+03 1.1 ++
3 -1.4 1.2 1 -1.6 -2.3 -0.052 0.14 -0.2 5.2e+03 2.1e-05 1e+04 1 ++
4 -1.4 1.2 1 -1.6 -2.3 -0.052 0.14 -0.2 5.2e+03 6.7e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 66/100
Biogeme parameters read from biogeme.toml.
Model with 11 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.
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 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 -0.66 -1 2 0 0 0 0 0 -0.67 0 0 6.3e+03 0.14 1 0.42 +
1 -0.89 0 2.1 0 0 0 0 0 0.077 0 0 6.2e+03 0.29 1 0.19 +
2 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 1 0.43 +
3 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.5 -14 -
4 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.25 -6.5 -
5 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.12 -4.2 -
6 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.062 -1.6 -
7 -1.4 0.0095 3.1 0 0 0 0 0 -0.53 0 0 5.9e+03 1.1 0.062 0.5 +
8 -1.4 0.0095 3.1 0 0 0 0 0 -0.53 0 0 5.9e+03 1.1 0.031 -0.15 -
9 -1.5 -0.022 3.1 0 0 0 0 0 -0.54 0 0 5.9e+03 0.33 0.031 0.14 +
10 -1.5 -0.022 3.1 0 0 0 0 0 -0.54 0 0 5.9e+03 0.33 0.016 -0.32 -
11 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.16 0.94 ++
12 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.078 -7.3 -
13 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.039 -5.4 -
14 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.02 -4.4 -
15 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.0098 -3.8 -
16 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.0049 -1.5 -
17 -1.5 -0.0013 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.0088 0.0049 0.86 +
18 -1.5 -0.0012 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.0032 0.049 1 ++
19 -1.5 -0.0014 3 0 0 0 0 0 -0.58 0 0 5.9e+03 0.0027 0.49 1 ++
20 -1.4 -0.0054 2.5 0 0 0 0 0 -0.54 0 0 5.9e+03 0.097 0.49 0.54 +
21 -1.5 -0.024 2 0 0 0 0 0 -0.57 0 0 5.8e+03 0.083 4.9 1.2 ++
22 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 4.9 0.65 +
23 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 2.4 -2.5e+02 -
24 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 1.2 -3.1 -
25 -0.48 -1.5 0.14 0 0 0 0 0 0.35 0 0 5.6e+03 0.032 12 1.3 ++
26 -0.35 -1.6 0.53 0 0 0 0 0 0.24 0 0 5.6e+03 0.0079 12 0.84 +
27 -0.4 -1.6 0.46 0 0 0 0 0 0.2 0 0 5.5e+03 0.00061 1.2e+02 1.1 ++
28 -0.41 -1.6 0.45 0 0 0 0 0 0.2 0 0 5.5e+03 6.9e-06 1.2e+03 1 ++
29 -0.41 -1.6 0.45 0 0 0 0 0 0.2 0 0 5.5e+03 3.9e-10 1.2e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 67/100
Biogeme parameters read from biogeme.toml.
Model with 5 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.
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
Attempt 68/100
Biogeme parameters read from biogeme.toml.
Model with 16 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.
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 0.5 10 0.92 ++
2 5.7e+03 0.5 5 -2.2e+03 -
3 5.7e+03 0.5 2.5 -18 -
4 5.7e+03 0.5 1.2 -0.14 -
5 5.5e+03 0.19 1.2 0.86 +
6 5.4e+03 0.053 12 1 ++
7 5.4e+03 0.00089 1.2e+02 0.97 ++
8 5.4e+03 2.4e-06 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 69/100
Biogeme parameters read from biogeme.toml.
Model with 23 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.
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 0/20 for current solution
Attempt 70/100
Considering neighbor 0/20 for current solution
Attempt 71/100
Biogeme parameters read from biogeme.toml.
Model with 9 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.
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 -0.0032 -0.59 -0.089 -0.14 5.4e+03 2.2 10 1.1 ++
1 -1.2 0.91 0.72 -0.99 -1.1 -0.005 -0.4 -0.033 -0.61 5.3e+03 0.38 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 0.027 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 0.0014 1e+04 1 ++
4 -1.4 1.2 0.96 -1 -1.1 -0.0055 -0.39 -0.036 -0.64 5.3e+03 0.00022 1e+05 1 ++
5 -1.4 1.2 0.96 -1 -1.1 -0.0055 -0.39 -0.036 -0.64 5.3e+03 3.7e-06 1e+05 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 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.
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 -0.67 0.5 -1 1.7 -0.91 -0.0026 -0.46 -0.23 5.5e+03 1.9 1 0.77 +
1 -0.94 1.5 -1.2 1 -1 -0.0053 -0.5 -0.36 5.1e+03 0.39 10 1.2 ++
2 -0.32 2 -2.3 0.00089 -1.1 -0.0061 0.085 -0.36 5e+03 0.076 10 0.29 +
3 -0.73 2.1 -1.7 0.21 -1.1 -0.0061 -0.17 -0.34 5e+03 0.003 1e+02 1.1 ++
4 -0.73 2.1 -1.7 0.38 -1.1 -0.0062 -0.18 -0.3 5e+03 0.0038 1e+03 0.99 ++
5 -0.74 2.1 -1.7 0.39 -1.1 -0.0062 -0.19 -0.29 5e+03 1.1e-05 1e+04 1 ++
6 -0.74 2.1 -1.7 0.39 -1.1 -0.0062 -0.19 -0.29 5e+03 2.9e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
Attempt 72/100
Considering neighbor 0/20 for current solution
Attempt 73/100
Considering neighbor 0/20 for current solution
Attempt 74/100
Biogeme parameters read from biogeme.toml.
Model with 15 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.
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 -0.91 -0.0041 -0.0085 -1 0 0 0 -0.0013 0 0 -0.41 -0.18 -0.05 0 0 5.7e+03 2.5 10 1 ++
1 -0.76 0.79 0.6 -2.8 0 0 0 -0.0044 0 0 0.096 -0.061 -0.67 0 0 5.5e+03 0.52 1e+02 1.1 ++
2 -0.94 1.1 0.84 -3 0 0 0 -0.0053 0 0 0.12 -0.073 -0.76 0 0 5.4e+03 0.059 1e+03 1.1 ++
3 -0.97 1.1 0.88 -3 0 0 0 -0.0053 0 0 0.12 -0.074 -0.76 0 0 5.4e+03 0.00097 1e+04 1 ++
4 -0.97 1.1 0.88 -3 0 0 0 -0.0053 0 0 0.12 -0.074 -0.76 0 0 5.4e+03 3.1e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 75/100
Biogeme parameters read from biogeme.toml.
Model with 16 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.
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
Attempt 76/100
Considering neighbor 0/20 for current solution
Attempt 77/100
Biogeme parameters read from biogeme.toml.
Model with 16 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.
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
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.
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 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 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.
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 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 -0.66 -1 2 0 0 0 0 0 -0.67 0 0 6.3e+03 0.14 1 0.42 +
1 -0.89 0 2.1 0 0 0 0 0 0.077 0 0 6.2e+03 0.29 1 0.19 +
2 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 1 0.43 +
3 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.5 -14 -
4 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.25 -6.5 -
5 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.12 -4.2 -
6 -1.4 -0.053 3.1 0 0 0 0 0 -0.53 0 0 6e+03 0.52 0.062 -1.6 -
7 -1.4 0.0095 3.1 0 0 0 0 0 -0.53 0 0 5.9e+03 1.1 0.062 0.5 +
8 -1.4 0.0095 3.1 0 0 0 0 0 -0.53 0 0 5.9e+03 1.1 0.031 -0.15 -
9 -1.5 -0.022 3.1 0 0 0 0 0 -0.54 0 0 5.9e+03 0.33 0.031 0.14 +
10 -1.5 -0.022 3.1 0 0 0 0 0 -0.54 0 0 5.9e+03 0.33 0.016 -0.32 -
11 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.16 0.94 ++
12 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.078 -7.3 -
13 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.039 -5.4 -
14 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.02 -4.4 -
15 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.0098 -3.8 -
16 -1.5 -0.0062 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.19 0.0049 -1.5 -
17 -1.5 -0.0013 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.0088 0.0049 0.86 +
18 -1.5 -0.0012 3 0 0 0 0 0 -0.56 0 0 5.9e+03 0.0032 0.049 1 ++
19 -1.5 -0.0014 3 0 0 0 0 0 -0.58 0 0 5.9e+03 0.0027 0.49 1 ++
20 -1.4 -0.0054 2.5 0 0 0 0 0 -0.54 0 0 5.9e+03 0.097 0.49 0.54 +
21 -1.5 -0.024 2 0 0 0 0 0 -0.57 0 0 5.8e+03 0.083 4.9 1.2 ++
22 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 4.9 0.65 +
23 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 2.4 -2.5e+02 -
24 -1.2 -0.26 -0.73 0 0 0 0 0 -0.39 0 0 5.8e+03 0.038 1.2 -3.1 -
25 -0.48 -1.5 0.14 0 0 0 0 0 0.35 0 0 5.6e+03 0.032 12 1.3 ++
26 -0.35 -1.6 0.53 0 0 0 0 0 0.24 0 0 5.6e+03 0.0079 12 0.84 +
27 -0.4 -1.6 0.46 0 0 0 0 0 0.2 0 0 5.5e+03 0.00061 1.2e+02 1.1 ++
28 -0.41 -1.6 0.45 0 0 0 0 0 0.2 0 0 5.5e+03 6.9e-06 1.2e+03 1 ++
29 -0.41 -1.6 0.45 0 0 0 0 0 0.2 0 0 5.5e+03 3.9e-10 1.2e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 4 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.
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 -0.95 -0.63 -0.93 -0.55 5.5e+03 0.036 10 1.1 ++
1 -0.86 -1 -1 -0.29 5.4e+03 0.0047 1e+02 1.1 ++
2 -0.85 -1.1 -1 -0.27 5.4e+03 7.3e-05 1e+03 1 ++
3 -0.85 -1.1 -1 -0.27 5.4e+03 1.7e-08 1e+03 1 ++
Considering neighbor 3/20 for current solution
Attempt 78/100
Considering neighbor 0/20 for current solution
Attempt 79/100
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.
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 0/20 for current solution
Attempt 80/100
Biogeme parameters read from biogeme.toml.
Model with 20 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.
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 0.044 10 1.1 ++
1 5.4e+03 0.01 1e+02 1.1 ++
2 5.4e+03 0.00066 1e+03 1 ++
3 5.4e+03 3.5e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 81/100
Biogeme parameters read from biogeme.toml.
Model with 14 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.
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 0/20 for current solution
Attempt 82/100
Biogeme parameters read from biogeme.toml.
Model with 11 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.
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 -0.71 0.11 -0.0033 0.73 -0.88 -1 -0.0025 -0.25 -0.13 -0.05 -0.3 5.2e+03 2.4 10 1.1 ++
1 -1.4 0.48 0.26 1.9 -0.98 -1.4 -0.0051 -0.52 0.11 -0.25 -1.3 5e+03 0.77 1e+02 1.1 ++
2 -1.6 0.66 0.48 1.9 -1 -1.5 -0.0062 -0.55 0.12 -0.4 -1.8 5e+03 0.055 1e+03 1.1 ++
3 -1.7 0.71 0.54 1.9 -1 -1.5 -0.0063 -0.55 0.12 -0.42 -1.9 5e+03 0.0009 1e+04 1 ++
4 -1.7 0.71 0.54 1.9 -1 -1.5 -0.0063 -0.55 0.12 -0.42 -1.9 5e+03 8e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 83/100
Biogeme parameters read from biogeme.toml.
Model with 15 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.
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 12 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.
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
Attempt 84/100
Biogeme parameters read from biogeme.toml.
Model with 8 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.
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_ma Function Relgrad Radius Rho
0 -0.57 -0.93 -0.9 1.9 -1 0.0068 -0.25 0.035 5.7e+03 2.3 1 0.71 +
1 0.067 -1.7 0.034 2.1 -1 -0.0079 -1.3 0.42 5.6e+03 0.81 1 0.37 +
2 0.067 -1.7 0.034 2.1 -1 -0.0079 -1.3 0.42 5.6e+03 0.81 0.5 -1.9 -
3 0.067 -1.7 0.034 2.1 -1 -0.0079 -1.3 0.42 5.6e+03 0.81 0.25 -0.28 -
4 0.0011 -1.6 -0.2 2.3 -1 -0.0082 -1.3 0.37 5.5e+03 0.069 0.25 0.25 +
5 -0.2 -1.5 -0.16 2.1 -1.1 -0.0072 -1.2 0.3 5.4e+03 0.013 2.5 1.1 ++
6 -0.2 -1.5 -0.16 2.1 -1.1 -0.0072 -1.2 0.3 5.4e+03 0.013 1.2 0.032 -
7 -0.33 -1.1 -0.68 0.82 -1.2 -0.0017 -0.87 0.44 5.3e+03 0.38 1.2 0.46 +
8 0.42 -1.3 -1.4 0.087 -1.1 -0.0055 -0.58 0.35 5.2e+03 0.056 12 0.94 ++
9 0.54 -1.3 -1.6 0.41 -1.1 -0.0057 -0.54 0.36 5.2e+03 0.016 12 0.72 +
10 0.5 -1.3 -1.5 0.35 -1.1 -0.0057 -0.55 0.34 5.2e+03 0.00061 1.2e+02 1 ++
11 0.5 -1.3 -1.5 0.34 -1.1 -0.0057 -0.55 0.34 5.2e+03 6.7e-06 1.2e+03 1 ++
12 0.5 -1.3 -1.5 0.34 -1.1 -0.0057 -0.55 0.34 5.2e+03 3.9e-08 1.2e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 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.
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 lambda_cost b_headway asc_car Function Relgrad Radius Rho
0 -0.68 -1 1.9 -0.84 1 -0.0011 -0.46 5.8e+03 1.7 1 0.67 +
1 -0.69 -1.8 0.85 -1.5 0.34 0.00068 -0.23 5.5e+03 0.27 10 0.92 ++
2 -0.28 -1.5 0.63 -1.1 0.45 -0.006 -0.23 5.4e+03 0.05 1e+02 1 ++
3 -0.29 -1.6 0.45 -1.1 0.61 -0.0054 -0.21 5.3e+03 0.0017 1e+03 0.93 ++
4 -0.31 -1.6 0.46 -1.1 0.58 -0.0054 -0.22 5.3e+03 4.7e-05 1e+04 1 ++
5 -0.31 -1.6 0.46 -1.1 0.58 -0.0054 -0.22 5.3e+03 5.3e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
Attempt 85/100
Biogeme parameters read from biogeme.toml.
Model with 22 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.
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
Attempt 86/100
Biogeme parameters read from biogeme.toml.
Model with 10 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.
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 -0.37 -0.71 0.82 -0.68 -1 1 -0.0024 -0.47 -0.17 -0.36 5.2e+03 2.6 10 1.1 ++
1 -0.48 -0.93 1.9 -0.98 -1.9 -0.44 -0.0054 -0.89 0.34 -1.5 5e+03 0.88 10 0.6 +
2 -0.48 -1.1 2 -0.95 -1.1 -0.44 -0.0065 -0.88 0.49 -1.9 4.9e+03 0.088 1e+02 0.95 ++
3 -0.42 -1.1 2 -1 -1.7 0.27 -0.0068 -0.96 0.47 -2 4.9e+03 0.05 1e+02 0.48 +
4 -0.38 -1.1 2 -1.1 -1.5 0.17 -0.0068 -0.89 0.45 -1.9 4.9e+03 0.0052 1e+03 1 ++
5 -0.4 -1.1 2 -1 -1.5 0.1 -0.0068 -0.9 0.46 -1.9 4.9e+03 0.00016 1e+04 0.99 ++
6 -0.4 -1.1 2 -1 -1.5 0.1 -0.0068 -0.9 0.46 -1.9 4.9e+03 3.8e-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_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.
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 b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.57 -0.89 0.63 -0.93 1.8 -1 1 0.0053 -0.28 -0.011 -0.28 5.5e+03 2.4 1 0.8 +
1 -0.23 -0.96 1.6 -0.93 1.3 -1 0.69 -0.0064 -0.55 0.085 -0.71 5e+03 0.63 10 1.1 ++
2 -0.23 -0.96 1.6 -0.93 1.3 -1 0.69 -0.0064 -0.55 0.085 -0.71 5e+03 0.63 2.8 -73 -
3 -0.23 -0.96 1.6 -0.93 1.3 -1 0.69 -0.0064 -0.55 0.085 -0.71 5e+03 0.63 1.4 -5.8 -
4 -0.08 -1.1 2.3 -2.2 -0.078 -1.7 0.06 -0.0035 -0.57 0.61 -1.3 4.9e+03 0.13 1.4 0.58 +
5 -0.036 -1.2 2.1 -1.6 0.091 -1.5 0.017 -0.0068 -0.68 0.46 -1.9 4.8e+03 0.018 14 1 ++
6 -0.057 -1.2 2.1 -1.6 0.23 -1.5 0.033 -0.0066 -0.69 0.47 -2 4.8e+03 0.0029 1.4e+02 0.97 ++
7 -0.057 -1.2 2.1 -1.6 0.23 -1.5 0.033 -0.0066 -0.69 0.47 -2 4.8e+03 3.3e-06 1.4e+02 1 ++
Considering neighbor 1/20 for current solution
Attempt 87/100
Biogeme parameters read from biogeme.toml.
Model with 11 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.
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 0/20 for current solution
Attempt 88/100
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.
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_ma beta_CAR_TT_SCA beta_CAR_TT_SCA Function Relgrad Radius Rho
0 -0.73 -0.88 0 0 0 -1 0 0 -0.6 -0.26 0 0 5.6e+03 0.039 10 1 ++
1 -0.77 -1.3 0 0 0 -2.1 0 0 -0.87 0.14 0 0 5.4e+03 0.0055 1e+02 1 ++
2 -0.78 -1.3 0 0 0 -2.2 0 0 -0.93 0.16 0 0 5.4e+03 0.00015 1e+03 1 ++
3 -0.78 -1.3 0 0 0 -2.2 0 0 -0.93 0.16 0 0 5.4e+03 1e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 89/100
Biogeme parameters read from biogeme.toml.
Model with 16 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.
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
Attempt 90/100
Considering neighbor 0/20 for current solution
Attempt 91/100
Biogeme parameters read from biogeme.toml.
Model with 5 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.
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 0/20 for current solution
Attempt 92/100
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.
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_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.61 -0.89 0.72 -1 -0.83 0.0016 -0.41 -0.079 -0.26 5.3e+03 3.1 10 1.1 ++
1 -0.16 -1 1.8 -2.7 -2.4 -0.0045 -0.54 0.32 0.79 4.8e+03 0.68 1e+02 1.1 ++
2 -0.044 -1.1 2 -3.1 -2.7 -0.0065 -0.65 0.42 1 4.8e+03 0.059 1e+03 1.1 ++
3 -0.041 -1.1 2 -3.1 -2.8 -0.0068 -0.68 0.44 1 4.8e+03 0.00068 1e+04 1 ++
4 -0.042 -1.1 2 -3.1 -2.8 -0.0068 -0.68 0.44 1 4.8e+03 1.8e-05 1e+05 1 ++
5 -0.042 -1.1 2 -3.1 -2.8 -0.0068 -0.68 0.44 1 4.8e+03 6.5e-08 1e+05 1 ++
Considering neighbor 0/20 for current solution
Attempt 93/100
Biogeme parameters read from biogeme.toml.
Model with 12 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.
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 0/20 for current solution
Attempt 94/100
Biogeme parameters read from biogeme.toml.
Model with 19 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.
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 0/20 for current solution
Attempt 95/100
Considering neighbor 0/20 for current solution
Attempt 96/100
Biogeme parameters read from biogeme.toml.
Model with 14 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.
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_ma asc_car_diff_wi beta_CAR_CO_SCA beta_CAR_CO_SCA Function Relgrad Radius Rho
0 -0.33 -0.57 0.63 -1 0 0 0 0 0 -0.27 -0.067 -0.26 0 0 5.4e+03 0.048 10 1 ++
1 -0.054 -0.99 1.7 -1.6 0 0 0 0 0 -0.078 0.37 -1.1 0 0 5.1e+03 0.016 1e+02 1 ++
2 -0.058 -1.2 1.8 -1.6 0 0 0 0 0 -0.1 0.39 -1.4 0 0 5.1e+03 0.00086 1e+03 1 ++
3 -0.06 -1.2 1.8 -1.6 0 0 0 0 0 -0.1 0.39 -1.4 0 0 5.1e+03 6.1e-06 1e+04 1 ++
4 -0.06 -1.2 1.8 -1.6 0 0 0 0 0 -0.1 0.39 -1.4 0 0 5.1e+03 3.1e-10 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 97/100
Biogeme parameters read from biogeme.toml.
Model with 16 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.
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 9 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.
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 1/20 for current solution
Attempt 98/100
Biogeme parameters read from biogeme.toml.
Model with 6 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.
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 -0.52 -0.74 -1 -0.94 -0.46 -0.16 5.4e+03 0.044 10 1 ++
1 0.21 -1.2 -2.9 -2.1 -0.24 0.26 5.1e+03 0.016 1e+02 1.1 ++
2 0.31 -1.3 -3.3 -2.4 -0.27 0.33 5.1e+03 0.00089 1e+03 1 ++
3 0.31 -1.3 -3.3 -2.4 -0.27 0.33 5.1e+03 2.8e-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_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.
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 1/20 for current solution
Attempt 99/100
Biogeme parameters read from biogeme.toml.
Model with 7 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.
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
Pareto file has been updated: b22multiple_models.pareto
Before the algorithm: 465 models, with 7 Pareto.
After the algorithm: 501 models, with 7 Pareto.
VNS algorithm completed. Postprocessing of the Pareto optimal solutions
Pareto set initialized from file with 474 elements [7 Pareto] and 0 invalid elements.
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22multiple_models_000000.iter
Parameter values restored from __b22multiple_models_000000.iter
Starting values for the algorithm: {'asc_train_ref': -0.07367671352474071, 'asc_train_diff_male': -1.1590679270761561, 'asc_train_diff_with_ga': 2.1085409793549172, 'b_time': -1.6114913593274356, 'lambda_tt': 0.2233736569872712, 'b_cost': -1.4884009465532348, 'b_headway': -0.006638084810181024, 'asc_car_ref': -0.6960052685126517, 'asc_car_diff_male': 0.47396665105972735, 'asc_car_diff_with_ga': -2.0020626594355013}
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 b_headway asc_car_ref asc_car_diff_ma asc_car_diff_wi Function Relgrad Radius Rho
0 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.5 -0.85 -
1 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.25 -0.84 -
2 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.12 -0.83 -
3 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.062 -0.83 -
4 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.031 -0.83 -
5 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.016 -0.83 -
6 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.0078 -0.83 -
7 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.0039 -0.83 -
8 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.002 -0.83 -
9 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.00098 -0.83 -
10 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.00049 -0.83 -
11 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.00024 -0.83 -
12 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 0.00012 -0.83 -
13 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 6.1e-05 -0.83 -
14 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 3.1e-05 -0.83 -
15 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 1.5e-05 -0.83 -
16 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 7.6e-06 -0.83 -
17 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 3.8e-06 -0.83 -
18 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 1.9e-06 -0.83 -
19 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 9.5e-07 -0.83 -
20 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 4.8e-07 -0.83 -
21 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 2.4e-07 -0.83 -
22 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 1.2e-07 -0.83 -
23 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 6e-08 -0.83 -
24 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 3e-08 -0.83 -
25 -0.074 -1.2 2.1 -1.6 -1.5 -0.0066 -0.7 0.47 -2 7.2e+04 1.6 1.5e-08 -0.83 -
Optimization algorithm has *not* converged.
Algorithm: Newton with trust region for simple bound constraints
Cause of termination: Trust region is too small: 1.4901161193847656e-08
Number of iterations: 26
Proportion of Hessian calculation: 1/1 = 100.0%
Optimization time: 0:00:00.861311
Calculate second derivatives and BHHH
It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm.
File b22multiple_models_000000~00.html has been generated.
File b22multiple_models_000000~00.yaml has been generated.
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22multiple_models_000001.iter
Parameter values restored from __b22multiple_models_000001.iter
Starting values for the algorithm: {'asc_train_ref': -0.06428873416454763, 'asc_train_diff_male': -1.1900399775238788, 'asc_train_diff_with_ga': 2.157415716942036, 'b_time': -1.6215306410844021, 'b_cost': -1.5023419723928042, 'b_headway': -0.006593342797065967, 'asc_car_ref': -0.683938377480246, 'asc_car_diff_male': 0.4537580026546972, 'asc_car_diff_with_ga': -1.9759512791566758}
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 0.94 -0.19 3.2 -2.6 5.6e-17 -0.5 0.99 -1.7 -0.55 -3 5.1e+04 0.62 1 0.9 +
1 0.94 -0.19 3.2 -2.6 5.6e-17 -0.5 0.99 -1.7 -0.55 -3 5.1e+04 0.62 0.5 -0.29 -
2 1.4 0.31 3.7 -2.1 0.5 -0.0023 1.5 -2.2 -1 -3.5 1.7e+04 1.1 0.5 0.62 +
3 1.4 0.27 3.7 -1.8 1 -0.041 1.5 -2.1 -0.99 -3.5 1.4e+04 0.049 0.5 0.53 +
4 1.4 0.27 3.7 -1.8 1 -0.041 1.5 -2.1 -0.99 -3.5 1.4e+04 0.049 0.25 -0.066 -
5 1.4 0.27 3.7 -1.8 1 -0.041 1.5 -2.1 -0.99 -3.5 1.4e+04 0.049 0.12 0.094 -
6 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.12 0.2 +
7 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.062 -0.017 -
8 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.031 0.022 -
9 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.016 0.039 -
10 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.0078 -0.71 -
11 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.0039 -0.4 -
12 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.002 -0.23 -
13 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.00098 -0.17 -
14 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.00049 -0.15 -
15 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.00024 -0.13 -
16 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 0.00012 -0.13 -
17 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 6.1e-05 -0.12 -
18 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 3.1e-05 -0.12 -
19 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 1.5e-05 -0.12 -
20 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 7.6e-06 -0.12 -
21 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 3.8e-06 -0.12 -
22 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 1.9e-06 -0.12 -
23 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 9.5e-07 -0.12 -
24 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 4.8e-07 -0.12 -
25 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 2.4e-07 -0.12 -
26 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 1.2e-07 -0.12 -
27 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 6e-08 -0.12 -
28 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 3e-08 -0.12 -
29 1.4 0.25 3.7 -1.8 1.1 -0.053 1.4 -2.1 -0.98 -3.5 1.4e+04 0.13 1.5e-08 -0.12 -
Optimization algorithm has *not* converged.
Algorithm: Newton with trust region for simple bound constraints
Cause of termination: Trust region is too small: 1.4901161193847656e-08
Number of iterations: 30
Proportion of Hessian calculation: 5/5 = 100.0%
Optimization time: 0:00:02.145363
Calculate second derivatives and BHHH
It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm.
File b22multiple_models_000001~00.html has been generated.
File b22multiple_models_000001~00.yaml has been generated.
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22multiple_models_000002.iter
Parameter values restored from __b22multiple_models_000002.iter
Starting values for the algorithm: {'asc_train_ref': -0.9179504404134234, 'asc_train_diff_with_ga': 2.2435709636922936, 'b_time': -1.5947261592634554, 'b_cost': -1.5032629167922742, 'b_headway': -0.0061230125995098954, 'asc_car_ref': -0.28199124501588113, 'asc_car_diff_with_ga': -1.8290175405420466}
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 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.5 -21 -
1 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.25 -80 -
2 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.12 -43 -
3 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.062 -19 -
4 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.031 -5.9 -
5 0 -1.6 -1.5 -0.0061 0 6e+03 9.2 0.016 -0.46 -
6 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.16 1 ++
7 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.078 -85 -
8 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.039 -56 -
9 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.02 -34 -
10 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.0098 -18 -
11 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.0049 -8.9 -
12 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.0024 -4.1 -
13 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.0012 -1.9 -
14 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.00061 -1.3 -
15 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.00031 -1.1 -
16 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 0.00015 -0.98 -
17 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 7.6e-05 -0.94 -
18 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 3.8e-05 -0.91 -
19 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 1.9e-05 -0.9 -
20 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 9.5e-06 -0.9 -
21 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 4.8e-06 -0.9 -
22 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 2.4e-06 -0.89 -
23 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 1.2e-06 -0.89 -
24 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 6e-07 -0.89 -
25 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 3e-07 -0.89 -
26 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 1.5e-07 -0.89 -
27 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 7.5e-08 -0.89 -
28 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 3.7e-08 -0.89 -
29 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 1.9e-08 -0.89 -
30 -0.016 -1.6 -1.5 -0.014 -0.013 5.8e+03 1.7 9.3e-09 -0.89 -
Optimization algorithm has *not* converged.
Algorithm: Newton with trust region for simple bound constraints
Cause of termination: Trust region is too small: 9.313225746154779e-09
Number of iterations: 31
Proportion of Hessian calculation: 2/2 = 100.0%
Optimization time: 0:00:00.579574
Calculate second derivatives and BHHH
It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm.
File b22multiple_models_000002~00.html has been generated.
File b22multiple_models_000002~00.yaml has been generated.
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22multiple_models_000003.iter
Parameter values restored from __b22multiple_models_000003.iter
Starting values for the algorithm: {'asc_train_ref': -0.37489922058477093, 'asc_train_diff_male': -1.171233665542124, 'asc_train_diff_with_ga': 2.1303933360865916, 'b_time': -1.6276384300357265, 'b_cost': -1.5013784331569677, 'asc_car_ref': -0.553122728238362, 'asc_car_diff_male': 0.4598529086351089, 'asc_car_diff_with_ga': -1.99738873512378}
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 -1.6 -1.5 0 6.9e+03 0.24 0.5 0.067 -
1 0 -1.6 -1.5 0 6.9e+03 0.24 0.25 0.088 -
2 -0.25 -1.9 -1.3 -0.12 6.8e+03 0.23 0.25 0.18 +
3 -0.5 -2.1 -1 -0.24 6.7e+03 0.25 0.25 0.17 +
4 -0.75 -2.4 -0.75 -0.34 6.6e+03 0.27 0.25 0.15 +
5 -1 -2.6 -0.5 -0.43 6.6e+03 0.29 0.25 0.14 +
6 -1.2 -2.9 -0.25 -0.51 6.5e+03 0.31 0.25 0.13 +
7 -1.5 -3.1 -0.0014 -0.59 6.4e+03 0.33 0.25 0.11 +
8 -1.8 -3.4 0.25 -0.66 6.4e+03 0.35 0.25 0.1 +
9 -1.8 -3.4 0.25 -0.66 6.4e+03 0.35 0.12 0.092 -
10 -1.9 -3.5 0.37 -0.73 6.3e+03 0.37 0.12 0.2 +
11 -2 -3.6 0.5 -0.8 6.3e+03 0.39 0.12 0.18 +
12 -2.1 -3.8 0.62 -0.86 6.3e+03 0.4 0.12 0.17 +
13 -2.2 -3.9 0.75 -0.91 6.2e+03 0.42 0.12 0.15 +
14 -2.4 -4 0.87 -0.96 6.2e+03 0.44 0.12 0.13 +
15 -2.5 -4.1 1 -1 6.2e+03 0.46 0.12 0.12 +
16 -2.6 -4.3 1.1 -1 6.1e+03 0.47 0.12 0.11 +
17 -2.6 -4.3 1.1 -1 6.1e+03 0.47 0.062 0.093 -
18 -2.7 -4.3 1.2 -1.1 6.1e+03 0.48 0.062 0.22 +
19 -2.8 -4.4 1.2 -1.1 6.1e+03 0.48 0.062 0.2 +
20 -2.8 -4.4 1.3 -1.2 6.1e+03 0.49 0.062 0.17 +
21 -2.9 -4.5 1.4 -1.2 6.1e+03 0.49 0.062 0.16 +
22 -2.9 -4.6 1.4 -1.2 6e+03 0.5 0.062 0.14 +
23 -3 -4.6 1.5 -1.2 6e+03 0.51 0.062 0.12 +
24 -3.1 -4.7 1.6 -1.2 6e+03 0.51 0.062 0.11 +
25 -3.1 -4.7 1.6 -1.2 6e+03 0.51 0.031 0.099 -
26 -3.1 -4.7 1.6 -1.3 6e+03 0.52 0.031 0.15 +
27 -3.1 -4.8 1.6 -1.3 6e+03 0.52 0.031 0.14 +
28 -3.2 -4.8 1.7 -1.3 6e+03 0.52 0.031 0.12 +
29 -3.2 -4.8 1.7 -1.3 6e+03 0.53 0.031 0.11 +
30 -3.2 -4.8 1.7 -1.3 6e+03 0.53 0.016 0.099 -
31 -3.2 -4.8 1.7 -1.3 6e+03 0.53 0.016 0.2 +
32 -3.2 -4.8 1.7 -1.3 6e+03 0.53 0.016 0.18 +
33 -3.2 -4.9 1.7 -1.3 6e+03 0.53 0.016 0.16 +
34 -3.2 -4.9 1.7 -1.3 6e+03 0.53 0.016 0.14 +
35 -3.3 -4.9 1.8 -1.3 6e+03 0.53 0.016 0.13 +
36 -3.3 -4.9 1.8 -1.3 6e+03 0.53 0.016 0.11 +
37 -3.3 -4.9 1.8 -1.4 6e+03 0.53 0.016 0.1 +
38 -3.3 -4.9 1.8 -1.4 6e+03 0.53 0.0078 0.089 -
39 -3.3 -4.9 1.8 -1.4 6e+03 0.54 0.0078 0.18 +
40 -3.3 -4.9 1.8 -1.4 6e+03 0.54 0.0078 0.16 +
41 -3.3 -4.9 1.8 -1.4 6e+03 0.54 0.0078 0.14 +
42 -3.3 -5 1.8 -1.4 6e+03 0.54 0.0078 0.13 +
43 -3.3 -5 1.8 -1.4 6e+03 0.54 0.0078 0.11 +
44 -3.3 -5 1.8 -1.4 6e+03 0.54 0.0078 0.1 +
45 -3.3 -5 1.8 -1.4 6e+03 0.54 0.0039 0.09 -
46 -3.3 -5 1.8 -1.4 6e+03 0.54 0.0039 0.18 +
47 -3.4 -5 1.9 -1.4 6e+03 0.54 0.0039 0.16 +
48 -3.4 -5 1.9 -1.4 6e+03 0.54 0.0039 0.14 +
49 -3.4 -5 1.9 -1.4 6e+03 0.54 0.0039 0.13 +
50 -3.4 -5 1.9 -1.4 6e+03 0.54 0.0039 0.11 +
51 -3.4 -5 1.9 -1.4 6e+03 0.54 0.0039 0.1 +
52 -3.4 -5 1.9 -1.4 6e+03 0.54 0.002 0.09 -
53 -3.4 -5 1.9 -1.4 6e+03 0.54 0.002 0.18 +
54 -3.4 -5 1.9 -1.4 6e+03 0.54 0.002 0.16 +
55 -3.4 -5 1.9 -1.4 6e+03 0.54 0.002 0.14 +
56 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.002 0.13 +
57 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.002 0.11 +
58 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.002 0.1 +
59 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.091 -
60 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.18 +
61 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.16 +
62 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.14 +
63 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.13 +
64 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.12 +
65 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00098 0.1 +
66 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.092 -
67 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.18 +
68 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.16 +
69 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.15 +
70 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.13 +
71 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.12 +
72 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00049 0.1 +
73 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.092 -
74 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.18 +
75 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.16 +
76 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.15 +
77 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.13 +
78 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.12 +
79 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00024 0.1 +
80 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.093 -
81 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.19 +
82 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.17 +
83 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.15 +
84 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.13 +
85 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.12 +
86 -3.4 -5 1.9 -1.4 5.9e+03 0.54 0.00012 0.1 +
87 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.093 -
88 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.19 +
89 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.17 +
90 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.15 +
91 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.13 +
92 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.12 +
93 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6.1e-05 0.11 +
94 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.094 -
95 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.19 +
96 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.17 +
97 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.15 +
98 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.13 +
99 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.12 +
100 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.1e-05 0.11 +
101 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.095 -
102 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.19 +
103 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.17 +
104 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.15 +
105 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.13 +
106 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.12 +
107 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-05 0.11 +
108 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.095 -
109 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.19 +
110 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.17 +
111 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.15 +
112 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.14 +
113 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.12 +
114 -3.4 -5 1.9 -1.4 5.9e+03 0.54 7.6e-06 0.11 +
115 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.096 -
116 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.19 +
117 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.17 +
118 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.15 +
119 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.14 +
120 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.12 +
121 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3.8e-06 0.11 +
122 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.097 -
123 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.19 +
124 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.17 +
125 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.15 +
126 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.14 +
127 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.12 +
128 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.9e-06 0.11 +
129 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.097 -
130 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.19 +
131 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.17 +
132 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.15 +
133 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.14 +
134 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.12 +
135 -3.4 -5 1.9 -1.4 5.9e+03 0.54 9.5e-07 0.11 +
136 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.098 -
137 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.2 +
138 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.17 +
139 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.16 +
140 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.14 +
141 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.12 +
142 -3.4 -5 1.9 -1.4 5.9e+03 0.54 4.8e-07 0.11 +
143 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.098 -
144 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.2 +
145 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.18 +
146 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.16 +
147 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.14 +
148 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.12 +
149 -3.4 -5 1.9 -1.4 5.9e+03 0.54 2.4e-07 0.11 +
150 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.099 -
151 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.2 +
152 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.18 +
153 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.16 +
154 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.14 +
155 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.13 +
156 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.2e-07 0.11 +
157 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.1 -
158 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.2 +
159 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.18 +
160 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.16 +
161 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.14 +
162 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.13 +
163 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.11 +
164 -3.4 -5 1.9 -1.4 5.9e+03 0.54 6e-08 0.32 +
165 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.066 -
166 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.13 +
167 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.12 +
168 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.11 +
169 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.32 +
170 -3.4 -5 1.9 -1.4 5.9e+03 0.54 3e-08 0.32 +
171 -3.4 -5 1.9 -1.4 5.9e+03 0.54 1.5e-08 0 +
Optimization algorithm has *not* converged.
Algorithm: Newton with trust region for simple bound constraints
Cause of termination: Trust region is too small: 1.4901161193847656e-08
Number of iterations: 172
Proportion of Hessian calculation: 147/147 = 100.0%
Optimization time: 0:00:01.829430
Calculate second derivatives and BHHH
It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm.
File b22multiple_models_000003~00.html has been generated.
File b22multiple_models_000003~00.yaml has been generated.
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22multiple_models_000004.iter
Parameter values restored from __b22multiple_models_000004.iter
Starting values for the algorithm: {'asc_train': -0.25828520695319407, 'b_time': -3.317406744595399, 'b_cost': -2.35293555588282, 'b_headway': -0.005287158125190783, 'asc_car': -0.054528515119256514}
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 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 1 0.56 +
1 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 0.5 -0.31 -
2 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 0.25 0.00087 -
3 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 0.12 -0.21 -
4 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 0.062 -0.1 -
5 -0.42 -0.58 0.49 -4.3 -3 -0.36 -0.18 -0.22 6e+03 0.35 0.031 0.072 -
6 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.031 0.11 +
7 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.016 0.017 -
8 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.0078 0.023 -
9 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.0039 0.012 -
10 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.002 -0.15 -
11 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.00098 -0.15 -
12 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.00049 -0.15 -
13 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.00024 -0.15 -
14 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 0.00012 -0.15 -
15 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 6.1e-05 -0.15 -
16 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 3.1e-05 -0.15 -
17 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 1.5e-05 -0.15 -
18 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 7.6e-06 -0.15 -
19 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 3.8e-06 -0.15 -
20 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 1.9e-06 -0.15 -
21 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 9.5e-07 -0.15 -
22 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 4.8e-07 -0.15 -
23 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 2.4e-07 -0.15 -
24 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 1.2e-07 -0.15 -
25 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 6e-08 -0.15 -
26 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 3e-08 -0.15 -
27 -0.45 -0.61 0.52 -4.3 -3 -0.39 -0.18 -0.25 6e+03 0.32 1.5e-08 -0.15 -
Optimization algorithm has *not* converged.
Algorithm: Newton with trust region for simple bound constraints
Cause of termination: Trust region is too small: 1.4901161193847656e-08
Number of iterations: 28
Proportion of Hessian calculation: 3/3 = 100.0%
Optimization time: 0:00:00.907065
Calculate second derivatives and BHHH
It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm.
File b22multiple_models_000004~00.html has been generated.
File b22multiple_models_000004~00.yaml has been generated.
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22multiple_models_000005.iter
Parameter values restored from __b22multiple_models_000005.iter
Starting values for the algorithm: {'asc_train_ref': -1.2363986219196865, 'asc_train_diff_with_ga': 2.116104799141088, 'b_time': -2.936778941355896, 'b_cost': -1.472890077438725, 'asc_car_ref': -0.18981688978327133, 'asc_car_diff_with_ga': -1.8968233567325306}
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 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.5 -0.94 -
1 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.25 -0.94 -
2 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.12 -0.94 -
3 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.062 -0.94 -
4 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.031 -0.94 -
5 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.016 -0.94 -
6 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.0078 -0.94 -
7 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.0039 -0.94 -
8 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.002 -0.94 -
9 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.00098 -0.94 -
10 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.00049 -0.94 -
11 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.00024 -0.94 -
12 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 0.00012 -0.94 -
13 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 6.1e-05 -0.94 -
14 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 3.1e-05 -0.94 -
15 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 1.5e-05 -0.94 -
16 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 7.6e-06 -0.94 -
17 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 3.8e-06 -0.94 -
18 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 1.9e-06 -0.94 -
19 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 9.5e-07 -0.94 -
20 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 4.8e-07 -0.94 -
21 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 2.4e-07 -0.94 -
22 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 1.2e-07 -0.94 -
23 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 6e-08 -0.94 -
24 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 3e-08 -0.94 -
25 -1.2 2.1 -2.9 -1.5 0 -0.19 -1.9 2.2e+05 0.54 1.5e-08 -0.94 -
Optimization algorithm has *not* converged.
Algorithm: Newton with trust region for simple bound constraints
Cause of termination: Trust region is too small: 1.4901161193847656e-08
Number of iterations: 26
Proportion of Hessian calculation: 1/1 = 100.0%
Optimization time: 0:00:00.763071
Calculate second derivatives and BHHH
It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm.
File b22multiple_models_000005~00.html has been generated.
File b22multiple_models_000005~00.yaml has been generated.
Biogeme parameters provided by the user.
*** Initial values of the parameters are obtained from the file __b22multiple_models_000006.iter
Parameter values restored from __b22multiple_models_000006.iter
Starting values for the algorithm: {'asc_train': -0.5046852334057811, 'b_time': -3.3215566932569422, 'b_cost': -2.3520626243030707, 'asc_car': 0.05192169085145166}
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 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.5 -0.27 -
1 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.25 -0.16 -
2 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.12 -0.11 -
3 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.062 -0.09 -
4 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.031 -0.08 -
5 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.016 -0.074 -
6 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.0078 -0.072 -
7 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.0039 -0.07 -
8 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.002 -0.07 -
9 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.00098 -0.069 -
10 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.00049 -0.069 -
11 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.00024 -0.069 -
12 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 0.00012 -0.069 -
13 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 6.1e-05 -0.069 -
14 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 3.1e-05 -0.069 -
15 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 1.5e-05 -0.069 -
16 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 7.6e-06 -0.069 -
17 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 3.8e-06 -0.069 -
18 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 1.9e-06 -0.069 -
19 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 9.5e-07 -0.069 -
20 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 4.8e-07 -0.069 -
21 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 2.4e-07 -0.069 -
22 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 1.2e-07 -0.069 -
23 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 6e-08 -0.069 -
24 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 3e-08 -0.069 -
25 0 0 -3.3 -2.4 0 0 8.7e+03 0.4 1.5e-08 -0.069 -
Optimization algorithm has *not* converged.
Algorithm: Newton with trust region for simple bound constraints
Cause of termination: Trust region is too small: 1.4901161193847656e-08
Number of iterations: 26
Proportion of Hessian calculation: 1/1 = 100.0%
Optimization time: 0:00:00.637017
Calculate second derivatives and BHHH
It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm.
File b22multiple_models_000006~00.html has been generated.
File b22multiple_models_000006~00.yaml has been generated.
Pareto: 7
Considered: 474
Removed: 26
summary, description = compile_estimation_results(
non_dominated_models, use_short_names=True
)
print(summary)
Model_000000 ... Model_000006
Number of estimated parameters 9 ... 6
Sample size 6768 ... 6768
Final log likelihood -4810.59 ... -5837.478
Akaike Information Criterion 9639.18 ... 11686.96
Bayesian Information Criterion 9700.56 ... 11727.88
asc_train_ref (t-test) -0.0737 (-0.705) ... 0 (0)
asc_train_diff_male (t-test) -1.16 (-13.7) ...
asc_train_diff_with_ga (t-test) 2.11 (23.1) ... 0 (0)
b_time (t-test) -1.61 (-20.2) ... -3.32 (-17.8)
b_cost (t-test) -1.49 (-18.4) ... -2.35 (-20.1)
b_headway (t-test) -0.00664 (-6.15) ...
asc_car_ref (t-test) -0.696 (-6.52) ... 0 (0)
asc_car_diff_male (t-test) 0.474 (4.39) ...
asc_car_diff_with_ga (t-test) -2 (-9.32) ... 0 (0)
lambda_tt (t-test) ...
asc_train (t-test) ...
asc_car (t-test) ...
[17 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:MALE-GA;train_cost_catalog:log;train_headway_catalog:with_headway;train_tt_catalog:log
Model_000001: asc:MALE-GA;train_cost_catalog:log;train_headway_catalog:with_headway;train_tt_catalog:boxcox
Model_000002: asc:no_seg;train_cost_catalog:sqrt;train_headway_catalog:with_headway;train_tt_catalog:sqrt
Model_000003: asc:no_seg;train_cost_catalog:sqrt;train_headway_catalog:without_headway;train_tt_catalog:sqrt
Model_000004: asc:MALE-GA;train_cost_catalog:log;train_headway_catalog:without_headway;train_tt_catalog:log
Model_000005: asc:GA;train_cost_catalog:log;train_headway_catalog:with_headway;train_tt_catalog:log
Model_000006: asc:GA;train_cost_catalog:log;train_headway_catalog:without_headway;train_tt_catalog:sqrt
Total running time of the script: (4 minutes 35.728 seconds)