Note
Go to the end to download the full example code.
Assisted specification
Example of the estimation of several versions of the model using assisted specification algorithm. The catalog of specifications is defined in Specification of a catalog of models . Compared to 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.
- author:
Michel Bierlaire, EPFL
- date:
Wed Apr 12 17:05:40 2023
import biogeme.biogeme_logging as blog
from biogeme.results import compile_estimation_results
from biogeme.multiobjectives import aic_bic_dimension
from biogeme.assisted import AssistedSpecification
from plot_b22multiple_models_spec import the_biogeme, PARETO_FILE_NAME
logger = blog.get_screen_logger(blog.INFO)
logger.info('Example b22multiple_models')
Example b22multiple_models
nbr = the_biogeme.log_like.number_of_multiple_expressions()
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 numner 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.
Unable to read file b22multiple_models.pareto. Pareto set empty.
The algorithm is run.
non_dominated_models = assisted_specification.run()
Biogeme parameters read from biogeme.toml.
Model with 4 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME Function Relgrad Radius Rho
0 -0.49 -0.92 -0.88 -0.67 5.4e+03 0.041 10 1.1 ++
1 -0.18 -0.73 -1 -1.2 5.3e+03 0.0072 1e+02 1.1 ++
2 -0.16 -0.7 -1.1 -1.3 5.3e+03 0.00018 1e+03 1 ++
3 -0.16 -0.7 -1.1 -1.3 5.3e+03 1.1e-07 1e+03 1 ++
default_specification=ASC:no_seg;TRAIN_COST_catalog:linear;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:linear
The number of possible specifications [504] exceeds the maximum number [100]. A heuristic algorithm is applied.
*** VNS ***
ASC:no_seg;TRAIN_COST_catalog:linear;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:linear [10670.504013832326, np.float64(10697.78385743747), 4]
Initial pareto: 1
Attempt 0/100
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME Function Relgrad Radius Rho
0 -0.55 -0.38 -0.028 -1 -0.23 -0.026 -0.45 -0.82 5.7e+03 0.051 10 1 ++
1 -0.087 0.081 -0.27 -1 0.77 0.61 -0.96 -2.9 5.2e+03 0.019 1e+02 1.1 ++
2 -0.069 0.07 -0.3 -1.3 1.1 0.9 -1.1 -3.3 5.2e+03 0.0018 1e+03 1.1 ++
3 -0.069 0.07 -0.3 -1.3 1.1 0.9 -1.1 -3.3 5.2e+03 3.8e-05 1e+03 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
ASC:LUGGAGE;TRAIN_COST_catalog:linear;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:sqrt [10418.29383437041, np.float64(10472.853521580699), 8]
Attempt 1/100
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.58 -0.25 -0.86 1 -0.91 -0.0036 -0.68 5.2e+03 2.5 10 1.1 ++
1 -0.38 -0.32 -0.95 1.9 -1.1 -0.0054 -1.1 5e+03 0.57 1e+02 1.1 ++
2 -0.38 -0.3 -0.99 2 -1.1 -0.0062 -1.2 5e+03 0.034 1e+03 1 ++
3 -0.38 -0.3 -0.99 2 -1.1 -0.0062 -1.2 5e+03 0.00011 1e+03 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
ASC:GA;TRAIN_COST_catalog:linear;TRAIN_HEADWAY_catalog:with_headway;TRAIN_TT_catalog:linear [10076.991197532167, np.float64(10124.730923841169), 7]
Attempt 2/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S lambda_COST Function Relgrad Radius Rho
0 -0.38 -0.27 -0.02 -1 -0.33 -0.026 -0.19 0 0 0 0 0 0 0 1 5.9e+03 0.04 10 1 ++
1 -0.38 -0.27 -0.02 -1 -0.33 -0.026 -0.19 0 0 0 0 0 0 0 1 5.9e+03 0.04 5 -1.7e+07 -
2 -0.38 -0.27 -0.02 -1 -0.33 -0.026 -0.19 0 0 0 0 0 0 0 1 5.9e+03 0.04 2.5 -3.2e+02 -
3 -0.38 -0.27 -0.02 -1 -0.33 -0.026 -0.19 0 0 0 0 0 0 0 1 5.9e+03 0.04 1.2 -0.92 -
4 -0.87 0.098 0.035 -2.1 0.81 0.89 -0.84 0 0 0 0 0 0 0 -0.25 5.6e+03 0.03 12 1.1 ++
5 -0.94 0.043 -0.38 -2.5 1.2 1.1 -1.3 0 0 0 0 0 0 0 0.45 5.6e+03 0.012 12 0.63 +
6 -0.91 0.036 -0.43 -2.6 1.2 1.2 -1.2 0 0 0 0 0 0 0 0.24 5.5e+03 0.0016 1.2e+02 1.1 ++
7 -0.91 0.034 -0.45 -2.6 1.2 1.2 -1.2 0 0 0 0 0 0 0 0.17 5.5e+03 0.00016 1.2e+03 1 ++
8 -0.91 0.034 -0.45 -2.6 1.2 1.2 -1.2 0 0 0 0 0 0 0 0.17 5.5e+03 3.3e-07 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 b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.047 -1 -1 0.11 -0.27 -1 1.1 5.6e+03 0.042 10 1.1 ++
1 -0.047 -1 -1 0.11 -0.27 -1 1.1 5.6e+03 0.042 5 -1.4e+07 -
2 -0.047 -1 -1 0.11 -0.27 -1 1.1 5.6e+03 0.042 2.5 -2.1e+02 -
3 -0.047 -1 -1 0.11 -0.27 -1 1.1 5.6e+03 0.042 1.2 -0.26 -
4 -0.38 -1.1 -1.4 1.4 -1.1 -1.9 0.99 5.1e+03 0.023 12 1.1 ++
5 -0.38 -1.1 -1.4 1.4 -1.1 -1.9 0.99 5.1e+03 0.023 1.2 -12 -
6 -0.31 -1.4 -1.3 2.5 -2 -3 -0.19 5e+03 0.041 1.2 0.5 +
7 -0.18 -1.8 -1.3 2.1 -1.4 -2.9 -0.13 5e+03 0.0015 12 0.98 ++
8 -0.19 -1.9 -1.2 2.1 -1.5 -3 0.1 4.9e+03 0.002 1.2e+02 0.91 ++
9 -0.19 -1.9 -1.2 2.1 -1.5 -3 0.1 4.9e+03 1.9e-05 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
*** New pareto solution:
ASC:GA;TRAIN_COST_catalog:boxcox;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:sqrt [9906.475577240222, np.float64(9954.215303549225), 7]
Attempt 3/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.74 -0.36 -0.043 -1 -0.18 -0.017 -0.67 -0.005 0 0 0 0 0 0 0 5.7e+03 2.3 10 1 ++
1 -1 0.18 -0.054 -1.9 0.85 0.8 -0.94 -0.0052 0 0 0 0 0 0 0 5.5e+03 0.56 1e+02 1.1 ++
2 -1 0.16 -0.097 -2.2 1.2 1.1 -0.96 -0.0055 0 0 0 0 0 0 0 5.5e+03 0.071 1e+03 1.1 ++
3 -1 0.16 -0.097 -2.3 1.2 1.2 -0.96 -0.0056 0 0 0 0 0 0 0 5.5e+03 0.0017 1e+04 1 ++
4 -1 0.16 -0.097 -2.3 1.2 1.2 -0.96 -0.0056 0 0 0 0 0 0 0 5.5e+03 1.1e-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 b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 2.5 10 1 ++
1 5.7e+03 2.5 3.3 -1.1e+04 -
2 5.7e+03 2.5 1.7 -13 -
3 5.3e+03 1.2 17 1.1 ++
4 5.3e+03 1.2 1.2 -29 -
5 5.3e+03 1.2 0.62 -0.081 -
6 5.2e+03 0.011 6.2 0.98 ++
7 5.2e+03 0.0076 62 0.96 ++
8 5.2e+03 0.0018 6.2e+02 1.1 ++
9 5.2e+03 1.2e-05 6.2e+02 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.04 10 1.1 ++
1 5.9e+03 0.04 5 -1.1e+07 -
2 5.9e+03 0.04 2.5 -1.8e+02 -
3 5.9e+03 0.04 1.2 0.051 -
4 5.4e+03 0.043 12 1.1 ++
5 5.4e+03 0.043 2.7 -3.1e+03 -
6 5.4e+03 0.043 1.4 -17 -
7 5.3e+03 0.057 1.4 0.48 +
8 5.2e+03 0.0044 14 0.91 ++
9 5.2e+03 0.0019 14 0.89 +
10 5.2e+03 3.7e-05 14 1 +
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 4/100
Considering neighbor 0/20 for current solution
Attempt 5/100
Biogeme parameters read from biogeme.toml.
Model with 5 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.61 -0.72 -0.95 -0.0037 -0.66 5.5e+03 2.2 10 1.1 ++
1 -0.41 -0.62 -1.1 -0.0051 -1 5.4e+03 0.2 1e+02 1.1 ++
2 -0.38 -0.6 -1 -0.0054 -1.1 5.4e+03 0.0025 1e+03 1 ++
3 -0.38 -0.6 -1 -0.0054 -1.1 5.4e+03 0.00027 1e+04 1 ++
4 -0.38 -0.6 -1 -0.0054 -1.1 5.4e+03 1.5e-05 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 6/100
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.38 -0.27 -0.021 -1 -0.33 -0.026 -0.19 0 0 0 0 0 0 0 5.9e+03 0.04 10 1 ++
1 -0.87 0.072 -0.35 -2.1 0.8 0.71 -1 0 0 0 0 0 0 0 5.6e+03 0.024 1e+02 1.1 ++
2 -0.9 0.028 -0.48 -2.5 1.2 1.1 -1.1 0 0 0 0 0 0 0 5.5e+03 0.004 1e+03 1.1 ++
3 -0.9 0.027 -0.48 -2.6 1.2 1.2 -1.1 0 0 0 0 0 0 0 5.5e+03 0.00013 1e+04 1 ++
4 -0.9 0.027 -0.48 -2.6 1.2 1.2 -1.1 0 0 0 0 0 0 0 5.5e+03 1.2e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.54 -0.26 -0.049 -0.92 -0.064 -0.011 -0.69 -0.0023 -1 5.5e+03 2.3 10 1 ++
1 -0.23 -0.071 -0.58 -0.99 0.81 0.61 -1 -0.0046 -2.6 5.3e+03 0.53 1e+02 1.1 ++
2 -0.22 -0.07 -0.71 -1.2 1.1 0.89 -1.1 -0.0054 -2.9 5.3e+03 0.062 1e+03 1.1 ++
3 -0.22 -0.069 -0.72 -1.2 1.2 0.93 -1.1 -0.0055 -2.9 5.3e+03 0.0016 1e+04 1 ++
4 -0.22 -0.069 -0.72 -1.2 1.2 0.93 -1.1 -0.0055 -2.9 5.3e+03 8.4e-06 1e+04 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.73 -0.37 -0.047 -1 -0.17 -0.015 -0.53 -0.0047 0 0 0 0 0 0 0 5.8e+03 2.4 10 1 ++
1 -1 0.05 -0.39 -1.9 0.83 0.76 -1.1 -0.0052 0 0 0 0 0 0 0 5.5e+03 0.53 1e+02 1.1 ++
2 -1 0.028 -0.47 -2.3 1.2 1.1 -1.1 -0.0055 0 0 0 0 0 0 0 5.5e+03 0.073 1e+03 1.1 ++
3 -1 0.028 -0.48 -2.3 1.2 1.2 -1.1 -0.0055 0 0 0 0 0 0 0 5.5e+03 0.0018 1e+04 1 ++
4 -1 0.028 -0.48 -2.3 1.2 1.2 -1.1 -0.0055 0 0 0 0 0 0 0 5.5e+03 1.2e-06 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.73 -0.37 -0.047 -1 -0.17 -0.015 -0.53 -0.0047 0 0 0 0 0 0 0 5.8e+03 2.4 10 1 ++
1 -1 0.05 -0.39 -1.9 0.83 0.76 -1.1 -0.0052 0 0 0 0 0 0 0 5.5e+03 0.53 1e+02 1.1 ++
2 -1 0.028 -0.47 -2.3 1.2 1.1 -1.1 -0.0055 0 0 0 0 0 0 0 5.5e+03 0.073 1e+03 1.1 ++
3 -1 0.028 -0.48 -2.3 1.2 1.2 -1.1 -0.0055 0 0 0 0 0 0 0 5.5e+03 0.0018 1e+04 1 ++
4 -1 0.028 -0.48 -2.3 1.2 1.2 -1.1 -0.0055 0 0 0 0 0 0 0 5.5e+03 1.2e-06 1e+04 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.55 -0.25 -0.045 -0.93 -0.076 -0.012 -0.84 -0.0026 -1 5.5e+03 2.2 10 1 ++
1 -0.21 0.082 -0.27 -0.87 0.82 0.64 -1 -0.0047 -2.9 5.2e+03 0.57 1e+02 1.1 ++
2 -0.17 0.067 -0.32 -1 1.1 0.87 -1.1 -0.0055 -3.3 5.2e+03 0.035 1e+03 1.1 ++
3 -0.18 0.071 -0.3 -1.1 1.2 0.95 -1.1 -0.0055 -3.3 5.2e+03 0.0086 1e+04 1 ++
4 -0.18 0.072 -0.3 -1.1 1.2 0.96 -1.1 -0.0055 -3.3 5.2e+03 0.00025 1e+05 1 ++
5 -0.18 0.072 -0.3 -1.1 1.2 0.96 -1.1 -0.0055 -3.3 5.2e+03 4.2e-07 1e+05 1 ++
Considering neighbor 4/20 for current solution
Considering neighbor 5/20 for current solution
Attempt 7/100
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S lambda_COST Function Relgrad Radius Rho
0 -0.85 -0.14 -0.67 -1 -0.83 0 0 0 0 0 0 0 1.1 5.6e+03 0.036 10 1 ++
1 -0.85 -0.14 -0.67 -1 -0.83 0 0 0 0 0 0 0 1.1 5.6e+03 0.036 1 -4 -
2 -1 0.19 -0.63 -1.4 -1.2 0 0 0 0 0 0 0 0.084 5.5e+03 0.013 10 0.91 ++
3 -1.1 0.21 -0.8 -1.3 -1.2 0 0 0 0 0 0 0 0.16 5.5e+03 0.00046 1e+02 1 ++
4 -1.1 0.21 -0.8 -1.3 -1.2 0 0 0 0 0 0 0 0.16 5.5e+03 1.4e-05 1e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 0.13 1 0.74 +
1 5.3e+03 0.032 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+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S lambda_COST Function Relgrad Radius Rho
0 -0.38 -0.27 -0.02 -1 -0.33 -0.026 -0.19 0 0 0 0 0 0 0 1 5.9e+03 0.04 10 1 ++
1 -0.38 -0.27 -0.02 -1 -0.33 -0.026 -0.19 0 0 0 0 0 0 0 1 5.9e+03 0.04 5 -1.7e+07 -
2 -0.38 -0.27 -0.02 -1 -0.33 -0.026 -0.19 0 0 0 0 0 0 0 1 5.9e+03 0.04 2.5 -3.2e+02 -
3 -0.38 -0.27 -0.02 -1 -0.33 -0.026 -0.19 0 0 0 0 0 0 0 1 5.9e+03 0.04 1.2 -0.92 -
4 -0.87 0.098 0.035 -2.1 0.81 0.89 -0.84 0 0 0 0 0 0 0 -0.25 5.6e+03 0.03 12 1.1 ++
5 -0.94 0.043 -0.38 -2.5 1.2 1.1 -1.3 0 0 0 0 0 0 0 0.45 5.6e+03 0.012 12 0.63 +
6 -0.91 0.036 -0.43 -2.6 1.2 1.2 -1.2 0 0 0 0 0 0 0 0.24 5.5e+03 0.0016 1.2e+02 1.1 ++
7 -0.91 0.034 -0.45 -2.6 1.2 1.2 -1.2 0 0 0 0 0 0 0 0.17 5.5e+03 0.00016 1.2e+03 1 ++
8 -0.91 0.034 -0.45 -2.6 1.2 1.2 -1.2 0 0 0 0 0 0 0 0.17 5.5e+03 3.3e-07 1.2e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 2.4 10 1 ++
1 5.8e+03 2.4 1.5 -17 -
2 5.6e+03 0.85 1.5 0.71 +
3 5.5e+03 0.11 15 1.1 ++
4 5.5e+03 0.0067 1.5e+02 1.1 ++
5 5.5e+03 0.00029 1.5e+03 1 ++
6 5.5e+03 2.8e-06 1.5e+03 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME lambda_COST lambda_TT Function Relgrad Radius Rho
0 -0.36 -0.19 -0.042 -0.24 -0.66 0.026 -0.0058 0.55 -0.8 -0.0024 -1 1 1.7 5.5e+03 1.9 1 0.77 +
1 -0.51 -0.16 -0.1 -0.65 -1 0.46 0.027 1.5 -0.97 -0.008 -1.3 0.83 0.99 5.1e+03 0.61 10 1.2 ++
2 -0.12 0.065 -0.42 -1.8 -0.98 0.66 0.38 1.9 -1.7 -0.0063 -2.1 -0.47 0.079 5e+03 0.076 10 0.49 +
3 -0.33 0.065 -0.51 -1.9 -1.4 0.7 0.42 2 -1.5 -0.0061 -1.5 -0.27 0.18 4.9e+03 0.04 1e+02 1.1 ++
4 -0.33 0.067 -0.46 -1.9 -1.4 0.7 0.44 2 -1.5 -0.0062 -1.6 -0.019 0.26 4.9e+03 0.0032 1e+03 1.1 ++
5 -0.31 0.062 -0.45 -1.8 -1.4 0.7 0.44 2 -1.5 -0.0062 -1.6 0.037 0.28 4.9e+03 0.00043 1e+04 1 ++
6 -0.31 0.062 -0.45 -1.8 -1.4 0.7 0.44 2 -1.5 -0.0062 -1.6 0.037 0.28 4.9e+03 2.3e-06 1e+04 1 ++
Considering neighbor 4/20 for current solution
*** New pareto solution:
ASC:LUGGAGE-GA;TRAIN_COST_catalog:boxcox;TRAIN_HEADWAY_catalog:with_headway;TRAIN_TT_catalog:boxcox [9810.284944597835, np.float64(9898.944436314554), 13]
Attempt 8/100
Considering neighbor 0/20 for current solution
Attempt 9/100
Biogeme parameters read from biogeme.toml.
Model with 22 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.041 10 1 ++
1 5.5e+03 0.053 1e+02 0.97 ++
2 5.5e+03 0.0043 1e+03 1 ++
3 5.5e+03 0.00016 1e+04 1 ++
4 5.5e+03 2.1e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S lambda_COST Function Relgrad Radius Rho
0 -0.7 -0.38 -0.33 -0.86 -0.94 0.73 -1 0 0 0 0 0 0 0 1 5.4e+03 0.036 10 1 ++
1 -0.7 -0.38 -0.33 -0.86 -0.94 0.73 -1 0 0 0 0 0 0 0 1 5.4e+03 0.036 1.5 -22 -
2 -1 0.068 -0.93 -1.2 -1.3 2.2 -1.4 0 0 0 0 0 0 0 0.17 5.1e+03 0.023 15 1 ++
3 -1.3 0.37 -1.7 -1.5 -1.2 2.2 -1.5 0 0 0 0 0 0 0 -0.18 5.1e+03 0.005 1.5e+02 0.98 ++
4 -1.3 0.38 -1.8 -1.5 -1.2 2.2 -1.5 0 0 0 0 0 0 0 -0.16 5.1e+03 0.00018 1.5e+03 1 ++
5 -1.3 0.38 -1.8 -1.5 -1.2 2.2 -1.5 0 0 0 0 0 0 0 -0.16 5.1e+03 1.1e-06 1.5e+03 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.94 -0.049 -0.63 -0.89 -0.88 -0.0029 0 0 0 0 0 0 0 5.5e+03 2.6 10 1 ++
1 -1.1 0.15 -0.5 -1.3 -0.94 -0.0053 0 0 0 0 0 0 0 5.5e+03 0.33 1e+02 1.1 ++
2 -1.1 0.14 -0.47 -1.4 -0.95 -0.0059 0 0 0 0 0 0 0 5.5e+03 0.012 1e+03 1 ++
3 -1.1 0.14 -0.47 -1.4 -0.95 -0.0059 0 0 0 0 0 0 0 5.5e+03 1.7e-05 1e+03 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.5e+03 2.9 10 1.1 ++
1 5.5e+03 2.9 2 -98 -
2 5.5e+03 2.9 1 0.009 -
3 5.2e+03 0.54 10 1 ++
4 5.2e+03 0.54 1 -6.2 -
5 5.1e+03 0.22 10 0.92 ++
6 5.1e+03 0.034 1e+02 1 ++
7 5.1e+03 0.00087 1e+03 1 ++
8 5.1e+03 2.2e-06 1e+03 1 ++
Considering neighbor 5/20 for current solution
Considering neighbor 6/20 for current solution
Attempt 10/100
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_HEADWAY B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.4 -0.65 0 -0.0026 -0.65 0 0 0 0 0 0 5.7e+03 2.5 10 1.1 ++
1 -0.064 -0.41 0 -0.0047 -1.1 0 0 0 0 0 0 5.6e+03 0.22 1e+02 1.1 ++
2 -0.062 -0.39 0 -0.0052 -1.1 0 0 0 0 0 0 5.6e+03 0.0049 1e+03 1 ++
3 -0.062 -0.39 0 -0.0052 -1.1 0 0 0 0 0 0 5.6e+03 2.4e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -1 -0.91 -0.42 -0.0075 0 0 0 0 0 0 0 5.8e+03 2.5 10 1 ++
1 -0.89 -1.3 -0.91 -0.0058 0 0 0 0 0 0 0 5.6e+03 0.098 1e+02 1 ++
2 -0.91 -1.4 -0.94 -0.0055 0 0 0 0 0 0 0 5.6e+03 0.0014 1e+03 1 ++
3 -0.91 -1.4 -0.94 -0.0055 0 0 0 0 0 0 0 5.6e+03 2.2e-07 1e+03 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 11/100
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.35 -0.24 -0.019 -1 -0.34 -0.026 -0.29 0 0 0 0 0 0 0 5.9e+03 0.039 10 1.1 ++
1 -0.88 0.18 0.015 -2.1 0.81 0.76 -0.86 0 0 0 0 0 0 0 5.6e+03 0.023 1e+02 1.1 ++
2 -0.9 0.16 -0.097 -2.5 1.2 1.1 -0.95 0 0 0 0 0 0 0 5.5e+03 0.0038 1e+03 1.1 ++
3 -0.9 0.16 -0.097 -2.5 1.2 1.1 -0.95 0 0 0 0 0 0 0 5.5e+03 0.00011 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.35 -0.27 -0.022 -0.66 -0.088 -0.015 -0.3 -1 1 5.6e+03 0.057 10 1 ++
1 -0.35 -0.27 -0.022 -0.66 -0.088 -0.015 -0.3 -1 1 5.6e+03 0.057 4.5 -2.9e+05 -
2 -0.35 -0.27 -0.022 -0.66 -0.088 -0.015 -0.3 -1 1 5.6e+03 0.057 2.2 -1.3e+02 -
3 -0.35 -0.27 -0.022 -0.66 -0.088 -0.015 -0.3 -1 1 5.6e+03 0.057 1.1 -4.7 -
4 0.0079 -0.16 -0.75 -1.1 0.83 0.97 -0.86 -1.6 2.1 5.6e+03 0.16 1.1 0.13 +
5 0.0079 -0.16 -0.75 -1.1 0.83 0.97 -0.86 -1.6 2.1 5.6e+03 0.16 0.56 -1.2 -
6 0.12 -0.074 -0.72 -1.1 1.1 0.92 -0.3 -1.6 2.1 5.4e+03 0.016 0.56 0.89 +
7 0.0027 -0.18 -0.7 -1.3 1.1 0.91 -0.56 -1.7 1.5 5.4e+03 0.031 5.6 0.96 ++
8 0.0027 -0.18 -0.7 -1.3 1.1 0.91 -0.56 -1.7 1.5 5.4e+03 0.031 1.1 -1.5 -
9 0.02 0.017 -0.68 -1.3 1.1 0.92 -0.9 -1.6 0.41 5.3e+03 0.0093 11 0.95 ++
10 -0.084 -0.068 -0.65 -1.4 1.2 1 -1.1 -1.5 0.41 5.3e+03 0.00049 1.1e+02 1 ++
11 -0.084 -0.068 -0.65 -1.4 1.2 1 -1.1 -1.5 0.41 5.3e+03 5.2e-06 1.1e+02 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 12/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME Function Relgrad Radius Rho
0 -0.69 0.0066 0.24 0.016 -0.65 -0.72 0.42 0.13 -0.9 -0.63 5.3e+03 0.043 10 1.1 ++
1 -0.56 0.3 0.12 -0.07 -0.58 -1 0.76 0.4 -1.1 -1.1 5.1e+03 0.01 1e+02 1.1 ++
2 -0.56 0.33 0.12 -0.069 -0.68 -1.1 0.94 0.56 -1.1 -1.2 5.1e+03 0.00059 1e+03 1 ++
3 -0.56 0.33 0.12 -0.069 -0.68 -1.1 0.94 0.56 -1.1 -1.2 5.1e+03 4.5e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.36 -0.25 -0.88 0.66 -1 -0.79 5.2e+03 0.047 10 1.1 ++
1 -0.28 -0.28 -1.2 1.9 -1.1 -1.1 5.1e+03 0.014 1e+02 1.1 ++
2 -0.25 -0.3 -1.3 2 -1.1 -1.2 5.1e+03 0.0007 1e+03 1 ++
3 -0.25 -0.3 -1.3 2 -1.1 -1.2 5.1e+03 2.4e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
*** New pareto solution:
ASC:GA;TRAIN_COST_catalog:linear;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:linear [10113.355391622936, np.float64(10154.275157030652), 6]
Attempt 13/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.045 -1 -1 0.15 -0.35 0 0 0 0 0 0 0 5.9e+03 0.076 10 1.1 ++
1 -0.86 -1.1 -2.1 2.2 -1.2 0 0 0 0 0 0 0 5.2e+03 0.031 1e+02 1 ++
2 -0.95 -1.5 -2.3 2.2 -1.5 0 0 0 0 0 0 0 5.2e+03 0.0012 1e+03 1 ++
3 -0.95 -1.5 -2.3 2.2 -1.5 0 0 0 0 0 0 0 5.2e+03 3.9e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.04 10 1.1 ++
1 5.2e+03 0.059 1e+02 1 ++
2 5.2e+03 0.0037 1e+03 1 ++
3 5.2e+03 0.00017 1e+04 1 ++
4 5.2e+03 2e-07 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.34 -0.19 -0.049 -0.7 0.084 -0.0035 -0.88 -0.0014 -1 1.9 5.8e+03 1.7 1 0.62 +
1 -0.32 0.082 -0.34 -1.3 1.1 0.26 -1.1 -0.0032 -1.3 1.2 5.4e+03 0.43 10 1.2 ++
2 -0.32 0.082 -0.34 -1.3 1.1 0.26 -1.1 -0.0032 -1.3 1.2 5.4e+03 0.43 1.5 -11 -
3 -0.32 0.082 -0.34 -1.3 1.1 0.26 -1.1 -0.0032 -1.3 1.2 5.4e+03 0.43 0.75 -0.67 -
4 -0.12 -0.011 -0.46 -1.3 1.1 0.5 -1.2 -0.0021 -1.7 0.48 5.3e+03 0.026 7.5 1 ++
5 -0.19 -0.087 -0.74 -1.2 1.1 1 -1.1 -0.0057 -1.5 0.41 5.3e+03 0.032 75 0.96 ++
6 -0.2 -0.072 -0.72 -1.2 1.2 0.94 -1.1 -0.0055 -1.5 0.38 5.3e+03 0.00036 7.5e+02 1 ++
7 -0.2 -0.072 -0.72 -1.2 1.2 0.94 -1.1 -0.0055 -1.5 0.38 5.3e+03 7.7e-07 7.5e+02 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 14/100
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME Function Relgrad Radius Rho
0 -0.62 0.0095 -0.38 -0.79 -0.93 -0.59 5.4e+03 0.038 10 1.1 ++
1 -0.63 0.34 -0.11 -1.1 -1 -0.98 5.3e+03 0.0068 1e+02 1.1 ++
2 -0.63 0.36 -0.075 -1.2 -1.1 -1 5.3e+03 0.00023 1e+03 1 ++
3 -0.63 0.36 -0.075 -1.2 -1.1 -1 5.3e+03 2.9e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.41 -0.18 -0.32 -0.45 -0.68 0.7 -1 -0.72 1 5.2e+03 0.04 10 1.1 ++
1 -0.76 0.35 -1.5 -0.72 -0.91 1.9 -1.8 -0.99 -0.33 5e+03 0.05 10 0.78 +
2 -0.78 0.48 -1.9 -0.78 -1.1 2 -1.4 -0.98 -0.25 4.9e+03 0.0018 1e+02 1.1 ++
3 -0.79 0.47 -1.9 -0.73 -1.1 2 -1.5 -1 0.1 4.9e+03 0.005 1e+03 0.91 ++
4 -0.79 0.47 -1.9 -0.73 -1.1 2 -1.5 -1 0.1 4.9e+03 6e-05 1e+03 0.99 ++
Considering neighbor 1/20 for current solution
*** New pareto solution:
ASC:MALE-GA;TRAIN_COST_catalog:boxcox;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:linear [9806.611706218542, np.float64(9867.991354330117), 9]
Attempt 15/100
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.38 -0.27 -0.02 -1 -0.33 -0.026 -0.22 0 0 0 0 0 0 0 5.9e+03 0.04 10 1 ++
1 -0.85 0.28 0.22 -2.1 0.8 0.74 -1.8 0 0 0 0 0 0 0 5.5e+03 0.021 1e+02 1.1 ++
2 -0.9 0.24 -0.01 -2.5 1.2 1.1 -2.2 0 0 0 0 0 0 0 5.5e+03 0.0037 1e+03 1.1 ++
3 -0.9 0.24 -0.01 -2.5 1.2 1.1 -2.2 0 0 0 0 0 0 0 5.5e+03 0.00012 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.45 -0.34 -0.03 -0.61 0.037 -0.014 -0.44 -1 1.5 5.7e+03 0.085 1 0.84 +
1 -0.022 0.043 -0.23 -1.1 1 0.12 -1.2 -1.8 0.54 5.3e+03 0.012 10 0.95 ++
2 -0.081 -0.081 -0.67 -1.4 1.1 1.1 -1.1 -1.5 0.43 5.3e+03 0.0022 1e+02 0.98 ++
3 -0.081 -0.081 -0.67 -1.4 1.1 1.1 -1.1 -1.5 0.43 5.3e+03 8.8e-05 1e+02 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.46 -0.27 -0.68 0.55 -0.74 -0.0021 -1 1.8 5.6e+03 2 1 0.69 +
1 -0.47 -0.74 -0.93 1.6 -1.2 -0.0064 -0.93 1.3 5.1e+03 0.24 10 1.1 ++
2 -0.47 -0.74 -0.93 1.6 -1.2 -0.0064 -0.93 1.3 5.1e+03 0.24 4.9 -2.4e+03 -
3 -0.47 -0.74 -0.93 1.6 -1.2 -0.0064 -0.93 1.3 5.1e+03 0.24 2.5 -34 -
4 -0.47 -0.74 -0.93 1.6 -1.2 -0.0064 -0.93 1.3 5.1e+03 0.24 1.2 -2.9 -
5 -0.15 -1.4 -1.1 2.5 -1.6 -0.0028 -1.9 0.12 5e+03 0.048 1.2 0.78 +
6 -0.28 -1.8 -0.87 2.2 -1.5 -0.0064 -1.6 0.24 4.9e+03 0.0097 12 0.98 ++
7 -0.28 -1.9 -0.9 2.2 -1.5 -0.0061 -1.6 0.27 4.9e+03 0.00021 1.2e+02 1 ++
8 -0.28 -1.9 -0.9 2.2 -1.5 -0.0061 -1.6 0.27 4.9e+03 5.5e-07 1.2e+02 1 ++
Considering neighbor 2/20 for current solution
*** New pareto solution:
ASC:GA;TRAIN_COST_catalog:log;TRAIN_HEADWAY_catalog:with_headway;TRAIN_TT_catalog:boxcox [9857.857591119024, np.float64(9912.417278329312), 8]
Attempt 16/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.78 -0.29 -0.18 -0.72 -0.9 1 -0.94 -0.0035 0 0 0 0 0 0 0 5.3e+03 2.9 10 1.1 ++
1 -1.2 0.25 -0.14 -1 -1.1 1.9 -0.99 -0.0058 0 0 0 0 0 0 0 5.2e+03 0.61 1e+02 1.1 ++
2 -1.2 0.25 -0.14 -1 -1.2 2.1 -1 -0.0068 0 0 0 0 0 0 0 5.2e+03 0.05 1e+03 1.1 ++
3 -1.2 0.25 -0.14 -1 -1.2 2.1 -1 -0.007 0 0 0 0 0 0 0 5.2e+03 0.00038 1e+04 1 ++
4 -1.2 0.25 -0.14 -1 -1.2 2.1 -1 -0.007 0 0 0 0 0 0 0 5.2e+03 2.2e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 0.038 10 1 ++
1 5.6e+03 0.038 0.95 -1.8 -
2 5.4e+03 0.021 9.5 0.92 ++
3 5.4e+03 0.00087 95 0.97 ++
4 5.4e+03 2.2e-05 95 0.99 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME lambda_COST lambda_TT Function Relgrad Radius Rho
0 -0.27 -0.004 -0.55 -0.88 -1 0.006 -0.9 1 1.8 5.6e+03 2.2 1 0.78 +
1 -0.68 0.34 0.45 -1.1 -0.97 -0.0078 -1.1 0.49 1.3 5.3e+03 0.48 10 1.1 ++
2 -0.68 0.34 0.45 -1.1 -0.97 -0.0078 -1.1 0.49 1.3 5.3e+03 0.48 1 -2.3 -
3 -0.54 0.46 0.52 -1.1 -1.3 -0.0039 -1.9 0.69 0.27 5.2e+03 0.13 1 0.68 +
4 -0.51 0.31 0.57 -1.3 -1.1 -0.0058 -1.6 0.62 0.36 5.2e+03 0.014 10 0.99 ++
5 -0.51 0.31 0.57 -1.3 -1.1 -0.0058 -1.6 0.62 0.36 5.2e+03 7.2e-05 10 0.98 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME lambda_COST lambda_TT Function Relgrad Radius Rho
0 -0.19 0.048 -0.13 -0.045 -0.55 -0.89 0.14 -0.0037 -1 0.0058 -0.93 1 1.8 5.6e+03 2.2 1 0.82 +
1 -0.5 0.3 0.032 -0.18 -0.16 -1.2 1.1 0.11 -1.1 -0.0043 -1.5 0.62 0.9 5.2e+03 1.2 10 1 ++
2 -0.36 0.31 -0.078 -0.52 -0.026 -1.1 0.96 0.52 -1.1 -0.0057 -1.9 0.62 0.35 5.1e+03 0.05 1e+02 0.95 ++
3 -0.47 0.28 -0.057 -0.52 -0.21 -1.1 0.96 0.51 -1.1 -0.0059 -1.6 0.53 0.37 5.1e+03 0.0038 1e+03 0.97 ++
4 -0.47 0.28 -0.057 -0.52 -0.21 -1.1 0.96 0.51 -1.1 -0.0059 -1.6 0.53 0.37 5.1e+03 2.3e-05 1e+03 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 17/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.88 -0.05 -0.77 -0.89 -0.88 0 0 0 0 0 0 0 5.5e+03 0.035 10 1 ++
1 -0.95 0.15 -0.75 -1.3 -0.94 0 0 0 0 0 0 0 5.5e+03 0.0059 1e+02 1.1 ++
2 -0.95 0.14 -0.75 -1.3 -0.95 0 0 0 0 0 0 0 5.5e+03 0.00018 1e+03 1 ++
3 -0.95 0.14 -0.75 -1.3 -0.95 0 0 0 0 0 0 0 5.5e+03 1.5e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.35 -0.24 -0.019 -1 -0.34 -0.026 -0.29 0 0 0 0 0 0 0 5.9e+03 0.039 10 1.1 ++
1 -0.88 0.18 0.015 -2.1 0.81 0.76 -0.86 0 0 0 0 0 0 0 5.6e+03 0.023 1e+02 1.1 ++
2 -0.9 0.16 -0.097 -2.5 1.2 1.1 -0.95 0 0 0 0 0 0 0 5.5e+03 0.0038 1e+03 1.1 ++
3 -0.9 0.16 -0.097 -2.5 1.2 1.1 -0.95 0 0 0 0 0 0 0 5.5e+03 0.00011 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -1 -0.032 -0.63 -0.9 -0.97 -0.003 0 0 0 0 0 0 0 5.6e+03 2.7 10 1.1 ++
1 -1.2 0.23 -0.55 -1.3 -1.1 -0.0052 0 0 0 0 0 0 0 5.5e+03 0.33 1e+02 1.1 ++
2 -1.2 0.23 -0.53 -1.3 -1.1 -0.0059 0 0 0 0 0 0 0 5.5e+03 0.013 1e+03 1 ++
3 -1.2 0.23 -0.53 -1.3 -1.1 -0.0059 0 0 0 0 0 0 0 5.5e+03 1.8e-05 1e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever ASC_TRAIN_with_ B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.42 -0.32 -0.029 -0.19 -0.62 0.017 -0.015 0.36 -0.41 -1 1.5 5.7e+03 0.088 1 0.85 +
1 0.16 0.092 -0.078 -0.39 -1.2 0.38 0.0051 1.3 -1.4 -1.3 0.9 5.1e+03 0.032 10 0.95 ++
2 -0.002 0.048 -0.35 0.95 -1.4 0.67 0.49 1.9 -2.6 -1.9 0.22 4.9e+03 0.0098 1e+02 0.97 ++
3 -0.15 0.048 -0.36 1.2 -1.6 0.71 0.49 1.9 -2.8 -1.6 0.31 4.9e+03 0.001 1e+03 1 ++
4 -0.15 0.048 -0.36 1.2 -1.6 0.71 0.49 1.9 -2.8 -1.6 0.31 4.9e+03 2.6e-05 1e+03 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.97 -0.0023 -0.81 -0.86 -0.97 0 0 0 0 0 0 0 5.6e+03 0.036 10 1.1 ++
1 -1.1 0.23 -0.81 -1.3 -1.1 0 0 0 0 0 0 0 5.5e+03 0.0062 1e+02 1.1 ++
2 -1.1 0.23 -0.81 -1.3 -1.1 0 0 0 0 0 0 0 5.5e+03 0.0002 1e+03 1 ++
3 -1.1 0.23 -0.81 -1.3 -1.1 0 0 0 0 0 0 0 5.5e+03 1.9e-07 1e+03 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 2.5 10 1 ++
1 5.2e+03 1 1e+02 1 ++
2 5.2e+03 0.088 1e+03 1.1 ++
3 5.2e+03 0.0024 1e+04 1 ++
4 5.2e+03 2e-06 1e+04 1 ++
Considering neighbor 5/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.49 -0.25 -0.4 -0.64 -0.89 -0.91 2 5.9e+03 0.31 1 0.57 +
1 -0.89 0.19 -0.48 -1.6 -0.93 -0.11 1.9 5.5e+03 0.037 1 0.83 +
2 -0.79 0.26 -0.33 -1 -1.4 -0.56 0.92 5.4e+03 0.052 1 0.77 +
3 -0.53 0.34 0.051 -1.2 -1 -1.3 0.15 5.2e+03 0.015 10 1.1 ++
4 -0.43 0.36 0.24 -1.3 -1.1 -1.6 0.45 5.2e+03 0.0062 10 0.68 +
5 -0.44 0.35 0.23 -1.3 -1.1 -1.5 0.35 5.2e+03 0.00068 1e+02 1.1 ++
6 -0.44 0.35 0.23 -1.3 -1.1 -1.5 0.35 5.2e+03 5.1e-06 1e+02 1 ++
Considering neighbor 6/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 7/20 for current solution
Considering neighbor 8/20 for current solution
Attempt 18/100
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.76 0.094 -0.22 -0.78 -0.87 -0.0029 -0.62 2 5.6e+03 2.2 10 0.94 ++
1 -0.76 0.094 -0.22 -0.78 -0.87 -0.0029 -0.62 2 5.6e+03 2.2 4.2 -1.5e+05 -
2 -0.76 0.094 -0.22 -0.78 -0.87 -0.0029 -0.62 2 5.6e+03 2.2 2.1 -1.3e+02 -
3 -0.76 0.094 -0.22 -0.78 -0.87 -0.0029 -0.62 2 5.6e+03 2.2 1.1 -10 -
4 -0.76 0.094 -0.22 -0.78 -0.87 -0.0029 -0.62 2 5.6e+03 2.2 0.53 -0.41 -
5 -0.59 0.36 0.11 -0.97 -0.59 -0.0036 -1.1 1.8 5.3e+03 0.44 5.3 1.1 ++
6 -0.59 0.36 0.11 -0.97 -0.59 -0.0036 -1.1 1.8 5.3e+03 0.44 2.6 -6e+02 -
7 -0.59 0.36 0.11 -0.97 -0.59 -0.0036 -1.1 1.8 5.3e+03 0.44 1.3 -5.2 -
8 -0.66 0.32 0.56 -1.5 -1.2 -0.0063 -1.2 0.46 5.2e+03 0.22 1.3 0.61 +
9 -0.71 0.32 0.28 -1.3 -1.1 -0.0058 -1.1 0.58 5.2e+03 0.015 13 1 ++
10 -0.71 0.32 0.28 -1.3 -1.1 -0.0058 -1.1 0.58 5.2e+03 5.9e-05 13 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 19/100
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST lambda_TT Function Relgrad Radius Rho
0 -0.18 0.11 -0.55 -0.94 0 0.0077 -0.82 0 0 0 0 0 0 2 6e+03 2.7 1 0.65 +
1 -0.18 0.11 -0.55 -0.94 0 0.0077 -0.82 0 0 0 0 0 0 2 6e+03 2.7 0.5 -0.62 -
2 -0.43 0.11 -0.047 -0.9 0 -0.006 -0.46 0 0 0 0 0 0 1.8 5.6e+03 0.32 5 1 ++
3 -0.43 0.11 -0.047 -0.9 0 -0.006 -0.46 0 0 0 0 0 0 1.8 5.6e+03 0.32 2.5 -22 -
4 -0.43 0.11 -0.047 -0.9 0 -0.006 -0.46 0 0 0 0 0 0 1.8 5.6e+03 0.32 1.2 -0.83 -
5 -0.27 0.54 -0.078 -1.4 0 0.004 -1.2 0 0 0 0 0 0 0.54 5.5e+03 0.41 1.2 0.78 +
6 -0.23 0.32 0.72 -1.2 0 -0.0061 -1.5 0 0 0 0 0 0 0.34 5.4e+03 0.19 12 0.95 ++
7 -0.21 0.31 0.69 -1.3 0 -0.0057 -1.6 0 0 0 0 0 0 0.39 5.4e+03 0.0054 1.2e+02 1 ++
8 -0.21 0.31 0.69 -1.3 0 -0.0057 -1.6 0 0 0 0 0 0 0.39 5.4e+03 3.1e-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 b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S lambda_COST Function Relgrad Radius Rho
0 -1 -0.046 -0.57 -0.94 -0.84 -0.0031 0 0 0 0 0 0 0 1.3 5.6e+03 2.6 10 1 ++
1 -1 -0.046 -0.57 -0.94 -0.84 -0.0031 0 0 0 0 0 0 0 1.3 5.6e+03 2.6 2.1 -2.1e+02 -
2 -1 -0.046 -0.57 -0.94 -0.84 -0.0031 0 0 0 0 0 0 0 1.3 5.6e+03 2.6 1 -2.6 -
3 -1.3 0.078 -0.39 -1.5 -1.3 -0.0064 0 0 0 0 0 0 0 0.24 5.5e+03 0.58 10 1 ++
4 -1.2 0.22 -0.51 -1.4 -1.1 -0.0059 0 0 0 0 0 0 0 0.19 5.5e+03 0.022 1e+02 0.97 ++
5 -1.2 0.22 -0.51 -1.4 -1.1 -0.0059 0 0 0 0 0 0 0 0.19 5.5e+03 5.2e-05 1e+02 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 20 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 20/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever ASC_TRAIN_with_ B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.4 -0.3 -0.026 -0.17 -0.64 -0.021 -0.016 0.32 -0.58 -1 1.4 5.5e+03 0.068 10 0.91 ++
1 -0.4 -0.3 -0.026 -0.17 -0.64 -0.021 -0.016 0.32 -0.58 -1 1.4 5.5e+03 0.068 5 -7.7e+02 -
2 -0.4 -0.3 -0.026 -0.17 -0.64 -0.021 -0.016 0.32 -0.58 -1 1.4 5.5e+03 0.068 2.5 -17 -
3 -0.4 -0.3 -0.026 -0.17 -0.64 -0.021 -0.016 0.32 -0.58 -1 1.4 5.5e+03 0.068 1.2 -1 -
4 -0.13 0.0032 -0.078 -0.38 -1.3 0.52 0.0085 1.6 -1.2 -1.6 0.6 5e+03 0.0097 12 1 ++
5 -0.067 0.027 -0.3 -0.29 -1.4 0.68 0.52 1.8 -1.1 -1.7 0.37 5e+03 0.0013 1.2e+02 0.99 ++
6 -0.067 0.027 -0.3 -0.29 -1.4 0.68 0.52 1.8 -1.1 -1.7 0.37 5e+03 1.5e-05 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 21/100
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 20 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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+03 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 22/100
Biogeme parameters read from biogeme.toml.
Model with 5 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.79 -0.93 -0.68 -0.0036 -1 5.5e+03 2.3 10 1 ++
1 -0.11 -0.36 -0.97 -0.0045 -3 5.3e+03 0.086 1e+02 1.1 ++
2 -0.11 -0.25 -1.1 -0.0052 -3.3 5.3e+03 0.003 1e+03 1 ++
3 -0.11 -0.23 -1.1 -0.0053 -3.4 5.3e+03 0.0021 1e+04 1 ++
4 -0.11 -0.23 -1.1 -0.0053 -3.4 5.3e+03 0.00013 1e+05 1 ++
5 -0.11 -0.23 -1.1 -0.0053 -3.4 5.3e+03 2.2e-08 1e+05 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
ASC:no_seg;TRAIN_COST_catalog:linear;TRAIN_HEADWAY_catalog:with_headway;TRAIN_TT_catalog:sqrt [10562.742717754141, np.float64(10596.842522260571), 5]
Attempt 23/100
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 3 10 1 ++
1 5.6e+03 0.41 1e+02 1.1 ++
2 5.6e+03 0.034 1e+03 1.1 ++
3 5.6e+03 0.00026 1e+04 1 ++
4 5.6e+03 2.1e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 24/100
Biogeme parameters read from biogeme.toml.
Model with 19 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 19 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.8e+03 2.6 10 1 ++
1 5.5e+03 0.94 1e+02 1 ++
2 5.5e+03 0.089 1e+03 1.1 ++
3 5.5e+03 0.0022 1e+04 1 ++
4 5.5e+03 1.6e-06 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.43 -0.12 -0.45 -0.7 0 -1 0 0 0 0 0 0 5.6e+03 0.044 10 1 ++
1 -0.13 0.31 0.32 -1.1 0 -2.8 0 0 0 0 0 0 5.4e+03 0.013 1e+02 1 ++
2 -0.12 0.32 0.38 -1.2 0 -3 0 0 0 0 0 0 5.4e+03 0.00038 1e+03 1 ++
3 -0.12 0.32 0.38 -1.2 0 -3 0 0 0 0 0 0 5.4e+03 3.7e-07 1e+03 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 25/100
Considering neighbor 0/20 for current solution
Attempt 26/100
Considering neighbor 0/20 for current solution
Attempt 27/100
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME lambda_COST lambda_TT Function Relgrad Radius Rho
0 -0.35 -0.19 -0.046 -0.68 0.065 -0.004 -0.88 -0.0015 -1 1 1.8 5.7e+03 1.6 1 0.7 +
1 -0.33 -0.04 -0.16 -0.96 1.1 0.085 -1 -0.0064 -1.4 0.73 1.1 5.3e+03 0.57 10 1.1 ++
2 0.05 -0.1 -0.7 -0.74 1.1 1 -1.1 -0.0056 -2.1 0.7 0.28 5.3e+03 0.069 10 0.69 +
3 -0.2 -0.059 -0.63 -1.2 1.2 0.95 -1.1 -0.0055 -1.5 0.55 0.38 5.2e+03 0.0064 1e+02 0.97 ++
4 -0.2 -0.062 -0.62 -1.1 1.2 0.95 -1.1 -0.0055 -1.6 0.55 0.44 5.2e+03 0.00054 1e+03 0.96 ++
5 -0.2 -0.062 -0.62 -1.1 1.2 0.95 -1.1 -0.0055 -1.6 0.55 0.44 5.2e+03 1.1e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 28/100
Considering neighbor 0/20 for current solution
Attempt 29/100
Considering neighbor 0/20 for current solution
Attempt 30/100
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 2.9 1 0.66 +
1 5.7e+03 0.71 1 0.43 +
2 5.7e+03 0.71 0.5 -1.9 -
3 5.7e+03 0.71 0.25 -0.21 -
4 5.7e+03 0.099 0.25 0.31 +
5 5.6e+03 0.04 2.5 1.1 ++
6 5.6e+03 0.04 1.2 -0.013 -
7 5.4e+03 0.27 1.2 0.41 +
8 5.3e+03 0.014 12 1 ++
9 5.3e+03 0.011 12 0.74 +
10 5.3e+03 0.00051 1.2e+02 1 ++
11 5.3e+03 2.2e-06 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 0.29 1 0.57 +
1 6e+03 0.29 0.5 -0.17 -
2 5.6e+03 0.051 5 0.91 ++
3 5.6e+03 0.051 2.5 -12 -
4 5.6e+03 0.051 1.2 -0.22 -
5 5.4e+03 0.037 1.2 0.85 +
6 5.3e+03 0.0087 1.2 0.89 +
7 5.3e+03 0.00037 12 0.99 ++
8 5.3e+03 2.9e-06 12 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S lambda_COST Function Relgrad Radius Rho
0 -1 -0.91 -0.22 -0.0073 0 0 0 0 0 0 0 1 5.9e+03 2.6 10 1 ++
1 -1 -0.91 -0.22 -0.0073 0 0 0 0 0 0 0 1 5.9e+03 2.6 4.5 -2.8e+05 -
2 -1 -0.91 -0.22 -0.0073 0 0 0 0 0 0 0 1 5.9e+03 2.6 2.2 -1.6e+02 -
3 -1 -0.91 -0.22 -0.0073 0 0 0 0 0 0 0 1 5.9e+03 2.6 1.1 -7.3 -
4 -1 -0.91 -0.22 -0.0073 0 0 0 0 0 0 0 1 5.9e+03 2.6 0.56 -0.51 -
5 -0.85 -1.2 -0.78 -0.0068 0 0 0 0 0 0 0 1 5.7e+03 0.032 5.6 0.97 ++
6 -0.85 -1.2 -0.78 -0.0068 0 0 0 0 0 0 0 1 5.7e+03 0.032 1 -8.7 -
7 -0.99 -1.7 -1.4 -0.0027 0 0 0 0 0 0 0 -0.021 5.7e+03 0.086 1 0.45 +
8 -0.98 -1.4 -1.1 -0.0055 0 0 0 0 0 0 0 0.077 5.6e+03 0.025 10 1 ++
9 -0.99 -1.4 -1.1 -0.0055 0 0 0 0 0 0 0 0.17 5.6e+03 0.00033 1e+02 1 ++
10 -0.99 -1.4 -1.1 -0.0055 0 0 0 0 0 0 0 0.17 5.6e+03 8.7e-07 1e+02 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 2.7 10 1 ++
1 5.6e+03 2.7 1.9 -91 -
2 5.6e+03 2.7 0.94 -1.1 -
3 5.4e+03 0.91 9.4 0.96 ++
4 5.4e+03 0.049 94 1 ++
5 5.4e+03 0.00026 9.4e+02 1 ++
6 5.4e+03 2.9e-08 9.4e+02 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 2.7 10 1 ++
1 5.6e+03 2.7 1.9 -91 -
2 5.6e+03 2.7 0.94 -1.1 -
3 5.4e+03 0.91 9.4 0.96 ++
4 5.4e+03 0.049 94 1 ++
5 5.4e+03 0.00026 9.4e+02 1 ++
6 5.4e+03 2.9e-08 9.4e+02 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 2.6 10 1 ++
1 5.8e+03 0.11 1e+02 1 ++
2 5.8e+03 0.0017 1e+03 1 ++
3 5.8e+03 1.9e-07 1e+03 1 ++
Considering neighbor 5/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 22 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 0.039 10 1 ++
1 5.6e+03 0.0083 1e+02 1.1 ++
2 5.6e+03 0.00067 1e+03 1 ++
3 5.6e+03 5.4e-06 1e+03 1 ++
Considering neighbor 6/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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+02 1 ++
Considering neighbor 7/20 for current solution
Considering neighbor 8/20 for current solution
Attempt 31/100
Considering neighbor 0/20 for current solution
Attempt 32/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 2.9 1 0.66 +
1 5.7e+03 0.71 1 0.43 +
2 5.7e+03 0.71 0.5 -1.9 -
3 5.7e+03 0.71 0.25 -0.21 -
4 5.7e+03 0.099 0.25 0.31 +
5 5.6e+03 0.04 2.5 1.1 ++
6 5.6e+03 0.04 1.2 -0.013 -
7 5.4e+03 0.27 1.2 0.41 +
8 5.3e+03 0.014 12 1 ++
9 5.3e+03 0.011 12 0.74 +
10 5.3e+03 0.00051 1.2e+02 1 ++
11 5.3e+03 2.2e-06 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 21 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST lambda_TT Function Relgrad Radius Rho
0 -0.41 -0.19 -0.31 -0.41 -0.64 0.67 0 -0.87 0 0 0 0 0 0 2 5.9e+03 0.3 1 0.57 +
1 -0.36 0.26 -0.8 -0.62 -1.2 1.7 0 -0.43 0 0 0 0 0 0 1.8 5.3e+03 0.052 10 0.96 ++
2 -0.36 0.26 -0.8 -0.62 -1.2 1.7 0 -0.43 0 0 0 0 0 0 1.8 5.3e+03 0.052 5 -1.9e+03 -
3 -0.36 0.26 -0.8 -0.62 -1.2 1.7 0 -0.43 0 0 0 0 0 0 1.8 5.3e+03 0.052 2.5 -14 -
4 -0.36 0.26 -0.8 -0.62 -1.2 1.7 0 -0.43 0 0 0 0 0 0 1.8 5.3e+03 0.052 1.2 -0.35 -
5 -0.37 0.38 -0.97 -0.8 -1.2 1.7 0 -1 0 0 0 0 0 0 0.52 5.2e+03 0.032 1.2 0.88 +
6 -0.12 0.42 -1.4 -0.056 -1.1 1.7 0 -1.6 0 0 0 0 0 0 0.2 5.1e+03 0.0062 12 0.93 ++
7 -0.12 0.42 -1.5 -0.069 -1.1 1.8 0 -1.6 0 0 0 0 0 0 0.3 5.1e+03 0.00045 1.2e+02 0.98 ++
8 -0.12 0.42 -1.5 -0.069 -1.1 1.8 0 -1.6 0 0 0 0 0 0 0.3 5.1e+03 2.4e-06 1.2e+02 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.31 -0.1 -0.23 -0.046 -0.43 -0.72 0.31 -0.011 -0.84 -0.9 2 5.8e+03 0.31 1 0.6 +
1 -0.41 0.11 0.024 -0.11 -0.69 -1.3 0.68 0.032 -1.8 -0.7 1.6 5.2e+03 0.067 10 1.1 ++
2 -0.41 0.11 0.024 -0.11 -0.69 -1.3 0.68 0.032 -1.8 -0.7 1.6 5.2e+03 0.067 5 -4e+03 -
3 -0.41 0.11 0.024 -0.11 -0.69 -1.3 0.68 0.032 -1.8 -0.7 1.6 5.2e+03 0.067 2.5 -24 -
4 -0.41 0.11 0.024 -0.11 -0.69 -1.3 0.68 0.032 -1.8 -0.7 1.6 5.2e+03 0.067 1.2 -1.4 -
5 -0.45 0.24 0.3 -0.13 -0.77 -1.2 0.78 0.11 -2.2 -1.4 0.35 5.1e+03 0.033 1.2 0.78 +
6 -0.4 0.36 0.18 -0.019 -0.45 -1.1 1 0.71 -2.4 -1.7 0.43 5e+03 0.0049 12 0.91 ++
7 -0.4 0.36 0.18 -0.019 -0.45 -1.1 1 0.71 -2.4 -1.7 0.43 5e+03 0.0001 12 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 3.1 1 0.67 +
1 5.4e+03 0.45 10 0.97 ++
2 5.4e+03 0.45 5 -1.4e+03 -
3 5.4e+03 0.45 2.5 -13 -
4 5.4e+03 0.45 1.2 -0.47 -
5 5.2e+03 0.13 1.2 0.86 +
6 5.1e+03 0.015 1.2 0.88 +
7 5.1e+03 0.0033 12 0.95 ++
8 5.1e+03 2.4e-05 12 1 ++
Considering neighbor 5/20 for current solution
Considering neighbor 6/20 for current solution
Attempt 33/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME lambda_COST lambda_TT Function Relgrad Radius Rho
0 -0.51 -0.28 -0.32 -0.45 -0.67 0.68 -0.96 -0.96 1 2 5.7e+03 0.33 1 0.6 +
1 -0.49 0.16 -0.78 -0.68 -1.3 1.7 -0.84 -0.62 0.66 1.7 5.1e+03 0.067 10 1.1 ++
2 -0.49 0.16 -0.78 -0.68 -1.3 1.7 -0.84 -0.62 0.66 1.7 5.1e+03 0.067 5 -3.1e+03 -
3 -0.49 0.16 -0.78 -0.68 -1.3 1.7 -0.84 -0.62 0.66 1.7 5.1e+03 0.067 2.5 -28 -
4 -0.49 0.16 -0.78 -0.68 -1.3 1.7 -0.84 -0.62 0.66 1.7 5.1e+03 0.067 1.2 -0.62 -
5 -0.6 0.3 -1.1 -0.85 -1.2 1.9 -1.6 -1.3 0.41 0.4 4.9e+03 0.033 1.2 0.9 +
6 -0.55 0.46 -1.8 -0.36 -1.1 2.1 -1.5 -1.6 0.12 0.22 4.8e+03 0.0056 12 0.99 ++
7 -0.56 0.48 -2 -0.38 -1.1 2.1 -1.5 -1.6 0.032 0.22 4.8e+03 0.00028 1.2e+02 1 ++
8 -0.56 0.48 -2 -0.38 -1.1 2.1 -1.5 -1.6 0.032 0.22 4.8e+03 2.1e-06 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
ASC:MALE-GA;TRAIN_COST_catalog:boxcox;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:boxcox [9665.916013944798, np.float64(9734.115622957659), 10]
Attempt 34/100
Considering neighbor 0/20 for current solution
Attempt 35/100
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.16 -0.055 -0.057 -0.33 -0.8 0.19 -0.0025 0.9 -1 -0.0018 -0.84 5.2e+03 2.7 10 1.1 ++
1 -0.46 0.093 -0.22 0.99 -1.4 0.52 0.38 1.7 -2.5 -0.0049 -1 5e+03 0.63 1e+02 1.1 ++
2 -0.5 0.1 -0.31 1.2 -1.5 0.69 0.54 1.9 -2.8 -0.0062 -1.1 5e+03 0.054 1e+03 1.1 ++
3 -0.5 0.1 -0.31 1.2 -1.5 0.72 0.56 1.9 -2.8 -0.0063 -1.1 5e+03 0.00056 1e+04 1 ++
4 -0.5 0.1 -0.31 1.2 -1.5 0.72 0.56 1.9 -2.8 -0.0063 -1.1 5e+03 9.2e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.29 -0.15 -0.05 -0.72 0.11 -0.0034 -0.8 -0.0012 -0.97 2 5.9e+03 1.9 1 0.55 +
1 -0.26 0.0067 -0.11 -0.93 0.75 0.059 -1.8 -0.008 -0.68 1.7 5.4e+03 0.082 10 1 ++
2 -0.26 0.0067 -0.11 -0.93 0.75 0.059 -1.8 -0.008 -0.68 1.7 5.4e+03 0.082 4.1 -6.6e+02 -
3 -0.26 0.0067 -0.11 -0.93 0.75 0.059 -1.8 -0.008 -0.68 1.7 5.4e+03 0.082 2 -12 -
4 -0.26 0.0067 -0.11 -0.93 0.75 0.059 -1.8 -0.008 -0.68 1.7 5.4e+03 0.082 1 0.069 -
5 -0.24 0.33 -0.15 -1.3 0.83 0.16 -2.1 -0.00037 -1.5 0.72 5.2e+03 0.19 10 0.97 ++
6 -0.14 0.16 -0.31 -1 1.1 1.1 -2.3 -0.0057 -1.7 0.43 5.1e+03 0.12 1e+02 0.93 ++
7 -0.17 0.16 -0.23 -1.1 1.2 0.95 -2.4 -0.0055 -1.6 0.46 5.1e+03 0.0019 1e+03 1 ++
8 -0.17 0.16 -0.23 -1.1 1.2 0.95 -2.4 -0.0055 -1.6 0.46 5.1e+03 1.6e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 36/100
Biogeme parameters read from biogeme.toml.
Model with 5 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.75 -0.93 -0.55 -0.0031 -1 5.6e+03 2.4 10 1 ++
1 -0.35 -0.42 -1.1 -0.0065 -2.6 5.4e+03 0.1 1e+02 1 ++
2 -0.23 -0.39 -1 -0.0054 -2.9 5.4e+03 0.0029 1e+03 1 ++
3 -0.24 -0.39 -1 -0.0054 -2.9 5.4e+03 0.007 1e+04 1 ++
4 -0.24 -0.39 -1 -0.0054 -2.9 5.4e+03 0.00011 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_HEADWAY B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.48 -0.53 -0.31 -0.0036 -1 1 5.6e+03 2.4 10 1 ++
1 -0.15 -0.31 -0.84 -0.005 -1.6 1.1 5.4e+03 0.058 1e+02 1 ++
2 -0.15 -0.31 -0.84 -0.005 -1.6 1.1 5.4e+03 0.058 0.51 0.089 -
3 -0.17 -0.28 -1 -0.0056 -1.6 0.57 5.4e+03 0.0041 5.1 0.99 ++
4 -0.22 -0.34 -1.1 -0.0054 -1.6 0.39 5.4e+03 0.0018 5.1 0.9 +
5 -0.22 -0.34 -1.1 -0.0054 -1.6 0.39 5.4e+03 3.5e-05 5.1 1 +
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.84 -0.24 -0.95 0.39 -0.47 -0.0042 -1 1 5.5e+03 2.5 10 1 ++
1 -0.84 -0.24 -0.95 0.39 -0.47 -0.0042 -1 1 5.5e+03 2.5 1.6 -7.6 -
2 -0.5 -0.7 -1.3 2 -1.2 -0.0031 -2 0.85 5e+03 0.72 16 1 ++
3 -0.5 -0.7 -1.3 2 -1.2 -0.0031 -2 0.85 5e+03 0.72 0.63 -0.076 -
4 -0.36 -0.92 -1 2.2 -1.3 -0.0065 -2.4 0.23 5e+03 0.035 6.3 1.1 ++
5 -0.32 -1.7 -0.95 2.1 -1.5 -0.0062 -2.9 0.065 4.9e+03 0.034 63 1 ++
6 -0.31 -1.8 -0.94 2.1 -1.5 -0.0062 -3 0.094 4.9e+03 0.0017 6.3e+02 1 ++
7 -0.31 -1.8 -0.94 2.1 -1.5 -0.0062 -3 0.094 4.9e+03 7.4e-06 6.3e+02 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.11 -1 -0.38 0 0 0 0 0 0 0 6e+03 0.074 10 1.1 ++
1 -0.75 -1.6 -0.83 0 0 0 0 0 0 0 5.6e+03 0.0058 1e+02 1 ++
2 -0.8 -1.7 -0.94 0 0 0 0 0 0 0 5.6e+03 0.00016 1e+03 1 ++
3 -0.8 -1.7 -0.94 0 0 0 0 0 0 0 5.6e+03 1.4e-07 1e+03 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 37/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.17 0.09 -0.12 -0.049 -0.57 -0.95 0.17 -0.0032 -1 0.0067 -0.94 1.9 5.7e+03 2.4 1 0.75 +
1 -0.79 0.55 0.25 -0.39 -0.34 -1.3 1.2 0.25 -1.1 -0.0025 -1.6 0.89 5.2e+03 0.86 10 1 ++
2 -0.38 0.33 -0.095 -0.66 -0.091 -1.1 0.94 0.51 -1.1 -0.0059 -1.8 0.39 5.2e+03 0.014 1e+02 1.1 ++
3 -0.49 0.31 -0.068 -0.62 -0.26 -1.1 0.96 0.5 -1.1 -0.0059 -1.5 0.35 5.1e+03 0.0012 1e+03 1 ++
4 -0.49 0.31 -0.068 -0.62 -0.26 -1.1 0.96 0.5 -1.1 -0.0059 -1.5 0.35 5.1e+03 5.7e-05 1e+03 0.99 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 38/100
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.25 0.0035 -0.22 -0.6 0 -0.0001 -1 0 0 0 0 0 0 5.5e+03 2.7 10 1 ++
1 -0.15 0.27 0.67 -1.2 0 -0.0046 -1.5 0 0 0 0 0 0 5.4e+03 0.46 1e+02 1 ++
2 -0.17 0.27 0.72 -1.3 0 -0.0056 -1.6 0 0 0 0 0 0 5.4e+03 0.021 1e+03 1 ++
3 -0.17 0.27 0.72 -1.3 0 -0.0056 -1.6 0 0 0 0 0 0 5.4e+03 4.4e-05 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST lambda_TT Function Relgrad Radius Rho
0 -0.18 0.11 -0.55 -0.94 0 0.0077 -0.82 0 0 0 0 0 0 2 6e+03 2.7 1 0.65 +
1 -0.18 0.11 -0.55 -0.94 0 0.0077 -0.82 0 0 0 0 0 0 2 6e+03 2.7 0.5 -0.62 -
2 -0.43 0.11 -0.047 -0.9 0 -0.006 -0.46 0 0 0 0 0 0 1.8 5.6e+03 0.32 5 1 ++
3 -0.43 0.11 -0.047 -0.9 0 -0.006 -0.46 0 0 0 0 0 0 1.8 5.6e+03 0.32 2.5 -22 -
4 -0.43 0.11 -0.047 -0.9 0 -0.006 -0.46 0 0 0 0 0 0 1.8 5.6e+03 0.32 1.2 -0.83 -
5 -0.27 0.54 -0.078 -1.4 0 0.004 -1.2 0 0 0 0 0 0 0.54 5.5e+03 0.41 1.2 0.78 +
6 -0.23 0.32 0.72 -1.2 0 -0.0061 -1.5 0 0 0 0 0 0 0.34 5.4e+03 0.19 12 0.95 ++
7 -0.21 0.31 0.69 -1.3 0 -0.0057 -1.6 0 0 0 0 0 0 0.39 5.4e+03 0.0054 1.2e+02 1 ++
8 -0.21 0.31 0.69 -1.3 0 -0.0057 -1.6 0 0 0 0 0 0 0.39 5.4e+03 3.1e-06 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.5e+03 2.8 10 1 ++
1 5.4e+03 0.53 1e+02 1.1 ++
2 5.3e+03 0.044 1e+03 1.1 ++
3 5.3e+03 0.00041 1e+04 1 ++
4 5.3e+03 4.5e-08 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 3 10 1 ++
1 5.6e+03 0.41 1e+02 1.1 ++
2 5.6e+03 0.034 1e+03 1.1 ++
3 5.6e+03 0.00026 1e+04 1 ++
4 5.6e+03 2.1e-08 1e+04 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 39/100
Considering neighbor 0/20 for current solution
Attempt 40/100
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.85 -0.21 -0.95 0.38 -0.61 -0.0045 -1 5.4e+03 2.4 10 1 ++
1 -0.22 -0.29 -0.76 2.1 -1 -0.0053 -2.9 5e+03 0.91 1e+02 0.99 ++
2 -0.21 -0.29 -0.77 2 -1.1 -0.0061 -3.2 5e+03 0.045 1e+03 1 ++
3 -0.21 -0.3 -0.77 2 -1.1 -0.0062 -3.2 5e+03 0.00024 1e+04 1 ++
4 -0.21 -0.3 -0.77 2 -1.1 -0.0062 -3.2 5e+03 9.4e-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 b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -1 -0.91 -0.22 -0.0073 0 0 0 0 0 0 0 5.9e+03 2.6 10 1 ++
1 -0.93 -1.4 -1.1 -0.0057 0 0 0 0 0 0 0 5.6e+03 0.042 1e+02 0.97 ++
2 -0.98 -1.4 -1.1 -0.0055 0 0 0 0 0 0 0 5.6e+03 0.00046 1e+03 1 ++
3 -0.98 -1.4 -1.1 -0.0055 0 0 0 0 0 0 0 5.6e+03 4.8e-08 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 0.037 10 1.1 ++
1 5.4e+03 0.0086 1e+02 1.1 ++
2 5.4e+03 0.00081 1e+03 1.1 ++
3 5.4e+03 7.7e-06 1e+03 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 41/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.079 -1 -1 0 0 0 0 0 0 0 5.9e+03 0.076 10 1.1 ++
1 -0.7 -1.6 -1.8 0 0 0 0 0 0 0 5.6e+03 0.0079 1e+02 1 ++
2 -0.76 -1.7 -2.2 0 0 0 0 0 0 0 5.6e+03 0.0005 1e+03 1 ++
3 -0.76 -1.7 -2.2 0 0 0 0 0 0 0 5.6e+03 1.8e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.11 -1 -0.28 0 0 0 0 0 0 0 6e+03 0.073 10 1.1 ++
1 -0.79 -1.6 -0.94 0 0 0 0 0 0 0 5.7e+03 0.006 1e+02 1.1 ++
2 -0.87 -1.7 -1.1 0 0 0 0 0 0 0 5.7e+03 0.00022 1e+03 1 ++
3 -0.87 -1.7 -1.1 0 0 0 0 0 0 0 5.7e+03 4e-07 1e+03 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 42/100
Biogeme parameters read from biogeme.toml.
Model with 4 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME Function Relgrad Radius Rho
0 -0.32 -0.71 -0.28 -1 5.6e+03 0.042 10 1 ++
1 -0.1 -0.65 -1 -1.5 5.4e+03 0.0059 1e+02 1 ++
2 -0.1 -0.65 -1 -1.5 5.4e+03 9.8e-05 1e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME Function Relgrad Radius Rho
0 -0.43 -0.16 -0.28 -0.053 -0.63 -0.86 0.3 -0.018 -1 -1 5.4e+03 0.034 10 1.1 ++
1 -0.43 0.28 -0.032 -0.47 -0.5 -1 0.75 0.33 -1 -2.6 5.2e+03 0.012 1e+02 1.1 ++
2 -0.42 0.32 -0.059 -0.59 -0.58 -1.1 0.93 0.49 -1.1 -2.9 5.2e+03 0.00069 1e+03 1 ++
3 -0.42 0.32 -0.059 -0.59 -0.58 -1.1 0.93 0.49 -1.1 -2.9 5.2e+03 4.5e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 5.2e-08 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 4 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME Function Relgrad Radius Rho
0 -0.11 -1 -0.38 -1 5.6e+03 0.041 10 1.1 ++
1 -0.037 -0.65 -0.89 -2.8 5.3e+03 0.016 1e+02 1.1 ++
2 -0.0039 -0.49 -1.1 -3.3 5.3e+03 0.0015 1e+03 1.1 ++
3 -0.0039 -0.49 -1.1 -3.3 5.3e+03 9.8e-06 1e+03 1 ++
Considering neighbor 3/20 for current solution
*** New pareto solution:
ASC:no_seg;TRAIN_COST_catalog:linear;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:sqrt [10592.228480651507, np.float64(10619.50832425665), 4]
Attempt 43/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.11 -1 -0.38 0 0 0 0 0 0 0 6e+03 0.074 10 1.1 ++
1 -0.75 -1.6 -0.83 0 0 0 0 0 0 0 5.6e+03 0.0058 1e+02 1 ++
2 -0.8 -1.7 -0.94 0 0 0 0 0 0 0 5.6e+03 0.00016 1e+03 1 ++
3 -0.8 -1.7 -0.94 0 0 0 0 0 0 0 5.6e+03 1.4e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 44/100
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S lambda_COST Function Relgrad Radius Rho
0 -0.13 -1 -0.3 0 0 0 0 0 0 0 2 6e+03 0.08 10 1 ++
1 -0.13 -1 -0.3 0 0 0 0 0 0 0 2 6e+03 0.08 5 -8.3e+05 -
2 -0.13 -1 -0.3 0 0 0 0 0 0 0 2 6e+03 0.08 2.5 -42 -
3 -1.3 -1.5 -1.7 0 0 0 0 0 0 0 -0.5 5.9e+03 0.082 2.5 0.2 +
4 -1.3 -1.5 -1.7 0 0 0 0 0 0 0 -0.5 5.9e+03 0.082 1 -0.66 -
5 -0.58 -1.4 -0.71 0 0 0 0 0 0 0 -0.45 5.7e+03 0.026 1 0.82 +
6 -0.93 -1.7 -1.4 0 0 0 0 0 0 0 0.59 5.7e+03 0.026 1 0.38 +
7 -0.88 -1.7 -1.1 0 0 0 0 0 0 0 0.38 5.7e+03 0.0041 10 1.1 ++
8 -0.88 -1.7 -1.1 0 0 0 0 0 0 0 0.17 5.7e+03 0.0012 1e+02 0.98 ++
9 -0.88 -1.7 -1.1 0 0 0 0 0 0 0 0.17 5.7e+03 3.3e-06 1e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 5 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.56 -0.93 -0.85 -0.64 2 5.7e+03 0.12 10 0.92 ++
1 -0.56 -0.93 -0.85 -0.64 2 5.7e+03 0.12 4.1 -1.3e+05 -
2 -0.56 -0.93 -0.85 -0.64 2 5.7e+03 0.12 2.1 -1.2e+02 -
3 -0.56 -0.93 -0.85 -0.64 2 5.7e+03 0.12 1 -9.9 -
4 -0.56 -0.93 -0.85 -0.64 2 5.7e+03 0.12 0.52 -0.53 -
5 -0.18 -0.7 -0.62 -1.2 1.7 5.5e+03 0.026 5.2 1.1 ++
6 -0.18 -0.7 -0.62 -1.2 1.7 5.5e+03 0.026 2.5 -5.6e+02 -
7 -0.18 -0.7 -0.62 -1.2 1.7 5.5e+03 0.026 1.3 -5.7 -
8 -0.14 -0.56 -1.2 -1.3 0.46 5.4e+03 0.012 1.3 0.57 +
9 -0.26 -0.78 -1.1 -1.2 0.6 5.4e+03 0.00052 13 1 ++
10 -0.26 -0.78 -1.1 -1.2 0.6 5.4e+03 2.1e-05 13 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.55 -0.27 -0.32 -0.65 -0.8 0.72 -0.95 -1 5.2e+03 0.045 10 1 ++
1 -0.55 0.38 -1.5 -0.53 -0.93 1.9 -1.4 -2.6 4.9e+03 0.019 1e+02 1.1 ++
2 -0.61 0.48 -2 -0.48 -1.1 2 -1.5 -2.9 4.8e+03 0.0013 1e+03 1.1 ++
3 -0.61 0.48 -2 -0.48 -1.1 2 -1.5 -2.9 4.8e+03 9.5e-06 1e+03 1 ++
Considering neighbor 2/20 for current solution
*** New pareto solution:
ASC:MALE-GA;TRAIN_COST_catalog:log;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:sqrt [9684.449236896578, np.float64(9739.008924106867), 8]
Attempt 45/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.74 -0.36 -0.043 -1 -0.18 -0.017 -0.67 -0.005 0 0 0 0 0 0 0 5.7e+03 2.3 10 1 ++
1 -1 0.18 -0.054 -1.9 0.85 0.8 -0.94 -0.0052 0 0 0 0 0 0 0 5.5e+03 0.56 1e+02 1.1 ++
2 -1 0.16 -0.097 -2.2 1.2 1.1 -0.96 -0.0055 0 0 0 0 0 0 0 5.5e+03 0.071 1e+03 1.1 ++
3 -1 0.16 -0.097 -2.3 1.2 1.2 -0.96 -0.0056 0 0 0 0 0 0 0 5.5e+03 0.0017 1e+04 1 ++
4 -1 0.16 -0.097 -2.3 1.2 1.2 -0.96 -0.0056 0 0 0 0 0 0 0 5.5e+03 1.1e-06 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever ASC_TRAIN_with_ B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.41 -0.32 -0.028 -0.19 -0.63 0.0022 -0.015 0.34 -0.42 -1 1.4 5.6e+03 0.078 1 0.87 +
1 -0.041 -0.059 -0.095 -0.63 -1.3 0.32 0.0016 1.3 -1.2 -1.3 0.81 5e+03 0.024 10 1 ++
2 -0.042 0.041 -0.37 -1.5 -1.4 0.66 0.46 2 -1.4 -1.8 0.15 4.9e+03 0.012 1e+02 0.94 ++
3 -0.18 0.059 -0.45 -1.8 -1.6 0.7 0.46 2 -1.5 -1.6 0.24 4.9e+03 0.0012 1e+03 1 ++
4 -0.18 0.059 -0.45 -1.8 -1.6 0.7 0.46 2 -1.5 -1.6 0.24 4.9e+03 3.8e-05 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 18 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 2.5 10 1 ++
1 5.7e+03 2.5 3.3 -1.1e+04 -
2 5.7e+03 2.5 1.7 -13 -
3 5.3e+03 1.2 17 1.1 ++
4 5.3e+03 1.2 1.2 -29 -
5 5.3e+03 1.2 0.62 -0.081 -
6 5.2e+03 0.011 6.2 0.98 ++
7 5.2e+03 0.0076 62 0.96 ++
8 5.2e+03 0.0018 6.2e+02 1.1 ++
9 5.2e+03 1.2e-05 6.2e+02 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 46/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.3 -0.15 -0.049 -0.28 -0.74 0.083 -0.0053 0.69 -0.78 -0.0018 -1 2 5.8e+03 2.2 1 0.58 +
1 -0.31 0.042 -0.11 -0.41 -1.2 0.49 0.044 1.6 -1.8 -0.008 -0.68 1.7 5.2e+03 0.1 10 1 ++
2 -0.31 0.042 -0.11 -0.41 -1.2 0.49 0.044 1.6 -1.8 -0.008 -0.68 1.7 5.2e+03 0.1 5 -3.3e+03 -
3 -0.31 0.042 -0.11 -0.41 -1.2 0.49 0.044 1.6 -1.8 -0.008 -0.68 1.7 5.2e+03 0.1 2.5 -29 -
4 -0.31 0.042 -0.11 -0.41 -1.2 0.49 0.044 1.6 -1.8 -0.008 -0.68 1.7 5.2e+03 0.1 1.2 -0.75 -
5 -0.22 0.39 -0.15 -0.23 -1.5 0.54 0.12 1.8 -2.2 -0.00014 -1.6 0.47 5e+03 0.059 1.2 0.88 +
6 -0.26 0.081 -0.32 1 -1.1 0.66 0.53 1.9 -2.7 -0.0071 -1.7 0.35 4.9e+03 0.046 12 0.96 ++
7 -0.28 0.051 -0.36 1.2 -1.3 0.71 0.47 1.9 -2.8 -0.0062 -1.6 0.34 4.9e+03 0.0038 1.2e+02 1 ++
8 -0.28 0.051 -0.36 1.2 -1.3 0.71 0.47 1.9 -2.8 -0.0062 -1.6 0.34 4.9e+03 6.6e-06 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 47/100
Considering neighbor 0/20 for current solution
Attempt 48/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 0.038 10 1.1 ++
1 5.4e+03 0.0096 1e+02 1.1 ++
2 5.4e+03 0.001 1e+03 1.1 ++
3 5.4e+03 1.2e-05 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.36 -0.14 -0.27 -0.049 -0.45 -0.73 0.31 -0.0099 -0.96 -0.93 2 5.8e+03 0.32 1 0.6 +
1 -0.87 0.3 0.056 -0.32 -0.96 -1.7 1.2 0.13 -0.99 0.057 2.1 5.7e+03 0.33 1 0.28 +
2 -0.87 0.3 0.056 -0.32 -0.96 -1.7 1.2 0.13 -0.99 0.057 2.1 5.7e+03 0.33 0.5 -2 -
3 -0.87 0.3 0.056 -0.32 -0.96 -1.7 1.2 0.13 -0.99 0.057 2.1 5.7e+03 0.33 0.25 -0.2 -
4 -1 0.12 -0.0082 -0.33 -1.2 -1.8 0.95 0.13 -0.96 -0.19 2 5.5e+03 0.047 0.25 0.74 +
5 -0.92 0.21 0.052 -0.33 -1.1 -1.5 0.99 0.15 -1.1 -0.24 1.9 5.4e+03 0.034 2.5 0.97 ++
6 -0.92 0.21 0.052 -0.33 -1.1 -1.5 0.99 0.15 -1.1 -0.24 1.9 5.4e+03 0.034 1.2 -0.032 -
7 -0.88 0.19 0.022 -0.35 -1.3 -1 0.76 0.23 -1.1 -0.71 0.63 5.3e+03 0.026 1.2 0.72 +
8 -0.38 0.31 -0.066 -0.59 -0.56 -1.1 0.97 0.59 -1.1 -1.5 0.12 5.2e+03 0.0056 12 0.93 ++
9 -0.35 0.31 -0.073 -0.62 -0.51 -1.1 0.95 0.51 -1.1 -1.6 0.35 5.2e+03 0.0027 12 0.86 +
10 -0.35 0.31 -0.073 -0.62 -0.51 -1.1 0.95 0.51 -1.1 -1.6 0.35 5.2e+03 6.7e-05 12 1 +
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 5 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.56 -0.69 -0.64 -1 1.8 5.8e+03 0.2 1 0.66 +
1 -0.11 -0.41 -1.6 -1.8 0.78 5.5e+03 0.036 1 0.87 +
2 -0.11 -0.65 -0.95 -1.4 0.6 5.4e+03 0.0079 10 0.98 ++
3 -0.097 -0.59 -1 -1.5 0.39 5.4e+03 0.0015 1e+02 0.95 ++
4 -0.097 -0.59 -1 -1.5 0.39 5.4e+03 2e-05 1e+02 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 49/100
Considering neighbor 0/20 for current solution
Attempt 50/100
Biogeme parameters read from biogeme.toml.
Model with 22 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.041 10 1 ++
1 5.5e+03 0.053 1e+02 0.97 ++
2 5.5e+03 0.0043 1e+03 1 ++
3 5.5e+03 0.00016 1e+04 1 ++
4 5.5e+03 2.1e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.61 -0.25 -0.32 -0.8 -0.93 0.87 -1 0 0 0 0 0 0 0 5.4e+03 0.041 10 1.1 ++
1 -1.1 0.26 1.2 -1.4 -1 2 -2.5 0 0 0 0 0 0 0 5.1e+03 0.012 1e+02 1.1 ++
2 -1.2 0.28 1.3 -1.4 -1.2 2.1 -2.7 0 0 0 0 0 0 0 5.1e+03 0.00083 1e+03 1 ++
3 -1.2 0.28 1.3 -1.4 -1.2 2.1 -2.7 0 0 0 0 0 0 0 5.1e+03 4.4e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 19 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.6 -0.26 -0.73 -0.88 -1 0 0 0 0 0 0 0 5.6e+03 0.039 10 1 ++
1 -0.87 0.14 -0.77 -1.3 -2.1 0 0 0 0 0 0 0 5.4e+03 0.0055 1e+02 1 ++
2 -0.93 0.16 -0.78 -1.3 -2.2 0 0 0 0 0 0 0 5.4e+03 0.00015 1e+03 1 ++
3 -0.93 0.16 -0.78 -1.3 -2.2 0 0 0 0 0 0 0 5.4e+03 1e-07 1e+03 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.44 -0.21 -0.39 -0.63 -0.81 -0.87 2 5.9e+03 0.3 1 0.58 +
1 -0.43 0.18 -0.41 -1.2 -1.8 -0.47 1.8 5.3e+03 0.056 10 0.96 ++
2 -0.43 0.18 -0.41 -1.2 -1.8 -0.47 1.8 5.3e+03 0.056 5 -2.4e+03 -
3 -0.43 0.18 -0.41 -1.2 -1.8 -0.47 1.8 5.3e+03 0.056 2.5 -15 -
4 -0.43 0.18 -0.41 -1.2 -1.8 -0.47 1.8 5.3e+03 0.056 1.2 -0.45 -
5 -0.49 0.27 -0.34 -1.3 -2.1 -1.1 0.52 5.2e+03 0.026 1.2 0.89 +
6 -0.27 0.33 0.33 -1.2 -2.3 -1.6 0.41 5.1e+03 0.0062 12 1 ++
7 -0.27 0.33 0.33 -1.2 -2.3 -1.6 0.41 5.1e+03 7.8e-05 12 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 0.13 1 0.74 +
1 5.3e+03 0.032 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+03 1 ++
Considering neighbor 5/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 6/20 for current solution
Considering neighbor 7/20 for current solution
Attempt 51/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.04 10 1 ++
1 5.3e+03 0.056 1e+02 1 ++
2 5.2e+03 0.004 1e+03 1 ++
3 5.2e+03 0.00015 1e+04 1 ++
4 5.2e+03 1.7e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 5 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.59 -0.68 -0.64 -1 1.9 6e+03 0.25 1 0.59 +
1 -0.024 -0.92 -1.6 -0.75 1.6 5.4e+03 0.057 10 0.96 ++
2 -0.024 -0.92 -1.6 -0.75 1.6 5.4e+03 0.057 1.2 -1.7 -
3 0.03 -0.68 -2.3 -1.8 0.39 5.3e+03 0.04 1.2 0.88 +
4 0.054 -0.48 -2.3 -1.7 0.47 5.2e+03 0.0029 12 0.95 ++
5 0.054 -0.48 -2.3 -1.7 0.47 5.2e+03 2.1e-05 12 1 ++
Considering neighbor 1/20 for current solution
*** New pareto solution:
ASC:no_seg;TRAIN_COST_catalog:sqrt;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:boxcox [10501.31233905937, np.float64(10535.4121435658), 5]
Attempt 52/100
Considering neighbor 0/20 for current solution
Attempt 53/100
Considering neighbor 0/20 for current solution
Attempt 54/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.21 0.00042 -0.13 -0.038 -0.5 -0.79 0.093 -0.0049 -1 0.0035 -0.88 1.6 5.4e+03 2.2 1 0.9 +
1 -0.46 0.21 0.093 -0.099 -0.18 -1.2 1.1 0.056 -1.1 -0.0049 -1.5 0.76 5.1e+03 0.88 10 1 ++
2 -0.41 0.3 0.068 -0.17 -0.051 -1.1 0.96 0.57 -1.1 -0.0058 -1.8 0.39 5.1e+03 0.034 1e+02 0.91 ++
3 -0.47 0.31 0.077 -0.14 -0.12 -1.1 0.96 0.53 -1.1 -0.0059 -1.7 0.44 5.1e+03 0.00022 1e+03 1 ++
4 -0.47 0.31 0.077 -0.14 -0.12 -1.1 0.96 0.53 -1.1 -0.0059 -1.7 0.44 5.1e+03 7.1e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.92 0.17 0.15 -0.38 -0.66 -0.64 0.49 0.18 -0.85 -0.0026 -0.59 2 5.5e+03 2.3 10 0.95 ++
1 -0.92 0.17 0.15 -0.38 -0.66 -0.64 0.49 0.18 -0.85 -0.0026 -0.59 2 5.5e+03 2.3 5 -1.2e+06 -
2 -0.92 0.17 0.15 -0.38 -0.66 -0.64 0.49 0.18 -0.85 -0.0026 -0.59 2 5.5e+03 2.3 2.5 -71 -
3 -0.92 0.17 0.15 -0.38 -0.66 -0.64 0.49 0.18 -0.85 -0.0026 -0.59 2 5.5e+03 2.3 1.2 -1.4 -
4 0.21 -0.59 -0.44 0.055 -0.69 -1.1 0.96 0.76 -1.6 -0.0025 -1 0.75 5.3e+03 0.53 1.2 0.77 +
5 -0.66 0.31 0.03 -0.24 -0.51 -1.1 0.98 0.56 -0.96 -0.0058 -1.1 0.78 5.2e+03 0.061 12 0.94 ++
6 -0.7 0.31 -0.013 -0.4 -0.5 -1.1 0.97 0.56 -1.1 -0.006 -1.1 0.54 5.2e+03 0.0016 1.2e+02 0.9 ++
7 -0.7 0.31 -0.013 -0.4 -0.5 -1.1 0.97 0.56 -1.1 -0.006 -1.1 0.54 5.2e+03 4.1e-05 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.84 0.15 0.14 -0.38 -0.8 -0.64 0.51 0.19 -0.84 -0.59 2 5.5e+03 0.12 10 0.95 ++
1 -0.84 0.15 0.14 -0.38 -0.8 -0.64 0.51 0.19 -0.84 -0.59 2 5.5e+03 0.12 5 -2.4e+06 -
2 -0.84 0.15 0.14 -0.38 -0.8 -0.64 0.51 0.19 -0.84 -0.59 2 5.5e+03 0.12 2.5 -1.2e+02 -
3 -0.84 0.15 0.14 -0.38 -0.8 -0.64 0.51 0.19 -0.84 -0.59 2 5.5e+03 0.12 1.2 -2.6 -
4 0.11 -0.34 -0.00021 -0.18 -0.039 -0.91 0.37 0.87 -1.6 -1.6 0.75 5.3e+03 0.037 1.2 0.68 +
5 -0.56 0.32 -0.013 -0.44 -0.71 -1 0.85 0.53 -0.97 -1.1 0.71 5.2e+03 0.0065 12 0.95 ++
6 -0.58 0.31 -0.016 -0.41 -0.78 -1.1 0.95 0.56 -1.1 -1.1 0.56 5.2e+03 0.00051 1.2e+02 0.97 ++
7 -0.58 0.31 -0.016 -0.41 -0.78 -1.1 0.95 0.56 -1.1 -1.1 0.56 5.2e+03 1.9e-05 1.2e+02 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.41 -0.17 -0.35 -0.45 -0.7 0.74 -1 -0.71 5.2e+03 0.041 10 1.1 ++
1 -0.71 0.39 -1.4 -0.71 -0.89 1.9 -1.4 -0.97 4.9e+03 0.012 1e+02 1.1 ++
2 -0.77 0.46 -1.9 -0.74 -1.1 2 -1.4 -1 4.9e+03 0.00093 1e+03 1.1 ++
3 -0.77 0.46 -1.9 -0.74 -1.1 2 -1.4 -1 4.9e+03 1.3e-05 1e+03 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.75 0.011 0.12 -0.35 -0.58 -0.69 0.43 0.12 -0.94 -0.0027 -0.58 5.3e+03 2.7 10 1.1 ++
1 -0.7 0.31 -0.022 -0.48 -0.47 -1 0.77 0.37 -1.1 -0.0051 -0.96 5.2e+03 0.45 1e+02 1.1 ++
2 -0.72 0.33 -0.021 -0.51 -0.56 -1.1 0.95 0.52 -1.1 -0.0059 -1 5.2e+03 0.036 1e+03 1.1 ++
3 -0.72 0.33 -0.022 -0.5 -0.57 -1.1 0.97 0.55 -1.1 -0.0059 -1 5.2e+03 0.00032 1e+04 1 ++
4 -0.72 0.33 -0.022 -0.5 -0.57 -1.1 0.97 0.55 -1.1 -0.0059 -1 5.2e+03 1.1e-06 1e+04 1 ++
Considering neighbor 4/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.34 -0.17 -0.18 -0.44 -0.59 0.47 -0.76 -1 5.2e+03 0.044 10 1 ++
1 -0.36 0.33 -0.35 -0.21 -1 2 -1 -1.6 4.9e+03 0.018 1e+02 1 ++
2 -0.39 0.37 -0.42 -0.2 -1.2 2 -1.1 -1.7 4.9e+03 0.0011 1e+03 1 ++
3 -0.39 0.37 -0.42 -0.2 -1.2 2 -1.1 -1.7 4.9e+03 8.5e-06 1e+03 1 ++
Considering neighbor 5/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 4 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME Function Relgrad Radius Rho
0 -0.3 -0.74 -0.39 -1 5.5e+03 0.044 10 1 ++
1 -0.0041 -0.54 -0.93 -1.6 5.3e+03 0.0052 1e+02 1.1 ++
2 0.0019 -0.51 -1 -1.7 5.3e+03 0.00015 1e+03 1 ++
3 0.0019 -0.51 -1 -1.7 5.3e+03 1.4e-07 1e+03 1 ++
Considering neighbor 6/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.24 -0.033 -0.13 -0.034 -0.42 -0.69 0.12 -0.0031 -0.82 0.0011 -1 5.3e+03 2.4 10 1.1 ++
1 -0.36 0.22 0.055 -0.15 0.027 -1.2 0.73 0.33 -1 -0.0044 -1.6 5.1e+03 0.62 1e+02 1.1 ++
2 -0.39 0.24 0.055 -0.18 -0.081 -1.2 0.94 0.52 -1 -0.0058 -1.7 5.1e+03 0.052 1e+03 1.1 ++
3 -0.4 0.25 0.055 -0.18 -0.1 -1.2 0.97 0.55 -1 -0.0059 -1.7 5.1e+03 0.00056 1e+04 1 ++
4 -0.4 0.25 0.055 -0.18 -0.1 -1.2 0.97 0.55 -1 -0.0059 -1.7 5.1e+03 2.6e-07 1e+04 1 ++
Considering neighbor 7/20 for current solution
Considering neighbor 8/20 for current solution
Attempt 55/100
Considering neighbor 0/20 for current solution
Attempt 56/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.04 10 1 ++
1 5.3e+03 0.056 1e+02 1 ++
2 5.2e+03 0.004 1e+03 1 ++
3 5.2e+03 0.00015 1e+04 1 ++
4 5.2e+03 1.7e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 57/100
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.47 0.1 -0.43 -0.97 0.51 0.33 0 -0.0026 -0.63 0 0 0 0 0 0 5.6e+03 2.7 10 1.1 ++
1 -0.042 -0.054 -0.68 -1 0.91 0.71 0 -0.0047 -1.1 0 0 0 0 0 0 5.5e+03 0.35 1e+02 1.1 ++
2 -0.05 -0.044 -0.68 -1.2 1.1 0.89 0 -0.0053 -1.1 0 0 0 0 0 0 5.5e+03 0.026 1e+03 1 ++
3 -0.05 -0.044 -0.68 -1.2 1.1 0.91 0 -0.0054 -1.1 0 0 0 0 0 0 5.5e+03 0.00018 1e+04 1 ++
4 -0.05 -0.044 -0.68 -1.2 1.1 0.91 0 -0.0054 -1.1 0 0 0 0 0 0 5.5e+03 1.1e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 58/100
Considering neighbor 0/20 for current solution
Attempt 59/100
Considering neighbor 0/20 for current solution
Attempt 60/100
Considering neighbor 0/20 for current solution
Attempt 61/100
Considering neighbor 0/20 for current solution
Attempt 62/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME Function Relgrad Radius Rho
0 -0.22 -0.037 -0.18 -0.036 -0.38 -0.63 0.25 -0.0076 -0.65 -1 5.4e+03 0.042 10 1 ++
1 -0.31 0.29 0.17 0.096 -0.27 -1.1 0.75 0.38 -2.1 -1.6 5.1e+03 0.013 1e+02 1.1 ++
2 -0.34 0.31 0.15 -0.0035 -0.39 -1.2 0.95 0.55 -2.3 -1.7 5.1e+03 0.00078 1e+03 1 ++
3 -0.34 0.31 0.15 -0.0035 -0.39 -1.2 0.95 0.55 -2.3 -1.7 5.1e+03 5.4e-06 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 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST lambda_TT Function Relgrad Radius Rho
0 -0.67 -0.66 0 -1 0 0 0 0 0 0 2 6.3e+03 0.33 1 0.42 +
1 0.077 -0.89 0 0 0 0 0 0 0 0 2.1 6.2e+03 0.29 1 0.19 +
2 -0.53 -1.4 0 -0.053 0 0 0 0 0 0 3.1 6e+03 0.52 1 0.43 +
3 -0.53 -1.4 0 -0.053 0 0 0 0 0 0 3.1 6e+03 0.52 0.5 -14 -
4 -0.53 -1.4 0 -0.053 0 0 0 0 0 0 3.1 6e+03 0.52 0.25 -6.5 -
5 -0.53 -1.4 0 -0.053 0 0 0 0 0 0 3.1 6e+03 0.52 0.12 -4.2 -
6 -0.53 -1.4 0 -0.053 0 0 0 0 0 0 3.1 6e+03 0.52 0.062 -1.6 -
7 -0.53 -1.4 0 0.0095 0 0 0 0 0 0 3.1 5.9e+03 1.1 0.062 0.5 +
8 -0.53 -1.4 0 0.0095 0 0 0 0 0 0 3.1 5.9e+03 1.1 0.031 -0.15 -
9 -0.54 -1.5 0 -0.022 0 0 0 0 0 0 3.1 5.9e+03 0.33 0.031 0.14 +
10 -0.54 -1.5 0 -0.022 0 0 0 0 0 0 3.1 5.9e+03 0.33 0.016 -0.32 -
11 -0.56 -1.5 0 -0.0062 0 0 0 0 0 0 3 5.9e+03 0.19 0.16 0.94 ++
12 -0.56 -1.5 0 -0.0062 0 0 0 0 0 0 3 5.9e+03 0.19 0.078 -7.3 -
13 -0.56 -1.5 0 -0.0062 0 0 0 0 0 0 3 5.9e+03 0.19 0.039 -5.4 -
14 -0.56 -1.5 0 -0.0062 0 0 0 0 0 0 3 5.9e+03 0.19 0.02 -4.4 -
15 -0.56 -1.5 0 -0.0062 0 0 0 0 0 0 3 5.9e+03 0.19 0.0098 -3.8 -
16 -0.56 -1.5 0 -0.0062 0 0 0 0 0 0 3 5.9e+03 0.19 0.0049 -1.5 -
17 -0.56 -1.5 0 -0.0013 0 0 0 0 0 0 3 5.9e+03 0.0088 0.0049 0.86 +
18 -0.56 -1.5 0 -0.0012 0 0 0 0 0 0 3 5.9e+03 0.0032 0.049 1 ++
19 -0.58 -1.5 0 -0.0014 0 0 0 0 0 0 3 5.9e+03 0.0027 0.49 1 ++
20 -0.54 -1.4 0 -0.0054 0 0 0 0 0 0 2.5 5.9e+03 0.097 0.49 0.54 +
21 -0.57 -1.5 0 -0.024 0 0 0 0 0 0 2 5.8e+03 0.083 4.9 1.2 ++
22 -0.39 -1.2 0 -0.26 0 0 0 0 0 0 -0.73 5.8e+03 0.038 4.9 0.65 +
23 -0.39 -1.2 0 -0.26 0 0 0 0 0 0 -0.73 5.8e+03 0.038 2.4 -2.5e+02 -
24 -0.39 -1.2 0 -0.26 0 0 0 0 0 0 -0.73 5.8e+03 0.038 1.2 -3.1 -
25 0.35 -0.48 0 -1.5 0 0 0 0 0 0 0.14 5.6e+03 0.032 12 1.3 ++
26 0.24 -0.35 0 -1.6 0 0 0 0 0 0 0.53 5.6e+03 0.0079 12 0.84 +
27 0.2 -0.4 0 -1.6 0 0 0 0 0 0 0.46 5.5e+03 0.00061 1.2e+02 1.1 ++
28 0.2 -0.4 0 -1.6 0 0 0 0 0 0 0.46 5.5e+03 6.9e-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 b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.37 -0.072 -0.43 -0.39 -0.76 1 0 -0.6 0 0 0 0 0 0 5.4e+03 0.044 10 1.1 ++
1 -0.31 0.41 -1.3 -0.37 -0.95 1.6 0 -1 0 0 0 0 0 0 5.2e+03 0.011 1e+02 1.1 ++
2 -0.32 0.42 -1.5 -0.39 -1.1 1.7 0 -1.1 0 0 0 0 0 0 5.2e+03 0.00068 1e+03 1 ++
3 -0.32 0.42 -1.5 -0.39 -1.1 1.7 0 -1.1 0 0 0 0 0 0 5.2e+03 4.2e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 64/100
Considering neighbor 0/20 for current solution
Attempt 65/100
Considering neighbor 0/20 for current solution
Attempt 66/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.43 -0.12 -0.45 -0.7 0 -1 0 0 0 0 0 0 5.6e+03 0.044 10 1 ++
1 -0.13 0.31 0.32 -1.1 0 -2.8 0 0 0 0 0 0 5.4e+03 0.013 1e+02 1 ++
2 -0.12 0.32 0.38 -1.2 0 -3 0 0 0 0 0 0 5.4e+03 0.00038 1e+03 1 ++
3 -0.12 0.32 0.38 -1.2 0 -3 0 0 0 0 0 0 5.4e+03 3.7e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.55 -0.21 -0.48 -0.77 -0.99 -0.0016 -1 5.4e+03 2.4 10 1 ++
1 -0.39 0.26 0.49 -1.2 -1 -0.0048 -3 5.1e+03 0.35 1e+02 1.1 ++
2 -0.42 0.31 0.62 -1.3 -1.1 -0.0057 -3.3 5.1e+03 0.017 1e+03 1 ++
3 -0.42 0.31 0.62 -1.3 -1.1 -0.0057 -3.3 5.1e+03 6.3e-05 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_HEADWAY B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST lambda_TT Function Relgrad Radius Rho
0 -0.4 -0.69 0 -0.00076 -0.92 0 0 0 0 0 0 2 6.1e+03 2 1 0.51 +
1 -0.4 -0.69 0 -0.00076 -0.92 0 0 0 0 0 0 2 6.1e+03 2 0.5 -0.051 -
2 -0.23 -0.7 0 -0.0059 -0.42 0 0 0 0 0 0 1.9 5.8e+03 0.26 5 0.92 ++
3 -0.23 -0.7 0 -0.0059 -0.42 0 0 0 0 0 0 1.9 5.8e+03 0.26 2.5 -19 -
4 -0.23 -0.7 0 -0.0059 -0.42 0 0 0 0 0 0 1.9 5.8e+03 0.26 1.2 -0.35 -
5 -0.1 -0.72 0 -0.0021 -1.1 0 0 0 0 0 0 0.6 5.6e+03 0.15 1.2 0.87 +
6 0.08 -0.17 0 -0.0053 -1.5 0 0 0 0 0 0 0.41 5.5e+03 0.033 12 1 ++
7 0.093 -0.16 0 -0.0052 -1.5 0 0 0 0 0 0 0.45 5.5e+03 0.00013 1.2e+02 0.97 ++
8 0.093 -0.16 0 -0.0052 -1.5 0 0 0 0 0 0 0.45 5.5e+03 5.4e-07 1.2e+02 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 67/100
Considering neighbor 0/20 for current solution
Attempt 68/100
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 3 10 1.1 ++
1 5.4e+03 0.45 1e+02 1.1 ++
2 5.4e+03 0.039 1e+03 1.1 ++
3 5.4e+03 0.00034 1e+04 1 ++
4 5.4e+03 3.4e-08 1e+04 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 22 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.041 10 1 ++
1 5.5e+03 0.053 1e+02 0.97 ++
2 5.5e+03 0.0043 1e+03 1 ++
3 5.5e+03 0.00016 1e+04 1 ++
4 5.5e+03 2.1e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 21 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 70/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.34 -0.1 -0.05 -0.22 -1 0.12 -0.012 0.86 -0.92 -0.75 5.2e+03 0.047 10 1.1 ++
1 -0.32 0.08 -0.21 -0.28 -1.5 0.49 0.36 1.7 -1.1 -1.1 5e+03 0.016 1e+02 1.1 ++
2 -0.29 0.075 -0.25 -0.29 -1.7 0.68 0.56 1.8 -1.1 -1.2 5e+03 0.0014 1e+03 1.1 ++
3 -0.29 0.075 -0.25 -0.29 -1.7 0.68 0.56 1.8 -1.1 -1.2 5e+03 1.8e-05 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 14 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.29 -0.071 -0.12 -1 0.4 0.04 0 -0.65 0 0 0 0 0 0 5.6e+03 0.037 10 1.1 ++
1 0.045 -0.044 -0.64 -1.2 0.89 0.71 0 -1.1 0 0 0 0 0 0 5.5e+03 0.0076 1e+02 1.1 ++
2 0.057 -0.046 -0.68 -1.4 1.1 0.89 0 -1.1 0 0 0 0 0 0 5.5e+03 0.00067 1e+03 1.1 ++
3 0.057 -0.046 -0.68 -1.4 1.1 0.89 0 -1.1 0 0 0 0 0 0 5.5e+03 6.1e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME Function Relgrad Radius Rho
0 -0.41 -0.18 -0.25 -0.04 -0.64 -0.82 0.18 -0.02 -1 -0.9 5.4e+03 0.034 10 1.1 ++
1 -0.39 0.28 0.11 -0.085 -0.34 -1.1 0.73 0.35 -1 -2.9 5.1e+03 0.016 1e+02 1.1 ++
2 -0.37 0.31 0.081 -0.14 -0.41 -1.1 0.93 0.52 -1.1 -3.3 5.1e+03 0.0011 1e+03 1 ++
3 -0.37 0.31 0.081 -0.14 -0.41 -1.1 0.93 0.52 -1.1 -3.3 5.1e+03 7.5e-06 1e+03 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 71/100
Biogeme parameters read from biogeme.toml.
Model with 19 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 3 10 1.1 ++
1 5.3e+03 0.43 1e+02 1.1 ++
2 5.3e+03 0.038 1e+03 1 ++
3 5.3e+03 0.00034 1e+04 1 ++
4 5.3e+03 3.1e-08 1e+04 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 72/100
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever ASC_TRAIN_with_ B_COST B_TIME lambda_COST lambda_TT Function Relgrad Radius Rho
0 -0.41 -0.31 -0.028 -0.19 -0.63 -0.0035 -0.015 0.34 -0.44 -1 1 1.4 5.6e+03 0.073 1 0.88 +
1 -0.079 -0.074 -0.084 -0.57 -1.3 0.35 0.0035 1.3 -1.1 -1.4 1.1 0.78 5.1e+03 0.021 10 1 ++
2 -0.079 -0.074 -0.084 -0.57 -1.3 0.35 0.0035 1.3 -1.1 -1.4 1.1 0.78 5.1e+03 0.021 0.78 -0.042 -
3 -0.097 0.032 -0.19 -1 -1.5 0.67 0.13 2.1 -1.4 -1.8 0.38 0.21 4.9e+03 0.012 7.8 0.97 ++
4 -0.19 0.062 -0.4 -1.8 -1.6 0.7 0.5 2 -1.6 -1.6 -0.0086 0.26 4.9e+03 0.0047 78 0.94 ++
5 -0.18 0.06 -0.45 -1.8 -1.6 0.7 0.47 2 -1.5 -1.6 0.032 0.27 4.9e+03 0.00021 7.8e+02 1 ++
6 -0.18 0.06 -0.45 -1.8 -1.6 0.7 0.47 2 -1.5 -1.6 0.032 0.27 4.9e+03 2.6e-06 7.8e+02 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 73/100
Considering neighbor 0/20 for current solution
Attempt 74/100
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.43 -0.21 -0.29 -0.43 -0.65 0.66 -0.79 -0.9 2 5.8e+03 0.31 1 0.6 +
1 -0.44 0.19 -0.44 -0.67 -1.3 1.6 -1.8 -0.47 1.8 5.1e+03 0.058 10 0.97 ++
2 -0.44 0.19 -0.44 -0.67 -1.3 1.6 -1.8 -0.47 1.8 5.1e+03 0.058 5 -2.5e+03 -
3 -0.44 0.19 -0.44 -0.67 -1.3 1.6 -1.8 -0.47 1.8 5.1e+03 0.058 2.5 -15 -
4 -0.44 0.19 -0.44 -0.67 -1.3 1.6 -1.8 -0.47 1.8 5.1e+03 0.058 1.2 -0.25 -
5 -0.52 0.33 -0.3 -0.91 -1.3 1.8 -2.1 -1.1 0.52 4.9e+03 0.036 12 0.91 ++
6 -0.48 0.43 1.1 -0.27 -1.1 2 -2.7 -1.6 0.21 4.8e+03 0.0074 1.2e+02 0.92 ++
7 -0.5 0.44 1 -0.29 -1.1 2 -2.8 -1.7 0.29 4.8e+03 0.00021 1.2e+03 1 ++
8 -0.5 0.44 1 -0.29 -1.1 2 -2.8 -1.7 0.29 4.8e+03 1.2e-06 1.2e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.11 0.16 -0.076 -0.05 -0.58 -1 0.2 -0.0031 -0.88 0.0079 -0.92 2 5.7e+03 2.7 1 0.7 +
1 -0.48 0.1 0.08 -0.11 -0.31 -1.1 0.81 0.069 -1.9 -0.0064 -0.8 1.6 5.2e+03 0.7 10 1.1 ++
2 -0.48 0.1 0.08 -0.11 -0.31 -1.1 0.81 0.069 -1.9 -0.0064 -0.8 1.6 5.2e+03 0.7 5 -2.4e+03 -
3 -0.48 0.1 0.08 -0.11 -0.31 -1.1 0.81 0.069 -1.9 -0.0064 -0.8 1.6 5.2e+03 0.7 2.5 -33 -
4 -0.48 0.1 0.08 -0.11 -0.31 -1.1 0.81 0.069 -1.9 -0.0064 -0.8 1.6 5.2e+03 0.7 1.2 -2 -
5 -0.38 0.49 0.35 -0.13 -0.44 -1.2 0.89 0.17 -2.3 0.002 -1.8 0.32 5.1e+03 0.15 1.2 0.7 +
6 -0.53 0.37 0.18 -0.02 -0.12 -1.1 0.96 0.6 -2.4 -0.0063 -1.7 0.4 5e+03 0.12 12 0.94 ++
7 -0.52 0.37 0.18 0.00028 -0.16 -1.1 0.96 0.54 -2.4 -0.0059 -1.7 0.41 5e+03 0.0023 1.2e+02 1 ++
8 -0.52 0.37 0.18 0.00028 -0.16 -1.1 0.96 0.54 -2.4 -0.0059 -1.7 0.41 5e+03 1e-06 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 75/100
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 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 3 10 1.1 ++
1 5.4e+03 0.45 1e+02 1.1 ++
2 5.4e+03 0.039 1e+03 1.1 ++
3 5.4e+03 0.00034 1e+04 1 ++
4 5.4e+03 3.4e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.35 -0.78 0 -0.65 0 0 0 0 0 0 5.7e+03 0.035 10 1.1 ++
1 0.032 -0.64 0 -1.1 0 0 0 0 0 0 5.6e+03 0.0056 1e+02 1 ++
2 0.032 -0.64 0 -1.1 0 0 0 0 0 0 5.6e+03 8.2e-05 1e+02 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.47 -0.19 -0.24 -0.35 -0.66 0.73 -1 -0.0036 -0.66 5.2e+03 2.5 10 1.1 ++
1 -0.7 0.35 -0.37 -0.26 -0.93 1.8 -1.1 -0.0056 -1.1 4.9e+03 0.68 1e+02 1.1 ++
2 -0.74 0.4 -0.39 -0.21 -1.1 1.9 -1.1 -0.0067 -1.2 4.9e+03 0.064 1e+03 1.1 ++
3 -0.75 0.4 -0.4 -0.21 -1.1 1.9 -1.1 -0.0069 -1.2 4.9e+03 0.00045 1e+04 1 ++
4 -0.75 0.4 -0.4 -0.21 -1.1 1.9 -1.1 -0.0069 -1.2 4.9e+03 4.9e-07 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 22 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 0.039 10 1 ++
1 5.6e+03 0.0083 1e+02 1.1 ++
2 5.6e+03 0.00067 1e+03 1 ++
3 5.6e+03 5.4e-06 1e+03 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 78/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.29 -0.66 0 -1 0 0 0 0 0 0 5.7e+03 0.039 10 1 ++
1 0.2 -0.41 0 -1.6 0 0 0 0 0 0 5.6e+03 0.0033 1e+02 1 ++
2 0.2 -0.41 0 -1.6 0 0 0 0 0 0 5.6e+03 1.3e-05 1e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 15 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.21 0.028 -0.23 -0.37 -0.67 0.59 0 0.0019 -1 0 0 0 0 0 0 5.4e+03 2.9 10 1.1 ++
1 -0.17 0.36 -1.1 0.14 -1 1.8 0 -0.0042 -1.6 0 0 0 0 0 0 5.1e+03 0.81 1e+02 1.1 ++
2 -0.22 0.39 -1.4 0.24 -1.2 1.8 0 -0.0062 -1.6 0 0 0 0 0 0 5.1e+03 0.069 1e+03 1.1 ++
3 -0.23 0.39 -1.4 0.25 -1.2 1.9 0 -0.0065 -1.6 0 0 0 0 0 0 5.1e+03 0.0007 1e+04 1 ++
4 -0.23 0.39 -1.4 0.25 -1.2 1.9 0 -0.0065 -1.6 0 0 0 0 0 0 5.1e+03 7.3e-08 1e+04 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 79/100
Biogeme parameters read from biogeme.toml.
Model with 4 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME Function Relgrad Radius Rho
0 -0.11 -1 -0.28 -1 5.6e+03 0.039 10 1.1 ++
1 -0.12 -0.75 -0.89 -2.5 5.4e+03 0.013 1e+02 1.1 ++
2 -0.13 -0.64 -1 -2.9 5.4e+03 0.0009 1e+03 1 ++
3 -0.13 -0.64 -1 -2.9 5.4e+03 3.5e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 0.037 10 1.1 ++
1 5.4e+03 0.0086 1e+02 1.1 ++
2 5.4e+03 0.00081 1e+03 1.1 ++
3 5.4e+03 7.7e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -1 -0.91 -0.42 -0.0075 0 0 0 0 0 0 0 5.8e+03 2.5 10 1 ++
1 -0.89 -1.3 -0.91 -0.0058 0 0 0 0 0 0 0 5.6e+03 0.098 1e+02 1 ++
2 -0.91 -1.4 -0.94 -0.0055 0 0 0 0 0 0 0 5.6e+03 0.0014 1e+03 1 ++
3 -0.91 -1.4 -0.94 -0.0055 0 0 0 0 0 0 0 5.6e+03 2.2e-07 1e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 0.038 10 1.1 ++
1 5.4e+03 0.0096 1e+02 1.1 ++
2 5.4e+03 0.001 1e+03 1.1 ++
3 5.4e+03 1.2e-05 1e+03 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 80/100
Biogeme parameters read from biogeme.toml.
Model with 6 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.048 -0.46 -1 -0.065 -0.72 -1 5.6e+03 0.042 10 1.1 ++
1 -0.17 0.87 -1.2 2.4 -2.2 -2.5 5e+03 0.039 1e+02 0.94 ++
2 -0.16 1.1 -1.1 2.1 -2.7 -3 5e+03 0.0016 1e+03 1 ++
3 -0.16 1.1 -1.1 2.1 -2.7 -3 5e+03 1.6e-05 1e+03 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
ASC:GA;TRAIN_COST_catalog:sqrt;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:sqrt [9929.037386373328, np.float64(9969.957151781044), 6]
Attempt 81/100
Considering neighbor 0/20 for current solution
Attempt 82/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.53 -0.37 -0.027 -0.17 -1 -0.23 -0.026 0.26 -0.32 -0.82 5.7e+03 0.061 10 1 ++
1 -0.2 0.13 -0.17 1.1 -1.3 0.35 0.18 2.1 -2.6 -2.7 5e+03 0.033 1e+02 0.96 ++
2 -0.2 0.067 -0.34 1.1 -1.5 0.65 0.45 1.9 -2.8 -3 4.9e+03 0.0021 1e+03 1 ++
3 -0.2 0.067 -0.34 1.1 -1.5 0.65 0.45 1.9 -2.8 -3 4.9e+03 5.3e-05 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.44 -0.088 -0.41 -0.74 0 -0.00051 -1 0 0 0 0 0 0 5.6e+03 2.8 10 1 ++
1 -0.22 0.3 0.54 -1.2 0 -0.0045 -2.8 0 0 0 0 0 0 5.4e+03 0.37 1e+02 1.1 ++
2 -0.23 0.32 0.65 -1.2 0 -0.0056 -3 0 0 0 0 0 0 5.4e+03 0.019 1e+03 1 ++
3 -0.23 0.32 0.65 -1.2 0 -0.0056 -3 0 0 0 0 0 0 5.4e+03 4.8e-05 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 2.7 10 1 ++
1 5.2e+03 0.77 1e+02 1 ++
2 5.2e+03 0.072 1e+03 1.1 ++
3 5.2e+03 0.0013 1e+04 1 ++
4 5.2e+03 5.2e-07 1e+04 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 83/100
Considering neighbor 0/20 for current solution
Attempt 84/100
Considering neighbor 0/20 for current solution
Attempt 85/100
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.36 -0.1 -0.34 -0.43 -0.73 0.84 -1 -0.65 5.2e+03 0.041 10 1.1 ++
1 -0.66 0.38 0.92 -0.62 -0.95 1.8 -2.5 -1 4.9e+03 0.012 1e+02 1.1 ++
2 -0.7 0.43 1 -0.63 -1.1 1.9 -2.7 -1.1 4.9e+03 0.00079 1e+03 1 ++
3 -0.7 0.43 1 -0.63 -1.1 1.9 -2.7 -1.1 4.9e+03 5.2e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 86/100
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.2 0.092 -0.57 -0.95 -0.88 0.0074 -0.86 2 5.8e+03 2.6 1 0.68 +
1 -0.56 0.079 0.056 -0.95 -1.9 -0.0083 -0.49 1.8 5.3e+03 0.36 10 0.99 ++
2 -0.56 0.079 0.056 -0.95 -1.9 -0.0083 -0.49 1.8 5.3e+03 0.36 5 -2.7e+03 -
3 -0.56 0.079 0.056 -0.95 -1.9 -0.0083 -0.49 1.8 5.3e+03 0.36 2.5 -16 -
4 -0.56 0.079 0.056 -0.95 -1.9 -0.0083 -0.49 1.8 5.3e+03 0.36 1.2 -0.53 -
5 -0.35 0.51 -0.036 -1.3 -2.2 0.0027 -1.3 0.56 5.1e+03 0.48 1.2 0.83 +
6 -0.37 0.34 0.63 -1.3 -2.3 -0.0057 -1.7 0.38 5.1e+03 0.14 12 0.99 ++
7 -0.36 0.33 0.63 -1.3 -2.4 -0.0057 -1.7 0.42 5.1e+03 0.0024 1.2e+02 0.99 ++
8 -0.36 0.33 0.63 -1.3 -2.4 -0.0057 -1.7 0.42 5.1e+03 2.5e-07 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME beta_CAR_CO_SCA beta_CAR_CO_SCA beta_SM_COST_SC beta_SM_COST_SC beta_TRAIN_COST beta_TRAIN_COST Function Relgrad Radius Rho
0 -0.47 0.023 -0.083 -0.77 0 -0.0027 -0.62 0 0 0 0 0 0 5.6e+03 2.9 10 1.1 ++
1 -0.38 0.33 0.34 -1.1 0 -0.005 -1.1 0 0 0 0 0 0 5.4e+03 0.37 1e+02 1.1 ++
2 -0.39 0.32 0.41 -1.2 0 -0.0057 -1.1 0 0 0 0 0 0 5.4e+03 0.018 1e+03 1 ++
3 -0.39 0.32 0.41 -1.2 0 -0.0057 -1.1 0 0 0 0 0 0 5.4e+03 3.6e-05 1e+03 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 87/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME Function Relgrad Radius Rho
0 -0.36 -0.1 -0.23 -0.05 -0.59 -0.85 0.34 -0.018 -0.9 -1 5.4e+03 0.037 10 1.1 ++
1 -0.41 0.32 0.2 0.07 -0.43 -1 0.78 0.4 -2.2 -2.8 5e+03 0.016 1e+02 1.1 ++
2 -0.43 0.37 0.19 0.0071 -0.46 -1.1 0.94 0.54 -2.4 -3.2 5e+03 0.0011 1e+03 1 ++
3 -0.43 0.37 0.19 0.0071 -0.46 -1.1 0.94 0.54 -2.4 -3.2 5e+03 5.6e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 0.04 10 1.1 ++
1 5.4e+03 0.0071 1e+02 1 ++
2 5.4e+03 0.0006 1e+03 1 ++
3 5.4e+03 4.6e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 21 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 23 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.7e+03 3 10 1 ++
1 5.6e+03 0.41 1e+02 1.1 ++
2 5.6e+03 0.034 1e+03 1.1 ++
3 5.6e+03 0.00026 1e+04 1 ++
4 5.6e+03 2.1e-08 1e+04 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 88/100
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.41 -0.32 -0.033 -0.64 0.07 -0.014 -0.49 -1 1.6 5.8e+03 0.12 1 0.76 +
1 0.12 0.063 -0.087 -1.1 0.55 0.02 -1.5 -1.2 1.1 5.3e+03 0.029 10 0.95 ++
2 0.14 0.15 -0.21 -1.1 1.1 0.98 -2.2 -2 0.23 5.2e+03 0.012 10 0.82 +
3 -0.047 0.16 -0.23 -1.3 1.1 0.95 -2.4 -1.7 0.41 5.2e+03 0.0014 1e+02 1.1 ++
4 -0.061 0.16 -0.23 -1.3 1.1 0.96 -2.4 -1.6 0.46 5.2e+03 0.00014 1e+03 1 ++
5 -0.061 0.16 -0.23 -1.3 1.1 0.96 -2.4 -1.6 0.46 5.2e+03 1.5e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 89/100
Considering neighbor 0/20 for current solution
Attempt 90/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 0.04 10 1.1 ++
1 5.4e+03 0.0071 1e+02 1 ++
2 5.4e+03 0.0006 1e+03 1 ++
3 5.4e+03 4.6e-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 b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.047 -0.85 -1 -0.065 -0.27 0 0 0 0 0 0 0 6e+03 0.075 10 1.1 ++
1 -0.84 0.038 -2 2.2 -0.88 0 0 0 0 0 0 0 5.3e+03 0.038 1e+02 1 ++
2 -0.88 -0.067 -2.2 2.1 -1 0 0 0 0 0 0 0 5.3e+03 0.0012 1e+03 1 ++
3 -0.88 -0.067 -2.2 2.1 -1 0 0 0 0 0 0 0 5.3e+03 5.4e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 -0.49 -0.24 -0.72 0.44 -0.56 -1 1.7 5.6e+03 0.15 1 0.75 +
1 -0.19 -0.7 -1.2 1.4 -1.2 -1.1 1.1 5.1e+03 0.034 10 1.1 ++
2 -0.19 -0.7 -1.2 1.4 -1.2 -1.1 1.1 5.1e+03 0.034 0.73 -0.33 -
3 -0.26 -1.1 -1.3 2.1 -1.3 -1.5 0.34 5e+03 0.016 7.3 1.1 ++
4 -0.15 -1.7 -1.2 2.2 -1.5 -1.6 0.27 4.9e+03 0.0012 73 1 ++
5 -0.15 -1.7 -1.2 2.2 -1.5 -1.6 0.27 4.9e+03 5.7e-05 73 1 ++
Considering neighbor 2/20 for current solution
*** New pareto solution:
ASC:GA;TRAIN_COST_catalog:log;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:boxcox [9892.518987759471, np.float64(9940.258714068474), 7]
Attempt 91/100
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.56 -0.22 -0.49 -0.78 -0.89 -0.0011 -1 1 5.4e+03 2.5 10 1 ++
1 -0.56 0.26 0.38 -1.2 -1.3 -0.0048 -2.7 0.25 5.2e+03 0.4 1e+02 0.98 ++
2 -0.56 0.33 0.51 -1.3 -1.1 -0.0057 -3 0.48 5.2e+03 0.027 1e+03 1.1 ++
3 -0.55 0.32 0.53 -1.3 -1.1 -0.0058 -3 0.58 5.2e+03 0.00048 1e+04 1 ++
4 -0.55 0.32 0.53 -1.3 -1.1 -0.0058 -3 0.58 5.2e+03 3.8e-06 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.51 -0.26 -0.24 -0.66 -0.78 0.62 -1 -0.95 5.2e+03 0.034 10 1.1 ++
1 -0.44 0.35 -0.43 -0.34 -0.96 1.8 -1 -2.8 4.9e+03 0.02 1e+02 1.1 ++
2 -0.45 0.42 -0.45 -0.27 -1.1 1.9 -1.1 -3.2 4.9e+03 0.0013 1e+03 1 ++
3 -0.45 0.42 -0.45 -0.27 -1.1 1.9 -1.1 -3.2 4.9e+03 7.3e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.28 -0.047 -0.37 -0.66 -0.7 0.0015 -1 5.4e+03 2.5 10 1.1 ++
1 -0.47 0.28 0.46 -1.3 -1.1 -0.0044 -1.5 5.2e+03 0.49 1e+02 1.1 ++
2 -0.52 0.3 0.53 -1.3 -1.1 -0.0057 -1.5 5.2e+03 0.024 1e+03 1 ++
3 -0.52 0.3 0.53 -1.3 -1.1 -0.0057 -1.5 5.2e+03 5.8e-05 1e+03 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 12 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -0.88 -0.05 -0.77 -0.89 -0.88 0 0 0 0 0 0 0 5.5e+03 0.035 10 1 ++
1 -0.95 0.15 -0.75 -1.3 -0.94 0 0 0 0 0 0 0 5.5e+03 0.0059 1e+02 1.1 ++
2 -0.95 0.14 -0.75 -1.3 -0.95 0 0 0 0 0 0 0 5.5e+03 0.00018 1e+03 1 ++
3 -0.95 0.14 -0.75 -1.3 -0.95 0 0 0 0 0 0 0 5.5e+03 1.5e-07 1e+03 1 ++
Considering neighbor 3/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME lambda_COST Function Relgrad Radius Rho
0 -0.54 -0.25 -0.57 -0.76 -0.96 -1 1 5.4e+03 0.034 10 1 ++
1 -0.46 0.27 0.16 -1.2 -1.3 -2.8 0.38 5.2e+03 0.013 1e+02 1 ++
2 -0.44 0.33 0.24 -1.3 -1.1 -3 0.53 5.2e+03 0.00083 1e+03 1.1 ++
3 -0.44 0.33 0.24 -1.3 -1.1 -3 0.53 5.2e+03 5.1e-05 1e+03 1 ++
Considering neighbor 4/20 for current solution
Considering neighbor 5/20 for current solution
Attempt 92/100
Considering neighbor 0/20 for current solution
Attempt 93/100
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_TRAIN ASC_TRAIN_male B_COST B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S lambda_COST Function Relgrad Radius Rho
0 -0.85 -0.14 -0.67 -1 -0.83 0 0 0 0 0 0 0 1.1 5.6e+03 0.036 10 1 ++
1 -0.85 -0.14 -0.67 -1 -0.83 0 0 0 0 0 0 0 1.1 5.6e+03 0.036 1 -4 -
2 -1 0.19 -0.63 -1.4 -1.2 0 0 0 0 0 0 0 0.084 5.5e+03 0.013 10 0.91 ++
3 -1.1 0.21 -0.8 -1.3 -1.2 0 0 0 0 0 0 0 0.16 5.5e+03 0.00046 1e+02 1 ++
4 -1.1 0.21 -0.8 -1.3 -1.2 0 0 0 0 0 0 0 0.16 5.5e+03 1.4e-05 1e+02 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME lambda_COST lambda_TT Function Relgrad Radius Rho
0 -0.44 -0.34 -0.03 -0.61 0.03 -0.014 -0.47 -1 1 1.5 5.7e+03 0.08 1 0.86 +
1 -0.076 -0.037 -0.16 -1.1 1 0.08 -1.2 -1.8 1.2 0.52 5.3e+03 0.021 10 0.9 ++
2 -0.1 -0.063 -0.55 -1.4 1.1 1.2 -1.2 -1.6 0.73 0.46 5.3e+03 0.0066 1e+02 1.1 ++
3 -0.089 -0.064 -0.62 -1.4 1.2 0.97 -1.1 -1.6 0.58 0.44 5.3e+03 0.00077 1e+03 1.1 ++
4 -0.089 -0.064 -0.62 -1.4 1.2 0.97 -1.1 -1.6 0.58 0.44 5.3e+03 3.6e-05 1e+03 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 94/100
Considering neighbor 0/20 for current solution
Attempt 95/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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+02 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 96/100
Biogeme parameters read from biogeme.toml.
Model with 7 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.37 -0.33 -0.72 0.73 -1 -0.0023 -0.83 5.2e+03 2.3 10 1.1 ++
1 -0.48 -1.2 -1.1 2 -1.4 -0.0051 -0.98 5e+03 0.77 1e+02 1.1 ++
2 -0.48 -1.8 -1.2 2.1 -1.4 -0.0061 -1 5e+03 0.032 1e+03 1.1 ++
3 -0.48 -1.8 -1.2 2.1 -1.4 -0.0061 -1 5e+03 5.4e-05 1e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 97/100
Biogeme parameters read from biogeme.toml.
Model with 10 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME Function Relgrad Radius Rho
0 -0.26 -0.084 -0.21 -0.036 -0.41 -0.63 0.21 -0.0079 -0.69 -1 5.4e+03 0.038 10 1 ++
1 -0.32 0.25 -0.071 -0.53 -0.33 -1.1 0.73 0.32 -1.1 -1.5 5.2e+03 0.0093 1e+02 1.1 ++
2 -0.33 0.27 -0.081 -0.63 -0.49 -1.2 0.93 0.51 -1.1 -1.5 5.2e+03 0.00065 1e+03 1.1 ++
3 -0.33 0.27 -0.081 -0.63 -0.49 -1.2 0.93 0.51 -1.1 -1.5 5.2e+03 6.9e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 8 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.3 -0.11 -0.21 -0.38 -0.58 0.55 -0.61 -1 5.3e+03 0.051 10 1 ++
1 -0.42 0.35 1 -0.32 -1 2 -2.5 -1.5 4.9e+03 0.02 1e+02 1 ++
2 -0.47 0.4 1.1 -0.28 -1.2 2.1 -2.7 -1.7 4.9e+03 0.0011 1e+03 1 ++
3 -0.47 0.4 1.1 -0.28 -1.2 2.1 -2.7 -1.7 4.9e+03 6.4e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.5e+03 2.9 10 1.1 ++
1 5.3e+03 0.37 1e+02 1.1 ++
2 5.3e+03 0.032 1e+03 1 ++
3 5.3e+03 0.00026 1e+04 1 ++
4 5.3e+03 2e-08 1e+04 1 ++
Considering neighbor 2/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 9 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.41 -0.079 -0.26 -0.61 -0.89 0.72 -0.83 0.0016 -1 5.3e+03 3.1 10 1.1 ++
1 -0.54 0.32 0.79 -0.16 -1 1.8 -2.4 -0.0045 -2.7 4.8e+03 0.68 1e+02 1.1 ++
2 -0.65 0.42 1 -0.045 -1.1 2 -2.7 -0.0066 -3.1 4.8e+03 0.051 1e+03 1.1 ++
3 -0.68 0.44 1 -0.041 -1.1 2 -2.8 -0.0068 -3.1 4.8e+03 0.0013 1e+04 1 ++
4 -0.68 0.44 1 -0.041 -1.1 2 -2.8 -0.0068 -3.1 4.8e+03 5.8e-06 1e+04 1 ++
Considering neighbor 3/20 for current solution
*** New pareto solution:
ASC:MALE-GA;TRAIN_COST_catalog:sqrt;TRAIN_HEADWAY_catalog:with_headway;TRAIN_TT_catalog:sqrt [9668.819379255528, np.float64(9730.199027367104), 9]
Attempt 98/100
Biogeme parameters read from biogeme.toml.
Model with 16 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.9e+03 0.04 10 1.1 ++
1 5.2e+03 0.059 1e+02 1 ++
2 5.2e+03 0.0037 1e+03 1 ++
3 5.2e+03 0.00017 1e+04 1 ++
4 5.2e+03 2e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 13 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME beta_CAR_TT_SCA beta_CAR_TT_SCA beta_SM_TT_SCAL beta_SM_TT_SCAL beta_TRAIN_TT_S beta_TRAIN_TT_S Function Relgrad Radius Rho
0 -1 -0.2 -0.91 0.23 -0.42 -0.008 0 0 0 0 0 0 0 5.7e+03 2.5 10 1 ++
1 -0.97 0.0081 -1.7 2.2 -0.97 -0.0062 0 0 0 0 0 0 0 5.3e+03 1.1 1e+02 1 ++
2 -1 -0.049 -1.9 2.2 -1 -0.0064 0 0 0 0 0 0 0 5.3e+03 0.062 1e+03 1 ++
3 -1 -0.042 -1.9 2.2 -1 -0.0064 0 0 0 0 0 0 0 5.3e+03 0.00046 1e+04 1 ++
4 -1 -0.042 -1.9 2.2 -1 -0.0064 0 0 0 0 0 0 0 5.3e+03 2.6e-08 1e+04 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 99/100
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.6e+03 3 10 1.1 ++
1 5.3e+03 0.43 1e+02 1.1 ++
2 5.3e+03 0.038 1e+03 1 ++
3 5.3e+03 0.00034 1e+04 1 ++
4 5.3e+03 3.1e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 17 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
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 2.6 10 1 ++
1 5.8e+03 0.11 1e+02 1 ++
2 5.8e+03 0.0017 1e+03 1 ++
3 5.8e+03 1.9e-07 1e+03 1 ++
Considering neighbor 1/20 for current solution
Biogeme parameters read from biogeme.toml.
Model with 11 unknown parameters [max: 50]
*** Estimate b22multiple_models_000000
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.46 -0.15 -0.22 -0.061 -0.56 -0.86 0.24 -0.0054 -0.93 -0.0011 -1 5.4e+03 2.7 10 1.1 ++
1 -0.52 0.27 -0.036 -0.43 -0.25 -1.1 0.74 0.32 -1 -0.0047 -2.6 5.2e+03 0.46 1e+02 1.1 ++
2 -0.53 0.32 -0.058 -0.57 -0.3 -1.1 0.93 0.49 -1.1 -0.0058 -2.9 5.1e+03 0.041 1e+03 1 ++
3 -0.53 0.32 -0.058 -0.59 -0.31 -1.1 0.96 0.51 -1.1 -0.0059 -2.9 5.1e+03 0.0004 1e+04 1 ++
4 -0.53 0.32 -0.058 -0.59 -0.31 -1.1 0.96 0.51 -1.1 -0.0059 -2.9 5.1e+03 7.4e-08 1e+04 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Pareto file has been updated: b22multiple_models.pareto
Before the algorithm: 1 models, with 1 Pareto.
After the algorithm: 193 models, with 7 Pareto.
VNS algorithm completed. Postprocessing of the Pareto optimal solutions
Pareto set initialized from file with 193 elements [7 Pareto] and 0 invalid elements.
Biogeme parameters provided by the user.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
*** Initial values of the parameters are obtained from the file __b22multiple_models_000000.iter
Parameter values restored from __b22multiple_models_000000.iter
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.52 0.38 -0.45 0.14 -0.96 1 -1.1 -2.7 5e+03 0.062 1 0.8 +
1 -0.51 0.38 -1.1 -0.28 -1.2 2 -1.3 -2.9 4.9e+03 0.017 10 1.1 ++
2 -0.61 0.49 -1.9 -0.47 -1.1 2 -1.5 -2.9 4.8e+03 0.0018 1e+02 1.1 ++
3 -0.61 0.49 -1.9 -0.47 -1.1 2 -1.5 -2.9 4.8e+03 9.3e-05 1e+02 1 ++
Results saved in file b22multiple_models_000000.html
Results saved in file b22multiple_models_000000.pickle
Biogeme parameters provided by the user.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
*** Initial values of the parameters are obtained from the file __b22multiple_models_000001.iter
Cannot read file __b22multiple_models_000001.iter. Statement is ignored.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 0.47 -0.49 0.032 1 -1.3 -2.1 0.67 5.3e+03 0.056 10 0.91 ++
1 -0.16 -1.3 -1.1 2 -1.4 -1.6 0.44 5e+03 0.0091 1e+02 1.1 ++
2 -0.14 -1.8 -1.2 2.1 -1.5 -1.6 0.26 4.9e+03 0.0015 1e+03 1 ++
3 -0.14 -1.8 -1.2 2.1 -1.5 -1.6 0.26 4.9e+03 2.2e-05 1e+03 1 ++
Results saved in file b22multiple_models_000001.html
Results saved in file b22multiple_models_000001.pickle
Biogeme parameters provided by the user.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
*** Initial values of the parameters are obtained from the file __b22multiple_models_000002.iter
Cannot read file __b22multiple_models_000002.iter. Statement is ignored.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_TIME lambda_COST lambda_TT Function Relgrad Radius Rho
0 -0.54 0.41 -1.8 -0.25 -1.2 1.9 -2.1 -1.8 0 0.25 4.9e+03 0.061 1 0.55 +
1 -0.56 0.48 -1.9 -0.4 -1.1 2.1 -1.4 -1.6 -0.02 0.22 4.8e+03 0.0044 10 0.93 ++
2 -0.56 0.48 -2 -0.38 -1.1 2.1 -1.5 -1.6 0.03 0.22 4.8e+03 0.0002 1e+02 0.99 ++
3 -0.56 0.48 -2 -0.38 -1.1 2.1 -1.5 -1.6 0.03 0.22 4.8e+03 1.8e-06 1e+02 1 ++
Results saved in file b22multiple_models_000002.html
Results saved in file b22multiple_models_000002.pickle
Biogeme parameters provided by the user.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
*** Initial values of the parameters are obtained from the file __b22multiple_models_000003.iter
Cannot read file __b22multiple_models_000003.iter. Statement is ignored.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME Function Relgrad Radius Rho
0 -0.18 -0.54 -0.82 -2.6 5.3e+03 0.026 10 1 ++
1 -0.011 -0.49 -1.1 -3.3 5.3e+03 0.0032 1e+02 1 ++
2 -0.011 -0.49 -1.1 -3.3 5.3e+03 2.7e-05 1e+02 1 ++
Results saved in file b22multiple_models_000003.html
Results saved in file b22multiple_models_000003.pickle
Biogeme parameters provided by the user.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
*** Initial values of the parameters are obtained from the file __b22multiple_models_000004.iter
Cannot read file __b22multiple_models_000004.iter. Statement is ignored.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_male ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_male ASC_TRAIN_with_ B_COST B_HEADWAY B_TIME Function Relgrad Radius Rho
0 -0.54 0.42 -1.6 0.097 -1.1 1.8 -2.1 -0.008 -3 4.9e+03 0.041 10 0.96 ++
1 -0.54 0.42 -1.6 0.097 -1.1 1.8 -2.1 -0.008 -3 4.9e+03 0.041 3.2 -3.2 -
2 -0.67 0.46 1.5 0.0059 -1.2 2 -3 -0.0067 -3.3 4.8e+03 0.065 3.2 0.77 +
3 -0.68 0.44 1.1 -0.043 -1.1 2 -2.8 -0.0068 -3.1 4.8e+03 0.0084 32 1 ++
4 -0.68 0.44 1 -0.042 -1.1 2 -2.8 -0.0068 -3.1 4.8e+03 0.00031 3.2e+02 1 ++
5 -0.68 0.44 1 -0.042 -1.1 2 -2.8 -0.0068 -3.1 4.8e+03 2.4e-06 3.2e+02 1 ++
Results saved in file b22multiple_models_000004.html
Results saved in file b22multiple_models_000004.pickle
Biogeme parameters provided by the user.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
*** Initial values of the parameters are obtained from the file __b22multiple_models_000005.iter
Cannot read file __b22multiple_models_000005.iter. Statement is ignored.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_with_ga ASC_TRAIN ASC_TRAIN_with_ B_COST B_TIME Function Relgrad Radius Rho
0 -0.015 -0.89 -0.98 1.6 -2.1 -3.1 5e+03 0.015 1 0.89 +
1 -0.06 0.11 -1.2 2.1 -2.6 -3.1 5e+03 0.0084 10 1 ++
2 -0.15 1.5 -1.1 2.1 -2.8 -3.1 5e+03 0.0052 10 0.69 +
3 -0.15 1.2 -1.1 2.1 -2.8 -3.1 5e+03 0.00054 1e+02 1.1 ++
4 -0.15 1.2 -1.1 2.1 -2.8 -3.1 5e+03 7.7e-06 1e+02 1 ++
Results saved in file b22multiple_models_000005.html
Results saved in file b22multiple_models_000005.pickle
Biogeme parameters provided by the user.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
*** Initial values of the parameters are obtained from the file __b22multiple_models_000006.iter
Cannot read file __b22multiple_models_000006.iter. Statement is ignored.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME lambda_TT Function Relgrad Radius Rho
0 0.4 0.14 -2.8 -2.1 0.32 5.3e+03 0.069 1 0.74 +
1 0.041 -0.48 -2.3 -1.7 0.44 5.2e+03 0.0038 10 1 ++
2 0.041 -0.48 -2.3 -1.7 0.44 5.2e+03 5.9e-05 10 1 ++
Results saved in file b22multiple_models_000006.html
Results saved in file b22multiple_models_000006.pickle
Pareto: 7
Considered: 193
Removed: 9
summary, description = compile_estimation_results(
non_dominated_models, use_short_names=True
)
print(summary)
Model_000000 ... Model_000006
Number of estimated parameters 8 ... 5
Sample size 6768 ... 6768
Final log likelihood -4834.225553 ... -5245.656374
Akaike Information Criterion 9684.451106 ... 10501.312749
Bayesian Information Criterion 9739.010793 ... 10535.412553
ASC_CAR (t-test) -0.619 (-5.91) ... 0.0569 (1.13)
ASC_CAR_male (t-test) 0.49 (4.55) ...
ASC_CAR_with_ga (t-test) -2.01 (-9.67) ...
ASC_TRAIN (t-test) -0.475 (-4.9) ... -0.497 (-7.59)
ASC_TRAIN_male (t-test) -1.11 (-13.2) ...
ASC_TRAIN_with_ga (t-test) 2.03 (22.4) ...
B_COST (t-test) -1.47 (-18) ... -2.35 (-18.3)
B_TIME (t-test) -2.95 (-16) ... -1.67 (-21.5)
lambda_TT (t-test) ... 0.477 (6.33)
lambda_COST (t-test) ...
B_HEADWAY (t-test) ...
[16 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:without_headway;TRAIN_TT_catalog:sqrt
Model_000001: ASC:GA;TRAIN_COST_catalog:log;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:boxcox
Model_000002: ASC:MALE-GA;TRAIN_COST_catalog:boxcox;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:boxcox
Model_000003: ASC:no_seg;TRAIN_COST_catalog:linear;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:sqrt
Model_000004: ASC:MALE-GA;TRAIN_COST_catalog:sqrt;TRAIN_HEADWAY_catalog:with_headway;TRAIN_TT_catalog:sqrt
Model_000005: ASC:GA;TRAIN_COST_catalog:sqrt;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:sqrt
Model_000006: ASC:no_seg;TRAIN_COST_catalog:sqrt;TRAIN_HEADWAY_catalog:without_headway;TRAIN_TT_catalog:boxcox
Total running time of the script: (1 minutes 5.660 seconds)