Note
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Combine many specifications: assisted specification algorithm
We combine many specifications, defined in Combination of many specifications. This leads to a total of 432 specifications. The algorithm implemented in the AssistedSpecification object is used to investigate some of these specifications. See Bierlaire and Ortelli (2023).
- author:
Michel Bierlaire, EPFL
- date:
Sat Jul 15 15:02:20 2023
from typing import Optional
import biogeme.biogeme_logging as blog
import biogeme.biogeme as bio
from biogeme.assisted import AssistedSpecification
from biogeme.multiobjectives import loglikelihood_dimension
from everything_spec import model_catalog, database
from biogeme.results import bioResults, compile_estimation_results
logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b07everything_assisted')
PARETO_FILE_NAME = 'b07everything_assisted.pareto'
Example b07everything_assisted
Function verifying that the estimation results are valid.
def validity(results: bioResults) -> tuple[bool, Optional[str]]:
"""Function verifying that the estimation results are valid.
The results are not valid if any of the time or cost coefficient is non negative.
"""
for beta in results.data.betas:
if 'TIME' in beta.name and beta.value >= 0:
return False, f'{beta.name} = {beta.value}'
if 'COST' in beta.name and beta.value >= 0:
return False, f'{beta.name} = {beta.value}'
return True, None
Create the Biogeme object
the_biogeme = bio.BIOGEME(database, model_catalog)
the_biogeme.modelName = 'b07everything'
the_biogeme.generate_html = False
the_biogeme.generate_pickle = False
File biogeme.toml has been parsed.
Estimate the parameters using assisted specification algorithm.
assisted_specification = AssistedSpecification(
biogeme_object=the_biogeme,
multi_objectives=loglikelihood_dimension,
pareto_file_name=PARETO_FILE_NAME,
validity=validity,
)
non_dominated_models = assisted_specification.run()
Pareto set initialized from file with 126 elements [10 Pareto] and 14 invalid elements.
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:linear
The number of possible specifications [432] exceeds the maximum number [100]. A heuristic algorithm is applied.
*** VNS ***
ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:linear [5331.252006916162, 4]
Initial pareto: 10
Attempt 0/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.14 -0.91 -0.26 -1 -0.95 -0.79 -0.6 -0.63 -0.39 -0.53 -0.31 1.7 1.9 6.2e+03 0.21 1 0.4 +
1 -0.78 0.086 -0.67 -0.54 -1.2 -0.12 -0.26 -0.55 -0.48 -0.77 -0.61 1.4 2.4 5.6e+03 0.17 1 0.39 +
2 -0.78 0.086 -0.67 -0.54 -1.2 -0.12 -0.26 -0.55 -0.48 -0.77 -0.61 1.4 2.4 5.6e+03 0.17 0.5 0.051 -
3 -0.28 -0.21 -0.52 -0.79 -1.3 -0.45 -0.6 -0.45 -0.45 -0.43 -0.4 1.1 2.3 5.1e+03 0.035 0.5 0.87 +
4 -0.39 -0.13 -0.59 -0.87 -1.6 -0.49 -0.93 -0.62 -0.59 -0.67 -0.44 0.64 2.2 5e+03 0.0076 5 1.1 ++
5 -0.39 -0.13 -0.59 -0.87 -1.6 -0.49 -0.93 -0.62 -0.59 -0.67 -0.44 0.64 2.2 5e+03 0.0076 0.58 -0.24 -
6 -0.28 0.13 -0.63 -0.92 -1.8 -0.79 -1.1 -0.82 -0.42 -1.1 -0.62 0.29 1.6 5e+03 0.019 5.8 0.94 ++
7 -0.19 0.46 -0.78 -1 -2.3 -1.2 -1.1 -1.4 -0.076 -1.6 -0.53 -0.035 1.4 5e+03 0.0067 5.8 0.9 +
8 -0.26 0.48 -0.8 -1.1 -2.6 -1.2 -1.1 -1.4 -0.14 -1.6 -0.51 0.093 1.3 5e+03 0.0014 58 1 ++
9 -0.27 0.47 -0.8 -1.1 -2.6 -1.2 -1.1 -1.3 -0.15 -1.6 -0.51 0.1 1.3 5e+03 1.9e-05 5.8e+02 1 ++
10 -0.27 0.47 -0.8 -1.1 -2.6 -1.2 -1.1 -1.3 -0.15 -1.6 -0.51 0.1 1.3 5e+03 2.1e-08 5.8e+02 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_SM B_TIME_TRAIN lambda_travel_t Function Relgrad Radius Rho
0 -0.53 -0.32 -0.045 -1 0.042 -0.023 -0.96 -0.71 -0.48 -0.88 1.8 5.6e+03 0.053 1 0.76 +
1 -0.33 0.32 -0.12 -1.5 1 0.06 -0.92 -1.2 -0.61 -1.3 1.1 5.3e+03 0.017 10 0.98 ++
2 -0.33 0.32 -0.12 -1.5 1 0.06 -0.92 -1.2 -0.61 -1.3 1.1 5.3e+03 0.017 1.6 -12 -
3 0.087 -0.075 -0.26 -1.2 1.4 0.71 -1.2 -2.2 -2.2 -2.7 -0.089 5.3e+03 0.081 1.6 0.14 +
4 -0.14 0.038 -0.31 -1 1.1 0.8 -1 -1.7 -1.2 -2.8 -0.25 5.2e+03 0.016 1.6 0.73 +
5 -0.075 0.045 -0.31 -1 1.1 0.77 -1.1 -1.7 -1.5 -2.6 0.2 5.2e+03 0.0048 16 0.95 ++
6 -0.081 0.044 -0.3 -1 1.1 0.77 -1.1 -1.7 -1.5 -2.6 0.12 5.2e+03 0.00025 1.6e+02 0.99 ++
7 -0.081 0.044 -0.3 -1 1.1 0.77 -1.1 -1.7 -1.5 -2.6 0.12 5.2e+03 3.2e-07 1.6e+02 1 ++
Considering neighbor 1/20 for current solution
Attempt 1/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 B_TIME_1st_clas cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -2.6 -
1 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 5 1 ++
2 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 2.5 -9.8 -
3 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 1.2 -6.8 -
4 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.62 -5 -
5 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.31 -4 -
6 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.16 -3.5 -
7 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.078 -3.4 -
8 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.039 -3.4 -
9 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.02 -3.5 -
10 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.0098 -3.6 -
11 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.0049 -2.3 -
12 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.0024 -1.7 -
13 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.0012 -1.3 -
14 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.00061 -0.81 -
15 -0.04 -0.038 -0.0057 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 0 1.4 0 5.5e+03 11 0.00031 -0.13 -
16 -0.04 -0.038 -0.006 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 -0.00031 1.4 -0.00031 5.5e+03 5.7 0.00031 0.63 +
17 -0.04 -0.038 -0.006 -0.5 -0.29 -0.015 -0.091 -0.5 -0.5 -0.00021 1.4 -0.00023 5.5e+03 5.4 0.00031 0.39 +
18 -0.04 -0.038 -0.006 -0.5 -0.29 -0.015 -0.092 -0.5 -0.5 -0.00025 1.4 -0.00015 5.5e+03 0.38 0.0031 0.95 ++
19 -0.04 -0.039 -0.0061 -0.5 -0.29 -0.015 -0.095 -0.5 -0.5 -0.00025 1.4 0.00062 5.5e+03 0.08 0.031 1 ++
20 -0.04 -0.045 -0.0071 -0.5 -0.28 -0.015 -0.13 -0.51 -0.51 -0.00029 1.4 0.0081 5.5e+03 0.32 0.31 1 ++
21 -0.035 -0.11 -0.018 -0.49 -0.13 -0.012 -0.43 -0.56 -0.55 -0.00046 1.5 0.051 5.3e+03 0.45 3.1 1 ++
22 -0.035 -0.11 -0.018 -0.49 -0.13 -0.012 -0.43 -0.56 -0.55 -0.00046 1.5 0.051 5.3e+03 0.45 1.5 -1.4e+02 -
23 -0.035 -0.11 -0.018 -0.49 -0.13 -0.012 -0.43 -0.56 -0.55 -0.00046 1.5 0.051 5.3e+03 0.45 0.76 -44 -
24 -0.035 -0.11 -0.018 -0.49 -0.13 -0.012 -0.43 -0.56 -0.55 -0.00046 1.5 0.051 5.3e+03 0.45 0.38 -4.9 -
25 0.024 -0.14 -0.038 -0.55 0.071 -0.0065 -0.81 -0.62 -0.61 -0.0001 1.6 -0.034 5.1e+03 0.34 3.8 0.92 ++
26 -0.12 0.083 -0.4 -0.83 0.63 0.37 -0.92 -0.97 -0.67 0.0001 1.9 -0.085 5.1e+03 4.5 38 1.1 ++
27 -0.05 0.09 -0.37 -0.79 0.68 0.36 -0.95 -1.3 -0.71 0.00019 1.9 -0.1 5e+03 2.5 3.8e+02 1.1 ++
28 -0.031 0.087 -0.36 -0.79 0.7 0.39 -0.96 -1.4 -0.72 0.00019 1.8 -0.1 5e+03 0.37 3.8e+03 1 ++
29 -0.031 0.088 -0.35 -0.79 0.7 0.4 -0.97 -1.4 -0.73 0.00019 1.8 -0.1 5e+03 0.0037 3.8e+04 1 ++
30 -0.031 0.088 -0.35 -0.79 0.7 0.4 -0.96 -1.4 -0.73 0.00019 1.8 -0.1 5e+03 6.8e-06 3.8e+05 1 ++
31 -0.031 0.088 -0.35 -0.79 0.7 0.4 -0.96 -1.4 -0.73 0.00019 1.8 -0.1 5e+03 1.7e-06 3.8e+05 1 ++
Considering neighbor 0/20 for current solution
Attempt 2/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN mu_existing Function Relgrad Radius Rho
0 0.088 -0.44 -0.31 -0.75 -1 -0.66 -0.4 -0.57 1.7 5.4e+03 0.19 1 0.9 +
1 0.088 -0.44 -0.31 -0.75 -1 -0.66 -0.4 -0.57 1.7 5.4e+03 0.19 0.5 0.0021 -
2 -0.24 -0.088 -0.44 -0.5 -1.5 -0.73 -0.68 -0.54 2.1 5.1e+03 0.048 0.5 0.86 +
3 -0.28 -0.038 -0.74 -0.81 -1.9 -0.98 -1 -0.89 1.6 5.1e+03 0.017 5 1.1 ++
4 -0.3 -0.007 -0.77 -0.98 -2.4 -1.1 -1.1 -0.94 1.2 5.1e+03 0.011 50 1.1 ++
5 -0.32 -0.015 -0.77 -1 -2.6 -1.2 -1.1 -0.93 1.2 5.1e+03 0.00076 5e+02 1 ++
6 -0.32 -0.017 -0.77 -1 -2.6 -1.2 -1.1 -0.92 1.2 5.1e+03 2.8e-05 5e+03 1 ++
7 -0.32 -0.017 -0.77 -1 -2.6 -1.2 -1.1 -0.92 1.2 5.1e+03 4.5e-09 5e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 3/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME lambda_travel_t mu_public Function Relgrad Radius Rho
0 -0.29 -0.24 -0.28 -0.035 -0.45 0.35 0.14 -0.012 -0.46 -0.29 -0.51 -1 1.4 1 5.6e+03 0.084 1 0.89 +
1 -0.38 -0.47 -0.071 -0.1 -0.63 1.2 0.56 0.032 -0.48 -0.86 -1.5 -1.3 0.77 1.2 5e+03 0.028 10 0.94 ++
2 -0.38 -0.47 -0.071 -0.1 -0.63 1.2 0.56 0.032 -0.48 -0.86 -1.5 -1.3 0.77 1.2 5e+03 0.028 4.5 -1.6e+03 -
3 -0.38 -0.47 -0.071 -0.1 -0.63 1.2 0.56 0.032 -0.48 -0.86 -1.5 -1.3 0.77 1.2 5e+03 0.028 2.3 -4.4e+02 -
4 -0.38 -0.47 -0.071 -0.1 -0.63 1.2 0.56 0.032 -0.48 -0.86 -1.5 -1.3 0.77 1.2 5e+03 0.028 1.1 -42 -
5 -0.38 -0.47 -0.071 -0.1 -0.63 1.2 0.56 0.032 -0.48 -0.86 -1.5 -1.3 0.77 1.2 5e+03 0.028 0.57 -1.4 -
6 -0.33 -0.42 0.045 -0.16 -0.46 0.96 0.5 0.2 -0.96 -1.1 -1.6 -1.7 0.2 1.4 4.9e+03 0.019 0.57 0.66 +
7 -0.33 -0.42 0.045 -0.16 -0.46 0.96 0.5 0.2 -0.96 -1.1 -1.6 -1.7 0.2 1.4 4.9e+03 0.019 0.28 -1.4 -
8 -0.28 -0.38 -0.011 -0.37 -0.22 0.76 0.39 0.23 -1.1 -1.1 -1.8 -1.4 0.3 1.6 4.9e+03 0.0048 0.28 0.88 +
9 -0.31 -0.37 0.014 -0.33 -0.37 0.88 0.47 0.2 -1 -1.1 -1.9 -1.5 0.34 1.3 4.9e+03 0.0075 2.8 0.91 ++
10 -0.25 -0.29 0.0051 -0.3 -0.46 0.96 0.52 0.29 -1 -1.1 -1.9 -1.6 0.32 1.2 4.9e+03 0.0015 28 1.1 ++
11 -0.22 -0.26 0.0069 -0.3 -0.56 1 0.57 0.33 -1 -1.1 -1.9 -1.6 0.32 1.1 4.9e+03 0.0017 2.8e+02 1 ++
12 -0.21 -0.25 0.0078 -0.3 -0.59 1.1 0.59 0.34 -1 -1.1 -2 -1.6 0.32 1.1 4.9e+03 9.5e-05 2.8e+03 1.1 ++
13 -0.21 -0.25 0.008 -0.3 -0.6 1.1 0.59 0.35 -0.99 -1.1 -2 -1.6 0.32 1.1 4.9e+03 1.2e-05 2.8e+04 1 ++
14 -0.21 -0.25 0.008 -0.3 -0.6 1.1 0.59 0.35 -0.99 -1.1 -2 -1.6 0.32 1.1 4.9e+03 4.5e-09 2.8e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.26 -0.3 -0.057 -0.037 -0.64 0.45 0.12 -0.012 0.048 -0.98 -0.8 -0.88 -0.65 1.7 2 6.3e+03 0.24 1 0.29 +
1 -0.52 -0.053 -0.058 -0.12 -0.51 0.56 0.23 0.034 -0.69 -0.19 -0.78 -0.16 -0.32 1 3 5.8e+03 0.29 1 0.29 +
2 -0.38 0.24 0.11 -0.13 -0.74 0.44 0.15 0.01 -0.13 -0.71 -0.26 -0.56 -0.5 0.89 4 5.4e+03 0.19 1 0.38 +
3 -0.38 0.24 0.11 -0.13 -0.74 0.44 0.15 0.01 -0.13 -0.71 -0.26 -0.56 -0.5 0.89 4 5.4e+03 0.19 0.5 0.094 -
4 -0.64 0.39 0.07 -0.17 -0.64 0.71 0.31 0.014 -0.28 -0.52 -0.59 -0.24 -0.47 0.59 4.5 5.2e+03 0.14 0.5 0.52 +
5 -0.75 0.87 0.049 -0.52 -0.84 1.1 0.21 -0.36 -0.15 -0.62 -0.39 -0.44 -0.44 0.41 5 5.1e+03 0.026 0.5 0.76 +
6 -0.72 0.77 -0.025 -0.45 -0.84 1.1 0.2 -0.3 -0.21 -0.73 -0.48 -0.57 -0.49 0.4 4.5 5e+03 0.0078 5 1.1 ++
7 -0.72 0.77 -0.025 -0.45 -0.84 1.1 0.2 -0.3 -0.21 -0.73 -0.48 -0.57 -0.49 0.4 4.5 5e+03 0.0078 1.8 -1.4 -
8 -0.47 0.76 0.11 -0.4 -0.7 1.2 0.49 -0.1 -0.19 -0.55 -0.55 -0.71 -0.66 0.48 2.8 5e+03 0.024 1.8 0.77 +
9 -0.42 0.57 -0.051 -0.37 -0.81 1.2 0.47 0.081 -0.48 -0.96 -1 -1.1 -0.74 0.42 1.2 5e+03 0.034 1.8 0.51 +
10 -0.35 0.17 0.003 -0.34 -0.88 1.3 0.47 0.17 -0.74 -1.1 -1.4 -1.1 -0.69 0.43 1.4 4.9e+03 0.0059 18 1 ++
11 -0.36 0.16 -0.0067 -0.38 -0.84 1.3 0.5 0.19 -0.78 -1.1 -1.5 -1.2 -0.71 0.43 1.3 4.9e+03 0.00054 1.8e+02 1.1 ++
12 -0.36 0.15 -0.0073 -0.38 -0.84 1.3 0.5 0.19 -0.79 -1.1 -1.6 -1.2 -0.71 0.43 1.3 4.9e+03 1.6e-05 1.8e+03 1 ++
13 -0.36 0.15 -0.0073 -0.38 -0.84 1.3 0.5 0.19 -0.79 -1.1 -1.6 -1.2 -0.71 0.43 1.3 4.9e+03 8.9e-09 1.8e+03 1 ++
Considering neighbor 1/20 for current solution
Attempt 4/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 0.072 1 0.72 +
1 5.1e+03 0.034 10 1.1 ++
2 5.1e+03 0.034 5 -2.5e+02 -
3 5.1e+03 0.034 2.5 -12 -
4 5.1e+03 0.034 1.2 -2.1 -
5 5e+03 0.044 1.2 0.63 +
6 4.9e+03 0.0072 1.2 0.88 +
7 4.9e+03 0.00045 12 1 ++
8 4.9e+03 2.4e-06 12 1 ++
Considering neighbor 0/20 for current solution
Attempt 5/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.5 -4.8 -
1 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.25 -1.4 -
2 0.16 -0.053 -0.049 -0.25 -0.25 -0.16 -0.25 0.25 -0.25 -0.25 0 0 5.9e+03 3.2 2.5 1 ++
3 0.16 -0.053 -0.049 -0.25 -0.25 -0.16 -0.25 0.25 -0.25 -0.25 0 0 5.9e+03 3.2 1.2 -4.4 -
4 0.16 -0.053 -0.049 -0.25 -0.25 -0.16 -0.25 0.25 -0.25 -0.25 0 0 5.9e+03 3.2 0.62 -2.8 -
5 0.16 -0.053 -0.049 -0.25 -0.25 -0.16 -0.25 0.25 -0.25 -0.25 0 0 5.9e+03 3.2 0.31 -1.6 -
6 0.16 -0.053 -0.049 -0.25 -0.25 -0.16 -0.25 0.25 -0.25 -0.25 0 0 5.9e+03 3.2 0.16 -0.21 -
7 0.17 -0.057 -0.052 -0.34 -0.26 -0.16 -0.24 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.16 0.23 +
8 0.17 -0.057 -0.052 -0.34 -0.26 -0.16 -0.24 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.078 -0.88 -
9 0.17 -0.057 -0.052 -0.34 -0.26 -0.16 -0.24 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.039 -0.81 -
10 0.17 -0.057 -0.052 -0.34 -0.26 -0.16 -0.24 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.02 -0.77 -
11 0.17 -0.057 -0.052 -0.34 -0.26 -0.16 -0.24 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.0098 -0.74 -
12 0.17 -0.057 -0.052 -0.34 -0.26 -0.16 -0.24 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.0049 -0.5 -
13 0.17 -0.057 -0.052 -0.34 -0.26 -0.16 -0.24 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.0024 -0.0018 -
14 0.17 -0.057 -0.052 -0.34 -0.26 -0.16 -0.24 0.24 -0.37 -0.41 -0.00033 0.094 5.7e+03 6.9 0.0024 0.63 +
15 0.17 -0.057 -0.052 -0.34 -0.26 -0.16 -0.24 0.24 -0.37 -0.41 -0.00033 0.094 5.7e+03 6.9 0.0012 -0.96 -
16 0.17 -0.057 -0.052 -0.34 -0.26 -0.16 -0.24 0.24 -0.37 -0.41 -0.00033 0.094 5.7e+03 6.9 0.00061 -0.35 -
17 0.17 -0.058 -0.053 -0.34 -0.26 -0.16 -0.23 0.24 -0.37 -0.41 -0.00094 0.093 5.7e+03 4.1 0.00061 0.25 +
18 0.17 -0.058 -0.053 -0.34 -0.26 -0.16 -0.23 0.24 -0.37 -0.41 -0.00094 0.093 5.7e+03 4.1 0.00031 -0.45 -
19 0.17 -0.058 -0.053 -0.34 -0.26 -0.16 -0.23 0.24 -0.37 -0.41 -0.00064 0.094 5.7e+03 1.7 0.00031 0.82 +
20 0.17 -0.058 -0.053 -0.34 -0.26 -0.16 -0.23 0.24 -0.37 -0.41 -0.00066 0.094 5.7e+03 0.058 0.0031 1 ++
21 0.17 -0.058 -0.053 -0.34 -0.26 -0.16 -0.23 0.24 -0.37 -0.41 -0.00067 0.095 5.7e+03 0.19 0.031 1 ++
22 0.18 -0.059 -0.054 -0.35 -0.25 -0.16 -0.23 0.21 -0.4 -0.43 -0.0007 0.1 5.6e+03 0.053 0.31 1 ++
23 0.21 -0.079 -0.066 -0.42 -0.14 -0.16 -0.18 -0.099 -0.63 -0.63 -0.0011 0.19 5.5e+03 0.28 3.1 0.97 ++
24 0.21 -0.079 -0.066 -0.42 -0.14 -0.16 -0.18 -0.099 -0.63 -0.63 -0.0011 0.19 5.5e+03 0.28 1.5 -46 -
25 0.21 -0.079 -0.066 -0.42 -0.14 -0.16 -0.18 -0.099 -0.63 -0.63 -0.0011 0.19 5.5e+03 0.28 0.76 -1.9 -
26 -0.18 -0.31 -0.13 -0.41 0.44 -0.13 -0.46 -0.64 -1.4 -0.99 0.00042 -0.17 5.4e+03 20 0.76 0.3 +
27 -0.18 -0.31 -0.13 -0.41 0.44 -0.13 -0.46 -0.64 -1.4 -0.99 0.00042 -0.17 5.4e+03 20 0.38 -0.11 -
28 -0.13 -0.23 -0.13 -0.49 0.41 -0.12 -0.43 -0.67 -1.5 -1.4 -0.00039 0.044 5.2e+03 24 0.38 0.43 +
29 -0.13 -0.23 -0.13 -0.49 0.41 -0.12 -0.43 -0.67 -1.5 -1.4 -0.00039 0.044 5.2e+03 24 0.19 -3 -
30 -0.13 -0.23 -0.13 -0.49 0.41 -0.12 -0.43 -0.67 -1.5 -1.4 -0.00039 0.044 5.2e+03 24 0.095 0.076 -
31 -0.13 -0.22 -0.13 -0.48 0.42 -0.12 -0.42 -0.69 -1.5 -1.4 -0.00019 -0.051 5.1e+03 20 0.095 0.71 +
32 -0.13 -0.22 -0.13 -0.48 0.42 -0.12 -0.42 -0.69 -1.5 -1.4 -0.00019 -0.051 5.1e+03 20 0.048 -0.29 -
33 -0.13 -0.22 -0.13 -0.48 0.42 -0.12 -0.42 -0.69 -1.5 -1.4 -0.00019 -0.051 5.1e+03 20 0.024 -0.22 -
34 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 0.00015 -0.075 5.1e+03 23 0.024 0.23 +
35 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 0.00015 -0.075 5.1e+03 23 0.012 -3 -
36 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 0.00015 -0.075 5.1e+03 23 0.006 -3.1 -
37 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 0.00015 -0.075 5.1e+03 23 0.003 -3.2 -
38 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 0.00015 -0.075 5.1e+03 23 0.0015 -3.2 -
39 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 0.00015 -0.075 5.1e+03 23 0.00075 -2 -
40 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 0.00015 -0.075 5.1e+03 23 0.00037 -1.3 -
41 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 0.00015 -0.075 5.1e+03 23 0.00019 -0.63 -
42 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 -3.4e-05 -0.075 5.1e+03 23 0.00019 0.13 +
43 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 -3.4e-05 -0.075 5.1e+03 23 9.3e-05 -0.15 -
44 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 5.9e-05 -0.075 5.1e+03 5 9.3e-05 0.83 +
45 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 5.2e-05 -0.075 5.1e+03 0.034 0.00093 1 ++
46 -0.14 -0.22 -0.13 -0.48 0.43 -0.12 -0.43 -0.68 -1.5 -1.4 4.8e-05 -0.074 5.1e+03 0.53 0.0093 1 ++
47 -0.15 -0.23 -0.14 -0.48 0.43 -0.12 -0.44 -0.67 -1.5 -1.4 7.1e-06 -0.065 5.1e+03 0.27 0.093 1 ++
48 -0.22 -0.21 -0.14 -0.46 0.48 -0.11 -0.53 -0.62 -1.6 -1.5 0.00023 -0.12 5.1e+03 13 0.093 0.59 +
49 -0.19 -0.16 -0.15 -0.46 0.53 -0.096 -0.51 -0.71 -1.7 -1.5 0.0001 -0.086 5e+03 0.59 0.93 0.96 ++
50 -0.26 -0.034 -0.28 -0.065 0.75 0.28 -0.79 -1.1 -2.7 -2.3 0.00037 -0.15 5e+03 13 0.93 0.72 +
51 -0.32 -0.01 -0.35 -0.0066 0.79 0.44 -0.76 -1.2 -2.8 -2.4 0.00019 -0.1 5e+03 34 0.93 0.44 +
52 -0.27 -0.0086 -0.35 -0.039 0.8 0.46 -0.83 -1.1 -2.8 -2.6 0.00027 -0.13 4.9e+03 38 0.93 0.4 +
53 -0.32 -5.7e-05 -0.35 -0.11 0.8 0.46 -0.84 -1.1 -2.8 -2.3 0.00022 -0.11 4.9e+03 1 9.3 0.9 ++
54 -0.3 0.00096 -0.34 -0.12 0.8 0.48 -0.85 -1.1 -2.8 -2.3 0.00024 -0.12 4.9e+03 0.35 93 1 ++
55 -0.31 0.0015 -0.34 -0.12 0.8 0.48 -0.85 -1.1 -2.8 -2.3 0.00024 -0.12 4.9e+03 0.0046 9.3e+02 1 ++
56 -0.31 0.0017 -0.34 -0.12 0.8 0.48 -0.85 -1.1 -2.8 -2.3 0.00024 -0.12 4.9e+03 4.1e-05 9.3e+03 1 ++
57 -0.31 0.0017 -0.34 -0.12 0.8 0.48 -0.85 -1.1 -2.8 -2.3 0.00024 -0.12 4.9e+03 4.5e-07 9.3e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 6/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_comm B_TIME_SM B_TIME_SM_commu B_TIME_TRAIN B_TIME_TRAIN_co lambda_travel_t mu_public Function Relgrad Radius Rho
0 -0.54 -0.32 -0.046 -1 0.063 -0.023 -0.99 -0.69 0.089 -0.48 -0.19 -0.87 -0.13 1.9 1 5.6e+03 0.051 1 0.74 +
1 -0.38 0.23 -0.12 -1.2 1.1 0.083 -1 -0.94 0.29 -0.8 0.34 -0.86 -0.37 1.5 1.2 5.3e+03 0.031 10 0.91 ++
2 -0.38 0.23 -0.12 -1.2 1.1 0.083 -1 -0.94 0.29 -0.8 0.34 -0.86 -0.37 1.5 1.2 5.3e+03 0.031 5 -3.1e+304 -
3 -0.38 0.23 -0.12 -1.2 1.1 0.083 -1 -0.94 0.29 -0.8 0.34 -0.86 -0.37 1.5 1.2 5.3e+03 0.031 2.5 -13 -
4 -0.38 0.23 -0.12 -1.2 1.1 0.083 -1 -0.94 0.29 -0.8 0.34 -0.86 -0.37 1.5 1.2 5.3e+03 0.031 1.2 -0.86 -
5 -0.22 -0.0039 -0.21 -1.1 1.1 0.64 -1.3 -1.5 0.26 -1.2 -0.1 -1.7 -0.66 0.2 1.2 5.2e+03 0.028 1.2 0.74 +
6 -0.076 0.051 -0.38 -0.98 1.1 0.66 -1 -1.8 0.26 -1.2 -0.83 -2.3 -0.85 -0.058 1 5.1e+03 0.015 12 1.1 ++
7 -0.079 0.051 -0.38 -1 1.1 0.66 -1 -1.8 0.25 -1.2 -0.84 -2.4 -0.86 0.026 1 5.1e+03 0.0051 1.2e+02 1.1 ++
8 -0.037 0.053 -0.39 -1 1.2 0.67 -1.1 -1.9 0.22 -1.3 -0.92 -2.6 -0.89 -0.015 1 5.1e+03 0.00041 1.2e+03 1 ++
9 -0.037 0.053 -0.39 -1 1.2 0.67 -1.1 -1.9 0.22 -1.3 -0.92 -2.6 -0.89 -0.015 1 5.1e+03 5.2e-07 1.2e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 7/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME mu_existing Function Relgrad Radius Rho
0 0.14 -0.23 -0.82 0.47 -1 -0.84 1.5 5.3e+03 0.084 10 0.93 ++
1 -0.26 0.05 -0.83 1.6 -0.85 -0.91 1.8 5e+03 0.018 10 0.87 +
2 -0.24 -0.013 -0.98 1.6 -1 -1.1 1.4 5e+03 0.0065 1e+02 0.94 ++
3 -0.23 0.06 -1 1.6 -0.99 -1.1 1.4 5e+03 0.00029 1e+03 1 ++
4 -0.23 0.06 -1 1.6 -0.99 -1.1 1.4 5e+03 4.1e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME mu_existing Function Relgrad Radius Rho
0 -0.019 -0.44 -0.48 0.75 -0.46 -0.89 -1 -1 1.7 5.2e+03 0.12 10 0.91 ++
1 -0.45 0.22 -0.6 1.2 -0.62 -0.86 -1.2 -0.94 1.7 5e+03 0.0044 1e+02 1 ++
2 -0.45 0.22 -0.6 1.2 -0.62 -0.86 -1.2 -0.94 1.7 5e+03 0.0044 0.31 4e-05 -
3 -0.45 0.18 -0.61 1.3 -0.7 -0.98 -1.4 -1.1 1.4 5e+03 0.0075 0.31 0.85 +
4 -0.44 0.095 -0.61 1.3 -0.78 -1 -1.5 -1.1 1.3 5e+03 0.00098 3.1 1.1 ++
5 -0.44 0.07 -0.61 1.3 -0.79 -1 -1.6 -1.1 1.3 5e+03 5.4e-05 31 1 ++
6 -0.44 0.07 -0.61 1.3 -0.79 -1 -1.6 -1.1 1.3 5e+03 4.4e-08 31 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME mu_existing Function Relgrad Radius Rho
0 0.095 -0.23 -0.11 -0.028 -0.75 0.47 0.059 -0.01 -1 -0.9 1.4 5.2e+03 0.049 10 0.97 ++
1 -0.28 0.1 0.071 -0.22 -1.2 1.5 0.48 0.31 -0.93 -0.97 1.6 5e+03 0.0081 1e+02 0.96 ++
2 -0.28 0.033 0.079 -0.24 -1.4 1.5 0.56 0.39 -1 -1.1 1.4 5e+03 0.0018 1e+03 0.99 ++
3 -0.28 0.047 0.077 -0.24 -1.4 1.5 0.57 0.4 -1 -1.1 1.4 5e+03 2e-05 1e+04 1 ++
4 -0.28 0.047 0.077 -0.24 -1.4 1.5 0.57 0.4 -1 -1.1 1.4 5e+03 1.8e-08 1e+04 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 8/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.4 0.5 -0.51 -
1 -0.018 -0.28 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.033 1.2 0.0041 6.1e+03 1.6 0.5 0.41 +
2 -0.018 -0.28 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.033 1.2 0.0041 6.1e+03 1.6 0.25 -6.9 -
3 -0.018 -0.28 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.033 1.2 0.0041 6.1e+03 1.6 0.12 -8.2 -
4 -0.018 -0.28 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.033 1.2 0.0041 6.1e+03 1.6 0.062 -7.4 -
5 -0.018 -0.28 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.033 1.2 0.0041 6.1e+03 1.6 0.031 -2.2 -
6 -0.02 -0.27 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0018 1.2 -0.0037 5.7e+03 0.73 0.31 0.99 ++
7 -0.02 -0.27 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0018 1.2 -0.0037 5.7e+03 0.73 0.16 -7.2 -
8 -0.02 -0.27 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0018 1.2 -0.0037 5.7e+03 0.73 0.078 -6.1 -
9 -0.02 -0.27 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0018 1.2 -0.0037 5.7e+03 0.73 0.039 -5.6 -
10 -0.02 -0.27 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0018 1.2 -0.0037 5.7e+03 0.73 0.02 -5.6 -
11 -0.02 -0.27 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0018 1.2 -0.0037 5.7e+03 0.73 0.0098 -6.1 -
12 -0.02 -0.27 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0018 1.2 -0.0037 5.7e+03 0.73 0.0049 -6.9 -
13 -0.02 -0.27 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0018 1.2 -0.0037 5.7e+03 0.73 0.0024 -1.9 -
14 -0.022 -0.27 -0.05 -0.046 -0.045 0.23 0.14 -0.49 -0.33 -0.00068 1.2 -0.0062 5.7e+03 0.89 0.0024 0.65 +
15 -0.023 -0.27 -0.052 -0.048 -0.046 0.23 0.14 -0.49 -0.33 -0.00014 1.2 -0.0071 5.7e+03 0.13 0.024 1 ++
16 -0.034 -0.27 -0.075 -0.073 -0.058 0.23 0.14 -0.48 -0.32 0.00013 1.2 -0.017 5.7e+03 0.36 0.24 0.99 ++
17 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.069 -0.43 -0.31 -0.0012 1.3 -0.055 5.6e+03 12 0.24 0.47 +
18 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.069 -0.43 -0.31 -0.0012 1.3 -0.055 5.6e+03 12 0.12 -0.42 -
19 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.069 -0.43 -0.31 -0.0012 1.3 -0.055 5.6e+03 12 0.061 -0.26 -
20 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.069 -0.43 -0.31 -0.0012 1.3 -0.055 5.6e+03 12 0.031 -0.19 -
21 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.069 -0.43 -0.31 -0.0012 1.3 -0.055 5.6e+03 12 0.015 -0.16 -
22 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.069 -0.43 -0.31 -0.0012 1.3 -0.055 5.6e+03 12 0.0076 -0.15 -
23 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.069 -0.43 -0.31 -0.0012 1.3 -0.055 5.6e+03 12 0.0038 -0.14 -
24 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.069 -0.43 -0.31 -0.0012 1.3 -0.055 5.6e+03 12 0.0019 0.093 -
25 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.067 -0.43 -0.32 0.0007 1.3 -0.053 5.5e+03 3.7 0.0019 0.47 +
26 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.067 -0.43 -0.32 0.0007 1.3 -0.053 5.5e+03 3.7 0.00095 -0.97 -
27 -0.1 -0.22 -0.32 -0.26 -0.14 0.16 0.066 -0.43 -0.32 -0.00025 1.3 -0.052 5.5e+03 6.6 0.00095 0.46 +
28 -0.1 -0.22 -0.32 -0.26 -0.14 0.15 0.066 -0.43 -0.32 0.00026 1.3 -0.051 5.5e+03 3.4 0.00095 0.25 +
29 -0.1 -0.22 -0.32 -0.26 -0.14 0.15 0.066 -0.43 -0.32 0.00026 1.3 -0.051 5.5e+03 3.4 0.00048 -1.2 -
30 -0.1 -0.23 -0.32 -0.26 -0.14 0.15 0.065 -0.44 -0.32 -0.00022 1.3 -0.051 5.5e+03 6 0.00048 0.15 +
31 -0.1 -0.23 -0.32 -0.26 -0.14 0.15 0.065 -0.44 -0.32 0.00013 1.3 -0.051 5.5e+03 2.9 0.00048 0.43 +
32 -0.1 -0.23 -0.32 -0.26 -0.14 0.15 0.065 -0.44 -0.32 0.00013 1.3 -0.051 5.5e+03 2.9 0.00024 -1 -
33 -0.1 -0.23 -0.32 -0.26 -0.14 0.15 0.065 -0.44 -0.32 -0.00011 1.3 -0.05 5.5e+03 3 0.00024 0.42 +
34 -0.1 -0.23 -0.32 -0.26 -0.13 0.15 0.065 -0.44 -0.32 -2.1e-05 1.3 -0.05 5.5e+03 0.11 0.0024 0.97 ++
35 -0.098 -0.23 -0.33 -0.26 -0.13 0.15 0.063 -0.44 -0.32 -3.9e-05 1.3 -0.048 5.5e+03 0.15 0.024 1 ++
36 -0.084 -0.24 -0.35 -0.24 -0.12 0.14 0.052 -0.46 -0.33 -0.00013 1.3 -0.026 5.5e+03 0.053 0.24 1 ++
37 -0.036 -0.26 -0.59 -0.26 -0.11 -0.017 -0.069 -0.52 -0.38 -0.00061 1.4 0.086 5.3e+03 0.72 0.24 0.9 +
38 -0.068 -0.2 -0.81 -0.38 -0.24 -0.25 -0.24 -0.43 -0.31 -0.00096 1.6 0.18 5.2e+03 1.8 0.24 0.59 +
39 -0.13 -0.25 -0.98 -0.33 -0.37 -0.29 -0.4 -0.53 -0.32 -0.0012 1.8 0.23 5.2e+03 1.3 2.4 0.94 ++
40 -0.13 -0.25 -0.98 -0.33 -0.37 -0.29 -0.4 -0.53 -0.32 -0.0012 1.8 0.23 5.2e+03 1.3 1.2 -2.5e+02 -
41 -0.13 -0.25 -0.98 -0.33 -0.37 -0.29 -0.4 -0.53 -0.32 -0.0012 1.8 0.23 5.2e+03 1.3 0.6 -99 -
42 -0.13 -0.25 -0.98 -0.33 -0.37 -0.29 -0.4 -0.53 -0.32 -0.0012 1.8 0.23 5.2e+03 1.3 0.3 -9.7 -
43 -0.13 -0.25 -0.98 -0.33 -0.37 -0.29 -0.4 -0.53 -0.32 -0.0012 1.8 0.23 5.2e+03 1.3 0.15 0.041 -
44 -0.24 -0.26 -0.95 -0.31 -0.46 -0.26 -0.45 -0.55 -0.47 -0.00058 1.9 0.084 5.1e+03 0.13 1.5 0.94 ++
45 -0.24 -0.26 -0.95 -0.31 -0.46 -0.26 -0.45 -0.55 -0.47 -0.00058 1.9 0.084 5.1e+03 0.13 0.75 -2e+02 -
46 -0.24 -0.26 -0.95 -0.31 -0.46 -0.26 -0.45 -0.55 -0.47 -0.00058 1.9 0.084 5.1e+03 0.13 0.37 -30 -
47 -0.39 -0.27 -0.93 -0.36 -0.72 -0.45 -0.82 -0.71 -0.69 -0.00014 2.2 -0.025 5.1e+03 0.74 3.7 1.2 ++
48 -0.39 -0.27 -0.93 -0.36 -0.72 -0.45 -0.82 -0.71 -0.69 -0.00014 2.2 -0.025 5.1e+03 0.74 0.49 -7 -
49 -0.39 -0.27 -0.93 -0.36 -0.72 -0.45 -0.82 -0.71 -0.69 -0.00014 2.2 -0.025 5.1e+03 0.74 0.24 -0.1 -
50 -0.36 -0.14 -0.88 -0.48 -0.9 -0.7 -0.89 -0.92 -0.88 8.8e-05 2.3 -0.077 5.1e+03 0.91 2.4 1.2 ++
51 -0.34 0.036 -0.94 -0.96 -0.87 -1.3 -0.77 -1.5 -0.87 0.00027 2.1 -0.12 5.1e+03 12 2.4 0.77 +
52 -0.32 0.18 -0.96 -1.1 -0.81 -1.4 -0.7 -1.7 -0.79 0.00021 2.1 -0.11 5.1e+03 4.8 2.4 0.83 +
53 -0.32 0.12 -0.96 -1.1 -0.77 -1.5 -0.63 -1.7 -0.77 0.00022 2.1 -0.11 5.1e+03 0.18 24 1 ++
54 -0.33 0.12 -0.95 -1.1 -0.68 -1.5 -0.51 -1.8 -0.68 0.00022 2.1 -0.11 5.1e+03 0.044 2.4e+02 1 ++
55 -0.33 0.13 -0.95 -1.1 -0.68 -1.6 -0.52 -1.8 -0.69 0.00022 2.1 -0.11 5.1e+03 0.00051 2.4e+03 1 ++
56 -0.33 0.12 -0.95 -1.1 -0.68 -1.6 -0.51 -1.8 -0.69 0.00022 2.1 -0.11 5.1e+03 2.9e-05 2.4e+04 1 ++
57 -0.33 0.13 -0.95 -1.1 -0.68 -1.6 -0.51 -1.8 -0.69 0.00022 2.1 -0.11 5.1e+03 0.0052 2.4e+05 1 ++
58 -0.33 0.13 -0.95 -1.1 -0.68 -1.6 -0.51 -1.8 -0.69 0.00022 2.1 -0.11 5.1e+03 0.00031 2.4e+06 1 ++
59 -0.33 0.13 -0.95 -1.1 -0.68 -1.6 -0.51 -1.8 -0.69 0.00022 2.1 -0.11 5.1e+03 8e-05 2.4e+07 1 ++
60 -0.33 0.13 -0.95 -1.1 -0.68 -1.6 -0.51 -1.8 -0.69 0.00022 2.1 -0.11 5.1e+03 4.6e-05 2.4e+08 1 ++
61 -0.33 0.13 -0.95 -1.1 -0.68 -1.6 -0.51 -1.8 -0.69 0.00022 2.1 -0.11 5.1e+03 1.2e-05 2.4e+08 1 ++
Considering neighbor 0/20 for current solution
Attempt 9/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME B_TIME_commuter mu_public Function Relgrad Radius Rho
0 -0.41 -0.26 -0.91 0.65 -1 -0.85 0.33 1 5.2e+03 0.04 10 1.1 ++
1 -0.41 -0.26 -0.91 0.65 -1 -0.85 0.33 1 5.2e+03 0.04 0.85 -0.087 -
2 -0.38 -0.34 -0.83 1.5 -1.1 -1.2 0.005 1.3 5.1e+03 0.02 0.85 0.9 +
3 -0.37 -0.46 -0.72 1.4 -1.1 -0.94 -0.39 1.5 5e+03 0.0078 0.85 0.84 +
4 -0.36 -0.45 -0.74 1.4 -1.1 -0.99 -0.32 1.5 5e+03 0.00027 8.5 1 ++
5 -0.35 -0.44 -0.77 1.4 -1.1 -1 -0.31 1.5 5e+03 0.00021 85 1 ++
6 -0.35 -0.44 -0.77 1.4 -1.1 -1 -0.31 1.5 5e+03 3.2e-07 85 1 ++
Considering neighbor 0/20 for current solution
Attempt 10/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.098 -0.4 -0.92 0.52 -0.32 -0.97 -1 -0.88 -0.64 -0.61 1.6 1.9 5.9e+03 0.21 1 0.53 +
1 0.098 -0.4 -0.92 0.52 -0.32 -0.97 -1 -0.88 -0.64 -0.61 1.6 1.9 5.9e+03 0.21 0.5 0.048 -
2 -0.4 -0.4 -0.57 0.69 -0.64 -0.66 -0.91 -0.64 -0.64 -0.6 1.1 1.9 5.1e+03 0.046 0.5 0.87 +
3 -0.35 -0.26 -0.52 0.98 -0.65 -0.76 -1.1 -0.87 -0.69 -0.91 0.58 2 5e+03 0.013 5 1.1 ++
4 -0.18 -0.12 -0.21 1.2 -0.86 -1 -1.7 -1.7 -1.5 -1.7 -0.076 1 5e+03 0.031 5 0.36 +
5 -0.24 -0.15 -0.21 1.3 -0.77 -1.1 -1.7 -1.9 -1.6 -2.1 0.24 1.2 4.9e+03 0.0033 50 0.91 ++
6 -0.22 0.017 -0.22 1.3 -0.81 -1 -1.6 -1.8 -1.4 -2.1 0.095 1.3 4.9e+03 0.0016 5e+02 1.1 ++
7 -0.22 0.037 -0.22 1.3 -0.8 -0.99 -1.5 -1.7 -1.4 -2.1 0.063 1.4 4.9e+03 0.00028 5e+03 1.1 ++
8 -0.22 0.037 -0.22 1.3 -0.8 -0.99 -1.5 -1.7 -1.4 -2.1 0.063 1.4 4.9e+03 4.4e-06 5e+03 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.5 -4.8 -
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.25 -1.4 -
2 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 0 0 5.8e+03 3.2 2.5 1 ++
3 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 0 0 5.8e+03 3.2 1.2 -4.3 -
4 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 0 0 5.8e+03 3.2 0.62 -2.7 -
5 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 0 0 5.8e+03 3.2 0.31 -1.5 -
6 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 0 0 5.8e+03 3.2 0.16 -0.17 -
7 0.12 -0.27 -0.043 -0.037 -0.34 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.16 0.24 +
8 0.12 -0.27 -0.043 -0.037 -0.34 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.078 -0.9 -
9 0.12 -0.27 -0.043 -0.037 -0.34 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.039 -0.83 -
10 0.12 -0.27 -0.043 -0.037 -0.34 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.02 -0.79 -
11 0.12 -0.27 -0.043 -0.037 -0.34 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.0098 -0.76 -
12 0.12 -0.27 -0.043 -0.037 -0.34 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.0049 -0.44 -
13 0.12 -0.27 -0.043 -0.037 -0.34 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.0028 0.092 5.8e+03 12 0.0024 0.033 -
14 0.12 -0.27 -0.043 -0.037 -0.35 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.00041 0.094 5.6e+03 6.8 0.0024 0.68 +
15 0.12 -0.27 -0.043 -0.037 -0.35 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.00041 0.094 5.6e+03 6.8 0.0012 -1.2 -
16 0.12 -0.27 -0.043 -0.037 -0.35 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.00041 0.094 5.6e+03 6.8 0.00061 -0.53 -
17 0.12 -0.27 -0.043 -0.037 -0.35 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.00041 0.094 5.6e+03 6.8 0.00031 0.055 -
18 0.12 -0.27 -0.042 -0.038 -0.35 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.00071 0.094 5.6e+03 2.2 0.00031 0.62 +
19 0.12 -0.27 -0.042 -0.038 -0.35 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.00064 0.094 5.6e+03 1.3 0.00031 0.75 +
20 0.12 -0.27 -0.042 -0.038 -0.35 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.00066 0.094 5.6e+03 0.057 0.0031 1 ++
21 0.12 -0.27 -0.042 -0.038 -0.35 0.11 -0.26 -0.12 -0.18 0.24 -0.37 -0.41 -0.00067 0.095 5.6e+03 0.25 0.031 1 ++
22 0.13 -0.28 -0.041 -0.039 -0.36 0.13 -0.25 -0.12 -0.17 0.21 -0.39 -0.43 -0.00071 0.11 5.6e+03 0.056 0.31 1 ++
23 0.18 -0.35 -0.049 -0.048 -0.45 0.27 -0.15 -0.12 -0.12 -0.098 -0.62 -0.65 -0.0011 0.21 5.4e+03 0.2 3.1 0.96 ++
24 0.18 -0.35 -0.049 -0.048 -0.45 0.27 -0.15 -0.12 -0.12 -0.098 -0.62 -0.65 -0.0011 0.21 5.4e+03 0.2 1.5 -68 -
25 0.18 -0.35 -0.049 -0.048 -0.45 0.27 -0.15 -0.12 -0.12 -0.098 -0.62 -0.65 -0.0011 0.21 5.4e+03 0.2 0.76 -7.6 -
26 -0.19 -0.6 -0.25 -0.1 -0.56 0.95 0.36 -0.098 -0.43 -0.73 -1.4 -1.1 0.00042 -0.17 5.3e+03 22 0.76 0.25 +
27 -0.19 -0.6 -0.25 -0.1 -0.56 0.95 0.36 -0.098 -0.43 -0.73 -1.4 -1.1 0.00042 -0.17 5.3e+03 22 0.38 0.048 -
28 -0.19 -0.6 -0.21 -0.11 -0.65 0.95 0.32 -0.094 -0.45 -0.72 -1.5 -1.5 -0.00033 0.029 5.1e+03 26 0.38 0.47 +
29 -0.19 -0.6 -0.21 -0.11 -0.65 0.95 0.32 -0.094 -0.45 -0.72 -1.5 -1.5 -0.00033 0.029 5.1e+03 26 0.19 -2.6 -
30 -0.19 -0.6 -0.21 -0.11 -0.65 0.95 0.32 -0.094 -0.45 -0.72 -1.5 -1.5 -0.00033 0.029 5.1e+03 26 0.095 -0.16 -
31 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 -0.00013 -0.066 5e+03 27 0.095 0.66 +
32 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 -0.00013 -0.066 5e+03 27 0.048 -0.6 -
33 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 -0.00013 -0.066 5e+03 27 0.024 -0.74 -
34 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 -0.00013 -0.066 5e+03 27 0.012 -0.42 -
35 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 -0.00013 -0.066 5e+03 27 0.006 -0.23 -
36 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 -0.00013 -0.066 5e+03 27 0.003 -0.14 -
37 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 -0.00013 -0.066 5e+03 27 0.0015 -0.091 -
38 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 -0.00013 -0.066 5e+03 27 0.00075 -0.067 -
39 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 -0.00013 -0.066 5e+03 27 0.00037 -0.055 -
40 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 -0.00013 -0.066 5e+03 27 0.00019 -0.049 -
41 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 6.1e-05 -0.066 5e+03 24 0.00019 0.52 +
42 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.4 -1.5 6.1e-05 -0.066 5e+03 24 9.3e-05 -0.59 -
43 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.5 -1.5 -3.2e-05 -0.066 5e+03 15 9.3e-05 0.16 +
44 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.5 -1.5 -3.2e-05 -0.066 5e+03 15 4.7e-05 -0.3 -
45 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.5 -1.5 1.5e-05 -0.066 5e+03 1.2 4.7e-05 0.79 +
46 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.5 -1.5 1.3e-05 -0.066 5e+03 0.026 0.00047 1 ++
47 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.093 -0.44 -0.74 -1.5 -1.5 1.3e-05 -0.066 5e+03 0.075 0.0047 1 ++
48 -0.19 -0.6 -0.2 -0.11 -0.65 0.95 0.32 -0.092 -0.45 -0.74 -1.5 -1.5 1.2e-05 -0.065 5e+03 0.023 0.047 1 ++
49 -0.23 -0.6 -0.19 -0.11 -0.66 0.97 0.33 -0.089 -0.49 -0.71 -1.5 -1.5 9.2e-05 -0.085 5e+03 1.8 0.47 0.98 ++
50 -0.28 -0.59 0.036 -0.15 -0.79 1.1 0.42 -0.048 -0.69 -0.91 -1.7 -2 0.00029 -0.13 4.9e+03 8.6 0.47 0.85 +
51 -0.34 -0.19 0.0082 -0.31 -0.71 1.2 0.63 0.4 -0.77 -1.1 -1.9 -2.4 0.0002 -0.11 4.9e+03 12 0.47 0.86 +
52 -0.33 -0.19 0.0061 -0.31 -0.76 1.2 0.66 0.4 -0.81 -1.1 -1.9 -2.4 0.00023 -0.11 4.9e+03 0.044 4.7 0.95 ++
53 -0.34 -0.19 0.0068 -0.31 -0.77 1.2 0.66 0.41 -0.81 -1.1 -1.9 -2.3 0.00023 -0.11 4.9e+03 0.037 47 1 ++
54 -0.34 -0.19 0.0069 -0.31 -0.77 1.2 0.66 0.41 -0.81 -1.1 -1.9 -2.3 0.00023 -0.11 4.9e+03 8.2e-06 4.7e+02 1 ++
55 -0.34 -0.19 0.0069 -0.31 -0.77 1.2 0.66 0.41 -0.81 -1.1 -1.9 -2.3 0.00023 -0.11 4.9e+03 9.7e-08 4.7e+02 1 ++
Considering neighbor 1/20 for current solution
Attempt 11/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 7e+03 0.4 0.5 -0.72 -
1 6.5e+03 1.7 0.5 0.18 +
2 6.5e+03 1.7 0.25 -4.3 -
3 6.5e+03 1.7 0.12 -4.5 -
4 6.5e+03 1.7 0.062 -2.3 -
5 5.9e+03 4.3 0.62 0.94 ++
6 5.9e+03 4.3 0.31 -0.13 -
7 5.7e+03 0.97 0.31 0.48 +
8 5.7e+03 0.97 0.16 -6.7 -
9 5.7e+03 0.97 0.078 -6.8 -
10 5.7e+03 0.97 0.039 -7.3 -
11 5.7e+03 0.97 0.02 -8.1 -
12 5.7e+03 0.97 0.0098 -11 -
13 5.7e+03 0.97 0.0049 -2.5 -
14 5.7e+03 0.91 0.0049 0.86 +
15 5.6e+03 0.2 0.049 1 ++
16 5.6e+03 0.12 0.49 1 ++
17 5.6e+03 0.12 0.24 -0.62 -
18 5.5e+03 15 0.24 0.22 +
19 5.5e+03 15 0.12 -0.65 -
20 5.5e+03 15 0.061 -0.47 -
21 5.5e+03 15 0.031 -0.39 -
22 5.5e+03 15 0.015 -0.36 -
23 5.5e+03 15 0.0076 -0.35 -
24 5.5e+03 15 0.0038 -0.24 -
25 5.4e+03 6.9 0.0038 0.23 +
26 5.4e+03 6.9 0.0019 -0.26 -
27 5.3e+03 2.7 0.019 0.95 ++
28 5.3e+03 0.33 0.19 1 ++
29 5.2e+03 0.43 1.9 0.96 ++
30 5e+03 2.5 19 1.1 ++
31 5e+03 2.5 9.5 -1.3e+305 -
32 5e+03 2.5 4.8 -2.1e+305 -
33 5e+03 2.5 2.4 -2.9e+02 -
34 5e+03 2.5 1.2 -91 -
35 5e+03 2.5 0.6 -24 -
36 5e+03 2.5 0.3 -5.8 -
37 5e+03 2.5 0.15 -1.4 -
38 5e+03 2.2 0.15 0.18 +
39 5e+03 6.6 1.5 1 ++
40 5e+03 6.6 0.75 -12 -
41 5e+03 6.6 0.37 -1.3 -
42 5e+03 13 0.37 0.25 +
43 4.9e+03 15 0.37 0.38 +
44 4.9e+03 15 0.19 -1.7 -
45 4.9e+03 15 0.093 -0.92 -
46 4.9e+03 15 0.047 -0.43 -
47 4.9e+03 17 0.047 0.73 +
48 4.9e+03 0.88 0.47 1 ++
49 4.9e+03 2.3 4.7 0.92 ++
50 4.9e+03 0.12 47 1.1 ++
51 4.9e+03 0.023 4.7e+02 1 ++
52 4.9e+03 2.6e-06 4.7e+02 1 ++
Considering neighbor 0/20 for current solution
Attempt 12/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 7e+03 0.27 0.5 -7.8 -
1 7e+03 0.27 0.25 -2.4 -
2 5.7e+03 7.4 2.5 1 ++
3 5.7e+03 7.4 1.2 -7.4 -
4 5.7e+03 7.4 0.62 -4.9 -
5 5.7e+03 7.4 0.31 -3.4 -
6 5.7e+03 7.4 0.16 -2.7 -
7 5.7e+03 7.4 0.078 -2.5 -
8 5.7e+03 7.4 0.039 -2.5 -
9 5.7e+03 7.4 0.02 -2.7 -
10 5.7e+03 7.4 0.0098 -2.9 -
11 5.7e+03 7.4 0.0049 -3.1 -
12 5.7e+03 7.4 0.0024 -2.3 -
13 5.7e+03 7.4 0.0012 -1.5 -
14 5.7e+03 7.4 0.00061 -0.81 -
15 5.7e+03 7.4 0.00031 -0.082 -
16 5.7e+03 3.1 0.00031 0.67 +
17 5.7e+03 0.81 0.0031 0.91 ++
18 5.7e+03 0.075 0.031 1 ++
19 5.7e+03 0.5 0.31 1 ++
20 5.4e+03 6.5 0.31 0.81 +
21 5.2e+03 9.7 0.31 0.58 +
22 5e+03 4.7 3.1 0.93 ++
23 4.9e+03 5.9 31 1.1 ++
24 4.8e+03 19 31 0.33 +
25 4.8e+03 39 31 0.45 +
26 4.8e+03 4.2 31 0.75 +
27 4.8e+03 0.19 3.1e+02 1 ++
28 4.8e+03 0.0018 3.1e+03 1 ++
29 4.8e+03 6.6e-07 3.1e+03 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic;model_catalog:nested existing;train_tt_catalog:power [4831.877155138444, 16]
Attempt 13/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME mu_public Function Relgrad Radius Rho
0 -0.4 -0.43 -0.75 0.88 -0.73 -0.77 -1 -0.73 1 5.2e+03 0.052 10 1.1 ++
1 -0.56 -0.24 -0.62 1.4 -0.9 -1.1 -1.6 -1.1 1.3 5e+03 0.011 1e+02 0.97 ++
2 -0.56 -0.24 -0.62 1.4 -0.9 -1.1 -1.6 -1.1 1.3 5e+03 0.011 0.37 -0.26 -
3 -0.55 -0.29 -0.41 1 -0.98 -1.2 -1.8 -1 1.3 5e+03 0.00094 3.7 0.92 ++
4 -0.52 -0.3 -0.44 1.1 -0.94 -1.1 -1.8 -1.1 1.3 5e+03 0.0003 37 1 ++
5 -0.52 -0.3 -0.44 1.1 -0.94 -1.1 -1.8 -1.1 1.3 5e+03 2.9e-06 37 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.4e+03 0.043 10 0.92 ++
1 5.4e+03 0.043 5 -23 -
2 5.4e+03 0.043 2.5 -6.7 -
3 5.4e+03 0.043 1.2 -5.1 -
4 5.4e+03 0.043 0.62 -1.7 -
5 5.1e+03 0.046 0.62 0.74 +
6 5e+03 0.04 0.62 0.49 +
7 4.9e+03 0.058 0.62 0.43 +
8 4.9e+03 0.028 0.62 0.36 +
9 4.9e+03 0.032 0.62 0.42 +
10 4.9e+03 0.0068 6.2 1.1 ++
11 4.9e+03 0.0068 0.97 -22 -
12 4.9e+03 0.0068 0.49 -0.75 -
13 4.9e+03 0.0099 4.9 1 ++
14 4.9e+03 0.02 4.9 0.77 +
15 4.9e+03 0.004 49 1.2 ++
16 4.9e+03 0.004 0.5 -13 -
17 4.9e+03 0.004 0.25 -0.43 -
18 4.9e+03 0.0053 2.5 0.98 ++
19 4.8e+03 0.0091 25 1 ++
20 4.8e+03 0.0019 2.5e+02 1.2 ++
21 4.8e+03 0.0051 2.5e+02 0.56 +
22 4.8e+03 0.00018 2.5e+03 1 ++
23 4.8e+03 0.00023 2.5e+04 0.99 ++
24 4.8e+03 2.7e-07 2.5e+04 1 ++
Considering neighbor 1/20 for current solution
Attempt 14/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 -0.39 -0.075 -0.066 -1 0.25 0.0045 -0.89 -0.71 5.4e+03 0.044 10 1.1 ++
1 -0.26 0.1 -0.22 -1.3 0.89 0.74 -1.1 -1.2 5.2e+03 0.0092 1e+02 1.1 ++
2 -0.24 0.1 -0.25 -1.5 1.1 0.95 -1.1 -1.2 5.2e+03 0.00096 1e+03 1.1 ++
3 -0.24 0.1 -0.25 -1.5 1.1 0.98 -1.1 -1.2 5.2e+03 1.1e-05 1e+04 1 ++
4 -0.24 0.1 -0.25 -1.5 1.1 0.98 -1.1 -1.2 5.2e+03 1.7e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 15/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME lambda_travel_t mu_public Function Relgrad Radius Rho
0 -0.29 -0.28 -0.035 -0.44 0.15 -0.012 -0.47 -0.3 -0.5 -1 1.4 1 5.7e+03 0.083 1 0.88 +
1 -0.27 -0.11 -0.093 -0.46 0.62 0.03 -0.37 -0.68 -1.5 -1 0.84 1.1 5.1e+03 0.04 10 0.99 ++
2 -0.27 -0.037 -0.24 0.18 0.57 0.58 -0.91 -1 -2.4 -1.6 0.044 1.4 5e+03 0.0079 10 0.87 +
3 -0.13 -0.0019 -0.29 0.07 0.68 0.42 -1 -1.1 -2.7 -1.7 0.2 1 5e+03 0.02 10 0.87 +
4 -0.14 -0.0045 -0.3 -0.028 0.75 0.41 -1 -1.1 -2.9 -1.6 0.24 1 5e+03 0.0015 1e+02 1 ++
5 -0.14 0.00065 -0.33 -0.044 0.77 0.46 -1 -1.1 -2.9 -1.6 0.25 1 5e+03 1.6e-05 1e+03 1 ++
6 -0.14 0.00065 -0.33 -0.044 0.77 0.46 -1 -1.1 -2.9 -1.6 0.25 1 5e+03 1.6e-08 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 16/100
Considering neighbor 0/20 for current solution
Attempt 17/100
Considering neighbor 0/20 for current solution
Attempt 18/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas mu_existing Function Relgrad Radius Rho
0 -0.0032 -0.44 -0.46 0.74 -0.39 -1 -0.92 -0.77 -0.74 1.7 5.2e+03 0.13 1 0.88 +
1 -0.65 0.39 -0.81 1.3 -0.57 -0.99 -1.1 -0.46 -0.78 1.9 5e+03 0.007 10 0.96 ++
2 -0.65 0.39 -0.81 1.3 -0.57 -0.99 -1.1 -0.46 -0.78 1.9 5e+03 0.007 0.45 -0.51 -
3 -0.63 0.34 -0.82 1.4 -0.62 -1.1 -1.3 -0.65 -0.88 1.4 4.9e+03 0.0077 4.5 0.91 ++
4 -0.63 0.24 -0.87 1.4 -0.73 -1.2 -1.4 -0.63 -0.88 1.4 4.9e+03 0.0014 45 1.1 ++
5 -0.63 0.2 -0.87 1.4 -0.75 -1.2 -1.5 -0.64 -0.89 1.3 4.9e+03 0.0001 4.5e+02 1 ++
6 -0.63 0.2 -0.87 1.4 -0.75 -1.2 -1.5 -0.64 -0.89 1.3 4.9e+03 1.8e-07 4.5e+02 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s lambda_travel_t mu_existing Function Relgrad Radius Rho
0 -0.14 -0.23 -0.033 -0.85 -0.0033 -0.016 -1 -0.62 -0.5 -0.38 -0.24 -0.63 -0.49 1.7 1.6 5.4e+03 0.061 1 0.75 +
1 -0.38 0.16 -0.21 -0.74 0.93 0.12 -0.85 -0.038 -0.89 -0.52 -0.77 -0.73 -0.69 1.2 2.6 5.2e+03 0.079 1 0.5 +
2 -0.11 0.17 -0.51 -0.56 0.37 0.48 -0.56 -0.15 -1 -0.59 -0.42 -0.66 -0.7 0.38 3.6 5.2e+03 0.084 1 0.16 +
3 -0.11 0.17 -0.51 -0.56 0.37 0.48 -0.56 -0.15 -1 -0.59 -0.42 -0.66 -0.7 0.38 3.6 5.2e+03 0.084 0.5 0.08 -
4 -0.24 0.099 -0.22 -0.44 0.47 0.13 -0.61 -0.36 -0.57 -0.88 0.054 -0.99 -0.4 0.14 4.1 5.1e+03 0.044 0.5 0.65 +
5 -0.19 0.12 -0.3 -0.47 0.52 0.2 -0.67 -0.51 -0.62 -0.81 -0.19 -1.1 -0.57 0.17 3.6 5.1e+03 0.011 5 1.2 ++
6 -0.19 0.12 -0.3 -0.47 0.52 0.2 -0.67 -0.51 -0.62 -0.81 -0.19 -1.1 -0.57 0.17 3.6 5.1e+03 0.011 1.3 -2 -
7 -0.13 0.027 -0.27 -0.53 0.53 0.15 -0.85 -0.49 -0.95 -0.92 -0.069 -1.3 -0.62 0.26 2.3 5e+03 0.012 1.3 0.88 +
8 -0.077 0.083 -0.32 -0.53 0.65 0.3 -0.92 -0.7 -0.99 -1.1 -0.18 -1.6 -0.8 0.087 2 5e+03 0.0031 13 1 ++
9 -0.074 0.084 -0.35 -0.56 0.68 0.33 -0.94 -0.72 -0.99 -1.1 -0.2 -1.6 -0.87 0.13 2 5e+03 0.00021 1.3e+02 1 ++
10 -0.074 0.084 -0.35 -0.56 0.68 0.33 -0.94 -0.72 -0.99 -1.1 -0.2 -1.6 -0.87 0.13 2 5e+03 1.4e-06 1.3e+02 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 B_TIME_1st_clas lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.15 -0.076 -0.028 -0.69 0.039 -0.012 -1 -0.69 -0.64 1.7 1.5 5.4e+03 0.079 1 0.66 +
1 -0.29 -0.078 -0.17 -0.8 0.74 0.076 -1 -0.25 -0.84 1.3 2.5 5.4e+03 0.12 1 0.24 +
2 -0.29 -0.078 -0.17 -0.8 0.74 0.076 -1 -0.25 -0.84 1.3 2.5 5.4e+03 0.12 0.5 -0.2 -
3 -0.33 0.26 -0.27 -0.88 0.46 0.17 -0.52 -0.3 -0.66 0.91 2.8 5.2e+03 0.041 0.5 0.61 +
4 -0.15 0.13 -0.33 -0.74 0.55 0.25 -0.75 -0.7 -0.62 0.41 2.6 5.1e+03 0.0033 5 0.95 ++
5 -0.15 0.13 -0.33 -0.74 0.55 0.25 -0.75 -0.7 -0.62 0.41 2.6 5.1e+03 0.0033 0.79 -2.1 -
6 -0.083 0.085 -0.32 -0.76 0.57 0.25 -0.87 -0.84 -0.65 0.74 1.8 5.1e+03 0.0067 0.79 0.56 +
7 -0.079 0.096 -0.35 -0.85 0.68 0.4 -0.96 -0.91 -0.7 0.53 1.8 5.1e+03 0.0011 7.9 1 ++
8 -0.094 0.097 -0.35 -0.89 0.7 0.41 -0.96 -0.87 -0.72 0.54 1.8 5.1e+03 2.1e-05 79 1 ++
9 -0.094 0.097 -0.35 -0.89 0.7 0.41 -0.96 -0.87 -0.72 0.54 1.8 5.1e+03 1.5e-08 79 1 ++
Considering neighbor 2/20 for current solution
Attempt 19/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME B_TIME_commuter cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -2.8 -
1 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.25 -2.2 -
2 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 2.5 1 ++
3 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 1.2 -6.7 -
4 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.62 -4.7 -
5 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.31 -3.5 -
6 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.16 -2.9 -
7 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.078 -2.8 -
8 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.039 -3 -
9 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.02 -3.2 -
10 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.0098 -3.5 -
11 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.0049 -2.4 -
12 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.0024 -1.8 -
13 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.0012 -1.4 -
14 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.00061 -0.8 -
15 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 0 1.2 0 5.9e+03 9.3 0.00031 -0.066 -
16 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 -0.00031 1.3 0.00031 5.9e+03 4.6 0.00031 0.7 +
17 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 -0.00024 1.3 0.00052 5.9e+03 2.4 0.00031 0.8 +
18 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 -0.00026 1.3 0.00073 5.9e+03 0.16 0.0031 1 ++
19 -0.11 -0.25 -0.25 0.08 -0.25 -0.25 -0.25 -0.00026 1.3 0.0029 5.9e+03 1.1 0.031 1 ++
20 -0.1 -0.25 -0.28 0.083 -0.26 -0.28 -0.26 -0.00036 1.3 0.024 5.8e+03 0.32 0.31 1 ++
21 -0.067 -0.27 -0.54 0.12 -0.32 -0.59 -0.3 -0.0012 1.4 0.24 5.4e+03 0.41 0.31 0.75 +
22 -0.028 -0.34 -0.62 0.35 -0.63 -0.66 -0.22 -0.00028 1.6 0.0079 5.2e+03 0.059 0.31 0.87 +
23 -0.21 -0.41 -0.64 0.65 -0.77 -0.96 -0.21 -8.6e-05 1.8 -0.038 5e+03 0.26 3.1 1.1 ++
24 -0.21 -0.41 -0.64 0.65 -0.77 -0.96 -0.21 -8.6e-05 1.8 -0.038 5e+03 0.26 1.5 -74 -
25 -0.21 -0.41 -0.64 0.65 -0.77 -0.96 -0.21 -8.6e-05 1.8 -0.038 5e+03 0.26 0.76 -21 -
26 -0.21 -0.41 -0.64 0.65 -0.77 -0.96 -0.21 -8.6e-05 1.8 -0.038 5e+03 0.26 0.38 -3.4 -
27 -0.21 -0.41 -0.64 0.65 -0.77 -0.96 -0.21 -8.6e-05 1.8 -0.038 5e+03 0.26 0.19 0.072 -
28 -0.1 -0.41 -0.77 0.82 -0.8 -1.2 -0.26 0.00013 1.9 -0.091 5e+03 22 1.9 0.93 ++
29 0.018 0.043 -0.57 1.5 -0.94 -1.8 -0.87 0.00028 1.6 -0.12 4.9e+03 8.2 19 0.96 ++
30 0.018 0.043 -0.57 1.5 -0.94 -1.8 -0.87 0.00028 1.6 -0.12 4.9e+03 8.2 0.14 -2.1 -
31 0.018 0.043 -0.57 1.5 -0.94 -1.8 -0.87 0.00028 1.6 -0.12 4.9e+03 8.2 0.068 -2.4 -
32 0.018 0.043 -0.57 1.5 -0.94 -1.8 -0.87 0.00028 1.6 -0.12 4.9e+03 8.2 0.034 -2.7 -
33 0.018 0.043 -0.57 1.5 -0.94 -1.8 -0.87 0.00028 1.6 -0.12 4.9e+03 8.2 0.017 -2.6 -
34 0.018 0.043 -0.57 1.5 -0.94 -1.8 -0.87 0.00028 1.6 -0.12 4.9e+03 8.2 0.0085 -1.2 -
35 0.018 0.043 -0.57 1.5 -0.94 -1.8 -0.87 0.00028 1.6 -0.12 4.9e+03 8.2 0.0042 -0.3 -
36 0.018 0.043 -0.57 1.5 -0.94 -1.8 -0.87 0.00023 1.6 -0.12 4.9e+03 65 0.0042 0.4 +
37 0.018 0.043 -0.57 1.5 -0.94 -1.8 -0.87 0.00023 1.6 -0.11 4.9e+03 3.4 0.042 0.94 ++
38 -0.013 0.034 -0.59 1.5 -0.94 -1.9 -0.89 0.0002 1.6 -0.11 4.9e+03 4.3 0.42 0.97 ++
39 -0.0072 0.0066 -0.69 1.8 -1 -1.9 -1.1 0.00021 1.4 -0.11 4.9e+03 0.39 4.2 0.96 ++
40 -0.013 -0.013 -0.71 1.8 -1 -1.9 -1.1 0.00021 1.4 -0.11 4.9e+03 0.011 42 1 ++
41 -0.011 -0.016 -0.7 1.8 -1 -1.9 -1.1 0.00021 1.4 -0.11 4.9e+03 0.00033 4.2e+02 1 ++
42 -0.011 -0.017 -0.7 1.8 -1 -1.9 -1.1 0.00021 1.4 -0.11 4.9e+03 9.4e-06 4.2e+03 1 ++
43 -0.011 -0.017 -0.7 1.8 -1 -1.9 -1.1 0.00021 1.4 -0.11 4.9e+03 1.1e-06 4.2e+03 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
ASC:GA;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic;model_catalog:nested existing;train_tt_catalog:power [4894.817872042836, 10]
Attempt 20/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 B_TIME_1st_clas mu_existing Function Relgrad Radius Rho
0 0.15 -0.073 -0.027 -0.63 0.071 -0.01 -1 -0.62 -0.59 1.5 5.2e+03 0.056 10 0.95 ++
1 -0.23 0.065 -0.42 -1 0.73 0.51 -0.96 -0.47 -0.77 1.8 5.1e+03 0.021 10 0.88 +
2 -0.24 0.12 -0.32 -1 0.67 0.4 -0.96 -0.5 -0.77 1.9 5.1e+03 0.00037 1e+02 1 ++
3 -0.23 0.12 -0.34 -1.1 0.69 0.41 -0.98 -0.51 -0.79 1.8 5.1e+03 0.00017 1e+03 0.96 ++
4 -0.23 0.12 -0.34 -1.1 0.69 0.41 -0.98 -0.51 -0.79 1.8 5.1e+03 7.4e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 21/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN mu_public Function Relgrad Radius Rho
0 -0.16 -0.34 -0.025 -0.063 -0.74 0.85 0.15 0.0034 -0.63 -0.74 -1 -0.8 -0.48 -0.63 1 5.2e+03 0.081 10 1.1 ++
1 -0.53 -0.25 0.082 -0.2 -1.2 1.2 0.51 0.4 -0.71 -1.1 -1.6 -1.2 -0.97 -0.83 1.3 5e+03 0.015 1e+02 1 ++
2 -0.53 -0.25 0.082 -0.2 -1.2 1.2 0.51 0.4 -0.71 -1.1 -1.6 -1.2 -0.97 -0.83 1.3 5e+03 0.015 1.1 -1.3e+02 -
3 -0.53 -0.25 0.082 -0.2 -1.2 1.2 0.51 0.4 -0.71 -1.1 -1.6 -1.2 -0.97 -0.83 1.3 5e+03 0.015 0.54 -3.3 -
4 -0.45 -0.33 0.063 -0.24 -0.74 0.67 0.42 0.44 -0.77 -1.3 -2 -1.3 -1 -0.81 1.5 5e+03 0.0084 0.54 0.73 +
5 -0.44 -0.4 0.048 -0.28 -0.66 0.66 0.34 0.22 -0.71 -1.2 -1.8 -1.2 -0.83 -0.68 1.8 5e+03 0.0072 5.4 1.1 ++
6 -0.43 -0.46 0.047 -0.27 -0.52 0.5 0.28 0.23 -0.7 -1.2 -1.8 -1.2 -0.73 -0.58 2.2 5e+03 0.013 54 1.1 ++
7 -0.41 -0.48 0.046 -0.28 -0.48 0.46 0.25 0.19 -0.69 -1.2 -1.8 -1.2 -0.66 -0.53 2.5 5e+03 0.003 5.4e+02 1.3 ++
8 -0.39 -0.53 0.045 -0.28 -0.38 0.36 0.2 0.16 -0.66 -1.2 -1.7 -1.1 -0.55 -0.43 3 5e+03 0.02 5.4e+02 0.5 +
9 -0.38 -0.54 0.044 -0.28 -0.37 0.36 0.2 0.15 -0.65 -1.2 -1.7 -1.1 -0.54 -0.42 3.1 5e+03 0.00053 5.4e+03 1 ++
10 -0.37 -0.56 0.043 -0.28 -0.34 0.33 0.18 0.14 -0.64 -1.2 -1.7 -1.1 -0.5 -0.39 3.4 5e+03 0.0031 5.4e+03 0.86 +
11 -0.37 -0.56 0.043 -0.28 -0.34 0.33 0.18 0.14 -0.64 -1.2 -1.7 -1.1 -0.5 -0.39 3.4 5e+03 2.5e-06 5.4e+03 1 +
Considering neighbor 0/20 for current solution
Attempt 22/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 7e+03 0.4 0.5 -0.53 -
1 -0.019 -0.28 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 0.0057 6.3e+03 1.4 0.5 0.29 +
2 -0.019 -0.28 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 0.0057 6.3e+03 1.4 0.25 -4.9 -
3 -0.019 -0.28 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 0.0057 6.3e+03 1.4 0.12 -5.6 -
4 -0.019 -0.28 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 0.0057 6.3e+03 1.4 0.062 -3.8 -
5 -0.017 -0.27 -0.037 0.28 -0.3 -0.013 0.23 -0.49 -0.0097 -0.0027 6.1e+03 5.3 0.062 0.37 +
6 -0.017 -0.27 -0.037 0.28 -0.3 -0.013 0.23 -0.49 -0.0097 -0.0027 6.1e+03 5.3 0.031 -0.32 -
7 -0.018 -0.27 -0.039 0.27 -0.3 0.018 0.22 -0.48 0.022 0.0054 5.9e+03 0.79 0.031 0.21 +
8 -0.018 -0.27 -0.039 0.27 -0.3 0.018 0.22 -0.48 0.022 0.0054 5.9e+03 0.79 0.016 -1.1 -
9 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 -0.01 5.8e+03 0.16 0.16 0.96 ++
10 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 -0.01 5.8e+03 0.16 0.078 -34 -
11 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 -0.01 5.8e+03 0.16 0.039 -27 -
12 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 -0.01 5.8e+03 0.16 0.02 -22 -
13 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 -0.01 5.8e+03 0.16 0.0098 -17 -
14 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 -0.01 5.8e+03 0.16 0.0049 -6 -
15 -0.034 -0.26 -0.055 0.25 -0.32 -0.0023 0.2 -0.46 0.00099 -0.011 5.8e+03 0.052 0.049 1.3 ++
16 -0.042 -0.25 -0.072 0.2 -0.34 -0.04 0.18 -0.46 -0.0013 -0.025 5.8e+03 2.8 0.049 0.82 +
17 -0.049 -0.25 -0.089 0.16 -0.37 -0.088 0.16 -0.47 0.00021 -0.025 5.7e+03 0.51 0.49 0.93 ++
18 -0.049 -0.25 -0.089 0.16 -0.37 -0.088 0.16 -0.47 0.00021 -0.025 5.7e+03 0.51 0.24 -0.076 -
19 -0.064 -0.27 -0.15 -0.088 -0.55 -0.26 0.039 -0.53 -0.0025 0.056 5.6e+03 9.5 0.24 0.4 +
20 -0.064 -0.27 -0.15 -0.088 -0.55 -0.26 0.039 -0.53 -0.0025 0.056 5.6e+03 9.5 0.12 -0.53 -
21 -0.064 -0.27 -0.15 -0.088 -0.55 -0.26 0.039 -0.53 -0.0025 0.056 5.6e+03 9.5 0.061 -0.29 -
22 -0.064 -0.27 -0.15 -0.088 -0.55 -0.26 0.039 -0.53 -0.0025 0.056 5.6e+03 9.5 0.031 -0.14 -
23 -0.064 -0.27 -0.15 -0.088 -0.55 -0.26 0.039 -0.53 -0.0025 0.056 5.6e+03 9.5 0.015 -0.058 -
24 -0.064 -0.27 -0.15 -0.088 -0.55 -0.26 0.039 -0.53 -0.0025 0.056 5.6e+03 9.5 0.0076 -0.015 -
25 -0.064 -0.27 -0.15 -0.088 -0.55 -0.26 0.039 -0.53 -0.0025 0.056 5.6e+03 9.5 0.0038 0.0073 -
26 -0.065 -0.27 -0.15 -0.092 -0.55 -0.26 0.035 -0.53 0.0013 0.059 5.6e+03 3.1 0.0038 0.33 +
27 -0.065 -0.27 -0.15 -0.092 -0.55 -0.26 0.035 -0.53 0.0013 0.059 5.6e+03 3.1 0.0019 -0.57 -
28 -0.067 -0.26 -0.16 -0.094 -0.56 -0.26 0.033 -0.53 -0.00059 0.061 5.5e+03 1.9 0.019 0.9 ++
29 -0.071 -0.26 -0.16 -0.11 -0.57 -0.28 0.018 -0.53 -0.00055 0.073 5.5e+03 0.56 0.19 0.99 ++
30 -0.091 -0.25 -0.22 -0.3 -0.76 -0.45 -0.17 -0.55 -0.0011 0.19 5.4e+03 2.2 1.9 0.93 ++
31 -0.091 -0.25 -0.22 -0.3 -0.76 -0.45 -0.17 -0.55 -0.0011 0.19 5.4e+03 2.2 0.95 -3.6 -
32 -0.22 -0.27 -0.38 -0.79 -1.7 -0.79 -0.57 -0.57 -0.0011 0.23 5.2e+03 9.1 9.5 1.1 ++
33 -0.22 -0.27 -0.38 -0.79 -1.7 -0.79 -0.57 -0.57 -0.0011 0.23 5.2e+03 9.1 4.8 -63 -
34 -0.22 -0.27 -0.38 -0.79 -1.7 -0.79 -0.57 -0.57 -0.0011 0.23 5.2e+03 9.1 2.4 -39 -
35 -0.22 -0.27 -0.38 -0.79 -1.7 -0.79 -0.57 -0.57 -0.0011 0.23 5.2e+03 9.1 1.2 -19 -
36 -0.22 -0.27 -0.38 -0.79 -1.7 -0.79 -0.57 -0.57 -0.0011 0.23 5.2e+03 9.1 0.6 -5.8 -
37 -0.22 -0.27 -0.38 -0.79 -1.7 -0.79 -0.57 -0.57 -0.0011 0.23 5.2e+03 9.1 0.3 -1.1 -
38 -0.3 -0.3 -0.46 -0.74 -2 -0.89 -0.54 -0.62 -4e-07 -0.073 5.2e+03 11 0.3 0.47 +
39 -0.25 -0.26 -0.51 -0.89 -2.3 -1.1 -0.84 -0.77 -0.00034 0.011 5.1e+03 8.8 3 0.92 ++
40 -0.25 -0.26 -0.51 -0.89 -2.3 -1.1 -0.84 -0.77 -0.00034 0.011 5.1e+03 8.8 1.5 -48 -
41 -0.25 -0.26 -0.51 -0.89 -2.3 -1.1 -0.84 -0.77 -0.00034 0.011 5.1e+03 8.8 0.75 -7 -
42 -0.25 -0.26 -0.51 -0.89 -2.3 -1.1 -0.84 -0.77 -0.00034 0.011 5.1e+03 8.8 0.37 -1.4 -
43 -0.25 -0.26 -0.51 -0.89 -2.3 -1.1 -0.84 -0.77 -0.00034 0.011 5.1e+03 8.8 0.19 -0.17 -
44 -0.25 -0.26 -0.51 -0.89 -2.3 -1.1 -0.84 -0.77 -0.00034 0.011 5.1e+03 8.8 0.093 -0.11 -
45 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 0.093 0.29 +
46 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 0.047 -1.4 -
47 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 0.023 -1.8 -
48 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 0.012 -2.1 -
49 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 0.0058 -2.3 -
50 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 0.0029 -2.4 -
51 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 0.0015 -2.5 -
52 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 0.00073 -2.6 -
53 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 0.00036 -2.6 -
54 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 0.00018 -1.5 -
55 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 0.00011 -0.075 5.1e+03 17 9.1e-05 -0.41 -
56 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 2.4e-05 -0.075 5.1e+03 12 9.1e-05 0.46 +
57 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 6.1e-05 -0.075 5.1e+03 7.3 9.1e-05 0.52 +
58 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 4.6e-05 -0.074 5.1e+03 1 0.00091 0.9 ++
59 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.85 -0.81 4.4e-05 -0.074 5.1e+03 0.033 0.0091 1 ++
60 -0.28 -0.26 -0.55 -0.86 -2.4 -1.2 -0.86 -0.81 6.3e-06 -0.064 5.1e+03 0.78 0.091 1 ++
61 -0.26 -0.24 -0.55 -0.91 -2.4 -1.3 -0.95 -0.84 -0.00011 -0.037 5.1e+03 1.2 0.91 1 ++
62 -0.26 -0.24 -0.55 -0.91 -2.4 -1.3 -0.95 -0.84 -0.00011 -0.037 5.1e+03 1.2 0.45 -0.76 -
63 -0.27 -0.21 -0.69 -0.97 -2.9 -1.6 -1.3 -1.1 0.00031 -0.14 5e+03 23 0.45 0.27 +
64 -0.21 -0.15 -0.6 -1.2 -3 -2.1 -1.7 -1.2 -2e-05 -0.053 5e+03 27 0.45 0.34 +
65 -0.21 -0.15 -0.6 -1.2 -3 -2.1 -1.7 -1.2 -2e-05 -0.053 5e+03 27 0.23 -1.9 -
66 -0.21 -0.15 -0.6 -1.2 -3 -2.1 -1.7 -1.2 -2e-05 -0.053 5e+03 27 0.11 -0.8 -
67 -0.21 -0.15 -0.6 -1.2 -3 -2.1 -1.7 -1.2 -2e-05 -0.053 5e+03 27 0.057 -0.45 -
68 -0.22 -0.16 -0.61 -1.1 -3 -2.1 -1.7 -1.2 0.00018 -0.11 5e+03 40 0.057 0.53 +
69 -0.2 -0.17 -0.61 -1.1 -3 -2.1 -1.7 -1.3 0.00019 -0.1 5e+03 22 0.057 0.74 +
70 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00013 -0.098 5e+03 33 0.057 0.15 +
71 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00013 -0.098 5e+03 33 0.028 0.077 -
72 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00013 -0.098 5e+03 33 0.014 0.024 -
73 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00013 -0.098 5e+03 33 0.0071 -0.043 -
74 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00013 -0.098 5e+03 33 0.0036 0.0024 -
75 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00013 -0.098 5e+03 33 0.0018 0.08 -
76 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00018 -0.099 5e+03 25 0.0018 0.12 +
77 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00018 -0.099 5e+03 25 0.00089 -0.83 -
78 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00018 -0.099 5e+03 25 0.00044 -0.8 -
79 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00018 -0.099 5e+03 25 0.00022 -0.79 -
80 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00018 -0.099 5e+03 25 0.00011 -0.78 -
81 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00018 -0.099 5e+03 25 5.6e-05 -0.78 -
82 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00018 -0.099 5e+03 25 2.8e-05 -0.71 -
83 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00015 -0.099 5e+03 11 2.8e-05 0.6 +
84 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00016 -0.099 5e+03 0.14 0.00028 0.99 ++
85 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00016 -0.1 5e+03 0.0058 0.0028 1 ++
86 -0.2 -0.13 -0.63 -1.1 -3.1 -2.1 -1.7 -1.3 0.00017 -0.1 5e+03 0.023 0.028 1 ++
87 -0.2 -0.12 -0.64 -1.1 -3.1 -2.1 -1.7 -1.3 0.00018 -0.1 5e+03 5.4 0.28 1 ++
88 -0.17 0.046 -0.74 -1.1 -3.2 -2.3 -2 -1.6 0.0002 -0.11 5e+03 1.3 2.8 1 ++
89 -0.16 0.22 -0.7 -1.2 -3.1 -2.5 -2.2 -1.9 0.00022 -0.11 5e+03 0.25 28 1 ++
90 -0.16 0.23 -0.7 -1.2 -3.1 -2.5 -2.2 -1.9 0.00022 -0.11 5e+03 0.01 2.8e+02 1 ++
91 -0.16 0.23 -0.7 -1.2 -3.1 -2.5 -2.2 -1.9 0.00022 -0.11 5e+03 1.8e-06 2.8e+02 1 ++
Considering neighbor 0/20 for current solution
Attempt 23/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s Function Relgrad Radius Rho
0 -0.19 -0.39 -0.66 0.87 -0.55 -0.82 -1 -0.76 -0.47 -0.43 -0.52 -0.56 -0.41 5.1e+03 0.08 10 1.1 ++
1 -0.58 -0.084 -1 1.5 -0.72 -1.2 -1.6 -0.61 -1.1 -0.37 -1.3 -0.53 -0.9 4.9e+03 0.02 1e+02 1.1 ++
2 -0.6 -0.064 -0.92 1.5 -0.76 -1.3 -1.9 -0.62 -1.2 -0.37 -1.4 -0.57 -1 4.9e+03 0.0032 1e+03 1.1 ++
3 -0.6 -0.061 -0.88 1.5 -0.77 -1.3 -2 -0.62 -1.2 -0.37 -1.4 -0.57 -1 4.9e+03 0.00011 1e+04 1 ++
4 -0.6 -0.061 -0.88 1.5 -0.77 -1.3 -2 -0.62 -1.2 -0.37 -1.4 -0.57 -1 4.9e+03 1.1e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s Function Relgrad Radius Rho
0 -0.083 -0.12 -0.027 -0.44 0.09 -0.0087 -1 -0.47 -0.24 -0.067 -0.19 -0.54 -0.3 5.4e+03 0.073 10 1.1 ++
1 -0.44 0.066 -0.35 -0.98 0.74 0.48 -1.1 -0.4 -0.99 -0.39 -1.2 -0.75 -0.97 5.2e+03 0.018 1e+02 1.1 ++
2 -0.45 0.064 -0.38 -1.1 1 0.71 -1.2 -0.4 -1.1 -0.41 -1.4 -0.84 -1.2 5.1e+03 0.0023 1e+03 1.1 ++
3 -0.46 0.063 -0.38 -1.1 1 0.74 -1.2 -0.4 -1.1 -0.41 -1.4 -0.85 -1.2 5.1e+03 5.7e-05 1e+04 1 ++
4 -0.46 0.063 -0.38 -1.1 1 0.74 -1.2 -0.4 -1.1 -0.41 -1.4 -0.85 -1.2 5.1e+03 1.5e-08 1e+04 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.5 -3.5 -
1 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.25 -1.2 -
2 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.2 2.5 1 ++
3 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.2 1.2 -7.4 -
4 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.2 0.62 -3.4 -
5 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.2 0.31 -1.7 -
6 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.2 0.16 -0.65 -
7 0.0054 -0.027 -0.0079 -0.34 -0.25 -0.016 -0.04 0.19 -0.38 -0.41 -0.32 -0.0035 0.14 5.7e+03 11 0.16 0.1 +
8 0.0054 -0.027 -0.0079 -0.34 -0.25 -0.016 -0.04 0.19 -0.38 -0.41 -0.32 -0.0035 0.14 5.7e+03 11 0.078 -0.92 -
9 0.0054 -0.027 -0.0079 -0.34 -0.25 -0.016 -0.04 0.19 -0.38 -0.41 -0.32 -0.0035 0.14 5.7e+03 11 0.039 -0.85 -
10 0.0054 -0.027 -0.0079 -0.34 -0.25 -0.016 -0.04 0.19 -0.38 -0.41 -0.32 -0.0035 0.14 5.7e+03 11 0.02 -0.8 -
11 0.0054 -0.027 -0.0079 -0.34 -0.25 -0.016 -0.04 0.19 -0.38 -0.41 -0.32 -0.0035 0.14 5.7e+03 11 0.0098 -0.77 -
12 0.0054 -0.027 -0.0079 -0.34 -0.25 -0.016 -0.04 0.19 -0.38 -0.41 -0.32 -0.0035 0.14 5.7e+03 11 0.0049 -0.43 -
13 0.0068 -0.027 -0.008 -0.34 -0.25 -0.016 -0.038 0.18 -0.38 -0.4 -0.32 0.0014 0.14 5.7e+03 7.2 0.0049 0.12 +
14 0.0068 -0.027 -0.008 -0.34 -0.25 -0.016 -0.038 0.18 -0.38 -0.4 -0.32 0.0014 0.14 5.7e+03 7.2 0.0024 -0.11 -
15 0.0092 -0.025 -0.01 -0.35 -0.25 -0.013 -0.036 0.18 -0.38 -0.4 -0.32 -0.001 0.14 5.6e+03 3.1 0.0024 0.89 +
16 0.0092 -0.025 -0.01 -0.35 -0.25 -0.013 -0.036 0.18 -0.38 -0.4 -0.32 -0.001 0.14 5.6e+03 3.1 0.0012 -2.6 -
17 0.0092 -0.025 -0.01 -0.35 -0.25 -0.013 -0.036 0.18 -0.38 -0.4 -0.32 -0.001 0.14 5.6e+03 3.1 0.00061 -2.9 -
18 0.0092 -0.025 -0.01 -0.35 -0.25 -0.013 -0.036 0.18 -0.38 -0.4 -0.32 -0.001 0.14 5.6e+03 3.1 0.00031 -1.5 -
19 0.0092 -0.025 -0.01 -0.35 -0.25 -0.013 -0.036 0.18 -0.38 -0.4 -0.32 -0.001 0.14 5.6e+03 3.1 0.00015 -0.14 -
20 0.0094 -0.025 -0.011 -0.35 -0.25 -0.013 -0.036 0.18 -0.38 -0.4 -0.32 -0.00088 0.14 5.6e+03 0.43 0.00015 0.82 +
21 0.0094 -0.025 -0.011 -0.35 -0.25 -0.013 -0.036 0.18 -0.38 -0.4 -0.32 -0.00087 0.14 5.6e+03 0.082 0.0015 1 ++
22 0.0097 -0.025 -0.011 -0.35 -0.25 -0.013 -0.035 0.18 -0.38 -0.4 -0.32 -0.00088 0.14 5.6e+03 0.2 0.015 1 ++
23 0.013 -0.025 -0.011 -0.35 -0.24 -0.013 -0.032 0.17 -0.39 -0.4 -0.32 -0.00086 0.14 5.6e+03 0.076 0.15 1 ++
24 0.04 -0.028 -0.015 -0.35 -0.2 -0.013 -0.0025 0.013 -0.45 -0.4 -0.33 -0.00073 0.11 5.5e+03 0.08 1.5 0.97 ++
25 -0.5 -0.12 -0.16 -0.6 0.57 0.029 -0.52 -0.9 -2 -0.56 -0.48 -0.00045 0.038 5e+03 0.73 15 1.1 ++
26 -0.53 0.018 -0.33 -0.52 0.74 0.47 -0.84 -1.1 -2.6 -0.93 -0.6 4.5e-05 -0.072 5e+03 5 1.5e+02 1.2 ++
27 -0.41 0.0024 -0.36 -0.32 0.78 0.5 -0.85 -1.2 -2.7 -1.6 -0.63 0.0002 -0.11 4.9e+03 28 1.5e+03 1.1 ++
28 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 1.5e+03 0.67 +
29 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 0.059 -1 -
30 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 0.03 -1.1 -
31 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 0.015 -1.1 -
32 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 0.0074 -1.1 -
33 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 0.0037 -1.1 -
34 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 0.0018 -0.89 -
35 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 0.00092 -0.49 -
36 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 0.00046 -0.25 -
37 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 0.00023 -0.15 -
38 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 0.00012 -0.096 -
39 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 5.8e-05 -0.071 -
40 -0.37 -0.0083 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00026 -0.12 4.9e+03 16 2.9e-05 -0.059 -
41 -0.37 -0.0084 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00023 -0.12 4.9e+03 5.3 2.9e-05 0.74 +
42 -0.37 -0.0084 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00023 -0.12 4.9e+03 0.44 0.00029 1.1 ++
43 -0.37 -0.0084 -0.39 -0.19 0.79 0.48 -0.81 -1.2 -2.8 -2 -0.62 0.00024 -0.12 4.9e+03 0.013 0.0029 1 ++
44 -0.37 -0.0097 -0.39 -0.19 0.79 0.48 -0.82 -1.2 -2.8 -2 -0.62 0.00024 -0.12 4.9e+03 0.0029 0.029 1 ++
45 -0.37 -0.017 -0.39 -0.2 0.78 0.47 -0.82 -1.2 -2.8 -2 -0.64 0.00024 -0.12 4.9e+03 0.93 0.29 1 ++
46 -0.38 -0.015 -0.41 -0.21 0.78 0.44 -0.83 -1.2 -2.8 -1.9 -0.68 0.00023 -0.12 4.9e+03 0.023 2.9 1 ++
47 -0.37 -0.016 -0.42 -0.21 0.78 0.44 -0.83 -1.2 -2.8 -1.9 -0.68 0.00023 -0.12 4.9e+03 0.00015 29 1 ++
48 -0.37 -0.016 -0.42 -0.21 0.78 0.44 -0.83 -1.2 -2.8 -1.9 -0.68 0.00023 -0.12 4.9e+03 9.2e-06 2.9e+02 1 ++
49 -0.37 -0.016 -0.42 -0.21 0.78 0.44 -0.83 -1.2 -2.8 -1.9 -0.68 0.00023 -0.12 4.9e+03 0.00014 2.9e+03 1 ++
50 -0.37 -0.016 -0.42 -0.21 0.78 0.44 -0.83 -1.2 -2.8 -1.9 -0.68 0.00023 -0.12 4.9e+03 2.4e-08 2.9e+03 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 24/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 0.072 1 0.72 +
1 5.1e+03 0.034 10 1.1 ++
2 5.1e+03 0.034 5 -3.8e+304 -
3 5.1e+03 0.034 2.5 -14 -
4 5.1e+03 0.034 1.2 -3.3 -
5 5.1e+03 0.034 0.62 -0.28 -
6 5e+03 0.012 6.2 1.1 ++
7 5e+03 0.012 3.1 -2.4e+02 -
8 5e+03 0.012 1.6 -47 -
9 5e+03 0.012 0.78 -4.8 -
10 5e+03 0.02 0.78 0.69 +
11 5e+03 0.02 0.38 -1.9 -
12 4.9e+03 0.0024 3.8 0.92 ++
13 4.9e+03 0.0024 0.25 -3.1 -
14 4.9e+03 0.008 0.25 0.67 +
15 4.9e+03 0.0026 2.5 1.2 ++
16 4.9e+03 0.0052 2.5 0.89 +
17 4.9e+03 0.00035 25 1 ++
18 4.9e+03 4e-06 25 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME B_TIME_1st_clas lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.26 -0.21 -0.87 0.4 -1 -0.71 -0.63 1.8 1.4 5.5e+03 0.081 1 0.64 +
1 -0.54 -0.34 -0.76 1.3 -0.98 -0.25 -0.81 1.2 2.4 5.2e+03 0.092 1 0.51 +
2 -0.057 0.66 -0.45 0.59 -0.83 -0.71 -0.73 0.6 2.1 5.1e+03 0.033 1 0.45 +
3 -0.12 0.099 -0.54 1.4 -0.71 -0.8 -0.51 0.39 2.7 5e+03 0.017 1 0.55 +
4 -0.043 0.28 -0.58 1.3 -0.98 -0.98 -0.67 0.47 1.7 4.9e+03 0.013 1 0.85 +
5 -0.079 0.16 -0.77 1.6 -1.1 -1.1 -0.71 0.42 1.4 4.9e+03 0.0032 10 1.1 ++
6 -0.078 0.042 -0.84 1.7 -1.1 -1.1 -0.73 0.43 1.4 4.9e+03 0.00036 1e+02 1.1 ++
7 -0.078 0.042 -0.84 1.7 -1.1 -1.1 -0.73 0.43 1.4 4.9e+03 4.6e-06 1e+02 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas mu_public Function Relgrad Radius Rho
0 -0.5 -0.47 -0.82 0.98 -0.77 -0.92 -1 -0.44 -0.67 1 5.1e+03 0.054 10 1.1 ++
1 -0.72 -0.14 -0.88 1.5 -0.87 -1.2 -1.5 -0.65 -0.76 1.2 5e+03 0.0087 1e+02 1 ++
2 -0.69 -0.17 -0.7 1.3 -0.88 -1.3 -1.7 -0.67 -0.82 1.2 4.9e+03 0.00087 1e+03 1 ++
3 -0.68 -0.13 -0.8 1.4 -0.86 -1.3 -1.8 -0.68 -0.87 1.1 4.9e+03 0.0018 1e+03 0.88 +
4 -0.68 -0.13 -0.81 1.4 -0.86 -1.3 -1.8 -0.68 -0.87 1.1 4.9e+03 1.7e-05 1e+04 1 ++
5 -0.68 -0.13 -0.81 1.4 -0.86 -1.3 -1.8 -0.68 -0.87 1.1 4.9e+03 7.5e-07 1e+04 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 25/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_comm B_TIME_SM B_TIME_SM_commu B_TIME_TRAIN B_TIME_TRAIN_co lambda_travel_t mu_existing Function Relgrad Radius Rho
0 -0.2 -0.26 -0.036 -0.89 0.012 -0.015 -1 -0.66 -0.068 -0.37 -0.16 -0.74 -0.092 1.5 1.6 5.4e+03 0.03 1 0.86 +
1 -0.37 0.25 -0.25 -0.7 0.91 0.22 -0.78 -0.8 0.41 -1 -0.19 -1.1 -0.3 0.81 2.6 5.2e+03 0.078 1 0.68 +
2 0.0012 0.21 -0.26 -0.37 0.57 0.37 -0.58 -1.3 0.14 -0.82 -0.83 -1.7 -0.16 -0.19 3.4 5.1e+03 0.053 1 0.38 +
3 -0.15 0.18 -0.46 -0.59 0.7 0.22 -0.76 -1.3 0.47 -0.86 -0.59 -1.9 0.11 -0.07 2.4 5e+03 0.0071 10 1 ++
4 -0.1 0.13 -0.33 -0.54 0.72 0.29 -0.84 -1.4 0.35 -0.98 -0.73 -2.1 -0.23 -0.042 2.1 5e+03 0.0013 1e+02 1.1 ++
5 -0.1 0.13 -0.32 -0.54 0.73 0.28 -0.85 -1.4 0.33 -0.99 -0.75 -2.1 -0.3 -0.052 2.1 5e+03 5.3e-05 1e+03 1 ++
6 -0.1 0.13 -0.32 -0.54 0.73 0.28 -0.85 -1.4 0.33 -0.99 -0.75 -2.1 -0.3 -0.052 2.1 5e+03 1e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 26/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.5 -5.7 -
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.25 -1.9 -
2 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 2.5 1 ++
3 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 1.2 -4.2 -
4 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.62 -3.5 -
5 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.31 -2.9 -
6 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.16 -2.5 -
7 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.078 -2.5 -
8 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.039 -2.6 -
9 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.02 -2.8 -
10 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0098 -3.1 -
11 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0049 -3.3 -
12 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0024 -2.1 -
13 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.0012 -1.4 -
14 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.00061 -0.77 -
15 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 8 0.00031 -0.03 -
16 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 -0.00031 0.00031 5.8e+03 2.9 0.00031 0.71 +
17 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 -0.00026 0.00049 5.8e+03 0.98 0.00031 0.89 +
18 0.098 -0.25 -0.043 -0.035 -0.25 0.082 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 -0.00027 0.00067 5.8e+03 0.082 0.0031 1 ++
19 0.099 -0.25 -0.043 -0.035 -0.25 0.083 -0.25 -0.12 -0.2 0.25 -0.25 -0.25 -0.25 -0.00026 0.0025 5.8e+03 1.9 0.031 1 ++
20 0.1 -0.26 -0.042 -0.036 -0.27 0.09 -0.25 -0.12 -0.19 0.24 -0.28 -0.28 -0.26 -0.00036 0.021 5.7e+03 0.36 0.31 1 ++
21 0.16 -0.31 -0.04 -0.043 -0.46 0.2 -0.23 -0.12 -0.14 0.11 -0.56 -0.59 -0.31 -0.001 0.18 5.5e+03 2.5 0.31 0.86 +
22 0.036 -0.4 -0.14 -0.06 -0.38 0.42 -0.00025 -0.11 -0.25 -0.2 -0.71 -0.72 -0.29 -0.00022 -0.0093 5.3e+03 1.6 0.31 0.83 +
23 0.047 -0.45 -0.11 -0.074 -0.55 0.57 0.046 -0.11 -0.25 -0.36 -1 -0.99 -0.34 -0.00027 0.0041 5.1e+03 0.87 3.1 1.1 ++
24 0.047 -0.45 -0.11 -0.074 -0.55 0.57 0.046 -0.11 -0.25 -0.36 -1 -0.99 -0.34 -0.00027 0.0041 5.1e+03 0.87 1.5 -49 -
25 0.047 -0.45 -0.11 -0.074 -0.55 0.57 0.046 -0.11 -0.25 -0.36 -1 -0.99 -0.34 -0.00027 0.0041 5.1e+03 0.87 0.76 -9.8 -
26 0.047 -0.45 -0.11 -0.074 -0.55 0.57 0.046 -0.11 -0.25 -0.36 -1 -0.99 -0.34 -0.00027 0.0041 5.1e+03 0.87 0.38 -1.1 -
27 -0.17 -0.52 -0.16 -0.1 -0.57 0.84 0.26 -0.096 -0.51 -0.5 -1.3 -1.4 -0.47 0.00042 -0.17 5.1e+03 52 0.38 0.32 +
28 -0.17 -0.52 -0.16 -0.1 -0.57 0.84 0.26 -0.096 -0.51 -0.5 -1.3 -1.4 -0.47 0.00042 -0.17 5.1e+03 52 0.19 -0.099 -
29 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.19 0.28 +
30 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.095 -3.8 -
31 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.048 -3.7 -
32 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.024 -2.4 -
33 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.012 -2 -
34 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.006 -1.7 -
35 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.003 -1.6 -
36 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.0015 -1.4 -
37 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.00075 -1.1 -
38 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.00037 -0.64 -
39 -0.081 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00011 -0.01 5e+03 35 0.00019 -0.14 -
40 -0.082 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.0003 -0.011 5e+03 15 0.00019 0.28 +
41 -0.082 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.0003 -0.011 5e+03 15 9.3e-05 -0.62 -
42 -0.082 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.0002 -0.011 5e+03 7.7 9.3e-05 0.82 +
43 -0.082 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.00021 -0.011 5e+03 0.43 0.00093 1 ++
44 -0.082 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.41 -0.7 -1.3 -1.4 -0.51 -0.0002 -0.012 5e+03 0.13 0.0093 1 ++
45 -0.083 -0.53 -0.1 -0.1 -0.58 0.87 0.28 -0.092 -0.42 -0.7 -1.3 -1.4 -0.51 -0.00017 -0.021 5e+03 0.4 0.093 1 ++
46 -0.14 -0.53 -0.12 -0.11 -0.58 0.89 0.3 -0.09 -0.48 -0.64 -1.3 -1.5 -0.52 0.00021 -0.11 5e+03 31 0.093 0.6 +
47 -0.17 -0.54 -0.11 -0.11 -0.61 0.93 0.31 -0.085 -0.52 -0.64 -1.4 -1.6 -0.56 0.00017 -0.099 4.9e+03 9.8 0.93 0.96 ++
48 -0.27 -0.26 0.0038 -0.3 -0.64 1.2 0.6 0.26 -0.8 -1.1 -1.9 -2.1 -1.3 0.00027 -0.12 4.8e+03 20 9.3 0.96 ++
49 -0.29 -0.19 0.012 -0.36 -0.67 1.3 0.67 0.35 -0.83 -1.1 -2 -2.1 -1.5 0.0002 -0.11 4.8e+03 21 9.3 0.58 +
50 -0.28 -0.19 0.011 -0.36 -0.69 1.3 0.68 0.35 -0.84 -1.1 -2 -2.1 -1.4 0.00023 -0.11 4.8e+03 1.6 93 0.99 ++
51 -0.28 -0.19 0.012 -0.36 -0.7 1.3 0.68 0.36 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 0.18 9.3e+02 0.98 ++
52 -0.28 -0.19 0.012 -0.36 -0.7 1.3 0.68 0.36 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 0.00095 9.3e+03 1 ++
53 -0.28 -0.19 0.012 -0.36 -0.7 1.3 0.68 0.36 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 4e-08 9.3e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 27/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 1 0 7e+03 0.4 0.5 -0.32 -
1 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 5 0.94 ++
2 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 2.5 -1.7e+302 -
3 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 1.2 -6.7e+302 -
4 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 0.62 -7.8 -
5 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 0.31 -8 -
6 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 0.16 -8.8 -
7 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 0.078 -10 -
8 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 0.039 -13 -
9 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 0.02 -5.1 -
10 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 0.0098 -3.9 -
11 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0047 1.2 0.0005 5.8e+03 1 0.0049 -1.7 -
12 -0.023 -0.28 -0.045 -0.079 0.25 -0.5 -0.0002 1.2 0.0054 5.7e+03 0.24 0.049 0.9 ++
13 -0.042 -0.28 -0.088 -0.13 0.25 -0.51 -0.00027 1.2 0.022 5.7e+03 0.18 0.49 1 ++
14 -0.11 -0.29 -0.58 -0.44 0.11 -0.59 -0.0013 1.5 0.14 5.5e+03 8.3 0.49 0.75 +
15 -0.11 -0.29 -0.58 -0.44 0.11 -0.59 -0.0013 1.5 0.14 5.5e+03 8.3 0.24 -0.45 -
16 -0.11 -0.29 -0.58 -0.44 0.11 -0.59 -0.0013 1.5 0.14 5.5e+03 8.3 0.12 0.076 -
17 -0.05 -0.29 -0.64 -0.39 -0.014 -0.6 0.0016 1.5 0.047 5.4e+03 7.4 0.12 0.24 +
18 -0.05 -0.29 -0.64 -0.39 -0.014 -0.6 0.0016 1.5 0.047 5.4e+03 7.4 0.061 -4.5 -
19 -0.05 -0.29 -0.64 -0.39 -0.014 -0.6 0.0016 1.5 0.047 5.4e+03 7.4 0.031 -3.3 -
20 -0.05 -0.29 -0.64 -0.39 -0.014 -0.6 0.0016 1.5 0.047 5.4e+03 7.4 0.015 -2.7 -
21 -0.05 -0.29 -0.64 -0.39 -0.014 -0.6 0.0016 1.5 0.047 5.4e+03 7.4 0.0076 -2.2 -
22 -0.05 -0.29 -0.64 -0.39 -0.014 -0.6 0.0016 1.5 0.047 5.4e+03 7.4 0.0038 -1.5 -
23 -0.05 -0.29 -0.64 -0.39 -0.014 -0.6 0.0016 1.5 0.047 5.4e+03 7.4 0.0019 -0.39 -
24 -0.048 -0.3 -0.64 -0.39 -0.016 -0.61 -0.00036 1.5 0.045 5.3e+03 5.3 0.019 0.99 ++
25 -0.047 -0.3 -0.66 -0.4 -0.032 -0.62 -0.00054 1.5 0.064 5.3e+03 3.2 0.19 0.92 ++
26 -0.048 -0.27 -0.81 -0.47 -0.22 -0.65 -0.00094 1.7 0.18 5.3e+03 3.1 1.9 0.92 ++
27 -0.048 -0.27 -0.81 -0.47 -0.22 -0.65 -0.00094 1.7 0.18 5.3e+03 3.1 0.95 -2e+02 -
28 -0.048 -0.27 -0.81 -0.47 -0.22 -0.65 -0.00094 1.7 0.18 5.3e+03 3.1 0.48 -15 -
29 -0.25 -0.22 -0.98 -0.64 -0.53 -0.86 -0.0002 2.1 -0.016 5.2e+03 8.2 0.48 0.74 +
30 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 4.8 0.99 ++
31 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 2.4 -1.8e+02 -
32 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 1.2 -40 -
33 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.6 -8.2 -
34 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.3 -3.2 -
35 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.15 -2 -
36 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.075 -1.9 -
37 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.037 -1.3 -
38 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.019 -1.3 -
39 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.0093 -1.3 -
40 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.0047 -1.4 -
41 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.0023 -1.5 -
42 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.0012 -1.5 -
43 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.00058 -1.6 -
44 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.00029 -1.6 -
45 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 0.00015 -1.6 -
46 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 7.3e-05 -0.71 -
47 -0.25 -0.11 -0.69 -0.89 -1 -1.2 6.4e-05 2.5 -0.065 5.1e+03 31 3.6e-05 0.081 -
48 -0.25 -0.11 -0.69 -0.89 -1 -1.2 2.8e-05 2.5 -0.065 5.1e+03 1.2 3.6e-05 0.7 +
49 -0.25 -0.11 -0.69 -0.89 -1 -1.2 2.9e-05 2.5 -0.065 5.1e+03 0.021 0.00036 1 ++
50 -0.25 -0.11 -0.69 -0.89 -1 -1.2 3e-05 2.5 -0.065 5.1e+03 0.03 0.0036 1 ++
51 -0.25 -0.11 -0.69 -0.89 -1 -1.2 3.8e-05 2.5 -0.067 5.1e+03 0.02 0.036 1 ++
52 -0.26 -0.12 -0.71 -0.91 -0.99 -1.3 0.0001 2.5 -0.083 5.1e+03 2 0.36 0.95 ++
53 -0.33 0.031 -0.82 -1.1 -1.3 -1.6 0.00019 2.6 -0.1 5.1e+03 2.2 3.6 0.98 ++
54 -0.32 0.15 -0.82 -1.4 -1.7 -2 0.00021 2.4 -0.11 5.1e+03 2.8 36 1.1 ++
55 -0.26 0.14 -0.85 -1.4 -1.7 -2 0.0002 2.3 -0.11 5.1e+03 4.3 3.6e+02 0.98 ++
56 -0.27 0.19 -0.85 -1.5 -1.7 -2.1 0.00021 2.3 -0.11 5.1e+03 1.5 3.6e+03 1 ++
57 -0.26 0.19 -0.86 -1.5 -1.7 -2.1 0.00021 2.2 -0.11 5.1e+03 0.21 3.6e+04 0.98 ++
58 -0.25 0.2 -0.86 -1.5 -1.7 -2.1 0.00021 2.2 -0.11 5.1e+03 0.46 3.6e+05 1 ++
59 -0.25 0.2 -0.86 -1.5 -1.7 -2.1 0.00021 2.2 -0.11 5.1e+03 0.088 3.6e+06 1 ++
60 -0.25 0.2 -0.86 -1.5 -1.7 -2.1 0.00021 2.2 -0.11 5.1e+03 0.14 3.6e+07 1 ++
61 -0.25 0.2 -0.86 -1.5 -1.7 -2.1 0.00021 2.2 -0.11 5.1e+03 0.0042 3.6e+08 1 ++
62 -0.25 0.2 -0.86 -1.5 -1.7 -2.1 0.00021 2.2 -0.11 5.1e+03 0.0091 3.6e+09 1 ++
63 -0.25 0.2 -0.86 -1.5 -1.7 -2.1 0.00021 2.2 -0.11 5.1e+03 0.0026 3.6e+10 1 ++
64 -0.25 0.2 -0.86 -1.5 -1.7 -2.1 0.00021 2.2 -0.11 5.1e+03 0.0023 3.6e+11 1 ++
65 -0.25 0.2 -0.86 -1.5 -1.7 -2.1 0.00021 2.2 -0.11 5.1e+03 0.00088 3.6e+12 1 ++
66 -0.25 0.2 -0.86 -1.5 -1.7 -2.1 0.00021 2.2 -0.11 5.1e+03 0.00024 3.6e+12 1 ++
Considering neighbor 0/20 for current solution
Attempt 28/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.1e+03 0.082 10 1.1 ++
1 4.9e+03 0.02 1e+02 1.1 ++
2 4.9e+03 0.0034 1e+03 1.1 ++
3 4.9e+03 0.00012 1e+04 1 ++
4 4.9e+03 1.4e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 29/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_SM B_TIME_TRAIN mu_existing Function Relgrad Radius Rho
0 0.032 -0.42 -1 -0.39 -0.099 -0.61 1.5 5.4e+03 0.09 10 0.96 ++
1 -0.3 0.14 -0.82 -0.69 -0.72 -1.2 2.1 5.2e+03 0.049 10 0.89 +
2 -0.31 -0.16 -0.81 -0.7 -0.78 -1 2.3 5.2e+03 0.0041 1e+02 0.94 ++
3 -0.33 -0.15 -0.83 -0.72 -0.81 -1.1 2.3 5.2e+03 0.00013 1e+03 1 ++
4 -0.33 -0.15 -0.83 -0.72 -0.81 -1.1 2.3 5.2e+03 3.2e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 30/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME lambda_travel_t Function Relgrad Radius Rho
0 -0.4 -0.17 -0.3 -0.026 -0.64 0.32 -0.021 -0.016 -0.58 -1 1.4 5.5e+03 0.068 10 0.91 ++
1 -0.4 -0.17 -0.3 -0.026 -0.64 0.32 -0.021 -0.016 -0.58 -1 1.4 5.5e+03 0.068 5 -7.7e+02 -
2 -0.4 -0.17 -0.3 -0.026 -0.64 0.32 -0.021 -0.016 -0.58 -1 1.4 5.5e+03 0.068 2.5 -17 -
3 -0.4 -0.17 -0.3 -0.026 -0.64 0.32 -0.021 -0.016 -0.58 -1 1.4 5.5e+03 0.068 1.2 -1 -
4 -0.13 -0.38 0.0032 -0.078 -1.3 1.6 0.52 0.0085 -1.2 -1.6 0.6 5e+03 0.0097 12 1 ++
5 -0.067 -0.29 0.027 -0.3 -1.4 1.8 0.68 0.52 -1.1 -1.7 0.37 5e+03 0.0013 1.2e+02 0.99 ++
6 -0.083 -0.28 0.027 -0.31 -1.5 1.9 0.71 0.51 -1.1 -1.7 0.38 5e+03 1.5e-05 1.2e+03 1 ++
7 -0.083 -0.28 0.027 -0.31 -1.5 1.9 0.71 0.51 -1.1 -1.7 0.38 5e+03 1.8e-09 1.2e+03 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas Function Relgrad Radius Rho
0 -0.48 -0.38 -0.09 -0.098 -1 1 0.08 -0.02 -0.82 -0.93 -0.95 -0.39 -0.65 5.1e+03 0.053 10 1.1 ++
1 -0.65 -0.1 0.028 -0.36 -1.3 1.5 0.4 0.19 -0.85 -1.2 -1.4 -0.63 -0.84 4.9e+03 0.018 1e+02 1.1 ++
2 -0.67 -0.051 0.021 -0.4 -1.4 1.4 0.58 0.36 -0.84 -1.3 -1.7 -0.69 -0.9 4.9e+03 0.0033 1e+03 1.1 ++
3 -0.67 -0.045 0.019 -0.4 -1.4 1.4 0.61 0.39 -0.84 -1.3 -1.8 -0.69 -0.91 4.9e+03 0.00012 1e+04 1 ++
4 -0.67 -0.045 0.019 -0.4 -1.4 1.4 0.61 0.39 -0.84 -1.3 -1.8 -0.69 -0.91 4.9e+03 1.7e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas lambda_travel_t Function Relgrad Radius Rho
0 -0.19 -0.62 -0.39 -0.82 -0.56 -1 -0.71 1.9 5.8e+03 0.091 1 0.59 +
1 -0.39 -0.55 -0.39 -0.76 -1.6 -0.53 -0.61 1.6 5.2e+03 0.026 10 1.1 ++
2 -0.39 -0.55 -0.39 -0.76 -1.6 -0.53 -0.61 1.6 5.2e+03 0.026 5 -1.8e+02 -
3 -0.39 -0.55 -0.39 -0.76 -1.6 -0.53 -0.61 1.6 5.2e+03 0.026 2.5 -5.7 -
4 -0.39 -0.55 -0.39 -0.76 -1.6 -0.53 -0.61 1.6 5.2e+03 0.026 1.2 -0.23 -
5 -0.71 0.054 -0.82 -1.5 -2.8 -0.99 -1 0.67 5e+03 0.033 12 0.93 ++
6 -0.23 0.45 -0.98 -1.2 -3 -1.3 -0.59 0.32 5e+03 0.003 1.2e+02 0.99 ++
7 -0.25 0.44 -1 -1.2 -3 -1.3 -0.57 0.33 5e+03 2.2e-05 1.2e+03 1 ++
8 -0.25 0.44 -1 -1.2 -3 -1.3 -0.57 0.33 5e+03 2.1e-09 1.2e+03 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 31/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter mu_public Function Relgrad Radius Rho
0 -0.31 -0.21 -0.073 -0.69 0.28 -0.0026 -0.64 -0.69 -1 -0.84 0.32 1 5.3e+03 0.06 10 1.1 ++
1 -0.61 0.088 -0.16 -0.17 0.73 0.66 -0.96 -0.98 -2.2 -0.8 -0.45 1.5 5.1e+03 0.043 10 0.87 +
2 -0.63 -0.00046 -0.32 -0.14 0.32 0.14 -1.1 -1.2 -2 -0.74 -0.36 1.9 5.1e+03 0.016 10 0.69 +
3 -0.63 -0.00046 -0.32 -0.14 0.32 0.14 -1.1 -1.2 -2 -0.74 -0.36 1.9 5.1e+03 0.016 4.1 -1.4e+03 -
4 -0.63 -0.00046 -0.32 -0.14 0.32 0.14 -1.1 -1.2 -2 -0.74 -0.36 1.9 5.1e+03 0.016 2 -4.9e+02 -
5 -0.63 -0.00046 -0.32 -0.14 0.32 0.14 -1.1 -1.2 -2 -0.74 -0.36 1.9 5.1e+03 0.016 1 -55 -
6 -0.63 -0.00046 -0.32 -0.14 0.32 0.14 -1.1 -1.2 -2 -0.74 -0.36 1.9 5.1e+03 0.016 0.51 -4.9 -
7 -0.63 -0.00046 -0.32 -0.14 0.32 0.14 -1.1 -1.2 -2 -0.74 -0.36 1.9 5.1e+03 0.016 0.25 -0.11 -
8 -0.63 0.022 -0.31 -0.066 0.4 0.36 -1.1 -1.2 -2.1 -0.66 -0.42 2.1 5e+03 0.011 0.25 0.63 +
9 -0.64 0.02 -0.27 -0.075 0.34 0.2 -1.2 -1.2 -2 -0.6 -0.38 2.4 5e+03 0.0033 0.25 0.89 +
10 -0.64 0.019 -0.27 -0.064 0.32 0.21 -1.2 -1.1 -2 -0.57 -0.38 2.5 5e+03 0.00098 2.5 1.1 ++
11 -0.64 0.019 -0.27 -0.064 0.32 0.21 -1.2 -1.1 -2 -0.57 -0.38 2.5 5e+03 0.00098 0.23 -4.4 -
12 -0.66 0.014 -0.27 -0.056 0.29 0.19 -1.2 -1.1 -1.9 -0.51 -0.38 2.7 5e+03 0.003 0.23 0.79 +
13 -0.66 0.015 -0.28 -0.049 0.28 0.18 -1.2 -1.1 -1.9 -0.48 -0.37 2.9 5e+03 0.0012 2.3 1.3 ++
14 -0.66 0.013 -0.27 -0.042 0.25 0.16 -1.2 -1.1 -1.9 -0.42 -0.36 3.2 5e+03 0.0042 2.3 0.5 +
15 -0.66 0.012 -0.28 -0.042 0.25 0.16 -1.2 -1.1 -1.8 -0.42 -0.36 3.3 5e+03 8.5e-05 23 1 ++
16 -0.67 0.011 -0.27 -0.04 0.24 0.16 -1.2 -1.1 -1.8 -0.4 -0.35 3.4 5e+03 0.00028 2.3e+02 0.92 ++
17 -0.67 0.012 -0.28 -0.04 0.24 0.16 -1.2 -1.1 -1.8 -0.4 -0.35 3.4 5e+03 1.4e-05 2.3e+03 1 ++
18 -0.67 0.011 -0.28 -0.04 0.24 0.16 -1.2 -1.1 -1.8 -0.4 -0.35 3.4 5e+03 1.7e-05 2.3e+04 0.99 ++
19 -0.67 0.011 -0.28 -0.04 0.24 0.16 -1.2 -1.1 -1.8 -0.4 -0.35 3.4 5e+03 2e-06 2.3e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME mu_existing Function Relgrad Radius Rho
0 0.054 -0.38 -0.17 -0.053 -0.53 0.64 0.28 0.0046 -0.32 -0.79 -0.94 -1 1.8 5.2e+03 0.12 10 0.93 ++
1 -0.5 0.23 0.064 -0.15 -0.97 1.1 0.5 0.3 -0.6 -0.87 -1.2 -0.96 1.7 5e+03 0.0066 1e+02 1 ++
2 -0.46 0.041 0.067 -0.19 -1 1.2 0.59 0.4 -0.8 -1 -1.6 -1.1 1.1 5e+03 0.0082 1e+02 0.3 +
3 -0.48 0.018 0.066 -0.22 -1 1.2 0.59 0.4 -0.81 -1 -1.6 -1.1 1.2 5e+03 0.0016 1e+03 1.1 ++
4 -0.48 0.072 0.065 -0.21 -1 1.2 0.57 0.38 -0.79 -1 -1.6 -1.1 1.3 5e+03 0.00038 1e+04 1.1 ++
5 -0.48 0.072 0.065 -0.21 -1 1.2 0.57 0.38 -0.79 -1 -1.6 -1.1 1.3 5e+03 4.6e-06 1e+04 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas mu_public Function Relgrad Radius Rho
0 -0.36 -0.22 -0.084 -0.69 0.25 -0.0062 -0.61 -0.77 -1 -0.45 -0.63 1 5.3e+03 0.06 10 1.1 ++
1 -0.67 0.053 -0.25 -0.42 0.68 0.49 -0.88 -1.1 -2.1 -0.69 -0.51 1.3 5e+03 0.026 1e+02 1 ++
2 -0.64 0.019 -0.37 -0.45 0.65 0.43 -0.91 -1.2 -2.4 -0.68 -0.67 1.2 5e+03 0.0068 1e+03 1.1 ++
3 -0.58 0.027 -0.37 -0.51 0.67 0.42 -0.85 -1.2 -2.6 -0.71 -0.76 1 5e+03 0.011 1e+04 0.93 ++
4 -0.59 -0.0092 -0.38 -0.58 0.74 0.42 -0.85 -1.2 -2.7 -0.71 -0.77 1 5e+03 0.0017 1e+05 1 ++
5 -0.6 0.029 -0.39 -0.61 0.77 0.51 -0.88 -1.2 -2.7 -0.7 -0.78 1 5e+03 3e-05 1e+06 1 ++
6 -0.6 0.029 -0.39 -0.61 0.77 0.51 -0.88 -1.2 -2.7 -0.7 -0.78 1 5e+03 3.9e-08 1e+06 1 ++
Considering neighbor 2/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -6.7 -
1 0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.25 -1.9 -
2 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 2.5 1 ++
3 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 1.2 -8.4 -
4 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.62 -5.1 -
5 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.31 -3.2 -
6 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.16 -0.81 -
7 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.078 -0.25 -
8 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.039 -0.19 -
9 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.02 -0.38 -
10 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.0098 -0.74 -
11 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.0049 -1.2 -
12 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.0024 -1.6 -
13 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.0012 -2 -
14 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.00061 -1.3 -
15 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.2 0.00031 -0.18 -
16 -0.034 -0.032 -0.0051 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.00031 1.3 0.00031 5.8e+03 1.7 0.00031 0.63 +
17 -0.034 -0.032 -0.0051 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.00025 1.3 0.00049 5.8e+03 0.15 0.0031 0.98 ++
18 -0.033 -0.032 -0.0052 -0.25 -0.25 -0.013 -0.07 0.25 -0.25 -0.25 -0.00027 1.3 0.0024 5.7e+03 0.15 0.031 1 ++
19 -0.023 -0.032 -0.0059 -0.27 -0.25 -0.014 -0.063 0.24 -0.28 -0.28 -0.00034 1.3 0.021 5.7e+03 0.13 0.31 1 ++
20 0.044 -0.063 -0.016 -0.4 -0.16 -0.015 -0.025 0.017 -0.57 -0.59 -0.0012 1.5 0.21 5.5e+03 6.6 0.31 0.72 +
21 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.31 0.57 +
22 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.15 -3.6 -
23 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.076 -3.5 -
24 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.038 -3.5 -
25 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.019 -3.5 -
26 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.0095 -2.5 -
27 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.0048 -1.9 -
28 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.0024 -1.6 -
29 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.0012 -1.2 -
30 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.0006 -0.76 -
31 -0.16 -0.23 -0.039 -0.2 0.14 -0.0025 -0.23 -0.24 -0.65 -0.64 -0.00033 1.5 0.055 5.3e+03 15 0.0003 -0.25 -
32 -0.16 -0.23 -0.039 -0.2 0.14 -0.0028 -0.23 -0.24 -0.65 -0.64 -0.00063 1.5 0.055 5.3e+03 9.7 0.0003 0.2 +
33 -0.16 -0.23 -0.039 -0.2 0.14 -0.0028 -0.23 -0.24 -0.65 -0.64 -0.00063 1.5 0.055 5.3e+03 9.7 0.00015 -0.23 -
34 -0.16 -0.23 -0.04 -0.2 0.14 -0.0029 -0.23 -0.24 -0.65 -0.64 -0.00048 1.5 0.055 5.3e+03 1.7 0.00015 0.79 +
35 -0.16 -0.23 -0.04 -0.2 0.14 -0.0029 -0.23 -0.24 -0.65 -0.64 -0.00049 1.5 0.055 5.3e+03 0.079 0.0015 1 ++
36 -0.16 -0.23 -0.04 -0.2 0.14 -0.0029 -0.23 -0.24 -0.65 -0.64 -0.00049 1.5 0.056 5.3e+03 0.57 0.015 1 ++
37 -0.15 -0.23 -0.04 -0.21 0.14 -0.0029 -0.22 -0.24 -0.66 -0.65 -0.00051 1.5 0.061 5.3e+03 0.07 0.15 1 ++
38 -0.078 -0.17 -0.047 -0.3 0.13 -0.00012 -0.16 -0.32 -0.81 -0.72 -0.00044 1.6 0.046 5.2e+03 0.23 1.5 0.99 ++
39 -0.078 -0.17 -0.047 -0.3 0.13 -0.00012 -0.16 -0.32 -0.81 -0.72 -0.00044 1.6 0.046 5.2e+03 0.23 0.75 -37 -
40 -0.078 -0.17 -0.047 -0.3 0.13 -0.00012 -0.16 -0.32 -0.81 -0.72 -0.00044 1.6 0.046 5.2e+03 0.23 0.37 -2.8 -
41 -0.078 -0.17 -0.047 -0.3 0.13 -0.00012 -0.16 -0.32 -0.81 -0.72 -0.00044 1.6 0.046 5.2e+03 0.23 0.19 -0.089 -
42 -0.19 -0.2 -0.071 -0.29 0.25 0.014 -0.3 -0.37 -1 -0.87 -7.2e-05 1.8 -0.044 5.1e+03 0.49 1.9 0.98 ++
43 -0.19 -0.2 -0.071 -0.29 0.25 0.014 -0.3 -0.37 -1 -0.87 -7.2e-05 1.8 -0.044 5.1e+03 0.49 0.73 -9.3 -
44 -0.19 -0.2 -0.071 -0.29 0.25 0.014 -0.3 -0.37 -1 -0.87 -7.2e-05 1.8 -0.044 5.1e+03 0.49 0.36 -0.52 -
45 -0.23 -0.081 -0.12 -0.29 0.4 0.051 -0.43 -0.61 -1.3 -1.2 0.00038 2 -0.15 5.1e+03 21 0.36 0.44 +
46 -0.23 -0.081 -0.12 -0.29 0.4 0.051 -0.43 -0.61 -1.3 -1.2 0.00038 2 -0.15 5.1e+03 21 0.18 -0.25 -
47 -0.24 -0.049 -0.14 -0.17 0.5 0.072 -0.43 -0.78 -1.3 -1.4 -0.00018 2 -0.014 5e+03 31 0.18 0.12 +
48 -0.24 -0.049 -0.14 -0.17 0.5 0.072 -0.43 -0.78 -1.3 -1.4 -0.00018 2 -0.014 5e+03 31 0.091 -0.51 -
49 -0.24 -0.038 -0.14 -0.19 0.48 0.073 -0.44 -0.75 -1.3 -1.4 0.00017 2 -0.1 5e+03 34 0.091 0.72 +
50 -0.26 -0.0072 -0.15 -0.23 0.46 0.084 -0.47 -0.68 -1.4 -1.5 0.00019 2 -0.1 5e+03 15 0.91 0.99 ++
51 -0.19 0.03 -0.25 -0.11 0.67 0.27 -0.71 -0.94 -2.1 -2.1 0.00022 1.2 -0.11 4.9e+03 2.9 0.91 0.59 +
52 -0.25 0.031 -0.29 -0.08 0.7 0.35 -0.73 -1 -2.3 -2.2 0.00023 1.3 -0.11 4.9e+03 0.46 9.1 1.1 ++
53 -0.24 0.034 -0.29 -0.078 0.69 0.36 -0.73 -0.98 -2.2 -2.1 0.00023 1.4 -0.11 4.9e+03 0.03 91 1.1 ++
54 -0.25 0.036 -0.29 -0.083 0.69 0.36 -0.72 -0.98 -2.2 -2.1 0.00023 1.4 -0.11 4.9e+03 0.00081 9.1e+02 1 ++
55 -0.25 0.036 -0.29 -0.083 0.69 0.36 -0.72 -0.98 -2.2 -2.1 0.00023 1.4 -0.11 4.9e+03 5.3e-05 9.1e+03 1 ++
56 -0.25 0.036 -0.29 -0.083 0.69 0.36 -0.72 -0.98 -2.2 -2.1 0.00023 1.4 -0.11 4.9e+03 0.00017 9.1e+04 1 ++
57 -0.25 0.036 -0.29 -0.083 0.69 0.36 -0.72 -0.98 -2.2 -2.1 0.00023 1.4 -0.11 4.9e+03 1.4e-05 9.1e+05 1 ++
58 -0.25 0.036 -0.29 -0.083 0.69 0.36 -0.72 -0.98 -2.2 -2.1 0.00023 1.4 -0.11 4.9e+03 0.00077 9.1e+06 1 ++
59 -0.25 0.036 -0.29 -0.083 0.69 0.36 -0.72 -0.98 -2.2 -2.1 0.00023 1.4 -0.11 4.9e+03 2.2e-07 9.1e+06 1 ++
Considering neighbor 3/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas Function Relgrad Radius Rho
0 -0.36 -0.22 -0.084 -0.69 0.25 -0.0062 -0.61 -0.77 -1 -0.45 -0.63 5.3e+03 0.06 10 1.1 ++
1 -0.56 0.04 -0.3 -0.71 0.64 0.37 -0.85 -1.1 -2 -0.67 -0.7 5e+03 0.038 1e+02 1.2 ++
2 -0.59 0.032 -0.38 -0.64 0.76 0.49 -0.88 -1.2 -2.6 -0.7 -0.77 5e+03 0.0093 1e+03 1.1 ++
3 -0.6 0.03 -0.39 -0.62 0.77 0.5 -0.88 -1.2 -2.7 -0.7 -0.78 5e+03 0.00053 1e+04 1 ++
4 -0.6 0.03 -0.39 -0.62 0.77 0.5 -0.88 -1.2 -2.7 -0.7 -0.78 5e+03 1.6e-06 1e+04 1 ++
Considering neighbor 4/20 for current solution
Attempt 32/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_travel_t Function Relgrad Radius Rho
0 -0.5 -0.71 -0.82 -1 1.7 5.6e+03 0.058 1 0.77 +
1 -0.35 -0.77 -1.3 -1.7 0.69 5.4e+03 0.049 10 1 ++
2 0.017 -0.47 -1 -1.7 0.53 5.3e+03 0.002 1e+02 0.96 ++
3 -0.0043 -0.48 -1.1 -1.7 0.51 5.3e+03 1.3e-05 1e+03 1 ++
4 -0.0043 -0.48 -1.1 -1.7 0.51 5.3e+03 1.8e-09 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 33/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas cube_tt_coef mu_public square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -3.4 -
1 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.25 -0.96 -
2 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 1 0 5.8e+03 3.1 2.5 1 ++
3 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 1 0 5.8e+03 3.1 1.2 -7 -
4 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 1 0 5.8e+03 3.1 0.62 -3 -
5 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 1 0 5.8e+03 3.1 0.31 -1.2 -
6 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 1 0 5.8e+03 3.1 0.16 -0.42 -
7 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 1.1 0.14 5.7e+03 11 0.16 0.3 +
8 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 1.1 0.14 5.7e+03 11 0.078 -0.87 -
9 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 1.1 0.14 5.7e+03 11 0.039 -0.81 -
10 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 1.1 0.14 5.7e+03 11 0.02 -0.77 -
11 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 1.1 0.14 5.7e+03 11 0.0098 -0.74 -
12 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 1.1 0.14 5.7e+03 11 0.0049 -0.5 -
13 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 1.1 0.14 5.7e+03 11 0.0024 0.082 -
14 0.13 -0.27 -0.37 0.11 -0.16 0.21 -0.38 -0.4 -0.32 -0.00093 1.1 0.14 5.5e+03 2.1 0.0024 0.8 +
15 0.13 -0.27 -0.37 0.11 -0.16 0.21 -0.38 -0.4 -0.32 -0.00083 1.1 0.14 5.5e+03 2.3 0.0024 0.82 +
16 0.13 -0.27 -0.37 0.11 -0.16 0.21 -0.38 -0.4 -0.32 -0.00086 1.1 0.14 5.5e+03 0.096 0.024 1 ++
17 0.13 -0.27 -0.37 0.11 -0.15 0.18 -0.39 -0.4 -0.32 -0.00083 1.1 0.13 5.5e+03 0.1 0.24 1 ++
18 0.14 -0.32 -0.37 0.21 -0.13 -0.062 -0.47 -0.42 -0.33 -0.00064 1.2 0.089 5.4e+03 0.049 2.4 0.93 ++
19 0.14 -0.32 -0.37 0.21 -0.13 -0.062 -0.47 -0.42 -0.33 -0.00064 1.2 0.089 5.4e+03 0.049 1.2 -7.2 -
20 -0.73 -0.65 -0.45 1.4 -0.72 -0.98 -1.5 -0.6 -0.44 0.00012 1.8 -0.095 5.1e+03 5.2 1.2 0.57 +
21 -0.73 -0.65 -0.45 1.4 -0.72 -0.98 -1.5 -0.6 -0.44 0.00012 1.8 -0.095 5.1e+03 5.2 0.61 -1.6 -
22 -0.67 -0.6 -0.28 1 -0.79 -1.3 -1.3 -1.2 -0.81 -0.00022 2 -0.0039 5.1e+03 13 0.61 0.21 +
23 -0.67 -0.6 -0.28 1 -0.79 -1.3 -1.3 -1.2 -0.81 -0.00022 2 -0.0039 5.1e+03 13 0.31 -12 -
24 -0.67 -0.6 -0.28 1 -0.79 -1.3 -1.3 -1.2 -0.81 -0.00022 2 -0.0039 5.1e+03 13 0.15 -1.8 -
25 -0.7 -0.6 -0.32 1 -0.81 -1.2 -1.3 -1.2 -0.82 0.00038 2 -0.16 5e+03 31 0.15 0.4 +
26 -0.62 -0.57 -0.19 0.96 -0.79 -1.2 -1.4 -1.3 -0.8 0.00022 1.8 -0.11 4.9e+03 10 1.5 0.98 ++
27 -0.62 -0.57 -0.19 0.96 -0.79 -1.2 -1.4 -1.3 -0.8 0.00022 1.8 -0.11 4.9e+03 10 0.42 -0.74 -
28 -0.54 -0.48 -0.25 1 -0.9 -1.3 -1.6 -1.5 -0.71 0.00015 1.4 -0.1 4.9e+03 25 0.42 0.86 +
29 -0.48 -0.32 -0.29 1.2 -0.84 -1.3 -1.7 -1.8 -0.77 0.00027 1.2 -0.12 4.9e+03 12 0.42 0.83 +
30 -0.48 -0.32 -0.29 1.2 -0.84 -1.3 -1.7 -1.8 -0.77 0.00027 1.2 -0.12 4.9e+03 12 0.08 -0.44 -
31 -0.48 -0.32 -0.29 1.2 -0.84 -1.3 -1.7 -1.8 -0.77 0.00027 1.2 -0.12 4.9e+03 12 0.04 -0.61 -
32 -0.48 -0.32 -0.29 1.2 -0.84 -1.3 -1.7 -1.8 -0.77 0.00027 1.2 -0.12 4.9e+03 12 0.02 -0.35 -
33 -0.48 -0.32 -0.29 1.2 -0.84 -1.3 -1.7 -1.8 -0.77 0.00027 1.2 -0.12 4.9e+03 12 0.01 -0.58 -
34 -0.48 -0.32 -0.29 1.2 -0.84 -1.3 -1.7 -1.8 -0.77 0.00027 1.2 -0.12 4.9e+03 12 0.005 -0.71 -
35 -0.48 -0.32 -0.29 1.2 -0.84 -1.3 -1.7 -1.8 -0.77 0.00027 1.2 -0.12 4.9e+03 12 0.0025 -0.25 -
36 -0.48 -0.32 -0.29 1.2 -0.84 -1.3 -1.7 -1.8 -0.77 0.00021 1.2 -0.11 4.9e+03 25 0.0025 0.1 +
37 -0.48 -0.32 -0.29 1.2 -0.84 -1.3 -1.7 -1.8 -0.77 0.00022 1.2 -0.11 4.9e+03 0.19 0.025 0.96 ++
38 -0.47 -0.32 -0.31 1.2 -0.83 -1.2 -1.8 -1.8 -0.78 0.00023 1.2 -0.11 4.9e+03 0.2 0.25 1 ++
39 -0.47 -0.26 -0.46 1.4 -0.81 -1.3 -1.9 -1.8 -0.88 0.00021 1 -0.11 4.9e+03 0.38 0.25 0.68 +
40 -0.44 -0.21 -0.49 1.4 -0.8 -1.2 -1.9 -1.8 -0.88 0.00022 1 -0.11 4.9e+03 3.1 2.5 1 ++
41 -0.44 -0.21 -0.49 1.4 -0.8 -1.2 -1.9 -1.8 -0.88 0.00022 1 -0.11 4.9e+03 0.27 25 0.94 ++
42 -0.45 -0.15 -0.51 1.5 -0.8 -1.3 -1.9 -1.8 -0.89 0.00022 1 -0.11 4.9e+03 0.0014 2.5e+02 1 ++
43 -0.45 -0.15 -0.51 1.5 -0.8 -1.3 -1.9 -1.8 -0.89 0.00022 1 -0.11 4.9e+03 1.9e-06 2.5e+02 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter Function Relgrad Radius Rho
0 -0.31 -0.21 -0.073 -0.69 0.28 -0.0026 -0.64 -0.69 -1 -0.84 0.32 5.3e+03 0.06 10 1.1 ++
1 -0.44 0.072 -0.19 -0.6 0.69 0.46 -0.9 -1 -2 -1 -0.043 5.1e+03 0.039 1e+02 1.2 ++
2 -0.46 0.061 -0.25 -0.49 0.79 0.56 -0.93 -1.1 -2.6 -1.1 -0.2 5e+03 0.0095 1e+03 1.1 ++
3 -0.46 0.059 -0.26 -0.46 0.81 0.57 -0.93 -1.1 -2.7 -1.1 -0.22 5e+03 0.00053 1e+04 1 ++
4 -0.46 0.059 -0.26 -0.46 0.81 0.57 -0.93 -1.1 -2.7 -1.1 -0.22 5e+03 1.6e-06 1e+04 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -7.9 -
1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.25 -2.4 -
2 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 2.5 1 ++
3 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 1.2 -7.4 -
4 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.62 -5 -
5 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.31 -3.5 -
6 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.16 -2.7 -
7 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.078 -2.5 -
8 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.039 -2.5 -
9 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.02 -2.7 -
10 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.0098 -3 -
11 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.0049 -3.2 -
12 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.0024 -2.3 -
13 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.0012 -1.5 -
14 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.00061 -0.82 -
15 -0.034 -0.032 -0.0048 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 0 1.2 0 5.7e+03 7.4 0.00031 -0.088 -
16 -0.034 -0.032 -0.0051 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 -0.00031 1.3 0.00031 5.7e+03 3.1 0.00031 0.67 +
17 -0.034 -0.032 -0.0051 -0.25 -0.25 -0.013 -0.071 0.25 -0.25 -0.25 -0.14 -0.00025 1.3 0.00047 5.7e+03 0.81 0.0031 0.9 ++
18 -0.033 -0.032 -0.0052 -0.25 -0.25 -0.013 -0.07 0.25 -0.25 -0.25 -0.14 -0.00027 1.3 0.0022 5.7e+03 0.075 0.031 1 ++
19 -0.022 -0.031 -0.0059 -0.27 -0.25 -0.014 -0.062 0.23 -0.28 -0.28 -0.15 -0.00034 1.3 0.019 5.7e+03 0.49 0.31 1 ++
20 0.04 -0.073 -0.019 -0.39 -0.13 -0.015 -0.031 -0.03 -0.59 -0.59 -0.21 -0.00097 1.5 0.17 5.4e+03 6.4 0.31 0.8 +
21 -0.18 -0.24 -0.044 -0.2 0.18 -0.0014 -0.26 -0.24 -0.71 -0.7 -0.19 -7.9e-05 1.6 -0.028 5.3e+03 21 0.31 0.55 +
22 -0.18 -0.24 -0.044 -0.2 0.18 -0.0014 -0.26 -0.24 -0.71 -0.7 -0.19 -7.9e-05 1.6 -0.028 5.3e+03 21 0.15 -0.84 -
23 -0.18 -0.24 -0.044 -0.2 0.18 -0.0014 -0.26 -0.24 -0.71 -0.7 -0.19 -7.9e-05 1.6 -0.028 5.3e+03 21 0.076 -0.094 -
24 -0.12 -0.2 -0.046 -0.27 0.15 -0.0015 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00099 1.6 0.021 5.3e+03 32 0.076 0.11 +
25 -0.12 -0.2 -0.046 -0.27 0.15 -0.0015 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00099 1.6 0.021 5.3e+03 32 0.038 -0.72 -
26 -0.12 -0.2 -0.046 -0.27 0.15 -0.0015 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00099 1.6 0.021 5.3e+03 32 0.019 -0.68 -
27 -0.12 -0.2 -0.046 -0.27 0.15 -0.0015 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00099 1.6 0.021 5.3e+03 32 0.0095 -0.67 -
28 -0.12 -0.2 -0.046 -0.27 0.15 -0.0015 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00099 1.6 0.021 5.3e+03 32 0.0048 -0.67 -
29 -0.12 -0.2 -0.046 -0.27 0.15 -0.0015 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00099 1.6 0.021 5.3e+03 32 0.0024 -0.67 -
30 -0.12 -0.2 -0.046 -0.27 0.15 -0.0015 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00099 1.6 0.021 5.3e+03 32 0.0012 -0.67 -
31 -0.12 -0.2 -0.046 -0.27 0.15 -0.0015 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00099 1.6 0.021 5.3e+03 32 0.0006 0.047 -
32 -0.12 -0.2 -0.046 -0.27 0.15 -0.0014 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00039 1.6 0.022 5.2e+03 14 0.0006 0.85 +
33 -0.12 -0.2 -0.046 -0.27 0.15 -0.0014 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00039 1.6 0.022 5.2e+03 14 0.0003 -1.9 -
34 -0.12 -0.2 -0.046 -0.27 0.15 -0.0014 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00039 1.6 0.022 5.2e+03 14 0.00015 -2 -
35 -0.12 -0.2 -0.046 -0.27 0.15 -0.0014 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00039 1.6 0.022 5.2e+03 14 7.5e-05 -1.1 -
36 -0.12 -0.2 -0.046 -0.27 0.15 -0.0013 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00032 1.6 0.022 5.2e+03 17 7.5e-05 0.28 +
37 -0.12 -0.2 -0.046 -0.27 0.15 -0.0013 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00035 1.6 0.022 5.2e+03 4.7 7.5e-05 0.73 +
38 -0.12 -0.2 -0.046 -0.27 0.15 -0.0013 -0.21 -0.27 -0.78 -0.76 -0.21 -0.00034 1.6 0.022 5.2e+03 0.74 0.00075 0.93 ++
39 -0.12 -0.2 -0.046 -0.27 0.15 -0.0013 -0.2 -0.27 -0.78 -0.76 -0.21 -0.00034 1.6 0.021 5.2e+03 0.034 0.0075 1 ++
40 -0.11 -0.2 -0.047 -0.27 0.15 -0.00099 -0.2 -0.27 -0.79 -0.76 -0.21 -0.00031 1.6 0.014 5.2e+03 2.2 0.075 1 ++
41 -0.082 -0.17 -0.052 -0.3 0.16 0.002 -0.18 -0.33 -0.87 -0.79 -0.22 -5.3e-05 1.6 -0.048 5.2e+03 0.22 0.75 0.91 ++
42 -0.082 -0.17 -0.052 -0.3 0.16 0.002 -0.18 -0.33 -0.87 -0.79 -0.22 -5.3e-05 1.6 -0.048 5.2e+03 0.22 0.37 -0.72 -
43 -0.23 -0.17 -0.1 -0.29 0.37 0.031 -0.4 -0.47 -1.2 -1.2 -0.31 0.00044 1.9 -0.17 5.1e+03 71 0.37 0.25 +
44 -0.23 -0.17 -0.1 -0.29 0.37 0.031 -0.4 -0.47 -1.2 -1.2 -0.31 0.00044 1.9 -0.17 5.1e+03 71 0.19 -0.48 -
45 -0.23 -0.17 -0.1 -0.29 0.37 0.031 -0.4 -0.47 -1.2 -1.2 -0.31 0.00044 1.9 -0.17 5.1e+03 71 0.093 0.065 -
46 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 0.00028 1.9 -0.082 5.1e+03 43 0.093 0.29 +
47 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 0.00028 1.9 -0.082 5.1e+03 43 0.047 -3.5 -
48 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 0.00028 1.9 -0.082 5.1e+03 43 0.023 -3.5 -
49 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 0.00028 1.9 -0.082 5.1e+03 43 0.012 -3.6 -
50 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 0.00028 1.9 -0.082 5.1e+03 43 0.0058 -2.5 -
51 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 0.00028 1.9 -0.082 5.1e+03 43 0.0029 -1.9 -
52 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 0.00028 1.9 -0.082 5.1e+03 43 0.0015 -1.6 -
53 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 0.00028 1.9 -0.082 5.1e+03 43 0.00073 -1.3 -
54 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 0.00028 1.9 -0.082 5.1e+03 43 0.00036 -0.82 -
55 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 0.00028 1.9 -0.082 5.1e+03 43 0.00018 -0.022 -
56 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 9.8e-05 1.9 -0.082 5e+03 23 0.0018 0.98 ++
57 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.47 -1.2 -1.2 -0.32 9.5e-05 1.9 -0.084 5e+03 3.6 0.018 0.95 ++
58 -0.22 -0.16 -0.1 -0.29 0.36 0.032 -0.4 -0.48 -1.2 -1.2 -0.32 0.00017 1.9 -0.1 5e+03 4.4 0.18 0.91 ++
59 -0.22 -0.059 -0.13 -0.2 0.46 0.06 -0.43 -0.66 -1.3 -1.3 -0.39 0.00013 2 -0.091 5e+03 0.27 1.8 1 ++
60 -0.2 0.042 -0.24 -0.048 0.65 0.19 -0.64 -0.85 -1.9 -1.9 -1 0.0003 1.5 -0.13 4.9e+03 15 1.8 0.8 +
61 -0.21 0.031 -0.33 0.038 0.72 0.3 -0.72 -1 -2.3 -2 -1.2 0.00015 1.3 -0.093 4.9e+03 55 1.8 0.25 +
62 -0.21 0.031 -0.33 0.038 0.72 0.3 -0.72 -1 -2.3 -2 -1.2 0.00015 1.3 -0.093 4.9e+03 55 0.78 -5.3 -
63 -0.21 0.031 -0.33 0.038 0.72 0.3 -0.72 -1 -2.3 -2 -1.2 0.00015 1.3 -0.093 4.9e+03 55 0.39 -2 -
64 -0.21 0.031 -0.33 0.038 0.72 0.3 -0.72 -1 -2.3 -2 -1.2 0.00015 1.3 -0.093 4.9e+03 55 0.19 -0.89 -
65 -0.21 0.031 -0.33 0.038 0.72 0.3 -0.72 -1 -2.3 -2 -1.2 0.00015 1.3 -0.093 4.9e+03 55 0.097 -0.4 -
66 -0.21 0.031 -0.33 0.038 0.72 0.3 -0.72 -1 -2.3 -2 -1.2 0.00015 1.3 -0.093 4.9e+03 55 0.049 -0.19 -
67 -0.21 0.031 -0.33 0.038 0.72 0.3 -0.72 -1 -2.3 -2 -1.2 0.00015 1.3 -0.093 4.9e+03 55 0.024 0.081 -
68 -0.21 0.031 -0.33 0.037 0.72 0.3 -0.72 -1 -2.3 -2 -1.2 0.00023 1.3 -0.12 4.9e+03 69 0.024 0.36 +
69 -0.22 0.03 -0.33 0.022 0.71 0.3 -0.73 -0.98 -2.3 -2 -1.2 0.00021 1.3 -0.11 4.9e+03 6.8 0.24 0.92 ++
70 -0.22 0.034 -0.35 -0.0034 0.73 0.33 -0.78 -1 -2.4 -2 -1.2 0.00022 1.2 -0.11 4.9e+03 0.83 2.4 1.1 ++
71 -0.22 0.033 -0.35 -0.0036 0.73 0.34 -0.79 -1 -2.5 -2 -1.2 0.00022 1.2 -0.11 4.9e+03 0.00051 24 1 ++
72 -0.22 0.033 -0.35 -0.0036 0.73 0.34 -0.79 -1 -2.5 -2 -1.2 0.00022 1.2 -0.11 4.9e+03 6.5e-07 24 1 ++
Considering neighbor 2/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME B_TIME_commuter cube_tt_coef mu_public square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -1.5 -
1 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 5 1.1 ++
2 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 2.5 -2.3e+302 -
3 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 1.2 -7.7e+302 -
4 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.62 -2.3e+303 -
5 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.31 -5.7e+303 -
6 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.16 -1.1e+304 -
7 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.078 -3.9 -
8 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.039 -2.8 -
9 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.02 -2.2 -
10 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.0098 -1.9 -
11 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.0049 -1.7 -
12 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.0024 -1.5 -
13 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.0012 -1.2 -
14 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.00061 -0.7 -
15 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 0 1 0 5.7e+03 17 0.00031 0.016 -
16 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00031 1 0.00031 5.6e+03 8.1 0.00031 0.73 +
17 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00031 1 0.00031 5.6e+03 8.1 0.00015 -1.2 -
18 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00031 1 0.00031 5.6e+03 8.1 7.6e-05 -1.4 -
19 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00023 1 0.00038 5.6e+03 11 7.6e-05 0.16 +
20 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00027 1 0.00046 5.6e+03 3.5 7.6e-05 0.72 +
21 -0.12 -0.11 -0.5 0.042 -0.27 -0.5 -0.5 -0.00026 1 0.00053 5.6e+03 0.72 0.00076 0.93 ++
22 -0.12 -0.11 -0.5 0.043 -0.27 -0.5 -0.5 -0.00026 1 0.0013 5.6e+03 0.1 0.0076 1 ++
23 -0.12 -0.12 -0.51 0.045 -0.27 -0.51 -0.5 -0.00029 1 0.0089 5.6e+03 0.95 0.076 1 ++
24 -0.12 -0.12 -0.56 0.066 -0.3 -0.58 -0.51 -0.00061 1.1 0.085 5.5e+03 0.27 0.76 0.96 ++
25 -0.26 -0.3 -0.73 0.83 -1 -1.1 -0.38 0.0004 1.4 -0.14 5.1e+03 3 7.6 0.99 ++
26 -0.26 -0.3 -0.73 0.83 -1 -1.1 -0.38 0.0004 1.4 -0.14 5.1e+03 3 1.2 -5.7 -
27 -0.26 -0.3 -0.73 0.83 -1 -1.1 -0.38 0.0004 1.4 -0.14 5.1e+03 3 0.58 -1.8 -
28 -0.26 -0.3 -0.73 0.83 -1 -1.1 -0.38 0.0004 1.4 -0.14 5.1e+03 3 0.29 0.04 -
29 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.29 0.3 +
30 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.14 -1.3 -
31 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.072 -0.82 -
32 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.036 -0.64 -
33 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.018 -0.61 -
34 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.009 -0.68 -
35 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.0045 -0.75 -
36 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.0023 -0.79 -
37 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.0011 -0.82 -
38 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.00057 -0.83 -
39 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00048 1.4 -0.021 5.1e+03 37 0.00028 -0.059 -
40 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.0002 1.4 -0.021 5e+03 13 0.00028 0.7 +
41 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.0002 1.4 -0.021 5e+03 13 0.00014 -2.8 -
42 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.0002 1.4 -0.021 5e+03 13 7.1e-05 -2.6 -
43 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.0002 1.4 -0.021 5e+03 13 3.5e-05 -0.62 -
44 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00016 1.4 -0.021 5e+03 5 3.5e-05 0.66 +
45 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00017 1.4 -0.021 5e+03 0.24 0.00035 1 ++
46 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00017 1.4 -0.021 5e+03 0.16 0.0035 1 ++
47 -0.38 -0.32 -0.5 1.1 -0.99 -1.3 -0.48 -0.00015 1.4 -0.025 5e+03 0.24 0.035 1 ++
48 -0.37 -0.32 -0.5 1.1 -1 -1.3 -0.48 -7.2e-06 1.5 -0.06 5e+03 0.89 0.35 0.97 ++
49 -0.37 -0.32 -0.5 1.1 -1 -1.3 -0.48 -7.2e-06 1.5 -0.06 5e+03 0.89 0.18 -0.63 -
50 -0.32 -0.34 -0.62 1.2 -1 -1.5 -0.56 0.00031 1.6 -0.14 5e+03 35 0.18 0.24 +
51 -0.14 -0.36 -0.45 1.4 -1.1 -1.6 -0.74 4.9e-05 1.6 -0.067 5e+03 34 0.18 0.26 +
52 -0.14 -0.36 -0.45 1.4 -1.1 -1.6 -0.74 4.9e-05 1.6 -0.067 5e+03 34 0.088 -0.96 -
53 -0.14 -0.36 -0.45 1.4 -1.1 -1.6 -0.74 4.9e-05 1.6 -0.067 5e+03 34 0.044 -0.27 -
54 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.044 0.12 +
55 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.022 -0.33 -
56 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.011 -0.36 -
57 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.0055 -0.4 -
58 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.0028 -0.45 -
59 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.0014 -0.48 -
60 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.00069 -0.5 -
61 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.00035 -0.51 -
62 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 0.00017 -0.52 -
63 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00018 1.6 -0.11 5e+03 65 8.6e-05 -0.17 -
64 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00027 1.6 -0.11 4.9e+03 22 8.6e-05 0.29 +
65 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00027 1.6 -0.11 4.9e+03 22 4.3e-05 0.03 -
66 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00022 1.6 -0.11 4.9e+03 3.8 4.3e-05 0.82 +
67 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00023 1.6 -0.11 4.9e+03 0.32 0.00043 1 ++
68 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.00022 1.6 -0.11 4.9e+03 0.15 0.0043 1 ++
69 -0.17 -0.36 -0.47 1.4 -1.1 -1.6 -0.75 0.0002 1.6 -0.11 4.9e+03 1.8 0.043 1 ++
70 -0.21 -0.36 -0.49 1.4 -1.1 -1.6 -0.77 0.00018 1.6 -0.1 4.9e+03 0.54 0.43 1 ++
71 -0.088 -0.44 -0.57 1.6 -1.1 -1.9 -1.2 0.00022 1.3 -0.11 4.9e+03 3.9 4.3 0.91 ++
72 -0.084 -0.44 -0.7 1.8 -1.1 -2 -1.2 0.00021 1.2 -0.11 4.9e+03 0.0075 43 1.1 ++
73 -0.045 -0.4 -0.81 1.9 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 0.04 4.3e+02 1 ++
74 -0.05 -0.37 -0.83 2 -1.1 -2 -1.3 0.00021 1.1 -0.11 4.9e+03 0.0017 4.3e+03 1 ++
75 -0.045 -0.37 -0.84 2 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 0.00027 4.3e+04 1 ++
76 -0.047 -0.37 -0.84 2 -1.1 -2 -1.3 0.00021 1.1 -0.11 4.9e+03 0.038 4.3e+05 1 ++
77 -0.046 -0.36 -0.84 2 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 6.2e-05 4.3e+06 1 ++
78 -0.046 -0.36 -0.84 2 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 7e-06 4.3e+07 1 ++
79 -0.046 -0.36 -0.84 2 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 0.0011 4.3e+08 1 ++
80 -0.046 -0.36 -0.84 2 -1.1 -2.1 -1.3 0.00021 1.1 -0.11 4.9e+03 1.4e-06 4.3e+08 1 ++
Considering neighbor 3/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME_CAR B_TIME_CAR_comm B_TIME_SM B_TIME_SM_commu B_TIME_TRAIN B_TIME_TRAIN_co cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.4 0.5 -0.26 -
1 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 -0.035 0.24 0.075 -0.5 -0.12 0.01 0.00099 5.9e+03 1.3 0.5 0.82 +
2 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 -0.035 0.24 0.075 -0.5 -0.12 0.01 0.00099 5.9e+03 1.3 0.25 -7.7 -
3 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 -0.035 0.24 0.075 -0.5 -0.12 0.01 0.00099 5.9e+03 1.3 0.12 -10 -
4 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 -0.035 0.24 0.075 -0.5 -0.12 0.01 0.00099 5.9e+03 1.3 0.062 -13 -
5 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 -0.035 0.24 0.075 -0.5 -0.12 0.01 0.00099 5.9e+03 1.3 0.031 -69 -
6 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 -0.035 0.24 0.075 -0.5 -0.12 0.01 0.00099 5.9e+03 1.3 0.016 -6.6 -
7 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 -0.035 0.24 0.075 -0.5 -0.12 0.01 0.00099 5.9e+03 1.3 0.0078 -1.5 -
8 -0.026 -0.025 -0.29 0.014 -0.048 -0.066 -0.027 0.25 0.082 -0.51 -0.12 0.0021 0.0088 5.8e+03 1 0.078 1 ++
9 -0.026 -0.025 -0.29 0.014 -0.048 -0.066 -0.027 0.25 0.082 -0.51 -0.12 0.0021 0.0088 5.8e+03 1 0.039 -11 -
10 -0.026 -0.025 -0.29 0.014 -0.048 -0.066 -0.027 0.25 0.082 -0.51 -0.12 0.0021 0.0088 5.8e+03 1 0.02 -15 -
11 -0.026 -0.025 -0.29 0.014 -0.048 -0.066 -0.027 0.25 0.082 -0.51 -0.12 0.0021 0.0088 5.8e+03 1 0.0098 -6.5 -
12 -0.026 -0.025 -0.29 0.014 -0.048 -0.066 -0.027 0.25 0.082 -0.51 -0.12 0.0021 0.0088 5.8e+03 1 0.0049 -4.3 -
13 -0.026 -0.025 -0.29 0.014 -0.048 -0.066 -0.027 0.25 0.082 -0.51 -0.12 0.0021 0.0088 5.8e+03 1 0.0024 -1.2 -
14 -0.028 -0.027 -0.29 0.017 -0.051 -0.068 -0.024 0.25 0.085 -0.51 -0.13 -0.00029 0.011 5.8e+03 0.18 0.0024 0.89 +
15 -0.029 -0.028 -0.29 0.018 -0.053 -0.071 -0.025 0.25 0.085 -0.51 -0.13 -0.00022 0.012 5.8e+03 0.087 0.024 1 ++
16 -0.039 -0.032 -0.29 0.028 -0.071 -0.095 -0.034 0.25 0.09 -0.52 -0.13 -0.00046 0.025 5.7e+03 0.73 0.24 0.99 ++
17 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.24 0.75 +
18 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.12 -4.5 -
19 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.061 -4.5 -
20 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.031 -4.7 -
21 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.015 -5 -
22 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.0076 -5.2 -
23 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.0038 -3.8 -
24 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.0019 -2.1 -
25 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.00095 -1.1 -
26 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.00048 -0.41 -
27 -0.11 -0.082 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00064 0.14 5.6e+03 6 0.00024 0.098 -
28 -0.11 -0.083 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00088 0.14 5.6e+03 0.1 0.00024 0.8 +
29 -0.11 -0.083 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00087 0.14 5.6e+03 0.51 0.0024 1 ++
30 -0.11 -0.083 -0.32 0.17 -0.32 -0.33 -0.12 0.22 0.13 -0.6 -0.17 -0.00087 0.14 5.6e+03 0.1 0.024 1 ++
31 -0.096 -0.084 -0.32 0.18 -0.33 -0.31 -0.12 0.2 0.13 -0.6 -0.17 -0.00079 0.12 5.5e+03 0.36 0.24 1 ++
32 -0.024 -0.12 -0.34 0.34 -0.57 -0.3 -0.14 -0.043 0.13 -0.66 -0.21 -0.00086 0.14 5.4e+03 0.059 2.4 0.96 ++
33 -0.024 -0.12 -0.34 0.34 -0.57 -0.3 -0.14 -0.043 0.13 -0.66 -0.21 -0.00086 0.14 5.4e+03 0.059 1.2 -12 -
34 -0.024 -0.12 -0.34 0.34 -0.57 -0.3 -0.14 -0.043 0.13 -0.66 -0.21 -0.00086 0.14 5.4e+03 0.059 0.6 -0.83 -
35 -0.066 -0.23 -0.34 0.87 -1 -0.66 -0.21 -0.64 0.13 -0.63 -0.3 -0.0017 0.37 5.2e+03 6 0.6 0.44 +
36 -0.092 -0.29 -0.66 1.4 -1.1 -0.55 -0.036 -0.5 0.73 -0.63 -0.27 -0.0016 0.33 5e+03 2.8 0.6 0.79 +
37 -0.092 -0.29 -0.66 1.4 -1.1 -0.55 -0.036 -0.5 0.73 -0.63 -0.27 -0.0016 0.33 5e+03 2.8 0.3 -1.4 -
38 -0.18 -0.31 -0.78 1.6 -1 -0.67 0.21 -0.8 0.46 -0.77 -0.18 -0.0018 0.37 5e+03 0.84 0.3 0.13 +
39 -0.22 -0.32 -0.83 1.6 -1.1 -0.64 0.14 -0.7 0.76 -0.78 -0.18 -0.0014 0.26 5e+03 0.26 0.3 0.76 +
40 -0.43 -0.35 -1.1 1.9 -1.1 -0.73 0.16 -0.99 0.73 -0.85 -0.13 -0.0012 0.24 5e+03 1.4 0.3 0.63 +
41 -0.33 -0.33 -1.1 2 -1.1 -0.79 0.24 -1 1 -0.85 -0.14 -0.0012 0.23 4.9e+03 0.67 0.3 0.57 +
42 -0.44 -0.3 -1.3 2.1 -1.1 -1 0.2 -1.3 1.1 -1.1 -0.043 -0.00033 0.017 4.9e+03 0.36 0.3 0.42 +
43 -0.22 -0.24 -1.1 2.1 -1.1 -1.3 0.35 -1.5 1.2 -1.4 -0.11 -0.00035 0.021 4.9e+03 1.3 3 1 ++
44 -0.22 -0.24 -1.1 2.1 -1.1 -1.3 0.35 -1.5 1.2 -1.4 -0.11 -0.00035 0.021 4.9e+03 1.3 1.5 -2.7e+02 -
45 -0.22 -0.24 -1.1 2.1 -1.1 -1.3 0.35 -1.5 1.2 -1.4 -0.11 -0.00035 0.021 4.9e+03 1.3 0.75 -40 -
46 -0.22 -0.24 -1.1 2.1 -1.1 -1.3 0.35 -1.5 1.2 -1.4 -0.11 -0.00035 0.021 4.9e+03 1.3 0.37 -4.9 -
47 -0.32 -0.23 -1 2.1 -1.1 -1.6 0.3 -1.8 1.1 -1.7 0.029 0.00012 -0.087 4.9e+03 9.1 0.37 0.36 +
48 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.37 0.86 +
49 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.19 -0.3 -
50 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.093 -0.035 -
51 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.047 -0.17 -
52 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.023 -0.21 -
53 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.012 -0.2 -
54 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.0058 -0.16 -
55 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.0029 -0.11 -
56 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.0015 -0.061 -
57 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.00073 -0.054 -
58 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.00036 -0.056 -
59 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 0.00018 -0.058 -
60 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 9.1e-05 -0.06 -
61 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 6.1e-05 -0.083 4.9e+03 23 4.5e-05 -0.061 -
62 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 0.00011 -0.083 4.9e+03 18 4.5e-05 0.25 +
63 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 7.1e-05 -0.083 4.9e+03 17 4.5e-05 0.17 +
64 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 9.4e-05 -0.083 4.9e+03 8 4.5e-05 0.63 +
65 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 8.7e-05 -0.083 4.9e+03 0.74 0.00045 0.93 ++
66 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 8.9e-05 -0.083 4.9e+03 0.021 0.0045 1 ++
67 -0.11 -0.21 -0.83 2.1 -1.1 -1.9 0.16 -2.1 1.1 -2.1 -0.22 0.00011 -0.088 4.9e+03 0.18 0.045 0.98 ++
68 -0.12 -0.21 -0.83 2.1 -1.1 -2 0.16 -2.1 1.1 -2.1 -0.21 0.00014 -0.094 4.9e+03 0.67 0.45 1 ++
69 -0.13 -0.22 -0.53 2 -1.1 -2.2 -0.042 -2.4 0.85 -2.6 -0.23 0.00021 -0.11 4.9e+03 2.6 0.45 0.87 +
70 -0.049 -0.22 -0.4 2 -1.1 -2.2 -0.16 -2.4 0.64 -2.7 -0.45 0.0002 -0.11 4.9e+03 0.58 4.5 0.98 ++
71 -0.086 -0.23 -0.46 2.1 -1.1 -2.2 -0.23 -2.4 0.57 -2.6 -0.49 0.0002 -0.11 4.9e+03 0.023 45 1 ++
72 -0.072 -0.22 -0.43 2.1 -1.1 -2.2 -0.25 -2.4 0.52 -2.6 -0.53 0.0002 -0.11 4.9e+03 0.0017 4.5e+02 1 ++
73 -0.078 -0.23 -0.45 2.1 -1.1 -2.2 -0.27 -2.4 0.51 -2.6 -0.54 0.0002 -0.11 4.9e+03 0.00015 4.5e+03 1 ++
74 -0.076 -0.23 -0.44 2.1 -1.1 -2.2 -0.28 -2.4 0.49 -2.6 -0.55 0.0002 -0.11 4.9e+03 0.00057 4.5e+04 1 ++
75 -0.076 -0.23 -0.44 2.1 -1.1 -2.2 -0.28 -2.4 0.49 -2.6 -0.55 0.0002 -0.11 4.9e+03 8.4e-05 4.5e+05 1 ++
76 -0.076 -0.23 -0.44 2.1 -1.1 -2.2 -0.28 -2.4 0.49 -2.6 -0.55 0.0002 -0.11 4.9e+03 0.00014 4.5e+06 1 ++
77 -0.076 -0.23 -0.44 2.1 -1.1 -2.2 -0.28 -2.4 0.49 -2.6 -0.55 0.0002 -0.11 4.9e+03 3.2e-06 4.5e+06 1 ++
Considering neighbor 4/20 for current solution
Attempt 34/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME_CAR B_TIME_SM B_TIME_TRAIN mu_existing Function Relgrad Radius Rho
0 0.034 -0.2 -0.44 0.46 -1 -0.4 -0.09 -0.63 1.6 5.3e+03 0.085 10 0.96 ++
1 -0.35 0.2 -0.62 1.5 -0.88 -0.75 -0.77 -1 1.7 5e+03 0.0097 1e+02 1 ++
2 -0.4 0.14 -0.72 1.5 -0.96 -0.91 -1 -1.2 1.5 5e+03 0.0026 1e+03 1 ++
3 -0.4 0.15 -0.73 1.5 -0.97 -0.92 -1.1 -1.2 1.5 5e+03 1.1e-05 1e+04 1 ++
4 -0.4 0.15 -0.73 1.5 -0.97 -0.92 -1.1 -1.2 1.5 5e+03 2.4e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 35/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME_CAR B_TIME_SM B_TIME_TRAIN lambda_travel_t Function Relgrad Radius Rho
0 -0.58 -0.16 -1 0.2 -0.43 -0.68 -0.37 -0.7 1.3 5.5e+03 0.048 10 0.98 ++
1 -0.58 -0.16 -1 0.2 -0.43 -0.68 -0.37 -0.7 1.3 5.5e+03 0.048 4.5 -2.4e+03 -
2 -0.58 -0.16 -1 0.2 -0.43 -0.68 -0.37 -0.7 1.3 5.5e+03 0.048 2.3 -16 -
3 -0.3 -0.38 -1.3 2.5 -0.99 -1.2 -1.1 -1.2 0.42 5e+03 0.036 23 0.9 ++
4 -0.1 -0.28 -0.88 2 -1.1 -1.7 -1.5 -2.1 0.18 5e+03 0.009 2.3e+02 1.1 ++
5 -0.079 -0.31 -0.81 2 -1.1 -1.8 -1.5 -2.4 0.068 5e+03 0.0013 2.3e+03 1.1 ++
6 -0.077 -0.31 -0.8 2 -1.1 -1.8 -1.5 -2.4 0.053 5e+03 2.2e-05 2.3e+04 1 ++
7 -0.077 -0.31 -0.8 2 -1.1 -1.8 -1.5 -2.4 0.053 5e+03 5.6e-09 2.3e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef mu_public square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 1 0 7e+03 0.4 0.5 -0.18 -
1 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 1 0.00088 5.9e+03 0.8 5 0.9 ++
2 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 1 0.00088 5.9e+03 0.8 2.5 -7 -
3 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 1 0.00088 5.9e+03 0.8 1.2 -7 -
4 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 1 0.00088 5.9e+03 0.8 0.62 -7.4 -
5 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 1 0.00088 5.9e+03 0.8 0.31 -8.3 -
6 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 1 0.00088 5.9e+03 0.8 0.16 -9.9 -
7 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 1 0.00088 5.9e+03 0.8 0.078 -13 -
8 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 1 0.00088 5.9e+03 0.8 0.039 -17 -
9 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 1 0.00088 5.9e+03 0.8 0.02 -8 -
10 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 1 0.00088 5.9e+03 0.8 0.0098 -3.9 -
11 -0.028 -0.29 -0.05 -0.066 0.25 -0.51 -0.0016 1 0.011 5.8e+03 4.1 0.0098 0.53 +
12 -0.031 -0.29 -0.056 -0.076 0.25 -0.51 -0.00015 1 0.014 5.8e+03 0.083 0.098 0.93 ++
13 -0.085 -0.31 -0.15 -0.17 0.27 -0.57 -0.0016 1 0.083 5.7e+03 4.1 0.098 0.76 +
14 -0.083 -0.3 -0.25 -0.25 0.2 -0.56 0.0015 1 0.04 5.7e+03 3 0.098 0.37 +
15 -0.083 -0.3 -0.25 -0.25 0.2 -0.56 0.0015 1 0.04 5.7e+03 3 0.049 -8.5 -
16 -0.083 -0.3 -0.25 -0.25 0.2 -0.56 0.0015 1 0.04 5.7e+03 3 0.024 -6.1 -
17 -0.083 -0.3 -0.25 -0.25 0.2 -0.56 0.0015 1 0.04 5.7e+03 3 0.012 -4.3 -
18 -0.083 -0.3 -0.25 -0.25 0.2 -0.56 0.0015 1 0.04 5.7e+03 3 0.0061 -3.1 -
19 -0.083 -0.3 -0.25 -0.25 0.2 -0.56 0.0015 1 0.04 5.7e+03 3 0.0031 -1.8 -
20 -0.083 -0.3 -0.25 -0.25 0.2 -0.56 0.0015 1 0.04 5.7e+03 3 0.0015 -0.0034 -
21 -0.081 -0.31 -0.25 -0.25 0.2 -0.56 -6.9e-05 1 0.041 5.6e+03 2.8 0.015 1 ++
22 -0.081 -0.31 -0.25 -0.25 0.2 -0.56 -6.9e-05 1 0.041 5.6e+03 2.8 0.0076 -5.4 -
23 -0.081 -0.31 -0.25 -0.25 0.2 -0.56 -6.9e-05 1 0.041 5.6e+03 2.8 0.0038 -5.9 -
24 -0.081 -0.31 -0.25 -0.25 0.2 -0.56 -6.9e-05 1 0.041 5.6e+03 2.8 0.0019 -3.6 -
25 -0.081 -0.31 -0.25 -0.25 0.2 -0.56 -6.9e-05 1 0.041 5.6e+03 2.8 0.00095 -1.7 -
26 -0.081 -0.31 -0.25 -0.25 0.2 -0.56 -6.9e-05 1 0.041 5.6e+03 2.8 0.00048 -0.43 -
27 -0.081 -0.31 -0.25 -0.25 0.2 -0.56 -0.00055 1 0.042 5.6e+03 2.3 0.00048 0.56 +
28 -0.081 -0.31 -0.25 -0.25 0.2 -0.56 -0.00042 1 0.042 5.6e+03 0.52 0.00048 0.86 +
29 -0.081 -0.31 -0.25 -0.25 0.2 -0.56 -0.00045 1 0.042 5.6e+03 0.053 0.0048 1 ++
30 -0.08 -0.31 -0.26 -0.25 0.2 -0.56 -0.00045 1 0.045 5.6e+03 0.16 0.048 1 ++
31 -0.074 -0.32 -0.31 -0.26 0.17 -0.59 -0.00058 1 0.072 5.6e+03 0.075 0.48 1 ++
32 -0.052 -0.34 -0.78 -0.42 -0.17 -0.68 -0.00095 1.1 0.17 5.4e+03 1.1 4.8 0.95 ++
33 -0.086 -0.34 -0.89 -0.52 -0.3 -0.68 -0.0012 1 0.23 5.4e+03 1.1 48 0.95 ++
34 -0.094 -0.38 -0.95 -0.53 -0.35 -0.74 -0.0012 1 0.22 5.4e+03 0.05 4.8e+02 1 ++
35 -0.094 -0.38 -0.95 -0.53 -0.35 -0.74 -0.0012 1 0.22 5.4e+03 0.05 0.46 -35 -
36 -0.094 -0.38 -0.95 -0.53 -0.35 -0.74 -0.0012 1 0.22 5.4e+03 0.05 0.23 -0.058 -
37 -0.22 -0.5 -1.2 -0.74 -0.59 -0.95 -0.00046 1 0.046 5.3e+03 0.53 2.3 0.92 ++
38 -0.23 -0.5 -1.1 -0.76 -0.6 -0.98 -0.00057 1 0.074 5.3e+03 0.022 23 0.99 ++
39 -0.23 -0.5 -1.1 -0.76 -0.6 -0.98 -0.00057 1 0.074 5.3e+03 0.022 5.1 -1e+03 -
40 -0.23 -0.5 -1.1 -0.76 -0.6 -0.98 -0.00057 1 0.074 5.3e+03 0.022 2.5 -3.5e+02 -
41 -0.23 -0.5 -1.1 -0.76 -0.6 -0.98 -0.00057 1 0.074 5.3e+03 0.022 1.3 -1.5e+02 -
42 -0.23 -0.5 -1.1 -0.76 -0.6 -0.98 -0.00057 1 0.074 5.3e+03 0.022 0.64 -39 -
43 -0.23 -0.5 -1.1 -0.76 -0.6 -0.98 -0.00057 1 0.074 5.3e+03 0.022 0.32 -1.1 -
44 -0.2 -0.48 -0.93 -0.92 -0.92 -1.2 -0.00035 1 0.021 5.3e+03 0.021 3.2 1.1 ++
45 -0.2 -0.48 -0.93 -0.92 -0.92 -1.2 -0.00035 1 0.021 5.3e+03 0.021 1.6 -67 -
46 -0.2 -0.48 -0.93 -0.92 -0.92 -1.2 -0.00035 1 0.021 5.3e+03 0.021 0.8 -15 -
47 -0.2 -0.48 -0.93 -0.92 -0.92 -1.2 -0.00035 1 0.021 5.3e+03 0.021 0.4 -1.9 -
48 -0.26 -0.39 -1.2 -1.3 -1.2 -1.6 0.00015 1 -0.099 5.3e+03 11 0.4 0.43 +
49 -0.26 -0.39 -1.2 -1.3 -1.2 -1.6 2.4e-05 1 -0.069 5.3e+03 3.8 4 1.1 ++
50 -0.26 -0.39 -1.2 -1.3 -1.2 -1.6 2.4e-05 1 -0.069 5.3e+03 3.8 0.57 0.09 -
51 -0.17 -0.15 -0.97 -1.7 -1.7 -2.2 0.0002 1 -0.11 5.2e+03 8 5.7 0.93 ++
52 -0.15 0.23 -1.1 -2.1 -2.2 -2.8 0.0002 1 -0.11 5.2e+03 4.1 57 1 ++
53 -0.15 0.32 -1.1 -2.1 -2.2 -2.9 0.00022 1 -0.11 5.2e+03 0.31 5.7e+02 0.97 ++
54 -0.14 0.31 -1.1 -2.1 -2.3 -2.9 0.00022 1 -0.11 5.2e+03 0.0017 5.7e+03 1 ++
55 -0.15 0.31 -1.1 -2.1 -2.3 -2.9 0.00022 1 -0.11 5.2e+03 0.00037 5.7e+04 1 ++
56 -0.15 0.31 -1.1 -2.1 -2.3 -2.9 0.00022 1 -0.11 5.2e+03 8.4e-06 5.7e+05 1 ++
57 -0.15 0.31 -1.1 -2.1 -2.3 -2.9 0.00022 1 -0.11 5.2e+03 4.5e-06 5.7e+05 1 ++
Considering neighbor 1/20 for current solution
Attempt 36/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 7e+03 0.27 0.5 -4.8 -
1 7e+03 0.27 0.25 -1.6 -
2 5.7e+03 4.1 2.5 1 ++
3 5.7e+03 4.1 1.2 -8.4 -
4 5.7e+03 4.1 0.62 -4.4 -
5 5.7e+03 4.1 0.31 -2.6 -
6 5.7e+03 4.1 0.16 -0.93 -
7 5.7e+03 4.1 0.078 -0.18 -
8 5.7e+03 4.1 0.039 -0.058 -
9 5.7e+03 4.1 0.02 -0.21 -
10 5.7e+03 4.1 0.0098 -0.55 -
11 5.7e+03 4.1 0.0049 -1 -
12 5.7e+03 4.1 0.0024 -1.5 -
13 5.7e+03 4.1 0.0012 -1.9 -
14 5.7e+03 4.1 0.00061 -1.2 -
15 5.7e+03 4.1 0.00031 -0.17 -
16 5.7e+03 1.7 0.00031 0.64 +
17 5.7e+03 0.15 0.0031 0.98 ++
18 5.7e+03 0.14 0.031 1 ++
19 5.6e+03 0.1 0.31 1 ++
20 5.4e+03 4.4 0.31 0.82 +
21 5.2e+03 13 0.31 0.63 +
22 5.2e+03 13 0.15 -0.33 -
23 5.1e+03 29 0.15 0.14 +
24 5.1e+03 29 0.076 -0.91 -
25 5.1e+03 29 0.038 -0.74 -
26 5.1e+03 29 0.019 -0.66 -
27 5.1e+03 29 0.0095 -0.62 -
28 5.1e+03 29 0.0048 -0.6 -
29 5.1e+03 29 0.0024 -0.59 -
30 5.1e+03 29 0.0012 -0.37 -
31 5.1e+03 19 0.0012 0.29 +
32 5.1e+03 19 0.0006 -0.33 -
33 5e+03 14 0.0006 0.58 +
34 5e+03 14 0.0003 -1.2 -
35 5e+03 14 0.00015 -0.28 -
36 5e+03 2.2 0.00015 0.88 +
37 5e+03 0.07 0.0015 1 ++
38 5e+03 0.041 0.015 1 ++
39 5e+03 0.024 0.15 1 ++
40 5e+03 13 0.15 0.66 +
41 4.9e+03 6.2 1.5 0.94 ++
42 4.9e+03 6.2 0.58 -1.4 -
43 4.9e+03 6.2 0.29 -0.12 -
44 4.9e+03 25 0.29 0.63 +
45 4.9e+03 17 0.29 0.8 +
46 4.8e+03 0.41 2.9 0.98 ++
47 4.8e+03 0.087 29 1 ++
48 4.8e+03 0.001 2.9e+02 1 ++
49 4.8e+03 1.2e-05 2.9e+03 1 ++
50 4.8e+03 9.7e-08 2.9e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 37/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.076 -0.54 -0.19 -0.78 -0.75 -1 1.6 1.9 5.9e+03 0.17 1 0.57 +
1 -0.53 0.1 -0.59 -0.41 -1.5 -0.45 0.96 2.9 5.4e+03 0.17 1 0.44 +
2 -0.15 -0.19 -0.44 -0.52 -0.78 -0.9 -0.035 3.3 5.2e+03 0.041 1 0.68 +
3 0.05 0.24 -0.56 -0.57 -1.2 -1.3 0.68 2.3 5.1e+03 0.016 1 0.7 +
4 -0.014 0.5 -0.69 -0.84 -2 -1.7 0.28 1.3 5e+03 0.03 1 0.8 +
5 -0.058 0.51 -0.89 -0.93 -2.4 -1.5 0.25 1.3 5e+03 0.0046 10 1 ++
6 -0.062 0.54 -0.92 -0.95 -2.6 -1.6 0.24 1.3 5e+03 0.00049 1e+02 1.1 ++
7 -0.062 0.54 -0.92 -0.95 -2.6 -1.6 0.24 1.3 5e+03 3.3e-06 1e+02 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.3e+03 0.063 1 0.76 +
1 5.1e+03 0.071 1 0.35 +
2 5.1e+03 0.071 0.5 -0.21 -
3 5e+03 0.033 0.5 0.75 +
4 4.9e+03 0.01 5 1 ++
5 4.9e+03 0.01 0.83 -3 -
6 4.9e+03 0.019 0.83 0.43 +
7 4.9e+03 0.0023 0.83 0.88 +
8 4.9e+03 0.0013 8.3 1 ++
9 4.9e+03 6.3e-06 83 1 ++
10 4.9e+03 8e-09 83 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.19 -0.29 -0.079 -0.035 -0.61 0.43 0.13 -0.0093 -0.036 -0.79 -0.8 -1 1.5 1.9 5.9e+03 0.21 1 0.54 +
1 -0.38 -0.13 0.0075 -0.12 -0.36 0.65 0.4 0.051 -0.64 -0.38 -0.79 -0.63 0.63 2.9 5.5e+03 0.23 1 0.33 +
2 -0.34 0.34 0.1 -0.12 -0.67 0.38 0.24 0.028 -0.12 -0.56 -0.3 -1 0.32 3.9 5.4e+03 0.16 1 0.23 +
3 -0.34 0.34 0.1 -0.12 -0.67 0.38 0.24 0.028 -0.12 -0.56 -0.3 -1 0.32 3.9 5.4e+03 0.16 0.5 -0.093 -
4 -0.58 0.24 0.17 -0.17 -0.42 0.87 0.3 0.065 -0.25 -0.48 -0.65 -0.61 0.23 4.4 5.2e+03 0.13 0.5 0.36 +
5 -0.56 0.74 0.11 -0.11 -0.53 0.53 0.27 -0.0072 -0.13 -0.48 -0.5 -0.81 0.31 4.3 5.2e+03 0.058 0.5 0.16 +
6 -0.62 0.64 0.044 -0.18 -0.68 1 0.26 0.017 -0.18 -0.55 -0.51 -0.78 0.24 4.4 5.1e+03 0.0075 0.5 0.9 +
7 -0.62 0.63 0.036 -0.18 -0.67 1 0.32 -0.019 -0.27 -0.7 -0.68 -1 0.29 3.9 5e+03 0.0057 5 1.1 ++
8 -0.62 0.63 0.036 -0.18 -0.67 1 0.32 -0.019 -0.27 -0.7 -0.68 -1 0.29 3.9 5e+03 0.0057 1.5 -1.3 -
9 -0.39 0.58 -0.096 -0.26 -0.72 1.1 0.36 0.032 -0.27 -0.6 -0.59 -1.1 0.29 2.5 5e+03 0.028 1.5 0.82 +
10 -0.28 0.44 -0.018 -0.23 -0.7 1.2 0.51 0.16 -0.55 -0.83 -1 -1.5 0.36 1.5 4.9e+03 0.022 15 1.1 ++
11 -0.24 0.22 0.019 -0.25 -0.62 1.1 0.5 0.21 -0.72 -0.93 -1.4 -1.5 0.32 1.5 4.9e+03 0.0052 1.5e+02 1.1 ++
12 -0.2 0.091 0.019 -0.27 -0.6 1.1 0.54 0.26 -0.82 -0.98 -1.6 -1.6 0.32 1.3 4.9e+03 0.0025 1.5e+03 1 ++
13 -0.2 0.068 0.02 -0.28 -0.6 1.1 0.54 0.26 -0.83 -0.99 -1.6 -1.6 0.31 1.3 4.9e+03 4.1e-05 1.5e+04 1 ++
14 -0.2 0.068 0.02 -0.28 -0.6 1.1 0.54 0.26 -0.83 -0.99 -1.6 -1.6 0.31 1.3 4.9e+03 5.5e-08 1.5e+04 1 ++
Considering neighbor 2/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN lambda_travel_t mu_public Function Relgrad Radius Rho
0 -0.35 -1 -0.68 -0.74 -0.82 -0.95 -0.78 -0.62 1.6 1 5.5e+03 0.046 10 0.9 ++
1 -0.31 -0.97 -0.59 -0.74 -1 -0.8 -0.73 -0.71 1.4 1 5.4e+03 0.025 1e+02 1.1 ++
2 -0.31 -0.97 -0.59 -0.74 -1 -0.8 -0.73 -0.71 1.4 1 5.4e+03 0.025 1.3 -1.8 -
3 -0.42 -0.099 -0.64 -0.82 -2.3 -1.1 -0.76 -0.97 0.73 1 5.1e+03 0.011 13 1.1 ++
4 -0.42 -0.099 -0.64 -0.82 -2.3 -1.1 -0.76 -0.97 0.73 1 5.1e+03 0.011 4.5 -9.7e+02 -
5 -0.42 -0.099 -0.64 -0.82 -2.3 -1.1 -0.76 -0.97 0.73 1 5.1e+03 0.011 2.2 -4.3e+02 -
6 -0.42 -0.099 -0.64 -0.82 -2.3 -1.1 -0.76 -0.97 0.73 1 5.1e+03 0.011 1.1 -1.1e+02 -
7 -0.42 -0.099 -0.64 -0.82 -2.3 -1.1 -0.76 -0.97 0.73 1 5.1e+03 0.011 0.56 -1.6 -
8 -0.6 0.29 -0.89 -1.2 -2.7 -1.5 -1.3 -0.95 0.2 1.4 5e+03 0.028 0.56 0.53 +
9 -0.6 0.29 -0.89 -1.2 -2.7 -1.5 -1.3 -0.95 0.2 1.4 5e+03 0.028 0.28 -0.19 -
10 -0.55 0.45 -0.86 -1.2 -2.7 -1.6 -1.1 -1.2 0.28 1.5 5e+03 0.018 0.28 0.83 +
11 -0.52 0.54 -0.79 -1.2 -2.6 -1.8 -1.3 -1.5 0.12 1.4 5e+03 0.0044 0.28 0.86 +
12 -0.4 0.54 -0.87 -1.2 -2.9 -1.8 -1.4 -1.6 0.14 1.1 5e+03 0.012 0.28 0.68 +
13 -0.3 0.59 -0.87 -1.1 -3 -1.9 -1.5 -1.8 0.095 1 5e+03 0.0028 2.8 1.2 ++
14 -0.29 0.57 -0.86 -1.1 -3 -1.9 -1.5 -1.8 0.1 1 5e+03 0.0012 28 1 ++
15 -0.28 0.59 -0.87 -1.1 -3.1 -1.9 -1.5 -1.8 0.087 1 5e+03 1.3e-05 2.8e+02 1 ++
16 -0.28 0.59 -0.87 -1.1 -3.1 -1.9 -1.5 -1.8 0.087 1 5e+03 1.9e-09 2.8e+02 1 ++
Considering neighbor 3/20 for current solution
Considering neighbor 4/20 for current solution
Attempt 38/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter lambda_travel_t Function Relgrad Radius Rho
0 -0.31 -0.3 -0.039 -0.46 0.18 -0.013 -0.51 -0.36 -0.54 -1 -0.1 1.5 5.7e+03 0.083 1 0.86 +
1 -0.27 -0.1 -0.097 -0.54 0.6 0.026 -0.39 -0.68 -1.5 -1.1 -0.12 0.85 5.1e+03 0.031 10 1 ++
2 0.077 -0.025 -0.42 0.27 0.76 0.37 -0.91 -0.96 -2.7 -1.9 -1.2 -0.37 5.1e+03 0.042 10 0.22 +
3 0.059 -0.021 -0.39 0.23 0.75 0.34 -1.1 -1 -3 -1.6 -0.75 -0.018 5e+03 0.0066 1e+02 1.1 ++
4 -0.056 0.0017 -0.38 0.077 0.76 0.38 -1.1 -1.1 -3 -1.5 -0.76 0.17 5e+03 0.0017 1e+03 1.1 ++
5 -0.069 0.004 -0.38 0.053 0.76 0.39 -1.1 -1.1 -3 -1.5 -0.74 0.19 5e+03 4.4e-05 1e+04 1 ++
6 -0.069 0.004 -0.38 0.053 0.76 0.39 -1.1 -1.1 -3 -1.5 -0.74 0.19 5e+03 5e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas lambda_travel_t Function Relgrad Radius Rho
0 -0.27 -0.31 -0.045 -0.5 0.2 -0.015 -0.46 -0.66 -0.54 -1 -0.73 1.8 5.8e+03 0.091 1 0.6 +
1 -0.37 -0.2 -0.11 -0.59 0.59 0.015 -0.35 -0.73 -1.5 -0.61 -0.61 1.4 5.1e+03 0.035 10 1.1 ++
2 -0.37 -0.2 -0.11 -0.59 0.59 0.015 -0.35 -0.73 -1.5 -0.61 -0.61 1.4 5.1e+03 0.035 5 -9.5e+02 -
3 -0.37 -0.2 -0.11 -0.59 0.59 0.015 -0.35 -0.73 -1.5 -0.61 -0.61 1.4 5.1e+03 0.035 2.5 -21 -
4 -0.37 -0.2 -0.11 -0.59 0.59 0.015 -0.35 -0.73 -1.5 -0.61 -0.61 1.4 5.1e+03 0.035 1.2 -2.4 -
5 -0.61 0.14 -0.28 -0.47 1 0.21 -0.84 -1.4 -2.7 -1.5 -0.84 0.13 5e+03 0.033 1.2 0.57 +
6 -0.18 -0.034 -0.42 -0.13 0.74 0.47 -0.99 -1.1 -2.8 -1.4 -0.51 0.25 5e+03 0.0028 12 0.99 ++
7 -0.25 -0.014 -0.42 -0.18 0.75 0.42 -0.98 -1.2 -2.8 -1.3 -0.57 0.34 5e+03 0.00033 1.2e+02 1 ++
8 -0.25 -0.014 -0.42 -0.18 0.75 0.42 -0.98 -1.2 -2.8 -1.3 -0.57 0.34 5e+03 4.6e-07 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
Attempt 39/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter lambda_travel_t Function Relgrad Radius Rho
0 -0.31 -0.27 -0.3 -0.038 -0.47 0.39 0.17 -0.013 -0.5 -0.34 -0.54 -1 -0.11 1.5 5.6e+03 0.087 1 0.88 +
1 -0.32 -0.48 -0.035 -0.1 -0.7 1.2 0.54 0.024 -0.45 -0.87 -1.5 -1.3 -0.26 0.74 5e+03 0.032 10 0.98 ++
2 -0.023 -0.24 -0.0067 -0.36 -0.44 1.2 0.63 0.25 -0.96 -1 -2 -1.8 -1 -0.015 4.9e+03 0.016 10 0.63 +
3 -0.076 -0.22 0.0062 -0.35 -0.58 1.2 0.65 0.31 -1 -1.1 -2.1 -1.6 -0.8 0.18 4.9e+03 0.0018 1e+02 1.1 ++
4 -0.11 -0.21 0.012 -0.34 -0.62 1.2 0.66 0.32 -1 -1.1 -2.1 -1.5 -0.78 0.24 4.9e+03 0.00017 1e+03 1 ++
5 -0.11 -0.21 0.012 -0.34 -0.62 1.2 0.66 0.32 -1 -1.1 -2.1 -1.5 -0.78 0.24 4.9e+03 4.8e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s lambda_travel_t Function Relgrad Radius Rho
0 -0.62 -1 -0.89 -0.68 -0.61 -0.51 -0.32 -0.71 -0.54 2 5.8e+03 0.1 1 0.58 +
1 -0.62 -1 -0.89 -0.68 -0.61 -0.51 -0.32 -0.71 -0.54 2 5.8e+03 0.1 0.5 -0.65 -
2 -0.3 -0.88 -1.2 -0.18 -0.35 -0.36 -0.48 -0.61 -0.64 1.9 5.4e+03 0.044 0.5 0.79 +
3 -0.48 -0.95 -1.1 -0.14 -0.66 -0.22 -0.98 -0.52 -0.91 1.5 5.3e+03 0.012 5 1.1 ++
4 -0.48 -0.95 -1.1 -0.14 -0.66 -0.22 -0.98 -0.52 -0.91 1.5 5.3e+03 0.012 2.5 -29 -
5 -0.48 -0.95 -1.1 -0.14 -0.66 -0.22 -0.98 -0.52 -0.91 1.5 5.3e+03 0.012 1.2 -4.5 -
6 -0.48 -0.95 -1.1 -0.14 -0.66 -0.22 -0.98 -0.52 -0.91 1.5 5.3e+03 0.012 0.62 -0.26 -
7 -0.38 -0.87 -1.2 -0.2 -1.1 -0.4 -1.3 -0.65 -1.2 0.92 5.2e+03 0.0092 6.2 0.97 ++
8 -0.38 -0.87 -1.2 -0.2 -1.1 -0.4 -1.3 -0.65 -1.2 0.92 5.2e+03 0.0092 1.7 -32 -
9 -0.38 -0.87 -1.2 -0.2 -1.1 -0.4 -1.3 -0.65 -1.2 0.92 5.2e+03 0.0092 0.83 -1.9 -
10 -0.13 -0.19 -1.1 -0.75 -1 -1.1 -0.52 -1.4 -1.6 0.15 5.2e+03 0.025 0.83 0.76 +
11 -0.04 -0.25 -1.2 -1 -1.1 -1.3 -0.42 -1.7 -1.5 0.22 5.2e+03 0.0018 8.3 1 ++
12 -0.03 -0.24 -1.2 -1 -1.1 -1.3 -0.39 -1.8 -1.6 0.14 5.2e+03 0.00014 83 0.99 ++
13 -0.03 -0.24 -1.2 -1 -1.1 -1.3 -0.39 -1.8 -1.6 0.14 5.2e+03 2.7e-07 83 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s Function Relgrad Radius Rho
0 -0.18 -0.55 -0.51 -0.73 -1 -0.74 -0.41 -0.39 -0.49 -0.51 -0.38 5.3e+03 0.069 10 1.1 ++
1 -0.49 -0.31 -0.71 -1.1 -2.3 -0.59 -1.1 -0.36 -1.3 -0.47 -0.77 5e+03 0.046 1e+02 1.2 ++
2 -0.54 -0.16 -0.78 -1.3 -3 -0.62 -1.2 -0.37 -1.4 -0.45 -0.85 5e+03 0.012 1e+03 1.1 ++
3 -0.56 -0.12 -0.78 -1.3 -3.2 -0.62 -1.2 -0.37 -1.4 -0.44 -0.86 5e+03 0.00077 1e+04 1 ++
4 -0.56 -0.12 -0.78 -1.3 -3.2 -0.62 -1.2 -0.37 -1.4 -0.44 -0.86 5e+03 2.9e-06 1e+04 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 40/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 7e+03 0.4 0.5 -0.6 -
1 7e+03 0.4 0.25 -0.027 -
2 5.8e+03 0.057 2.5 1.3 ++
3 5.8e+03 0.057 1.2 -6.2 -
4 5.8e+03 0.057 0.62 -1.4 -
5 5.6e+03 14 0.62 0.29 +
6 5.6e+03 14 0.31 -1.2 -
7 5.6e+03 14 0.16 -0.67 -
8 5.6e+03 14 0.078 -0.4 -
9 5.6e+03 14 0.039 -0.28 -
10 5.6e+03 14 0.02 -0.22 -
11 5.6e+03 14 0.0098 -0.2 -
12 5.6e+03 14 0.0049 0.0054 -
13 5.4e+03 6.1 0.0049 0.53 +
14 5.4e+03 6.1 0.0024 -0.83 -
15 5.4e+03 9.4 0.0024 0.17 +
16 5.4e+03 5.6 0.024 1 ++
17 5.3e+03 5.4 0.024 0.6 +
18 5.3e+03 5.4 0.012 -2.2 -
19 5.3e+03 5.4 0.0061 -1.7 -
20 5.3e+03 5.4 0.0031 -1.2 -
21 5.3e+03 5.4 0.0015 -0.92 -
22 5.3e+03 5.4 0.00076 -0.6 -
23 5.3e+03 5.4 0.00038 -0.065 -
24 5.3e+03 1.4 0.00038 0.84 +
25 5.3e+03 0.049 0.0038 1 ++
26 5.3e+03 0.28 0.038 1 ++
27 5.3e+03 0.035 0.38 0.99 ++
28 5.2e+03 0.12 3.8 0.96 ++
29 5.2e+03 0.12 1.9 -35 -
30 5.2e+03 0.12 0.95 -3.3 -
31 5.2e+03 10 0.95 0.16 +
32 5.2e+03 10 0.48 -2.4 -
33 5.2e+03 10 0.24 -0.21 -
34 5e+03 5.4 0.24 0.59 +
35 5e+03 5.4 0.12 -0.31 -
36 5e+03 15 0.12 0.71 +
37 5e+03 6.4 1.2 1 ++
38 5e+03 6.4 0.6 -5.3 -
39 4.9e+03 3.9 6 1 ++
40 4.9e+03 12 60 0.94 ++
41 4.9e+03 1.4 6e+02 0.93 ++
42 4.9e+03 0.012 6e+03 1 ++
43 4.9e+03 3e-05 6e+04 1 ++
44 4.9e+03 3.2e-07 6e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_CAR_comm B_TIME_SM B_TIME_SM_commu B_TIME_TRAIN B_TIME_TRAIN_co cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.4 0.5 -0.59 -
1 -0.019 -0.28 -0.038 0.29 -0.3 -0.075 -0.035 0.24 0.075 -0.5 -0.12 0.051 0.005 6.4e+03 1.8 0.5 0.22 +
2 -0.019 -0.28 -0.038 0.29 -0.3 -0.075 -0.035 0.24 0.075 -0.5 -0.12 0.051 0.005 6.4e+03 1.8 0.25 -4.4 -
3 -0.019 -0.28 -0.038 0.29 -0.3 -0.075 -0.035 0.24 0.075 -0.5 -0.12 0.051 0.005 6.4e+03 1.8 0.12 -5.1 -
4 -0.019 -0.28 -0.038 0.29 -0.3 -0.075 -0.035 0.24 0.075 -0.5 -0.12 0.051 0.005 6.4e+03 1.8 0.062 -3.7 -
5 -0.016 -0.27 -0.037 0.28 -0.3 -0.013 0.025 0.23 0.074 -0.49 -0.12 -0.012 -0.0043 6.2e+03 6.5 0.062 0.27 +
6 -0.016 -0.27 -0.037 0.28 -0.3 -0.013 0.025 0.23 0.074 -0.49 -0.12 -0.012 -0.0043 6.2e+03 6.5 0.031 -0.067 -
7 -0.017 -0.27 -0.039 0.27 -0.3 -0.044 -0.0065 0.21 0.066 -0.47 -0.1 0.019 0.0094 5.9e+03 0.88 0.031 0.32 +
8 -0.017 -0.27 -0.039 0.27 -0.3 -0.044 -0.0065 0.21 0.066 -0.47 -0.1 0.019 0.0094 5.9e+03 0.88 0.016 -2.8 -
9 -0.0027 -0.26 -0.055 0.25 -0.32 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0038 -0.0062 5.8e+03 0.31 0.16 0.99 ++
10 -0.0027 -0.26 -0.055 0.25 -0.32 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0038 -0.0062 5.8e+03 0.31 0.078 -8.6 -
11 -0.0027 -0.26 -0.055 0.25 -0.32 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0038 -0.0062 5.8e+03 0.31 0.039 -11 -
12 -0.0027 -0.26 -0.055 0.25 -0.32 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0038 -0.0062 5.8e+03 0.31 0.02 -15 -
13 -0.0027 -0.26 -0.055 0.25 -0.32 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0038 -0.0062 5.8e+03 0.31 0.0098 -20 -
14 -0.0027 -0.26 -0.055 0.25 -0.32 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0038 -0.0062 5.8e+03 0.31 0.0049 -11 -
15 -0.0042 -0.25 -0.058 0.25 -0.32 -0.027 0.014 0.19 0.084 -0.45 -0.12 -0.0011 -0.011 5.8e+03 1.3 0.0049 0.58 +
16 -0.005 -0.25 -0.06 0.24 -0.32 -0.03 0.011 0.19 0.085 -0.45 -0.12 -0.00075 -0.012 5.8e+03 0.47 0.049 1.1 ++
17 -0.013 -0.24 -0.075 0.19 -0.34 -0.066 -0.012 0.17 0.095 -0.45 -0.12 0.0002 -0.021 5.7e+03 0.45 0.49 0.99 ++
18 -0.013 -0.24 -0.075 0.19 -0.34 -0.066 -0.012 0.17 0.095 -0.45 -0.12 0.0002 -0.021 5.7e+03 0.45 0.24 -1.9 -
19 -0.013 -0.24 -0.075 0.19 -0.34 -0.066 -0.012 0.17 0.095 -0.45 -0.12 0.0002 -0.021 5.7e+03 0.45 0.12 -0.76 -
20 -0.013 -0.24 -0.075 0.19 -0.34 -0.066 -0.012 0.17 0.095 -0.45 -0.12 0.0002 -0.021 5.7e+03 0.45 0.061 0.02 -
21 -0.021 -0.24 -0.094 0.13 -0.38 -0.11 -0.046 0.14 0.11 -0.45 -0.14 -0.0012 -0.0091 5.7e+03 5.7 0.061 0.51 +
22 -0.025 -0.24 -0.11 0.072 -0.43 -0.17 -0.086 0.1 0.12 -0.47 -0.15 0.00048 -0.015 5.6e+03 3 0.061 0.71 +
23 -0.025 -0.24 -0.11 0.072 -0.43 -0.17 -0.086 0.1 0.12 -0.47 -0.15 0.00048 -0.015 5.6e+03 3 0.031 -6.7 -
24 -0.025 -0.24 -0.11 0.072 -0.43 -0.17 -0.086 0.1 0.12 -0.47 -0.15 0.00048 -0.015 5.6e+03 3 0.015 -7.2 -
25 -0.025 -0.24 -0.11 0.072 -0.43 -0.17 -0.086 0.1 0.12 -0.47 -0.15 0.00048 -0.015 5.6e+03 3 0.0076 -5.6 -
26 -0.025 -0.24 -0.11 0.072 -0.43 -0.17 -0.086 0.1 0.12 -0.47 -0.15 0.00048 -0.015 5.6e+03 3 0.0038 -3.9 -
27 -0.025 -0.24 -0.11 0.072 -0.43 -0.17 -0.086 0.1 0.12 -0.47 -0.15 0.00048 -0.015 5.6e+03 3 0.0019 -2.6 -
28 -0.025 -0.24 -0.11 0.072 -0.43 -0.17 -0.086 0.1 0.12 -0.47 -0.15 0.00048 -0.015 5.6e+03 3 0.00095 -0.99 -
29 -0.025 -0.24 -0.11 0.071 -0.43 -0.17 -0.087 0.1 0.12 -0.47 -0.15 -0.00047 -0.014 5.6e+03 4.7 0.00095 0.39 +
30 -0.025 -0.24 -0.11 0.071 -0.43 -0.17 -0.087 0.1 0.12 -0.47 -0.15 -0.00047 -0.014 5.6e+03 4.7 0.00048 -0.013 -
31 -0.025 -0.24 -0.11 0.071 -0.43 -0.17 -0.088 0.1 0.12 -0.47 -0.16 3e-06 -0.013 5.6e+03 2.7 0.00048 0.31 +
32 -0.025 -0.24 -0.11 0.071 -0.43 -0.17 -0.088 0.1 0.12 -0.47 -0.16 3e-06 -0.013 5.6e+03 2.7 0.00024 -0.28 -
33 -0.025 -0.24 -0.11 0.07 -0.43 -0.17 -0.088 0.1 0.13 -0.47 -0.16 -0.00024 -0.013 5.6e+03 0.61 0.00024 0.71 +
34 -0.025 -0.24 -0.11 0.07 -0.43 -0.17 -0.088 0.1 0.13 -0.47 -0.16 -0.00021 -0.013 5.6e+03 0.036 0.0024 1 ++
35 -0.025 -0.24 -0.11 0.068 -0.43 -0.17 -0.089 0.098 0.13 -0.47 -0.16 -0.00018 -0.011 5.6e+03 0.89 0.024 1 ++
36 -0.025 -0.25 -0.12 0.044 -0.45 -0.19 -0.098 0.083 0.13 -0.48 -0.16 -0.00031 0.005 5.6e+03 0.44 0.24 1 ++
37 -0.032 -0.25 -0.17 -0.19 -0.65 -0.34 -0.19 -0.085 0.17 -0.53 -0.21 -0.0009 0.15 5.4e+03 1.3 2.4 0.97 ++
38 -0.032 -0.25 -0.17 -0.19 -0.65 -0.34 -0.19 -0.085 0.17 -0.53 -0.21 -0.0009 0.15 5.4e+03 1.3 1.2 -12 -
39 -0.032 -0.25 -0.17 -0.19 -0.65 -0.34 -0.19 -0.085 0.17 -0.53 -0.21 -0.0009 0.15 5.4e+03 1.3 0.6 -1.7 -
40 -0.032 -0.25 -0.17 -0.19 -0.65 -0.34 -0.19 -0.085 0.17 -0.53 -0.21 -0.0009 0.15 5.4e+03 1.3 0.3 -0.16 -
41 -0.059 -0.19 -0.23 -0.39 -0.86 -0.52 -0.21 -0.38 0.16 -0.46 -0.24 -0.0012 0.23 5.3e+03 3.8 0.3 0.77 +
42 -0.08 -0.22 -0.26 -0.52 -1.2 -0.55 -0.19 -0.47 0.32 -0.54 -0.27 -0.0016 0.3 5.2e+03 8.3 3 1 ++
43 -0.08 -0.22 -0.26 -0.52 -1.2 -0.55 -0.19 -0.47 0.32 -0.54 -0.27 -0.0016 0.3 5.2e+03 8.3 1.5 -2e+02 -
44 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 15 1 ++
45 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 7.5 -4.5 -
46 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 3.7 -4.5 -
47 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 1.9 -4.5 -
48 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.93 -4.6 -
49 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.47 -4.7 -
50 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.23 -4.8 -
51 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.12 -5.1 -
52 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.058 -5.4 -
53 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.029 -4 -
54 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.015 -3 -
55 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.0073 -2.5 -
56 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.0036 -2.2 -
57 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.0018 -1.7 -
58 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.00091 -1.1 -
59 -0.4 -0.16 -0.57 -0.98 -2.6 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.00078 0.19 5e+03 10 0.00045 -0.42 -
60 -0.4 -0.16 -0.57 -0.98 -2.7 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.0012 0.19 5e+03 10 0.00045 0.14 +
61 -0.4 -0.16 -0.57 -0.98 -2.7 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.0012 0.19 5e+03 10 0.00023 -0.12 -
62 -0.4 -0.16 -0.57 -0.98 -2.7 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.001 0.19 5e+03 6 0.00023 0.72 +
63 -0.4 -0.16 -0.57 -0.98 -2.7 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.001 0.19 5e+03 2 0.00023 0.76 +
64 -0.4 -0.16 -0.57 -0.98 -2.7 -0.87 0.067 -0.92 0.8 -0.56 -0.13 -0.001 0.19 5e+03 0.062 0.0023 0.99 ++
65 -0.4 -0.16 -0.58 -0.98 -2.7 -0.87 0.068 -0.92 0.8 -0.56 -0.13 -0.001 0.19 5e+03 0.016 0.023 1 ++
66 -0.41 -0.16 -0.59 -0.97 -2.7 -0.89 0.091 -0.92 0.8 -0.58 -0.13 -0.001 0.18 5e+03 0.013 0.23 1 ++
67 -0.44 -0.12 -0.68 -1 -2.9 -0.98 0.27 -1 0.87 -0.65 -0.11 -0.00078 0.13 5e+03 0.037 2.3 1.1 ++
68 -0.44 -0.12 -0.68 -1 -2.9 -0.98 0.27 -1 0.87 -0.65 -0.11 -0.00078 0.13 5e+03 0.037 1.1 -1.5e+02 -
69 -0.44 -0.12 -0.68 -1 -2.9 -0.98 0.27 -1 0.87 -0.65 -0.11 -0.00078 0.13 5e+03 0.037 0.57 -29 -
70 -0.44 -0.12 -0.68 -1 -2.9 -0.98 0.27 -1 0.87 -0.65 -0.11 -0.00078 0.13 5e+03 0.037 0.28 -0.15 -
71 -0.45 -0.055 -0.77 -1.1 -3.2 -1.2 0.28 -1.3 1.1 -0.77 -0.027 -0.00047 0.049 5e+03 0.11 2.8 1 ++
72 -0.45 -0.055 -0.77 -1.1 -3.2 -1.2 0.28 -1.3 1.1 -0.77 -0.027 -0.00047 0.049 5e+03 0.11 0.34 -0.91 -
73 -0.35 -0.012 -0.78 -1.2 -3.2 -1.5 0.35 -1.5 1.2 -0.98 -0.08 -0.00021 -0.011 4.9e+03 0.64 3.4 1.2 ++
74 -0.35 -0.012 -0.78 -1.2 -3.2 -1.5 0.35 -1.5 1.2 -0.98 -0.08 -0.00021 -0.011 4.9e+03 0.64 0.56 -16 -
75 -0.17 0.14 -0.76 -1.1 -3.2 -2.1 0.16 -2 1.2 -1.4 -0.0056 0.00021 -0.11 4.9e+03 22 0.56 0.26 +
76 -0.0013 0.31 -0.63 -1.2 -3.2 -2.6 0.06 -2.3 0.93 -1.8 -0.28 0.00014 -0.093 4.9e+03 23 0.56 0.71 +
77 -0.0013 0.31 -0.63 -1.2 -3.2 -2.6 0.06 -2.3 0.93 -1.8 -0.28 0.00014 -0.093 4.9e+03 23 0.28 -1.6 -
78 -0.0013 0.31 -0.63 -1.2 -3.2 -2.6 0.06 -2.3 0.93 -1.8 -0.28 0.00014 -0.093 4.9e+03 23 0.14 -0.2 -
79 -0.015 0.36 -0.7 -1.1 -3.2 -2.7 -0.017 -2.4 0.79 -1.9 -0.24 0.00023 -0.12 4.9e+03 23 0.14 0.4 +
80 -0.057 0.46 -0.72 -1.2 -3.2 -2.5 -0.13 -2.3 0.69 -1.9 -0.37 0.00019 -0.11 4.9e+03 0.83 1.4 0.92 ++
81 -0.043 0.4 -0.71 -1.2 -3.2 -2.5 -0.18 -2.3 0.61 -1.9 -0.42 0.0002 -0.11 4.9e+03 0.072 14 1 ++
82 -0.044 0.43 -0.72 -1.1 -3.2 -2.5 -0.21 -2.3 0.57 -1.9 -0.45 0.0002 -0.11 4.9e+03 0.0015 1.4e+02 1 ++
83 -0.042 0.41 -0.72 -1.1 -3.2 -2.5 -0.23 -2.3 0.54 -1.9 -0.46 0.0002 -0.11 4.9e+03 0.0012 1.4e+03 1 ++
84 -0.043 0.42 -0.72 -1.1 -3.2 -2.5 -0.24 -2.3 0.53 -1.9 -0.47 0.0002 -0.11 4.9e+03 0.00023 1.4e+04 1 ++
85 -0.042 0.42 -0.72 -1.1 -3.2 -2.5 -0.24 -2.3 0.53 -1.9 -0.47 0.0002 -0.11 4.9e+03 0.00023 1.4e+05 1 ++
86 -0.042 0.42 -0.72 -1.1 -3.2 -2.5 -0.24 -2.3 0.52 -1.9 -0.48 0.0002 -0.11 4.9e+03 4.6e-05 1.4e+06 1 ++
87 -0.042 0.42 -0.72 -1.1 -3.2 -2.5 -0.25 -2.3 0.52 -1.9 -0.48 0.0002 -0.11 4.9e+03 0.0083 1.4e+07 1 ++
88 -0.042 0.42 -0.72 -1.1 -3.2 -2.5 -0.25 -2.3 0.52 -1.9 -0.48 0.0002 -0.11 4.9e+03 0.0001 1.4e+08 1 ++
89 -0.042 0.42 -0.72 -1.1 -3.2 -2.5 -0.25 -2.3 0.52 -1.9 -0.48 0.0002 -0.11 4.9e+03 3.6e-06 1.4e+08 1 ++
Considering neighbor 1/20 for current solution
Attempt 41/100
Considering neighbor 0/20 for current solution
Attempt 42/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter cube_tt_coef mu_public square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -5.5 -
1 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.25 -1.5 -
2 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 2.5 1 ++
3 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 1.2 -7.6e+303 -
4 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.62 -2.1e+304 -
5 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.31 -2.9 -
6 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.16 -2.4 -
7 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.078 -2.2 -
8 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.039 -2.4 -
9 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.02 -2.7 -
10 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.0098 -3 -
11 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.0049 -3.2 -
12 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.0024 -2 -
13 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.0012 -1.4 -
14 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.00061 -0.73 -
15 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 0 1 0 5.9e+03 8 0.00031 0.0082 -
16 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 -0.00031 1 0.00031 5.9e+03 2.5 0.00031 0.74 +
17 -0.059 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 -0.00027 1 0.00049 5.9e+03 0.61 0.0031 0.95 ++
18 -0.058 -0.25 -0.12 0.25 -0.25 -0.25 -0.25 -0.00028 1 0.0024 5.8e+03 0.092 0.031 1 ++
19 -0.049 -0.28 -0.12 0.25 -0.28 -0.28 -0.26 -0.00036 1 0.021 5.8e+03 0.5 0.31 1 ++
20 -0.00071 -0.41 -0.084 -0.047 -0.54 -0.59 -0.3 -0.0009 1.3 0.15 5.5e+03 2.3 0.31 0.82 +
21 -0.31 -0.21 -0.39 -0.25 -0.78 -0.79 -0.29 7.3e-05 1.2 -0.1 5.4e+03 28 0.31 0.6 +
22 -0.31 -0.21 -0.39 -0.25 -0.78 -0.79 -0.29 7.3e-05 1.2 -0.1 5.4e+03 28 0.15 0.013 -
23 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.0006 1.3 -0.092 5.3e+03 22 0.15 0.37 +
24 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.0006 1.3 -0.092 5.3e+03 22 0.076 -3.4 -
25 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.0006 1.3 -0.092 5.3e+03 22 0.038 -3.3 -
26 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.0006 1.3 -0.092 5.3e+03 22 0.019 -3.4 -
27 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.0006 1.3 -0.092 5.3e+03 22 0.0095 -3.5 -
28 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.0006 1.3 -0.092 5.3e+03 22 0.0048 -2.6 -
29 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.0006 1.3 -0.092 5.3e+03 22 0.0024 -1.9 -
30 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.0006 1.3 -0.092 5.3e+03 22 0.0012 -1.3 -
31 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.0006 1.3 -0.092 5.3e+03 22 0.0006 -0.64 -
32 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 5.6e-06 1.3 -0.092 5.3e+03 32 0.0006 0.51 +
33 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 5.6e-06 1.3 -0.092 5.3e+03 32 0.0003 -0.62 -
34 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 5.6e-06 1.3 -0.092 5.3e+03 32 0.00015 -0.0013 -
35 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.00015 1.3 -0.092 5.2e+03 12 0.00015 0.68 +
36 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.00012 1.3 -0.092 5.2e+03 9.3 0.00015 0.46 +
37 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.00013 1.3 -0.092 5.2e+03 0.66 0.0015 0.95 ++
38 -0.24 -0.29 -0.33 -0.32 -0.94 -0.93 -0.37 0.00012 1.3 -0.09 5.2e+03 0.025 0.015 1 ++
39 -0.24 -0.27 -0.34 -0.33 -0.95 -0.94 -0.38 5.6e-05 1.3 -0.076 5.2e+03 0.98 0.15 1 ++
40 -0.3 -0.13 -0.41 -0.48 -1.1 -1 -0.43 0.0001 1.3 -0.085 5.2e+03 0.043 1.5 0.99 ++
41 -0.45 0.82 -0.95 -1 -2.3 -1.4 -1.1 0.00032 2 -0.14 5.1e+03 23 1.5 0.38 +
42 -0.45 0.82 -0.95 -1 -2.3 -1.4 -1.1 0.00032 2 -0.14 5.1e+03 23 0.75 -2.7 -
43 -0.73 0.2 -1.2 -1.3 -2.1 -1.2 -0.41 0.00032 2.7 -0.13 5e+03 6.5 0.75 0.26 +
44 -0.73 0.2 -1.2 -1.3 -2.1 -1.2 -0.41 0.00032 2.7 -0.13 5e+03 6.5 0.37 -0.26 -
45 -0.59 0.39 -1.2 -1.2 -2.1 -0.98 -0.78 0.00019 2.7 -0.1 5e+03 2.1 0.37 0.86 +
46 -0.54 0.39 -1.2 -1.2 -2.1 -1.1 -0.73 0.00023 2.4 -0.11 5e+03 0.89 3.7 1 ++
47 -0.54 0.39 -1.2 -1.2 -2.1 -1.1 -0.73 0.00023 2.4 -0.11 5e+03 0.89 0.68 -2.3 -
48 -0.52 0.56 -1 -1.2 -2.4 -1.5 -1.1 0.00024 1.7 -0.11 5e+03 11 6.8 1.1 ++
49 -0.36 0.61 -0.97 -1.1 -2.5 -1.8 -1.1 0.00019 1.4 -0.11 5e+03 52 68 0.94 ++
50 -0.29 0.68 -0.89 -1.1 -2.9 -2.1 -1.3 0.00025 1.1 -0.12 4.9e+03 9.7 68 0.81 +
51 -0.25 0.63 -0.88 -1.1 -3 -2 -1.3 0.00019 1 -0.11 4.9e+03 67 68 0.13 +
52 -0.22 0.69 -0.89 -1.1 -3.1 -2.2 -1.4 0.00027 1 -0.12 4.9e+03 12 68 0.31 +
53 -0.22 0.69 -0.89 -1.1 -3.1 -2.2 -1.4 0.00027 1 -0.12 4.9e+03 12 0.087 -3.1 -
54 -0.22 0.69 -0.89 -1.1 -3.1 -2.2 -1.4 0.00027 1 -0.12 4.9e+03 12 0.044 -2.9 -
55 -0.22 0.69 -0.89 -1.1 -3.1 -2.2 -1.4 0.00027 1 -0.12 4.9e+03 12 0.022 -3.2 -
56 -0.22 0.69 -0.89 -1.1 -3.1 -2.2 -1.4 0.00027 1 -0.12 4.9e+03 12 0.011 -2.5 -
57 -0.22 0.69 -0.89 -1.1 -3.1 -2.2 -1.4 0.00027 1 -0.12 4.9e+03 12 0.0054 -2.1 -
58 -0.22 0.69 -0.89 -1.1 -3.1 -2.2 -1.4 0.00027 1 -0.12 4.9e+03 12 0.0027 -0.74 -
59 -0.22 0.69 -0.89 -1.1 -3.1 -2.2 -1.4 0.00023 1 -0.11 4.9e+03 11 0.0027 0.73 +
60 -0.22 0.69 -0.89 -1.1 -3.1 -2.1 -1.4 0.00023 1 -0.11 4.9e+03 1.6 0.027 1.1 ++
61 -0.23 0.69 -0.89 -1.1 -3.1 -2.1 -1.4 0.00023 1 -0.11 4.9e+03 0.0079 0.27 1 ++
62 -0.24 0.64 -0.9 -1.1 -3.1 -2 -1.3 0.00023 1 -0.11 4.9e+03 0.08 2.7 0.99 ++
63 -0.25 0.64 -0.9 -1.1 -3.1 -2 -1.3 0.00023 1 -0.11 4.9e+03 0.033 27 1 ++
64 -0.25 0.64 -0.9 -1.1 -3.1 -2 -1.3 0.00023 1 -0.11 4.9e+03 0.0016 2.7e+02 1 ++
65 -0.25 0.64 -0.9 -1.1 -3.1 -2 -1.3 0.00023 1 -0.11 4.9e+03 0.0004 2.7e+03 1 ++
66 -0.25 0.64 -0.9 -1.1 -3.1 -2 -1.3 0.00023 1 -0.11 4.9e+03 1.2e-08 2.7e+03 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 7e+03 0.4 0.5 -0.18 -
1 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 0.00088 5.9e+03 0.8 5 0.9 ++
2 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 0.00088 5.9e+03 0.8 2.5 -6.5 -
3 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 0.00088 5.9e+03 0.8 1.2 -6.7 -
4 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 0.00088 5.9e+03 0.8 0.62 -7.2 -
5 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 0.00088 5.9e+03 0.8 0.31 -8.2 -
6 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 0.00088 5.9e+03 0.8 0.16 -9.8 -
7 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 0.00088 5.9e+03 0.8 0.078 -12 -
8 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 0.00088 5.9e+03 0.8 0.039 -16 -
9 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 0.00088 5.9e+03 0.8 0.02 -8.1 -
10 -0.018 -0.28 -0.041 -0.074 0.24 -0.5 0.0082 0.00088 5.9e+03 0.8 0.0098 -3.9 -
11 -0.028 -0.29 -0.05 -0.066 0.25 -0.51 -0.0016 0.011 5.8e+03 4.1 0.0098 0.53 +
12 -0.031 -0.29 -0.056 -0.076 0.25 -0.51 -0.00015 0.014 5.8e+03 0.082 0.098 0.93 ++
13 -0.084 -0.31 -0.15 -0.17 0.27 -0.57 -0.0019 0.085 5.7e+03 4.9 0.098 0.69 +
14 -0.08 -0.31 -0.25 -0.22 0.21 -0.57 0.00014 0.043 5.7e+03 2.5 0.098 0.84 +
15 -0.08 -0.31 -0.25 -0.22 0.21 -0.57 0.00014 0.043 5.7e+03 2.5 0.049 -6.5 -
16 -0.08 -0.31 -0.25 -0.22 0.21 -0.57 0.00014 0.043 5.7e+03 2.5 0.024 -6.9 -
17 -0.08 -0.31 -0.25 -0.22 0.21 -0.57 0.00014 0.043 5.7e+03 2.5 0.012 -7.4 -
18 -0.08 -0.31 -0.25 -0.22 0.21 -0.57 0.00014 0.043 5.7e+03 2.5 0.0061 -5.9 -
19 -0.08 -0.31 -0.25 -0.22 0.21 -0.57 0.00014 0.043 5.7e+03 2.5 0.0031 -3.8 -
20 -0.08 -0.31 -0.25 -0.22 0.21 -0.57 0.00014 0.043 5.7e+03 2.5 0.0015 -2.1 -
21 -0.08 -0.31 -0.25 -0.22 0.21 -0.57 0.00014 0.043 5.7e+03 2.5 0.00076 -0.6 -
22 -0.08 -0.31 -0.25 -0.22 0.21 -0.57 -0.00063 0.044 5.6e+03 2.8 0.00076 0.53 +
23 -0.08 -0.31 -0.25 -0.22 0.21 -0.58 -0.00035 0.044 5.6e+03 1.5 0.00076 0.56 +
24 -0.08 -0.31 -0.25 -0.22 0.21 -0.58 -0.0005 0.044 5.6e+03 0.9 0.00076 0.75 +
25 -0.08 -0.31 -0.25 -0.22 0.21 -0.58 -0.00046 0.045 5.6e+03 0.052 0.0076 1 ++
26 -0.08 -0.31 -0.26 -0.22 0.2 -0.58 -0.00052 0.049 5.6e+03 0.76 0.076 1 ++
27 -0.08 -0.33 -0.34 -0.26 0.17 -0.61 -0.00065 0.09 5.6e+03 0.16 0.76 1 ++
28 -0.054 -0.35 -1.1 -0.56 -0.45 -0.73 -0.0012 0.24 5.4e+03 3.7 0.76 0.79 +
29 -0.43 -0.6 -1 -0.81 -0.88 -1.1 -0.00024 -0.015 5.3e+03 5.4 7.6 1 ++
30 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 76 1 ++
31 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 38 -8.6 -
32 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 19 -8.5 -
33 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 9.5 -8.3 -
34 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 4.8 -7.9 -
35 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 2.4 -7.4 -
36 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 1.2 -6.9 -
37 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.6 -6.8 -
38 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.3 -4.4 -
39 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.15 -3.7 -
40 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.075 -3.5 -
41 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.037 -3.5 -
42 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.019 -3.4 -
43 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.0093 -3.4 -
44 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.0047 -3.5 -
45 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.0023 -2.9 -
46 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.0012 -2 -
47 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.00058 -1.4 -
48 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.00029 -0.86 -
49 -0.14 -0.29 -1 -1.4 -1.4 -1.7 4.9e-05 -0.05 5.3e+03 24 0.00015 -0.22 -
50 -0.14 -0.29 -1 -1.4 -1.4 -1.7 -9.6e-05 -0.051 5.3e+03 13 0.00015 0.4 +
51 -0.14 -0.29 -1 -1.4 -1.4 -1.7 -9.6e-05 -0.051 5.3e+03 13 7.3e-05 -1.2 -
52 -0.14 -0.29 -1 -1.4 -1.4 -1.7 -2.4e-05 -0.051 5.3e+03 16 7.3e-05 0.32 +
53 -0.14 -0.29 -1 -1.4 -1.4 -1.7 -5.5e-05 -0.051 5.3e+03 4.5 7.3e-05 0.71 +
54 -0.14 -0.29 -1 -1.4 -1.4 -1.7 -4.5e-05 -0.051 5.3e+03 0.87 7.3e-05 0.88 +
55 -0.14 -0.29 -1 -1.4 -1.4 -1.7 -4.7e-05 -0.051 5.3e+03 0.021 0.00073 1 ++
56 -0.14 -0.29 -1 -1.4 -1.4 -1.7 -4.4e-05 -0.051 5.3e+03 0.032 0.0073 1 ++
57 -0.14 -0.29 -1 -1.4 -1.4 -1.7 -2.5e-05 -0.056 5.3e+03 0.028 0.073 1 ++
58 -0.17 -0.31 -1 -1.4 -1.3 -1.8 7.8e-05 -0.081 5.3e+03 2.1 0.73 0.94 ++
59 -0.22 0.027 -1.2 -1.9 -2 -2.5 0.00021 -0.11 5.2e+03 6.3 7.3 0.97 ++
60 -0.12 0.3 -1.1 -2.1 -2.2 -2.8 0.00021 -0.11 5.2e+03 0.78 73 1 ++
61 -0.15 0.29 -1.1 -2.1 -2.3 -2.9 0.00022 -0.11 5.2e+03 0.14 7.3e+02 0.99 ++
62 -0.14 0.31 -1.1 -2.1 -2.3 -2.9 0.00022 -0.11 5.2e+03 0.0024 7.3e+03 1 ++
63 -0.15 0.31 -1.1 -2.1 -2.3 -2.9 0.00022 -0.11 5.2e+03 0.00026 7.3e+04 1 ++
64 -0.15 0.31 -1.1 -2.1 -2.3 -2.9 0.00022 -0.11 5.2e+03 9e-06 7.3e+05 1 ++
65 -0.15 0.31 -1.1 -2.1 -2.3 -2.9 0.00022 -0.11 5.2e+03 1.6e-06 7.3e+05 1 ++
Considering neighbor 1/20 for current solution
Attempt 43/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 0.17 1 0.69 +
1 5.6e+03 0.17 0.5 -0.032 -
2 5.1e+03 0.035 5 0.92 ++
3 5.1e+03 0.035 0.88 -1.2 -
4 4.9e+03 0.014 8.8 0.91 ++
5 4.9e+03 0.023 8.8 0.18 +
6 4.9e+03 0.0033 88 1.1 ++
7 4.9e+03 0.0016 8.8e+02 1.1 ++
8 4.9e+03 0.0003 8.8e+03 1.1 ++
9 4.9e+03 6.1e-06 8.8e+04 1 ++
10 4.9e+03 1.7e-09 8.8e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 44/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN lambda_travel_t Function Relgrad Radius Rho
0 -0.35 -1 -0.68 -0.74 -0.82 -0.95 -0.78 -0.62 1.6 5.5e+03 0.046 10 0.9 ++
1 -0.35 -1 -0.68 -0.74 -0.82 -0.95 -0.78 -0.62 1.6 5.5e+03 0.046 5 -48 -
2 -0.35 -1 -0.68 -0.74 -0.82 -0.95 -0.78 -0.62 1.6 5.5e+03 0.046 2.5 -4.4 -
3 -0.87 0.15 -0.57 -1.6 -3.3 -2.3 -1.9 -0.8 0.38 5.2e+03 0.07 2.5 0.69 +
4 -0.31 0.48 -0.85 -1 -2.9 -1.7 -1.3 -1.4 0.26 5e+03 0.015 25 0.94 ++
5 -0.28 0.6 -0.87 -1.1 -3.1 -1.9 -1.5 -1.8 0.069 5e+03 0.0018 2.5e+02 0.97 ++
6 -0.28 0.59 -0.88 -1.1 -3.1 -1.9 -1.5 -1.8 0.087 5e+03 2.3e-05 2.5e+03 1 ++
7 -0.28 0.59 -0.88 -1.1 -3.1 -1.9 -1.5 -1.8 0.087 5e+03 1.5e-08 2.5e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 45/100
Considering neighbor 0/20 for current solution
Attempt 46/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas lambda_travel_t Function Relgrad Radius Rho
0 -0.18 -0.32 -0.63 0.43 -0.38 -0.8 -0.56 -1 -0.71 1.8 5.6e+03 0.087 1 0.62 +
1 -0.52 -0.52 -0.68 1.4 -0.48 -0.91 -1.6 -0.57 -0.6 1.6 5.1e+03 0.02 10 1.1 ++
2 -0.52 -0.52 -0.68 1.4 -0.48 -0.91 -1.6 -0.57 -0.6 1.6 5.1e+03 0.02 5 -1.3e+03 -
3 -0.52 -0.52 -0.68 1.4 -0.48 -0.91 -1.6 -0.57 -0.6 1.6 5.1e+03 0.02 2.5 -15 -
4 -0.52 -0.52 -0.68 1.4 -0.48 -0.91 -1.6 -0.57 -0.6 1.6 5.1e+03 0.02 1.2 -2.1 -
5 -0.76 -0.45 -0.8 1.5 -0.9 -1.7 -1.6 -1.4 -1.1 0.32 5e+03 0.064 1.2 0.37 +
6 -0.3 -0.077 -0.57 1.5 -0.93 -1.2 -1.8 -1.2 -0.71 0.37 4.9e+03 0.0053 12 0.97 ++
7 -0.37 -0.16 -0.55 1.4 -0.92 -1.2 -1.9 -1.2 -0.76 0.44 4.9e+03 0.00031 1.2e+02 1 ++
8 -0.37 -0.16 -0.55 1.4 -0.92 -1.2 -1.9 -1.2 -0.76 0.44 4.9e+03 1.2e-06 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Attempt 47/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s lambda_travel_t Function Relgrad Radius Rho
0 -0.27 -0.36 -1 0.44 -0.56 -0.71 -0.76 -0.82 -0.64 -0.74 -0.45 -0.54 -0.29 1.8 5.5e+03 0.066 1 0.79 +
1 -0.55 -0.51 -0.8 1.4 -0.59 -0.99 -1.6 -0.62 -0.53 -0.55 -0.63 -0.53 -0.47 1.5 5e+03 0.021 10 1 ++
2 -0.55 -0.51 -0.8 1.4 -0.59 -0.99 -1.6 -0.62 -0.53 -0.55 -0.63 -0.53 -0.47 1.5 5e+03 0.021 5 -8.2e+02 -
3 -0.55 -0.51 -0.8 1.4 -0.59 -0.99 -1.6 -0.62 -0.53 -0.55 -0.63 -0.53 -0.47 1.5 5e+03 0.021 2.5 -23 -
4 -0.55 -0.51 -0.8 1.4 -0.59 -0.99 -1.6 -0.62 -0.53 -0.55 -0.63 -0.53 -0.47 1.5 5e+03 0.021 1.2 -4.5 -
5 -0.55 -0.51 -0.8 1.4 -0.59 -0.99 -1.6 -0.62 -0.53 -0.55 -0.63 -0.53 -0.47 1.5 5e+03 0.021 0.62 -0.06 -
6 -0.63 -0.46 -1.1 1.4 -0.73 -1.3 -1.8 -0.91 -0.92 -0.67 -0.79 -0.67 -0.64 0.84 4.9e+03 0.016 6.2 1.1 ++
7 -0.63 -0.46 -1.1 1.4 -0.73 -1.3 -1.8 -0.91 -0.92 -0.67 -0.79 -0.67 -0.64 0.84 4.9e+03 0.016 0.84 -2 -
8 -0.57 -0.14 -0.53 1.6 -0.69 -1.4 -1.9 -1.4 -1.4 -1.5 -0.42 -1.4 -1 0.042 4.9e+03 0.02 0.84 0.57 +
9 -0.44 -0.23 -0.41 1.4 -0.81 -1.3 -1.9 -1.3 -1.2 -1.3 -0.28 -1.7 -0.93 0.079 4.9e+03 0.0012 8.4 0.99 ++
10 -0.45 -0.21 -0.44 1.5 -0.8 -1.3 -1.9 -1.3 -1.2 -1.3 -0.3 -1.7 -0.93 0.11 4.9e+03 1.8e-05 84 1 ++
11 -0.45 -0.21 -0.44 1.5 -0.8 -1.3 -1.9 -1.3 -1.2 -1.3 -0.3 -1.7 -0.93 0.11 4.9e+03 2.6e-09 84 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s lambda_travel_t mu_public Function Relgrad Radius Rho
0 -0.6 -0.19 -1 0.32 -0.65 -0.69 -0.57 -0.45 -0.26 -0.66 -0.48 1.7 1 5.5e+03 0.073 1 0.76 +
1 -0.34 -0.33 -0.83 1.3 -1.3 -0.34 -0.41 -0.48 -0.53 -0.59 -0.67 1.5 1.2 5.1e+03 0.029 1 0.81 +
2 -0.34 -0.33 -0.83 1.3 -1.3 -0.34 -0.41 -0.48 -0.53 -0.59 -0.67 1.5 1.2 5.1e+03 0.029 0.5 -0.08 -
3 -0.5 -0.33 -1.1 1.4 -1.1 -0.4 -0.63 -0.54 -0.68 -0.47 -0.64 1 1.4 5e+03 0.018 5 0.92 ++
4 -0.5 -0.33 -1.1 1.4 -1.1 -0.4 -0.63 -0.54 -0.68 -0.47 -0.64 1 1.4 5e+03 0.018 1.2 -7.6 -
5 -0.5 -0.33 -1.1 1.4 -1.1 -0.4 -0.63 -0.54 -0.68 -0.47 -0.64 1 1.4 5e+03 0.018 0.58 0.045 -
6 -0.3 -0.34 -0.78 1.4 -1.3 -0.64 -1.1 -0.8 -0.75 -0.8 -0.86 0.45 1.4 5e+03 0.0089 5.8 1 ++
7 -0.18 -0.41 -0.53 1.4 -1.2 -0.94 -1.1 -1.2 -0.24 -1.4 -0.94 0.12 1.4 4.9e+03 0.0068 58 1 ++
8 -0.089 -0.31 -0.68 1.7 -1.2 -1.1 -1.1 -1.4 -0.31 -1.7 -1.1 0.12 1 4.9e+03 0.0081 58 0.81 +
9 -0.072 -0.28 -0.77 1.9 -1.2 -1.1 -1.1 -1.4 -0.32 -1.8 -1.2 0.086 1 4.9e+03 0.00098 5.8e+02 1.1 ++
10 -0.07 -0.28 -0.79 1.9 -1.2 -1.1 -1.1 -1.4 -0.32 -1.8 -1.2 0.089 1 4.9e+03 0.0012 5.8e+03 1 ++
11 -0.067 -0.27 -0.79 1.9 -1.2 -1.1 -1.1 -1.4 -0.32 -1.8 -1.2 0.072 1 4.9e+03 9.9e-06 5.8e+04 1 ++
12 -0.067 -0.27 -0.79 1.9 -1.2 -1.1 -1.1 -1.4 -0.32 -1.8 -1.2 0.072 1 4.9e+03 1e-09 5.8e+04 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 7e+03 0.4 0.5 -0.58 -
1 7e+03 0.4 0.25 0.017 -
2 5.8e+03 0.052 2.5 1.2 ++
3 5.8e+03 0.052 1.2 -11 -
4 5.8e+03 0.052 0.62 -2.6 -
5 5.8e+03 0.052 0.31 -0.1 -
6 5.6e+03 7.5 0.31 0.72 +
7 5.6e+03 7.5 0.16 -0.31 -
8 5.6e+03 7.5 0.078 0.05 -
9 5.5e+03 4 0.078 0.32 +
10 5.5e+03 4 0.039 -4.6 -
11 5.5e+03 4 0.02 -3.5 -
12 5.5e+03 4 0.0098 -2.6 -
13 5.5e+03 4 0.0049 -1.7 -
14 5.5e+03 4 0.0024 -0.41 -
15 5.4e+03 3.2 0.024 0.99 ++
16 5.4e+03 4.3 0.024 0.83 +
17 5.4e+03 4.4 0.024 0.58 +
18 5.4e+03 4.4 0.012 -3.9 -
19 5.4e+03 4.4 0.0061 -3 -
20 5.4e+03 4.4 0.0031 -2.3 -
21 5.4e+03 4.4 0.0015 -1.6 -
22 5.4e+03 4.4 0.00076 -0.86 -
23 5.4e+03 4.4 0.00038 -0.04 -
24 5.4e+03 0.59 0.00038 0.81 +
25 5.4e+03 0.03 0.0038 1 ++
26 5.4e+03 0.39 0.038 1 ++
27 5.3e+03 0.064 0.38 1 ++
28 5.2e+03 0.87 0.38 0.62 +
29 5.1e+03 1.3 0.38 0.75 +
30 5e+03 0.27 0.38 0.81 +
31 5e+03 0.27 0.19 -3 -
32 4.9e+03 3.1 0.19 0.43 +
33 4.9e+03 1.4 1.9 0.91 ++
34 4.9e+03 1.4 0.95 -1.9e+02 -
35 4.9e+03 1.4 0.48 -22 -
36 4.9e+03 1.4 0.24 -0.15 -
37 4.9e+03 1.9 0.24 0.84 +
38 4.9e+03 0.42 2.4 1 ++
39 4.9e+03 0.42 0.28 -0.062 -
40 4.9e+03 0.94 2.8 1 ++
41 4.9e+03 3.7 2.8 0.84 +
42 4.9e+03 0.5 28 1.1 ++
43 4.9e+03 0.48 28 0.74 +
44 4.9e+03 0.011 2.8e+02 1 ++
45 4.9e+03 0.56 2.8e+03 1 ++
46 4.9e+03 0.0051 2.8e+04 1 ++
47 4.9e+03 5.4e-05 2.8e+05 1 ++
48 4.9e+03 0.014 2.8e+06 1 ++
49 4.9e+03 3.6e-06 2.8e+06 1 ++
Considering neighbor 2/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 B_TIME_1st_clas lambda_travel_t mu_public Function Relgrad Radius Rho
0 -0.48 -0.4 -0.04 -0.6 0.16 -0.017 -1 -0.87 -0.81 2 1 6e+03 0.14 1 0.49 +
1 -0.48 -0.4 -0.04 -0.6 0.16 -0.017 -1 -0.87 -0.81 2 1 6e+03 0.14 0.5 -0.17 -
2 -0.056 -0.095 -0.052 -0.79 0.22 -0.01 -1.2 -0.37 -0.6 1.7 1.2 5.3e+03 0.043 0.5 0.89 +
3 -0.35 -0.094 -0.098 -1 0.47 0.03 -1.1 -0.38 -0.82 1.2 1.4 5.2e+03 0.014 5 1 ++
4 -0.35 -0.094 -0.098 -1 0.47 0.03 -1.1 -0.38 -0.82 1.2 1.4 5.2e+03 0.014 0.61 -0.1 -
5 -0.26 0.21 -0.21 -1.1 0.9 0.23 -1.3 -0.89 -0.97 0.59 1.2 5.2e+03 0.0067 0.61 0.83 +
6 -0.057 0.0045 -0.34 -1.2 0.98 0.5 -1.2 -1.1 -0.91 0.54 1 5.1e+03 0.0085 6.1 1 ++
7 -0.058 0.015 -0.35 -1.3 0.96 0.52 -1.2 -1.1 -0.91 0.56 1 5.1e+03 0.00094 61 1 ++
8 -0.086 0.033 -0.45 -1.4 1 0.8 -1.2 -1 -0.89 0.58 1 5.1e+03 0.00015 6.1e+02 0.99 ++
9 -0.086 0.033 -0.45 -1.4 1 0.8 -1.2 -1 -0.89 0.58 1 5.1e+03 3.4e-07 6.1e+02 1 ++
Considering neighbor 3/20 for current solution
Attempt 48/100
Considering neighbor 0/20 for current solution
Attempt 49/100
Considering neighbor 0/20 for current solution
Attempt 50/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.4 0.5 -0.68 -
1 -0.018 -0.28 -0.038 0.29 -0.3 -0.075 0.24 -0.5 0.052 1.2 0.0056 6.3e+03 1.6 0.5 0.25 +
2 -0.018 -0.28 -0.038 0.29 -0.3 -0.075 0.24 -0.5 0.052 1.2 0.0056 6.3e+03 1.6 0.25 -4.7 -
3 -0.018 -0.28 -0.038 0.29 -0.3 -0.075 0.24 -0.5 0.052 1.2 0.0056 6.3e+03 1.6 0.12 -5 -
4 -0.018 -0.28 -0.038 0.29 -0.3 -0.075 0.24 -0.5 0.052 1.2 0.0056 6.3e+03 1.6 0.062 -3.2 -
5 -0.017 -0.27 -0.037 0.28 -0.29 -0.012 0.23 -0.49 -0.011 1.2 -0.0033 6.1e+03 5.3 0.062 0.39 +
6 -0.017 -0.27 -0.037 0.28 -0.29 -0.012 0.23 -0.49 -0.011 1.2 -0.0033 6.1e+03 5.3 0.031 -0.45 -
7 -0.018 -0.27 -0.038 0.27 -0.3 0.019 0.22 -0.48 0.02 1.2 0.00051 5.9e+03 0.96 0.031 0.24 +
8 -0.018 -0.27 -0.038 0.27 -0.3 0.019 0.22 -0.48 0.02 1.2 0.00051 5.9e+03 0.96 0.016 -1 -
9 -0.033 -0.26 -0.054 0.26 -0.3 0.0031 0.21 -0.46 0.0048 1.2 -0.015 5.8e+03 0.24 0.16 0.97 ++
10 -0.033 -0.26 -0.054 0.26 -0.3 0.0031 0.21 -0.46 0.0048 1.2 -0.015 5.8e+03 0.24 0.078 -8.9 -
11 -0.033 -0.26 -0.054 0.26 -0.3 0.0031 0.21 -0.46 0.0048 1.2 -0.015 5.8e+03 0.24 0.039 -11 -
12 -0.033 -0.26 -0.054 0.26 -0.3 0.0031 0.21 -0.46 0.0048 1.2 -0.015 5.8e+03 0.24 0.02 -12 -
13 -0.033 -0.26 -0.054 0.26 -0.3 0.0031 0.21 -0.46 0.0048 1.2 -0.015 5.8e+03 0.24 0.0098 -12 -
14 -0.033 -0.26 -0.054 0.26 -0.3 0.0031 0.21 -0.46 0.0048 1.2 -0.015 5.8e+03 0.24 0.0049 -7.8 -
15 -0.038 -0.25 -0.059 0.25 -0.3 -0.0017 0.2 -0.46 -9.4e-05 1.2 -0.02 5.7e+03 0.065 0.049 1.1 ++
16 -0.043 -0.24 -0.071 0.2 -0.31 -0.03 0.17 -0.44 0.0033 1.2 -0.041 5.7e+03 0.44 0.049 0.85 +
17 -0.043 -0.24 -0.071 0.2 -0.31 -0.03 0.17 -0.44 0.0033 1.2 -0.041 5.7e+03 0.44 0.024 -9.3 -
18 -0.043 -0.24 -0.071 0.2 -0.31 -0.03 0.17 -0.44 0.0033 1.2 -0.041 5.7e+03 0.44 0.012 -10 -
19 -0.043 -0.24 -0.071 0.2 -0.31 -0.03 0.17 -0.44 0.0033 1.2 -0.041 5.7e+03 0.44 0.0061 -11 -
20 -0.043 -0.24 -0.071 0.2 -0.31 -0.03 0.17 -0.44 0.0033 1.2 -0.041 5.7e+03 0.44 0.0031 -2.7 -
21 -0.045 -0.23 -0.074 0.2 -0.32 -0.028 0.17 -0.44 0.00024 1.2 -0.044 5.7e+03 0.15 0.0031 0.8 +
22 -0.045 -0.23 -0.075 0.2 -0.32 -0.03 0.17 -0.44 0.00061 1.2 -0.045 5.7e+03 0.049 0.031 1 ++
23 -0.049 -0.23 -0.085 0.17 -0.34 -0.05 0.16 -0.44 -0.00044 1.2 -0.047 5.7e+03 1.2 0.31 0.9 ++
24 -0.093 -0.2 -0.19 -0.14 -0.53 -0.32 0.017 -0.46 0.0025 1.4 -0.026 5.5e+03 5.5 0.31 0.46 +
25 -0.093 -0.2 -0.19 -0.14 -0.53 -0.32 0.017 -0.46 0.0025 1.4 -0.026 5.5e+03 5.5 0.15 -6.7 -
26 -0.093 -0.2 -0.19 -0.14 -0.53 -0.32 0.017 -0.46 0.0025 1.4 -0.026 5.5e+03 5.5 0.076 -5.9 -
27 -0.093 -0.2 -0.19 -0.14 -0.53 -0.32 0.017 -0.46 0.0025 1.4 -0.026 5.5e+03 5.5 0.038 -3.9 -
28 -0.093 -0.2 -0.19 -0.14 -0.53 -0.32 0.017 -0.46 0.0025 1.4 -0.026 5.5e+03 5.5 0.019 -3 -
29 -0.093 -0.2 -0.19 -0.14 -0.53 -0.32 0.017 -0.46 0.0025 1.4 -0.026 5.5e+03 5.5 0.0095 -2.3 -
30 -0.093 -0.2 -0.19 -0.14 -0.53 -0.32 0.017 -0.46 0.0025 1.4 -0.026 5.5e+03 5.5 0.0048 -1.6 -
31 -0.093 -0.2 -0.19 -0.14 -0.53 -0.32 0.017 -0.46 0.0025 1.4 -0.026 5.5e+03 5.5 0.0024 -0.34 -
32 -0.091 -0.2 -0.19 -0.14 -0.54 -0.32 0.015 -0.46 0.00015 1.4 -0.023 5.5e+03 5.3 0.024 1 ++
33 -0.091 -0.2 -0.19 -0.14 -0.54 -0.32 0.015 -0.46 0.00015 1.4 -0.023 5.5e+03 5.3 0.012 -4.8 -
34 -0.091 -0.2 -0.19 -0.14 -0.54 -0.32 0.015 -0.46 0.00015 1.4 -0.023 5.5e+03 5.3 0.006 -5 -
35 -0.091 -0.2 -0.19 -0.14 -0.54 -0.32 0.015 -0.46 0.00015 1.4 -0.023 5.5e+03 5.3 0.003 -3.6 -
36 -0.091 -0.2 -0.19 -0.14 -0.54 -0.32 0.015 -0.46 0.00015 1.4 -0.023 5.5e+03 5.3 0.0015 -2.3 -
37 -0.091 -0.2 -0.19 -0.14 -0.54 -0.32 0.015 -0.46 0.00015 1.4 -0.023 5.5e+03 5.3 0.00075 -1.4 -
38 -0.091 -0.2 -0.19 -0.14 -0.54 -0.32 0.015 -0.46 0.00015 1.4 -0.023 5.5e+03 5.3 0.00037 -0.44 -
39 -0.09 -0.2 -0.19 -0.14 -0.54 -0.32 0.014 -0.46 -0.00022 1.4 -0.023 5.4e+03 4.2 0.00037 0.59 +
40 -0.09 -0.2 -0.19 -0.14 -0.54 -0.32 0.014 -0.46 -0.00013 1.4 -0.023 5.4e+03 0.91 0.00037 0.87 +
41 -0.09 -0.2 -0.19 -0.14 -0.54 -0.32 0.014 -0.46 -0.00015 1.4 -0.022 5.4e+03 0.044 0.0037 1 ++
42 -0.089 -0.21 -0.19 -0.14 -0.54 -0.32 0.012 -0.46 -0.00016 1.4 -0.019 5.4e+03 0.21 0.037 1 ++
43 -0.077 -0.22 -0.18 -0.17 -0.58 -0.31 -0.0084 -0.49 -0.0003 1.5 0.014 5.4e+03 0.1 0.37 0.99 ++
44 -0.11 -0.15 -0.3 -0.52 -0.95 -0.61 -0.34 -0.49 -0.0015 1.7 0.3 5.3e+03 8.7 0.37 0.6 +
45 -0.22 -0.14 -0.36 -0.55 -1.3 -0.61 -0.3 -0.49 -8.9e-05 1.9 -0.031 5.1e+03 11 0.37 0.77 +
46 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.37 0.66 +
47 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.19 -3.1 -
48 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.093 -1.3 -
49 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.047 -1 -
50 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.023 -1.3 -
51 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.012 -1.5 -
52 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.0058 -1.7 -
53 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.0029 -1.9 -
54 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.0015 -2 -
55 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.00073 -2 -
56 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.00036 -2.1 -
57 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 0.00018 -1.6 -
58 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00059 2 0.067 5.1e+03 18 9.1e-05 -0.65 -
59 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.0005 2 0.067 5.1e+03 20 9.1e-05 0.39 +
60 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00054 2 0.067 5.1e+03 10 9.1e-05 0.42 +
61 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00051 2 0.067 5.1e+03 7.5 9.1e-05 0.53 +
62 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00052 2 0.067 5.1e+03 0.23 0.00091 1 ++
63 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00052 2 0.066 5.1e+03 0.05 0.0091 1 ++
64 -0.24 -0.066 -0.45 -0.74 -1.5 -0.86 -0.67 -0.7 -0.00048 2 0.057 5.1e+03 0.19 0.091 1 ++
65 -0.25 -0.073 -0.47 -0.72 -1.5 -0.88 -0.67 -0.73 -0.00011 2 -0.034 5.1e+03 0.44 0.091 0.86 +
66 -0.24 -0.08 -0.49 -0.7 -1.6 -0.95 -0.75 -0.82 -3e-05 2 -0.055 5.1e+03 6.2 0.91 1 ++
67 -0.24 -0.08 -0.49 -0.7 -1.6 -0.95 -0.75 -0.82 -3e-05 2 -0.055 5.1e+03 6.2 0.45 0.03 -
68 -0.2 -0.0053 -0.57 -0.78 -1.7 -1.3 -1.2 -1.1 6.7e-05 1.9 -0.075 5e+03 9.5 4.5 1 ++
69 -0.2 -0.0053 -0.57 -0.78 -1.7 -1.3 -1.2 -1.1 6.7e-05 1.9 -0.075 5e+03 9.5 0.6 -1.5 -
70 -0.24 0.14 -0.59 -0.82 -1.9 -1.8 -1.8 -1.7 0.00033 1.6 -0.14 5e+03 37 0.6 0.22 +
71 -0.24 0.14 -0.59 -0.82 -1.9 -1.8 -1.8 -1.7 0.00033 1.6 -0.14 5e+03 37 0.3 -0.1 -
72 -0.24 0.14 -0.59 -0.82 -1.9 -1.8 -1.8 -1.7 0.00033 1.6 -0.14 5e+03 37 0.15 0.067 -
73 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.15 0.1 +
74 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.075 -1.5 -
75 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.037 -1.3 -
76 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.019 -0.99 -
77 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.0093 -0.8 -
78 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.0047 -0.94 -
79 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.0023 -1.1 -
80 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.0012 -1.3 -
81 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.00058 -1.3 -
82 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.00029 -1.4 -
83 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 0.00015 -1.4 -
84 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 7.3e-05 -1.2 -
85 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 8.7e-05 1.7 -0.076 5e+03 49 3.6e-05 -0.25 -
86 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 5e-05 1.7 -0.076 5e+03 26 3.6e-05 0.38 +
87 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 5e-05 1.7 -0.076 5e+03 26 1.8e-05 0.071 -
88 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 6.9e-05 1.7 -0.076 5e+03 19 1.8e-05 0.47 +
89 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 6.3e-05 1.7 -0.076 5e+03 0.18 0.00018 0.99 ++
90 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 6.4e-05 1.7 -0.076 5e+03 0.43 0.0018 1 ++
91 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 7.1e-05 1.7 -0.078 5e+03 0.17 0.018 1 ++
92 -0.22 0.25 -0.52 -0.95 -2 -2 -1.9 -1.7 0.00015 1.7 -0.096 5e+03 8.8 0.18 0.92 ++
93 -0.15 0.24 -0.61 -0.9 -2.1 -2 -1.9 -1.8 0.00021 1.5 -0.11 5e+03 3.9 1.8 0.99 ++
94 -0.13 0.29 -0.66 -1 -2.5 -2.3 -2.1 -1.9 0.00022 1.3 -0.11 5e+03 0.61 18 1.1 ++
95 -0.14 0.3 -0.65 -1 -2.5 -2.3 -2.1 -1.9 0.00022 1.3 -0.11 5e+03 0.017 1.8e+02 1 ++
96 -0.14 0.3 -0.65 -1 -2.5 -2.3 -2.1 -1.9 0.00022 1.3 -0.11 5e+03 4.3e-05 1.8e+03 1 ++
97 -0.14 0.3 -0.65 -1 -2.5 -2.3 -2.1 -1.9 0.00022 1.3 -0.11 5e+03 5.4e-07 1.8e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 51/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 0.069 1 0.76 +
1 5e+03 0.018 10 1.1 ++
2 5e+03 0.018 5 -6.3e+02 -
3 5e+03 0.018 2.5 -29 -
4 5e+03 0.018 1.2 -7.3 -
5 5e+03 0.018 0.62 -0.067 -
6 4.9e+03 0.0074 6.2 1.1 ++
7 4.9e+03 0.0074 1.1 -5.7 -
8 4.9e+03 0.0074 0.53 -0.19 -
9 4.9e+03 0.0076 5.3 1.1 ++
10 4.9e+03 0.0044 5.3 0.76 +
11 4.9e+03 0.00044 53 1.1 ++
12 4.9e+03 7.8e-06 5.3e+02 1 ++
13 4.9e+03 2.3e-09 5.3e+02 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 B_TIME_1st_clas lambda_travel_t Function Relgrad Radius Rho
0 -0.48 -0.4 -0.04 -0.6 0.16 -0.017 -1 -0.87 -0.81 2 6e+03 0.14 1 0.49 +
1 -0.00011 0.13 -0.11 -1.2 0.54 0.0088 -1.2 0.13 -0.6 2.1 5.7e+03 0.31 1 0.28 +
2 -0.00011 0.13 -0.11 -1.2 0.54 0.0088 -1.2 0.13 -0.6 2.1 5.7e+03 0.31 0.5 -1.6 -
3 -0.16 0.038 -0.11 -1.3 0.51 0.011 -1.1 -0.37 -0.73 2.2 5.5e+03 0.11 0.5 0.36 +
4 -0.51 -0.096 -0.14 -1.5 0.5 0.037 -1.1 0.13 -0.91 2.1 5.4e+03 0.16 0.5 0.37 +
5 -0.51 -0.096 -0.14 -1.5 0.5 0.037 -1.1 0.13 -0.91 2.1 5.4e+03 0.16 0.25 -0.22 -
6 -0.43 -0.032 -0.15 -1.6 0.5 0.045 -1.2 -0.12 -0.89 1.9 5.3e+03 0.044 0.25 0.73 +
7 -0.44 0.039 -0.19 -1.6 0.75 0.1 -1.2 -0.017 -0.98 1.8 5.2e+03 0.019 0.25 0.89 +
8 -0.51 0.038 -0.24 -1.8 0.83 0.18 -1.2 -0.054 -0.96 1.6 5.2e+03 0.014 2.5 1.2 ++
9 -0.51 0.038 -0.24 -1.8 0.83 0.18 -1.2 -0.054 -0.96 1.6 5.2e+03 0.014 1.2 -4.9 -
10 -0.32 0.17 -0.43 -1.8 1.2 0.64 -1.2 -0.34 -1.4 0.37 5.2e+03 0.019 1.2 0.14 +
11 -0.092 0.05 -0.45 -1.4 1 0.79 -1.2 -1 -0.91 0.88 5.2e+03 0.0093 1.2 0.65 +
12 -0.034 0.027 -0.46 -1.3 1 0.77 -1.2 -1.2 -0.87 0.54 5.1e+03 0.0041 12 0.97 ++
13 -0.086 0.033 -0.45 -1.4 1.1 0.79 -1.2 -1 -0.89 0.58 5.1e+03 0.00015 1.2e+02 0.99 ++
14 -0.086 0.033 -0.45 -1.4 1.1 0.79 -1.2 -1 -0.89 0.58 5.1e+03 3.8e-07 1.2e+02 1 ++
Considering neighbor 1/20 for current solution
Attempt 52/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME lambda_travel_t Function Relgrad Radius Rho
0 -0.45 -0.21 -0.73 0.39 -0.73 -1 1.6 5.5e+03 0.049 1 0.83 +
1 -0.2 -0.35 -1.1 1.4 -1.1 -1.3 0.9 5.1e+03 0.022 10 1.1 ++
2 0.037 -0.36 -0.87 2 -1.1 -1.9 0.26 5e+03 0.012 10 0.82 +
3 -0.061 -0.31 -1 2 -1.1 -1.7 0.36 5e+03 0.0012 1e+02 1 ++
4 -0.064 -0.31 -1 2 -1.1 -1.7 0.38 5e+03 3.1e-05 1e+03 1 ++
5 -0.064 -0.31 -1 2 -1.1 -1.7 0.38 5e+03 3.6e-09 1e+03 1 ++
Considering neighbor 0/20 for current solution
*** New pareto solution:
ASC:GA;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:boxcox [4995.755387259556, 7]
Attempt 53/100
Considering neighbor 0/20 for current solution
Attempt 54/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s Function Relgrad Radius Rho
0 -0.099 -0.46 -1 -0.49 -0.25 -0.061 -0.2 -0.53 -0.27 5.4e+03 0.068 10 1.1 ++
1 -0.38 -0.47 -1.1 -0.41 -0.98 -0.4 -1.2 -0.75 -1 5.2e+03 0.014 1e+02 1.1 ++
2 -0.41 -0.35 -1.2 -0.42 -1.1 -0.43 -1.4 -0.84 -1.2 5.2e+03 0.0018 1e+03 1.1 ++
3 -0.41 -0.34 -1.2 -0.42 -1.1 -0.43 -1.4 -0.85 -1.3 5.2e+03 2.4e-05 1e+04 1 ++
4 -0.41 -0.34 -1.2 -0.42 -1.1 -0.43 -1.4 -0.85 -1.3 5.2e+03 4.5e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME_CAR B_TIME_CAR_comm B_TIME_SM B_TIME_SM_commu B_TIME_TRAIN B_TIME_TRAIN_co Function Relgrad Radius Rho
0 -0.091 -0.19 -0.11 -0.028 -0.48 0.52 0.14 -0.0055 -1 -0.47 -0.16 -0.07 0.12 -0.65 -0.098 5.3e+03 0.075 10 1.1 ++
1 -0.41 -0.2 0.04 -0.25 -1.3 1.8 0.48 0.36 -1 -1.3 0.54 -1.6 1.4 -1.4 0.29 4.9e+03 0.035 1e+02 1.1 ++
2 -0.4 -0.19 0.03 -0.28 -1.4 1.9 0.67 0.55 -1.1 -1.4 0.68 -1.8 1.6 -1.6 0.2 4.9e+03 0.0046 1e+03 1.1 ++
3 -0.39 -0.19 0.029 -0.28 -1.5 1.9 0.71 0.59 -1.1 -1.4 0.69 -1.8 1.6 -1.6 0.16 4.9e+03 0.00013 1e+04 1 ++
4 -0.39 -0.19 0.029 -0.28 -1.5 1.9 0.71 0.59 -1.1 -1.4 0.69 -1.8 1.6 -1.6 0.16 4.9e+03 1.2e-07 1e+04 1 ++
Considering neighbor 1/20 for current solution
Attempt 55/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.4 0.5 -0.51 -
1 -0.018 -0.017 -0.28 0.0066 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.033 1.2 0.004 6e+03 1.6 0.5 0.42 +
2 -0.018 -0.017 -0.28 0.0066 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.033 1.2 0.004 6e+03 1.6 0.25 -6.9 -
3 -0.018 -0.017 -0.28 0.0066 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.033 1.2 0.004 6e+03 1.6 0.12 -8.2 -
4 -0.018 -0.017 -0.28 0.0066 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.033 1.2 0.004 6e+03 1.6 0.062 -7.4 -
5 -0.018 -0.017 -0.28 0.0066 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.033 1.2 0.004 6e+03 1.6 0.031 -2.2 -
6 -0.02 -0.019 -0.27 0.0097 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0016 1.2 -0.0038 5.7e+03 0.71 0.31 0.99 ++
7 -0.02 -0.019 -0.27 0.0097 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0016 1.2 -0.0038 5.7e+03 0.71 0.16 -7 -
8 -0.02 -0.019 -0.27 0.0097 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0016 1.2 -0.0038 5.7e+03 0.71 0.078 -5.5 -
9 -0.02 -0.019 -0.27 0.0097 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0016 1.2 -0.0038 5.7e+03 0.71 0.039 -4.7 -
10 -0.02 -0.019 -0.27 0.0097 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0016 1.2 -0.0038 5.7e+03 0.71 0.02 -4.6 -
11 -0.02 -0.019 -0.27 0.0097 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0016 1.2 -0.0038 5.7e+03 0.71 0.0098 -5 -
12 -0.02 -0.019 -0.27 0.0097 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0016 1.2 -0.0038 5.7e+03 0.71 0.0049 -5.8 -
13 -0.02 -0.019 -0.27 0.0097 -0.047 -0.043 -0.043 0.24 0.14 -0.49 -0.33 0.0016 1.2 -0.0038 5.7e+03 0.71 0.0024 -2.1 -
14 -0.022 -0.021 -0.27 0.012 -0.05 -0.046 -0.045 0.23 0.14 -0.49 -0.33 -0.00085 1.2 -0.0062 5.7e+03 1.2 0.0024 0.57 +
15 -0.023 -0.021 -0.27 0.013 -0.052 -0.048 -0.046 0.23 0.14 -0.49 -0.33 -0.00033 1.2 -0.0072 5.7e+03 0.39 0.024 1.1 ++
16 -0.034 -0.027 -0.27 0.025 -0.075 -0.073 -0.058 0.23 0.14 -0.48 -0.32 0.0002 1.2 -0.016 5.7e+03 0.48 0.24 0.98 ++
17 -0.11 -0.081 -0.22 0.15 -0.32 -0.28 -0.15 0.16 0.07 -0.43 -0.31 -0.0018 1.3 -0.064 5.6e+03 13 0.24 0.14 +
18 -0.11 -0.081 -0.22 0.15 -0.32 -0.28 -0.15 0.16 0.07 -0.43 -0.31 -0.0018 1.3 -0.064 5.6e+03 13 0.12 -0.7 -
19 -0.11 -0.081 -0.22 0.15 -0.32 -0.28 -0.15 0.16 0.07 -0.43 -0.31 -0.0018 1.3 -0.064 5.6e+03 13 0.061 -0.57 -
20 -0.11 -0.081 -0.22 0.15 -0.32 -0.28 -0.15 0.16 0.07 -0.43 -0.31 -0.0018 1.3 -0.064 5.6e+03 13 0.031 -0.47 -
21 -0.11 -0.081 -0.22 0.15 -0.32 -0.28 -0.15 0.16 0.07 -0.43 -0.31 -0.0018 1.3 -0.064 5.6e+03 13 0.015 -0.42 -
22 -0.11 -0.081 -0.22 0.15 -0.32 -0.28 -0.15 0.16 0.07 -0.43 -0.31 -0.0018 1.3 -0.064 5.6e+03 13 0.0076 -0.37 -
23 -0.11 -0.081 -0.22 0.15 -0.32 -0.28 -0.15 0.16 0.07 -0.43 -0.31 -0.0018 1.3 -0.064 5.6e+03 13 0.0038 0.0047 -
24 -0.11 -0.085 -0.22 0.15 -0.32 -0.27 -0.14 0.16 0.067 -0.43 -0.32 0.002 1.3 -0.06 5.6e+03 4 0.0038 0.3 +
25 -0.11 -0.085 -0.22 0.15 -0.32 -0.27 -0.14 0.16 0.067 -0.43 -0.32 0.002 1.3 -0.06 5.6e+03 4 0.0019 -0.54 -
26 -0.1 -0.087 -0.23 0.16 -0.32 -0.27 -0.14 0.15 0.065 -0.43 -0.32 0.00013 1.3 -0.058 5.5e+03 2.8 0.019 0.98 ++
27 -0.091 -0.089 -0.24 0.16 -0.34 -0.26 -0.13 0.15 0.057 -0.45 -0.33 -0.00023 1.3 -0.039 5.5e+03 5 0.19 0.9 ++
28 -0.026 -0.13 -0.28 0.28 -0.53 -0.24 -0.097 0.019 -0.045 -0.54 -0.38 -0.00048 1.4 0.06 5.3e+03 0.076 0.19 0.89 +
29 -0.058 -0.18 -0.24 0.47 -0.72 -0.38 -0.22 -0.17 -0.18 -0.47 -0.32 -0.00083 1.5 0.14 5.1e+03 1.5 0.19 0.79 +
30 -0.066 -0.23 -0.31 0.66 -0.85 -0.39 -0.31 -0.26 -0.31 -0.56 -0.33 -0.0012 1.7 0.24 5.1e+03 0.19 1.9 0.95 ++
31 -0.066 -0.23 -0.31 0.66 -0.85 -0.39 -0.31 -0.26 -0.31 -0.56 -0.33 -0.0012 1.7 0.24 5.1e+03 0.19 0.95 -62 -
32 -0.066 -0.23 -0.31 0.66 -0.85 -0.39 -0.31 -0.26 -0.31 -0.56 -0.33 -0.0012 1.7 0.24 5.1e+03 0.19 0.48 -9.9 -
33 -0.066 -0.23 -0.31 0.66 -0.85 -0.39 -0.31 -0.26 -0.31 -0.56 -0.33 -0.0012 1.7 0.24 5.1e+03 0.19 0.24 -0.44 -
34 -0.16 -0.26 -0.4 0.9 -0.92 -0.4 -0.39 -0.21 -0.4 -0.58 -0.42 -0.00039 1.8 0.038 5e+03 0.39 0.24 0.73 +
35 -0.26 -0.19 -0.48 1.1 -0.88 -0.44 -0.59 -0.45 -0.64 -0.71 -0.51 -0.00047 1.9 0.057 5e+03 0.051 2.4 0.99 ++
36 -0.26 -0.19 -0.48 1.1 -0.88 -0.44 -0.59 -0.45 -0.64 -0.71 -0.51 -0.00047 1.9 0.057 5e+03 0.051 1.2 -75 -
37 -0.26 -0.19 -0.48 1.1 -0.88 -0.44 -0.59 -0.45 -0.64 -0.71 -0.51 -0.00047 1.9 0.057 5e+03 0.051 0.6 -38 -
38 -0.26 -0.19 -0.48 1.1 -0.88 -0.44 -0.59 -0.45 -0.64 -0.71 -0.51 -0.00047 1.9 0.057 5e+03 0.051 0.3 -11 -
39 -0.26 -0.19 -0.48 1.1 -0.88 -0.44 -0.59 -0.45 -0.64 -0.71 -0.51 -0.00047 1.9 0.057 5e+03 0.051 0.15 -1.5 -
40 -0.31 -0.1 -0.56 1.2 -0.92 -0.56 -0.68 -0.52 -0.73 -0.84 -0.66 8.6e-05 1.8 -0.079 5e+03 5 0.15 0.38 +
41 -0.3 -0.056 -0.54 1.2 -0.92 -0.6 -0.73 -0.67 -0.81 -0.9 -0.69 -0.0001 1.8 -0.035 4.9e+03 1.1 1.5 0.96 ++
42 -0.3 -0.056 -0.54 1.2 -0.92 -0.6 -0.73 -0.67 -0.81 -0.9 -0.69 -0.0001 1.8 -0.035 4.9e+03 1.1 0.75 -25 -
43 -0.3 -0.056 -0.54 1.2 -0.92 -0.6 -0.73 -0.67 -0.81 -0.9 -0.69 -0.0001 1.8 -0.035 4.9e+03 1.1 0.37 -2.6 -
44 -0.43 0.16 -0.57 1.4 -1 -0.88 -0.91 -1 -1 -1.3 -0.95 0.00028 1.7 -0.12 4.9e+03 11 0.37 0.37 +
45 -0.38 0.2 -0.42 1.5 -1.1 -1.1 -1 -1.4 -1.1 -1.6 -0.98 0.00017 1.6 -0.096 4.9e+03 8.6 0.37 0.77 +
46 -0.34 0.1 -0.42 1.6 -1.1 -1.4 -0.89 -1.8 -0.8 -2 -0.89 0.00027 1.3 -0.12 4.9e+03 8 0.37 0.42 +
47 -0.37 0.13 -0.39 1.5 -1.1 -1.4 -0.74 -1.8 -0.64 -1.9 -0.74 0.00023 1.5 -0.11 4.9e+03 1.4 3.7 1 ++
48 -0.39 0.14 -0.41 1.5 -1.1 -1.4 -0.68 -1.8 -0.56 -2 -0.7 0.00023 1.5 -0.11 4.9e+03 0.14 37 1 ++
49 -0.37 0.13 -0.4 1.5 -1.1 -1.4 -0.68 -1.8 -0.56 -2 -0.7 0.00023 1.5 -0.11 4.9e+03 0.00097 3.7e+02 1 ++
50 -0.38 0.13 -0.4 1.5 -1.1 -1.4 -0.67 -1.8 -0.55 -2 -0.69 0.00023 1.5 -0.11 4.9e+03 9.8e-05 3.7e+03 1 ++
51 -0.37 0.13 -0.4 1.5 -1.1 -1.4 -0.67 -1.8 -0.55 -2 -0.69 0.00023 1.5 -0.11 4.9e+03 6.1e-06 3.7e+04 1 ++
52 -0.37 0.13 -0.4 1.5 -1.1 -1.4 -0.67 -1.8 -0.55 -2 -0.69 0.00023 1.5 -0.11 4.9e+03 8.9e-06 3.7e+05 1 ++
53 -0.37 0.13 -0.4 1.5 -1.1 -1.4 -0.67 -1.8 -0.55 -2 -0.69 0.00023 1.5 -0.11 4.9e+03 9.2e-07 3.7e+05 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_comm B_TIME_SM B_TIME_SM_commu B_TIME_TRAIN B_TIME_TRAIN_co mu_existing Function Relgrad Radius Rho
0 0.043 -0.42 -1 -0.34 -0.19 -0.13 0.092 -0.59 -0.054 1.5 5.4e+03 0.1 10 0.96 ++
1 -0.53 0.13 -0.78 -0.96 0.83 -1.5 1.7 -1.4 0.52 2.3 5.2e+03 0.12 10 0.73 +
2 -0.39 -0.32 -0.74 -0.83 0.67 -1.3 1.5 -1.1 0.55 2.8 5.1e+03 0.022 10 0.7 +
3 -0.39 -0.32 -0.74 -0.83 0.67 -1.3 1.5 -1.1 0.55 2.8 5.1e+03 0.022 0.58 -0.23 -
4 -0.45 -0.24 -0.84 -0.97 0.72 -1.4 1.5 -1.3 0.56 2.3 5.1e+03 0.0061 5.8 0.95 ++
5 -0.44 -0.2 -0.85 -0.98 0.74 -1.5 1.6 -1.3 0.55 2.2 5.1e+03 0.00054 58 1.1 ++
6 -0.43 -0.19 -0.85 -0.98 0.75 -1.5 1.6 -1.3 0.55 2.2 5.1e+03 1.3e-05 5.8e+02 1 ++
7 -0.43 -0.19 -0.85 -0.98 0.75 -1.5 1.6 -1.3 0.55 2.2 5.1e+03 1.2e-08 5.8e+02 1 ++
Considering neighbor 1/20 for current solution
Attempt 56/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN mu_existing Function Relgrad Radius Rho
0 0.11 -0.12 -0.049 -0.46 0.29 0.0088 -0.28 -0.73 -1 -0.61 -0.38 -0.62 1.8 5.4e+03 0.21 10 0.9 ++
1 0.11 -0.12 -0.049 -0.46 0.29 0.0088 -0.28 -0.73 -1 -0.61 -0.38 -0.62 1.8 5.4e+03 0.21 3.3 -1e+02 -
2 0.11 -0.12 -0.049 -0.46 0.29 0.0088 -0.28 -0.73 -1 -0.61 -0.38 -0.62 1.8 5.4e+03 0.21 1.6 -21 -
3 0.11 -0.12 -0.049 -0.46 0.29 0.0088 -0.28 -0.73 -1 -0.61 -0.38 -0.62 1.8 5.4e+03 0.21 0.82 -1.7 -
4 -0.53 0.12 -0.25 -0.13 0.53 0.21 -0.58 -0.79 -1.5 -0.78 -0.79 -0.8 2.6 5.2e+03 0.14 0.82 0.56 +
5 -0.68 0.25 -0.037 -0.81 0.5 0.22 -0.39 -0.45 -0.69 -0.47 -0.76 -0.62 3.3 5.2e+03 0.078 0.82 0.18 +
6 -0.22 0.12 -0.13 -0.2 0.46 0.31 -0.67 -0.55 -1.1 -0.62 -0.68 -0.87 2.5 5.1e+03 0.018 8.2 1.1 ++
7 -0.22 0.12 -0.13 -0.2 0.46 0.31 -0.67 -0.55 -1.1 -0.62 -0.68 -0.87 2.5 5.1e+03 0.018 0.77 -0.17 -
8 -0.46 0.13 -0.19 -0.47 0.53 0.28 -0.58 -0.74 -1.6 -0.89 -0.94 -0.85 1.8 5.1e+03 0.0079 7.7 1.2 ++
9 -0.37 0.099 -0.2 -0.5 0.68 0.45 -0.74 -1 -2.4 -1.2 -1.1 -1 1 5.1e+03 0.024 7.7 0.19 +
10 -0.38 0.072 -0.25 -0.62 0.74 0.52 -0.76 -1.1 -2.6 -1.2 -1.1 -0.93 1.1 5e+03 0.0016 77 1.1 ++
11 -0.38 0.08 -0.24 -0.58 0.73 0.5 -0.75 -1 -2.5 -1.2 -1.1 -0.95 1.2 5e+03 0.00063 7.7e+02 1.1 ++
12 -0.38 0.081 -0.23 -0.58 0.72 0.49 -0.75 -1 -2.5 -1.2 -1.1 -0.95 1.2 5e+03 1.9e-05 7.7e+03 1 ++
13 -0.38 0.081 -0.23 -0.58 0.72 0.49 -0.75 -1 -2.5 -1.2 -1.1 -0.95 1.2 5e+03 8.6e-08 7.7e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 57/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN mu_public Function Relgrad Radius Rho
0 -0.18 -0.37 -0.65 0.84 -0.57 -0.76 -1 -0.82 -0.43 -0.63 1 5.2e+03 0.078 10 1.1 ++
1 -0.48 -0.23 -0.8 1.4 -0.71 -1.1 -1.7 -1.2 -0.97 -0.83 1.3 5e+03 0.016 1e+02 0.96 ++
2 -0.48 -0.23 -0.8 1.4 -0.71 -1.1 -1.7 -1.2 -0.97 -0.83 1.3 5e+03 0.016 6.5 -6.5e+02 -
3 -0.48 -0.23 -0.8 1.4 -0.71 -1.1 -1.7 -1.2 -0.97 -0.83 1.3 5e+03 0.016 3.3 -3.5e+02 -
4 -0.48 -0.23 -0.8 1.4 -0.71 -1.1 -1.7 -1.2 -0.97 -0.83 1.3 5e+03 0.016 1.6 -1.1e+02 -
5 -0.48 -0.23 -0.8 1.4 -0.71 -1.1 -1.7 -1.2 -0.97 -0.83 1.3 5e+03 0.016 0.82 -9.2 -
6 -0.48 -0.23 -0.8 1.4 -0.71 -1.1 -1.7 -1.2 -0.97 -0.83 1.3 5e+03 0.016 0.41 -0.08 -
7 -0.42 -0.26 -0.59 0.97 -0.76 -1.2 -1.8 -1.3 -1.1 -0.9 1.4 5e+03 0.0031 0.41 0.9 +
8 -0.48 -0.35 -0.4 0.68 -0.72 -1.2 -1.8 -1.2 -0.76 -0.62 1.8 5e+03 0.029 0.41 0.23 +
9 -0.42 -0.43 -0.37 0.65 -0.72 -1.2 -1.8 -1.2 -0.79 -0.65 2 5e+03 0.0023 4.1 1.1 ++
10 -0.42 -0.43 -0.37 0.65 -0.72 -1.2 -1.8 -1.2 -0.79 -0.65 2 5e+03 0.0023 0.48 -7.4 -
11 -0.4 -0.42 -0.29 0.52 -0.71 -1.2 -1.8 -1.2 -0.69 -0.55 2.5 5e+03 0.025 0.48 0.43 +
12 -0.38 -0.51 -0.26 0.46 -0.67 -1.2 -1.8 -1.2 -0.59 -0.48 2.8 5e+03 0.0026 4.8 1.1 ++
13 -0.36 -0.55 -0.21 0.36 -0.65 -1.2 -1.7 -1.1 -0.5 -0.39 3.3 5e+03 0.022 4.8 0.34 +
14 -0.36 -0.56 -0.21 0.36 -0.64 -1.2 -1.7 -1.1 -0.49 -0.39 3.4 5e+03 0.00022 48 1 ++
15 -0.35 -0.57 -0.2 0.35 -0.64 -1.2 -1.7 -1.1 -0.48 -0.38 3.5 5e+03 0.00054 4.8e+02 0.98 ++
16 -0.35 -0.57 -0.2 0.35 -0.64 -1.2 -1.7 -1.1 -0.48 -0.38 3.5 5e+03 2e-07 4.8e+02 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 58/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.14 -0.3 -0.6 0.43 -0.12 -0.78 -0.77 -1 1.6 1.9 5.9e+03 0.19 1 0.55 +
1 -0.43 -0.24 -0.26 0.87 -0.63 -0.42 -0.95 -0.59 0.83 2.9 5.3e+03 0.15 1 0.52 +
2 -0.43 0.7 -0.33 0.51 -0.16 -0.54 -0.65 -1 -0.17 3 5.2e+03 0.05 1 0.39 +
3 -0.67 0.56 -0.46 1.4 -0.18 -0.71 -0.61 -1.1 0.38 4 5.1e+03 0.042 1 0.34 +
4 -0.42 0.88 -0.33 1.1 -0.48 -0.77 -0.9 -1.1 0.3 3 5e+03 0.012 10 0.96 ++
5 -0.42 0.88 -0.33 1.1 -0.48 -0.77 -0.9 -1.1 0.3 3 5e+03 0.012 1 -1.1 -
6 -0.39 0.46 -0.43 1.2 -0.45 -0.77 -0.89 -1.3 0.34 2 5e+03 0.019 10 1 ++
7 -0.23 0.28 -0.29 1.2 -0.7 -0.91 -1.3 -1.5 0.33 1.5 4.9e+03 0.014 1e+02 1.1 ++
8 -0.19 0.11 -0.25 1.2 -0.8 -0.96 -1.6 -1.6 0.32 1.4 4.9e+03 0.0026 1e+03 1.1 ++
9 -0.18 0.045 -0.23 1.2 -0.83 -0.99 -1.7 -1.6 0.31 1.3 4.9e+03 0.00047 1e+04 1 ++
10 -0.18 0.045 -0.23 1.2 -0.83 -0.99 -1.7 -1.6 0.31 1.3 4.9e+03 2.4e-06 1e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -3 -
1 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 5 1.1 ++
2 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 2.5 -8.5 -
3 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 1.2 -6.1 -
4 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.62 -4.7 -
5 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.31 -3.8 -
6 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.16 -3.4 -
7 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.078 -3.4 -
8 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.039 -3.5 -
9 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.02 -3.6 -
10 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.0098 -3 -
11 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.0049 -2.1 -
12 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.0024 -1.7 -
13 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.0012 -1.3 -
14 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.00061 -0.83 -
15 -0.047 -0.045 -0.0067 -0.5 -0.35 -0.018 -0.11 -0.5 0 1.5 0 5.6e+03 11 0.00031 -0.15 -
16 -0.048 -0.045 -0.007 -0.5 -0.35 -0.018 -0.11 -0.5 -0.00031 1.5 0.00031 5.6e+03 6.5 0.00031 0.62 +
17 -0.048 -0.045 -0.007 -0.5 -0.35 -0.018 -0.11 -0.5 -0.00021 1.5 0.00058 5.6e+03 6.4 0.00031 0.39 +
18 -0.048 -0.045 -0.007 -0.5 -0.35 -0.018 -0.11 -0.5 -0.00025 1.5 0.00085 5.6e+03 0.87 0.0031 0.91 ++
19 -0.049 -0.046 -0.0072 -0.5 -0.35 -0.018 -0.11 -0.5 -0.00026 1.5 0.0036 5.6e+03 0.07 0.031 1 ++
20 -0.058 -0.059 -0.0084 -0.51 -0.33 -0.018 -0.14 -0.53 -0.00038 1.5 0.03 5.5e+03 0.52 0.31 1 ++
21 -0.11 -0.16 -0.02 -0.53 -0.16 -0.015 -0.45 -0.72 -0.001 1.6 0.19 5.4e+03 1.7 0.31 0.87 +
22 -0.0084 -0.15 -0.037 -0.57 0.049 -0.0057 -0.75 -0.8 -0.0002 1.7 -0.011 5.2e+03 2 0.31 0.83 +
23 -0.11 -0.17 -0.076 -0.64 0.3 0.022 -0.86 -1.1 1.7e-05 1.9 -0.064 5.1e+03 0.86 3.1 1.1 ++
24 -0.11 -0.17 -0.076 -0.64 0.3 0.022 -0.86 -1.1 1.7e-05 1.9 -0.064 5.1e+03 0.86 0.34 -0.3 -
25 -0.1 0.013 -0.13 -0.72 0.45 0.079 -0.8 -1.4 0.00028 2.1 -0.13 5.1e+03 26 0.34 0.62 +
26 -0.017 0.14 -0.26 -0.61 0.67 0.25 -0.85 -1.8 0.00012 2.1 -0.086 5.1e+03 37 0.34 0.48 +
27 -0.017 0.14 -0.26 -0.61 0.67 0.25 -0.85 -1.8 0.00012 2.1 -0.086 5.1e+03 37 0.17 -1.1 -
28 -0.017 0.14 -0.26 -0.61 0.67 0.25 -0.85 -1.8 0.00012 2.1 -0.086 5.1e+03 37 0.085 -0.35 -
29 -0.017 0.14 -0.26 -0.61 0.67 0.25 -0.85 -1.8 0.00012 2.1 -0.086 5.1e+03 37 0.043 -0.054 -
30 -0.024 0.13 -0.26 -0.66 0.64 0.25 -0.84 -1.8 0.00025 2.1 -0.12 5.1e+03 29 0.043 0.24 +
31 -0.022 0.12 -0.28 -0.65 0.67 0.28 -0.87 -1.8 0.00017 2.1 -0.1 5.1e+03 2 0.043 0.79 +
32 -0.04 0.11 -0.29 -0.68 0.67 0.31 -0.86 -1.8 0.00019 2 -0.1 5.1e+03 0.69 0.43 1 ++
33 -0.043 0.12 -0.29 -0.74 0.73 0.45 -0.89 -1.8 0.00019 1.9 -0.1 5.1e+03 0.011 4.3 1 ++
34 -0.049 0.12 -0.27 -0.76 0.74 0.47 -0.89 -1.8 0.00019 1.9 -0.1 5.1e+03 0.0048 43 1 ++
35 -0.048 0.12 -0.26 -0.75 0.74 0.48 -0.89 -1.8 0.00019 1.9 -0.1 5.1e+03 0.00072 4.3e+02 1 ++
36 -0.049 0.12 -0.26 -0.76 0.74 0.48 -0.89 -1.8 0.00019 1.9 -0.1 5.1e+03 4.2e-05 4.3e+03 1 ++
37 -0.049 0.12 -0.26 -0.76 0.74 0.48 -0.89 -1.8 0.00019 1.9 -0.1 5.1e+03 0.00012 4.3e+04 1 ++
38 -0.049 0.12 -0.26 -0.76 0.74 0.48 -0.89 -1.8 0.00019 1.9 -0.1 5.1e+03 1.6e-06 4.3e+04 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 B_TIME_commuter mu_existing Function Relgrad Radius Rho
0 0.092 -0.11 -0.029 -0.75 0.058 -0.01 -1 -0.83 0.09 1.5 5.3e+03 0.062 10 0.94 ++
1 -0.24 0.09 -0.34 -0.98 0.81 0.72 -0.85 -0.94 0.18 1.9 5.2e+03 0.034 10 0.8 +
2 -0.28 0.18 -0.12 -0.98 0.67 0.49 -0.82 -0.89 0.23 2.2 5.2e+03 0.0049 10 0.88 +
3 -0.27 0.17 -0.18 -1 0.72 0.53 -0.87 -0.95 0.2 2 5.2e+03 0.00091 1e+02 0.95 ++
4 -0.27 0.16 -0.19 -1 0.72 0.53 -0.87 -0.95 0.2 2 5.2e+03 2.4e-05 1e+03 1 ++
5 -0.27 0.16 -0.19 -1 0.72 0.53 -0.87 -0.95 0.2 2 5.2e+03 2.9e-08 1e+03 1 ++
Considering neighbor 2/20 for current solution
Attempt 59/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.4e+03 0.19 10 0.94 ++
1 5.4e+03 0.19 1 -3.4 -
2 5.2e+03 0.19 1 0.31 +
3 5.2e+03 0.19 0.51 -0.48 -
4 5e+03 0.021 0.51 0.85 +
5 5e+03 0.0046 5.1 1.1 ++
6 5e+03 0.0046 0.69 -0.29 -
7 4.9e+03 0.016 6.9 1.1 ++
8 4.9e+03 0.016 69 0.98 ++
9 4.9e+03 0.0015 6.9e+02 1 ++
10 4.9e+03 8.2e-05 6.9e+03 1 ++
11 4.9e+03 6.5e-08 6.9e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 60/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN mu_existing Function Relgrad Radius Rho
0 0.13 -0.35 -0.11 -0.048 -0.51 0.67 0.27 0.006 -0.27 -0.8 -1 -0.62 -0.39 -0.66 1.9 5.4e+03 0.24 1 0.84 +
1 -0.85 0.36 -0.038 -0.26 -0.46 0.79 0.5 0.19 -0.71 -0.76 -0.73 -0.62 -1.1 -1.1 2.9 5.3e+03 0.22 1 0.35 +
2 -0.85 0.36 -0.038 -0.26 -0.46 0.79 0.5 0.19 -0.71 -0.76 -0.73 -0.62 -1.1 -1.1 2.9 5.3e+03 0.22 0.5 0.026 -
3 -0.45 0.48 0.12 -0.19 -0.91 0.95 0.37 0.1 -0.5 -0.61 -0.52 -0.6 -0.65 -0.68 3.4 5.1e+03 0.052 0.5 0.63 +
4 -0.65 0.65 0.074 -0.2 -0.74 1 0.33 0.11 -0.58 -0.74 -0.81 -0.64 -0.77 -0.82 2.9 5e+03 0.0052 5 1.1 ++
5 -0.65 0.65 0.074 -0.2 -0.74 1 0.33 0.11 -0.58 -0.74 -0.81 -0.64 -0.77 -0.82 2.9 5e+03 0.0052 0.94 -1.3 -
6 -0.55 0.36 0.085 -0.27 -0.85 1.2 0.38 0.16 -0.62 -0.84 -0.78 -0.78 -0.77 -0.97 1.9 5e+03 0.015 9.4 0.98 ++
7 -0.47 0.19 0.073 -0.21 -0.91 1.2 0.49 0.3 -0.72 -0.96 -1.2 -1 -1 -1.1 1.4 5e+03 0.014 94 1.1 ++
8 -0.46 0.11 0.068 -0.21 -0.98 1.2 0.55 0.36 -0.73 -1 -1.5 -1.1 -1.1 -1.1 1.4 5e+03 0.002 9.4e+02 1.1 ++
9 -0.45 0.062 0.068 -0.22 -1 1.2 0.57 0.38 -0.74 -1 -1.6 -1.2 -1.1 -1.1 1.3 5e+03 0.00039 9.4e+03 1 ++
10 -0.45 0.062 0.068 -0.22 -1 1.2 0.57 0.38 -0.74 -1 -1.6 -1.2 -1.1 -1.1 1.3 5e+03 1.5e-06 9.4e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 61/100
Considering neighbor 0/20 for current solution
Attempt 62/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.23 -0.31 -0.65 0.45 -0.0054 -1 -0.78 -0.89 -0.67 1.8 2 6.5e+03 0.23 1 0.22 +
1 0.23 -0.31 -0.65 0.45 -0.0054 -1 -0.78 -0.89 -0.67 1.8 2 6.5e+03 0.23 0.5 -0.63 -
2 -0.27 -0.37 -0.39 0.56 -0.51 -0.61 -0.68 -0.77 -0.73 1.5 1.8 5.3e+03 0.068 5 0.91 ++
3 -0.94 1.2 -0.65 2.1 -0.51 -1.5 -1.3 -2.2 -0.34 0.27 1 5.1e+03 0.046 5 0.35 +
4 -0.024 -0.22 -0.65 1.4 -0.87 -0.92 -1.3 -0.9 -0.74 0.26 1.4 4.9e+03 0.024 5 0.75 +
5 -0.4 0.2 -0.5 1.3 -0.72 -1.1 -1.5 -1.1 -0.75 0.54 1.4 4.9e+03 0.0017 5 0.88 +
6 -0.36 0.12 -0.49 1.4 -0.79 -1.1 -1.6 -1.2 -0.73 0.44 1.3 4.9e+03 0.0009 50 0.99 ++
7 -0.36 0.12 -0.5 1.4 -0.79 -1.1 -1.6 -1.1 -0.73 0.44 1.3 4.9e+03 9.7e-06 5e+02 1 ++
8 -0.36 0.12 -0.5 1.4 -0.79 -1.1 -1.6 -1.1 -0.73 0.44 1.3 4.9e+03 8e-09 5e+02 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 B_TIME_1st_clas lambda_travel_t mu_public Function Relgrad Radius Rho
0 -0.37 -0.7 -0.95 -0.77 -0.71 2 1 5.7e+03 0.14 1 0.55 +
1 -0.31 -1.4 -1.3 0.23 -0.87 2.1 1 5.6e+03 0.22 1 0.23 +
2 -0.31 -1.4 -1.3 0.23 -0.87 2.1 1 5.6e+03 0.22 0.5 -0.82 -
3 -0.41 -1.3 -1.3 -0.27 -0.86 2.1 1 5.4e+03 0.11 0.5 0.36 +
4 -0.41 -1.3 -1.3 -0.27 -0.86 2.1 1 5.4e+03 0.11 0.25 -0.6 -
5 -0.4 -1.3 -1.3 -0.016 -0.8 2 1 5.3e+03 0.024 2.5 0.95 ++
6 -0.35 -1.1 -1.2 -0.11 -1.4 0.66 1 5.3e+03 0.031 2.5 0.53 +
7 -0.14 -0.75 -1.2 -0.73 -0.98 1.2 1 5.3e+03 0.019 2.5 0.21 +
8 -0.14 -0.75 -1.2 -0.73 -0.98 1.2 1 5.3e+03 0.019 1.2 -13 -
9 -0.14 -0.75 -1.2 -0.73 -0.98 1.2 1 5.3e+03 0.019 0.62 -0.73 -
10 -0.029 -0.66 -1.3 -1 -1.1 0.62 1 5.2e+03 0.0089 0.62 0.9 +
11 -0.057 -0.61 -1.2 -1 -0.98 0.6 1 5.2e+03 0.00041 6.2 0.97 ++
12 -0.057 -0.62 -1.2 -1 -0.98 0.6 1 5.2e+03 0.00014 62 1 ++
13 -0.057 -0.62 -1.2 -1 -0.98 0.6 1 5.2e+03 3.9e-07 62 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s mu_existing Function Relgrad Radius Rho
0 0.056 -0.07 -0.025 -0.4 0.1 -0.004 -1 -0.33 -0.16 -0.14 -0.2 -0.53 -0.31 1.6 5.3e+03 0.11 10 0.95 ++
1 -0.58 0.025 -0.59 -0.45 0.68 0.51 -0.81 -0.022 -1 -0.22 -1.2 -0.68 -0.78 2.4 5.1e+03 0.076 10 0.73 +
2 -0.51 0.17 -0.14 -0.62 0.5 0.2 -0.76 -0.13 -0.87 -0.23 -1.1 -0.49 -0.77 2.9 5.1e+03 0.019 10 0.67 +
3 -0.51 0.17 -0.14 -0.62 0.5 0.2 -0.76 -0.13 -0.87 -0.23 -1.1 -0.49 -0.77 2.9 5.1e+03 0.019 0.94 -4 -
4 -0.46 0.11 -0.41 -0.68 0.63 0.36 -0.93 -0.18 -0.99 -0.26 -1.2 -0.63 -0.86 2 5.1e+03 0.006 0.94 0.78 +
5 -0.52 0.12 -0.36 -0.64 0.64 0.35 -0.93 -0.16 -1 -0.28 -1.2 -0.66 -0.91 2.1 5.1e+03 0.00081 9.4 1 ++
6 -0.52 0.12 -0.36 -0.64 0.64 0.35 -0.93 -0.16 -1 -0.28 -1.2 -0.66 -0.9 2.2 5.1e+03 2.2e-05 94 1 ++
7 -0.52 0.12 -0.36 -0.64 0.64 0.35 -0.93 -0.16 -1 -0.28 -1.2 -0.66 -0.9 2.2 5.1e+03 4.3e-07 94 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 63/100
Considering neighbor 0/20 for current solution
Attempt 64/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 mu_existing Function Relgrad Radius Rho
0 0.091 -0.11 -0.028 -0.72 0.076 -0.009 -1 -0.87 1.4 5.3e+03 0.053 10 0.97 ++
1 -0.24 0.11 -0.32 -1 0.82 0.71 -0.87 -0.94 1.8 5.2e+03 0.023 10 0.88 +
2 -0.27 0.18 -0.16 -1 0.73 0.53 -0.86 -0.92 2 5.2e+03 0.0021 1e+02 0.93 ++
3 -0.26 0.17 -0.2 -1 0.74 0.55 -0.88 -0.94 1.9 5.2e+03 7e-05 1e+03 1 ++
4 -0.26 0.17 -0.2 -1 0.74 0.55 -0.88 -0.94 1.9 5.2e+03 1.3e-07 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 65/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 B_TIME_1st_clas Function Relgrad Radius Rho
0 -0.39 -0.07 -0.084 -1 0.25 -0.0074 -0.92 -0.32 -0.74 5.3e+03 0.038 10 1.1 ++
1 -0.27 0.061 -0.39 -1.4 0.81 0.57 -1.2 -0.59 -0.91 5.2e+03 0.009 1e+02 1.1 ++
2 -0.24 0.062 -0.43 -1.6 1 0.77 -1.2 -0.65 -0.94 5.2e+03 0.00087 1e+03 1.1 ++
3 -0.24 0.062 -0.43 -1.6 1 0.8 -1.2 -0.66 -0.94 5.2e+03 9e-06 1e+04 1 ++
4 -0.24 0.062 -0.43 -1.6 1 0.8 -1.2 -0.66 -0.94 5.2e+03 1e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 B_TIME_1st_clas cube_tt_coef mu_public square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -1.7 -
1 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 5 1.1 ++
2 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 2.5 -8.7 -
3 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 1.2 -2.8e+304 -
4 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.62 -8.2 -
5 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.31 -6.2 -
6 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.16 -3.4 -
7 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.078 -2.7 -
8 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.039 -2.8 -
9 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.019 -3 -
10 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.0097 -3.2 -
11 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.0049 -3.4 -
12 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.0024 -2.2 -
13 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.0012 -1.5 -
14 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.00061 -0.8 -
15 -0.068 -0.064 -0.0096 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 0 1 0 5.6e+03 8 0.0003 -0.046 -
16 -0.068 -0.064 -0.0099 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 -0.0003 1 0.0003 5.6e+03 2.9 0.0003 0.7 +
17 -0.068 -0.064 -0.0099 -0.5 -0.5 -0.026 -0.15 -0.5 -0.5 -0.00026 1 0.00061 5.6e+03 0.95 0.0003 0.87 +
18 -0.068 -0.064 -0.0099 -0.5 -0.5 -0.026 -0.16 -0.5 -0.5 -0.00027 1 0.00091 5.6e+03 0.068 0.003 1 ++
19 -0.068 -0.065 -0.01 -0.5 -0.5 -0.026 -0.16 -0.5 -0.5 -0.00024 1 0.004 5.6e+03 4.2 0.03 1 ++
20 -0.069 -0.069 -0.011 -0.51 -0.49 -0.027 -0.19 -0.52 -0.51 -0.00043 1 0.034 5.5e+03 1.2 0.3 0.99 ++
21 -0.053 -0.1 -0.021 -0.55 -0.33 -0.029 -0.49 -0.61 -0.59 -0.0009 1 0.15 5.4e+03 0.86 3 0.98 ++
22 -0.053 -0.1 -0.021 -0.55 -0.33 -0.029 -0.49 -0.61 -0.59 -0.0009 1 0.15 5.4e+03 0.86 1.5 -40 -
23 -0.053 -0.1 -0.021 -0.55 -0.33 -0.029 -0.49 -0.61 -0.59 -0.0009 1 0.15 5.4e+03 0.86 0.76 -5.7 -
24 0.00029 -0.16 -0.055 -0.66 0.18 -0.032 -1.3 -0.82 -0.78 0.00027 1.2 -0.13 5.3e+03 12 0.76 0.29 +
25 0.00029 -0.16 -0.055 -0.66 0.18 -0.032 -1.3 -0.82 -0.78 0.00027 1.2 -0.13 5.3e+03 12 0.38 0.041 -
26 -0.14 -0.17 -0.076 -0.63 0.39 -0.014 -1.1 -1.2 -1 -5.7e-05 1.3 -0.057 5.2e+03 9.8 0.38 0.68 +
27 -0.14 -0.17 -0.076 -0.63 0.39 -0.014 -1.1 -1.2 -1 -5.7e-05 1.3 -0.057 5.2e+03 9.8 0.19 -0.43 -
28 -0.037 -0.038 -0.1 -0.7 0.53 0.021 -1.1 -1.4 -1.1 0.00041 1.4 -0.14 5.2e+03 2.6 0.19 0.16 +
29 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.19 0.84 +
30 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.095 -0.26 -
31 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.048 -0.46 -
32 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.024 -0.17 -
33 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.012 -0.16 -
34 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.0059 -0.21 -
35 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.003 -0.19 -
36 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.0015 -0.15 -
37 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.00074 -0.14 -
38 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.00037 -0.14 -
39 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 0.00019 -0.14 -
40 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 9.3e-05 -0.14 -
41 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 8.9e-05 1.2 -0.091 5.1e+03 25 4.6e-05 -0.14 -
42 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 0.00014 1.2 -0.091 5.1e+03 6 4.6e-05 0.67 +
43 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 0.00013 1.2 -0.091 5.1e+03 0.51 0.00046 0.94 ++
44 -0.022 -0.011 -0.14 -0.71 0.64 0.092 -1.2 -1.5 -0.99 0.00013 1.2 -0.092 5.1e+03 0.022 0.0046 1 ++
45 -0.024 -0.012 -0.14 -0.72 0.64 0.093 -1.2 -1.5 -0.99 0.00015 1.2 -0.096 5.1e+03 0.14 0.046 0.99 ++
46 -0.048 -0.024 -0.15 -0.76 0.63 0.1 -1.2 -1.5 -1 0.00018 1.2 -0.1 5.1e+03 0.26 0.46 1 ++
47 -0.026 0.014 -0.29 -1.1 0.92 0.32 -1.2 -1.7 -0.93 0.00019 1 -0.1 5.1e+03 0.51 4.6 1 ++
48 -0.0038 0.02 -0.31 -1.1 0.93 0.35 -1.2 -1.7 -0.94 0.00019 1 -0.1 5.1e+03 0.15 46 1 ++
49 -0.0078 0.015 -0.48 -1.3 1.1 0.8 -1.2 -1.7 -0.97 0.00019 1 -0.11 5.1e+03 0.01 4.6e+02 0.98 ++
50 -0.012 0.021 -0.46 -1.3 1.1 0.78 -1.2 -1.7 -0.96 0.00019 1 -0.11 5.1e+03 4.2e-05 4.6e+03 1 ++
51 -0.011 0.02 -0.46 -1.3 1.1 0.78 -1.2 -1.7 -0.96 0.0002 1 -0.11 5.1e+03 2e-05 4.6e+04 1 ++
52 -0.011 0.02 -0.46 -1.3 1.1 0.78 -1.2 -1.7 -0.96 0.0002 1 -0.11 5.1e+03 8.7e-07 4.6e+04 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas Function Relgrad Radius Rho
0 -0.5 -0.47 -0.82 0.98 -0.77 -0.92 -1 -0.44 -0.67 5.1e+03 0.054 10 1.1 ++
1 -0.64 -0.12 -1 1.6 -0.85 -1.2 -1.5 -0.65 -0.86 5e+03 0.018 1e+02 1.1 ++
2 -0.66 -0.08 -0.97 1.5 -0.85 -1.3 -1.8 -0.69 -0.92 4.9e+03 0.0028 1e+03 1.1 ++
3 -0.66 -0.076 -0.94 1.5 -0.85 -1.3 -1.8 -0.69 -0.92 4.9e+03 8.7e-05 1e+04 1 ++
4 -0.66 -0.076 -0.94 1.5 -0.85 -1.3 -1.8 -0.69 -0.92 4.9e+03 8.9e-08 1e+04 1 ++
Considering neighbor 2/20 for current solution
Attempt 66/100
Considering neighbor 0/20 for current solution
Attempt 67/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.4 0.5 -0.39 -
1 -0.018 -0.017 -0.017 -0.0025 -0.28 0.0064 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.5 0.81 +
2 -0.018 -0.017 -0.017 -0.0025 -0.28 0.0064 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.25 -8.4 -
3 -0.018 -0.017 -0.017 -0.0025 -0.28 0.0064 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.12 -10 -
4 -0.018 -0.017 -0.017 -0.0025 -0.28 0.0064 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.062 -13 -
5 -0.018 -0.017 -0.017 -0.0025 -0.28 0.0064 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.031 -21 -
6 -0.018 -0.017 -0.017 -0.0025 -0.28 0.0064 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.016 -5 -
7 -0.018 -0.017 -0.017 -0.0025 -0.28 0.0064 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.0078 -1 -
8 -0.026 -0.025 -0.025 -0.0048 -0.27 0.014 -0.12 -0.0059 -0.048 -0.066 0.23 -0.49 0.0023 1.2 -0.005 5.8e+03 0.87 0.078 0.99 ++
9 -0.026 -0.025 -0.025 -0.0048 -0.27 0.014 -0.12 -0.0059 -0.048 -0.066 0.23 -0.49 0.0023 1.2 -0.005 5.8e+03 0.87 0.039 -7.9 -
10 -0.026 -0.025 -0.025 -0.0048 -0.27 0.014 -0.12 -0.0059 -0.048 -0.066 0.23 -0.49 0.0023 1.2 -0.005 5.8e+03 0.87 0.02 -8.8 -
11 -0.026 -0.025 -0.025 -0.0048 -0.27 0.014 -0.12 -0.0059 -0.048 -0.066 0.23 -0.49 0.0023 1.2 -0.005 5.8e+03 0.87 0.0098 -10 -
12 -0.026 -0.025 -0.025 -0.0048 -0.27 0.014 -0.12 -0.0059 -0.048 -0.066 0.23 -0.49 0.0023 1.2 -0.005 5.8e+03 0.87 0.0049 -3.9 -
13 -0.026 -0.025 -0.025 -0.0048 -0.27 0.014 -0.12 -0.0059 -0.048 -0.066 0.23 -0.49 0.0023 1.2 -0.005 5.8e+03 0.87 0.0024 -1 -
14 -0.028 -0.027 -0.027 -0.0072 -0.27 0.017 -0.12 -0.0035 -0.051 -0.068 0.24 -0.49 -0.00015 1.2 -0.0025 5.8e+03 0.29 0.0024 0.85 +
15 -0.029 -0.028 -0.028 -0.0073 -0.27 0.018 -0.12 -0.0035 -0.053 -0.071 0.24 -0.49 -1.2e-05 1.2 -0.0018 5.7e+03 0.079 0.024 1 ++
16 -0.039 -0.033 -0.036 -0.0081 -0.27 0.029 -0.11 -0.0032 -0.074 -0.095 0.24 -0.5 -0.00038 1.2 0.0053 5.7e+03 0.98 0.24 0.98 ++
17 -0.12 -0.09 -0.11 -0.017 -0.27 0.16 -0.015 -0.00028 -0.32 -0.33 0.22 -0.54 -9.8e-05 1.3 0.089 5.6e+03 5.8 0.24 0.74 +
18 -0.12 -0.09 -0.11 -0.017 -0.27 0.16 -0.015 -0.00028 -0.32 -0.33 0.22 -0.54 -9.8e-05 1.3 0.089 5.6e+03 5.8 0.12 -6.3 -
19 -0.12 -0.09 -0.11 -0.017 -0.27 0.16 -0.015 -0.00028 -0.32 -0.33 0.22 -0.54 -9.8e-05 1.3 0.089 5.6e+03 5.8 0.061 -6.7 -
20 -0.12 -0.09 -0.11 -0.017 -0.27 0.16 -0.015 -0.00028 -0.32 -0.33 0.22 -0.54 -9.8e-05 1.3 0.089 5.6e+03 5.8 0.031 -4.9 -
21 -0.12 -0.09 -0.11 -0.017 -0.27 0.16 -0.015 -0.00028 -0.32 -0.33 0.22 -0.54 -9.8e-05 1.3 0.089 5.6e+03 5.8 0.015 -3.3 -
22 -0.12 -0.09 -0.11 -0.017 -0.27 0.16 -0.015 -0.00028 -0.32 -0.33 0.22 -0.54 -9.8e-05 1.3 0.089 5.6e+03 5.8 0.0076 -2.7 -
23 -0.12 -0.09 -0.11 -0.017 -0.27 0.16 -0.015 -0.00028 -0.32 -0.33 0.22 -0.54 -9.8e-05 1.3 0.089 5.6e+03 5.8 0.0038 -2.1 -
24 -0.12 -0.09 -0.11 -0.017 -0.27 0.16 -0.015 -0.00028 -0.32 -0.33 0.22 -0.54 -9.8e-05 1.3 0.089 5.6e+03 5.8 0.0019 -1.4 -
25 -0.12 -0.09 -0.11 -0.017 -0.27 0.16 -0.015 -0.00028 -0.32 -0.33 0.22 -0.54 -9.8e-05 1.3 0.089 5.6e+03 5.8 0.00095 -0.64 -
26 -0.12 -0.091 -0.1 -0.017 -0.27 0.17 -0.014 0.00068 -0.32 -0.33 0.22 -0.55 -0.0011 1.3 0.088 5.6e+03 6.7 0.00095 0.19 +
27 -0.12 -0.091 -0.1 -0.017 -0.27 0.17 -0.014 0.00068 -0.32 -0.33 0.22 -0.55 -0.0011 1.3 0.088 5.6e+03 6.7 0.00048 -0.016 -
28 -0.12 -0.092 -0.1 -0.018 -0.28 0.17 -0.014 0.0012 -0.32 -0.33 0.22 -0.55 -0.00057 1.3 0.087 5.5e+03 2.8 0.00048 0.82 +
29 -0.12 -0.092 -0.1 -0.018 -0.28 0.17 -0.014 0.0012 -0.32 -0.33 0.22 -0.55 -0.00064 1.3 0.087 5.5e+03 0.39 0.0048 0.96 ++
30 -0.11 -0.092 -0.1 -0.018 -0.28 0.17 -0.013 0.0012 -0.32 -0.33 0.21 -0.55 -0.00062 1.3 0.085 5.5e+03 0.088 0.048 1 ++
31 -0.081 -0.097 -0.087 -0.018 -0.28 0.19 -0.0033 0.0017 -0.36 -0.28 0.17 -0.56 -0.00052 1.4 0.062 5.4e+03 0.29 0.48 1 ++
32 0.014 -0.2 -0.069 -0.036 -0.31 0.54 0.22 0.014 -0.83 -0.38 -0.23 -0.67 -0.001 1.7 0.2 5.1e+03 2.2 0.48 0.83 +
33 -0.1 -0.23 0.021 -0.097 -0.58 1 0.32 0.064 -0.83 -0.58 -0.45 -0.75 -0.00067 2 0.099 5e+03 7 4.8 1.1 ++
34 -0.1 -0.23 0.021 -0.097 -0.58 1 0.32 0.064 -0.83 -0.58 -0.45 -0.75 -0.00067 2 0.099 5e+03 7 2.4 -3.2e+02 -
35 -0.1 -0.23 0.021 -0.097 -0.58 1 0.32 0.064 -0.83 -0.58 -0.45 -0.75 -0.00067 2 0.099 5e+03 7 1.2 -1.1e+02 -
36 -0.1 -0.23 0.021 -0.097 -0.58 1 0.32 0.064 -0.83 -0.58 -0.45 -0.75 -0.00067 2 0.099 5e+03 7 0.6 -24 -
37 -0.1 -0.23 0.021 -0.097 -0.58 1 0.32 0.064 -0.83 -0.58 -0.45 -0.75 -0.00067 2 0.099 5e+03 7 0.3 -5.1 -
38 -0.1 -0.23 0.021 -0.097 -0.58 1 0.32 0.064 -0.83 -0.58 -0.45 -0.75 -0.00067 2 0.099 5e+03 7 0.15 -0.51 -
39 -0.18 -0.18 0.017 -0.11 -0.67 1 0.31 0.076 -0.78 -0.68 -0.46 -0.88 -6.1e-06 2 -0.05 5e+03 11 0.15 0.67 +
40 -0.19 -0.14 0.03 -0.12 -0.64 1 0.36 0.086 -0.8 -0.74 -0.61 -0.92 -0.00031 2 0.014 5e+03 6.4 1.5 0.93 ++
41 -0.19 -0.14 0.03 -0.12 -0.64 1 0.36 0.086 -0.8 -0.74 -0.61 -0.92 -0.00031 2 0.014 5e+03 6.4 0.75 -24 -
42 -0.19 -0.14 0.03 -0.12 -0.64 1 0.36 0.086 -0.8 -0.74 -0.61 -0.92 -0.00031 2 0.014 5e+03 6.4 0.37 -4.7 -
43 -0.19 -0.14 0.03 -0.12 -0.64 1 0.36 0.086 -0.8 -0.74 -0.61 -0.92 -0.00031 2 0.014 5e+03 6.4 0.19 -0.64 -
44 -0.25 -0.079 0.035 -0.14 -0.73 1.1 0.35 0.1 -0.79 -0.87 -0.68 -1.1 0.00017 2 -0.097 5e+03 1.9 0.19 0.47 +
45 -0.25 -0.041 0.044 -0.15 -0.68 1.1 0.42 0.11 -0.81 -0.94 -0.87 -1.1 -4.5e-05 2 -0.049 5e+03 5.2 1.9 0.96 ++
46 -0.25 -0.041 0.044 -0.15 -0.68 1.1 0.42 0.11 -0.81 -0.94 -0.87 -1.1 -4.5e-05 2 -0.049 5e+03 5.2 0.73 -8.6 -
47 -0.25 -0.041 0.044 -0.15 -0.68 1.1 0.42 0.11 -0.81 -0.94 -0.87 -1.1 -4.5e-05 2 -0.049 5e+03 5.2 0.37 -0.48 -
48 -0.33 0.11 0.064 -0.19 -0.75 1.1 0.43 0.16 -0.82 -1.2 -1.2 -1.5 0.00029 2 -0.13 4.9e+03 14 0.37 0.48 +
49 -0.34 0.18 0.036 -0.21 -0.66 1.2 0.48 0.18 -0.86 -1.4 -1.6 -1.8 0.00011 1.9 -0.083 4.9e+03 14 0.37 0.68 +
50 -0.4 0.28 0.13 -0.24 -0.59 1.3 0.44 0.22 -0.85 -1.7 -1.9 -2.1 0.00029 1.8 -0.13 4.9e+03 20 0.37 0.14 +
51 -0.29 0.2 0.023 -0.28 -0.58 1.4 0.49 0.23 -0.97 -1.8 -2.1 -2.3 0.00019 1.6 -0.1 4.9e+03 16 0.37 0.71 +
52 -0.31 0.18 0.013 -0.28 -0.61 1.4 0.51 0.24 -0.97 -1.9 -2.2 -2.4 0.00024 1.6 -0.11 4.9e+03 1.4 0.37 0.86 +
53 -0.31 0.18 0.016 -0.28 -0.62 1.4 0.52 0.24 -0.97 -1.8 -2.1 -2.4 0.00023 1.6 -0.11 4.9e+03 0.027 3.7 1 ++
54 -0.32 0.18 0.015 -0.28 -0.63 1.4 0.52 0.24 -0.97 -1.8 -2.1 -2.4 0.00023 1.6 -0.11 4.9e+03 8.9e-05 37 1 ++
55 -0.32 0.18 0.015 -0.28 -0.63 1.4 0.52 0.24 -0.97 -1.8 -2.1 -2.4 0.00023 1.6 -0.11 4.9e+03 0.0012 3.7e+02 1 ++
56 -0.32 0.18 0.015 -0.28 -0.63 1.4 0.52 0.24 -0.97 -1.8 -2.1 -2.4 0.00023 1.6 -0.11 4.9e+03 3e-07 3.7e+02 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 7e+03 0.4 0.5 -0.75 -
1 7e+03 0.4 0.25 -0.072 -
2 5.8e+03 0.09 2.5 1.2 ++
3 5.8e+03 0.09 1.2 -6.9 -
4 5.8e+03 0.09 0.62 -0.89 -
5 5.4e+03 17 0.62 0.48 +
6 5.4e+03 17 0.31 -1.3 -
7 5.4e+03 17 0.16 -0.97 -
8 5.4e+03 17 0.078 -0.77 -
9 5.4e+03 17 0.039 -0.67 -
10 5.4e+03 17 0.02 -0.63 -
11 5.4e+03 17 0.0098 -0.61 -
12 5.4e+03 17 0.0049 -0.17 -
13 5.3e+03 8.1 0.0049 0.22 +
14 5.3e+03 8.1 0.0024 -0.3 -
15 5.2e+03 1.1 0.024 0.98 ++
16 5.1e+03 0.18 0.24 0.99 ++
17 5.1e+03 0.059 0.24 0.81 +
18 5e+03 0.55 0.24 0.77 +
19 4.9e+03 0.59 2.4 1 ++
20 4.9e+03 0.59 1.2 -1.3e+02 -
21 4.9e+03 0.59 0.59 -21 -
22 4.9e+03 0.59 0.29 -1.4 -
23 4.9e+03 3.4 2.9 1 ++
24 4.9e+03 3.4 0.6 -0.59 -
25 4.9e+03 10 0.6 0.79 +
26 4.8e+03 17 0.6 0.64 +
27 4.8e+03 2.9 0.6 0.77 +
28 4.8e+03 0.055 6 1 ++
29 4.8e+03 0.14 60 1 ++
30 4.8e+03 0.0012 6e+02 1 ++
31 4.8e+03 1.2e-05 6e+03 1 ++
32 4.8e+03 5.9e-07 6e+03 1 ++
Considering neighbor 1/20 for current solution
Attempt 68/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.5 -3.4 -
1 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.25 -0.96 -
2 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.1 2.5 1 ++
3 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.1 1.2 -8.4 -
4 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.1 0.62 -3 -
5 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.1 0.31 -1.2 -
6 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.1 0.16 -0.39 -
7 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 0.14 5.7e+03 11 0.16 0.28 +
8 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 0.14 5.7e+03 11 0.078 -0.87 -
9 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 0.14 5.7e+03 11 0.039 -0.8 -
10 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 0.14 5.7e+03 11 0.02 -0.75 -
11 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 0.14 5.7e+03 11 0.0098 -0.73 -
12 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 0.14 5.7e+03 11 0.0049 -0.5 -
13 0.13 -0.27 -0.37 0.1 -0.16 0.21 -0.38 -0.41 -0.32 -0.0034 0.14 5.7e+03 11 0.0024 0.081 -
14 0.13 -0.27 -0.37 0.11 -0.16 0.21 -0.38 -0.4 -0.32 -0.00092 0.14 5.5e+03 1.8 0.0024 0.8 +
15 0.13 -0.27 -0.37 0.11 -0.16 0.21 -0.38 -0.4 -0.32 -0.00085 0.14 5.5e+03 1.1 0.024 0.94 ++
16 0.14 -0.27 -0.38 0.11 -0.15 0.18 -0.39 -0.4 -0.32 -0.00085 0.14 5.5e+03 0.075 0.24 1 ++
17 0.2 -0.31 -0.43 0.19 -0.078 -0.06 -0.5 -0.43 -0.34 -0.0007 0.1 5.4e+03 0.059 2.4 0.91 ++
18 -0.7 0.24 -1.1 1.6 -0.59 -1.1 -1.5 -0.46 -0.58 -0.00074 0.11 5e+03 0.43 24 1.1 ++
19 -0.7 0.24 -1.1 1.6 -0.59 -1.1 -1.5 -0.46 -0.58 -0.00074 0.11 5e+03 0.43 0.81 -1.8e+02 -
20 -0.7 0.24 -1.1 1.6 -0.59 -1.1 -1.5 -0.46 -0.58 -0.00074 0.11 5e+03 0.43 0.41 -31 -
21 -0.7 0.24 -1.1 1.6 -0.59 -1.1 -1.5 -0.46 -0.58 -0.00074 0.11 5e+03 0.43 0.2 -4.3 -
22 -0.73 0.17 -1.1 1.6 -0.67 -1.2 -1.6 -0.66 -0.78 -0.00017 -0.023 4.9e+03 1.2 2 0.97 ++
23 -0.52 -0.098 -0.67 1.4 -0.81 -1.2 -1.9 -1.3 -0.86 0.00024 -0.12 4.9e+03 17 2 0.82 +
24 -0.46 -0.12 -0.49 1.5 -0.74 -1.3 -1.8 -1.9 -0.81 0.00016 -0.094 4.9e+03 25 2 0.6 +
25 -0.46 -0.12 -0.49 1.5 -0.74 -1.3 -1.8 -1.9 -0.81 0.00016 -0.094 4.9e+03 25 0.25 -1.2 -
26 -0.46 -0.12 -0.49 1.5 -0.74 -1.3 -1.8 -1.9 -0.81 0.00016 -0.094 4.9e+03 25 0.12 -0.23 -
27 -0.44 -0.13 -0.49 1.5 -0.79 -1.3 -1.9 -2 -0.89 0.00027 -0.13 4.9e+03 26 0.12 0.25 +
28 -0.45 -0.14 -0.48 1.5 -0.77 -1.3 -1.9 -1.9 -0.84 0.00021 -0.11 4.9e+03 1.3 1.2 0.93 ++
29 -0.45 -0.15 -0.51 1.5 -0.8 -1.3 -1.9 -1.8 -0.89 0.00022 -0.11 4.9e+03 0.09 12 1 ++
30 -0.46 -0.15 -0.51 1.5 -0.8 -1.3 -1.9 -1.8 -0.89 0.00022 -0.11 4.9e+03 5.4e-05 1.2e+02 1 ++
31 -0.46 -0.15 -0.51 1.5 -0.8 -1.3 -1.9 -1.8 -0.89 0.00022 -0.11 4.9e+03 2.9e-06 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_CAR_comm B_TIME_SM B_TIME_SM_commu B_TIME_TRAIN B_TIME_TRAIN_co cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.4 0.5 -0.59 -
1 -0.019 -0.017 -0.28 0.0067 -0.038 0.29 -0.3 -0.075 -0.035 0.24 0.075 -0.5 -0.12 0.051 0.005 6.4e+03 1.8 0.5 0.22 +
2 -0.019 -0.017 -0.28 0.0067 -0.038 0.29 -0.3 -0.075 -0.035 0.24 0.075 -0.5 -0.12 0.051 0.005 6.4e+03 1.8 0.25 -4.4 -
3 -0.019 -0.017 -0.28 0.0067 -0.038 0.29 -0.3 -0.075 -0.035 0.24 0.075 -0.5 -0.12 0.051 0.005 6.4e+03 1.8 0.12 -5.1 -
4 -0.019 -0.017 -0.28 0.0067 -0.038 0.29 -0.3 -0.075 -0.035 0.24 0.075 -0.5 -0.12 0.051 0.005 6.4e+03 1.8 0.062 -3.7 -
5 -0.016 -0.019 -0.27 0.01 -0.037 0.28 -0.3 -0.013 0.025 0.23 0.074 -0.49 -0.12 -0.012 -0.0043 6.2e+03 6.5 0.062 0.26 +
6 -0.016 -0.019 -0.27 0.01 -0.037 0.28 -0.3 -0.013 0.025 0.23 0.074 -0.49 -0.12 -0.012 -0.0043 6.2e+03 6.5 0.031 -0.058 -
7 -0.017 -0.022 -0.27 0.016 -0.039 0.27 -0.3 -0.044 -0.0065 0.21 0.066 -0.47 -0.1 0.019 0.0094 5.9e+03 0.87 0.031 0.33 +
8 -0.017 -0.022 -0.27 0.016 -0.039 0.27 -0.3 -0.044 -0.0065 0.21 0.066 -0.47 -0.1 0.019 0.0094 5.9e+03 0.87 0.016 -2.9 -
9 -0.0015 -0.037 -0.26 0.032 -0.055 0.25 -0.31 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0036 -0.0062 5.8e+03 0.3 0.16 0.99 ++
10 -0.0015 -0.037 -0.26 0.032 -0.055 0.25 -0.31 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0036 -0.0062 5.8e+03 0.3 0.078 -8.4 -
11 -0.0015 -0.037 -0.26 0.032 -0.055 0.25 -0.31 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0036 -0.0062 5.8e+03 0.3 0.039 -11 -
12 -0.0015 -0.037 -0.26 0.032 -0.055 0.25 -0.31 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0036 -0.0062 5.8e+03 0.3 0.02 -14 -
13 -0.0015 -0.037 -0.26 0.032 -0.055 0.25 -0.31 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0036 -0.0062 5.8e+03 0.3 0.0098 -19 -
14 -0.0015 -0.037 -0.26 0.032 -0.055 0.25 -0.31 -0.028 0.0092 0.2 0.082 -0.46 -0.12 0.0036 -0.0062 5.8e+03 0.3 0.0049 -10 -
15 -0.0033 -0.041 -0.25 0.037 -0.059 0.25 -0.32 -0.027 0.014 0.19 0.084 -0.45 -0.12 -0.0012 -0.011 5.8e+03 2 0.0049 0.48 +
16 -0.004 -0.043 -0.25 0.04 -0.06 0.24 -0.32 -0.031 0.012 0.19 0.085 -0.45 -0.12 -0.00075 -0.012 5.7e+03 0.47 0.049 1 ++
17 -0.011 -0.057 -0.24 0.068 -0.075 0.19 -0.34 -0.065 -0.012 0.17 0.096 -0.45 -0.12 0.00018 -0.02 5.7e+03 0.42 0.49 1 ++
18 -0.011 -0.057 -0.24 0.068 -0.075 0.19 -0.34 -0.065 -0.012 0.17 0.096 -0.45 -0.12 0.00018 -0.02 5.7e+03 0.42 0.24 -1.4 -
19 -0.011 -0.057 -0.24 0.068 -0.075 0.19 -0.34 -0.065 -0.012 0.17 0.096 -0.45 -0.12 0.00018 -0.02 5.7e+03 0.42 0.12 -0.39 -
20 -0.025 -0.095 -0.24 0.15 -0.11 0.072 -0.42 -0.16 -0.076 0.11 0.12 -0.47 -0.15 -0.0021 0.0054 5.7e+03 10 0.12 0.26 +
21 -0.025 -0.095 -0.24 0.15 -0.11 0.072 -0.42 -0.16 -0.076 0.11 0.12 -0.47 -0.15 -0.0021 0.0054 5.7e+03 10 0.061 -0.13 -
22 -0.025 -0.095 -0.24 0.15 -0.11 0.072 -0.42 -0.16 -0.076 0.11 0.12 -0.47 -0.15 -0.0021 0.0054 5.7e+03 10 0.031 0.083 -
23 -0.025 -0.1 -0.24 0.16 -0.12 0.056 -0.43 -0.19 -0.1 0.1 0.13 -0.48 -0.16 0.0022 -0.012 5.6e+03 3.5 0.031 0.23 +
24 -0.025 -0.1 -0.24 0.16 -0.12 0.056 -0.43 -0.19 -0.1 0.1 0.13 -0.48 -0.16 0.0022 -0.012 5.6e+03 3.5 0.015 -4.2 -
25 -0.025 -0.1 -0.24 0.16 -0.12 0.056 -0.43 -0.19 -0.1 0.1 0.13 -0.48 -0.16 0.0022 -0.012 5.6e+03 3.5 0.0076 -3.2 -
26 -0.025 -0.1 -0.24 0.16 -0.12 0.056 -0.43 -0.19 -0.1 0.1 0.13 -0.48 -0.16 0.0022 -0.012 5.6e+03 3.5 0.0038 -2 -
27 -0.025 -0.1 -0.24 0.16 -0.12 0.056 -0.43 -0.19 -0.1 0.1 0.13 -0.48 -0.16 0.0022 -0.012 5.6e+03 3.5 0.0019 -0.086 -
28 -0.023 -0.1 -0.25 0.16 -0.12 0.054 -0.43 -0.19 -0.1 0.098 0.13 -0.48 -0.16 0.00034 -0.0098 5.6e+03 3.4 0.019 1 ++
29 -0.023 -0.1 -0.25 0.16 -0.12 0.054 -0.43 -0.19 -0.1 0.098 0.13 -0.48 -0.16 0.00034 -0.0098 5.6e+03 3.4 0.0095 -7.4 -
30 -0.023 -0.1 -0.25 0.16 -0.12 0.054 -0.43 -0.19 -0.1 0.098 0.13 -0.48 -0.16 0.00034 -0.0098 5.6e+03 3.4 0.0048 -4.2 -
31 -0.023 -0.1 -0.25 0.16 -0.12 0.054 -0.43 -0.19 -0.1 0.098 0.13 -0.48 -0.16 0.00034 -0.0098 5.6e+03 3.4 0.0024 -3 -
32 -0.023 -0.1 -0.25 0.16 -0.12 0.054 -0.43 -0.19 -0.1 0.098 0.13 -0.48 -0.16 0.00034 -0.0098 5.6e+03 3.4 0.0012 -1.6 -
33 -0.023 -0.1 -0.25 0.16 -0.12 0.054 -0.43 -0.19 -0.1 0.098 0.13 -0.48 -0.16 0.00034 -0.0098 5.6e+03 3.4 0.0006 -0.31 -
34 -0.023 -0.1 -0.25 0.16 -0.12 0.053 -0.43 -0.19 -0.1 0.097 0.13 -0.48 -0.16 -0.00026 -0.0092 5.6e+03 0.94 0.0006 0.82 +
35 -0.023 -0.1 -0.25 0.16 -0.12 0.053 -0.43 -0.19 -0.1 0.097 0.13 -0.48 -0.16 -0.00023 -0.0088 5.6e+03 0.039 0.006 1 ++
36 -0.022 -0.1 -0.25 0.17 -0.12 0.047 -0.44 -0.19 -0.1 0.093 0.14 -0.48 -0.16 -0.0002 -0.0047 5.6e+03 1.5 0.06 1 ++
37 -0.017 -0.12 -0.26 0.21 -0.13 -0.013 -0.49 -0.22 -0.12 0.054 0.14 -0.51 -0.18 -0.00046 0.036 5.5e+03 1.6 0.6 0.99 ++
38 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.6 0.57 +
39 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.3 -4.4 -
40 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.15 -4.1 -
41 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.075 -3.3 -
42 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.037 -3.3 -
43 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.019 -3.5 -
44 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.0093 -3.6 -
45 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.0047 -3.2 -
46 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.0023 -1.8 -
47 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.0012 -1.1 -
48 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.00058 -0.61 -
49 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.00029 -0.41 -
50 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0017 0.39 5.2e+03 15 0.00015 -0.052 -
51 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0019 0.38 5.2e+03 8.4 0.00015 0.61 +
52 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0018 0.38 5.2e+03 6.9 0.00015 0.47 +
53 -0.067 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0018 0.38 5.2e+03 1.1 0.0015 0.93 ++
54 -0.066 -0.31 -0.25 0.7 -0.31 -0.61 -0.97 -0.72 -0.32 -0.44 0.22 -0.58 -0.32 -0.0018 0.38 5.2e+03 0.14 0.015 1 ++
55 -0.061 -0.31 -0.25 0.7 -0.3 -0.62 -0.97 -0.7 -0.32 -0.44 0.22 -0.58 -0.32 -0.0018 0.37 5.2e+03 0.66 0.15 1 ++
56 -0.062 -0.33 -0.31 0.8 -0.3 -0.63 -1.1 -0.63 -0.23 -0.44 0.3 -0.64 -0.33 -0.0014 0.26 5.1e+03 8.4 1.5 1 ++
57 -0.062 -0.33 -0.31 0.8 -0.3 -0.63 -1.1 -0.63 -0.23 -0.44 0.3 -0.64 -0.33 -0.0014 0.26 5.1e+03 8.4 0.73 -21 -
58 -0.27 -0.46 -0.52 1.4 -0.53 -0.86 -1.8 -0.89 0.035 -0.77 0.62 -0.73 -0.36 -0.00055 0.079 4.9e+03 8.3 7.3 1 ++
59 -0.47 -0.19 -0.7 1.4 -0.71 -1.1 -2.1 -1.2 0.4 -1.3 1.2 -0.96 -0.024 -0.00069 0.096 4.9e+03 6 73 1 ++
60 -0.47 -0.19 -0.7 1.4 -0.71 -1.1 -2.1 -1.2 0.4 -1.3 1.2 -0.96 -0.024 -0.00069 0.096 4.9e+03 6 36 -1.4e+03 -
61 -0.47 -0.19 -0.7 1.4 -0.71 -1.1 -2.1 -1.2 0.4 -1.3 1.2 -0.96 -0.024 -0.00069 0.096 4.9e+03 6 18 -1.2e+03 -
62 -0.47 -0.19 -0.7 1.4 -0.71 -1.1 -2.1 -1.2 0.4 -1.3 1.2 -0.96 -0.024 -0.00069 0.096 4.9e+03 6 9.1 -9.2e+02 -
63 -0.47 -0.19 -0.7 1.4 -0.71 -1.1 -2.1 -1.2 0.4 -1.3 1.2 -0.96 -0.024 -0.00069 0.096 4.9e+03 6 4.5 -6.2e+02 -
64 -0.47 -0.19 -0.7 1.4 -0.71 -1.1 -2.1 -1.2 0.4 -1.3 1.2 -0.96 -0.024 -0.00069 0.096 4.9e+03 6 2.3 -3.5e+02 -
65 -0.47 -0.19 -0.7 1.4 -0.71 -1.1 -2.1 -1.2 0.4 -1.3 1.2 -0.96 -0.024 -0.00069 0.096 4.9e+03 6 1.1 -1.5e+02 -
66 -0.47 -0.19 -0.7 1.4 -0.71 -1.1 -2.1 -1.2 0.4 -1.3 1.2 -0.96 -0.024 -0.00069 0.096 4.9e+03 6 0.57 -36 -
67 -0.47 -0.19 -0.7 1.4 -0.71 -1.1 -2.1 -1.2 0.4 -1.3 1.2 -0.96 -0.024 -0.00069 0.096 4.9e+03 6 0.28 -5 -
68 -0.42 -0.18 -0.75 1.3 -0.79 -1.2 -2.1 -1.5 0.38 -1.5 1.1 -1.2 -0.083 -2.2e-05 -0.051 4.9e+03 11 0.28 0.16 +
69 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.28 0.57 +
70 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.14 -2.4 -
71 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.071 -1.9 -
72 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.036 -1.9 -
73 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.018 -1.8 -
74 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.0089 -1.8 -
75 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.0044 -1.8 -
76 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.0022 -1.8 -
77 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.0011 -1.9 -
78 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.00056 -1.9 -
79 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.00028 -1.6 -
80 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 0.00014 -0.88 -
81 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00015 -0.039 4.9e+03 20 6.9e-05 -0.32 -
82 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -8.4e-05 -0.039 4.9e+03 14 6.9e-05 0.49 +
83 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.00011 -0.039 4.9e+03 6.9 6.9e-05 0.6 +
84 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -0.0001 -0.039 4.9e+03 0.74 0.00069 0.92 ++
85 -0.27 -0.17 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -9.7e-05 -0.039 4.9e+03 0.016 0.0069 1 ++
86 -0.27 -0.16 -0.63 1.4 -0.71 -1.2 -2.1 -1.8 0.19 -1.7 1.2 -1.4 -0.11 -6.9e-05 -0.046 4.9e+03 0.012 0.069 0.99 ++
87 -0.27 -0.16 -0.64 1.4 -0.73 -1.1 -2.1 -1.9 0.26 -1.7 1.1 -1.4 -0.12 2.4e-05 -0.066 4.9e+03 9.2 0.69 1 ++
88 -0.27 -0.16 -0.64 1.4 -0.73 -1.1 -2.1 -1.9 0.26 -1.7 1.1 -1.4 -0.12 2.4e-05 -0.066 4.9e+03 9.2 0.35 -2 -
89 -0.2 -0.15 -0.5 1.4 -0.78 -1.1 -2.1 -2.2 0.23 -2 1 -1.7 -0.22 0.00018 -0.11 4.9e+03 24 0.35 0.48 +
90 -0.12 -0.17 -0.33 1.4 -0.7 -1.2 -2.1 -2.5 -0.04 -2.3 0.91 -2.1 -0.27 0.00016 -0.099 4.8e+03 17 0.35 0.87 +
91 -0.023 -0.18 -0.11 1.4 -0.69 -1.2 -2.1 -2.7 -0.22 -2.4 0.57 -2.3 -0.51 0.00023 -0.12 4.8e+03 17 0.35 0.42 +
92 -0.069 -0.18 -0.18 1.4 -0.7 -1.2 -2.1 -2.5 -0.24 -2.3 0.52 -2.2 -0.54 0.0002 -0.11 4.8e+03 1 3.5 1 ++
93 -0.066 -0.18 -0.18 1.4 -0.69 -1.2 -2.1 -2.5 -0.26 -2.3 0.49 -2.2 -0.57 0.0002 -0.11 4.8e+03 0.033 35 1 ++
94 -0.068 -0.18 -0.18 1.4 -0.7 -1.2 -2.1 -2.5 -0.27 -2.3 0.48 -2.2 -0.57 0.0002 -0.11 4.8e+03 0.00036 3.5e+02 1 ++
95 -0.068 -0.18 -0.18 1.4 -0.7 -1.2 -2.1 -2.5 -0.27 -2.3 0.48 -2.2 -0.58 0.0002 -0.11 4.8e+03 0.00069 3.5e+03 1 ++
96 -0.068 -0.18 -0.18 1.4 -0.7 -1.2 -2.1 -2.5 -0.27 -2.3 0.48 -2.2 -0.58 0.0002 -0.11 4.8e+03 9.5e-06 3.5e+04 1 ++
97 -0.068 -0.18 -0.18 1.4 -0.7 -1.2 -2.1 -2.5 -0.27 -2.3 0.48 -2.2 -0.58 0.0002 -0.11 4.8e+03 0.00097 3.5e+05 1 ++
98 -0.068 -0.18 -0.18 1.4 -0.7 -1.2 -2.1 -2.5 -0.27 -2.3 0.48 -2.2 -0.58 0.0002 -0.11 4.8e+03 3.3e-05 3.5e+06 1 ++
99 -0.068 -0.18 -0.18 1.4 -0.7 -1.2 -2.1 -2.5 -0.27 -2.3 0.48 -2.2 -0.58 0.0002 -0.11 4.8e+03 5.3e-05 3.5e+06 1 ++
Considering neighbor 1/20 for current solution
Attempt 69/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter lambda_travel_t Function Relgrad Radius Rho
0 -0.24 -0.28 -0.57 0.37 -0.45 -0.49 -0.55 -1 -0.087 1.5 5.4e+03 0.053 1 0.88 +
1 -0.45 -0.52 -0.48 1.4 -0.61 -0.9 -1.5 -1.3 -0.21 0.79 5e+03 0.023 10 1.1 ++
2 0.018 -0.27 0.092 1.3 -0.97 -1.1 -2.1 -1.9 -1.1 -0.16 5e+03 0.033 10 0.12 +
3 -0.03 -0.26 -0.068 1.3 -1.1 -1 -2.2 -1.6 -0.78 0.08 4.9e+03 0.0022 1e+02 1.1 ++
4 -0.096 -0.26 -0.14 1.3 -1 -1.1 -2.2 -1.6 -0.77 0.24 4.9e+03 0.0013 1e+03 1 ++
5 -0.096 -0.26 -0.14 1.3 -1 -1.1 -2.2 -1.6 -0.77 0.24 4.9e+03 5.9e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 70/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_SM B_TIME_TRAIN Function Relgrad Radius Rho
0 -0.66 -0.81 -0.89 -0.6 -0.74 -0.77 5.4e+03 0.065 10 1.1 ++
1 -0.26 -0.36 -1 -1.1 -1.1 -1.4 5.3e+03 0.016 1e+02 1.1 ++
2 -0.27 -0.21 -1.1 -1.1 -1.2 -1.6 5.3e+03 0.0011 1e+03 1 ++
3 -0.27 -0.21 -1.1 -1.1 -1.2 -1.6 5.3e+03 4.9e-06 1e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 71/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME B_TIME_1st_clas cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -2.1 -
1 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 5 1 ++
2 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 2.5 -1.1e+303 -
3 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 1.2 -6.5 -
4 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.62 -4.9 -
5 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.31 -4 -
6 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.16 -3.5 -
7 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.078 -3.4 -
8 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.039 -3.5 -
9 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.02 -3.7 -
10 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.0098 -2.9 -
11 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.0049 -2.1 -
12 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.0024 -1.7 -
13 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.0012 -1.3 -
14 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.00061 -0.82 -
15 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 0 1.5 0 5.3e+03 11 0.00031 -0.14 -
16 0.12 -0.088 -0.5 0.044 -0.27 -0.5 -0.5 -0.00031 1.5 0.00031 5.3e+03 6.6 0.00031 0.62 +
17 0.12 -0.088 -0.5 0.045 -0.27 -0.5 -0.5 -0.0002 1.5 0.00061 5.3e+03 8 0.00031 0.17 +
18 0.12 -0.088 -0.5 0.045 -0.27 -0.5 -0.5 -0.00027 1.5 0.00092 5.3e+03 3.1 0.00031 0.7 +
19 0.12 -0.088 -0.5 0.045 -0.27 -0.5 -0.5 -0.00025 1.5 0.0012 5.3e+03 0.53 0.0031 0.95 ++
20 0.12 -0.089 -0.5 0.047 -0.27 -0.5 -0.5 -0.00026 1.5 0.0043 5.3e+03 0.061 0.031 1 ++
21 0.1 -0.099 -0.5 0.065 -0.3 -0.52 -0.51 -0.00039 1.5 0.035 5.3e+03 0.22 0.31 1 ++
22 -0.038 -0.2 -0.51 0.28 -0.6 -0.63 -0.59 -0.00065 1.6 0.098 5.1e+03 0.86 3.1 1 ++
23 -0.038 -0.2 -0.51 0.28 -0.6 -0.63 -0.59 -0.00065 1.6 0.098 5.1e+03 0.86 1.5 -49 -
24 -0.038 -0.2 -0.51 0.28 -0.6 -0.63 -0.59 -0.00065 1.6 0.098 5.1e+03 0.86 0.76 -20 -
25 -0.038 -0.2 -0.51 0.28 -0.6 -0.63 -0.59 -0.00065 1.6 0.098 5.1e+03 0.86 0.38 -2.6 -
26 -0.032 -0.32 -0.67 0.66 -0.95 -0.82 -0.71 0.0004 1.9 -0.15 5.1e+03 18 0.38 0.22 +
27 -0.096 -0.32 -0.55 0.89 -0.9 -1.2 -0.9 2.1e-05 1.9 -0.056 5e+03 32 0.38 0.49 +
28 -0.096 -0.32 -0.55 0.89 -0.9 -1.2 -0.9 2.1e-05 1.9 -0.056 5e+03 32 0.19 -2.3 -
29 -0.096 -0.32 -0.55 0.89 -0.9 -1.2 -0.9 2.1e-05 1.9 -0.056 5e+03 32 0.095 -0.76 -
30 -0.096 -0.32 -0.55 0.89 -0.9 -1.2 -0.9 2.1e-05 1.9 -0.056 5e+03 32 0.048 -0.16 -
31 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00012 1.9 -0.1 5e+03 50 0.048 0.47 +
32 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00012 1.9 -0.1 5e+03 50 0.024 -0.86 -
33 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00012 1.9 -0.1 5e+03 50 0.012 -0.79 -
34 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00012 1.9 -0.1 5e+03 50 0.006 -0.74 -
35 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00012 1.9 -0.1 5e+03 50 0.003 -0.74 -
36 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00012 1.9 -0.1 5e+03 50 0.0015 -0.75 -
37 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00012 1.9 -0.1 5e+03 50 0.00075 -0.76 -
38 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00012 1.9 -0.1 5e+03 50 0.00037 -0.76 -
39 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00012 1.9 -0.1 5e+03 50 0.00019 -0.52 -
40 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00012 1.9 -0.1 5e+03 50 9.3e-05 0.019 -
41 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00021 1.9 -0.1 4.9e+03 17 9.3e-05 0.61 +
42 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00021 1.9 -0.1 4.9e+03 17 4.7e-05 -0.09 -
43 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00021 1.9 -0.1 4.9e+03 17 2.3e-05 -0.092 -
44 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00019 1.9 -0.1 4.9e+03 9.4 2.3e-05 0.56 +
45 -0.091 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.0002 1.9 -0.1 4.9e+03 0.18 0.00023 1 ++
46 -0.09 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.0002 1.9 -0.1 4.9e+03 0.015 0.0023 1 ++
47 -0.088 -0.32 -0.55 0.9 -0.91 -1.2 -0.89 0.00019 1.9 -0.1 4.9e+03 0.18 0.023 1 ++
48 -0.065 -0.32 -0.56 0.91 -0.91 -1.2 -0.89 0.00019 1.9 -0.1 4.9e+03 0.0093 0.23 1 ++
49 0.014 -0.27 -0.56 1.1 -0.93 -1.2 -0.83 0.00017 1.8 -0.097 4.9e+03 0.75 2.3 1 ++
50 -0.027 -0.026 -0.75 1.6 -1.1 -1.6 -0.8 0.00021 1.3 -0.11 4.9e+03 0.86 2.3 0.87 +
51 -0.038 -0.023 -0.78 1.6 -1.1 -1.6 -0.78 0.00021 1.4 -0.11 4.9e+03 0.048 23 1 ++
52 -0.04 -0.0047 -0.77 1.6 -1.1 -1.6 -0.78 0.00021 1.4 -0.11 4.9e+03 0.0034 2.3e+02 1 ++
53 -0.04 -0.00031 -0.77 1.6 -1.1 -1.6 -0.78 0.00021 1.4 -0.11 4.9e+03 6.8e-06 2.3e+03 1 ++
54 -0.04 0.00028 -0.77 1.6 -1.1 -1.6 -0.78 0.00021 1.4 -0.11 4.9e+03 0.0065 2.3e+04 1 ++
55 -0.04 0.00028 -0.77 1.6 -1.1 -1.6 -0.78 0.00021 1.4 -0.11 4.9e+03 4.1e-07 2.3e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME B_TIME_commuter lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.092 -0.23 -0.11 -0.029 -0.82 0.47 0.021 -0.012 -1 -0.91 0.071 1.5 1.5 5.3e+03 0.051 1 0.78 +
1 -0.18 -0.27 0.11 -0.13 -0.82 1.5 0.65 0.072 -0.78 -1.2 -0.096 0.71 2.2 5.1e+03 0.047 1 0.68 +
2 0.17 0.64 -0.02 -0.27 -0.71 1.6 0.66 0.27 -1 -2 -1 -0.15 1.2 5e+03 0.019 1 0.21 +
3 -0.023 0.0072 0.053 -0.28 -1.1 1.7 0.56 0.25 -0.92 -1.5 -0.6 0.09 1.4 4.9e+03 0.0044 10 1 ++
4 -0.042 0.027 0.039 -0.32 -1.1 1.7 0.58 0.29 -0.98 -1.5 -0.61 0.31 1.3 4.9e+03 0.0023 1e+02 0.95 ++
5 -0.062 0.046 0.04 -0.31 -1.1 1.7 0.58 0.29 -0.98 -1.5 -0.58 0.3 1.4 4.9e+03 2.8e-05 1e+03 1 ++
6 -0.062 0.046 0.04 -0.31 -1.1 1.7 0.58 0.29 -0.98 -1.5 -0.58 0.3 1.4 4.9e+03 1.5e-08 1e+03 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -6.4 -
1 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.25 -1.4 -
2 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 2.5 1 ++
3 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 1.2 -7 -
4 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.62 -4 -
5 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.31 -2.3 -
6 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.16 -0.57 -
7 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.078 -0.041 -
8 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.039 0.021 -
9 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.02 -0.15 -
10 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.0098 -0.51 -
11 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.0049 -1 -
12 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.0024 -1.5 -
13 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.0012 -1.9 -
14 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.00061 -1.2 -
15 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4.1 0.00031 -0.17 -
16 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.00031 1.3 0.00031 5.7e+03 1.8 0.00031 0.64 +
17 0.083 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 -0.00025 1.3 0.00049 5.7e+03 0.19 0.0031 0.98 ++
18 0.084 -0.25 -0.25 0.082 -0.2 0.25 -0.25 -0.25 -0.00027 1.3 0.0023 5.7e+03 0.19 0.031 1 ++
19 0.096 -0.25 -0.28 0.087 -0.19 0.24 -0.28 -0.28 -0.00034 1.3 0.021 5.7e+03 0.18 0.31 1 ++
20 0.17 -0.32 -0.44 0.19 -0.12 0.013 -0.55 -0.59 -0.0012 1.5 0.21 5.4e+03 6.9 0.31 0.75 +
21 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.31 0.58 +
22 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.15 -4 -
23 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.076 -3.9 -
24 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.038 -3.8 -
25 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.019 -3 -
26 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.0095 -2.1 -
27 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.0048 -1.8 -
28 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.0024 -1.6 -
29 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.0012 -1.3 -
30 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.0006 -0.86 -
31 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00016 1.6 0.02 5.2e+03 16 0.0003 -0.29 -
32 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00046 1.6 0.02 5.2e+03 11 0.0003 0.28 +
33 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00046 1.6 0.02 5.2e+03 11 0.00015 -0.39 -
34 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00031 1.6 0.02 5.2e+03 7.8 0.00015 0.64 +
35 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00034 1.6 0.02 5.2e+03 0.35 0.0015 0.96 ++
36 -0.13 -0.44 -0.23 0.44 -0.38 -0.25 -0.61 -0.67 -0.00034 1.6 0.021 5.2e+03 0.069 0.015 1 ++
37 -0.12 -0.44 -0.24 0.44 -0.37 -0.26 -0.63 -0.68 -0.00037 1.6 0.028 5.2e+03 0.28 0.15 1 ++
38 -0.049 -0.46 -0.35 0.49 -0.3 -0.37 -0.78 -0.77 -0.00021 1.7 -0.0077 5.1e+03 0.44 1.5 0.98 ++
39 -0.36 0.22 -0.43 1.1 -0.61 -0.84 -1.2 -1.4 0.00041 1.8 -0.16 5e+03 40 1.5 0.53 +
40 -0.36 0.22 -0.43 1.1 -0.61 -0.84 -1.2 -1.4 0.00041 1.8 -0.16 5e+03 40 0.75 -2.7 -
41 -0.36 0.22 -0.43 1.1 -0.61 -0.84 -1.2 -1.4 0.00041 1.8 -0.16 5e+03 40 0.37 -1.3 -
42 -0.36 0.22 -0.43 1.1 -0.61 -0.84 -1.2 -1.4 0.00041 1.8 -0.16 5e+03 40 0.19 -0.27 -
43 -0.33 0.22 -0.35 1.1 -0.55 -1 -1.2 -1.6 -0.00018 1.8 -0.0092 5e+03 41 0.19 0.13 +
44 -0.33 0.22 -0.35 1.1 -0.55 -1 -1.2 -1.6 -0.00018 1.8 -0.0092 5e+03 41 0.093 -0.4 -
45 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 0.093 0.51 +
46 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 0.047 -0.67 -
47 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 0.023 -0.56 -
48 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 0.012 -0.58 -
49 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 0.0058 -0.63 -
50 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 0.0029 -0.66 -
51 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 0.0015 -0.68 -
52 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 0.00073 -0.69 -
53 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 0.00036 -0.7 -
54 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 0.00018 -0.7 -
55 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00013 1.8 -0.1 5e+03 61 9.1e-05 -0.28 -
56 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00022 1.8 -0.1 4.9e+03 34 9.1e-05 0.31 +
57 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00022 1.8 -0.1 4.9e+03 34 4.5e-05 -0.69 -
58 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00017 1.8 -0.1 4.9e+03 20 4.5e-05 0.63 +
59 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00018 1.8 -0.1 4.9e+03 1.4 0.00045 0.94 ++
60 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00018 1.8 -0.1 4.9e+03 0.13 0.0045 1 ++
61 -0.35 0.22 -0.35 1.1 -0.57 -0.98 -1.2 -1.6 0.00016 1.8 -0.097 4.9e+03 0.22 0.045 1 ++
62 -0.37 0.22 -0.37 1.1 -0.59 -0.94 -1.2 -1.6 0.00013 1.8 -0.09 4.9e+03 0.71 0.45 1 ++
63 -0.36 0.22 -0.32 1.2 -0.58 -0.96 -1.3 -2.1 0.00026 1.7 -0.12 4.9e+03 8.9 0.45 0.77 +
64 -0.31 0.14 -0.28 1.3 -0.69 -1 -1.5 -2.2 0.00021 1.4 -0.11 4.9e+03 4.7 0.45 0.86 +
65 -0.32 0.12 -0.28 1.3 -0.68 -1 -1.5 -2.2 0.00022 1.4 -0.11 4.9e+03 0.12 4.5 1 ++
66 -0.32 0.12 -0.28 1.3 -0.68 -1 -1.5 -2.2 0.00022 1.4 -0.11 4.9e+03 1.5e-05 45 1 ++
67 -0.32 0.12 -0.28 1.3 -0.68 -1 -1.5 -2.2 0.00022 1.4 -0.11 4.9e+03 1.1e-06 45 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 72/100
Considering neighbor 0/20 for current solution
Attempt 73/100
Considering neighbor 0/20 for current solution
Attempt 74/100
Considering neighbor 0/20 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
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME B_TIME_commuter mu_public Function Relgrad Radius Rho
0 -0.26 -0.24 -0.19 -0.04 -0.9 0.63 0.066 -0.018 -1 -0.87 0.28 1 5.2e+03 0.044 10 1.1 ++
1 -0.26 -0.24 -0.19 -0.04 -0.9 0.63 0.066 -0.018 -1 -0.87 0.28 1 5.2e+03 0.044 0.77 -0.2 -
2 -0.37 -0.31 -0.081 -0.07 -0.93 1.4 0.57 0.015 -0.99 -1.3 -0.016 1.4 5.1e+03 0.033 0.77 0.7 +
3 -0.37 -0.31 -0.081 -0.07 -0.93 1.4 0.57 0.015 -0.99 -1.3 -0.016 1.4 5.1e+03 0.033 0.39 -0.15 -
4 -0.37 -0.35 0.024 -0.12 -1.1 1.4 0.41 0.13 -1.1 -0.95 -0.28 1.4 5e+03 0.0044 3.9 0.94 ++
5 -0.37 -0.42 0.052 -0.26 -1.1 1.3 0.49 0.41 -1.1 -1 -0.33 1.4 5e+03 0.00028 39 0.98 ++
6 -0.36 -0.41 0.054 -0.27 -1.2 1.4 0.51 0.41 -1.1 -1 -0.32 1.4 5e+03 0.00043 3.9e+02 1 ++
7 -0.36 -0.41 0.054 -0.27 -1.2 1.4 0.51 0.41 -1.1 -1 -0.32 1.4 5e+03 9.1e-07 3.9e+02 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 78/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_SM B_TIME_TRAIN lambda_travel_t Function Relgrad Radius Rho
0 -0.62 -1 -0.46 -0.71 -0.39 -0.7 1.3 5.5e+03 0.053 10 0.96 ++
1 -0.62 -1 -0.46 -0.71 -0.39 -0.7 1.3 5.5e+03 0.053 5 -7.9e+03 -
2 -0.62 -1 -0.46 -0.71 -0.39 -0.7 1.3 5.5e+03 0.053 2.5 -48 -
3 -0.62 -1 -0.46 -0.71 -0.39 -0.7 1.3 5.5e+03 0.053 1.2 -1.8 -
4 -0.23 -0.62 -1.1 -1.6 -1.4 -1.7 0.031 5.3e+03 0.029 1.2 0.78 +
5 0.0083 -0.11 -1.1 -1.8 -1.7 -2.7 0.39 5.3e+03 0.0079 1.2 0.72 +
6 -0.029 -0.21 -1.1 -1.7 -1.6 -2.6 0.17 5.2e+03 0.0015 12 1.1 ++
7 -0.042 -0.23 -1.1 -1.7 -1.5 -2.6 0.13 5.2e+03 7.4e-05 1.2e+02 1 ++
8 -0.042 -0.23 -1.1 -1.7 -1.5 -2.6 0.13 5.2e+03 1.3e-07 1.2e+02 1 ++
Considering neighbor 0/20 for current solution
Attempt 79/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME mu_public Function Relgrad Radius Rho
0 -0.31 -0.17 -0.072 -0.65 0.27 0.0021 -0.62 -0.62 -1 -0.72 1 5.3e+03 0.06 10 1.1 ++
1 -0.58 0.072 -0.14 -0.32 0.71 0.6 -0.92 -1 -2.1 -0.97 1.3 5.1e+03 0.029 1e+02 1 ++
2 -0.55 0.036 -0.26 -0.3 0.61 0.44 -0.98 -1.1 -2.3 -0.99 1.3 5e+03 0.0025 1e+03 1 ++
3 -0.55 0.036 -0.26 -0.3 0.61 0.44 -0.98 -1.1 -2.3 -0.99 1.3 5e+03 0.0025 0.17 -0.051 -
4 -0.52 0.053 -0.25 -0.32 0.62 0.44 -0.95 -1.1 -2.5 -1 1.2 5e+03 0.0065 0.17 0.85 +
5 -0.5 0.055 -0.26 -0.42 0.74 0.47 -0.94 -1.1 -2.6 -1.1 1.1 5e+03 0.0036 1.7 1.2 ++
6 -0.46 0.05 -0.26 -0.45 0.76 0.51 -0.91 -1.1 -2.7 -1.1 1 5e+03 0.0018 17 1.1 ++
7 -0.46 0.051 -0.26 -0.45 0.77 0.51 -0.91 -1.1 -2.7 -1.1 1 5e+03 0.00071 1.7e+02 1 ++
8 -0.48 0.058 -0.25 -0.48 0.81 0.58 -0.92 -1.1 -2.7 -1.1 1 5e+03 1.9e-05 1.7e+03 1 ++
9 -0.48 0.058 -0.25 -0.48 0.81 0.58 -0.92 -1.1 -2.7 -1.1 1 5e+03 2.4e-09 1.7e+03 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 80/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -4.7 -
1 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.25 -1.3 -
2 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 2.5 1 ++
3 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 1.2 -8.9 -
4 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.62 -4.1 -
5 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.31 -2.2 -
6 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.16 -1.1 -
7 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.078 -0.11 -
8 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.039 0.048 -
9 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.02 -0.079 -
10 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.0098 -0.4 -
11 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.0049 -0.86 -
12 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.0024 -1.4 -
13 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.0012 -1.8 -
14 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.00061 -1.3 -
15 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 1.2 0 5.7e+03 4 0.00031 -0.21 -
16 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 -0.00031 1.3 0.00031 5.7e+03 2 0.00031 0.62 +
17 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 -0.00024 1.3 0.00055 5.7e+03 0.22 0.0031 0.97 ++
18 0.14 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 -0.00026 1.3 0.0029 5.7e+03 0.13 0.031 1 ++
19 0.15 -0.28 -0.23 0.23 -0.28 -0.28 -0.27 -0.00036 1.3 0.027 5.6e+03 0.15 0.31 1 ++
20 0.21 -0.38 -0.14 -0.077 -0.51 -0.45 -0.37 -0.00094 1.4 0.16 5.4e+03 3.7 0.31 0.85 +
21 -0.091 -0.23 -0.37 -0.26 -0.64 -0.5 -0.43 -0.00038 1.5 0.039 5.3e+03 6.5 0.31 0.84 +
22 -0.13 -0.27 -0.36 -0.48 -0.94 -0.61 -0.5 -0.0001 1.7 -0.043 5.1e+03 8.1 3.1 0.93 ++
23 -0.13 -0.27 -0.36 -0.48 -0.94 -0.61 -0.5 -0.0001 1.7 -0.043 5.1e+03 8.1 0.42 0.016 -
24 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.42 0.8 +
25 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.21 -3.6 -
26 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.11 -2.6 -
27 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.053 -2.3 -
28 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.026 -1.9 -
29 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.013 -1.8 -
30 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.0066 -2 -
31 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.0033 -2.2 -
32 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.0017 -2.4 -
33 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.00083 -2.5 -
34 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.00041 -2.5 -
35 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.00021 -1.6 -
36 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00039 2 -0.12 5e+03 23 0.0001 -0.00063 -
37 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00028 2 -0.12 5e+03 12 0.0001 0.89 +
38 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00028 2 -0.12 5e+03 12 5.2e-05 -0.98 -
39 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00028 2 -0.12 5e+03 12 2.6e-05 -0.068 -
40 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00026 2 -0.12 5e+03 2.6 2.6e-05 0.88 +
41 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00025 2 -0.12 5e+03 0.29 0.00026 0.96 ++
42 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00025 2 -0.12 5e+03 0.13 0.0026 1 ++
43 -0.38 -0.11 -0.51 -0.76 -1.4 -0.93 -0.69 0.00024 2 -0.12 5e+03 0.3 0.026 1 ++
44 -0.37 -0.12 -0.5 -0.77 -1.4 -0.94 -0.69 0.00013 2 -0.09 5e+03 1.5 0.26 1 ++
45 -0.35 0.066 -0.52 -0.88 -1.6 -1.2 -0.76 0.00016 2 -0.097 5e+03 0.8 2.6 1 ++
46 -0.35 0.066 -0.52 -0.88 -1.6 -1.2 -0.76 0.00016 2 -0.097 5e+03 0.8 0.48 -0.29 -
47 -0.2 0.28 -0.62 -0.89 -1.9 -1.7 -0.67 0.00025 1.5 -0.12 5e+03 4.5 0.48 0.82 +
48 -0.27 0.34 -0.72 -1 -2.3 -1.7 -0.63 0.00022 1.4 -0.11 4.9e+03 0.78 4.8 1.1 ++
49 -0.28 0.37 -0.74 -1.1 -2.4 -1.8 -0.64 0.00022 1.3 -0.11 4.9e+03 0.0045 48 1 ++
50 -0.28 0.37 -0.75 -1.1 -2.4 -1.8 -0.63 0.00022 1.3 -0.11 4.9e+03 0.00019 4.8e+02 1 ++
51 -0.28 0.37 -0.75 -1.1 -2.4 -1.8 -0.64 0.00022 1.3 -0.11 4.9e+03 0.0018 4.8e+03 1 ++
52 -0.28 0.38 -0.75 -1.1 -2.4 -1.8 -0.64 0.00022 1.3 -0.11 4.9e+03 7.7e-06 4.8e+04 1 ++
53 -0.28 0.38 -0.75 -1.1 -2.4 -1.8 -0.64 0.00022 1.3 -0.11 4.9e+03 8.2e-06 4.8e+05 1 ++
54 -0.28 0.38 -0.75 -1.1 -2.4 -1.8 -0.64 0.00022 1.3 -0.11 4.9e+03 8.3e-08 4.8e+05 1 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 81/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.4 0.5 -0.56 -
1 -0.019 -0.017 -0.0026 -0.28 -0.13 -0.0068 -0.039 0.29 -0.3 -0.076 0.24 -0.5 0.061 0.0066 6.4e+03 1.4 0.5 0.22 +
2 -0.019 -0.017 -0.0026 -0.28 -0.13 -0.0068 -0.039 0.29 -0.3 -0.076 0.24 -0.5 0.061 0.0066 6.4e+03 1.4 0.25 -4.5 -
3 -0.019 -0.017 -0.0026 -0.28 -0.13 -0.0068 -0.039 0.29 -0.3 -0.076 0.24 -0.5 0.061 0.0066 6.4e+03 1.4 0.12 -5 -
4 -0.019 -0.017 -0.0026 -0.28 -0.13 -0.0068 -0.039 0.29 -0.3 -0.076 0.24 -0.5 0.061 0.0066 6.4e+03 1.4 0.062 -2.8 -
5 -0.017 -0.017 -0.0027 -0.27 -0.13 -0.0067 -0.037 0.28 -0.3 -0.013 0.23 -0.49 -0.0015 -0.0016 5.8e+03 1.9 0.62 0.98 ++
6 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 6.2 1.3 ++
7 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 3.1 -5.9 -
8 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 1.6 -6 -
9 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.78 -6.1 -
10 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.39 -6.4 -
11 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.2 -6.7 -
12 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.098 -7.2 -
13 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.049 -7.6 -
14 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.024 -4.7 -
15 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.012 -3.9 -
16 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.0061 -3.3 -
17 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.0031 -2.6 -
18 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.0015 -1.4 -
19 -0.16 -0.19 -0.037 -0.26 0.25 0.0041 -0.32 -0.34 -0.77 -0.26 -0.0043 -0.68 0.00019 0.061 5.4e+03 3.2 0.00076 -0.21 -
20 -0.17 -0.19 -0.037 -0.26 0.25 0.0049 -0.32 -0.34 -0.77 -0.26 -0.005 -0.68 -0.00057 0.061 5.4e+03 0.85 0.00076 0.84 +
21 -0.17 -0.19 -0.037 -0.26 0.25 0.0049 -0.32 -0.34 -0.77 -0.26 -0.0057 -0.68 -0.00054 0.062 5.4e+03 0.029 0.0076 1 ++
22 -0.17 -0.19 -0.038 -0.26 0.26 0.0052 -0.32 -0.35 -0.78 -0.27 -0.012 -0.68 -0.00057 0.067 5.4e+03 0.14 0.076 1 ++
23 -0.16 -0.17 -0.042 -0.27 0.29 0.0084 -0.34 -0.37 -0.85 -0.34 -0.082 -0.69 -0.00074 0.11 5.4e+03 0.038 0.76 0.99 ++
24 -0.12 -0.0063 -0.086 -0.37 0.56 0.046 -0.43 -0.74 -1.6 -0.82 -0.73 -0.65 -0.0019 0.39 5.3e+03 0.36 0.76 0.48 +
25 -0.12 -0.0063 -0.086 -0.37 0.56 0.046 -0.43 -0.74 -1.6 -0.82 -0.73 -0.65 -0.0019 0.39 5.3e+03 0.36 0.38 -1.8 -
26 -0.19 -0.039 -0.096 -0.45 0.54 0.054 -0.46 -0.71 -1.7 -0.81 -0.54 -0.71 -0.00032 0.012 5.1e+03 1 0.38 0.88 +
27 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 3.8 1 ++
28 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 1.9 -20 -
29 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.95 -7.9 -
30 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.48 -3.6 -
31 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.24 -2.8 -
32 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.12 -2.9 -
33 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.06 -2.3 -
34 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.03 -2.2 -
35 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.015 -2.1 -
36 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.0075 -2.1 -
37 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.0037 -2.1 -
38 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.0019 -2.2 -
39 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.00093 -2.2 -
40 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.00047 -1.7 -
41 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.00023 -0.63 -
42 -0.23 0.00049 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00021 0.0075 5.1e+03 17 0.00012 -0.11 -
43 -0.23 0.00037 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00033 0.0074 5.1e+03 8.7 0.00012 0.44 +
44 -0.23 0.00037 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00033 0.0074 5.1e+03 8.7 5.8e-05 -0.64 -
45 -0.23 0.00031 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00027 0.0074 5.1e+03 9.8 5.8e-05 0.19 +
46 -0.23 0.00031 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.0003 0.0074 5.1e+03 2 5.8e-05 0.81 +
47 -0.23 0.0003 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00029 0.0073 5e+03 0.12 0.00058 0.97 ++
48 -0.23 0.00024 -0.13 -0.54 0.63 0.096 -0.53 -0.89 -2.1 -1.1 -0.73 -0.87 -0.00029 0.0068 5e+03 0.015 0.0058 1 ++
49 -0.23 -0.00034 -0.13 -0.54 0.64 0.097 -0.54 -0.88 -2.1 -1.1 -0.73 -0.87 -0.00027 0.0017 5e+03 0.11 0.058 1 ++
50 -0.25 -0.004 -0.13 -0.54 0.65 0.11 -0.56 -0.87 -2.2 -1.1 -0.76 -0.86 -0.00012 -0.035 5e+03 0.25 0.58 1 ++
51 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 5.8 1 ++
52 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.52 -0.92 -
53 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.26 -0.5 -
54 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.13 -0.49 -
55 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.065 -0.85 -
56 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.032 -0.47 -
57 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.016 -0.54 -
58 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.0081 -0.73 -
59 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.004 -0.82 -
60 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.002 -0.99 -
61 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.001 -1.1 -
62 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.00051 -1.2 -
63 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.00025 -1.2 -
64 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 0.00013 -1.3 -
65 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00015 -0.088 5e+03 22 6.3e-05 -1.1 -
66 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 8.2e-05 -0.088 5e+03 21 6.3e-05 0.19 +
67 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00013 -0.088 5e+03 18 6.3e-05 0.19 +
68 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 9e-05 -0.088 5e+03 16 6.3e-05 0.2 +
69 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00011 -0.088 5e+03 7.4 6.3e-05 0.65 +
70 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.00011 -0.088 5e+03 0.59 0.00063 0.95 ++
71 -0.27 0.08 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 0.0001 -0.087 5e+03 0.039 0.0063 1 ++
72 -0.27 0.079 -0.19 -0.64 0.77 0.21 -0.7 -1 -2.8 -1.6 -1.3 -1.1 7.8e-05 -0.081 5e+03 0.67 0.063 1 ++
73 -0.28 0.06 -0.19 -0.66 0.75 0.23 -0.72 -1 -2.8 -1.7 -1.3 -1.2 0.00011 -0.09 5e+03 0.12 0.63 1 ++
74 -0.25 -0.02 -0.26 -0.67 0.73 0.39 -0.79 -1.2 -3 -2.3 -2 -1.6 0.00024 -0.12 4.9e+03 8.2 6.3 0.92 ++
75 -0.16 0.0091 -0.34 -0.38 0.8 0.51 -0.68 -1.2 -2.9 -2.5 -2.2 -2 0.00021 -0.11 4.9e+03 2.7 63 0.96 ++
76 -0.17 0.0078 -0.37 -0.38 0.79 0.48 -0.68 -1.2 -2.9 -2.5 -2.2 -2 0.00023 -0.11 4.9e+03 0.16 6.3e+02 1 ++
77 -0.17 0.0068 -0.37 -0.38 0.79 0.48 -0.68 -1.2 -2.9 -2.5 -2.2 -2 0.00023 -0.11 4.9e+03 0.00088 6.3e+03 1 ++
78 -0.17 0.0068 -0.37 -0.38 0.79 0.48 -0.68 -1.2 -2.9 -2.5 -2.2 -2 0.00023 -0.11 4.9e+03 1.4e-06 6.3e+03 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_travel_t mu_existing Function Relgrad Radius Rho
0 0.087 -0.11 -0.028 -0.76 0.054 -0.01 -1 -0.92 1.5 1.5 5.4e+03 0.048 1 0.77 +
1 -0.1 0.11 -0.2 -0.53 1 0.17 -0.89 -1.4 0.54 2.4 5.3e+03 0.11 1 0.3 +
2 -0.1 0.11 -0.2 -0.53 1 0.17 -0.89 -1.4 0.54 2.4 5.3e+03 0.11 0.5 -0.29 -
3 -0.073 0.24 -0.28 -0.7 0.59 0.29 -0.66 -0.92 0.29 2.7 5.2e+03 0.057 0.5 0.65 +
4 -0.12 0.14 -0.26 -0.75 0.59 0.33 -0.78 -1.1 0.6 2.2 5.1e+03 0.0079 5 0.98 ++
5 -0.084 0.15 -0.22 -0.82 0.74 0.51 -0.87 -1.3 0.42 1.8 5.1e+03 0.0021 5 0.88 +
6 -0.1 0.14 -0.23 -0.83 0.74 0.52 -0.87 -1.3 0.44 1.9 5.1e+03 0.00025 50 1 ++
7 -0.1 0.14 -0.23 -0.83 0.74 0.52 -0.87 -1.3 0.44 1.9 5.1e+03 2.5e-06 50 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_travel_t Function Relgrad Radius Rho
0 -0.43 -0.32 -0.028 -0.62 0.0082 -0.015 -0.62 -1 1.4 5.6e+03 0.071 1 0.89 +
1 -0.096 0.095 -0.11 -1.1 1 0.064 -1.3 -1.8 0.47 5.2e+03 0.011 10 0.95 ++
2 -0.062 0.066 -0.3 -1.3 1.1 1.2 -1.1 -1.6 0.49 5.2e+03 0.0019 10 0.87 +
3 -0.065 0.068 -0.3 -1.3 1.1 0.97 -1.1 -1.6 0.49 5.2e+03 7.8e-05 1e+02 1 ++
4 -0.065 0.068 -0.3 -1.3 1.1 0.97 -1.1 -1.6 0.49 5.2e+03 3e-07 1e+02 1 ++
Considering neighbor 2/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.4 0.5 -0.39 -
1 -0.018 -0.017 -0.0025 -0.28 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.5 0.8 +
2 -0.018 -0.017 -0.0025 -0.28 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.25 -8.4 -
3 -0.018 -0.017 -0.0025 -0.28 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.12 -10 -
4 -0.018 -0.017 -0.0025 -0.28 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.062 -13 -
5 -0.018 -0.017 -0.0025 -0.28 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.031 -25 -
6 -0.018 -0.017 -0.0025 -0.28 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.016 -5.2 -
7 -0.018 -0.017 -0.0025 -0.28 -0.13 -0.0068 -0.041 -0.074 0.24 -0.5 0.01 1.2 0.0011 5.8e+03 1.1 0.0078 -0.98 -
8 -0.026 -0.025 -0.0046 -0.27 -0.12 -0.006 -0.048 -0.066 0.23 -0.49 0.0025 1.2 -0.005 5.8e+03 0.88 0.078 0.99 ++
9 -0.026 -0.025 -0.0046 -0.27 -0.12 -0.006 -0.048 -0.066 0.23 -0.49 0.0025 1.2 -0.005 5.8e+03 0.88 0.039 -8.4 -
10 -0.026 -0.025 -0.0046 -0.27 -0.12 -0.006 -0.048 -0.066 0.23 -0.49 0.0025 1.2 -0.005 5.8e+03 0.88 0.02 -9.4 -
11 -0.026 -0.025 -0.0046 -0.27 -0.12 -0.006 -0.048 -0.066 0.23 -0.49 0.0025 1.2 -0.005 5.8e+03 0.88 0.0098 -12 -
12 -0.026 -0.025 -0.0046 -0.27 -0.12 -0.006 -0.048 -0.066 0.23 -0.49 0.0025 1.2 -0.005 5.8e+03 0.88 0.0049 -3.8 -
13 -0.026 -0.025 -0.0046 -0.27 -0.12 -0.006 -0.048 -0.066 0.23 -0.49 0.0025 1.2 -0.005 5.8e+03 0.88 0.0024 -0.84 -
14 -0.028 -0.027 -0.0071 -0.27 -0.12 -0.0035 -0.051 -0.068 0.24 -0.49 4.4e-05 1.2 -0.0025 5.8e+03 0.12 0.0024 0.9 +
15 -0.029 -0.028 -0.0072 -0.27 -0.12 -0.0035 -0.053 -0.071 0.24 -0.49 -0.00012 1.2 -0.0018 5.8e+03 0.19 0.024 0.99 ++
16 -0.039 -0.036 -0.008 -0.27 -0.11 -0.0032 -0.074 -0.095 0.24 -0.5 -0.00012 1.2 0.0051 5.7e+03 0.17 0.24 1 ++
17 -0.12 -0.11 -0.017 -0.27 -0.013 -0.00021 -0.32 -0.33 0.22 -0.54 -0.0014 1.3 0.087 5.6e+03 9 0.24 0.62 +
18 0.011 -0.054 -0.023 -0.31 0.078 0.005 -0.56 -0.34 -0.0074 -0.64 0.00058 1.5 0.053 5.4e+03 6.4 0.24 0.72 +
19 0.011 -0.054 -0.023 -0.31 0.078 0.005 -0.56 -0.34 -0.0074 -0.64 0.00058 1.5 0.053 5.4e+03 6.4 0.12 -7.8 -
20 0.011 -0.054 -0.023 -0.31 0.078 0.005 -0.56 -0.34 -0.0074 -0.64 0.00058 1.5 0.053 5.4e+03 6.4 0.061 -3.9 -
21 0.011 -0.054 -0.023 -0.31 0.078 0.005 -0.56 -0.34 -0.0074 -0.64 0.00058 1.5 0.053 5.4e+03 6.4 0.031 -3.3 -
22 0.011 -0.054 -0.023 -0.31 0.078 0.005 -0.56 -0.34 -0.0074 -0.64 0.00058 1.5 0.053 5.4e+03 6.4 0.015 -2.9 -
23 0.011 -0.054 -0.023 -0.31 0.078 0.005 -0.56 -0.34 -0.0074 -0.64 0.00058 1.5 0.053 5.4e+03 6.4 0.0076 -2.5 -
24 0.011 -0.054 -0.023 -0.31 0.078 0.005 -0.56 -0.34 -0.0074 -0.64 0.00058 1.5 0.053 5.4e+03 6.4 0.0038 -2 -
25 0.011 -0.054 -0.023 -0.31 0.078 0.005 -0.56 -0.34 -0.0074 -0.64 0.00058 1.5 0.053 5.4e+03 6.4 0.0019 -1.2 -
26 0.011 -0.054 -0.023 -0.31 0.078 0.005 -0.56 -0.34 -0.0074 -0.64 0.00058 1.5 0.053 5.4e+03 6.4 0.00095 -0.1 -
27 0.01 -0.055 -0.024 -0.31 0.079 0.0059 -0.56 -0.34 -0.0084 -0.64 -0.00037 1.5 0.052 5.4e+03 4.6 0.0095 0.99 ++
28 0.0063 -0.058 -0.025 -0.31 0.085 0.0063 -0.57 -0.35 -0.012 -0.64 -0.00051 1.5 0.055 5.3e+03 2.8 0.0095 0.89 +
29 0.0025 -0.061 -0.025 -0.3 0.091 0.0067 -0.57 -0.36 -0.016 -0.64 -0.00047 1.5 0.059 5.3e+03 0.57 0.095 0.99 ++
30 -0.032 -0.089 -0.032 -0.28 0.17 0.012 -0.65 -0.45 -0.081 -0.62 -0.0007 1.6 0.11 5.3e+03 1.1 0.095 0.88 +
31 -0.0011 -0.072 -0.038 -0.29 0.21 0.018 -0.72 -0.42 -0.18 -0.66 -0.0008 1.6 0.14 5.2e+03 0.031 0.95 0.95 ++
32 -0.0011 -0.072 -0.038 -0.29 0.21 0.018 -0.72 -0.42 -0.18 -0.66 -0.0008 1.6 0.14 5.2e+03 0.031 0.35 -6.9 -
33 -0.073 -0.035 -0.098 -0.38 0.47 0.072 -0.93 -0.58 -0.43 -0.79 -0.00068 2 0.11 5.2e+03 0.11 3.5 1 ++
34 -0.073 -0.035 -0.098 -0.38 0.47 0.072 -0.93 -0.58 -0.43 -0.79 -0.00068 2 0.11 5.2e+03 0.11 1.7 -4.3e+02 -
35 -0.073 -0.035 -0.098 -0.38 0.47 0.072 -0.93 -0.58 -0.43 -0.79 -0.00068 2 0.11 5.2e+03 0.11 0.87 -1.6e+02 -
36 -0.073 -0.035 -0.098 -0.38 0.47 0.072 -0.93 -0.58 -0.43 -0.79 -0.00068 2 0.11 5.2e+03 0.11 0.43 -23 -
37 -0.073 -0.035 -0.098 -0.38 0.47 0.072 -0.93 -0.58 -0.43 -0.79 -0.00068 2 0.11 5.2e+03 0.11 0.22 -3 -
38 -0.19 0.091 -0.16 -0.48 0.49 0.14 -0.77 -0.72 -0.53 -1 0.0002 2.1 -0.11 5.2e+03 12 0.22 0.15 +
39 -0.21 0.083 -0.19 -0.42 0.61 0.18 -0.84 -0.84 -0.75 -1.2 -6.8e-05 2.2 -0.043 5.1e+03 2.4 0.22 0.89 +
40 -0.3 0.15 -0.24 -0.44 0.57 0.24 -0.74 -0.99 -0.91 -1.4 0.00025 2.3 -0.12 5.1e+03 19 0.22 0.28 +
41 -0.3 0.12 -0.25 -0.38 0.64 0.27 -0.81 -1.1 -1.1 -1.5 0.00011 2.4 -0.083 5.1e+03 9.2 0.22 0.86 +
42 -0.36 0.18 -0.27 -0.34 0.61 0.31 -0.72 -1.2 -1.3 -1.7 0.00021 2.5 -0.11 5e+03 8.5 0.22 0.77 +
43 -0.38 0.13 -0.27 -0.31 0.63 0.32 -0.81 -1.3 -1.6 -1.9 0.0002 2.4 -0.1 5e+03 1.4 2.2 1 ++
44 -0.33 0.11 -0.27 -0.24 0.7 0.39 -0.87 -1.6 -1.8 -2.2 0.00022 2.1 -0.11 5e+03 1.6 2.2 0.87 +
45 -0.34 0.11 -0.26 -0.24 0.7 0.4 -0.86 -1.6 -1.8 -2.2 0.00022 2.1 -0.11 5e+03 0.05 22 1 ++
46 -0.34 0.12 -0.25 -0.24 0.7 0.4 -0.86 -1.6 -1.8 -2.2 0.00022 2.1 -0.11 5e+03 0.0031 2.2e+02 1 ++
47 -0.34 0.12 -0.25 -0.24 0.7 0.4 -0.86 -1.6 -1.8 -2.2 0.00022 2.1 -0.11 5e+03 6.1e-06 2.2e+03 1 ++
48 -0.34 0.12 -0.25 -0.24 0.7 0.4 -0.86 -1.6 -1.8 -2.2 0.00022 2.1 -0.11 5e+03 0.0021 2.2e+04 1 ++
49 -0.34 0.12 -0.25 -0.24 0.7 0.4 -0.86 -1.6 -1.8 -2.2 0.00022 2.1 -0.11 5e+03 5.3e-07 2.2e+04 1 ++
Considering neighbor 3/20 for current solution
Attempt 82/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.5 -3.4 -
1 0 0 0 0 0 0 0 0 0 7e+03 0.27 0.25 -0.97 -
2 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.1 2.5 1 ++
3 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.1 1.2 -8.8 -
4 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.1 0.62 -3.3 -
5 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.1 0.31 -1.2 -
6 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 0 0 5.8e+03 3.1 0.16 -0.47 -
7 0.17 -0.37 -0.21 0.21 -0.38 -0.41 -0.32 -0.0033 0.14 5.7e+03 11 0.16 0.27 +
8 0.17 -0.37 -0.21 0.21 -0.38 -0.41 -0.32 -0.0033 0.14 5.7e+03 11 0.078 -0.84 -
9 0.17 -0.37 -0.21 0.21 -0.38 -0.41 -0.32 -0.0033 0.14 5.7e+03 11 0.039 -0.77 -
10 0.17 -0.37 -0.21 0.21 -0.38 -0.41 -0.32 -0.0033 0.14 5.7e+03 11 0.02 -0.72 -
11 0.17 -0.37 -0.21 0.21 -0.38 -0.41 -0.32 -0.0033 0.14 5.7e+03 11 0.0098 -0.7 -
12 0.17 -0.37 -0.21 0.21 -0.38 -0.41 -0.32 -0.0033 0.14 5.7e+03 11 0.0049 -0.58 -
13 0.17 -0.37 -0.21 0.21 -0.38 -0.41 -0.32 -0.0033 0.14 5.7e+03 11 0.0024 0.041 -
14 0.17 -0.37 -0.21 0.21 -0.39 -0.4 -0.33 -0.00083 0.14 5.6e+03 2.8 0.0024 0.78 +
15 0.18 -0.37 -0.21 0.21 -0.39 -0.4 -0.33 -0.00087 0.14 5.6e+03 0.085 0.024 1 ++
16 0.18 -0.37 -0.2 0.18 -0.4 -0.4 -0.32 -0.00085 0.14 5.6e+03 0.22 0.24 1 ++
17 0.22 -0.42 -0.14 -0.06 -0.51 -0.44 -0.34 -0.00071 0.11 5.5e+03 0.071 2.4 0.93 ++
18 -0.57 -0.24 -0.6 -0.96 -2.1 -0.47 -0.56 -0.00058 0.068 5.1e+03 0.35 24 1.1 ++
19 -0.57 -0.24 -0.6 -0.96 -2.1 -0.47 -0.56 -0.00058 0.068 5.1e+03 0.35 0.35 -1.6 -
20 -0.57 -0.24 -0.6 -0.96 -2.1 -0.47 -0.56 -0.00058 0.068 5.1e+03 0.35 0.18 -0.61 -
21 -0.57 -0.27 -0.65 -0.99 -2.3 -0.64 -0.69 -0.00017 -0.026 5e+03 0.57 1.8 1.1 ++
22 -0.57 -0.27 -0.65 -0.99 -2.3 -0.64 -0.69 -0.00017 -0.026 5e+03 0.57 0.54 -3.8 -
23 -0.55 0.09 -0.79 -1.2 -2.6 -1.2 -0.99 0.00024 -0.12 5e+03 11 0.54 0.68 +
24 -0.39 0.39 -0.78 -1.2 -2.8 -1.7 -0.97 0.00018 -0.1 5e+03 16 0.54 0.82 +
25 -0.36 0.46 -0.85 -1.2 -3 -2 -0.71 0.00025 -0.12 5e+03 17 0.54 0.72 +
26 -0.37 0.44 -0.85 -1.2 -3 -1.9 -0.69 0.00023 -0.11 5e+03 0.85 5.4 1 ++
27 -0.38 0.43 -0.85 -1.2 -3 -1.9 -0.7 0.00023 -0.11 5e+03 0.057 54 1 ++
28 -0.38 0.43 -0.85 -1.2 -3 -1.9 -0.7 0.00023 -0.11 5e+03 0.0093 5.4e+02 1 ++
29 -0.38 0.43 -0.85 -1.2 -3 -1.9 -0.7 0.00023 -0.11 5e+03 0.0012 5.4e+03 1 ++
30 -0.38 0.43 -0.85 -1.2 -3 -1.9 -0.7 0.00023 -0.11 5e+03 7.2e-05 5.4e+04 1 ++
31 -0.38 0.43 -0.85 -1.2 -3 -1.9 -0.7 0.00023 -0.11 5e+03 2.7e-06 5.4e+04 0.99 ++
Considering neighbor 0/20 for current solution
Considering neighbor 1/20 for current solution
Attempt 83/100
Considering neighbor 0/20 for current solution
Attempt 84/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef mu_public square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.4 0.5 -0.53 -
1 -0.019 -0.28 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 1 0.0057 6.3e+03 1.4 0.5 0.29 +
2 -0.019 -0.28 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 1 0.0057 6.3e+03 1.4 0.25 -4.9 -
3 -0.019 -0.28 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 1 0.0057 6.3e+03 1.4 0.12 -5.6 -
4 -0.019 -0.28 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 1 0.0057 6.3e+03 1.4 0.062 -3.8 -
5 -0.017 -0.27 -0.037 0.28 -0.3 -0.013 0.23 -0.49 -0.0097 1 -0.0027 6.1e+03 5.3 0.062 0.37 +
6 -0.017 -0.27 -0.037 0.28 -0.3 -0.013 0.23 -0.49 -0.0097 1 -0.0027 6.1e+03 5.3 0.031 -0.32 -
7 -0.018 -0.27 -0.039 0.27 -0.3 0.018 0.22 -0.48 0.022 1 0.0054 5.9e+03 0.79 0.031 0.21 +
8 -0.018 -0.27 -0.039 0.27 -0.3 0.018 0.22 -0.48 0.022 1 0.0054 5.9e+03 0.79 0.016 -1.1 -
9 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 1 -0.01 5.8e+03 0.16 0.16 0.96 ++
10 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 1 -0.01 5.8e+03 0.16 0.078 -34 -
11 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 1 -0.01 5.8e+03 0.16 0.039 -27 -
12 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 1 -0.01 5.8e+03 0.16 0.02 -22 -
13 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 1 -0.01 5.8e+03 0.16 0.0098 -17 -
14 -0.033 -0.26 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0059 1 -0.01 5.8e+03 0.16 0.0049 -6 -
15 -0.034 -0.26 -0.055 0.25 -0.32 -0.0023 0.2 -0.46 0.00099 1 -0.011 5.8e+03 0.052 0.049 1.3 ++
16 -0.042 -0.25 -0.072 0.2 -0.34 -0.04 0.18 -0.46 -0.0013 1 -0.025 5.8e+03 2.8 0.049 0.82 +
17 -0.049 -0.25 -0.089 0.16 -0.37 -0.088 0.16 -0.47 0.00022 1 -0.025 5.7e+03 0.53 0.49 0.93 ++
18 -0.049 -0.25 -0.089 0.16 -0.37 -0.088 0.16 -0.47 0.00022 1 -0.025 5.7e+03 0.53 0.24 -0.19 -
19 -0.07 -0.25 -0.16 -0.088 -0.54 -0.27 0.036 -0.52 -0.0025 1.1 0.039 5.6e+03 10 0.24 0.33 +
20 -0.07 -0.25 -0.16 -0.088 -0.54 -0.27 0.036 -0.52 -0.0025 1.1 0.039 5.6e+03 10 0.12 -0.45 -
21 -0.07 -0.25 -0.16 -0.088 -0.54 -0.27 0.036 -0.52 -0.0025 1.1 0.039 5.6e+03 10 0.061 -0.22 -
22 -0.07 -0.25 -0.16 -0.088 -0.54 -0.27 0.036 -0.52 -0.0025 1.1 0.039 5.6e+03 10 0.031 -0.069 -
23 -0.07 -0.25 -0.16 -0.088 -0.54 -0.27 0.036 -0.52 -0.0025 1.1 0.039 5.6e+03 10 0.015 0.016 -
24 -0.07 -0.25 -0.16 -0.088 -0.54 -0.27 0.036 -0.52 -0.0025 1.1 0.039 5.6e+03 10 0.0076 0.061 -
25 -0.07 -0.25 -0.16 -0.088 -0.54 -0.27 0.036 -0.52 -0.0025 1.1 0.039 5.6e+03 10 0.0038 0.085 -
26 -0.07 -0.25 -0.16 -0.089 -0.54 -0.27 0.035 -0.52 0.0013 1.1 0.043 5.6e+03 3.2 0.0038 0.38 +
27 -0.07 -0.25 -0.16 -0.089 -0.54 -0.27 0.035 -0.52 0.0013 1.1 0.043 5.6e+03 3.2 0.0019 -0.73 -
28 -0.072 -0.25 -0.16 -0.091 -0.54 -0.27 0.033 -0.51 -0.00062 1.1 0.041 5.5e+03 3.7 0.0019 0.8 +
29 -0.072 -0.25 -0.16 -0.092 -0.54 -0.27 0.031 -0.51 -0.00023 1.1 0.042 5.5e+03 3 0.0019 0.18 +
30 -0.072 -0.25 -0.16 -0.092 -0.54 -0.27 0.031 -0.51 -0.00023 1.1 0.042 5.5e+03 3 0.00095 -1.7 -
31 -0.072 -0.25 -0.16 -0.092 -0.54 -0.27 0.031 -0.51 -0.00023 1.1 0.042 5.5e+03 3 0.00048 -1.7 -
32 -0.072 -0.25 -0.16 -0.092 -0.54 -0.27 0.031 -0.51 -0.00023 1.1 0.042 5.5e+03 3 0.00024 -0.2 -
33 -0.073 -0.25 -0.16 -0.092 -0.54 -0.27 0.031 -0.51 -0.00046 1.1 0.042 5.5e+03 0.7 0.00024 0.69 +
34 -0.073 -0.25 -0.16 -0.093 -0.54 -0.27 0.031 -0.51 -0.00044 1.1 0.042 5.5e+03 0.031 0.0024 1 ++
35 -0.073 -0.25 -0.16 -0.095 -0.55 -0.27 0.029 -0.51 -0.00047 1.1 0.043 5.5e+03 0.74 0.024 1 ++
36 -0.078 -0.25 -0.17 -0.11 -0.57 -0.3 0.0088 -0.51 -0.0005 1.1 0.056 5.5e+03 0.13 0.24 1 ++
37 -0.11 -0.22 -0.24 -0.35 -0.8 -0.5 -0.23 -0.5 -0.0011 1.1 0.19 5.4e+03 1.2 0.24 0.9 +
38 -0.13 -0.24 -0.27 -0.45 -1 -0.56 -0.33 -0.53 -0.0011 1.2 0.21 5.3e+03 0.28 2.4 1 ++
39 -0.13 -0.24 -0.27 -0.45 -1 -0.56 -0.33 -0.53 -0.0011 1.2 0.21 5.3e+03 0.28 1.2 -2.7 -
40 -0.43 -0.19 -0.53 -0.89 -2.2 -1 -0.68 -0.42 7.7e-05 1.6 -0.085 5.1e+03 6.2 1.2 0.78 +
41 -0.43 -0.19 -0.53 -0.89 -2.2 -1 -0.68 -0.42 7.7e-05 1.6 -0.085 5.1e+03 6.2 0.6 -1.9 -
42 -0.4 -0.039 -0.65 -1.3 -2.3 -1.6 -0.96 -0.8 -0.00015 1.9 -0.026 5e+03 1.4 0.6 0.63 +
43 -0.4 -0.039 -0.65 -1.3 -2.3 -1.6 -0.96 -0.8 -0.00015 1.9 -0.026 5e+03 1.4 0.3 -2.4 -
44 -0.4 -0.039 -0.65 -1.3 -2.3 -1.6 -0.96 -0.8 -0.00015 1.9 -0.026 5e+03 1.4 0.15 -0.24 -
45 -0.36 0.00094 -0.66 -1.2 -2.4 -1.7 -1.1 -0.83 0.00023 2 -0.12 5e+03 20 0.15 0.52 +
46 -0.32 -0.009 -0.68 -1.3 -2.3 -1.8 -1.2 -0.93 0.00012 2 -0.09 5e+03 6.4 0.15 0.84 +
47 -0.25 0.066 -0.7 -1.3 -2.4 -2 -1.3 -1 0.00019 2.1 -0.11 5e+03 5.6 0.15 0.85 +
48 -0.2 0.076 -0.67 -1.3 -2.4 -2.1 -1.4 -1.1 0.00019 2 -0.11 5e+03 0.54 1.5 1 ++
49 -0.2 0.076 -0.67 -1.3 -2.4 -2.1 -1.4 -1.1 0.00019 2 -0.11 5e+03 0.54 0.51 -7.8 -
50 -0.14 0.16 -0.54 -1.3 -2.6 -2.4 -1.6 -1.3 0.0002 1.5 -0.11 5e+03 0.66 0.51 0.24 +
51 -0.2 0.15 -0.68 -1.2 -2.7 -2.3 -1.8 -1.5 0.00021 1.4 -0.11 5e+03 0.085 5.1 1.2 ++
52 -0.2 0.15 -0.68 -1.2 -2.7 -2.3 -1.8 -1.5 0.00021 1.4 -0.11 5e+03 0.085 0.2 -0.02 -
53 -0.18 0.13 -0.66 -1.2 -2.8 -2.4 -1.9 -1.6 0.00021 1.2 -0.11 5e+03 0.35 2 1 ++
54 -0.18 0.19 -0.73 -1.2 -3.1 -2.5 -2.2 -1.9 0.00022 1 -0.11 5e+03 0.18 20 1.1 ++
55 -0.17 0.19 -0.72 -1.2 -3.1 -2.5 -2.2 -1.9 0.00022 1 -0.11 5e+03 0.003 2e+02 1 ++
56 -0.16 0.23 -0.71 -1.2 -3.1 -2.5 -2.2 -1.9 0.00022 1 -0.11 5e+03 0.00072 2e+03 1 ++
57 -0.16 0.23 -0.7 -1.2 -3.1 -2.5 -2.2 -1.9 0.00022 1 -0.11 5e+03 0.0064 2e+04 1 ++
58 -0.16 0.23 -0.7 -1.2 -3.1 -2.5 -2.2 -1.9 0.00022 1 -0.11 5e+03 4.3e-06 2e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.4 0.5 -0.41 -
1 -0.019 -0.017 -0.0026 -0.28 -0.13 -0.0068 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.042 0.0052 6.1e+03 1.4 0.5 0.35 +
2 -0.019 -0.017 -0.0026 -0.28 -0.13 -0.0068 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.042 0.0052 6.1e+03 1.4 0.25 -6.6 -
3 -0.019 -0.017 -0.0026 -0.28 -0.13 -0.0068 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.042 0.0052 6.1e+03 1.4 0.12 -7.9 -
4 -0.019 -0.017 -0.0026 -0.28 -0.13 -0.0068 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.042 0.0052 6.1e+03 1.4 0.062 -6.5 -
5 -0.019 -0.017 -0.0026 -0.28 -0.13 -0.0068 -0.041 -0.075 -0.04 0.24 0.15 -0.5 -0.33 0.042 0.0052 6.1e+03 1.4 0.031 -1 -
6 -0.05 -0.049 -0.034 -0.25 -0.1 0.024 -0.072 -0.044 -0.072 0.21 0.12 -0.47 -0.3 0.011 -0.026 5.8e+03 0.58 0.31 0.98 ++
7 -0.05 -0.049 -0.034 -0.25 -0.1 0.024 -0.072 -0.044 -0.072 0.21 0.12 -0.47 -0.3 0.011 -0.026 5.8e+03 0.58 0.16 -8.8 -
8 -0.05 -0.049 -0.034 -0.25 -0.1 0.024 -0.072 -0.044 -0.072 0.21 0.12 -0.47 -0.3 0.011 -0.026 5.8e+03 0.58 0.078 -11 -
9 -0.05 -0.049 -0.034 -0.25 -0.1 0.024 -0.072 -0.044 -0.072 0.21 0.12 -0.47 -0.3 0.011 -0.026 5.8e+03 0.58 0.039 -13 -
10 -0.05 -0.049 -0.034 -0.25 -0.1 0.024 -0.072 -0.044 -0.072 0.21 0.12 -0.47 -0.3 0.011 -0.026 5.8e+03 0.58 0.02 -14 -
11 -0.05 -0.049 -0.034 -0.25 -0.1 0.024 -0.072 -0.044 -0.072 0.21 0.12 -0.47 -0.3 0.011 -0.026 5.8e+03 0.58 0.0098 -4.8 -
12 -0.06 -0.058 -0.044 -0.24 -0.09 0.026 -0.082 -0.034 -0.081 0.2 0.11 -0.46 -0.31 0.0012 -0.031 5.7e+03 0.065 0.098 0.94 ++
13 -0.06 -0.058 -0.044 -0.24 -0.09 0.026 -0.082 -0.034 -0.081 0.2 0.11 -0.46 -0.31 0.0012 -0.031 5.7e+03 0.065 0.049 -1.9 -
14 -0.06 -0.058 -0.044 -0.24 -0.09 0.026 -0.082 -0.034 -0.081 0.2 0.11 -0.46 -0.31 0.0012 -0.031 5.7e+03 0.065 0.024 -0.8 -
15 -0.06 -0.058 -0.044 -0.24 -0.09 0.026 -0.082 -0.034 -0.081 0.2 0.11 -0.46 -0.31 0.0012 -0.031 5.7e+03 0.065 0.012 0.042 -
16 -0.064 -0.061 -0.044 -0.24 -0.086 0.026 -0.094 -0.044 -0.087 0.2 0.11 -0.47 -0.31 -0.00089 -0.025 5.7e+03 2.1 0.012 0.54 +
17 -0.068 -0.064 -0.044 -0.25 -0.083 0.026 -0.11 -0.056 -0.092 0.2 0.1 -0.47 -0.32 -0.0003 -0.019 5.7e+03 0.65 0.12 1.1 ++
18 -0.1 -0.087 -0.048 -0.26 -0.046 0.026 -0.23 -0.15 -0.13 0.19 0.081 -0.5 -0.35 -0.0001 0.037 5.6e+03 1.6 1.2 0.92 ++
19 -0.1 -0.087 -0.048 -0.26 -0.046 0.026 -0.23 -0.15 -0.13 0.19 0.081 -0.5 -0.35 -0.0001 0.037 5.6e+03 1.6 0.61 -0.86 -
20 -0.029 -0.072 -0.063 -0.3 0.16 0.029 -0.84 -0.41 -0.094 -0.19 -0.23 -0.59 -0.47 -0.0044 0.19 5.5e+03 14 0.61 0.2 +
21 -0.029 -0.072 -0.063 -0.3 0.16 0.029 -0.84 -0.41 -0.094 -0.19 -0.23 -0.59 -0.47 -0.0044 0.19 5.5e+03 14 0.31 -1.3 -
22 -0.029 -0.072 -0.063 -0.3 0.16 0.029 -0.84 -0.41 -0.094 -0.19 -0.23 -0.59 -0.47 -0.0044 0.19 5.5e+03 14 0.15 -0.83 -
23 -0.029 -0.072 -0.063 -0.3 0.16 0.029 -0.84 -0.41 -0.094 -0.19 -0.23 -0.59 -0.47 -0.0044 0.19 5.5e+03 14 0.076 -0.56 -
24 -0.029 -0.072 -0.063 -0.3 0.16 0.029 -0.84 -0.41 -0.094 -0.19 -0.23 -0.59 -0.47 -0.0044 0.19 5.5e+03 14 0.038 -0.42 -
25 -0.029 -0.072 -0.063 -0.3 0.16 0.029 -0.84 -0.41 -0.094 -0.19 -0.23 -0.59 -0.47 -0.0044 0.19 5.5e+03 14 0.019 -0.36 -
26 -0.029 -0.072 -0.063 -0.3 0.16 0.029 -0.84 -0.41 -0.094 -0.19 -0.23 -0.59 -0.47 -0.0044 0.19 5.5e+03 14 0.0095 -0.33 -
27 -0.029 -0.072 -0.063 -0.3 0.16 0.029 -0.84 -0.41 -0.094 -0.19 -0.23 -0.59 -0.47 -0.0044 0.19 5.5e+03 14 0.0048 -0.046 -
28 -0.034 -0.076 -0.064 -0.3 0.16 0.029 -0.84 -0.41 -0.098 -0.18 -0.23 -0.59 -0.47 0.00036 0.2 5.4e+03 6 0.0048 0.47 +
29 -0.034 -0.076 -0.064 -0.3 0.16 0.029 -0.84 -0.41 -0.098 -0.18 -0.23 -0.59 -0.47 0.00036 0.2 5.4e+03 6 0.0024 -0.84 -
30 -0.036 -0.078 -0.066 -0.3 0.16 0.031 -0.84 -0.41 -0.1 -0.18 -0.23 -0.59 -0.47 -0.002 0.2 5.3e+03 8.2 0.0024 0.26 +
31 -0.037 -0.078 -0.066 -0.3 0.17 0.031 -0.84 -0.42 -0.1 -0.18 -0.23 -0.59 -0.47 -0.0011 0.2 5.3e+03 3.4 0.024 1.3 ++
32 -0.046 -0.082 -0.067 -0.31 0.17 0.031 -0.84 -0.44 -0.13 -0.18 -0.22 -0.59 -0.46 -0.0011 0.2 5.3e+03 4.1 0.024 0.9 +
33 -0.052 -0.083 -0.068 -0.31 0.18 0.032 -0.85 -0.45 -0.15 -0.18 -0.23 -0.59 -0.46 -0.0012 0.21 5.3e+03 2.1 0.24 0.92 ++
34 -0.061 -0.052 -0.083 -0.44 0.35 0.038 -1 -0.46 -0.39 -0.37 -0.42 -0.66 -0.47 -0.0012 0.23 5.2e+03 0.091 2.4 1.1 ++
35 -0.061 -0.052 -0.083 -0.44 0.35 0.038 -1 -0.46 -0.39 -0.37 -0.42 -0.66 -0.47 -0.0012 0.23 5.2e+03 0.091 1.2 -1.8e+02 -
36 -0.061 -0.052 -0.083 -0.44 0.35 0.038 -1 -0.46 -0.39 -0.37 -0.42 -0.66 -0.47 -0.0012 0.23 5.2e+03 0.091 0.6 -44 -
37 -0.061 -0.052 -0.083 -0.44 0.35 0.038 -1 -0.46 -0.39 -0.37 -0.42 -0.66 -0.47 -0.0012 0.23 5.2e+03 0.091 0.3 -1.5 -
38 -0.24 -0.034 -0.13 -0.73 0.65 0.078 -1.1 -0.54 -0.57 -0.39 -0.67 -0.81 -0.74 -0.00016 -0.029 5.2e+03 1.8 0.3 0.47 +
39 -0.22 0.081 -0.17 -0.81 0.82 0.14 -1.1 -0.68 -0.83 -0.69 -0.85 -1 -0.87 -0.00031 0.0088 5.1e+03 0.036 3 0.9 ++
40 -0.22 0.081 -0.17 -0.81 0.82 0.14 -1.1 -0.68 -0.83 -0.69 -0.85 -1 -0.87 -0.00031 0.0088 5.1e+03 0.036 1.5 -72 -
41 -0.22 0.081 -0.17 -0.81 0.82 0.14 -1.1 -0.68 -0.83 -0.69 -0.85 -1 -0.87 -0.00031 0.0088 5.1e+03 0.036 0.75 -26 -
42 -0.22 0.081 -0.17 -0.81 0.82 0.14 -1.1 -0.68 -0.83 -0.69 -0.85 -1 -0.87 -0.00031 0.0088 5.1e+03 0.036 0.37 -4.7 -
43 -0.22 0.081 -0.17 -0.81 0.82 0.14 -1.1 -0.68 -0.83 -0.69 -0.85 -1 -0.87 -0.00031 0.0088 5.1e+03 0.036 0.19 -0.14 -
44 -0.33 -0.0081 -0.21 -0.89 0.9 0.22 -1.2 -0.86 -0.81 -0.82 -1 -1.2 -1.1 1.8e-05 -0.068 5.1e+03 1.7 0.19 0.85 +
45 -0.29 0.065 -0.25 -0.9 0.95 0.3 -1.1 -0.92 -0.97 -1 -1 -1.3 -1.1 1.9e-05 -0.067 5.1e+03 0.27 1.9 0.99 ++
46 -0.29 0.065 -0.25 -0.9 0.95 0.3 -1.1 -0.92 -0.97 -1 -1 -1.3 -1.1 1.9e-05 -0.067 5.1e+03 0.27 0.93 -14 -
47 -0.29 0.065 -0.25 -0.9 0.95 0.3 -1.1 -0.92 -0.97 -1 -1 -1.3 -1.1 1.9e-05 -0.067 5.1e+03 0.27 0.47 -3.1 -
48 -0.33 0.013 -0.36 -0.88 1.1 0.56 -1.2 -1.3 -1.1 -1.4 -1.2 -1.8 -1.3 0.00027 -0.12 5.1e+03 9.2 0.47 0.42 +
49 -0.24 -0.0023 -0.43 -0.54 1 0.76 -1.2 -1.5 -0.99 -1.7 -0.96 -2.3 -1.1 0.00022 -0.11 5.1e+03 5.6 0.47 0.83 +
50 -0.26 0.021 -0.42 -0.65 1 0.68 -1.2 -1.6 -0.68 -1.9 -0.54 -2.3 -0.84 0.00022 -0.11 5.1e+03 2.1 4.7 0.99 ++
51 -0.27 0.01 -0.42 -0.63 1 0.69 -1.2 -1.7 -0.68 -2 -0.54 -2.3 -0.85 0.00023 -0.11 5.1e+03 0.21 47 1 ++
52 -0.26 0.009 -0.41 -0.63 1 0.68 -1.2 -1.7 -0.66 -2 -0.5 -2.3 -0.83 0.00023 -0.11 5.1e+03 0.2 4.7e+02 1 ++
53 -0.26 0.0094 -0.42 -0.63 1 0.69 -1.2 -1.7 -0.66 -2 -0.5 -2.4 -0.83 0.00023 -0.11 5.1e+03 0.028 4.7e+03 1 ++
54 -0.26 0.0094 -0.42 -0.63 1 0.68 -1.2 -1.7 -0.66 -2 -0.5 -2.4 -0.83 0.00023 -0.11 5.1e+03 0.0065 4.7e+04 1 ++
55 -0.26 0.0094 -0.42 -0.63 1 0.68 -1.2 -1.7 -0.66 -2 -0.5 -2.4 -0.83 0.00023 -0.11 5.1e+03 0.0027 4.7e+05 1 ++
56 -0.26 0.0094 -0.42 -0.63 1 0.68 -1.2 -1.7 -0.66 -2 -0.5 -2.4 -0.83 0.00023 -0.11 5.1e+03 2.1e-05 4.7e+06 1 ++
57 -0.26 0.0094 -0.42 -0.63 1 0.68 -1.2 -1.7 -0.66 -2 -0.5 -2.4 -0.83 0.00023 -0.11 5.1e+03 8.7e-05 4.7e+07 1 ++
58 -0.26 0.0094 -0.42 -0.63 1 0.68 -1.2 -1.7 -0.66 -2 -0.5 -2.4 -0.83 0.00023 -0.11 5.1e+03 1.3e-05 4.7e+07 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.4 0.5 -0.58 -
1 0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.4 0.25 0.015 -
2 -0.013 -0.21 -0.028 0.22 -0.22 -0.056 -0.03 0.18 0.11 -0.25 -0.25 0 0 5.8e+03 0.052 2.5 1.2 ++
3 -0.013 -0.21 -0.028 0.22 -0.22 -0.056 -0.03 0.18 0.11 -0.25 -0.25 0 0 5.8e+03 0.052 1.2 -8.4 -
4 -0.013 -0.21 -0.028 0.22 -0.22 -0.056 -0.03 0.18 0.11 -0.25 -0.25 0 0 5.8e+03 0.052 0.62 -3.9 -
5 -0.013 -0.21 -0.028 0.22 -0.22 -0.056 -0.03 0.18 0.11 -0.25 -0.25 0 0 5.8e+03 0.052 0.31 -0.23 -
6 -0.0072 -0.32 -0.084 -0.092 -0.47 -0.22 -0.033 0.049 -0.07 -0.46 -0.37 -0.0023 0.12 5.6e+03 6.2 0.31 0.72 +
7 -0.0072 -0.32 -0.084 -0.092 -0.47 -0.22 -0.033 0.049 -0.07 -0.46 -0.37 -0.0023 0.12 5.6e+03 6.2 0.16 -0.36 -
8 -0.0072 -0.32 -0.084 -0.092 -0.47 -0.22 -0.033 0.049 -0.07 -0.46 -0.37 -0.0023 0.12 5.6e+03 6.2 0.078 0.0058 -
9 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.069 0.037 -0.077 -0.43 -0.36 0.0015 0.1 5.6e+03 3.8 0.078 0.34 +
10 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.069 0.037 -0.077 -0.43 -0.36 0.0015 0.1 5.6e+03 3.8 0.039 -4.5 -
11 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.069 0.037 -0.077 -0.43 -0.36 0.0015 0.1 5.6e+03 3.8 0.02 -3.5 -
12 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.069 0.037 -0.077 -0.43 -0.36 0.0015 0.1 5.6e+03 3.8 0.0098 -2.7 -
13 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.069 0.037 -0.077 -0.43 -0.36 0.0015 0.1 5.6e+03 3.8 0.0049 -1.9 -
14 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.069 0.037 -0.077 -0.43 -0.36 0.0015 0.1 5.6e+03 3.8 0.0024 -0.66 -
15 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.07 0.035 -0.08 -0.43 -0.36 -0.00099 0.1 5.5e+03 4.3 0.0024 0.76 +
16 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.07 0.035 -0.08 -0.43 -0.36 -0.00099 0.1 5.5e+03 4.3 0.0012 -0.79 -
17 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.07 0.035 -0.08 -0.43 -0.36 -0.00099 0.1 5.5e+03 4.3 0.00061 -0.62 -
18 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.07 0.035 -0.08 -0.43 -0.36 -0.00099 0.1 5.5e+03 4.3 0.00031 0.07 -
19 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.071 0.035 -0.08 -0.43 -0.36 -0.00069 0.1 5.5e+03 0.3 0.00031 0.78 +
20 -0.03 -0.3 -0.11 -0.1 -0.48 -0.3 -0.071 0.034 -0.08 -0.43 -0.36 -0.0007 0.1 5.5e+03 0.042 0.0031 1 ++
21 -0.031 -0.3 -0.11 -0.11 -0.48 -0.3 -0.071 0.032 -0.082 -0.42 -0.36 -0.00069 0.1 5.5e+03 0.25 0.031 1 ++
22 -0.034 -0.29 -0.12 -0.14 -0.5 -0.32 -0.079 0.0036 -0.1 -0.41 -0.35 -0.0007 0.1 5.5e+03 0.036 0.31 1 ++
23 -0.11 -0.25 -0.25 -0.44 -0.75 -0.61 -0.24 -0.28 -0.34 -0.36 -0.36 -0.0016 0.32 5.4e+03 0.48 0.31 0.31 +
24 -0.15 -0.32 -0.26 -0.56 -1.1 -0.47 -0.2 -0.22 -0.43 -0.38 -0.37 -0.0017 0.34 5.2e+03 0.052 3.1 0.95 ++
25 -0.15 -0.32 -0.26 -0.56 -1.1 -0.47 -0.2 -0.22 -0.43 -0.38 -0.37 -0.0017 0.34 5.2e+03 0.052 1.5 -52 -
26 -0.59 -0.25 -0.64 -1.1 -2.6 -0.45 -0.61 -0.23 -0.85 -0.34 -0.54 -0.00063 0.082 5e+03 0.55 15 1.1 ++
27 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 1.5e+02 1 ++
28 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 76 -6.7e+02 -
29 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 38 -6.5e+02 -
30 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 19 -6.2e+02 -
31 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 9.5 -5.6e+02 -
32 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 4.8 -4.7e+02 -
33 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 2.4 -3.5e+02 -
34 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 1.2 -2.2e+02 -
35 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 0.6 -1e+02 -
36 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 0.3 -21 -
37 -0.59 -0.2 -0.67 -1.3 -3.2 -0.51 -0.89 -0.22 -1.1 -0.28 -0.65 -0.00073 0.11 5e+03 0.16 0.15 -1.8 -
38 -0.57 -0.22 -0.7 -1.2 -3.2 -0.61 -1 -0.37 -1.1 -0.35 -0.7 -0.00023 -0.022 5e+03 5.4 0.15 0.43 +
39 -0.55 -0.2 -0.71 -1.3 -3.2 -0.76 -1 -0.45 -1.2 -0.42 -0.74 -0.00029 0.00073 5e+03 2.4 1.5 0.95 ++
40 -0.55 -0.2 -0.71 -1.3 -3.2 -0.76 -1 -0.45 -1.2 -0.42 -0.74 -0.00029 0.00073 5e+03 2.4 0.75 -33 -
41 -0.55 -0.2 -0.71 -1.3 -3.2 -0.76 -1 -0.45 -1.2 -0.42 -0.74 -0.00029 0.00073 5e+03 2.4 0.37 -6.4 -
42 -0.55 -0.2 -0.71 -1.3 -3.2 -0.76 -1 -0.45 -1.2 -0.42 -0.74 -0.00029 0.00073 5e+03 2.4 0.19 -0.3 -
43 -0.47 -0.18 -0.7 -1.2 -3.2 -0.89 -1.2 -0.64 -1.2 -0.53 -0.82 5.9e-05 -0.078 5e+03 0.26 0.19 0.7 +
44 -0.48 -0.18 -0.72 -1.3 -3.2 -1.1 -1.2 -0.73 -1.3 -0.67 -0.85 6.8e-06 -0.066 5e+03 0.42 1.9 0.97 ++
45 -0.48 -0.18 -0.72 -1.3 -3.2 -1.1 -1.2 -0.73 -1.3 -0.67 -0.85 6.8e-06 -0.066 5e+03 0.42 0.93 -28 -
46 -0.48 -0.18 -0.72 -1.3 -3.2 -1.1 -1.2 -0.73 -1.3 -0.67 -0.85 6.8e-06 -0.066 5e+03 0.42 0.47 -3.3 -
47 -0.48 -0.18 -0.72 -1.3 -3.2 -1.1 -1.2 -0.73 -1.3 -0.67 -0.85 6.8e-06 -0.066 5e+03 0.42 0.23 -0.073 -
48 -0.38 -0.12 -0.7 -1.2 -3.2 -1.2 -1.3 -0.96 -1.3 -0.83 -0.92 0.0002 -0.11 5e+03 5.4 0.23 0.69 +
49 -0.37 -0.066 -0.7 -1.3 -3.2 -1.5 -1.3 -1.1 -1.3 -0.99 -0.91 0.00013 -0.098 5e+03 16 2.3 0.95 ++
50 -0.19 0.27 -0.63 -1.3 -3.2 -2.4 -0.79 -2.1 -0.51 -1.8 -0.47 0.00034 -0.13 5e+03 4.3 2.3 0.38 +
51 -0.19 0.27 -0.63 -1.3 -3.2 -2.4 -0.79 -2.1 -0.51 -1.8 -0.47 0.00034 -0.13 5e+03 4.3 0.13 -1.4 -
52 -0.19 0.27 -0.63 -1.3 -3.2 -2.4 -0.79 -2.1 -0.51 -1.8 -0.47 0.00034 -0.13 5e+03 4.3 0.064 -1.3 -
53 -0.19 0.27 -0.63 -1.3 -3.2 -2.4 -0.79 -2.1 -0.51 -1.8 -0.47 0.00034 -0.13 5e+03 4.3 0.032 -1.3 -
54 -0.19 0.27 -0.63 -1.3 -3.2 -2.4 -0.79 -2.1 -0.51 -1.8 -0.47 0.00034 -0.13 5e+03 4.3 0.016 -1.2 -
55 -0.19 0.27 -0.63 -1.3 -3.2 -2.4 -0.79 -2.1 -0.51 -1.8 -0.47 0.00034 -0.13 5e+03 4.3 0.008 -0.55 -
56 -0.19 0.27 -0.63 -1.3 -3.2 -2.4 -0.79 -2.1 -0.51 -1.8 -0.47 0.00026 -0.12 4.9e+03 15 0.008 0.8 +
57 -0.19 0.27 -0.62 -1.3 -3.2 -2.3 -0.78 -2.1 -0.52 -1.8 -0.47 0.00025 -0.12 4.9e+03 0.97 0.08 0.97 ++
58 -0.26 0.25 -0.64 -1.3 -3.2 -2.3 -0.74 -2 -0.51 -1.8 -0.43 0.00024 -0.12 4.9e+03 0.86 0.8 1 ++
59 -0.29 0.18 -0.67 -1.3 -3.2 -2.1 -0.75 -1.9 -0.5 -1.7 -0.44 0.00023 -0.11 4.9e+03 0.095 8 0.98 ++
60 -0.3 0.19 -0.67 -1.3 -3.2 -2.1 -0.78 -1.9 -0.54 -1.7 -0.46 0.00023 -0.11 4.9e+03 0.00016 80 1 ++
61 -0.29 0.19 -0.67 -1.3 -3.2 -2.1 -0.78 -1.9 -0.54 -1.7 -0.46 0.00023 -0.11 4.9e+03 0.0031 8e+02 1 ++
62 -0.3 0.19 -0.67 -1.3 -3.2 -2.1 -0.79 -1.9 -0.55 -1.7 -0.47 0.00023 -0.11 4.9e+03 0.00014 8e+03 1 ++
63 -0.3 0.19 -0.67 -1.3 -3.2 -2.1 -0.79 -1.9 -0.55 -1.7 -0.47 0.00023 -0.11 4.9e+03 0.00058 8e+04 1 ++
64 -0.3 0.19 -0.67 -1.3 -3.2 -2.1 -0.79 -1.9 -0.55 -1.7 -0.47 0.00023 -0.11 4.9e+03 5.2e-07 8e+04 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 85/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.4 0.5 -0.31 -
1 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 5 0.95 ++
2 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 2.5 -1.8e+302 -
3 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 1.2 -7e+302 -
4 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 0.62 -7.8 -
5 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 0.31 -8 -
6 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 0.16 -8.7 -
7 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 0.078 -10 -
8 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 0.039 -13 -
9 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 0.02 -5 -
10 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 0.0098 -3.8 -
11 -0.018 -0.017 -0.28 0.0063 -0.041 -0.074 0.24 -0.5 0.0045 1.2 0.00048 5.8e+03 1 0.0049 -1.7 -
12 -0.023 -0.022 -0.28 0.011 -0.045 -0.079 0.25 -0.5 -0.00038 1.2 0.0054 5.7e+03 0.83 0.0049 0.86 +
13 -0.025 -0.023 -0.28 0.013 -0.05 -0.083 0.25 -0.51 -0.00012 1.2 0.007 5.7e+03 0.076 0.049 0.99 ++
14 -0.044 -0.034 -0.29 0.037 -0.094 -0.13 0.25 -0.52 -0.0005 1.2 0.024 5.7e+03 1.6 0.49 0.98 ++
15 -0.13 -0.14 -0.3 0.3 -0.58 -0.49 0.13 -0.61 -0.00016 1.5 0.16 5.5e+03 9.8 0.49 0.54 +
16 -0.13 -0.14 -0.3 0.3 -0.58 -0.49 0.13 -0.61 -0.00016 1.5 0.16 5.5e+03 9.8 0.24 -6.1 -
17 -0.13 -0.14 -0.3 0.3 -0.58 -0.49 0.13 -0.61 -0.00016 1.5 0.16 5.5e+03 9.8 0.12 -6.4 -
18 -0.13 -0.14 -0.3 0.3 -0.58 -0.49 0.13 -0.61 -0.00016 1.5 0.16 5.5e+03 9.8 0.061 -4.6 -
19 -0.13 -0.14 -0.3 0.3 -0.58 -0.49 0.13 -0.61 -0.00016 1.5 0.16 5.5e+03 9.8 0.031 -2.9 -
20 -0.13 -0.14 -0.3 0.3 -0.58 -0.49 0.13 -0.61 -0.00016 1.5 0.16 5.5e+03 9.8 0.015 -2.4 -
21 -0.13 -0.14 -0.3 0.3 -0.58 -0.49 0.13 -0.61 -0.00016 1.5 0.16 5.5e+03 9.8 0.0076 -2 -
22 -0.13 -0.14 -0.3 0.3 -0.58 -0.49 0.13 -0.61 -0.00016 1.5 0.16 5.5e+03 9.8 0.0038 -1.6 -
23 -0.13 -0.14 -0.3 0.3 -0.58 -0.49 0.13 -0.61 -0.00016 1.5 0.16 5.5e+03 9.8 0.0019 -1 -
24 -0.13 -0.14 -0.3 0.3 -0.58 -0.49 0.13 -0.61 -0.00016 1.5 0.16 5.5e+03 9.8 0.00095 -0.25 -
25 -0.13 -0.14 -0.31 0.3 -0.58 -0.49 0.13 -0.61 -0.0011 1.5 0.16 5.5e+03 8.8 0.00095 0.6 +
26 -0.13 -0.14 -0.31 0.3 -0.58 -0.49 0.13 -0.61 -0.0011 1.5 0.16 5.5e+03 8.8 0.00048 -0.56 -
27 -0.13 -0.14 -0.31 0.3 -0.58 -0.49 0.13 -0.61 -0.0011 1.5 0.16 5.5e+03 8.8 0.00024 -0.079 -
28 -0.13 -0.14 -0.31 0.3 -0.58 -0.49 0.13 -0.61 -0.00087 1.5 0.15 5.5e+03 2.8 0.00024 0.71 +
29 -0.13 -0.14 -0.31 0.3 -0.58 -0.49 0.13 -0.61 -0.00089 1.5 0.15 5.5e+03 0.16 0.0024 1 ++
30 -0.13 -0.14 -0.31 0.3 -0.58 -0.49 0.13 -0.61 -0.00086 1.5 0.15 5.5e+03 3.2 0.024 1 ++
31 -0.12 -0.14 -0.31 0.31 -0.59 -0.47 0.11 -0.61 -0.0008 1.5 0.13 5.4e+03 0.18 0.24 1 ++
32 -0.0096 -0.18 -0.32 0.46 -0.73 -0.38 -0.13 -0.66 -0.00091 1.6 0.16 5.2e+03 0.16 0.24 0.86 +
33 -0.046 -0.23 -0.3 0.7 -0.84 -0.49 -0.34 -0.61 -0.001 1.8 0.19 5.1e+03 0.47 0.24 0.86 +
34 -0.13 -0.25 -0.42 0.94 -0.84 -0.57 -0.35 -0.74 -0.00038 1.9 0.039 5.1e+03 5.4 2.4 1.1 ++
35 -0.13 -0.25 -0.42 0.94 -0.84 -0.57 -0.35 -0.74 -0.00038 1.9 0.039 5.1e+03 5.4 0.35 -0.21 -
36 -0.17 -0.18 -0.46 1.2 -0.79 -0.75 -0.7 -0.9 -0.00048 2 0.054 5e+03 8.4 3.5 1 ++
37 -0.17 -0.18 -0.46 1.2 -0.79 -0.75 -0.7 -0.9 -0.00048 2 0.054 5e+03 8.4 1.8 -66 -
38 -0.17 -0.18 -0.46 1.2 -0.79 -0.75 -0.7 -0.9 -0.00048 2 0.054 5e+03 8.4 0.89 -38 -
39 -0.17 -0.18 -0.46 1.2 -0.79 -0.75 -0.7 -0.9 -0.00048 2 0.054 5e+03 8.4 0.44 -13 -
40 -0.17 -0.18 -0.46 1.2 -0.79 -0.75 -0.7 -0.9 -0.00048 2 0.054 5e+03 8.4 0.22 -3.3 -
41 -0.17 -0.18 -0.46 1.2 -0.79 -0.75 -0.7 -0.9 -0.00048 2 0.054 5e+03 8.4 0.11 -0.57 -
42 -0.2 -0.16 -0.52 1.2 -0.79 -0.8 -0.67 -1 5.6e-06 2 -0.056 5e+03 6.9 0.11 0.81 +
43 -0.22 -0.12 -0.49 1.2 -0.81 -0.86 -0.78 -1 -0.00012 1.9 -0.029 5e+03 2 1.1 0.97 ++
44 -0.22 -0.12 -0.49 1.2 -0.81 -0.86 -0.78 -1 -0.00012 1.9 -0.029 5e+03 2 0.55 -5.3 -
45 -0.22 -0.12 -0.49 1.2 -0.81 -0.86 -0.78 -1 -0.00012 1.9 -0.029 5e+03 2 0.28 -1.1 -
46 -0.22 -0.12 -0.49 1.2 -0.81 -0.86 -0.78 -1 -0.00012 1.9 -0.029 5e+03 2 0.14 0.082 -
47 -0.24 -0.076 -0.53 1.2 -0.81 -0.94 -0.84 -1.2 0.00011 1.9 -0.086 5e+03 11 0.14 0.77 +
48 -0.23 -0.042 -0.5 1.2 -0.83 -0.98 -0.98 -1.2 7.3e-06 1.9 -0.06 5e+03 1.7 1.4 1 ++
49 -0.23 -0.042 -0.5 1.2 -0.83 -0.98 -0.98 -1.2 7.3e-06 1.9 -0.06 5e+03 1.7 0.61 -2.5 -
50 -0.33 0.21 -0.49 1.4 -0.86 -1.4 -1.6 -1.8 0.00035 1.9 -0.14 5e+03 18 0.61 0.15 +
51 -0.33 0.21 -0.49 1.4 -0.86 -1.4 -1.6 -1.8 0.00035 1.9 -0.14 5e+03 18 0.3 -0.12 -
52 -0.23 0.23 -0.29 1.4 -1 -1.7 -1.9 -2 6.6e-05 1.8 -0.071 5e+03 39 0.3 0.23 +
53 -0.23 0.23 -0.29 1.4 -1 -1.7 -1.9 -2 6.6e-05 1.8 -0.071 5e+03 39 0.15 -1.3 -
54 -0.23 0.23 -0.29 1.4 -1 -1.7 -1.9 -2 6.6e-05 1.8 -0.071 5e+03 39 0.076 -0.73 -
55 -0.23 0.23 -0.29 1.4 -1 -1.7 -1.9 -2 6.6e-05 1.8 -0.071 5e+03 39 0.038 -0.14 -
56 -0.23 0.23 -0.29 1.4 -1 -1.7 -1.9 -2 0.0002 1.8 -0.11 4.9e+03 49 0.038 0.55 +
57 -0.23 0.23 -0.32 1.4 -0.99 -1.7 -1.9 -2.1 0.00021 1.8 -0.11 4.9e+03 16 0.038 0.87 +
58 -0.25 0.23 -0.32 1.4 -0.95 -1.7 -1.9 -2.1 0.0002 1.8 -0.11 4.9e+03 8.9 0.38 0.9 ++
59 -0.3 0.17 -0.35 1.5 -0.98 -1.8 -2.1 -2.3 0.00022 1.6 -0.11 4.9e+03 1.6 3.8 0.97 ++
60 -0.3 0.15 -0.31 1.5 -0.97 -1.8 -2.1 -2.3 0.00022 1.6 -0.11 4.9e+03 9.6e-05 38 1 ++
61 -0.29 0.15 -0.31 1.5 -0.97 -1.8 -2.1 -2.3 0.00022 1.6 -0.11 4.9e+03 0.00016 3.8e+02 1 ++
62 -0.29 0.15 -0.31 1.5 -0.97 -1.8 -2.1 -2.3 0.00022 1.6 -0.11 4.9e+03 6.7e-07 3.8e+02 1 ++
Considering neighbor 0/20 for current solution
Attempt 86/100
Considering neighbor 0/20 for current solution
Attempt 87/100
Considering neighbor 0/20 for current solution
Attempt 88/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME B_TIME_1st_clas mu_public Function Relgrad Radius Rho
0 -0.28 -0.24 -0.76 0.57 -1 -0.55 -0.62 1 5.2e+03 0.041 10 1.1 ++
1 -0.54 -0.078 -1 2.2 -1.2 -0.6 -0.67 1.5 5.1e+03 0.092 10 0.31 +
2 -0.54 -0.078 -1 2.2 -1.2 -0.6 -0.67 1.5 5.1e+03 0.092 5 -25 -
3 -0.54 -0.078 -1 2.2 -1.2 -0.6 -0.67 1.5 5.1e+03 0.092 2.5 -13 -
4 -0.54 -0.078 -1 2.2 -1.2 -0.6 -0.67 1.5 5.1e+03 0.092 1.2 -1.9 -
5 -0.36 -0.25 -0.66 0.91 -1.2 -0.41 -0.96 1.9 5e+03 0.017 1.2 0.49 +
6 -0.36 -0.25 -0.66 0.91 -1.2 -0.41 -0.96 1.9 5e+03 0.017 0.43 -0.26 -
7 -0.36 -0.59 -0.8 1.2 -1.2 -0.56 -0.82 1.4 5e+03 0.0076 4.3 1 ++
8 -0.36 -0.37 -0.9 1.4 -1.2 -0.6 -0.77 1.4 5e+03 0.00083 43 1.1 ++
9 -0.34 -0.34 -1 1.5 -1.2 -0.62 -0.81 1.3 5e+03 0.0016 4.3e+02 0.94 ++
10 -0.33 -0.33 -1 1.6 -1.2 -0.62 -0.82 1.3 5e+03 2.1e-05 4.3e+03 1 ++
11 -0.33 -0.33 -1 1.6 -1.2 -0.62 -0.82 1.3 5e+03 1.1e-06 4.3e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 89/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_1st_clas lambda_travel_t mu_public Function Relgrad Radius Rho
0 -0.26 -0.31 -0.31 -0.044 -0.5 0.45 0.19 -0.016 -0.45 -0.65 -0.54 -1 -0.73 1.8 1 5.7e+03 0.087 1 0.63 +
1 -0.49 -0.47 -0.14 -0.13 -0.71 1.3 0.51 0.023 -0.41 -0.84 -1.5 -0.51 -0.47 1.5 1.4 5.1e+03 0.047 1 0.8 +
2 -0.81 -0.45 0.018 -0.18 -0.94 0.94 0.37 0.084 -0.9 -1.4 -1.3 -0.71 -0.9 0.46 1.8 5e+03 0.039 1 0.55 +
3 -0.81 -0.45 0.018 -0.18 -0.94 0.94 0.37 0.084 -0.9 -1.4 -1.3 -0.71 -0.9 0.46 1.8 5e+03 0.039 0.5 0.026 -
4 -0.48 -0.36 -0.041 -0.25 -0.44 0.88 0.34 0.23 -0.97 -1.2 -1.6 -1 -0.51 0.38 1.7 4.9e+03 0.0059 5 1 ++
5 -0.48 -0.36 -0.041 -0.25 -0.44 0.88 0.34 0.23 -0.97 -1.2 -1.6 -1 -0.51 0.38 1.7 4.9e+03 0.0059 0.35 -1.3 -
6 -0.43 -0.34 -0.014 -0.29 -0.47 0.92 0.39 0.18 -0.96 -1.2 -1.8 -1.1 -0.6 0.44 1.3 4.9e+03 0.01 0.35 0.9 +
7 -0.4 -0.21 -0.02 -0.4 -0.63 1.1 0.46 0.23 -0.95 -1.2 -1.8 -1.2 -0.65 0.44 1.2 4.9e+03 0.0025 3.5 1.2 ++
8 -0.38 -0.17 -0.018 -0.41 -0.78 1.2 0.53 0.27 -0.93 -1.2 -1.8 -1.2 -0.7 0.44 1.1 4.9e+03 0.0027 35 1.1 ++
9 -0.38 -0.15 -0.017 -0.41 -0.84 1.2 0.55 0.29 -0.93 -1.2 -1.8 -1.2 -0.71 0.43 1.1 4.9e+03 0.00025 3.5e+02 1.1 ++
10 -0.37 -0.14 -0.016 -0.42 -0.87 1.2 0.56 0.29 -0.93 -1.2 -1.9 -1.2 -0.72 0.43 1.1 4.9e+03 6.6e-05 3.5e+03 1 ++
11 -0.37 -0.14 -0.016 -0.42 -0.87 1.2 0.56 0.29 -0.93 -1.2 -1.9 -1.2 -0.72 0.43 1.1 4.9e+03 2.1e-07 3.5e+03 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST B_TIME B_TIME_1st_clas lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.15 -0.22 -0.073 -0.027 -0.72 0.44 0.026 -0.012 -1 -0.71 -0.65 1.7 1.5 5.3e+03 0.08 1 0.67 +
1 -0.5 -0.068 0.072 -0.26 -1.1 1.4 0.46 0.12 -0.71 0.034 -0.5 1.6 2.5 5.2e+03 0.17 1 0.22 +
2 -0.5 -0.068 0.072 -0.26 -1.1 1.4 0.46 0.12 -0.71 0.034 -0.5 1.6 2.5 5.2e+03 0.17 0.5 -2.1 -
3 -0.5 0.18 0.06 -0.26 -1.2 0.92 0.28 0.097 -0.74 -0.13 -0.54 2.1 2.5 5.2e+03 0.15 0.5 0.34 +
4 -0.5 0.18 0.06 -0.26 -1.2 0.92 0.28 0.097 -0.74 -0.13 -0.54 2.1 2.5 5.2e+03 0.15 0.25 -3.1 -
5 -0.5 0.18 0.06 -0.26 -1.2 0.92 0.28 0.097 -0.74 -0.13 -0.54 2.1 2.5 5.2e+03 0.15 0.12 -0.74 -
6 -0.47 0.18 0.065 -0.26 -1.2 0.94 0.32 0.098 -0.78 -0.0038 -0.47 2 2.4 5.1e+03 0.085 1.2 0.95 ++
7 -0.47 0.47 -0.029 -0.35 -1.5 1.3 0.42 0.089 -1.1 -0.0091 -0.8 1.5 1.2 5e+03 0.058 1.2 0.32 +
8 -0.44 0.1 0.052 -0.42 -1.7 1.5 0.48 0.23 -1.1 -0.079 -0.93 1.3 1.3 5e+03 0.021 12 1.1 ++
9 -0.44 0.1 0.052 -0.42 -1.7 1.5 0.48 0.23 -1.1 -0.079 -0.93 1.3 1.3 5e+03 0.021 0.85 -1.3 -
10 -0.36 0.13 0.054 -0.42 -1.6 1.5 0.54 0.25 -1.2 -0.4 -1 0.5 1.3 4.9e+03 0.017 8.5 1.1 ++
11 -0.13 0.084 0.017 -0.41 -1.2 1.5 0.48 0.2 -1.1 -0.92 -0.76 0.54 1.4 4.9e+03 0.0022 85 1 ++
12 -0.071 0.042 0.0039 -0.41 -1.2 1.6 0.51 0.22 -1.1 -1.1 -0.72 0.4 1.3 4.9e+03 0.00069 8.5e+02 0.9 ++
13 -0.077 0.044 0.0041 -0.41 -1.2 1.6 0.51 0.22 -1.1 -1.1 -0.72 0.42 1.3 4.9e+03 9.5e-06 8.5e+03 1 ++
14 -0.077 0.044 0.0041 -0.41 -1.2 1.6 0.51 0.22 -1.1 -1.1 -0.72 0.42 1.3 4.9e+03 5.1e-09 8.5e+03 1 ++
Considering neighbor 1/20 for current solution
Attempt 90/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef mu_public square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.4 0.5 -0.18 -
1 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 0.24 -0.5 0.008 1 0.00086 5.9e+03 0.79 5 0.91 ++
2 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 0.24 -0.5 0.008 1 0.00086 5.9e+03 0.79 2.5 -7 -
3 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 0.24 -0.5 0.008 1 0.00086 5.9e+03 0.79 1.2 -7 -
4 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 0.24 -0.5 0.008 1 0.00086 5.9e+03 0.79 0.62 -7.4 -
5 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 0.24 -0.5 0.008 1 0.00086 5.9e+03 0.79 0.31 -8.3 -
6 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 0.24 -0.5 0.008 1 0.00086 5.9e+03 0.79 0.16 -9.9 -
7 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 0.24 -0.5 0.008 1 0.00086 5.9e+03 0.79 0.078 -12 -
8 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 0.24 -0.5 0.008 1 0.00086 5.9e+03 0.79 0.039 -17 -
9 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 0.24 -0.5 0.008 1 0.00086 5.9e+03 0.79 0.02 -7.5 -
10 -0.018 -0.017 -0.28 0.0064 -0.041 -0.074 0.24 -0.5 0.008 1 0.00086 5.9e+03 0.79 0.0098 -3.7 -
11 -0.028 -0.025 -0.29 0.016 -0.05 -0.067 0.25 -0.51 -0.0018 1 0.011 5.8e+03 4.6 0.0098 0.46 +
12 -0.031 -0.027 -0.29 0.019 -0.056 -0.077 0.25 -0.51 0.00011 1 0.013 5.8e+03 0.37 0.0098 0.84 +
13 -0.035 -0.028 -0.29 0.023 -0.063 -0.087 0.25 -0.52 -0.00052 1 0.018 5.8e+03 0.93 0.098 0.91 ++
14 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.98 0.91 ++
15 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.49 -2.5 -
16 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.24 -1.1 -
17 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.12 -0.59 -
18 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.061 -0.43 -
19 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.031 -0.54 -
20 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.015 -0.89 -
21 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.0076 -1.4 -
22 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.0038 -1.9 -
23 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.0019 -2.3 -
24 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.00095 -2.3 -
25 -0.071 -0.045 -0.31 0.064 -0.14 -0.18 0.26 -0.56 -0.00022 1 0.068 5.7e+03 2.1 0.00048 -0.48 -
26 -0.071 -0.046 -0.31 0.065 -0.14 -0.18 0.26 -0.56 -0.0007 1 0.068 5.7e+03 1.6 0.00048 0.5 +
27 -0.071 -0.046 -0.31 0.065 -0.14 -0.18 0.26 -0.56 -0.00054 1 0.068 5.7e+03 0.35 0.00048 0.88 +
28 -0.071 -0.046 -0.31 0.065 -0.14 -0.19 0.26 -0.56 -0.00057 1 0.068 5.7e+03 0.063 0.0048 1 ++
29 -0.071 -0.047 -0.31 0.068 -0.14 -0.19 0.26 -0.56 -0.00055 1 0.068 5.7e+03 0.21 0.048 1 ++
30 -0.07 -0.056 -0.31 0.095 -0.19 -0.2 0.23 -0.56 -0.00057 1 0.069 5.6e+03 0.082 0.48 1 ++
31 -0.065 -0.14 -0.34 0.38 -0.67 -0.4 -0.042 -0.66 -0.0008 1.1 0.13 5.3e+03 1 4.8 0.99 ++
32 -0.065 -0.14 -0.34 0.38 -0.67 -0.4 -0.042 -0.66 -0.0008 1.1 0.13 5.3e+03 1 2.4 -4.6 -
33 -0.065 -0.14 -0.34 0.38 -0.67 -0.4 -0.042 -0.66 -0.0008 1.1 0.13 5.3e+03 1 1.2 -2.5 -
34 -0.18 -0.33 -0.57 1.6 -1.2 -0.73 -0.84 -0.67 -0.0016 1.6 0.32 5.3e+03 3.2 1.2 0.24 +
35 -0.18 -0.33 -0.57 1.6 -1.2 -0.73 -0.84 -0.67 -0.0016 1.6 0.32 5.3e+03 3.2 0.6 -4.1 -
36 -0.41 -0.34 -1.2 1 -0.99 -0.84 -0.86 -0.61 2.6e-05 1.7 -0.063 5.1e+03 5.6 0.6 0.65 +
37 -0.22 -0.4 -0.57 1.2 -1.2 -1.3 -1.1 -1.1 -0.00013 1.8 -0.037 5e+03 10 0.6 0.85 +
38 -0.22 -0.4 -0.57 1.2 -1.2 -1.3 -1.1 -1.1 -0.00013 1.8 -0.037 5e+03 10 0.3 -2.6 -
39 -0.22 -0.4 -0.57 1.2 -1.2 -1.3 -1.1 -1.1 -0.00013 1.8 -0.037 5e+03 10 0.15 -0.43 -
40 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00031 1.7 -0.12 5e+03 8.3 0.15 0.19 +
41 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00031 1.7 -0.12 5e+03 8.3 0.075 -0.21 -
42 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00031 1.7 -0.12 5e+03 8.3 0.037 -0.27 -
43 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00031 1.7 -0.12 5e+03 8.3 0.019 -0.17 -
44 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00012 1.7 -0.1 5e+03 34 0.019 0.21 +
45 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00012 1.7 -0.1 5e+03 34 0.0093 -0.23 -
46 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00012 1.7 -0.1 5e+03 34 0.0047 -0.29 -
47 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00012 1.7 -0.1 5e+03 34 0.0023 -0.29 -
48 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00012 1.7 -0.1 5e+03 34 0.0012 -0.27 -
49 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00012 1.7 -0.1 5e+03 34 0.00058 -0.26 -
50 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00012 1.7 -0.1 5e+03 34 0.00029 -0.26 -
51 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00012 1.7 -0.1 5e+03 34 0.00015 -0.26 -
52 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00012 1.7 -0.1 5e+03 34 7.3e-05 0.0031 -
53 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00019 1.7 -0.1 5e+03 8.9 7.3e-05 0.57 +
54 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00017 1.7 -0.1 5e+03 5.6 7.3e-05 0.59 +
55 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00018 1.7 -0.1 5e+03 0.31 0.00073 1 ++
56 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00017 1.7 -0.1 5e+03 0.02 0.0073 1 ++
57 -0.24 -0.4 -0.6 1.2 -1 -1.4 -1.2 -1.3 0.00014 1.7 -0.093 5e+03 0.92 0.073 1 ++
58 -0.2 -0.41 -0.59 1.2 -1.1 -1.4 -1.3 -1.3 0.00011 1.7 -0.086 5e+03 0.15 0.73 0.99 ++
59 -0.21 -0.55 -0.53 1.4 -1.1 -1.9 -1.9 -2 0.00028 1.2 -0.13 5e+03 11 0.73 0.43 +
60 -0.19 -0.42 -0.51 1.7 -1.1 -2.1 -2.2 -2.4 0.0002 1.1 -0.1 5e+03 11 7.3 0.93 ++
61 -0.17 -0.41 -0.58 1.8 -1.1 -2.1 -2.2 -2.5 0.00022 1 -0.11 4.9e+03 0.62 73 1 ++
62 -0.17 -0.39 -0.6 1.9 -1.1 -2.1 -2.2 -2.5 0.00022 1 -0.11 4.9e+03 0.37 7.3e+02 1 ++
63 -0.19 -0.32 -0.63 2 -1.1 -2.2 -2.3 -2.6 0.00023 1 -0.11 4.9e+03 0.096 7.3e+03 1 ++
64 -0.19 -0.3 -0.61 2 -1.1 -2.1 -2.3 -2.6 0.00023 1 -0.11 4.9e+03 0.00084 7.3e+04 1 ++
65 -0.19 -0.3 -0.61 2 -1.1 -2.1 -2.3 -2.6 0.00023 1 -0.11 4.9e+03 0.00013 7.3e+05 1 ++
66 -0.19 -0.3 -0.61 2 -1.1 -2.1 -2.3 -2.6 0.00023 1 -0.11 4.9e+03 4.4e-06 7.3e+05 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME cube_tt_coef mu_public square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -4.6 -
1 0 0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.25 -1 -
2 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1 0 5.9e+03 3.2 2.5 1 ++
3 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1 0 5.9e+03 3.2 1.2 -4.5 -
4 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1 0 5.9e+03 3.2 0.62 -2.7 -
5 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1 0 5.9e+03 3.2 0.31 -1.3 -
6 0.082 -0.25 -0.25 0.081 -0.2 0.25 -0.25 -0.25 0 1 0 5.9e+03 3.2 0.16 -0.11 -
7 0.11 -0.26 -0.36 0.097 -0.18 0.26 -0.37 -0.41 -0.003 1.1 0.093 5.7e+03 12 0.16 0.39 +
8 0.11 -0.26 -0.36 0.097 -0.18 0.26 -0.37 -0.41 -0.003 1.1 0.093 5.7e+03 12 0.078 -0.93 -
9 0.11 -0.26 -0.36 0.097 -0.18 0.26 -0.37 -0.41 -0.003 1.1 0.093 5.7e+03 12 0.039 -0.85 -
10 0.11 -0.26 -0.36 0.097 -0.18 0.26 -0.37 -0.41 -0.003 1.1 0.093 5.7e+03 12 0.02 -0.81 -
11 0.11 -0.26 -0.36 0.097 -0.18 0.26 -0.37 -0.41 -0.003 1.1 0.093 5.7e+03 12 0.0098 -0.79 -
12 0.11 -0.26 -0.36 0.097 -0.18 0.26 -0.37 -0.41 -0.003 1.1 0.093 5.7e+03 12 0.0049 -0.38 -
13 0.11 -0.26 -0.36 0.097 -0.18 0.26 -0.37 -0.41 -0.003 1.1 0.093 5.7e+03 12 0.0024 0.069 -
14 0.11 -0.27 -0.36 0.1 -0.18 0.26 -0.37 -0.4 -0.00051 1.1 0.095 5.6e+03 6.4 0.0024 0.73 +
15 0.11 -0.27 -0.36 0.1 -0.18 0.26 -0.37 -0.4 -0.00051 1.1 0.095 5.6e+03 6.4 0.0012 -0.34 -
16 0.11 -0.27 -0.36 0.1 -0.18 0.26 -0.37 -0.4 -0.00051 1.1 0.095 5.6e+03 6.4 0.00061 -0.45 -
17 0.11 -0.27 -0.36 0.1 -0.18 0.26 -0.37 -0.4 -0.00051 1.1 0.095 5.6e+03 6.4 0.00031 -0.42 -
18 0.11 -0.27 -0.36 0.1 -0.18 0.26 -0.37 -0.41 -0.00082 1.1 0.095 5.6e+03 3.6 0.00031 0.22 +
19 0.11 -0.27 -0.36 0.1 -0.18 0.26 -0.37 -0.41 -0.00082 1.1 0.095 5.6e+03 3.6 0.00015 -0.2 -
20 0.11 -0.27 -0.36 0.1 -0.18 0.26 -0.37 -0.41 -0.00066 1.1 0.095 5.6e+03 0.28 0.00015 0.77 +
21 0.11 -0.27 -0.36 0.1 -0.18 0.26 -0.37 -0.41 -0.00067 1.1 0.095 5.6e+03 0.06 0.0015 1 ++
22 0.11 -0.27 -0.37 0.1 -0.18 0.26 -0.37 -0.41 -0.00067 1.1 0.096 5.6e+03 0.33 0.015 1 ++
23 0.11 -0.27 -0.37 0.11 -0.18 0.24 -0.38 -0.42 -0.00069 1.1 0.1 5.6e+03 0.058 0.15 1 ++
24 0.13 -0.3 -0.41 0.17 -0.15 0.088 -0.48 -0.51 -0.00085 1.2 0.14 5.4e+03 0.16 1.5 0.99 ++
25 0.13 -0.3 -0.41 0.17 -0.15 0.088 -0.48 -0.51 -0.00085 1.2 0.14 5.4e+03 0.16 0.76 -12 -
26 -0.26 -0.55 -0.27 0.77 -0.48 -0.67 -0.89 -0.91 -0.00054 1.3 0.068 5.1e+03 0.42 7.6 0.99 ++
27 -0.26 -0.55 -0.27 0.77 -0.48 -0.67 -0.89 -0.91 -0.00054 1.3 0.068 5.1e+03 0.42 0.35 -0.025 -
28 -0.46 -0.59 -0.43 0.95 -0.7 -0.7 -1.2 -1.2 0.00054 1.6 -0.2 5.1e+03 28 0.35 0.17 +
29 -0.46 -0.59 -0.43 0.95 -0.7 -0.7 -1.2 -1.2 0.00054 1.6 -0.2 5.1e+03 28 0.17 0.068 -
30 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.17 0.69 +
31 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.087 -2 -
32 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.043 -0.77 -
33 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.022 -0.05 -
34 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.011 -0.056 -
35 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.0054 -0.12 -
36 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.0027 -0.23 -
37 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.0014 -0.33 -
38 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.00068 -0.39 -
39 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.00034 -0.42 -
40 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 0.00017 -0.44 -
41 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -3.4e-05 1.6 -0.043 5e+03 20 8.5e-05 -0.39 -
42 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -0.00012 1.6 -0.043 5e+03 10 8.5e-05 0.3 +
43 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -0.00012 1.6 -0.043 5e+03 10 4.2e-05 -0.73 -
44 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -7.6e-05 1.6 -0.043 5e+03 3.9 4.2e-05 0.64 +
45 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -8.2e-05 1.6 -0.043 5e+03 0.14 0.00042 1 ++
46 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -8.1e-05 1.6 -0.043 5e+03 0.073 0.0042 1 ++
47 -0.42 -0.59 -0.37 0.95 -0.66 -0.87 -1.2 -1.2 -6.3e-05 1.6 -0.047 5e+03 0.1 0.042 1 ++
48 -0.44 -0.6 -0.37 0.96 -0.68 -0.85 -1.2 -1.3 0.00011 1.6 -0.09 5e+03 4.9 0.042 0.82 +
49 -0.45 -0.6 -0.37 0.96 -0.7 -0.84 -1.2 -1.3 0.00013 1.6 -0.091 5e+03 0.37 0.42 1 ++
50 -0.49 -0.6 -0.24 1 -0.84 -1.1 -1.5 -1.7 0.00022 1.6 -0.11 4.9e+03 2.3 4.2 0.99 ++
51 -0.36 -0.37 -0.25 1.3 -0.87 -1.2 -1.8 -2.3 0.00021 1 -0.11 4.9e+03 2.1 4.2 0.23 +
52 -0.34 -0.24 -0.29 1.3 -0.82 -1.1 -1.9 -2.3 0.00022 1 -0.11 4.9e+03 0.32 42 1 ++
53 -0.33 -0.24 -0.32 1.3 -0.81 -1.1 -2 -2.3 0.00023 1 -0.11 4.9e+03 0.072 4.2e+02 1.1 ++
54 -0.33 -0.24 -0.31 1.3 -0.82 -1.1 -2 -2.3 0.00023 1 -0.11 4.9e+03 0.035 4.2e+03 1 ++
55 -0.33 -0.23 -0.31 1.3 -0.82 -1.1 -2 -2.3 0.00023 1 -0.11 4.9e+03 1.3e-05 4.2e+04 1 ++
56 -0.33 -0.23 -0.31 1.3 -0.82 -1.1 -2 -2.3 0.00023 1 -0.11 4.9e+03 0.0082 4.2e+05 1 ++
57 -0.33 -0.23 -0.31 1.3 -0.82 -1.1 -2 -2.3 0.00023 1 -0.11 4.9e+03 1.1e-06 4.2e+05 1 ++
Considering neighbor 1/20 for current solution
Considering neighbor 2/20 for current solution
Attempt 91/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME mu_existing Function Relgrad Radius Rho
0 -0.058 -0.39 -0.47 -0.78 -1 -0.92 1.6 5.3e+03 0.095 10 0.96 ++
1 -0.37 0.16 -0.62 -0.76 -1.7 -0.83 2.1 5.1e+03 0.039 10 0.78 +
2 -0.42 0.21 -0.85 -1 -2.4 -1.1 1 5.1e+03 0.051 10 0.13 +
3 -0.32 0.15 -0.89 -1 -2.5 -1.1 1.2 5.1e+03 0.0049 1e+02 1.1 ++
4 -0.34 0.15 -0.87 -0.98 -2.5 -1.1 1.3 5.1e+03 0.0013 1e+03 1.1 ++
5 -0.33 0.14 -0.86 -0.97 -2.4 -1.1 1.3 5.1e+03 7.9e-05 1e+04 1 ++
6 -0.33 0.14 -0.86 -0.97 -2.4 -1.1 1.3 5.1e+03 2.6e-07 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 92/100
Considering neighbor 0/20 for current solution
Attempt 93/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME_CAR B_TIME_SM B_TIME_TRAIN cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 0 0 0 0 0 7e+03 0.4 0.5 -0.52 -
1 -0.019 -0.017 -0.28 0.0068 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 0.0057 6.3e+03 1.4 0.5 0.29 +
2 -0.019 -0.017 -0.28 0.0068 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 0.0057 6.3e+03 1.4 0.25 -4.9 -
3 -0.019 -0.017 -0.28 0.0068 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 0.0057 6.3e+03 1.4 0.12 -5.6 -
4 -0.019 -0.017 -0.28 0.0068 -0.038 0.29 -0.3 -0.076 0.24 -0.5 0.053 0.0057 6.3e+03 1.4 0.062 -3.8 -
5 -0.017 -0.019 -0.27 0.01 -0.037 0.28 -0.3 -0.013 0.23 -0.49 -0.0099 -0.0027 6.1e+03 5.3 0.062 0.35 +
6 -0.017 -0.019 -0.27 0.01 -0.037 0.28 -0.3 -0.013 0.23 -0.49 -0.0099 -0.0027 6.1e+03 5.3 0.031 -0.31 -
7 -0.017 -0.021 -0.27 0.015 -0.039 0.27 -0.3 0.018 0.22 -0.48 0.021 0.0052 5.9e+03 0.79 0.031 0.22 +
8 -0.017 -0.021 -0.27 0.015 -0.039 0.27 -0.3 0.018 0.22 -0.48 0.021 0.0052 5.9e+03 0.79 0.016 -1.1 -
9 -0.033 -0.037 -0.26 0.03 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0057 -0.01 5.8e+03 0.15 0.16 0.96 ++
10 -0.033 -0.037 -0.26 0.03 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0057 -0.01 5.8e+03 0.15 0.078 -33 -
11 -0.033 -0.037 -0.26 0.03 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0057 -0.01 5.8e+03 0.15 0.039 -25 -
12 -0.033 -0.037 -0.26 0.03 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0057 -0.01 5.8e+03 0.15 0.02 -19 -
13 -0.033 -0.037 -0.26 0.03 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0057 -0.01 5.8e+03 0.15 0.0098 -15 -
14 -0.033 -0.037 -0.26 0.03 -0.054 0.26 -0.31 0.0026 0.21 -0.47 0.0057 -0.01 5.8e+03 0.15 0.0049 -6 -
15 -0.038 -0.042 -0.25 0.035 -0.059 0.25 -0.32 -0.0023 0.2 -0.46 0.00082 -0.015 5.8e+03 0.051 0.049 1.2 ++
16 -0.045 -0.056 -0.25 0.063 -0.074 0.2 -0.34 -0.039 0.18 -0.46 0.0086 -0.026 5.8e+03 0.54 0.049 0.51 +
17 -0.045 -0.056 -0.25 0.063 -0.074 0.2 -0.34 -0.039 0.18 -0.46 0.0086 -0.026 5.8e+03 0.54 0.024 -16 -
18 -0.045 -0.056 -0.25 0.063 -0.074 0.2 -0.34 -0.039 0.18 -0.46 0.0086 -0.026 5.8e+03 0.54 0.012 -11 -
19 -0.045 -0.056 -0.25 0.063 -0.074 0.2 -0.34 -0.039 0.18 -0.46 0.0086 -0.026 5.8e+03 0.54 0.0061 -1.3 -
20 -0.051 -0.062 -0.24 0.069 -0.08 0.2 -0.35 -0.033 0.17 -0.46 0.0025 -0.032 5.7e+03 0.32 0.061 0.97 ++
21 -0.051 -0.062 -0.24 0.069 -0.08 0.2 -0.35 -0.033 0.17 -0.46 0.0025 -0.032 5.7e+03 0.32 0.031 -7.6 -
22 -0.051 -0.062 -0.24 0.069 -0.08 0.2 -0.35 -0.033 0.17 -0.46 0.0025 -0.032 5.7e+03 0.32 0.015 -8 -
23 -0.051 -0.062 -0.24 0.069 -0.08 0.2 -0.35 -0.033 0.17 -0.46 0.0025 -0.032 5.7e+03 0.32 0.0076 -8.2 -
24 -0.051 -0.062 -0.24 0.069 -0.08 0.2 -0.35 -0.033 0.17 -0.46 0.0025 -0.032 5.7e+03 0.32 0.0038 -8.4 -
25 -0.051 -0.062 -0.24 0.069 -0.08 0.2 -0.35 -0.033 0.17 -0.46 0.0025 -0.032 5.7e+03 0.32 0.0019 -0.27 -
26 -0.053 -0.064 -0.24 0.071 -0.082 0.19 -0.35 -0.035 0.17 -0.47 0.00057 -0.031 5.7e+03 0.066 0.019 0.91 ++
27 -0.056 -0.07 -0.25 0.084 -0.09 0.18 -0.36 -0.05 0.16 -0.47 -0.00038 -0.027 5.7e+03 0.64 0.19 0.93 ++
28 -0.082 -0.13 -0.26 0.22 -0.16 -0.015 -0.51 -0.23 0.078 -0.53 0.00091 0.024 5.6e+03 2.7 0.19 0.81 +
29 -0.082 -0.13 -0.26 0.22 -0.16 -0.015 -0.51 -0.23 0.078 -0.53 0.00091 0.024 5.6e+03 2.7 0.095 -7 -
30 -0.082 -0.13 -0.26 0.22 -0.16 -0.015 -0.51 -0.23 0.078 -0.53 0.00091 0.024 5.6e+03 2.7 0.048 -7.5 -
31 -0.082 -0.13 -0.26 0.22 -0.16 -0.015 -0.51 -0.23 0.078 -0.53 0.00091 0.024 5.6e+03 2.7 0.024 -6.8 -
32 -0.082 -0.13 -0.26 0.22 -0.16 -0.015 -0.51 -0.23 0.078 -0.53 0.00091 0.024 5.6e+03 2.7 0.012 -4.4 -
33 -0.082 -0.13 -0.26 0.22 -0.16 -0.015 -0.51 -0.23 0.078 -0.53 0.00091 0.024 5.6e+03 2.7 0.006 -3.3 -
34 -0.082 -0.13 -0.26 0.22 -0.16 -0.015 -0.51 -0.23 0.078 -0.53 0.00091 0.024 5.6e+03 2.7 0.003 -2.3 -
35 -0.082 -0.13 -0.26 0.22 -0.16 -0.015 -0.51 -0.23 0.078 -0.53 0.00091 0.024 5.6e+03 2.7 0.0015 -0.76 -
36 -0.081 -0.14 -0.26 0.22 -0.16 -0.016 -0.51 -0.23 0.076 -0.53 -0.00058 0.024 5.5e+03 3.4 0.0015 0.69 +
37 -0.08 -0.14 -0.26 0.22 -0.16 -0.018 -0.51 -0.23 0.075 -0.53 -0.00015 0.024 5.5e+03 2.3 0.0015 0.31 +
38 -0.08 -0.14 -0.26 0.22 -0.16 -0.018 -0.51 -0.23 0.075 -0.53 -0.00015 0.024 5.5e+03 2.3 0.00075 -1 -
39 -0.08 -0.14 -0.26 0.22 -0.16 -0.018 -0.51 -0.23 0.075 -0.53 -0.00015 0.024 5.5e+03 2.3 0.00037 -1.2 -
40 -0.08 -0.14 -0.26 0.22 -0.16 -0.018 -0.51 -0.23 0.075 -0.53 -0.00052 0.025 5.5e+03 2.8 0.00037 0.22 +
41 -0.08 -0.14 -0.26 0.22 -0.16 -0.019 -0.51 -0.23 0.074 -0.53 -0.00029 0.025 5.5e+03 1.3 0.00037 0.62 +
42 -0.08 -0.14 -0.26 0.22 -0.16 -0.019 -0.51 -0.23 0.074 -0.53 -0.00039 0.025 5.5e+03 0.42 0.00037 0.86 +
43 -0.08 -0.14 -0.26 0.22 -0.16 -0.019 -0.51 -0.23 0.074 -0.53 -0.00037 0.025 5.5e+03 0.042 0.0037 1 ++
44 -0.079 -0.14 -0.26 0.22 -0.16 -0.023 -0.51 -0.23 0.071 -0.53 -0.00042 0.027 5.5e+03 0.95 0.037 1 ++
45 -0.074 -0.15 -0.27 0.25 -0.16 -0.06 -0.54 -0.24 0.044 -0.54 -0.00044 0.043 5.5e+03 0.09 0.37 1 ++
46 -0.089 -0.27 -0.31 0.55 -0.26 -0.43 -0.87 -0.55 -0.25 -0.64 -0.0016 0.3 5.3e+03 4.6 0.37 0.79 +
47 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.37 0.84 +
48 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.19 -4.7 -
49 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.093 -4.1 -
50 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.047 -4 -
51 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.023 -4 -
52 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.012 -4 -
53 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.0058 -2.7 -
54 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.0029 -2 -
55 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.0015 -1.5 -
56 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.00073 -0.89 -
57 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00059 0.12 5.1e+03 11 0.00036 -0.3 -
58 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00095 0.12 5.1e+03 7.6 0.00036 0.14 +
59 -0.16 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00095 0.12 5.1e+03 7.6 0.00018 -0.17 -
60 -0.15 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00077 0.12 5.1e+03 1 0.00018 0.81 +
61 -0.15 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.61 -0.00078 0.12 5.1e+03 0.04 0.0018 1 ++
62 -0.15 -0.37 -0.38 0.88 -0.33 -0.65 -1.2 -0.72 -0.36 -0.62 -0.00078 0.13 5.1e+03 0.4 0.018 1 ++
63 -0.15 -0.37 -0.4 0.88 -0.33 -0.65 -1.3 -0.72 -0.36 -0.63 -0.00082 0.13 5.1e+03 0.025 0.18 1 ++
64 -0.17 -0.4 -0.49 1 -0.37 -0.76 -1.4 -0.77 -0.46 -0.73 -0.00059 0.079 5.1e+03 0.049 1.8 1 ++
65 -0.17 -0.4 -0.49 1 -0.37 -0.76 -1.4 -0.77 -0.46 -0.73 -0.00059 0.079 5.1e+03 0.049 0.41 0.096 -
66 -0.28 -0.46 -0.69 1.3 -0.54 -1 -1.8 -1.1 -0.73 -0.89 -0.00016 -0.024 5e+03 0.24 4.1 1.2 ++
67 -0.28 -0.46 -0.69 1.3 -0.54 -1 -1.8 -1.1 -0.73 -0.89 -0.00016 -0.024 5e+03 0.24 0.58 -1.4 -
68 -0.25 -0.41 -0.84 1.3 -0.72 -1.1 -2 -1.7 -1.3 -1.2 8.2e-05 -0.083 4.9e+03 6.1 5.8 1.1 ++
69 -0.2 -0.26 -0.51 1.3 -0.7 -1.1 -2 -2.4 -2.1 -2 0.00026 -0.12 4.9e+03 5.5 58 0.97 ++
70 -0.17 -0.25 -0.35 1.3 -0.65 -1.2 -2 -2.6 -2.2 -2.2 0.00021 -0.11 4.9e+03 3.4 58 0.85 +
71 -0.18 -0.25 -0.36 1.3 -0.68 -1.2 -2 -2.5 -2.2 -2.2 0.00022 -0.11 4.9e+03 0.12 5.8e+02 1 ++
72 -0.18 -0.25 -0.37 1.3 -0.67 -1.2 -2 -2.5 -2.2 -2.2 0.00022 -0.11 4.9e+03 0.0029 5.8e+03 1 ++
73 -0.18 -0.25 -0.37 1.3 -0.67 -1.2 -2 -2.5 -2.2 -2.2 0.00022 -0.11 4.9e+03 3.4e-06 5.8e+03 1 ++
Considering neighbor 0/20 for current solution
Attempt 94/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_SM B_TIME_TRAIN mu_public Function Relgrad Radius Rho
0 -0.66 -0.81 -0.89 -0.6 -0.74 -0.77 1 5.4e+03 0.065 10 1.1 ++
1 -0.28 -0.25 -1 -1.1 -1.1 -1.4 1.1 5.3e+03 0.0098 1e+02 1 ++
2 -0.28 -0.27 -1.1 -1.1 -1.1 -1.5 1 5.3e+03 0.004 1e+03 1.1 ++
3 -0.27 -0.2 -1.1 -1.1 -1.2 -1.6 1 5.3e+03 0.00015 1e+04 1 ++
4 -0.27 -0.2 -1.1 -1.1 -1.2 -1.6 1 5.3e+03 8.6e-08 1e+04 1 ++
Considering neighbor 0/20 for current solution
Attempt 95/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter lambda_travel_t mu_existing Function Relgrad Radius Rho
0 0.059 -0.54 -0.24 -0.85 -0.8 -1 -0.0022 1.7 2 6e+03 0.18 1 0.52 +
1 -0.52 -0.034 -0.58 -0.22 -1.6 -0.24 0.28 1.2 3 5.5e+03 0.14 1 0.46 +
2 -0.52 -0.034 -0.58 -0.22 -1.6 -0.24 0.28 1.2 3 5.5e+03 0.14 0.5 -0.44 -
3 -0.3 -0.28 -0.62 -0.7 -1.1 -0.66 0.49 1.6 2.9 5.2e+03 0.042 0.5 0.73 +
4 -0.3 -0.28 -0.62 -0.7 -1.1 -0.66 0.49 1.6 2.9 5.2e+03 0.042 0.25 -6.3 -
5 -0.3 -0.28 -0.62 -0.7 -1.1 -0.66 0.49 1.6 2.9 5.2e+03 0.042 0.12 -1.7 -
6 -0.33 -0.3 -0.61 -0.63 -1.1 -0.54 0.56 1.5 2.9 5.2e+03 0.078 0.12 0.59 +
7 -0.39 -0.27 -0.53 -0.63 -1.2 -0.51 0.44 1.6 2.8 5.2e+03 0.013 0.12 0.45 +
8 -0.42 -0.33 -0.56 -0.63 -1.2 -0.51 0.51 1.5 2.7 5.2e+03 0.04 0.12 0.56 +
9 -0.44 -0.31 -0.56 -0.63 -1.3 -0.51 0.48 1.5 2.6 5.2e+03 0.019 1.2 1.2 ++
10 -0.44 -0.31 -0.56 -0.63 -1.3 -0.51 0.48 1.5 2.6 5.2e+03 0.019 0.62 -0.25 -
11 -0.34 -0.29 -0.73 -0.77 -1.4 -0.8 0.71 0.99 2 5.2e+03 0.025 6.2 0.98 ++
12 -0.19 0.035 -0.8 -0.78 -1.8 -1.1 0.53 0.25 1.7 5.1e+03 0.022 62 1.4 ++
13 -0.024 0.47 -0.94 -0.88 -2.4 -1.3 -0.3 0.17 1.3 5e+03 0.013 6.2e+02 1.2 ++
14 -0.019 0.62 -0.99 -0.95 -2.7 -1.5 -0.61 0.19 1.2 5e+03 0.0022 6.2e+03 1.1 ++
15 -0.022 0.65 -1 -0.97 -2.8 -1.5 -0.67 0.18 1.2 5e+03 0.0002 6.2e+04 1 ++
16 -0.022 0.65 -1 -0.97 -2.8 -1.5 -0.67 0.18 1.2 5e+03 2.5e-07 6.2e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME lambda_travel_t mu_public Function Relgrad Radius Rho
0 -0.23 -0.25 -0.54 0.33 -0.41 -0.42 -0.51 -1 1.5 1 5.4e+03 0.054 10 0.9 ++
1 -0.23 -0.25 -0.54 0.33 -0.41 -0.42 -0.51 -1 1.5 1 5.4e+03 0.054 2.3 -12 -
2 -0.23 -0.25 -0.54 0.33 -0.41 -0.42 -0.51 -1 1.5 1 5.4e+03 0.054 1.2 -0.4 -
3 -0.51 -0.53 -0.29 1.5 -0.63 -0.9 -1.5 -1.2 0.75 1.4 5e+03 0.042 1.2 0.85 +
4 -0.51 -0.53 -0.29 1.5 -0.63 -0.9 -1.5 -1.2 0.75 1.4 5e+03 0.042 0.58 -2 -
5 -0.48 -0.48 -0.31 1.1 -0.9 -1.1 -1.5 -1.5 0.16 1.5 5e+03 0.018 0.58 0.6 +
6 -0.41 0.1 0.16 0.73 -1.1 -1.2 -1.8 -1.3 0.36 1.8 5e+03 0.017 0.58 0.55 +
7 -0.32 -0.31 0.047 0.84 -1.1 -1.1 -1.8 -1.4 0.34 1.5 5e+03 0.0081 5.8 1.2 ++
8 -0.28 -0.35 -0.028 0.96 -1 -1.1 -1.9 -1.5 0.33 1.3 5e+03 0.0056 58 1.2 ++
9 -0.25 -0.33 -0.082 1 -1 -1.1 -1.9 -1.6 0.33 1.3 4.9e+03 0.0019 5.8e+02 1.2 ++
10 -0.23 -0.31 -0.12 1.1 -1 -1.1 -2 -1.6 0.32 1.2 4.9e+03 0.00085 5.8e+03 1.2 ++
11 -0.23 -0.31 -0.13 1.1 -1 -1.1 -2 -1.6 0.32 1.2 4.9e+03 7.9e-05 5.8e+04 1.1 ++
12 -0.23 -0.31 -0.13 1.1 -1 -1.1 -2 -1.6 0.32 1.2 4.9e+03 2.2e-06 5.8e+04 1 ++
Considering neighbor 1/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME lambda_travel_t mu_public Function Relgrad Radius Rho
0 -0.23 -0.53 -0.42 -0.43 -0.51 -1 1.5 1 5.5e+03 0.056 1 0.9 +
1 -0.4 -0.24 -0.51 -0.73 -1.5 -1 0.86 1.1 5.2e+03 0.033 10 1.1 ++
2 -0.32 1.1 -0.99 -1.1 -2.6 -1.6 0.03 1.6 5.1e+03 0.084 10 0.22 +
3 -0.32 1.1 -0.99 -1.1 -2.6 -1.6 0.03 1.6 5.1e+03 0.084 0.89 -7.7 -
4 -0.46 0.22 -1.4 -1.3 -2.1 -1 0.37 2.2 5.1e+03 0.048 0.89 0.38 +
5 -0.46 0.22 -1.4 -1.3 -2.1 -1 0.37 2.2 5.1e+03 0.048 0.44 -1.8 -
6 -0.49 0.59 -1.1 -1.2 -2.1 -1.1 0.36 2.7 5.1e+03 0.053 0.44 0.11 +
7 -0.43 0.51 -1.2 -1.2 -2.2 -1.1 0.29 2.2 5e+03 0.019 4.4 1.2 ++
8 -0.43 0.51 -1.2 -1.2 -2.2 -1.1 0.29 2.2 5e+03 0.019 0.53 -1.9 -
9 -0.38 0.5 -1.1 -1.1 -2.3 -1.2 0.35 1.7 5e+03 0.025 5.3 1 ++
10 -0.29 0.57 -1.1 -1.1 -2.5 -1.4 0.26 1.5 5e+03 0.013 53 1.3 ++
11 -0.2 0.61 -1.1 -1.1 -2.8 -1.6 0.25 1.2 5e+03 0.015 5.3e+02 1.1 ++
12 -0.15 0.61 -1.1 -1.1 -2.9 -1.6 0.24 1.1 5e+03 0.0041 5.3e+03 1.2 ++
13 -0.13 0.56 -1 -1.1 -3 -1.6 0.24 1 5e+03 0.0035 5.3e+04 1.1 ++
14 -0.12 0.57 -1 -1.1 -3 -1.6 0.24 1 5e+03 0.0014 5.3e+05 1 ++
15 -0.12 0.6 -1 -1.1 -3.1 -1.6 0.23 1 5e+03 1.9e-05 5.3e+06 1 ++
16 -0.12 0.6 -1 -1.1 -3.1 -1.6 0.23 1 5e+03 1.8e-09 5.3e+06 1 ++
Considering neighbor 2/20 for current solution
Considering neighbor 3/20 for current solution
Attempt 96/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME_CAR B_TIME_SM B_TIME_TRAIN Function Relgrad Radius Rho
0 -0.12 -0.2 -0.49 0.5 -1 -0.54 -0.042 -0.63 5.4e+03 0.072 10 1.1 ++
1 -0.25 -0.25 -1 1.9 -1.1 -1 -0.93 -1.1 5.1e+03 0.026 1e+02 1.1 ++
2 -0.33 -0.27 -1.1 1.9 -1.1 -1.1 -1.2 -1.3 5e+03 0.0021 1e+03 1 ++
3 -0.33 -0.27 -1.1 1.9 -1.1 -1.1 -1.2 -1.3 5e+03 1.8e-05 1e+04 1 ++
4 -0.33 -0.27 -1.1 1.9 -1.1 -1.1 -1.2 -1.3 5e+03 1.6e-09 1e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_comm B_TIME_SM B_TIME_SM_commu B_TIME_TRAIN B_TIME_TRAIN_co Function Relgrad Radius Rho
0 -0.56 -0.88 -0.87 -0.81 0.25 -1 0.4 -0.85 0.15 5.4e+03 0.063 10 1.1 ++
1 -0.24 -0.32 -1.1 -1.4 0.96 -1.7 2.1 -1.7 0.75 5.2e+03 0.022 1e+02 0.93 ++
2 -0.38 -0.17 -1.1 -1.4 0.47 -1.8 1.2 -1.9 0.21 5.2e+03 0.018 1e+02 0.13 +
3 -0.28 -0.12 -1.1 -1.4 0.91 -1.8 2 -1.9 0.58 5.2e+03 0.0074 1e+02 0.43 +
4 -0.34 -0.14 -1.1 -1.4 0.59 -1.8 1.5 -1.9 0.31 5.2e+03 0.0088 1e+02 0.24 +
5 -0.31 -0.13 -1.1 -1.4 0.71 -1.8 1.7 -1.9 0.42 5.2e+03 0.0001 1e+03 0.96 ++
6 -0.31 -0.13 -1.1 -1.4 0.71 -1.8 1.7 -1.9 0.42 5.2e+03 1e-06 1e+03 1 ++
Considering neighbor 1/20 for current solution
Attempt 97/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_COST_SM B_COST_TRAIN B_TIME cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -6.4 -
1 0 0 0 0 0 0 0 1 0 7e+03 0.27 0.25 -1.5 -
2 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 2.5 1 ++
3 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 1.2 -7 -
4 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.62 -3.9 -
5 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.31 -2.3 -
6 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.16 -0.62 -
7 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.078 -0.051 -
8 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.039 0.023 -
9 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.02 -0.14 -
10 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.0098 -0.49 -
11 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.0049 -0.97 -
12 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.0024 -1.5 -
13 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.0012 -1.9 -
14 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.00061 -1.3 -
15 0.13 -0.25 -0.25 0.25 -0.25 -0.25 0 1.2 0 5.8e+03 4.1 0.00031 -0.21 -
16 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.00031 1.3 0.00031 5.8e+03 1.9 0.00031 0.62 +
17 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.00024 1.3 0.00049 5.8e+03 0.22 0.0031 0.97 ++
18 0.13 -0.25 -0.25 0.25 -0.25 -0.25 -0.00026 1.3 0.0023 5.8e+03 0.15 0.031 1 ++
19 0.14 -0.28 -0.24 0.24 -0.28 -0.28 -0.00033 1.3 0.021 5.7e+03 0.19 0.31 1 ++
20 0.19 -0.41 -0.18 -0.016 -0.56 -0.59 -0.0012 1.5 0.21 5.4e+03 7 0.31 0.73 +
21 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.31 0.57 +
22 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.15 -4.1 -
23 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.076 -3.9 -
24 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.038 -3.9 -
25 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.019 -2.8 -
26 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.0095 -2.1 -
27 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.0048 -1.8 -
28 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.0024 -1.5 -
29 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.0012 -1.3 -
30 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.0006 -0.79 -
31 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00015 1.5 0.022 5.3e+03 15 0.0003 -0.19 -
32 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00044 1.5 0.022 5.3e+03 10 0.0003 0.41 +
33 -0.12 -0.19 -0.42 -0.24 -0.66 -0.65 -0.00044 1.5 0.022 5.3e+03 10 0.00015 -0.7 -
34 -0.11 -0.19 -0.41 -0.24 -0.66 -0.65 -0.0003 1.5 0.022 5.3e+03 13 0.00015 0.32 +
35 -0.11 -0.19 -0.41 -0.24 -0.66 -0.65 -0.00038 1.5 0.022 5.3e+03 7.1 0.00015 0.37 +
36 -0.11 -0.19 -0.41 -0.24 -0.66 -0.65 -0.00032 1.5 0.022 5.3e+03 8.2 0.00015 0.27 +
37 -0.11 -0.19 -0.41 -0.24 -0.66 -0.65 -0.00035 1.5 0.022 5.3e+03 0.67 0.0015 0.93 ++
38 -0.11 -0.19 -0.41 -0.24 -0.66 -0.65 -0.00035 1.5 0.023 5.3e+03 0.07 0.015 1 ++
39 -0.1 -0.2 -0.4 -0.25 -0.67 -0.66 -0.00037 1.5 0.027 5.3e+03 0.35 0.15 1 ++
40 -0.044 -0.27 -0.34 -0.32 -0.82 -0.72 -0.00035 1.6 0.025 5.2e+03 0.24 1.5 1 ++
41 -0.044 -0.27 -0.34 -0.32 -0.82 -0.72 -0.00035 1.6 0.025 5.2e+03 0.24 0.75 -25 -
42 -0.044 -0.27 -0.34 -0.32 -0.82 -0.72 -0.00035 1.6 0.025 5.2e+03 0.24 0.37 -3.3 -
43 -0.044 -0.27 -0.34 -0.32 -0.82 -0.72 -0.00035 1.6 0.025 5.2e+03 0.24 0.19 -0.31 -
44 -0.22 -0.19 -0.47 -0.39 -1 -0.88 7.7e-06 1.7 -0.063 5.1e+03 1.9 1.9 0.97 ++
45 -0.22 -0.19 -0.47 -0.39 -1 -0.88 7.7e-06 1.7 -0.063 5.1e+03 1.9 0.57 -1.1 -
46 -0.38 0.027 -0.58 -0.83 -1.6 -1.4 0.00036 2.1 -0.14 5.1e+03 3.5 0.57 0.65 +
47 -0.38 0.027 -0.58 -0.83 -1.6 -1.4 0.00036 2.1 -0.14 5.1e+03 3.5 0.29 -0.23 -
48 -0.38 0.027 -0.58 -0.83 -1.6 -1.4 0.00036 2.1 -0.14 5.1e+03 3.5 0.14 0.068 -
49 -0.33 0.17 -0.54 -0.89 -1.5 -1.5 -1e-05 2.1 -0.056 5e+03 23 0.14 0.2 +
50 -0.33 0.17 -0.54 -0.89 -1.5 -1.5 -1e-05 2.1 -0.056 5e+03 23 0.071 -0.4 -
51 -0.33 0.14 -0.55 -0.84 -1.6 -1.6 0.00027 2.1 -0.13 5e+03 45 0.071 0.24 +
52 -0.29 0.15 -0.54 -0.79 -1.6 -1.6 0.00016 2.1 -0.097 5e+03 24 0.071 0.79 +
53 -0.26 0.2 -0.55 -0.76 -1.6 -1.7 0.00022 2 -0.11 5e+03 2.8 0.071 0.89 +
54 -0.24 0.25 -0.55 -0.8 -1.7 -1.7 0.00019 1.9 -0.1 5e+03 0.48 0.71 0.99 ++
55 -0.18 0.48 -0.74 -0.97 -2.3 -2.1 0.00022 1.2 -0.11 5e+03 1.6 0.71 0.52 +
56 -0.2 0.47 -0.76 -0.99 -2.4 -2.1 0.00023 1.3 -0.11 5e+03 0.29 7.1 1.1 ++
57 -0.2 0.48 -0.76 -0.98 -2.4 -2.1 0.00023 1.4 -0.11 5e+03 1.1 71 1 ++
58 -0.2 0.47 -0.75 -0.98 -2.4 -2.1 0.00022 1.4 -0.11 5e+03 0.36 7.1e+02 0.99 ++
59 -0.2 0.47 -0.75 -0.98 -2.4 -2.1 0.00022 1.4 -0.11 5e+03 0.015 7.1e+03 1 ++
60 -0.2 0.47 -0.75 -0.98 -2.4 -2.1 0.00022 1.4 -0.11 5e+03 0.019 7.1e+04 1 ++
61 -0.2 0.47 -0.75 -0.98 -2.4 -2.1 0.00022 1.4 -0.11 5e+03 0.002 7.1e+05 1 ++
62 -0.2 0.47 -0.75 -0.98 -2.4 -2.1 0.00022 1.4 -0.11 5e+03 0.00028 7.1e+06 1 ++
63 -0.2 0.47 -0.75 -0.98 -2.4 -2.1 0.00022 1.4 -0.11 5e+03 0.00031 7.1e+07 1 ++
64 -0.2 0.47 -0.75 -0.98 -2.4 -2.1 0.00022 1.4 -0.11 5e+03 3e-07 7.1e+07 1 ++
Considering neighbor 0/20 for current solution
Attempt 98/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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 cube_tt_coef mu_public square_tt_coef Function Relgrad Radius Rho
0 0 0 0 0 0 1 0 7e+03 0.27 0.5 -0.86 -
1 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 5 1 ++
2 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 2.5 -8.1e+303 -
3 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 1.2 -1.8e+304 -
4 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.62 -4.1 -
5 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.31 -2.4 -
6 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.16 -1.7 -
7 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.078 -1.7 -
8 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.039 -2.1 -
9 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.02 -2.6 -
10 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.0098 -3.1 -
11 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.0049 -3.5 -
12 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.0024 -2.4 -
13 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.0012 -1.6 -
14 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.00061 -0.9 -
15 -0.079 -0.5 -0.5 -0.5 0 1 0 5.7e+03 7.4 0.00031 -0.099 -
16 -0.08 -0.5 -0.5 -0.5 -0.00031 1 0.00031 5.7e+03 3.5 0.00031 0.69 +
17 -0.08 -0.5 -0.5 -0.5 -0.00025 1 0.00061 5.7e+03 1.2 0.00031 0.87 +
18 -0.08 -0.5 -0.5 -0.5 -0.00027 1 0.00092 5.7e+03 0.15 0.0031 1 ++
19 -0.08 -0.5 -0.5 -0.5 -0.00027 1 0.004 5.7e+03 0.97 0.031 1 ++
20 -0.086 -0.52 -0.5 -0.53 -0.00041 1 0.034 5.6e+03 0.28 0.31 1 ++
21 -0.14 -0.64 -0.6 -0.83 -0.0013 1.2 0.26 5.5e+03 2.5 0.31 0.39 +
22 -0.22 -0.61 -0.85 -0.95 -6.4e-05 1.2 -0.046 5.4e+03 1.4 0.31 0.61 +
23 -0.32 -0.6 -0.95 -1.3 -5.6e-05 1.2 -0.052 5.3e+03 6 3.1 1.1 ++
24 -0.029 -0.4 -1.2 -2.1 0.00029 1 -0.12 5.3e+03 2.6 3.1 0.23 +
25 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 3.1 0.72 +
26 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.73 -3.9 -
27 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.36 -1.5 -
28 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.18 -0.81 -
29 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.091 -0.68 -
30 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.045 -0.43 -
31 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.023 -0.38 -
32 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.011 -0.38 -
33 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.0057 -0.22 -
34 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.0028 -0.22 -
35 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.0014 -0.26 -
36 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.00071 -0.3 -
37 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.00035 -0.33 -
38 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 0.00018 -0.35 -
39 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 8.9e-05 -0.35 -
40 0.049 -0.39 -1.1 -2.3 0.00011 1 -0.093 5.2e+03 40 4.4e-05 -0.36 -
41 0.049 -0.39 -1.1 -2.3 0.00015 1 -0.093 5.2e+03 25 4.4e-05 0.42 +
42 0.049 -0.39 -1.1 -2.3 0.00013 1 -0.093 5.2e+03 22 4.4e-05 0.3 +
43 0.049 -0.39 -1.1 -2.3 0.00014 1 -0.093 5.2e+03 5.5 4.4e-05 0.8 +
44 0.049 -0.39 -1.1 -2.3 0.00014 1 -0.094 5.2e+03 0.15 0.00044 0.99 ++
45 0.049 -0.39 -1.1 -2.3 0.00014 1 -0.094 5.2e+03 0.082 0.0044 1 ++
46 0.049 -0.39 -1.1 -2.3 0.00016 1 -0.098 5.2e+03 0.54 0.044 0.99 ++
47 0.036 -0.42 -1.1 -2.3 0.00019 1 -0.1 5.2e+03 1 0.44 0.95 ++
48 0.042 -0.41 -1.1 -2.3 0.00019 1 -0.1 5.2e+03 0.35 4.4 1 ++
49 0.043 -0.41 -1.1 -2.3 0.00019 1 -0.1 5.2e+03 0.035 44 1 ++
50 0.043 -0.41 -1.1 -2.3 0.00019 1 -0.1 5.2e+03 0.00014 4.4e+02 1 ++
51 0.043 -0.41 -1.1 -2.3 0.00019 1 -0.1 5.2e+03 2.9e-06 4.4e+02 1 ++
Considering neighbor 0/20 for current solution
Attempt 99/100
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 7e+03 0.4 0.5 -0.55 -
1 6.2e+03 1.7 0.5 0.33 +
2 6.2e+03 1.7 0.25 -6.4 -
3 6.2e+03 1.7 0.12 -7.5 -
4 6.2e+03 1.7 0.062 -6 -
5 6.2e+03 1.7 0.031 -0.99 -
6 5.7e+03 0.73 0.31 0.98 ++
7 5.7e+03 0.73 0.16 -8.7 -
8 5.7e+03 0.73 0.078 -11 -
9 5.7e+03 0.73 0.039 -13 -
10 5.7e+03 0.73 0.02 -31 -
11 5.7e+03 0.73 0.0098 -5.3 -
12 5.7e+03 4.5 0.0098 0.2 +
13 5.7e+03 0.27 0.0098 0.73 +
14 5.7e+03 0.91 0.0098 0.82 +
15 5.7e+03 0.07 0.098 0.99 ++
16 5.6e+03 0.36 0.98 0.99 ++
17 5.2e+03 5.6 0.98 0.67 +
18 5.2e+03 5.6 0.49 -2.2 -
19 5.2e+03 0.37 0.49 0.11 +
20 5.2e+03 0.37 0.24 -0.34 -
21 5.1e+03 0.092 2.4 0.9 ++
22 5.1e+03 0.092 0.15 0.061 -
23 5.1e+03 0.056 0.15 0.88 +
24 5.1e+03 0.0087 1.5 0.98 ++
25 5.1e+03 0.0087 0.13 -0.19 -
26 5.1e+03 0.93 1.3 1 ++
27 5.1e+03 5.2 1.3 0.82 +
28 5.1e+03 5.2 0.63 -6.5 -
29 5.1e+03 5.2 0.32 -5 -
30 5.1e+03 5.2 0.16 -3.4 -
31 5.1e+03 5.2 0.079 -2.1 -
32 5.1e+03 5.2 0.04 -1.3 -
33 5.1e+03 5.2 0.02 -0.85 -
34 5.1e+03 5.2 0.0099 -0.29 -
35 5.1e+03 5.2 0.0049 -0.29 -
36 5.1e+03 5.2 0.0025 -0.4 -
37 5.1e+03 5.2 0.0012 -0.5 -
38 5.1e+03 5.2 0.00062 -0.57 -
39 5.1e+03 5.2 0.00031 -0.61 -
40 5.1e+03 5.2 0.00015 -0.032 -
41 5.1e+03 0.66 0.00015 0.68 +
42 5.1e+03 0.03 0.0015 0.99 ++
43 5.1e+03 0.056 0.015 1 ++
44 5e+03 0.03 0.15 1 ++
45 5e+03 0.83 0.15 0.7 +
46 5e+03 0.74 1.5 1.1 ++
47 5e+03 0.74 0.77 -58 -
48 5e+03 0.74 0.39 -7.4 -
49 5e+03 11 0.39 0.43 +
50 5e+03 11 3.9 0.92 ++
51 5e+03 20 3.9 0.75 +
52 5e+03 0.71 39 1 ++
53 5e+03 0.017 3.9e+02 1 ++
54 5e+03 2.8e-05 3.9e+03 1 ++
55 5e+03 0.0023 3.9e+04 1 ++
56 5e+03 1.5e-07 3.9e+04 1 ++
Considering neighbor 0/20 for current solution
File biogeme.toml has been parsed.
*** Estimate b07everything_000000
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_CAR B_TIME_CAR_1st_ B_TIME_SM B_TIME_SM_1st_c B_TIME_TRAIN B_TIME_TRAIN_1s mu_existing Function Relgrad Radius Rho
0 0.049 -0.41 -1 -0.34 -0.17 -0.13 -0.21 -0.52 -0.28 1.6 5.3e+03 0.12 10 0.94 ++
1 -0.58 0.11 -0.83 -0.017 -1 -0.21 -1.2 -0.68 -0.83 2.4 5.2e+03 0.093 10 0.61 +
2 -0.31 -0.37 -0.7 -0.15 -0.79 -0.2 -0.93 -0.39 -0.72 3.3 5.1e+03 0.04 10 0.46 +
3 -0.31 -0.37 -0.7 -0.15 -0.79 -0.2 -0.93 -0.39 -0.72 3.3 5.1e+03 0.04 1.1 -1.8 -
4 -0.45 -0.37 -0.87 -0.18 -1 -0.34 -1.3 -0.56 -0.95 2.1 5.1e+03 0.0063 1.1 0.85 +
5 -0.43 -0.22 -0.93 -0.16 -1 -0.27 -1.2 -0.62 -0.92 2.2 5.1e+03 0.0009 11 0.98 ++
6 -0.43 -0.22 -0.93 -0.16 -1 -0.28 -1.2 -0.62 -0.93 2.2 5.1e+03 1.3e-05 1.1e+02 1 ++
7 -0.43 -0.22 -0.93 -0.16 -1 -0.28 -1.2 -0.62 -0.93 2.2 5.1e+03 4.5e-08 1.1e+02 1 ++
Considering neighbor 1/20 for current solution
Pareto file has been updated: b07everything_assisted.pareto
Before the algorithm: 126 models, with 10 Pareto.
After the algorithm: 204 models, with 10 Pareto.
VNS algorithm completed. Postprocessing of the Pareto optimal solutions
Pareto set initialized from file with 204 elements [10 Pareto] and 23 invalid elements.
File biogeme.toml has been parsed.
*** Initial values of the parameters are obtained from the file __b07everything_000000.iter
Parameter values restored from __b07everything_000000.iter
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 -0.43 0 -0.22 0 -0.93 0 0 0 6.6e+03 0.26 0.5 -2.4 -
1 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 5 1.1 ++
2 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 2.5 -9.7 -
3 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 1.2 -6.5 -
4 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.62 -4.1 -
5 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.31 -2.7 -
6 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.16 -2.1 -
7 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.078 -2.1 -
8 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.039 -2.5 -
9 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.02 -2.9 -
10 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.0098 -3.3 -
11 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.0049 -3.5 -
12 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.0024 -2.3 -
13 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.0012 -1.6 -
14 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.00061 -0.86 -
15 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 0 0 5.6e+03 8 0.00031 -0.078 -
16 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 -0.00031 0.00031 5.6e+03 3.4 0.00031 0.7 +
17 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 -0.00026 0.00061 5.6e+03 1.1 0.00031 0.87 +
18 -0.16 -0.074 -0.72 0.12 -0.43 -0.5 -0.00027 0.00092 5.6e+03 0.12 0.0031 1 ++
19 -0.16 -0.075 -0.72 0.12 -0.43 -0.5 -0.00027 0.004 5.6e+03 1.1 0.031 1 ++
20 -0.16 -0.077 -0.74 0.13 -0.44 -0.53 -0.00041 0.034 5.5e+03 0.26 0.31 1 ++
21 -0.16 -0.13 -0.85 0.33 -0.65 -0.83 -0.0011 0.2 5.3e+03 2.2 0.31 0.75 +
22 -0.17 -0.2 -0.88 0.63 -0.87 -0.96 -0.00021 -0.011 5.2e+03 1.8 0.31 0.8 +
23 -0.22 -0.25 -0.96 0.94 -0.96 -1.2 -0.00017 -0.021 5.1e+03 1 3.1 1 ++
24 -0.22 -0.25 -0.96 0.94 -0.96 -1.2 -0.00017 -0.021 5.1e+03 1 1.5 -58 -
25 -0.22 -0.25 -0.96 0.94 -0.96 -1.2 -0.00017 -0.021 5.1e+03 1 0.76 -4.2 -
26 -0.22 -0.25 -0.96 0.94 -0.96 -1.2 -0.00017 -0.021 5.1e+03 1 0.38 -0.085 -
27 -0.2 -0.3 -1.1 1.3 -1 -1.5 0.00021 -0.11 5e+03 20 0.38 0.69 +
28 -0.22 -0.33 -1.1 1.7 -1.1 -1.9 0.00016 -0.095 5e+03 14 3.8 0.92 ++
29 -0.026 -0.35 -0.96 2 -1.2 -2.4 0.00028 -0.12 5e+03 16 3.8 0.64 +
30 -0.021 -0.35 -0.95 2 -1.1 -2.3 0.00021 -0.11 5e+03 7.5 3.8 0.85 +
31 -0.034 -0.35 -0.98 2 -1.1 -2.3 0.00022 -0.11 5e+03 0.32 38 1 ++
32 -0.034 -0.35 -0.98 2 -1.1 -2.3 0.00022 -0.11 5e+03 0.003 3.8e+02 1 ++
33 -0.034 -0.35 -0.98 2 -1.1 -2.3 0.00022 -0.11 5e+03 6e-06 3.8e+02 1 ++
File biogeme.toml has been parsed.
*** Initial values of the parameters are obtained from the file __b07everything_000001.iter
Parameter values restored from __b07everything_000001.iter
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.5 -3.8 -
1 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.25 -3.9 -
2 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.12 -4 -
3 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.062 -4.1 -
4 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.031 -2.6 -
5 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.016 -2 -
6 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.0078 -1.7 -
7 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.0039 -1.5 -
8 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.002 -1.3 -
9 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.00098 -0.95 -
10 -0.15 0 -0.7 0 0 0 0 -1.3 0 0 0 5.6e+03 29 0.00049 -0.38 -
11 -0.15 -0.00049 -0.7 0.00049 0.00049 -0.00049 -0.00049 -1.3 0.00049 -0.00049 -0.00049 5.6e+03 17 0.00049 0.3 +
12 -0.15 -0.00052 -0.7 0.0006 0.00058 -0.0007 -0.0006 -1.3 0.00049 -0.00031 -0.00098 5.6e+03 12 0.0049 1.5 ++
13 -0.15 -0.00052 -0.7 0.0006 0.00058 -0.0007 -0.0006 -1.3 0.00049 -0.00031 -0.00098 5.6e+03 12 0.0024 -2.4 -
14 -0.15 -0.00052 -0.7 0.0006 0.00058 -0.0007 -0.0006 -1.3 0.00049 -0.00031 -0.00098 5.6e+03 12 0.0012 -3 -
15 -0.15 -0.00052 -0.7 0.0006 0.00058 -0.0007 -0.0006 -1.3 0.00049 -0.00031 -0.00098 5.6e+03 12 0.00061 -3.5 -
16 -0.15 -0.00052 -0.7 0.0006 0.00058 -0.0007 -0.0006 -1.3 0.00049 -0.00031 -0.00098 5.6e+03 12 0.00031 -3.7 -
17 -0.15 -0.00052 -0.7 0.0006 0.00058 -0.0007 -0.0006 -1.3 0.00049 -0.00031 -0.00098 5.6e+03 12 0.00015 -2.3 -
18 -0.15 -0.00052 -0.7 0.0006 0.00058 -0.0007 -0.0006 -1.3 0.00049 -0.00031 -0.00098 5.6e+03 12 7.6e-05 -1 -
19 -0.15 -0.0006 -0.7 0.00067 0.00066 -0.00078 -0.00067 -1.3 0.00041 -0.00023 -0.0011 5.6e+03 16 7.6e-05 0.37 +
20 -0.15 -0.00061 -0.7 0.00069 0.00068 -0.00082 -0.00069 -1.3 0.00041 -0.00026 -0.0011 5.6e+03 1.5 7.6e-05 0.9 +
21 -0.15 -0.00061 -0.7 0.00071 0.00069 -0.00086 -0.00071 -1.3 0.00041 -0.00025 -0.0012 5.6e+03 0.19 0.00076 1 ++
22 -0.15 -0.00068 -0.7 0.00091 0.00086 -0.0013 -0.00091 -1.3 0.0004 -0.00025 -0.002 5.6e+03 0.49 0.0076 1 ++
23 -0.15 -0.0014 -0.7 0.0029 0.0026 -0.0052 -0.0029 -1.3 0.00032 -0.00022 -0.0096 5.6e+03 0.17 0.076 1 ++
24 -0.13 -0.0089 -0.7 0.025 0.02 -0.046 -0.026 -1.3 -0.0011 9.4e-05 -0.086 5.5e+03 8.1 0.076 0.87 +
25 -0.12 -0.028 -0.7 0.076 0.025 -0.074 -0.1 -1.3 -0.016 -6.4e-05 -0.05 5.5e+03 6.1 0.76 1.1 ++
26 -0.11 -0.25 -0.68 0.68 -0.033 -0.41 -0.87 -1.5 -0.18 0.00021 -0.11 5.2e+03 1.7 7.6 0.95 ++
27 -0.33 -0.057 -0.37 1.5 -0.56 -0.94 -1.7 -1.9 -1 0.00016 -0.1 4.9e+03 8.3 76 1.1 ++
28 -0.25 -0.17 -0.24 1.4 -0.84 -1.1 -2 -2.2 -1.4 0.00025 -0.12 4.9e+03 10 76 0.87 +
29 -0.28 -0.22 -0.23 1.4 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.9e+03 2.4 7.6e+02 0.95 ++
30 -0.28 -0.23 -0.22 1.4 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.9e+03 0.037 7.6e+03 1 ++
31 -0.28 -0.23 -0.22 1.4 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.9e+03 5.5e-05 7.6e+04 1 ++
32 -0.28 -0.23 -0.22 1.4 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.9e+03 7.9e-07 7.6e+04 1 ++
File biogeme.toml has been parsed.
*** Initial values of the parameters are obtained from the file __b07everything_000002.iter
Parameter values restored from __b07everything_000002.iter
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 B_TIME_1st_clas Function Relgrad Radius Rho
0 -0.37 -0.89 -1 -0.36 -0.8 5.3e+03 0.036 10 1.1 ++
1 -0.21 -0.84 -1.2 -0.6 -0.99 5.2e+03 0.0047 1e+02 1.1 ++
2 -0.19 -0.81 -1.2 -0.65 -1 5.2e+03 0.0001 1e+03 1 ++
3 -0.19 -0.81 -1.2 -0.65 -1 5.2e+03 5e-08 1e+03 1 ++
File biogeme.toml has been parsed.
*** Initial values of the parameters are obtained from the file __b07everything_000003.iter
Parameter values restored from __b07everything_000003.iter
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 -0.18 -1.2 -1.3 5.5e+03 0.084 1 0.55 +
1 -0.17 -0.64 -1.1 -1.3 5.3e+03 0.01 10 1.1 ++
2 -0.15 -0.7 -1.1 -1.3 5.3e+03 0.00025 1e+02 1 ++
3 -0.15 -0.7 -1.1 -1.3 5.3e+03 1.7e-07 1e+02 1 ++
File biogeme.toml has been parsed.
*** Initial values of the parameters are obtained from the file __b07everything_000004.iter
Parameter values restored from __b07everything_000004.iter
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME B_TIME_commuter cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.5 -3.8 -
1 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.25 -3.9 -
2 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.12 -4 -
3 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.062 -4.2 -
4 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.031 -2.9 -
5 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.016 -2.2 -
6 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.0078 -1.8 -
7 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.0039 -1.6 -
8 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.002 -1.4 -
9 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.00098 -1 -
10 -0.25 -0.3 -1.3 2 -1.1 -1.2 0 0 0 5.1e+03 29 0.00049 -0.45 -
11 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00049 -0.00049 -0.00049 5e+03 19 0.00049 0.26 +
12 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00054 -0.0003 -0.00098 5e+03 12 0.0049 1.5 ++
13 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00054 -0.0003 -0.00098 5e+03 12 0.0024 -2 -
14 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00054 -0.0003 -0.00098 5e+03 12 0.0012 -2.3 -
15 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00054 -0.0003 -0.00098 5e+03 12 0.00061 -2.5 -
16 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00054 -0.0003 -0.00098 5e+03 12 0.00031 -2.7 -
17 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00054 -0.0003 -0.00098 5e+03 12 0.00015 -2.7 -
18 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00054 -0.0003 -0.00098 5e+03 12 7.6e-05 -1.8 -
19 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00054 -0.0003 -0.00098 5e+03 12 3.8e-05 0.012 -
20 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00058 -0.00026 -0.001 5e+03 3.3 3.8e-05 0.84 +
21 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00058 -0.00025 -0.0011 5e+03 0.37 0.00038 0.94 ++
22 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.00064 -0.00025 -0.0014 5e+03 0.062 0.0038 1 ++
23 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.0013 -0.00023 -0.0052 5e+03 0.43 0.038 1 ++
24 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.014 -7.9e-05 -0.043 5e+03 0.63 0.38 0.92 ++
25 -0.25 -0.3 -1.3 2 -1.1 -1.2 -0.014 -7.9e-05 -0.043 5e+03 0.63 0.19 -0.28 -
26 -0.31 -0.3 -1.3 2 -1.1 -1.4 -0.13 0.0001 -0.086 5e+03 5 1.9 1 ++
27 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00034 -0.14 4.9e+03 19 1.9 0.6 +
28 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00034 -0.14 4.9e+03 19 0.27 -0.41 -
29 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00034 -0.14 4.9e+03 19 0.14 -0.18 -
30 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00034 -0.14 4.9e+03 19 0.068 -0.079 -
31 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00034 -0.14 4.9e+03 19 0.034 -0.0066 -
32 -0.019 -0.29 -0.97 2.1 -1.1 -2.1 -0.99 0.00023 -0.1 4.9e+03 70 0.034 0.3 +
33 -0.019 -0.29 -0.97 2.1 -1.1 -2.1 -0.99 0.00023 -0.1 4.9e+03 70 0.017 -2.7 -
34 -0.019 -0.29 -0.97 2.1 -1.1 -2.1 -0.99 0.00023 -0.1 4.9e+03 70 0.0085 -2.7 -
35 -0.019 -0.29 -0.97 2.1 -1.1 -2.1 -0.99 0.00023 -0.1 4.9e+03 70 0.0042 -2.7 -
36 -0.019 -0.29 -0.97 2.1 -1.1 -2.1 -0.99 0.00023 -0.1 4.9e+03 70 0.0021 -2.6 -
37 -0.019 -0.29 -0.97 2.1 -1.1 -2.1 -0.99 0.00023 -0.1 4.9e+03 70 0.0011 -2.6 -
38 -0.019 -0.29 -0.97 2.1 -1.1 -2.1 -0.99 0.00023 -0.1 4.9e+03 70 0.00053 -2.5 -
39 -0.019 -0.29 -0.97 2.1 -1.1 -2.1 -0.99 0.00023 -0.1 4.9e+03 70 0.00026 -2.5 -
40 -0.019 -0.29 -0.97 2.1 -1.1 -2.1 -0.99 0.00023 -0.1 4.9e+03 70 0.00013 -1.8 -
41 -0.019 -0.29 -0.97 2.1 -1.1 -2.1 -0.99 0.00023 -0.1 4.9e+03 70 6.6e-05 -0.59 -
42 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00017 -0.1 4.9e+03 69 6.6e-05 0.56 +
43 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00019 -0.1 4.9e+03 28 6.6e-05 0.57 +
44 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00018 -0.1 4.9e+03 14 6.6e-05 0.7 +
45 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00018 -0.1 4.9e+03 0.53 0.00066 1 ++
46 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00018 -0.1 4.9e+03 0.13 0.0066 1 ++
47 -0.019 -0.29 -0.96 2.1 -1.1 -2.1 -0.99 0.00022 -0.11 4.9e+03 2.8 0.066 0.99 ++
48 -0.031 -0.3 -0.94 2.1 -1.1 -2.1 -1.1 0.00021 -0.11 4.9e+03 0.17 0.66 1 ++
49 -0.022 -0.33 -0.94 2.1 -1.1 -2.1 -1.3 0.00021 -0.11 4.9e+03 1.1 6.6 1 ++
50 -0.022 -0.33 -0.95 2.1 -1.1 -2.1 -1.3 0.00021 -0.11 4.9e+03 0.0041 66 1 ++
51 -0.022 -0.33 -0.95 2.1 -1.1 -2.1 -1.3 0.00021 -0.11 4.9e+03 0.022 6.6e+02 1 ++
52 -0.022 -0.33 -0.95 2.1 -1.1 -2.1 -1.3 0.00021 -0.11 4.9e+03 0.0032 6.6e+03 1 ++
53 -0.022 -0.33 -0.95 2.1 -1.1 -2.1 -1.3 0.00021 -0.11 4.9e+03 5.8e-06 6.6e+03 1 ++
File biogeme.toml has been parsed.
*** Initial values of the parameters are obtained from the file __b07everything_000005.iter
Parameter values restored from __b07everything_000005.iter
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. Function Relgrad Radius Rho
0 5.1e+03 41 0.5 -3.8 -
1 5.1e+03 41 0.25 -3.9 -
2 5.1e+03 41 0.12 -4.2 -
3 5.1e+03 41 0.062 -3.3 -
4 5.1e+03 41 0.031 -2.3 -
5 5.1e+03 41 0.016 -1.9 -
6 5.1e+03 41 0.0078 -1.7 -
7 5.1e+03 41 0.0039 -1.5 -
8 5.1e+03 41 0.002 -1.3 -
9 5.1e+03 41 0.00098 -0.92 -
10 5.1e+03 41 0.00049 -0.36 -
11 5.1e+03 22 0.00049 0.31 +
12 5.1e+03 16 0.0049 1.5 ++
13 5.1e+03 16 0.0024 -3.8 -
14 5.1e+03 16 0.0012 -2.8 -
15 5.1e+03 16 0.00061 -2.4 -
16 5.1e+03 16 0.00031 -2 -
17 5.1e+03 16 0.00015 -1.3 -
18 5.1e+03 16 7.6e-05 -0.18 -
19 5.1e+03 11 0.00076 0.96 ++
20 5.1e+03 13 0.00076 0.7 +
21 5.1e+03 0.7 0.0076 1 ++
22 5.1e+03 0.27 0.076 1 ++
23 5e+03 9.2 0.076 0.86 +
24 5e+03 2.7 0.76 1 ++
25 4.9e+03 0.42 7.6 1 ++
26 4.9e+03 61 7.6 0.23 +
27 4.8e+03 0.3 76 1.2 ++
28 4.8e+03 0.098 7.6e+02 1.1 ++
29 4.8e+03 0.0069 7.6e+03 1 ++
30 4.8e+03 0.0001 7.6e+04 1 ++
31 4.8e+03 6.1e-07 7.6e+04 1 ++
File biogeme.toml has been parsed.
*** Initial values of the parameters are obtained from the file __b07everything_000006.iter
Parameter values restored from __b07everything_000006.iter
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_CAR_one_lug ASC_CAR_several ASC_TRAIN ASC_TRAIN_GA ASC_TRAIN_one_l ASC_TRAIN_sever B_COST_CAR B_COST_SM B_COST_TRAIN B_TIME B_TIME_commuter cube_tt_coef square_tt_coef Function Relgrad Radius Rho
0 -0.3 -0.21 -0.013 -0.11 -0.58 1.1 0.66 0.028 -0.82 -1.2 -2 -2.3 -1 0.00022 -0.11 4.8e+03 0.79 10 1 ++
1 -0.29 -0.19 0.017 -0.35 -0.69 1.3 0.68 0.37 -0.84 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 0.14 1e+02 1 ++
2 -0.29 -0.19 0.012 -0.36 -0.7 1.3 0.68 0.36 -0.84 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 0.0022 1e+03 1 ++
3 -0.28 -0.19 0.012 -0.36 -0.7 1.3 0.68 0.36 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 0.0017 1e+04 1 ++
4 -0.28 -0.19 0.012 -0.36 -0.7 1.3 0.68 0.36 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 0.00016 1e+05 1 ++
5 -0.28 -0.19 0.012 -0.36 -0.7 1.3 0.68 0.36 -0.85 -1.1 -2 -2.1 -1.4 0.00022 -0.11 4.8e+03 2.4e-09 1e+05 1 ++
File biogeme.toml has been parsed.
*** Initial values of the parameters are obtained from the file __b07everything_000007.iter
Parameter values restored from __b07everything_000007.iter
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME Function Relgrad Radius Rho
0 -0.03 -0.29 -1.2 1.6 -0.93 -1.2 5.1e+03 0.031 1 0.87 +
1 -0.24 -0.25 -1.3 2 -1.1 -1.2 5.1e+03 0.0013 10 1 ++
2 -0.25 -0.3 -1.3 2 -1.1 -1.2 5.1e+03 1.2e-05 1e+02 1 ++
3 -0.25 -0.3 -1.3 2 -1.1 -1.2 5.1e+03 4.3e-09 1e+02 1 ++
File biogeme.toml has been parsed.
*** Initial values of the parameters are obtained from the file __b07everything_000008.iter
Parameter values restored from __b07everything_000008.iter
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME B_TIME_commuter cube_tt_coef mu_existing square_tt_coef Function Relgrad Radius Rho
0 0.015 -0.098 -0.72 1.8 -1 -2 -1.2 0.00021 1.2 -0.11 4.9e+03 4.8 10 1.1 ++
1 -0.0074 -0.067 -0.72 1.8 -1 -1.9 -1.2 0.00021 1.3 -0.11 4.9e+03 0.58 1e+02 1.1 ++
2 -0.0093 -0.025 -0.7 1.8 -1 -1.9 -1.1 0.00021 1.4 -0.11 4.9e+03 0.0045 1e+03 1 ++
3 -0.011 -0.02 -0.7 1.8 -1 -1.9 -1.1 0.00021 1.4 -0.11 4.9e+03 5.6e-05 1e+04 1 ++
4 -0.011 -0.017 -0.7 1.8 -1 -1.9 -1.1 0.00021 1.4 -0.11 4.9e+03 5.2e-05 1e+05 1 ++
5 -0.011 -0.017 -0.7 1.8 -1 -1.9 -1.1 0.00021 1.4 -0.11 4.9e+03 1.4e-06 1e+05 1 ++
File biogeme.toml has been parsed.
*** Initial values of the parameters are obtained from the file __b07everything_000009.iter
Parameter values restored from __b07everything_000009.iter
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_CAR_GA ASC_TRAIN ASC_TRAIN_GA B_COST B_TIME lambda_travel_t Function Relgrad Radius Rho
0 -0.29 -0.34 -1.2 1.8 -1 -1.2 0.39 5e+03 0.029 10 1.1 ++
1 -0.08 -0.32 -1 2 -1.1 -1.6 0.45 5e+03 0.0027 1e+02 1 ++
2 -0.062 -0.31 -1 2 -1.1 -1.7 0.38 5e+03 0.00024 1e+03 0.97 ++
3 -0.062 -0.31 -1 2 -1.1 -1.7 0.38 5e+03 7.2e-07 1e+03 1 ++
Pareto: 10
Condidered: 204
Removed: 13
print(f'A total of {len(non_dominated_models)} models have been generated.')
A total of 10 models have been generated.
compiled_results, specs = compile_estimation_results(
non_dominated_models, use_short_names=True
)
compiled_results
Glossary
for short_name, spec in specs.items():
print(f'{short_name}\t{spec}')
Model_000000 ASC:GA;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:power
Model_000001 ASC:GA;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:power
Model_000002 ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:linear
Model_000003 ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:linear
Model_000004 ASC:GA;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:power
Model_000005 ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic;model_catalog:nested existing;train_tt_catalog:power
Model_000006 ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:power
Model_000007 ASC:GA;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:linear
Model_000008 ASC:GA;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic;model_catalog:nested existing;train_tt_catalog:power
Model_000009 ASC:GA;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic;model_catalog:logit;train_tt_catalog:boxcox
Total running time of the script: (4 minutes 4.085 seconds)