gemseo.algos.opt.augmented_lagrangian.settings.base_augmented_lagrangian_settings module#
Settings for the augmented lagrangian algorithm.
- Settings BaseAugmentedLagragianSettings(*, enable_progress_bar=None, eq_tolerance=0.01, ineq_tolerance=0.0001, log_problem=True, max_time=0.0, normalize_design_space=True, reset_iteration_counters=True, round_ints=True, use_database=True, use_one_line_progress_bar=False, store_jacobian=True, ftol_rel=1e-09, ftol_abs=1e-09, max_iter=1000, scaling_threshold=None, stop_crit_n_x=3, xtol_rel=1e-09, xtol_abs=1e-09, initial_rho=10.0, sub_algorithm_name, sub_algorithm_settings=<factory>, sub_problem_constraints=(), update_options_callback=None)[source]#
Bases:
BaseOptimizerSettingsThe base augmented lagrangian settings.
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
enable_progress_bar (bool | None)
eq_tolerance (Annotated[float, Ge(ge=0), Ge(ge=0), Ge(ge=0)]) --
By default it is set to 0.01.
ineq_tolerance (Annotated[float, Ge(ge=0), Ge(ge=0)]) --
By default it is set to 0.0001.
log_problem (bool) --
By default it is set to True.
max_time (Annotated[float, Ge(ge=0), Ge(ge=0)]) --
By default it is set to 0.0.
normalize_design_space (bool) --
By default it is set to True.
reset_iteration_counters (bool) --
By default it is set to True.
round_ints (bool) --
By default it is set to True.
use_database (bool) --
By default it is set to True.
use_one_line_progress_bar (bool) --
By default it is set to False.
store_jacobian (bool) --
By default it is set to True.
ftol_rel (Annotated[float, Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0)]) --
By default it is set to 1e-09.
ftol_abs (Annotated[float, Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0)]) --
By default it is set to 1e-09.
max_iter (Annotated[int, Gt(gt=0), Gt(gt=0), Gt(gt=0), Gt(gt=0)]) --
By default it is set to 1000.
stop_crit_n_x (Annotated[int, Ge(ge=2)]) --
By default it is set to 3.
xtol_rel (Annotated[float, Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0)]) --
By default it is set to 1e-09.
xtol_abs (Annotated[float, Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0), Ge(ge=0)]) --
By default it is set to 1e-09.
initial_rho (Annotated[float, Ge(ge=0)]) --
By default it is set to 10.0.
sub_algorithm_name (str)
sub_algorithm_settings (Mapping[str, Any]) --
By default it is set to <factory>.
sub_problem_constraints (Iterable[str]) --
By default it is set to ().
update_options_callback (Annotated[Callable[[Any], Any], WithJsonSchema(json_schema={}, mode=None)] | None)
- Return type:
None
- ftol_abs: NonNegativeFloat = 1e-09#
The absolute tolerance on the objective function.
- Constraints:
ge = 0
- ftol_rel: NonNegativeFloat = 1e-09#
The relative tolerance on the objective function.
- Constraints:
ge = 0
- initial_rho: NonNegativeFloat = 10.0#
The initial penalty value.
- Constraints:
ge = 0
- sub_algorithm_settings: StrKeyMapping [Optional]#
The settings of the optimizer used to solve each sub-problem.
- sub_problem_constraints: Iterable[str] = ()#
The constraints to keep in the sub-problem.
If
empty, all constraints are handled by the Augmented Lagrangian method which implies that the sub-problem is unconstrained.
- update_options_callback: Annotated[Callable[[Any], Any], WithJsonSchema({})] | None = None#
A callable for updating parameters or a function call.
- xtol_abs: NonNegativeFloat = 1e-09#
The absolute tolerance on the design parameters.
- Constraints:
ge = 0
- xtol_rel: NonNegativeFloat = 1e-09#
The relative tolerance on the design parameters.
- Constraints:
ge = 0