gemseo.algos.opt.scipy_global.settings.dual_annealing module

Settings for the SciPy dual annealing algorithm.

Settings DUAL_ANNEALING_Settings(*, enable_progress_bar=None, eq_tolerance=1e-06, ineq_tolerance=0.0001, log_problem=True, max_time=0.0, normalize_design_space=True, progress_bar_data_name='ProgressBarData', reset_iteration_counters=True, round_ints=True, store_jacobian=True, use_database=True, use_one_line_progress_bar=False, ftol_rel=1e-09, ftol_abs=1e-09, max_iter=9223372036854775807, scaling_threshold=None, stop_crit_n_x=3, xtol_rel=1e-09, xtol_abs=1e-09, initial_temp=5230, restart_temp_ratio=2e-05, visit=2.62, accept=-5, seed=0, no_local_search=False)

Bases: BaseSciPyGlobalSettings

The SciPy dual annealing setting.

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)]) --

  By default it is set to 1e-06.

• ineq_tolerance (Annotated[float, 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)]) --

  By default it is set to 0.0.

• normalize_design_space (bool) --

  By default it is set to True.

• progress_bar_data_name (ProgressBarDataName) --

  By default it is set to "ProgressBarData".

• reset_iteration_counters (bool) --

  By default it is set to True.

• round_ints (bool) --

  By default it is set to True.

• store_jacobian (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.

• ftol_rel (Annotated[float, Ge(ge=0), Ge(ge=0)]) --

  By default it is set to 1e-09.

• ftol_abs (Annotated[float, Ge(ge=0), Ge(ge=0)]) --

  By default it is set to 1e-09.

• max_iter (Annotated[int, Gt(gt=0), Gt(gt=0)]) --

  By default it is set to 9223372036854775807.

• scaling_threshold (Annotated[float, Ge(ge=0)] | None)

• 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)]) --

  By default it is set to 1e-09.

• xtol_abs (Annotated[float, Ge(ge=0), Ge(ge=0)]) --

  By default it is set to 1e-09.

• initial_temp (Annotated[float, Gt(gt=0.01), Le(le=50000.0)]) --

  By default it is set to 5230.

• restart_temp_ratio (Annotated[float, Lt(lt=1), Gt(gt=0)]) --

  By default it is set to 2e-05.

• visit (Annotated[float, Gt(gt=1), Le(le=3)]) --

  By default it is set to 2.62.

• accept (Annotated[float, Gt(gt=-10000.0), Le(le=-5)]) --

  By default it is set to -5.

• seed (int) --

  By default it is set to 0.

• no_local_search (bool) --

  By default it is set to False.

Return type:

None

accept: float = -5

The acceptance distribution parameter.

The lower the acceptance parameter, the smaller the probability of acceptance.

Constraints:

• gt = -10000.0

• le = -5

initial_temp: float = 5230

The initial temperature.

Use higher values to facilitates a wider search of the energy landscape.

Constraints:

• gt = 0.01

• le = 50000.0

no_local_search: bool = False

Whether to perform local search.

restart_temp_ratio: PositiveFloat = 2e-05

The temperature ratio under which the reannealing process is triggered.

Constraints:

• lt = 1

• gt = 0

seed: int = 0

The random seed.

visit: float = 2.62

The visiting distribution parameter.

Higher values give the visiting distribution a heavier tail, this makes the algorithm jump to a more distant region.

Constraints:

• gt = 1

• le = 3

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that's what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) -- The BaseModel instance.

• context (Any) -- The context.

Return type:

None