gemseo.algos.opt.scipy_linprog.settings.highs_interior_point module#

Settings for the HiGHS interior point method.

Settings INTERIOR_POINT_Settings(*, 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=0.0, ftol_abs=0.0, max_iter=1000, scaling_threshold=None, stop_crit_n_x=3, xtol_rel=0.0, xtol_abs=0.0, autoscale=False, disp=False, presolve=True, rr=True, rr_method=None, dual_feasibility_tolerance=1e-07, ipm_optimality_tolerance=1e-08, primal_feasibility_tolerance=1e-07)[source]#

Bases: BaseSciPyLinProgSettings

The HiGHS interior point method 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), Ge(ge=0)]) --

    By default it is set to 0.01.

  • 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.

  • 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)]) --

    By default it is set to 0.0.

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

    By default it is set to 0.0.

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

    By default it is set to 1000.

  • 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), Ge(ge=0), Ge(ge=0), Ge(ge=0)]) --

    By default it is set to 0.0.

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

    By default it is set to 0.0.

  • autoscale (bool) --

    By default it is set to False.

  • disp (bool) --

    By default it is set to False.

  • presolve (bool) --

    By default it is set to True.

  • rr (bool) --

    By default it is set to True.

  • rr_method (Literal['SVD', 'pivot', 'ID', None])

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

    By default it is set to 1e-07.

  • ipm_optimality_tolerance (Annotated[float, Ge(ge=1e-12), Ge(ge=0)]) --

    By default it is set to 1e-08.

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

    By default it is set to 1e-07.

Return type:

None

dual_feasibility_tolerance: NonNegativeFloat = 1e-07#

The tolerance on the feasibility of the dual problem.

Constraints:

  • ge = 0

ipm_optimality_tolerance: NonNegativeFloat = 1e-08#

Optimality tolerance.

Constraints:

  • ge = 0

primal_feasibility_tolerance: NonNegativeFloat = 1e-07#

The tolerance on the feasibility of the primal problem.

Constraints:

  • ge = 0

model_post_init(context, /)#

We need to both initialize private attributes and call the user-defined model_post_init method.

Parameters:

  • self (BaseModel)

  • context (Any)

Return type:

None