gemseo / algos

Show inherited members

opt_result_multiobj module

Multi-objective optimization result.

class gemseo.algos.opt_result_multiobj.MultiObjectiveOptimizationResult(x_0=None, x_0_as_dict=<factory>, x_opt=None, x_opt_as_dict=<factory>, f_opt=None, objective_name='', status=None, optimizer_name=None, message=None, n_obj_call=None, n_grad_call=None, n_constr_call=None, is_feasible=False, optimum_index=None, constraint_values=None, constraints_grad=None, pareto_front=None, _MultiObjectiveOptimizationResult__PARETO_FRONT='pareto_front')[source]

Bases: OptimizationResult

The result of a multi-objective optimization.

Parameters:

  • x_0 (ndarray | None) –

  • x_0_as_dict (dict[str, ndarray]) –

    By default it is set to <factory>.

  • x_opt (ndarray | None) –

  • x_opt_as_dict (dict[str, ndarray]) –

    By default it is set to <factory>.

  • f_opt (ndarray | None) –

  • objective_name (str) –

    By default it is set to “”.

  • status (int | None) –

  • optimizer_name (str | None) –

  • message (str | None) –

  • n_obj_call (int | None) –

  • n_grad_call (int | None) –

  • n_constr_call (int | None) –

  • is_feasible (bool) –

    By default it is set to False.

  • optimum_index (int | None) –

  • constraint_values (Mapping[str, ndarray] | None) –

  • constraints_grad (Mapping[str, ndarray | None] | None) –

  • pareto_front (ParetoFront | None) –

  • _MultiObjectiveOptimizationResult__PARETO_FRONT (Final[str]) –

    By default it is set to “pareto_front”.

to_dict()[source]

Convert the optimization result to a dictionary.

The keys are the names of the optimization result fields, except for the constraint values and gradients. The key "constr:y" maps to result.constraint_values["y"] while "constr_grad:y" maps to result.constraints_grad["y"].

Returns:

A dictionary representation of the optimization result.

pareto_front: ParetoFront | None = None

The Pareto front when the solution is feasible.

x_0_as_dict: dict[str, ndarray]

The design variable names bound to the initial design values.

x_opt_as_dict: dict[str, ndarray]

The design variable names bound to the optimal design values.

Examples using MultiObjectiveOptimizationResult

Multi-objective Binh-Korn example with the mNBI algorithm

Multi-objective Binh-Korn example with the mNBI algorithm

Multi-objective Poloni example with the mNBI algorithm

Multi-objective Poloni example with the mNBI algorithm