gemseo.algos.optimization_result module#

Optimization result.

class OptimizationResult(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)[source]#

Bases: object

The result of an 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)

classmethod from_dict(dict_)[source]#

Create an optimization result from a dictionary.

Parameters:

  • dict -- The dictionary representation of the optimization result. The keys are the names of the optimization result fields, except for the constraint values and gradients. The value associated with the key "constr:y" will be stored in result.constraint_values["y"] while the value associated with the key "constr_grad:y" will be stored in result.constraints_grad["y"].

  • dict_ (Mapping[str, Value])

Returns:

An optimization result.

Return type:

OptimizationResult

classmethod from_optimization_problem(problem, **fields_)[source]#

Create an optimization result from an optimization problem.

Parameters:

  • problem (OptimizationProblem) -- The optimization problem.

  • **fields_ -- The fields of the OptimizationResult that cannot be deduced from the optimization problem; e.g. "optimizer_name".

Returns:

The optimization result associated with the optimization problem.

Return type:

OptimizationResult

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.

Return type:

dict[str, str | int | bool | ndarray]

constraint_values: Mapping[str, ndarray] | None = None#

The values of the constraints at the optimum.

constraints_grad: Mapping[str, ndarray | None] | None = None#

The values of the gradients of the constraints at the optimum.

f_opt: ndarray | None = None#

The value of the objective function at the optimum.

is_feasible: bool = False#

Whether the solution is feasible.

message: str | None = None#

The message returned by the optimizer.

n_constr_call: int | None = None#

The number of calls to the constraints function.

n_grad_call: int | None = None#

The number of calls to the gradient function.

n_obj_call: int | None = None#

The number of calls to the objective function.

objective_name: str = ''#

The name of the objective function.

optimizer_name: str | None = None#

The name of the optimizer.

optimum_index: int | None = None#

The zero-based position of the optimum in the optimization history.

status: int | None = None#

The status of the optimization.

x_0: ndarray | None = None#

The initial values of the design variables.

x_0_as_dict: dict[str, ndarray]#

The design variable names bound to the initial design values.

x_opt: ndarray | None = None#

The optimal values of the design variables, called the optimum.

x_opt_as_dict: dict[str, ndarray]#

The design variable names bound to the optimal design values.