opt_result module¶
Optimization result.
- class gemseo.algos.opt_result.OptimizationResult(x_0=None, x_opt=None, f_opt=None, 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_opt (ndarray | None) –
f_opt (ndarray | None) –
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) –
- 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 inresult.constraint_values["y"]
while the value associated with the key"constr_grad:y"
will be stored inresult.constraints_grad["y"]
.
- Returns:
An optimization result.
- Return type:
- 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 toresult.constraint_values["y"]
while"constr_grad:y"
maps toresult.constraints_grad["y"]
.
- constraint_values: Mapping[str, ndarray] | None = None
The values of the constraints at the optimum.
- constraints_grad: Mapping[str, ndarray] | 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.
- optimum_index: int | None = None
The zero-based position of the optimum in the optimization history.
- x_0: ndarray | None = None
The initial values of the design variables.
- x_opt: ndarray | None = None
The optimal values of the design variables, called the optimum.