payoff_table_method_general

desdeo_mcdm.utilities.payoff_table_method_general(objective_evaluator, n_of_objectives, variable_bounds, constraint_evaluator=None, initial_guess=None, solver_method='scipy_de')

Solves a representation for the nadir and ideal points for a multiobjective minimization problem with objectives defined as the result of some objective evaluator.

Parameters

• objective_evaluator (Callable[[np.ndarray], np.ndarray]) – The evaluator which returns the objective values given a set of variabels.

• n_of_objectives (int) – Number of objectives returned by calling objective_evaluator.

• variable_bounds (np.ndarray) – The lower and upper bounds of the variables passed as argument to objective_evaluator. Should be a 2D numpy array with the limits for each variable being on each row. The first column should contain the lower bounds, and the second column the upper bounds. Use np.inf to indicate no bounds.

• constraint_evaluator (Optional[Callable[[np.ndarray], np.ndarray]], optional) – An evaluator accepting the same arguments as objective_evaluator, which returns the constraint values of the multiobjective minimization problem being solved. A negative constraint value indicates a broken constraint. Defaults to None.

• initial_guess (Optional[np.ndarray], optional) – The initial guess used for the variable values while solving the payoff table. The relevancy of this parameter depends on the solver_method being used. Defaults to None.

• solver_method (Optional[Union[ScalarMethod, str]], optional) – The method to solve the scalarized problems in the payoff table method. Defaults to “scipy_de”, which ignores initial_guess.

Returns

The representations computed using the payoff table for the ideal and nadir points respectively.

Return type

Tuple[np.ndarray, np.ndarray]