pyanno4rt.optimization.solvers.configurations._configure_scipy

SciPy algorithm configuration.

Overview

Function

configure_scipy(problem_instance, lower_variable_bounds, upper_variable_bounds, lower_constraint_bounds, upper_constraint_bounds, algorithm, max_iter, tolerance, callback)

Configure the SciPy solver.

Functions

pyanno4rt.optimization.solvers.configurations._configure_scipy.configure_scipy(problem_instance, lower_variable_bounds, upper_variable_bounds, lower_constraint_bounds, upper_constraint_bounds, algorithm, max_iter, tolerance, callback)[source]

Configure the SciPy solver.

Supported algorithms: L-BFGS-B, TNC, trust-constr.

Parameters:

  • problem_instance (object of class LexicographicOptimization WeightedSumOptimization) – The object representing the optimization problem.

  • lower_variable_bounds (list) – Lower bounds on the decision variables.

  • upper_variable_bounds (list) – Upper bounds on the decision variables.

  • lower_constraint_bounds (list) – Lower bounds on the constraints.

  • upper_constraint_bounds (list) – Upper bounds on the constraints.

  • algorithm (str) – Label for the solution algorithm.

  • max_iter (int) – Maximum number of iterations.

  • tolerance (float) – Precision goal for the objective function value.

  • callback (callable) – Callback function from the class SciPySolver.

Returns:

  • fun (callable) – Minimization function from the SciPy library.

  • arguments (dict) – Dictionary with the function arguments.