opt.classes.problems.ProblemFeasibleInitPoint.solve

ProblemFeasibleInitPoint.solve(x=None, t_B=None, x_ast=None, p_ast=None, gf_B=None, Hf_B=None, plot=True, tol_inner_iter=1e-08, tol_outer_iter=1e-06, tol_backtracking=1e-12, max_inner_iter=30, max_total_iterations=30, method=None)[source]

Solve problem using initial feasible point. Problem can be one of: UCO, CICO, CECO or CIECO.

Parameters

  • x (numpy ndarray) – initial point for logarithmic barrier or descent method.

  • t_B (float) – initial point for parameter barrier in logarithmic barrier method.

  • x_ast (numpy ndarray) – solution of optimization problem.

  • p_ast (float) –

  • gf_B (opt function class) – instance of class for gradient of objective function, could be of original or logarithmic barrier.

  • Hf_B (opt function class) – instance of class for Hessian of objective function, could be of original or logarithmic barrier.

  • tol_inner_iter (float) – tolerance that will halt method. Controls stopping criteria for inner iterations (iterations of descent method).

  • tol_outer_iter (float) – tolerance that will halt method. Controls stopping criteria.

  • tol_backtracking (float) – tolerance that will halt method. Controls value of line search by backtracking.

  • max_inner_iter (int) – maximum number of inner iterations (iterations of descent method).

  • max_total_iter (int) – maximum number of total iterations.

  • method (str) – type of method that will be used, gradient, Newton

Returns

Depends of problem to be solved. See opt.logarithmic_barrier.logarithmic_barrier_methods.primal_dual_feasible_init_point_method and opt.descent_methods.feasible_init_point_descent_method