%This Example for using users m-function Optimization_subroutine.m was created automatically by PSG Toolbox.

%Function description:

%for {matrix_fact_in; matrix_fact_out; num}=crossvalidation(4, matrix_data) 
%
%Problem: problem_first_num, maximize
%logexp_sum(spline_sum(matrix_parameters_vars, matrix_fact_in))
%
%Problem: problem_second_num, calculate
%Point: point_problem_first_num
%logistic_in_num(spline_sum(matrix_parameters_vars, matrix_fact_in, matrix_data_knots_in))
%logistic_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%logexp_sum_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%
%end for
%
%
%Input variables:
%
%Inputs                         PSG Type    PSG Object                Location in Problem Statement                                                                    Class
%matrix_data_benchmark          bench       matrix_data               for {matrix_fact_in; matrix_fact_out; num}=crossvalidation(4, matrix_data)                       double
%                                                                     logistic_in_num(spline_sum(matrix_parameters_vars, matrix_fact_in, matrix_data_knots_in))
%                                                                     logistic_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%                                                                     logexp_sum_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%matrix_data_data               data        matrix_data               for {matrix_fact_in; matrix_fact_out; num}=crossvalidation(4, matrix_data)                       double
%                                                                     logistic_in_num(spline_sum(matrix_parameters_vars, matrix_fact_in, matrix_data_knots_in))
%                                                                     logistic_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%                                                                     logexp_sum_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%matrix_data_vars               vars        matrix_data               for {matrix_fact_in; matrix_fact_out; num}=crossvalidation(4, matrix_data)                       cell
%                                                                     logistic_in_num(spline_sum(matrix_parameters_vars, matrix_fact_in, matrix_data_knots_in))
%                                                                     logistic_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%                                                                     logexp_sum_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%matrix_data_knots_in_data      data        matrix_data_knots_in      logistic_in_num(spline_sum(matrix_parameters_vars, matrix_fact_in, matrix_data_knots_in))        double
%                                                                     logistic_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%                                                                     logexp_sum_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%matrix_data_knots_in_vars      vars        matrix_data_knots_in      logistic_in_num(spline_sum(matrix_parameters_vars, matrix_fact_in, matrix_data_knots_in))        cell
%                                                                     logistic_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%                                                                     logexp_sum_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%matrix_parameters_vars_data    data        matrix_parameters_vars    logexp_sum(spline_sum(matrix_parameters_vars, matrix_fact_in))                                   double
%                                                                     logistic_in_num(spline_sum(matrix_parameters_vars, matrix_fact_in, matrix_data_knots_in))
%                                                                     logistic_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%                                                                     logexp_sum_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%matrix_parameters_vars_vars    vars        matrix_parameters_vars    logexp_sum(spline_sum(matrix_parameters_vars, matrix_fact_in))                                   cell
%                                                                     logistic_in_num(spline_sum(matrix_parameters_vars, matrix_fact_in, matrix_data_knots_in))
%                                                                     logistic_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%                                                                     logexp_sum_out_num(spline_sum(matrix_parameters_vars, matrix_fact_out, matrix_data_knots_in))
%
%Output variables:
%
%solution_str = string with solution of problem;
%outargstruc_arr = array of output PSG data structures;


%Load data from mat-file:
load('D:\American Optimal Decisions\PSG\MATLAB_Stan\All\Spline Regression\problem_2_Logexp_sum_of_15_splines_cv\Optimization_subroutine_data.mat')

%Save variables from mat-file to Workspace:
tbpsg_export_to_workspace(toolboxstruc_arr)

%Run users m-function Optimization_subroutine:

[solution_str,outargstruc_arr] = Optimization_subroutine(matrix_data_benchmark,matrix_data_data,matrix_data_vars,matrix_data_knots_in_data,matrix_data_knots_in_vars,matrix_parameters_vars_data,matrix_parameters_vars_vars);

%Extract Objective:
val_obj = tbpsg_objective(solution_str, outargstruc_arr);

disp(' ');
disp('Objective = ');
disp(val_obj);

%Extract optimal solution:
point_data = tbpsg_optimal_point_data(solution_str, outargstruc_arr);

disp(' ');
disp('Optimal point = ');
disp(point_data);

%Extract structure containing PSG solution reports:
output_structure = tbpsg_solution_struct(solution_str, outargstruc_arr);

disp(' ');
disp('Structure with PSG solution = ');
disp(output_structure);

%Uncomment the following lines to extract solutions details:

%output = tbpsg_isoptimal(solution_str, outargstruc_arr);

%output = tbpsg_function_data(solution_str, outargstruc_arr);

%output = tbpsg_function_names(solution_str, outargstruc_arr);

%output = tbpsg_time(solution_str, outargstruc_arr);

%output = tbpsg_optimal_point_vars(solution_str, outargstruc_arr);

%output = tbpsg_constraints_vars(solution_str, outargstruc_arr);

%output = tbpsg_slack_data(solution_str, outargstruc_arr);

%output = tbpsg_dual_data(solution_str, outargstruc_arr);

%output = tbpsg_vector_constraint_data(solution_str, outargstruc_arr);

%output = tbpsg_vector_dual_data(solution_str, outargstruc_arr);

%output = tbpsg_vector_slack_data(solution_str, outargstruc_arr);

%output = tbpsg_matrix_data(solution_str, outargstruc_arr);

%output = tbpsg_matrix_vars(solution_str, outargstruc_arr);

%output = tbpsg_vector_data(solution_str, outargstruc_arr);