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

%Function description:

%minimize
%meansquare_err(ltranche(matrix_tranche,vector_tparam))
%box: >=parameter_bound, <=parameter_bound_1
%value:
%meanabs_pen(ltranche(matrix_tranche,vector_tparam))
%
%Input variables:
%
%Inputs                      PSG Type    PSG Object           Location in Problem Statement                             Class
%matrix_tranche_benchmark    bench       matrix_tranche       meansquare_err(ltranche(matrix_tranche,vector_tparam))    double
%                                                             meanabs_pen(ltranche(matrix_tranche,vector_tparam))
%matrix_tranche_data         data        matrix_tranche       meansquare_err(ltranche(matrix_tranche,vector_tparam))    double
%                                                             meanabs_pen(ltranche(matrix_tranche,vector_tparam))
%matrix_tranche_vars         vars        matrix_tranche       meansquare_err(ltranche(matrix_tranche,vector_tparam))    cell
%                                                             meanabs_pen(ltranche(matrix_tranche,vector_tparam))
%vector_tparam_data          data        vector_tparam        meansquare_err(ltranche(matrix_tranche,vector_tparam))    double
%                                                             meanabs_pen(ltranche(matrix_tranche,vector_tparam))
%parameter_bound_data        data        parameter_bound      box: >=parameter_bound, <=parameter_bound_1               double
%parameter_bound_1_data      data        parameter_bound_1    box: >=parameter_bound, <=parameter_bound_1               double
%
%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\ready\Linear Regression in Tranche\data_problem_lthranche_test\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_tranche_benchmark,matrix_tranche_data,matrix_tranche_vars,vector_tparam_data,parameter_bound_data,parameter_bound_1_data);

%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);