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

%Function description:

%minimize
%variance(matrix_artificial_scenarios)
%Constraint: <= parameter_bound
%1.0001*linear(matrix_linear)
%+0.0001*cardn_pos(parameter_alpha, matrix_linear_card)
%Constraint: >= parameter_bound_1
%-0.0001*linear(matrix_linear)
%+avg_g(matrix_artificial_scenarios)
%-0.0001*cardn_pos(parameter_alpha_1, matrix_linear_card)
%Constraint: <= parameter_bound_2
%pr_pen(parameter_alpha_2,matrix_probability)
%Box: >= parameter_bound_3, <= parameter_bound_4
%
%Input variables:
%
%Inputs                              PSG Type    PSG Object                     Location in Problem Statement                               Class
%matrix_artificial_scenarios_data    data        matrix_artificial_scenarios    variance(matrix_artificial_scenarios)                       double
%                                                                               +avg_g(matrix_artificial_scenarios)
%matrix_artificial_scenarios_vars    vars        matrix_artificial_scenarios    variance(matrix_artificial_scenarios)                       cell
%                                                                               +avg_g(matrix_artificial_scenarios)
%matrix_linear_data                  data        matrix_linear                  1.0001*linear(matrix_linear)                                double
%                                                                               -0.0001*linear(matrix_linear)
%matrix_linear_vars                  vars        matrix_linear                  1.0001*linear(matrix_linear)                                cell
%                                                                               -0.0001*linear(matrix_linear)
%matrix_linear_card_data             data        matrix_linear_card             +0.0001*cardn_pos(parameter_alpha, matrix_linear_card)      double
%                                                                               -0.0001*cardn_pos(parameter_alpha_1, matrix_linear_card)
%matrix_linear_card_vars             vars        matrix_linear_card             +0.0001*cardn_pos(parameter_alpha, matrix_linear_card)      cell
%                                                                               -0.0001*cardn_pos(parameter_alpha_1, matrix_linear_card)
%matrix_probability_data             data        matrix_probability             pr_pen(parameter_alpha_2,matrix_probability)                double
%matrix_probability_vars             vars        matrix_probability             pr_pen(parameter_alpha_2,matrix_probability)                cell
%parameter_bound_data                data        parameter_bound                Constraint: <= parameter_bound                              double
%parameter_alpha_data                data        parameter_alpha                +0.0001*cardn_pos(parameter_alpha, matrix_linear_card)      double
%parameter_bound_1_data              data        parameter_bound_1              Constraint: >= parameter_bound_1                            double
%parameter_alpha_1_data              data        parameter_alpha_1              -0.0001*cardn_pos(parameter_alpha_1, matrix_linear_card)    double
%parameter_bound_2_data              data        parameter_bound_2              Constraint: <= parameter_bound_2                            double
%parameter_alpha_2_data              data        parameter_alpha_2              pr_pen(parameter_alpha_2,matrix_probability)                double
%parameter_bound_3_data              data        parameter_bound_3              Box: >= parameter_bound_3, <= parameter_bound_4             double
%parameter_bound_4_data              data        parameter_bound_4              Box: >= parameter_bound_3, <= parameter_bound_4             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\Portfolio Optimization with Probabilistic Constraint\data_problem_spm_cardinality_1_100__short\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_artificial_scenarios_data,matrix_artificial_scenarios_vars,matrix_linear_data,matrix_linear_vars,matrix_linear_card_data,matrix_linear_card_vars,matrix_probability_data,matrix_probability_vars,parameter_bound_data,parameter_alpha_data,parameter_bound_1_data,parameter_alpha_1_data,parameter_bound_2_data,parameter_alpha_2_data,parameter_bound_3_data,parameter_bound_4_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);