%This Example for using users m-function Optimization_subroutine.m was created automatically by PSG Toolbox. %Function description: %minimize %var_risk(parameter_alpha, matrix_scenario) %Constraint: >= parameter_bound %linear(matrix_returns) %Box: >= point_lowerbounds, <= point_upperbounds % % %Input variables: % %Inputs PSG Type PSG Object Location in Problem Statement Class %matrix_returns_data data matrix_returns linear(matrix_returns) double %matrix_returns_vars vars matrix_returns linear(matrix_returns) cell %matrix_scenario_probability prob matrix_scenario var_risk(parameter_alpha, matrix_scenario) double %matrix_scenario_data data matrix_scenario var_risk(parameter_alpha, matrix_scenario) double %matrix_scenario_vars vars matrix_scenario var_risk(parameter_alpha, matrix_scenario) cell %point_lowerbounds_data data point_lowerbounds Box: >= point_lowerbounds, <= point_upperbounds double %point_lowerbounds_vars vars point_lowerbounds Box: >= point_lowerbounds, <= point_upperbounds cell %point_upperbounds_data data point_upperbounds Box: >= point_lowerbounds, <= point_upperbounds double %point_upperbounds_vars vars point_upperbounds Box: >= point_lowerbounds, <= point_upperbounds cell %parameter_alpha_data data parameter_alpha var_risk(parameter_alpha, matrix_scenario) double %parameter_bound_data data parameter_bound Constraint: >= parameter_bound 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\All\VaR vs Difference of CVaRs Minimization\problem_1_10000_VaR_0p95\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_returns_data,matrix_returns_vars,matrix_scenario_probability,matrix_scenario_data,matrix_scenario_vars,point_lowerbounds_data,point_lowerbounds_vars,point_upperbounds_data,point_upperbounds_vars,parameter_alpha_data,parameter_bound_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);