www.gusucode.com > optim 案例源码 matlab代码程序 > optim/ExaminetheSolutionExample.m
%% Obtain More Output to Examine the Solution Process % Obtain the exit flag and output structure to better understand the solution % process and quality. %% % For this example, use these linear inequality constraints: % % $$ x(1) + x(2) \le 2$$ % % $$x(1) + x(2)/4 \le 1$$ % % $$x(1) - x(2) \le 2$$ % % $$-x(1)/4 - x(2) \le 1$$ % % $$-x(1) - x(2) \le -1$$ % % $$-x(1) + x(2) \le 2.$$ A = [1 1 1 1/4 1 -1 -1/4 -1 -1 -1 -1 1]; b = [2 1 2 1 -1 2]; %% % Use the linear equality constraint $x(1) + x(2)/4 = 1/2$. % Aeq = [1 1/4]; beq = 1/2; %% % Set these bounds: % % $$-1 \le x(1) \le 1.5$$ % % $$-0.5 \le x(2) \le 1.25 .$$ lb = [-1,-0.5]; ub = [1.5,1.25]; %% % Use the objective function $-x(1) - x(2)/3$. f = [-1 -1/3]; %% % Set options to use the |'dual-simplex'| algorithm. options = optimoptions('linprog','Algorithm','dual-simplex'); %% % Solve the linear program and request the function value, exit flag, and % output structure. [x,fval,exitflag,output] = linprog(f,A,b,Aeq,beq,lb,ub,options) %% % * |fval|, the objective function value, is larger than <docid:optim_ug.buus4rk-1_2>, % because there are more constraints. % * |exitflag| = 1 indicates that the solution is reliable. % * |output.iterations| = 0 indicates that |linprog| found the solution % during presolve, and did not have to iterate at all.