fmincon（fmincon）.docVIP

fmincon（fmincon） The specific format of function fmincon () is: X=fmincon (fun, x0, A, b), X=fmincon (fun, x0, A, B, Ub, Aeq, Beq, Lb,) X=fmincon (fun, x0, A, B, Aeq, Beq, Lb, Ub, nonlcon, options) [X, Fval, exitflag, output]=fmincon (fun, x0),... ) [X, Fval, exitflag, output, lambda, grad, Hessian]=fmincon (fun, x0),... ) The fun in the parameter is the objective function, x0 is the initial value of the variable, and X is the value of the returned variable satisfying the requirement. A and B represent the linear inequality constraints, Aeq, Beq Lb and Ub linear equality constraints, respectively lower and upper bound variables, nonlcon nonlinear constraints, options optimization parameter vector control optimization process. The return value Fval is the objective function. Exitflag0 indicates that the optimization result converges to the solution, and exitflag=0 indicates that the optimization exceeds the number of times the function value is calculated, and exitflag0 indicates that the optimization is not convergent. Lambda is the Lagrange multiplier, which shows that the constraint is valid. Grad represents the gradient, and Hessian represents the Hansen matrix. Example: make the objective function obtain the minimum under the constraint condition. The design process is as follows: first, the objective function and constraints are written into separate M files, note that such a m file must start with function, and the file name must be consistent with the function name. The object function files are: function, f=objfun (x), f=exp (x (1)) *, (4*x (1), ^2+2*x (2), ^2+4*x (1), *x (2), +2*x (2) +1); The constraints of the document are: function, [c, ceq]=confun (x), c=[1.5+x (1), *x (2), -x (1), -x (2), -x (1), *x (2) -10];% represents inequality, nonlinear constraint ceq=[];% represents equality, nonlinear constraint The optimized procedure is as follows: Clear x0=[-1 1]; Options=optimset (largescale,off,display,iter); [x, Fval, exitflag, output]=fmincon (@objfun, x