A robust optimization model was built on matlab, and yalmip called gurobi to solve it. The info shown that it was solved successfully and shown the optimal value in the process, but when viewed with the value function at the end, it shown NaN instead.
The robust optimization model is as follows:
p=sdpvar(T,1,'full'); %The scalar T was given.
x=sdpvar(size(C,2),1,'full'); %The matrix C was given.
F=[Ex<=f]; %The matrix E and the vector F were given.
W=[Ap<=b,uncertain(p)]; %The matrix A and the vector b were given, and the vector variable p was an uncertain variable.
obj=norm(C*x+d-p,1);
sol=optimize(F+W,obj)
obj=value(obj)
p=value(p)
The running results were shown in matlab as follow:
A robust optimization model was built on matlab, and yalmip called gurobi to solve it. The info shown that it was solved successfully and shown the optimal value in the process, but when viewed with the value function at the end, it shown NaN instead.
The robust optimization model is as follows: p=sdpvar(T,1,'full'); %The scalar T was given. x=sdpvar(size(C,2),1,'full'); %The matrix C was given. F=[Ex<=f]; %The matrix E and the vector F were given. W=[Ap<=b,uncertain(p)]; %The matrix A and the vector b were given, and the vector variable p was an uncertain variable. obj=norm(C*x+d-p,1); sol=optimize(F+W,obj) obj=value(obj) p=value(p)
The running results were shown in matlab as follow: