Non-linear constraints with several input variables in fmincon
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Hello everyone,
I am trying to solve the following optimization problem with fmincon:
To do this, I have made the following code:
fun = @(x) x'*L*x;
nonlcon = @(x) consfun(A,B,x);
x = fmincon(fun,x0,[],[],[],[],[],[],nonlcon);
Where ''consfun'' is the following function:
function [c,ceq] = consfun(A,B,x)
c = (norm(A*x,inf)/b)-D);
ceq = [];
end
However, the final solution, x, does not satisfy the non-linear constraint so I wonder if the code is correct in relation to the optimisation problem posed.
Can anyone help me?
Thank you very much for your time!
1 Comment
Alan Weiss
on 20 Sep 2021
Did fmincon claim to give a feasible solution? If so, then in what way was the constraint violated? I mean, was consfun(A,B,x) > 0? If not, then you may need to search for a feasible solution. See Converged to an Infeasible Point.
Alan Weiss
MATLAB mathematical toolbox documentation
Accepted Answer
Matt J
on 20 Sep 2021
Edited: Matt J
on 20 Sep 2021
Your nonlinear constraints are not differentiable. That doesn't always spell disaster, but it breaks the assumptions of fmincon. Also, since your problem can be reformulated as a quadratic program, it would be better to use quadprog.
[m,n]=size(A);
e=ones(m,1);
Aineq=[A;-A];
bineq=[(B+b*D).*e ; -(B-b*D).*e];
x=quadprog(L,zeros(n,1),Aineq,bineq);
4 Comments
Perry Mason
on 22 Sep 2021
I meant that the constraint in the original problem of javm6 is defined in terms of the infinity norm.
I reckon your reformulation works if the constraint is defined using euclidean norm, but will this reformulation still work even if the infinity norm is used?
Cheers
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