Solving a constrained optimization problem
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I am trying to solve an optimization problem in matlab:
minimize( sum_{i,j}(f(M(i,j))) + err )
subject to: - norm(M) < err
- err > 0
- if j == i+1: M(i,j) > 1
- if j == i-1: M(i,j) < -1
- else: -1 <= M(i,j) <= 1
where: - f is a non convex function in general
- M is an n by n matrix
Can anyone point me which function I should use?
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Accepted Answer
Alan Weiss
on 24 Apr 2015
If you look in the Optimization Decision Table you see that for a constrained nonlinear problem you should use fmincon. The constraint norm(M) < err is a nonlinear inequality constraint. The other constraints on M(i,j) are bound constraints.
Good luck,
Alan Weiss
MATLAB mathematical toolbox documentation
3 Comments
Alan Weiss
on 30 Apr 2015
That sounds right, with the proviso that you make
A = [M(:);err];
I mean, make a column vector, using a semicolon before err.
Alan Weiss
MATLAB mathematical toolbox documentation
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