- If the local optimum (supplied by the first optimization) is actually a feasible point according to the set of constraints
- The number of iterations does fmincon runs in both cases. You may need to increase the number of iterations.
- The tolerance values might need to be changed using optimoptions
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Why fmincon cannot find the local minumum back when it is supplied as initial point
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Hi all,
I'm using fmincon to solve a contrained optimization problem. Fmincon finds different local minima for different initial points, which is normal given that it's not a global technique. However, I'm confused why it cannot find the same local optimum once I supply it as the inital point. It says it converged to an infeasible point, with exitflag -2, which is the local optimum that it found before. Any idea why?
Many thanks!
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Nachiket Katakkar
2017-6-1
The error message indicates that the given set of constraints are impossible to solve due to an inconsistency. To troubleshoot this, it is helpful to remove these constraints one at a time to get a general idea of where the problem may originate.
Some other things to check:
Have you considered global searching algorithms like "ga" or "patternsearch", considering that the problem you wish to solve is non-convex?
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