# Simulated Annealing Parameters and Results [closed]

I am using SimulatedAnnealing method for a simulation based optimization problem and was wondering if someone can enlighten me on the following features:

1. Is there some guidelines on optimal size of "SearchPoints" based on the size of the solution space knowing that the objective function is non-linear?

2. It is intuitive that the number of iterations will depend on the objective function, but I have noticed that the number of iterations seem to be a multiple of number of variables. is this true? Sample code is provided in Simulated Annealing Convergence

3. I think it is difficult to prove global optimization results, but was wondering if any one has ideas on alternative ways to do that.

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## closed as off-topic by blochwave, MarcoB, Sjoerd C. de Vries, m_goldberg, Karsten 7.Sep 26 '15 at 17:42

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Does anyone have a comment or a response? – brama Feb 25 '14 at 22:47
I'm voting to close this question as off-topic because it is a question about the simulated annealing method for global optimization, rather than a question about Mathematica's implementation. – blochwave Sep 25 '15 at 18:53
@blochwave Not sure. I could benefit from an answer to points one and two – Dr. belisarius Sep 25 '15 at 20:13
@belisarius, true but I still think that (1) is a SA question rather than the specific MMA implementation. – blochwave Sep 25 '15 at 20:54
The docs only has this to say "The default number of starting points, given by the option "SearchPoints", is min(2d, 50), where d is the number of variables." - Reference. That even answers (2) to a point. – blochwave Sep 25 '15 at 20:55