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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    $\begingroup$ Does anyone have a comment or a response? $\endgroup$
    – brama
    Commented Feb 25, 2014 at 22:47
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    $\begingroup$ 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. $\endgroup$ Commented Sep 25, 2015 at 18:53
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    $\begingroup$ @blochwave Not sure. I could benefit from an answer to points one and two $\endgroup$ Commented Sep 25, 2015 at 20:13
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    $\begingroup$ @belisarius, true but I still think that (1) is a SA question rather than the specific MMA implementation. $\endgroup$ Commented Sep 25, 2015 at 20:54
  • $\begingroup$ 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. $\endgroup$ Commented Sep 25, 2015 at 20:55


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