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There are so many different optimization algorithms out there, and lots of research going on. However, I have difficulties to find good comparison between them, and all articles / books / papers seem to evaluate them in different ways.

Isn't there some set of standard functions to run "standardized" benchmarks in order to test and compare all such algorithms? Is there some reference website showing how each algorithm performs?

(I'm especially interested in numerical derivative-free optimization techniques)

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    What kinds of optimization problems are you interested in? For certain problem classes there are sets of standard problems.2011-09-19
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    In my case, I guess it is: continuous, unconstrained, non-differentiable, noisy, non-convex *...in other words: just some noisy function*2011-09-19
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    I don't know if there is such a site (or set of standard functions), but here are two reasons why such a site might not exist: 1) A lot of the optimization problems that are solved in practice have a great deal of structure to them, and so they are often solved by specialized algorithms that exploit that structure. Such algorithms may work poorly or not at all on general problems. 2) An algorithm's performance is often implementation-dependent, and so the same algorithm may work much faster or slower depending on all kinds of things related to the implementation.2011-09-19
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    Still, if you don't get an answer here and you end up finding one yourself later, would you mind posting it back here? I would be interested to know about such a site or set of standard functions.2011-09-19
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    I presume you've gone through [these](http://www.neos-guide.org/NEOS/index.php/Test_Problems)?2011-09-19
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    @Mike: concerning your arguments, 2) I meant convergence speed per function evaluation and stability, therefore independent of its implementation. 1) I think on the contrary that many real world problems have to optimize "some" random functions. I doubt many can tweak existing optimization processes using specific problem knowledge in order to make them converge faster / more stable.2011-09-20

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