Parallel Optimization: Loop or Finite-Difference Gradient
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According to the documentation on parallel optimization, one cannot use the 'UseParallel' option inside a 'parfor' loop.
I am optimizing a very messy likelihood function over 32 parameters w/ fmincon. Unfortunately I run the optimization around 1,000 times each at a different initial point.
Would it be more computationally efficient to parallelize the finite-difference gradient calculation, or parallelize the loop of 1,000 calls to fmincon.
Thanks!
PS I have 25 cores available to me.
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Walter Roberson
2017 年 1 月 20 日
編集済み: Walter Roberson
2017 年 1 月 20 日
Is what you are doing really a global optimization? If so then the tools such as MultiStart in Global Optimization Toolbox would seem appropriate. Or patternsearch.
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Alan Weiss
2017 年 1 月 23 日
It is very difficult to know which way would work better. Can you test a small set of representative points and see which way is faster?
Sorry for the lack of info, but parallel processing is hard to predict, as there are a lot of things that go into its performance, or lack thereof.
Alan Weiss
MATLAB mathematical toolbox documentation
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