MATLAB Answers

Decision making on optimization method

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Stephan Heidrich
Stephan Heidrich on 8 Mar 2017
Answered: Alan Weiss on 17 Mar 2017
Hello Everyone!
I am currently working on a Simulink model for which I want to fit parameters using the Simulink Design Optimization Toolbox (2015b). Since I need to fit quite a few parameters for long lasting simulations I was wondering which optimization method to use in advance.
The design variables are restricted by bounds and do use the sum squared error as cost function. Thus its a Minimization problem.
I read the users guide (<http://www.apmath.spbu.ru/ru/staff/smirnovmn/files/sldo_ug.pdf>) but didnt find other sources.
I understand: - I cant use Simplex pattern due to the bounds on the design variables
I dont understand: - how to choose from the others since all of them appear to be usable to me.
So my question is how to decide which method I should use. Is there any document which shows the reasoning (benefits and drawbacks) in a handy way (for one that doesnt have mathematical background)?
Thanks in Advance!
Best Regards, Stephan

Answers (2)

Prateek Khandelwal
Prateek Khandelwal on 16 Mar 2017
I think the section "Optimization Methods and Problem Formulations" in the documentation of Simulink Design Optimization would help you.
  1 Comment
Stephan Heidrich
Stephan Heidrich on 16 Mar 2017
Hi Prateek,
thank you for your answer, but unfortunately the website is quite redundant with the users guide i linked above. I was looking for some more detailed explanation.

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Alan Weiss
Alan Weiss on 17 Mar 2017
For general optimization there are some general guidelines on which solver to choose. For a sum of squares, you should likely choose lsqnonlin, unless your objective function always outputs the sum of squares already calculated, in which case choose fmincon ( lsqnonlin wants to raw components, and internally sums the squares). Be careful, when optimizing a simulation you often have to choose larger-than-default finite differences.
If your objective function is not a smooth function of the control variables, and if you have a Global Optimization Toolbox license, then you might want to try patternsearch.
Good luck,
Alan Weiss
MATLAB mathematical toolbox documentation

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