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From Luc Maisonobe <Luc.Maison...@free.fr>
Subject Re: [math] Optimization: Nelder-Mead and Levenberg-Marquardt
Date Wed, 23 Nov 2011 12:58:14 GMT
Le 23/11/2011 12:22, Mikkel Meyer Andersen a écrit :
> 2011/11/23 annaykay <a.simon@fz-juelich.de>:
>> Hello everyone,

Hi Annakay,

>> I am working on the optimization of some model parameters in my simulation,
>> which simulates the impact of communication on the attitude towards a
>> specific topic.
>> I want to optimize 17 parameters in a non-linear function to get a minimal
>> error-value. Therefor I implemented two different Optimization-Algorithms in
>> my Simulation.
>> First I tried the Nelder-Mead Algorithm. But in this case I have the
>> problem, that first my error-value increases, then decreases again and
>> starts to stagnate on a non-satisfying value. Is this even possible for the
>> Nelder-Mead method, that the error increases however I want to minimize it?
>> Then I also tried a different Optimization-Algorithm, the
>> Levenberg-Marquardt-Algorithm. Here the problem is that the changes in the
>> parameters are too small, so that the optimization already stops after one
>> iteration.
>> Do you maybe have an idea about approaching this problem or do you know a
>> different Optimization-Algorithm that could suit my problem?
>>
>> Thanks in advance!
>>
>>
>> --
>> View this message in context: http://apache-commons.680414.n4.nabble.com/math-Optimization-Nelder-Mead-and-Levenberg-Marquardt-tp4099186p4099186.html
>> Sent from the Commons - User mailing list archive at Nabble.com.
>>
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>>
> 
> Hi,
> 
> If the function you want to optimize has several local maxima, then it
> is (almost) always problematic. Especially with 17 parameters, that is
> a lot. Are you sure that you cannot obtain an analytical solution?
> Have you tried different starting values for the Nelder-Mead
> Algorithm?

One way to try different starting values as suggested by Mikkel is to
use the multi-start wrappers around the raw optimizers.

Luc

> 
> Cheers, Mikkel.
> 
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