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Posted to user@commons.apache.org by annaykay <a....@fz-juelich.de> on 2011/11/23 11:54:51 UTC
[math] Optimization: Nelder-Mead and Levenberg-Marquardt
Hello everyone,
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!
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Re: [math] Optimization: Nelder-Mead and Levenberg-Marquardt
Posted by annaykay <a....@fz-juelich.de>.
Hej,
thank you very much for the quick answer!
I already tried different start values, but it did not change so much. But I
will try to write a wrapper around my start values to try more combinations.
Kind Regards,
annaykay
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Re: [math] Optimization: Nelder-Mead and Levenberg-Marquardt
Posted by Luc Maisonobe <Lu...@free.fr>.
Le 23/11/2011 12:22, Mikkel Meyer Andersen a écrit :
> 2011/11/23 annaykay <a....@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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>> To unsubscribe, e-mail: user-unsubscribe@commons.apache.org
>> For additional commands, e-mail: user-help@commons.apache.org
>>
>>
>
> 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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Re: [math] Optimization: Nelder-Mead and Levenberg-Marquardt
Posted by Mikkel Meyer Andersen <mi...@mikl.dk>.
2011/11/23 annaykay <a....@fz-juelich.de>:
> Hello everyone,
> 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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> To unsubscribe, e-mail: user-unsubscribe@commons.apache.org
> For additional commands, e-mail: user-help@commons.apache.org
>
>
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?
Cheers, Mikkel.
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