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Topic: parameter normalization for optimization functions such as fmincon and fminsearch
Replies: 5   Last Post: Jun 26, 2009 8:35 AM

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Hanan Shteingart

Posts: 4
Registered: 6/25/09
Re: parameter normalization for optimization functions such as fmincon and fminsearch
Posted: Jun 25, 2009 1:23 PM
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Thanks John for your quick reply.

Any idea of how to do what you've proposed (same order of magnitude for different parameters) ?
I am using a non-analytic function that measure the likelihood of a model to an observed measurements, so how can I tell in advance the derivation of the likelihood by each parameters? I don't have an explicit expression for the likelihood since the model is not stationary and is data dependent.

Thanks,
Hanan.


"John D'Errico" <woodchips@rochester.rr.com> wrote in message <h20959$i6u$1@fred.mathworks.com>...
> "Hanan Shteingart" <chanansh@gmail.com> wrote in message <h208cv$s5u$1@fred.mathworks.com>...
> > Hi,
> > From other discussions I've seen here, it seems the TolX parameter in the option parameter to fmincon/fminsearch is absolute, which means those functions assume the same order of magnitude to all parameters. Is there any smart way to solve this? Should I normalized all parameters to be around [0 1] and re-normalize it within the subject function (which I want to minimize)?
> >

>
> Better than normalizing all parameters to be unity, would
> be to scale them to have derivatives that are all roughly
> the same order of magnitude.
>
> Lacking that, scaling them to be roughly the same
> magnitude may be reasonable.
>
> John




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