Search All of the Math Forum:

Views expressed in these public forums are not endorsed by NCTM or The Math Forum.

Notice: We are no longer accepting new posts, but the forums will continue to be readable.

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

 Messages: [ Previous | Next ]
 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

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

Date Subject Author
6/25/09 Hanan Shteingart
6/25/09 John D'Errico
6/25/09 Hanan Shteingart
6/25/09 John D'Errico
6/26/09 Alan Weiss
6/26/09 John D'Errico