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Re: Re: Constraints to parameters in FindFit?


Jill et al.,

In general, you do need global optimization to solve constrained model 
calibration problems.

In the monograph 
http://www.springeronline.com/sgw/cda/frontpage/0,0,4-0-22-33656158-0,0.html?referer=www.springeronline.com/isbn/0-7923-3757-3 
I discuss several related case studies. The short tutorial book 
http://www.lionhrtpub.com/books/globaloptimization.html includes a demo 
example that is [sounds] very similar to Jill's model. Please also see 
e.g., Globally optimized calibration of nonlinear models: techniques, 
software, and applications. Optimization Methods and Software 18 (2003) (3) 
335-355.

Regards,
Janos Pinter



At 05:18 AM 10/9/2004, Peter Valko wrote:
>For sign restiction an old trick is to use instead of paramold the
>expression  Exp[paramnew] or -Exp[paramnew] in your model, and search
>for paramnew (instead of paramold).
>
>P.
>
>
>Jill Foley <efoley at princeton.edu> wrote in message 
>news:<ck5e57$oio$1 at smc.vnet.net>...
> > Hi All,
> >
> > I am using FindFit to fit a series of peaks to some data. I would like
> > to be able to constrain some of the parameters of my fit to correspond
> > to physical reality. For example, some peaks should have a negative
> > amplitude, others positive, where the amplitudes are the parameters
> > that Mathematica is finding in FindFit. The peaks are all very near
> > each other, so without any constraint, it is making the wrong ones
> > negative. I'd like to specify that a given parameter should always be
> > negative. I am already giving an initial guess of the proper sign, but
> > it doesn't fix the problem.
> >
> > Please advise - Thanks!
> >
> > Jill.



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