Re: [Help-gsl] Nonlinear Solver With Bounds

[Eskandar Ensafi]

Dear Barrett,

Thank you for the tip.  I thoght about doing something
similar, but I was worried that it would impact the
convergence properties of the algorithm and alter the
parameter correlation matrix.  Perhaps this is not an
issue after all -- is it?

Thanks again,

Eskandar


--- "Barrett C. Foat" <address@hidden> wrote:

> Hi Eskandar,
> 
> You can impose constraints on model parameters by
> defining auxiliary 
> functions. For example if you want a parameter b to
> always be positive, 
> you add it to the function as c^2 and then after the
> algorithm converges 
> on a value (positive or negative) for c, you just
> square it to get your 
> final value of b. Likewise, you can impose any
> arbitrary range using sin 
> or cos. For example if you want values of b only
> between 0 and 1, you can 
> represent it by (0.5 * (cos(c) + 1)). No matter what
> value c takes during 
> the minimization, b = (0.5 * (cos(c) + 1)) is always
> between, 0 and 1 
> inclusive.
> 
> Sorry, I'm not familiar with the PORT library.
> 
> Barrett Foat
> 
> On Thursday 13 September 2007 17:04, Eskandar Ensafi
> wrote:
> > Hi,
> >
> > I would like to use the functions defined in
> > "gsl_multifit_nlin.h" to solve nonlinear
> least-squares
> > and general nonlinear minimization problems. 
> However,
> > I do not see a way to impose constraints (simple
> > bounds) on the model parameters (independent
> > variables).  This is particularly important in
> > problems where one or more model parameters must
> > always be positive, or if a weight parameter must
> > always be between 0 and 1.  How can this problem
> be
> > addressed in the current version of GSL?
> >
> > Also, for those of you familiar with the PORT
> Library
> > <http://www.bell-labs.com/project/PORT>, how does
> > GSL's nonlinear solver compare to the PORT
> versions in
> > terms of robustness?  I am specifically referring
> to
> > the PORT routines DN2FB and DN2GB, which were
> based on
> > NL2SOL, an adaptive nonlinear least-squares
> algorithm
> > (ACM TOMS Algorithm 573).
> >
> > Thanks!
> >
> > Eskandar
> >
> >
> >
> >
> >
>
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