Re: [Help-gsl] Re: Re: non-linear least squares fitting

[Jay Howard]

Side question:

What's the best way to enforce boundaries on the parameters being
fitted?  Just have f() and df() return huge values whenever one of
them falls outside its acceptable range?

For instance, if I wanted to minimize F(x,y) over the range:  a <= x
<= b, c <= y <= d.


On Dec 6, 2007 8:25 AM, Barrett C. Foat <address@hidden> wrote:
> I guess that I misunderstood. I have always used analytical partial
> derivatives. I am kind of surprised that numerical partial derivatives
> work. However, if the optimization converges without errors, it must be a
> good enough approximation for your function. In my experience, this
> package fails to converge if you do something wrong with the derivatives.
>
> Barrett Foat
>
>
> On Thursday 06 December 2007 03:21, Richard Henwood wrote:
> > <posted & mailed>
> >
> > Barrett Foat wrote:
> > >> I implement fdf using numerical partial differentiation of f.
> > >>
> > >> This method (numerical partial differentials) appeared to work;
> > >> is this a reasonable approach?
> > >
> > > Yes, the df and fdf functions need to caluclate the numerical partial
> > > derivatives given the current values of the arguments (vector x in the
> > > docs) and your independent variable data.
> > >
> > > To my knowledge, the approach you took is the only reasonable
> > > approach.
> >
> > Good.
> >
> > Is the only alternative approach to use the analytic partial derivatives
> > in df and fdf?
> >
> > My reading is that this approach is used in the example given in the GSL
> > manual example.
> >
> > r,
> >
> >
> >
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