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Re: SV: [Help-gsl] On conjugate gradient algorithms in multidimentional
Re: SV: [Help-gsl] On conjugate gradient algorithms in multidimentional minimisation problems.
Sat, 17 Dec 2005 19:00:31 +0000
Max Belushkin writes:
> It's a standard chi squared of a model function (which is quite
> complicated, but only has 9 parameters for the problem in this post).
> The function itself is, technically, a sum of "a/(b+x)" functions. The
> chi squared is computed in the standard way based on this function, the
> data, and the errors of the data. To the fit, chi squared is being fed,
> the gradient is computed numerically in each parameter.
Did you try the gsl_multifit functions? If it's a least-squares
problem they will work much better than multimin.
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