Re: SV: [Help-gsl] On conjugate gradient algorithms in multidimentional minimisation problems.

[Brian Gough]

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.

Hello,

Did you try the gsl_multifit functions? If it's a least-squares
problem they will work much better than multimin.

-- 
Brian Gough

Network Theory Ltd,
Publishing the GSL Manual --- http://www.network-theory.co.uk/gsl/manual/