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

[Max Belushkin]

Brian Gough wrote:
> Just for clarification, what type of function are you minimising?

  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.

  Back to your question - I'm minimizing a real function without any 
singularities in the region of minimization. I don't see why any exact 
form of a continuous, non-oscillatory function should affect the 
performance of conjugate gradient algorithms, when any simple random 
walk algorithm does at least some part of the job.