I'm not sure I understand what you're trying to do 100%, but,
generally, for any regression, you choose a function to minimize - i.e.
chi squared. That takes some parameters over which you're minimizing,
and, if one is lucky, is continuous in those parameters - then you need
to take the derivatives in parameters, the actual function you're
"fitting" doesn't matter, since chi squared is a sum over the whole
range(s) of the squares of the function values minus the data bin (i.e.
experimental data). Well, I lost the experimental errors in there.
So, how are you composing your function to be minimized? Is it
somehow discretized in terms of the parameters?
I think a bit more information on how you're trying to get to the end
result would be helpful.
[EMAIL PROTECTED] wrote:
Hello,
I was looking for a non-linear fitting algorithm, and the manual only
covers Algorithms with Derivatives, but unfortunatly this does not apply
for code as I have a histogram convolved with a Gaussian as my fitting
function. Is there any Algorithm without Derivatives that I may use in
GSL, and if not what other software that maight be helpful, knowing that I
only program in C. Thank you in advance for your response.
Sincerely,
Hassan
----------------------------------------------
Hassan Saadaoui,
PhD candidate,
TRIUMF
4004 Wesbrook Mall
Vancouver, B.C.
Canada V6T 2A3
1-604-222-1047, ext. 6194
----------------------------------------------
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