I have a convergence problem with lsqnonlin and I hope that someone can help me speed it up.
Ok, so what do I have:
- 18 different data sets with 8 data points each with each data point having a standard deviation
- 18 different and really nasty objective functions that I build with the help of a for loop
- 28 overall free parameters from which all have a lower bound and only one has an upper bound.
What I'm doing now:
- Generate initial conditions from log normal distribution
- Pass them to lsqnonlin and let lsqnonlin do its magic
- Save goodness of fit (not really chi^2 because I don't use weighted fitting)
- Repeat a lot of times and then choose the parameter set according to "chi^2" value
After diagnosing one of those many iteration, I see that it really takes long time for lsqnonlin to converge to a solution. And with long time I mean about 35 seconds for one iteration which is just not tolerable if you wanna do this 10000 times.
I hope I made myself clear and that anyone could hint me on ways to speed up the convergence. Of course I'll try to provide anything necessary for you to help me.
Thanks a lot in advance.