[SciPy-User] efficiency of the simplex routine: R (optim) vs scipy.optimize.fmin
Mon Jul 23 04:53:47 CDT 2012
Thanks for your response. For the fmin function in scipy, I took the
default ftol and stol values. I'm just trying to minize a chi square
between observed experimental data and simulated data. I've done this in
python and R with the Nelder-Mead algorithm, with exactly the same starting
values. While the solutions produced by R and python are not very
different, R systematicaly produces a lower chi-square after the same
amount of iterations. This may be related to ftol and stol, but I don't
know which value I should give to these parameters....
2012/7/20 denis <email@example.com>
> Hi Mathieu,
> (months later) two differences among implementations of Nelder-Mead:
> 1) the start simplex: x0 +- what ? It's common to take x0 + a fixed
> (user-specified) stepsize in each dimension. NLOpt takes a "walking
> simplex", don't know what R does
> 2) termination: what ftol, xtol did you specify ? NLOpt looks at
> fhi - flo: fhi changes at each iteration, flo is sticky.
> Could you post a testcase similar to yours ?
> That would sure be helpful.
> -- denis
> On 24/05/2012 10:15, servant mathieu wrote:
> > Dear scipy users,
> > Again a question about optimization.
> > I've just compared the efficiency of the simplex routine in R
> > (optim) vs scipy (fmin), when minimizing a chi-square. fmin is faster
> > than optim, but appears to be less efficient. In R, the value of the
> > function is always minimized step by step (there are of course some
> > exceptions) while there is lot of fluctuations in python. Given that the
> > underlying simplex algorithm is supposed to be the same, which mechanism
> > is responsible for this difference? Is it possible to constrain fmin so
> > it could be more rigorous?
> > Cheers,
> > Mathieu
> SciPy-User mailing list
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