Unless you know the function to be non-smooth, I suggest you use
method="BFGS" in R.
BTW, all such algorithms are only designed to find local minima, and so
the choice of starting point may be crucial.
On Mon, 12 Jun 2006, Anthony Bishara wrote:
> Hi,
> I'm having a problem converting a Matlab program into R. The R code
works
> almost all the time, but about 4% of the time R's optim function gets
stuck
> on a local minimum whereas matlab's fminsearch function does not (or at
> least fminsearch finds a better minimum than optim). My understanding is
> that both functions default to Nelder-Mead optimization, but what's
> different about the two functions? Below, I've pasted the relevant
default
> options I could find. Are there other options I should to consider? Does
> Matlab have default settings for reflection, contraction, and expansion,
and
> if so what are they? Are there other reasons optim and fminsearch might
> work differently?
> Thanks.
>
> ***Matlab's fminsearch defaults***
> MaxFunEvals: '200*numberofvariables'
> MaxIter: '200*numberofvariables'
> TolFun: 1.0000e-004 #Termination tolerance on the function
> value.
> TolX: 1.0000e-004 #Termination tolerance on x.
>
> ***R's optim defaults (for Nelder-Mead)***
> maxit=500
> reltol=1e-8
> alpha=1.0 #Reflection
> beta=.5 #Contraction
> gamma=2.0 #Expansion
>
>
> Anthony J. Bishara
> Post-Doctoral Fellow
> Department of Psychological & Brain Sciences
> Indiana University
> 1101 E. Tenth St.
> Bloomington, IN 47405
> (812)856-4678
>
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>
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
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