[R] r's optim vs. matlab's fminsearch

[Prof Brian Ripley]

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)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595