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

Spencer Graves spencer.graves at pdf.com
Mon Jun 12 20:29:01 CEST 2006

	  Have you also tried 'nlminb'?  A year or so ago, Doug Bates switched 
from optim to nlminb for mixed-effects estimation.  I'm not certain I 
know why, but I think 'nlminb' may automatically adjust the 'scale' 
parameter by default, while "optim" does not automatically adjust the 
comparable 'control$parscale'.  If Matlab does reasonable auto-scaling, 
that might explain the difference.  Others (e.g., Professors Ripley and 
Bates) might be able to add more.

	  There might be more information on this in 'RSiteSearch', but I think 
it's temporarily off line right now.  And, of course, you can always 
read the R code.  Both 'nlminb' and 'optim' call compiled code, but the 
source should be available.

	  Hope this helps,
	  Spencer Graves	

Prof Brian Ripley wrote:
> 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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