r's optim vs. matlab's fminsearch
Thanks for the feedback. I should've mentioned before that the function is non-smooth. Also, it has a 3-element free parameter vector, and I've been using a grid of 27 vectors of starting parameters. Anthony -----Original Message----- From: Prof Brian Ripley [mailto:ripley at stats.ox.ac.uk] Sent: Monday, June 12, 2006 1:40 PM To: Anthony Bishara Cc: r-help at stat.math.ethz.ch Subject: Re: [R] r's optim vs. matlab's fminsearch 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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