After Googling and trial and errors, the major cause of optimization was not
functions, but data setting.
Originally, I was using data.frame for likelihood calculation. Then, I changed
data.frame to vector and matrix for the same likelihood calculation. Now
convergence takes ~ 14 sec instead of 25 min. Certainly, I didn't know this
simple change makes huge computational difference.
Toshihide Hamachan Hamazaki, 濱崎俊秀PhD
Alaska Department of Fish and Game: アラスカ州漁業野生動物課
Diivision of Commercial Fisheries: 商業漁業部
333 Raspberry Rd. Anchorage, AK 99518
Phone: (907)267-2158
Cell: (907)440-9934
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Ben Bolker
Sent: Wednesday, July 13, 2011 12:21 PM
To: r-h...@stat.math.ethz.ch
Subject: Re: [R] Very slow optim()
Hamazaki, Hamachan (DFG toshihide.hamazaki at alaska.gov writes:
Dear list,
I am using optim() function to MLE ~55 parameters, but it is very slow to
converge (~ 25 min), whereas I can do
the same in ~1 sec. using ADMB, and ~10 sec using MS EXCEL Solver.
Are there any tricks to speed up?
Are there better optimization functions?
There's absolutely no way to tell without knowing more about your code. You
might try method=CG:
Method ‘CG’ is a conjugate gradients method based on that by
Fletcher and Reeves (1964) (but with the option of Polak-Ribiere
or Beale-Sorenson updates). Conjugate gradient methods will
generally be more fragile than the BFGS method, but as they do not
store a matrix they may be successful in much larger optimization
problems.
If ADMB works better, why not use it? You can use the R2admb
package (on R forge) to wrap your ADMB calls in R code, if you
prefer that workflow.
Ben
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