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 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
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.