----------------------------------------
> Date: Thu, 14 Jul 2011 12:44:18 -0800
> From: toshihide.hamaz...@alaska.gov
> To: r-h...@stat.math.ethz.ch
> Subject: Re: [R] Very slow optim(): solved
>
> 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.
Thanks, can you pass along any additional details like google links you found
or comment on
the resulting limitation( were you CPU limited converting data formats or did
this cause memory
problems leading to VM thrashing?)? I've often had c++ code that turns out to
be IO limited
when I expected I was doing real complicated computations, it never hurts to go
beyond
the usual suspects LOL.
>
>
>
> 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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