[R] Very slow optim()

[John C Nash]

As I'm at least partly responsible for CG in optim, and packager of Rcgmin, I'll recommend
the latter based on experience since it was introduced. I've so far seen no example where
CG does better than Rcgmin, though I'm sure there are cases to be found.

However, Ben is right that if ADMB does so well (it uses effectively analytic
derivatives), then use it. Rcgmin really wants you to provide gradient code, and that is
work.

JN


On 07/14/2011 06:00 AM, r-help-request at r-project.org wrote:
> Message: 85 Date: Wed, 13 Jul 2011 20:20:47 +0000 From: Ben Bolker <bbolker at gmail.com> To:
> <r-help at stat.math.ethz.ch> Subject: Re: [R] Very slow optim() Message-ID:
> <loom.20110713T221924-956 at post.gmane.org> Content-Type: text/plain; charset="utf-8"
> 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
> 
> 
>