Parallel optimization in R

Question

This question came at the right time, as I'm struggling with optimization as well. I am aware of the different "normal" optimization routines in R, and I am aware of parallel packages like snow, snowfall, Rmpi and the likes. Yet, I didn't manage to get an optimization running in parallel on my computer.

Some toy code to illustrate :

f <- function(x) sum((x-1:length(x))^2)
a <- 1:5
optim(a,f)
nlm(f,a)

What I want to do, is to parallelize the optim() function ( or the nlm() function, which does basically the same). My real function f() is a lot more complicated, and one optimization round lasts about half an hour. If I want to run a simulation of 100 samples, that one takes ages. I'd like to avoid writing my own Newton-like algorithm for parallel computing, so I hope somebody could give me some hints on how to use parallel computing for complex optimization problems in R.

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I reckon this problem is of a different nature than the one in the related question. My request is specifically directed towards parallel computing, not some faster alternative for optim.

Answer 1

If I were after speed, the first thing I would do is go from R to C or C++. (Also Fortran, I hate to say.) When I had squeezed out every possible cycle using this technique, I would introduce MPI to take advantage of parallel hardware.

Answer 2

Sprint might be of interest. I know nothing about it but stumbled across it recently.

Answer 3

To answer my own question :

There is a package in development that looks promising. It has Particle Swarm Optimization methods and builds on the Rmpi package for parallel computing. It can be found on Rforge :

http://www.rforge.net/ppso/index.html

It's still in beta AFAIK, but it looks promising. I'm going to take a look at it later on, I'll report back when I know more. Still, I leave the question open, so if anybody else has another option...