[R] How to improve the "OPTIM" results

[Hans W Borchers]

> MORE GENERAL OPTIM ISSUES
> 
>       I'm considering creating a package 'optimMLE' that would automate 
> some of this and package it with common 'methods' that would assume that 
> sum(fn(...)) was either a log(likelihood) or the negative of a 
> log(likelihood), etc.  However, before I do, I need to make more 
> progress on some of my other commitments, review RSiteSearch("optim", 
> "fun") to make sure I'm not duplicating something that already exists, 
> etc.  If anyone is interested in collaborating on this, please contact 
> me off-line. 
> 
>       Hope this helps. 
>       Spencer

Thanks for your tips on using `optim()'. I believe `optim' is a reasonable good
implementation of numerical optimization techniques such as quasi-Newton BFGS or
conjugate gradients. Maybe methods using modern line searches or trust regions
can follow someday.

Everybody applying `optim' should be aware that it is a *Local Optimization*
(LO) approach. What you describe appears to be a step towards *Global
Optimization* (GO) in R. And indeed more and more requests in to the R-help list
are about `optim' as a tool for global optimization, not always being fully
aware of the differences.

I am wondering whether it would be more useful to provide one or two global
optimization approaches to the R community. And as this is an active research
area, there are many and with different advantages and drawbacks.

I would like to propose IPOPT as one of he newer and more advanced methods for
global optimization. This powerful software package is open source and available
through the COIN-OR initiative and its Web pages:

    http://www.coin-or.org/Ipopt/documentation/

    ``Ipopt (Interior Point OPTimizer, pronounced I-P-Opt) is a software package 
    for large-scale nonlinear optimization. Ipopt is written in C++ and is 
    released as open source code under the Common Public License (CPL). It is
    available from the COIN-OR initiative. The code has been written by Carl
    Laird (Carnegie Mellon University) and Andreas Wachter (IBM's T.J. Watson 
    Research Center), who is the COIN project leader for Ipopt.''

PERHAPS the COIN project would agree for IPOPT to be integrated into the open
source project R as a package. For testing it right now there is an AMPL-based
interface to IPOPT at the NEOS solver:

    http://neos.mcs.anl.gov/neos/solvers/nco:Ipopt/AMPL.html

There may be other rewarding projects in the COIN-OR initiative, such as
`SYMPHONY' for solving mixed-integer linear programs (MILP, stronger than `glpk'
and `lp-solve'), or the BONMIN open source *non-linear* mixed integer
programming (MINLP) solver. I could imagine R to be a good platform for
integrating some of these algorithms.

------------
Hans W. Borchers
Control and Optimization Group
ABB Corporate Research Germany
hans-werner.borchers at de.abb.com