Hi, You really need to study the documentation of "optim" carefully before you make broad generalizations. There are several algorithms available in optim. The default is a simplex-type algorithm called Nelder-Mead. I think this is an unfortunate choice as the default algorithm. Nelder-Mead is a robust algorithm that can work well for almost any kind of objective function (smooth or nasty). However, the trade-off is that it is very slow in terms of convergence rate. For simple, smooth problems, such as yours, you should use "BFGS" (or "L-BFGS" if you have simple box-constraints). Also, take a look at the "optimx" package and the most recent paper in J Stat Software on optimx for a better understanding of the wide array of optimization options available in R.
Best, Ravi. ______________________________________________ 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.
