Hi: A reasonable place to start would be the Optimization task view at CRAN:
http://cran.r-project.org/web/views/ HTH, Dennis On Tue, Aug 24, 2010 at 10:47 AM, David Beacham <d.beacha...@imperial.ac.uk>wrote: > I'm relatively new to R, but I'm attempting to do a non-linear maximum > likelihood estimation (mle) in R, with the added problem that I have a > non-linear constraint. > > The basic problem is linear in the parameters (a_i) and has only one > non-linear component, b, with the problem being linear when b = 0 and > non-linear otherwise. Furthermore, f(a_i) <= b <= g(a_i) for some (simple) f > and g. > > Using optim, I can get the optimisation to work when the non-linearity is > included but not constrained, but gives poor results (as I'd expect). > However, I'm not sure how best to go about the constraint condition. My > initial attempts revolve around the use of logarithmic barrier function, but > this only appears to work when using method="CG". When using "BFGS", the > value of b 'goes out of bounds' and the loglikelihood starts throwing NaN, > which is particularly bad if I want to box constrain the a_i using the > "L-BFGS-B" method. > > Are there any other methods/approaches/variations on the above available to > me in the form of other packages/R functions etc? Or any good > references/books to help me out? > > Any help would be greatly appreciated, > David. > > ______________________________________________ > 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. > [[alternative HTML version deleted]] ______________________________________________ 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.
