As suggested by Prof. Ripley, you should read a good book in the optimization area. One that I would highly recommend is the book by Dennis and Schnabel (1983) - Numerical methods for unconstrained optimization, which does a great job of explaining both "line-search" and "trust-region" approaches for achieving globally-convergent versions of a fast numerical scheme such as Gauss-Newton.
Best, Ravi. ----- Original Message ----- From: Prof Brian Ripley <[EMAIL PROTECTED]> Date: Saturday, September 2, 2006 5:51 am Subject: Re: [R] nonlinear least squares fitting Trust-Region" To: Martin Ivanov <[EMAIL PROTECTED]> Cc: [email protected] > I believe people (including me) did not reply because you appeared > not to > have done your homework. The help page for ?nls _does_ have a > reference > to the 'port' documentation, and RSiteSearch("trust region") is > informative and leads to an R package that does trust-region > optimization. > (So would looking in the R FAQ.) > > You say: > > > Since I am not an expert in the field of optimization, I am just > > conforming to what matlab documentation > > Please note that some of the R developers are really expert in > that area, > and their advice (in the R documentation) should be taken as > seriously as > that in some commercial package that is merely commenting about > the very > sparse choice it offers. Or if R is not in your personal trust > region, > just use 'matlab'. > > Please > > 1) do not shout at your helpers: using all caps is regarded as > shouting. > 2) study and follow the posting guide. People are much more > likely to > help you if you demonstrate you have made efforts to help yourself. > > 3) read the literature. The R FAQ leads to books that cover > fitting > non-linear models in S/R in considerable detail. > > > On Sat, 2 Sep 2006, Martin Ivanov wrote: > > > Dear Mr Graves, > > > Thank you very much for your response. Nobody else from this > mailing > > list ventured to reply to me for the two weeks since I posted my > > question. "nlminb" and "optim" are just optimization procedures. > What I > > need is not just optimization, but a nonlinear CURVE FITTING > procedure. > Which is just optimization: usually by least squares (although you > have > not actually specified that and there are better modern > statistical > ideas). > > > If there is some way to perform nonlinear curve fitting with the > > "Trust-Region" algorithm using any of these functions, I would > me much > > obliged to you if you suggest to me how to achieve that. You > asked me > > why I do not want Gauss-Newton. Since I am not an expert in the > field of > > optimization, I am just conforming to what matlab documentation > > suggests, namely: "Algorithm used for the fitting procedure: > > Trust-Region -- This is the default algorithm and must be used > if you > > specify coefficient constraints. Levenberg-Marquardt -- If the > > trust-region algorithm does not produce a reasonable fit, and > you do not > > have coefficient constraints, you should try the Levenberg- > Marquardt > > algorithm. Gauss-Newton --THIS ALGORITHM IS POTENTIALLY FASTER > THAN THE > > OTHER ALGORITHMS, BUT IT ASSUMES THAT THE RESIDUALS ARE CLOSE TO > ZERO. > > IT IS INCLUDED FOR PEDAGOGICAL REASONS AND SHOULD BE THE LAST > CHOICE FOR > > MOST MODELS AND DATA SETS. I browsed some literature about the > garchfit > > function, but I did not see the "Trust-Region" algorithm there > either: > > algorithm = c("sqp", "nlminb", "lbfgsb", "nlminb+nm", > "lbfgsb+nm"), > > control = list(), title = NULL, description = NULL, ...) > > > > Thank you for your attention. I am looking forward to your reply. > > Regards, > > Martin > > > > ----------------------------------------------------------------- > > vbox7.com - ??????? ????? ???????! > > > > ______________________________________________ > > [email protected] 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. > > > > -- > Brian D. Ripley, [EMAIL PROTECTED] > Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ > University of Oxford, Tel: +44 1865 272861 (self) > 1 South Parks Road, +44 1865 272866 (PA) > Oxford OX1 3TG, UK Fax: +44 1865 272595 > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.htmland provide commented, minimal, self-contained, > reproducible code. > ______________________________________________ [email protected] 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.
