Ingmar Visser <I.Visser <at> uva.nl> writes: > > If you have only boundary constraints on parameters you can use method > L-BFGS in optim. > Hth, ingmar > > > From: Weijie Cai <wcai11 <at> hotmail.com>
> > > > I am dealing with a noisy function (gradient,hessian not available) with > > simple boundary constraints (x_i>0). I've tried constrOptim() using nelder > > mead to minimize it but it is way too slow and the returned results are not > > satisfying. simulated annealing is so hard to tune and it always crashes R > > program in my case. I wonder if there are any packages or functions can do > > direct search optimization? > > Noisy functions are really challenging to optimize; (1) there is no "best" method (despite all the papers doing comparisons of stochastic global optimizers on various sets of test functions); (2) the fancier methods are hard to program [and existing implementations tend have more restricted licenses]; (3) they tend to be slow (thousands of function evaluations). Packages on CRAN that *might* be helpful are genalg, DEoptim. A typical "poor man's" approach to boundary constraints is to add a quadratic penalty (perhaps not even trying to evaluate the objective function -- e.g. substituting the value at the closest boundary point) for parameters outside the constraints into the objective function. With more information (number of parameters, time to compute a single function evaluation, kind of noise) we might be able to help more. Ben Bolker ______________________________________________ [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
