I fit also model with many variables (>100) and I get good result when I
mix several method iteratively, for example: 500 iterations of
Nelder-Mead followed by 500 iterations of BFGS followed by 500
iterations of Nelder-Mead followed by 500 iterations of BFGS etc. until
it stabilized. It can take several days.
I use or several rounds of optimx or simply succession of optim.
Marc
Le 28/11/2018 à 09:29, Ruben a écrit :
Hi,
Sarah Goslee (jn reply to Basic optimization question (I'm a
rookie)): "R is quite good at optimization."
I wonder what is the experience of the R user community with high
dimensional problems, various objective functions and various
numerical methods in R.
In my experience with my package CatDyn (which depends on optimx), I
have fitted nonlinear models with nearly 50 free parameters using
normal, lognormal, gamma, Poisson and negative binomial exact
loglikelihoods, and adjusted profile normal and adjusted profile
lognormal approximate loglikelihoods.
Most numerical methods crash, but CG and spg often, and BFGS, bobyqa,
newuoa and Nelder-Mead sometimes, do yield good results (all numerical
gradients less than 1) after 1 day or more running in a normal 64 bit
PC with Ubuntu 16.04 or Windows 7.
Ruben
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