[R] High dimensional optimization in R

[Marc Girondot]

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
>