[R] High dimensional optimization in R

Jeremie Juste jeremieju@te @ending from gm@il@com
Sat Dec 1 09:52:54 CET 2018


Genetic algorithm can prove handy as well here. see for instance

with non-convex objective functions I usually try a genetic algorithm for
a few rounds then finish using nlminb

Best regards,

Marc Girondot via R-help <r-help using r-project.org> writes:

> 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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