[R] High dimensional optimization in R

J C Nash profjcn@@h @ending from gm@il@com
Sat Dec 1 18:05:17 CET 2018


The postings about polyalgorithms don't mention that optimx has a
tool called polyopt() for this. Though I included it in the package,
it has not been widely tested or applied, and more experience with such
approaches would certainly be of interest to a number of workers, though
I suspect the results are rather context-dependent.

JN

On 2018-12-01 3:52 a.m., Jeremie Juste wrote:
> 
> Hello,
> 
> Genetic algorithm can prove handy as well here. see for instance
> https://cran.r-project.org/web/packages/GA/vignettes/GA.html
> 
> with non-convex objective functions I usually try a genetic algorithm for
> a few rounds then finish using nlminb
> 
> 
> Best regards,
> Jeremie
> 
> 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
>>>
>>
>> ______________________________________________
>> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> 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.
> 
> ______________________________________________
> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> 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.
>



More information about the R-help mailing list