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
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
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
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
4 matches
Mail list logo
