In my experience statistical fitting problems are more typically compute-bound (CPU) rather than memory-bound; again, speaking only from my experience, having too *little* memory will cause severe problems, but having more memory than necessary doesn't help.
Usually the work has to go into speeding up the objective function: providing gradients of the objective function (either analytically or by autodiff) can make a huge difference (e.g. see the RTMB package ... [R]TMB are heavily used in fisheries, FWIW) you might be able to parallelize the objective-function computations. Parallelized optimization algorithms do exist (e.g. Kyle and Neira 2014), but I don't know if anyone has implemented them in R ... translating objective functions into C++ etc. (possibly with threaded computation using OpenMP) Klein, Kyle, and Julian Neira. 2014. “Nelder-Mead Simplex Optimization Routine for Large-Scale Problems: A Distributed Memory Implementation.” *Computational Economics* 43 (4): 447–61. https://doi.org/10.1007/s10614-013-9377-8. I'm not sure those address your problem, but that's my best guess based on what you've told us On Fri, Dec 26, 2025 at 5:01 AM Ruben Roa Ureta via R-help < [email protected]> wrote: > Dear R experts. > > I am running customized versions of nonlinear models in my package CatDyn. > These are models with 140 parameters to estimate and composite likelihoods > made of mixtures of adjusted profile normal, adjusted profile lognormal, > and a robust version of the lognormal. > There are 3^6 composite likelihoods, because of 3 core likelihoods and 6 > agents acting to produce the data for the model, each one having one of the > 3 likelihoods. > The numerical methods I'm using are CG and spg, as these worked the best > for these models in other, smaller optimization problems within the same > set of models in CatDyn. > > My motivation for this message is that the optimization is taking days for > each of the 3^6 composite likelihoods on an Ubuntu 24.04 AMD Ryzen™ 7 8700G > w/ Radeon™ 780M Graphics×16 with 128 GB RAM. > I was expecting much faster optimization with 128 GB RAM. > > Some of you may have experience in running large nonlinear optimization > problems in R. > Is there any advice on how to speed up these rather large-ish optimization > problems in R? > Either software, hardware, or both? > > I apologize in advance if you consider this not a proper question for the > mail list. > > Ruben > --- > Ruben H. Roa-Ureta, Ph. D. > Consultant in Statistical Modeling > ORCID ID 0000-0002-9620-5224 > > ______________________________________________ > [email protected] mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > https://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
