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 <
r-help at r-project.org> 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
>
> ______________________________________________
> R-help at r-project.org 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]]