I'd generally echo Ben's suggestions. In particular, it is likely useful to time sections of objective calculation with microbenchmark. My experience is that only some sections of objective are critical, so only parts need be coded outside R. Sometimes changing to different R code can be enough. Ben didn't mention Fortran. Now getting old-fashioned, but it can be very fast for some operations, but be careful to count the call and return i.e., time outside the replaced objective code, not just inside the sub-programs.
About 10 years ago managed to get better than 10* speedup with approach replacing "bits and pieces" Cheers, JN On 2025-12-29 14:42, Ben Bolker wrote:
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.
______________________________________________ [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.
