Implementations in R of classical general-purpose algorithms for local optimization generally have two major limitations which cause difficulties in applications to complex problems: too loose convergence criteria and too long calculation time. By relying on a Marquardt-Levenberg algorithm (MLA), a Newton-like method particularly robust for solving local optimization problems, we provide with marqLevAlg package an efficient and general-purpose local optimizer which (i) prevents con vergence to saddle points by using a stringent convergence criterion based on the relative distance to minimum/maximum in addition to the stability of the parameters and of the objective function; and (ii) reduces the computation time in complex settings by allowing parallel calculations at each iteration. We demonstrate through a variety of cases from the literature that our implementation reli ably and consistently reaches the optimum (even when other optimizers fail) and also largely reduces computational time in complex settings through the example of maximum likelihood estimation of different sophisticated statistical models.
base, optimx, minpack.lm, nlmrt, marqLevAlg, doParallel, foreach, JM, lcmm, optimParallel, optim, roptim, DEoptim, GA, rgenoud, hydroPSO
Optimization, HighPerformanceComputing, ChemPhys, Cluster, Hydrology, MachineLearning, Survival
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Philipps, et al., "The R Journal: Robust and Efficient Optimization Using a Marquardt-Levenberg Algorithm with R Package marqLevAlg", The R Journal, 2021
BibTeX citation
@article{RJ-2021-089,
author = {Philipps, Viviane and Hejblum, Boris P. and Prague, Mélanie and Commenges, Daniel and Proust-Lima, Cécile},
title = {The R Journal: Robust and Efficient Optimization Using a Marquardt-Levenberg Algorithm with R Package marqLevAlg},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-089},
doi = {10.32614/RJ-2021-089},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {365-379}
}