marqLevAlg: A Parallelized General-Purpose Optimization Based onMarquardt-Levenberg Algorithm
This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 <doi:10.32614/RJ-2021-089>.
| Version: | 2.0.8 |
| Depends: | R (≥ 3.5.0) |
| Imports: | doParallel,foreach |
| Suggests: | microbenchmark,knitr,rmarkdown,ggplot2,viridis,patchwork,xtable |
| Published: | 2023-03-22 |
| DOI: | 10.32614/CRAN.package.marqLevAlg |
| Author: | Viviane Philipps, Cecile Proust-Lima, Melanie Prague, Boris Hejblum, Daniel Commenges, Amadou Diakite |
| Maintainer: | Viviane Philipps <viviane.philipps at u-bordeaux.fr> |
| BugReports: | https://github.com/VivianePhilipps/marqLevAlgParallel/issues |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2.0)] |
| NeedsCompilation: | yes |
| In views: | Optimization |
| CRAN checks: | marqLevAlg results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=marqLevAlgto link to this page.