marqLevAlg: A Parallelized General-Purpose Optimization Based on
Marquardt-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 |
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