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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>.
Getting started
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Package details |
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| Author | Viviane Philipps, Cecile Proust-Lima, Melanie Prague, Boris Hejblum, Daniel Commenges, Amadou Diakite |
| Maintainer | Viviane Philipps <viviane.philipps@u-bordeaux.fr> |
| License | GPL (>= 2.0) |
| Version | 2.0.8 |
| Package repository | View on CRAN |
| Installation |
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