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, 2020 .


Reference manual

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2.0.5 by Viviane Philipps, 7 months ago

Report a bug at https://github.com/VivianePhilipps/marqLevAlgParallel/issues

Browse source code at https://github.com/cran/marqLevAlg

Authors: Viviane Philipps , Cecile Proust-Lima , Melanie Prague , Boris Hejblum , Daniel Commenges , Amadou Diakite

Documentation:   PDF Manual  

GPL (>= 2.0) license

Imports doParallel, foreach

Suggests microbenchmark, knitr, rmarkdown, rticles, ggplot2, viridis, patchwork, xtable

Imported by sfaR.

See at CRAN