A Parallelized General-Purpose Optimization Based on Marquardt-Levenberg Algorithm

This algorithm provides a numerical solution to the problem of minimizing (or maximizing) a function. 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 .


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install.packages("marqLevAlg")

2.0.4 by Viviane Philipps, 3 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


See at CRAN