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 form https://CRAN.R-project.org/package=marqLevAlg to link to this page.
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