A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
| Version: | 1.2.1.2 |
| Imports: | Rcpp (≥ 0.11.2), methods |
| LinkingTo: | Rcpp |
| Published: | 2022-06-23 |
| DOI: | 10.32614/CRAN.package.lbfgs |
| Author: | Antonio Coppola [aut, cre, cph], Brandon Stewart [aut, cph], Naoaki Okazaki [aut, cph], David Ardia [ctb, cph], Dirk Eddelbuettel [ctb, cph], Katharine Mullen [ctb, cph], Jorge Nocedal [ctb, cph] |
| Maintainer: | Antonio Coppola <acoppola at stanford.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| In views: | Optimization |
| CRAN checks: | lbfgs results |
| Reference manual: | lbfgs.html , lbfgs.pdf |
| Vignettes: |
An R Package for Limited-memory BFGS Optimization (source) |
| Package source: | lbfgs_1.2.1.2.tar.gz |
| Windows binaries: | r-devel: lbfgs_1.2.1.2.zip, r-release: lbfgs_1.2.1.2.zip, r-oldrel: lbfgs_1.2.1.2.zip |
| macOS binaries: | r-release (arm64): lbfgs_1.2.1.2.tgz, r-oldrel (arm64): lbfgs_1.2.1.2.tgz, r-release (x86_64): lbfgs_1.2.1.2.tgz, r-oldrel (x86_64): lbfgs_1.2.1.2.tgz |
| Old sources: | lbfgs archive |
| Reverse depends: | hierSDR |
| Reverse imports: | bandle, Dire, FactorHet, GauPro, GCEstim, splitfngr, xtune |
| Reverse suggests: | optimx, PlackettLuce, psqn, regsem, ROI.plugin.optimx |
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