lbfgs: Limited-memory BFGS Optimization

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.

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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
In views: Optimization
CRAN checks: lbfgs results


Reference manual: lbfgs.pdf
Vignettes: An R Package for Limited-memory BFGS Optimization


Package source: lbfgs_1.2.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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 dependencies:

Reverse depends: hierSDR
Reverse imports: bandle, Dire, edmcr, GauPro, splitfngr, xtune
Reverse suggests: optimx, PlackettLuce, psqn, regsem, ROI.plugin.optimx


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