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.

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

Documentation:

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

Downloads:

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 dependencies:

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

Linking:

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