AntonioCoppola/lbfgs: L-BFGS and OWL-QN Optimization

A wrapper built around the libLBFGS optimization library written 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, and can be used to train log-linear models with L1-regularization. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.

Getting started

Package details

AuthorAntonio Coppola and Brandon Stewart, Harvard University
MaintainerAntonio Coppola <[email protected]>
LicenseMIT + file LICENSE
Version1.0
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("AntonioCoppola/lbfgs")
AntonioCoppola/lbfgs documentation built on May 28, 2017, 5:52 p.m.