@misc{33891,
  author = {Antonio Coppola and Brandon Stewart},
  title = {lbfgs: Efficient L-BFGS and OWL-QN Optimization in R},
  abstract = {<p>This vignette introduces the lbfgs package for R, which consists of 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 Limited-memory Quasi-Newton (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{\textquoteright}s parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems. The lbfgs package compares favorably with other optimization packages for R in microbenchmark tests.</p>
},
  year = {2014},
  publisher = {Harvard University},
  address = {Cambridge},
  language = {eng},
}