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.
|Author||Antonio Coppola and Brandon Stewart, Harvard University|
|Maintainer||Antonio Coppola <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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