stochQN: Stochastic Limited Memory Quasi-Newton Optimizers

Implementations of stochastic, limited-memory quasi-Newton optimizers, similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm, for smooth stochastic optimization. Implements the following methods: oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <>), SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>), adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>). Provides functions for easily creating R objects with partial_fit/predict methods from some given objective/gradient/predict functions. Includes an example stochastic logistic regression using these optimizers. Provides header files and registered C routines for using it directly from C/C++.

Getting started

Package details

AuthorDavid Cortes
MaintainerDavid Cortes <[email protected]>
LicenseBSD_2_clause + file LICENSE
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the stochQN package in your browser

Any scripts or data that you put into this service are public.

stochQN documentation built on Sept. 5, 2019, 9:03 a.m.