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 <http://proceedings.mlr.press/v2/schraudolph07a.html>), 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++.
| Version: | 0.1.2 |
| Published: | 2019-09-05 |
| Author: | David Cortes |
| Maintainer: | David Cortes <david.cortes.rivera at gmail.com> |
| BugReports: | https://github.com/david-cortes/stochQN/issues |
| License: | BSD_2_clause + file LICENSE |
| URL: | https://github.com/david-cortes/stochQN |
| NeedsCompilation: | yes |
| CRAN checks: | stochQN results |
| Reference manual: | stochQN.pdf |
| Package source: | stochQN_0.1.2.tar.gz |
| Windows binaries: | r-devel: stochQN_0.1.2.zip, r-release: stochQN_0.1.2.zip, r-oldrel: stochQN_0.1.2.zip |
| macOS binaries: | r-release: stochQN_0.1.2.tgz, r-oldrel: stochQN_0.1.2.tgz |
| Old sources: | stochQN archive |
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