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 <http://proceedings.mlr.press/v2/schraudolph07a.html>), SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <doi:10.48550/arXiv.1401.7020>), adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <doi:10.48550/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:	0.1.2-1
Published:	2021-09-26
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
In views:	Optimization
CRAN checks:	stochQN results

Documentation:

Reference manual:

stochQN.pdf

Downloads:

Package source:	stochQN_0.1.2-1.tar.gz
Windows binaries:	r-devel: stochQN_0.1.2-1.zip, r-release: stochQN_0.1.2-1.zip, r-oldrel: stochQN_0.1.2-1.zip
macOS binaries:	r-release (arm64): stochQN_0.1.2-1.tgz, r-oldrel (arm64): stochQN_0.1.2-1.tgz, r-release (x86_64): stochQN_0.1.2-1.tgz, r-oldrel (x86_64): stochQN_0.1.2-1.tgz
Old sources:	stochQN archive

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