Package: stochQN
Type: Package
Title: Stochastic Limited Memory Quasi-Newton Optimizers
Version: 0.1.2-1
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
URL: https://github.com/david-cortes/stochQN
BugReports: https://github.com/david-cortes/stochQN/issues
Description: 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++.
License: BSD_2_clause + file LICENSE
NeedsCompilation: yes
RoxygenNote: 6.1.1
Packaged: 2026-11-02 11:21:41 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2021-09-26 03:10:02 UTC
RemoteUrl: https://github.com/cran/stochQN
RemoteRef: HEAD
RemoteSha: c595f81ca70ab26bf44263d8664b7ef4bf0084fa