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), adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016). 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++.
- Version0.1.2-1
- R versionunknown
- LicenseBSD_2_clause
- LicenseLICENSE
- Needs compilation?Yes
- Last release09/26/2021
Documentation
Team
David Cortes
MaintainerShow author details
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Last 30 days
This package has been downloaded 205 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 11 times.
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Last 365 days
This package has been downloaded 3,234 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Jul 21, 2024 with 73 downloads.
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