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 191 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 3 times.
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Last 365 days
This package has been downloaded 3,218 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 21, 2024 with 73 downloads.
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