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stochQN  

Stochastic Limited Memory Quasi-Newton Optimizers
View on CRAN: Click here


Download and install stochQN package within the R console
Install from CRAN:
install.packages("stochQN")

Install from Github:
library("remotes")
install_github("cran/stochQN")

Install by package version:
library("remotes")
install_version("stochQN", "0.1.2-1")



Attach the package and use:
library("stochQN")
Maintained by
David Cortes
[Scholar Profile | Author Map]
First Published: 2019-08-29
Latest Update: 2021-09-26
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 <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++.
How to cite:
David Cortes (2019). stochQN: Stochastic Limited Memory Quasi-Newton Optimizers. R package version 0.1.2-1, https://cran.r-project.org/web/packages/stochQN. Accessed 21 Apr. 2025.
Previous versions and publish date:
0.1.0 (2019-08-29 11:30), 0.1.1 (2019-08-30 01:10), 0.1.2 (2019-09-05 04:10)
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Complete documentation for stochQN
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