madgrad: 'MADGRAD' Method for Stochastic Optimization

A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization algorithm. MADGRAD is a 'best-of-both-worlds' optimizer with the generalization performance of stochastic gradient descent and at least as fast convergence as that of Adam, often faster. A drop-in optim_madgrad() implementation is provided based on Defazio et al (2020) <arXiv:2101.11075>.

Version:	0.1.0
Imports:	torch (≥ 0.3.0), rlang
Suggests:	testthat (≥ 3.0.0)
Published:	2021-05-10
Author:	Daniel Falbel [aut, cre, cph], RStudio [cph], MADGRAD original implementation authors. [cph]
Maintainer:	Daniel Falbel <daniel at rstudio.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	README
CRAN checks:	madgrad results

Documentation:

Reference manual:

madgrad.pdf

Downloads:

Package source:	madgrad_0.1.0.tar.gz
Windows binaries:	r-devel: madgrad_0.1.0.zip, r-release: madgrad_0.1.0.zip, r-oldrel: madgrad_0.1.0.zip
macOS binaries:	r-release (arm64): madgrad_0.1.0.tgz, r-oldrel (arm64): madgrad_0.1.0.tgz, r-release (x86_64): madgrad_0.1.0.tgz, r-oldrel (x86_64): madgrad_0.1.0.tgz

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