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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) <doi:10.48550/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 |
| Reference manual: | madgrad.pdf |
| 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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