madgrad: 'MADGRAD' Method for Stochastic Optimization (original) (raw)
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 |
| DOI: | 10.32614/CRAN.package.madgrad |
| Author: | Daniel Falbel [aut, cre, cph], RStudio [cph], MADGRAD original implementation authors. [cph] |
| Maintainer: | Daniel Falbel |
| License: | MIT + file |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | madgrad results |
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