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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>.
Getting started
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Package details |
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| Author | Daniel Falbel [aut, cre, cph], RStudio [cph], MADGRAD original implementation authors. [cph] |
| Maintainer | Daniel Falbel <daniel@rstudio.com> |
| License | MIT + file LICENSE |
| Version | 0.1.0 |
| Package repository | View on CRAN |
| Installation |
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