Methods › General › Stochastic Optimization › MADGRAD
Momentumized, adaptive, dual averaged gradient
MADGRAD
Introduced by Aaron Defazio et al. in Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The MADGRAD method contains a series of modifications to the AdaGrad-DA method to improve its performance on deep learning optimization problems. It gives state-of-the-art generalization performance across a diverse set of problems, including those that Adam normally under-performs on.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization 26 Jan 2021 · 5 repositories · arXiv:2101.11075
Tasks archive 2025-07-28
1 task the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Stochastic Optimization | 1 |
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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