Methods > General > Stochastic Optimization

Momentumized, adaptive, dual averaged gradient

Introduced by Defazio et al. in Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization

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.

Source: Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization

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