'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) .


Reference manual

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0.1.0 by Daniel Falbel, 7 months ago

Browse source code at https://github.com/cran/madgrad

Authors: Daniel Falbel [aut, cre, cph] , RStudio [cph] , MADGRAD original implementation authors. [cph]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports torch, rlang

Suggests testthat

See at CRAN