PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization

25 Aug 2020Zhize LiHongyan BaoXiangliang ZhangPeter Richtárik

In this paper, we propose a novel stochastic gradient estimator---ProbAbilistic Gradient Estimator (PAGE)---for nonconvex optimization. PAGE is easy to implement as it is designed via a small adjustment to vanilla SGD: in each iteration, PAGE uses the vanilla minibatch SGD update with probability $p$ and reuses the previous gradient with a small adjustment, at a much lower computational cost, with probability $1-p$... (read more)

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