PAGE: A simple and optimal probabilistic gradient estimator for nonconvex optimization

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...

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Bibliographic Details
Main Authors: LI, Zhize, BAO, Hongyan, ZHANG, Xiangliang, RICHTARIK, Peter
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/8683
https://ink.library.smu.edu.sg/context/sis_research/article/9686/viewcontent/ICML21_full_page.pdf
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Institution: Singapore Management University
Language: English

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