New insight into hybrid stochastic gradient descent: Beyond with-replacement sampling and convexity

New insight into hybrid stochastic gradient descent: Beyond with-replacement sampling and convexity

As an incremental-gradient algorithm, the hybrid stochastic gradient descent (HSGD) enjoys merits of both stochastic and full gradient methods for finite-sum problem optimization. However, the existing rate-of-convergence analysis for HSGD is made under with-replacement sampling (WRS) and is restric...

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Bibliographic Details
Main Authors: ZHOU, Pan, YUAN, Xiao-Tong, FENG, Jiashi
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
Subjects:
OS and Networks
Online Access:https://ink.library.smu.edu.sg/sis_research/9007
https://ink.library.smu.edu.sg/context/sis_research/article/10010/viewcontent/NeurIPS_2018_new_insight_into_hybrid_stochastic_gradient_descent_beyond_with_replacement_sampling_and_convexity_Paper.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/9007
https://ink.library.smu.edu.sg/context/sis_research/article/10010/viewcontent/NeurIPS_2018_new_insight_into_hybrid_stochastic_gradient_descent_beyond_with_replacement_sampling_and_convexity_Paper.pdf

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