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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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2018
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| 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 |