Random shuffling beats SGD after finite epochs

A long-standing problem in optimization is proving that RANDOMSHUFFLE, the without-replacement version of SGD, converges faster than (the usual) with-replacement SGD. Building upon (Giirbiizbalaban et al., 2015b), we present the first non-asymptotic results for this problem, proving that after a rea...

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Bibliographic Details
Main Authors: HaoChen, Jeff (Author), Sra, Suvrit (Author)
Other Authors: Massachusetts Institute of Technology. Institute for Data, Systems, and Society (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: 2021-11-03T15:30:23Z.
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