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SGD without Replacement: Sharper Rates for General Smooth Convex Functions

ICML 2019 · Optimization › Nonconvex and smooth optimization · approach: Stochastic gradient last-iterate analysis · 23 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
sample without replacement rather than with replacementSGD on general smooth strongly-convex functions with large K · General smooth strongly-convex functions, large Ksuboptimality: O\left(\tfrac{1}{nK}\right) -> \tilde{O}\left(\tfrac{1}{nK^2}\right)Helped
sample without replacement rather than with replacementSGD on general smooth strongly-convex functions with small K · General smooth strongly-convex functions, small Ksuboptimality: O\left(\tfrac{1}{nK}\right) -> O\left(\tfrac{1}{nK} \right)No clear effect
sample without replacement rather than with replacementSGD on general smooth convex functions without strong convexity · General smooth convex functions without strong convexitysuboptimality: O(G\cdot D/\sqrt{nK}) -> O\left(\tfrac{1}{\sqrt{nK}} \right)No clear effect
remove smoothness from the assumptionsSGDo on strongly-convex functions · Strongly-convex functionssuboptimality: G^2/(8\mu nK)Hurt

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