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• W2896572968 abstract "We study the convergence of Stochastic Gradient Descent (SGD) for strongly convex objective functions. We prove for all $t$ a lower bound on the expected convergence rate after the $t$-th SGD iteration; the lower bound is over all possible sequences of diminishing step sizes. It implies that recently proposed sequences of step sizes at ICML 2018 and ICML 2019 are {em universally} close to optimal in that the expected convergence rate after {em each} iteration is within a factor $32$ of our lower bound. This factor is independent of dimension $d$. We offer a framework for comparing with lower bounds in state-of-the-art literature and when applied to SGD for strongly convex objective functions our lower bound is a significant factor $775cdot d$ larger compared to existing work." @default.
• W2896572968 created "2018-10-26" @default.
• W2896572968 creator A5049043092 @default.
• W2896572968 creator A5061545468 @default.
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• W2896572968 date "2018-10-10" @default.
• W2896572968 modified "2023-09-27" @default.
• W2896572968 title "Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD" @default.
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