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• W3131753936 abstract "In this paper, we introduce a simplified and unified method for finite-sum convex optimization, named emph{Variance Reduction via Accelerated Dual Averaging (VRADA)}. In both general convex and strongly convex settings, VRADA can attain an $Obig(frac{1}{n}big)$-accurate solution in $O(nloglog n)$ number of stochastic gradient evaluations which improves the best-known result $O(nlog n)$, where $n$ is the number of samples. Meanwhile, VRADA matches the lower bound of the general convex setting up to a $loglog n$ factor and matches the lower bounds in both regimes $nle Theta(kappa)$ and $ngg kappa$ of the strongly convex setting, where $kappa$ denotes the condition number. Besides improving the best-known results and matching all the above lower bounds simultaneously, VRADA has more unified and simplified algorithmic implementation and convergence analysis for both the general convex and strongly convex settings. The underlying novel approaches such as the novel initialization strategy in VRADA may be of independent interest. Through experiments on real datasets, we show the good performance of VRADA over existing methods for large-scale machine learning problems." @default.
• W3131753936 created "2021-03-01" @default.
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• W3131753936 date "2020-06-18" @default.
• W3131753936 modified "2023-09-27" @default.
• W3131753936 title "Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization" @default.
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• W3131753936 hasPublicationYear "2020" @default.
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