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Accelerated Mini-Batch Stochastic Dual Coordinate Ascent

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arxiv 1305.2581 v1 pith:DA35I75X submitted 2013-05-12 stat.ML cs.LG

Shai Shalev-Shwartz , Tong Zhang This is my paper · ORCID
classification stat.MLcs.LG
keywords acceleratedascentcoordinatedualmethodmini-batchsdcastochastic
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Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized loss minimization problems in machine learning. This paper considers an extension of SDCA under the mini-batch setting that is often used in practice. Our main contribution is to introduce an accelerated mini-batch version of SDCA and prove a fast convergence rate for this method. We discuss an implementation of our method over a parallel computing system, and compare the results to both the vanilla stochastic dual coordinate ascent and to the accelerated deterministic gradient descent method of \cite{nesterov2007gradient}.

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  1. Braid Group Representations and Defect Operators in AdS/CFT Correspondence

    hep-th 2025-05 reject novelty 2.0 of 10

    Bulk Wilson loop braidings are claimed to correspond to boundary defect operators via AdS/CFT, but the mapping is a restatement of the standard dictionary and lacks derivation.

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