Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization

Shai Shalev-Shwartz; Tong Zhang

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arXiv · 1209.1873

Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization

Shai Shalev-Shwartz·Tong Zhang

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Abstract

Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closely related Dual Coordinate Ascent (DCA) method has been implemented in various software packages, it has so far lacked good convergence analysis. This paper presents a new analysis of Stochastic Dual Coordinate Ascent (SDCA) showing that this class of methods enjoy strong theoretical guarantees that are comparable or better than SGD. This analysis justifies the effectiveness of SDCA for practical applications.

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BibTeX RIS

Shai Shalev-Shwartz, Tong Zhang. 2013-01-30. Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization. https://arxiv.org/abs/1209.1873

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