Proximal Stochastic Dual Coordinate Ascent

2012/11/12 by Shalev-Shwartz, Shai, Zhang, Tong
#FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)

paper · doi:10.48550/arxiv.1211.2717

Abstract

We introduce a proximal version of dual coordinate ascent method. We demonstrate how the derived algorithmic framework can be used for numerous regularized loss minimization problems, including ℓ₁ regularization and structured output SVM. The convergence rates we obtain match, and sometimes improve, state-of-the-art results.

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