Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization

We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runtime of the framework and obtain rates that improve stateof-the-art results for various key machine learning optimization problems including SVM, logistic regression, ridge regression, Lasso, and multiclass SVM. Experiments validate our theoretical findings.

• Type: Journal
• Year: 2016
• Where: MP
• Authors: Shai Shalev-Shwartz, Tong Zhang 0001
• Keywords: Information Technology, MP 2016