Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization

Understand

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 state-of-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.