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 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.
Math. Program.