Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization

作者：Shalev Shwartz Shai; Zhang Tong^*

来源：Mathematical Programming, 2016, 155(1-2): 105-145.

DOI：10.1007/s10107-014-0839-0

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.

• 出版日期2016-1