Proximal Stochastic Dual Coordinate Ascent

الأصل · EN

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.