Optimal Regularized Dual Averaging Methods for Stochastic Optimization

no code implementations • NeurIPS 2012 • Xi Chen, Qihang Lin, Javier Pena

We develop a novel algorithm based on the regularized dual averaging (RDA) method, that can simultaneously achieve the optimal convergence rates for both convex and strongly convex loss.

Stochastic Optimization