The Extended Regularized Dual Averaging Method for Composite Optimization

Abstract

We present a new algorithm, extended regularized dual averaging (XRDA), for solving composite optimization problems, which are a generalization of the regularized dual averaging (RDA) method. The main novelty of the method is that it allows more flexible control of the backward step size. For instance, the backward step size for RDA grows without bound, while XRDA the backward step size can be kept bounded