  Dual averaging-type methods are widely used in industrial machine learning
applications due to their ability to promoting solution structure (e.g.,
sparsity) efficiently. In this paper, we propose a novel accelerated
dual-averaging primal-dual algorithm for minimizing a composite convex
function. We also derive a stochastic version of the proposed method which
solves empirical risk minimization, and its advantages on handling sparse data
are demonstrated both theoretically and empirically.
