Accelerated dual-averaging primal–dual method for composite convex minimization. (3rd July 2020) - Tan, Conghui; Qian, Yuqiu; Ma, Shiqian; Zhang, Tong
ABSTRACT: 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 that solves empirical risk minimization, and its advantages on handling sparse data are demonstrated both theoretically and empirically.