Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization

15 Jan 2020  ·  Conghui Tan, Yuqiu Qian, Shiqian Ma, Tong Zhang ·

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. read more

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