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arXiv · 2001.05537

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

Conghui Tan·Yuqiu Qian·Shiqian Ma·Tong Zhang
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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 which solves empirical risk minimization, and its advantages on handling sparse data are demonstrated both theoretically and empirically.

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Conghui Tan, Yuqiu Qian, Shiqian Ma, Tong Zhang. 2020-01-15. Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization. https://doi.org/10.1080/10556788.2020.1713779

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