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Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization (2001.05537v1)

Published 15 Jan 2020 in math.OC, cs.LG, and stat.ML

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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