Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization
Optimization and Control
2020-01-17 v1 Machine Learning
Machine Learning
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.
Cite
@article{arxiv.2001.05537,
title = {Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization},
author = {Conghui Tan and Yuqiu Qian and Shiqian Ma and Tong Zhang},
journal= {arXiv preprint arXiv:2001.05537},
year = {2020}
}