Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Authors

Conghui Tan, Tong Zhang, Shiqian Ma, Ji Liu

Abstract

<p>Regularized empirical risk minimization problem with linear predictor appears frequently in machine learning. In this paper, we propose a new stochastic primal-dual method to solve this class of problems. Different from existing methods, our proposed methods only require O(1) operations in each iteration. We also develop a variance-reduction variant of the algorithm that converges linearly. Numerical experiments suggest that our methods are faster than existing ones such as proximal SGD, SVRG and SAGA on high-dimensional problems.</p>