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arXiv · 1910.09396

Efficient Projection-Free Online Methods with Stochastic Recursive Gradient

Jiahao Xie·Zebang Shen·Chao Zhang·Boyu Wang·Hui Qian
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Abstract

This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems. Existing projection-free methods either achieve suboptimal regret bounds or have high per-iteration computational costs. To fill this gap, two efficient projection-free online methods called ORGFW and MORGFW are proposed for solving stochastic and adversarial OCO problems, respectively. By employing a recursive gradient estimator, our methods achieve optimal regret bounds (up to a logarithmic factor) while possessing low per-iteration computational costs. Experimental results demonstrate the efficiency of the proposed methods compared to state-of-the-arts.

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BibTeXRIS

Jiahao Xie, Zebang Shen, Chao Zhang, Boyu Wang, Hui Qian. 2019-10-21. Efficient Projection-Free Online Methods with Stochastic Recursive Gradient. https://arxiv.org/abs/1910.09396

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