Efficient Projection-Free Online Methods with Stochastic Recursive Gradient [article]

Jiahao Xie, Zebang Shen, Chao Zhang, Boyu Wang, Hui Qian
2019 arXiv   pre-print
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
more » ... mic factor) while possessing low per-iteration computational costs. Experimental results demonstrate the efficiency of the proposed methods compared to state-of-the-arts.
arXiv:1910.09396v2 fatcat:ygt457pl25e7jm47mud5jvlpx4