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.Comment: 15 pages, 3 figure
