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Poster
Efficient Projection-free Algorithms for Saddle Point Problems
Cheng Chen · Luo Luo · Weinan Zhang · Yong Yu

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1182
in Poster Session 5 »
The Frank-Wolfe algorithm is a classic method for constrained optimization problems. It has recently been popular in many machine learning applications because its projection-free property leads to more efficient iterations. In this paper, we study projection-free algorithms for convex-strongly-concave saddle point problems with complicated constraints. Our method combines Conditional Gradient Sliding with Mirror-Prox and show that it only requires $\tilde{\cO}(1/\sqrt{\epsilon})$ gradient evaluations and $\tilde{\cO}(1/\epsilon^2)$ linear optimizations in the batch setting. We also extend our method to the stochastic setting and propose first stochastic projection-free algorithms for saddle point problems. Experimental results demonstrate the effectiveness of our algorithms and verify our theoretical guarantees.
[ Paper ]

Author Information

Cheng Chen (Shanghai Jiao Tong University)
Luo Luo (The Hong Kong University of Science and Technology)
Weinan Zhang (Shanghai Jiao Tong University)
Yong Yu (Shanghai Jiao Tong Unviersity)

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