English

Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm

Optimization and Control 2023-05-30 v2 Machine Learning Machine Learning

Abstract

Deciding whether saddle points exist or are approximable for nonconvex-nonconcave problems is usually intractable. This paper takes a step towards understanding a broad class of nonconvex-nonconcave minimax problems that do remain tractable. Specifically, it studies minimax problems over geodesic metric spaces, which provide a vast generalization of the usual convex-concave saddle point problems. The first main result of the paper is a geodesic metric space version of Sion's minimax theorem; we believe our proof is novel and broadly accessible as it relies on the finite intersection property alone. The second main result is a specialization to geodesically complete Riemannian manifolds: here, we devise and analyze the complexity of first-order methods for smooth minimax problems.

Keywords

Cite

@article{arxiv.2202.06950,
  title  = {Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm},
  author = {Peiyuan Zhang and Jingzhao Zhang and Suvrit Sra},
  journal= {arXiv preprint arXiv:2202.06950},
  year   = {2023}
}

Comments

23 pages, 3 figures