First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

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Michael Jordan, Tianyi Lin, Emmanouil-Vasileios Vlatakis-Gkaragkounis


From optimal transport to robust dimensionality reduction, many machine learning applicationscan be cast into the min-max optimization problems over Riemannian manifolds. Though manymin-max algorithms have been analyzed in the Euclidean setting, it has been elusive how theseresults translate to the Riemannian case. Zhang et al. (2022) have recently identified that geodesic convexconcave Riemannian problems admit always Sion’s saddle point solutions. Immediately, an importantquestion that arises is if a performance gap between the Riemannian and the optimal Euclidean spaceconvex concave algorithms is necessary. Our work is the first to answer the question in the negative:We prove that the Riemannian corrected extragradient (RCEG) method achieves last-iterate at alinear convergence rate at the geodesically strongly convex concave case, matching the euclidean one.Our results also extend to the stochastic or non-smooth case where RCEG & Riemanian gradientascent descent (RGDA) achieve respectively near-optimal convergence rates up to factors dependingon curvature of the manifold. Finally, we empirically demonstrate the effectiveness of RCEG insolving robust PCA.