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arXiv · 2007.04528

Higher-order methods for convex-concave min-max optimization and monotone variational inequalities

Brian Bullins·Kevin A. Lai
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Abstract

We provide improved convergence rates for constrained convex-concave min-max problems and monotone variational inequalities with higher-order smoothness. In min-max settings where the $p^{th}$-order derivatives are Lipschitz continuous, we give an algorithm HigherOrderMirrorProx that achieves an iteration complexity of $O(1/T^{\frac{p+1}{2}})$ when given access to an oracle for finding a fixed point of a $p^{th}$-order equation. We give analogous rates for the weak monotone variational inequality problem. For $p>2$, our results improve upon the iteration complexity of the first-order Mirror Prox method of Nemirovski [2004] and the second-order method of Monteiro and Svaiter [2012]. We further instantiate our entire algorithm in the unconstrained $p=2$ case.

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Brian Bullins, Kevin A. Lai. 2020-07-09. Higher-order methods for convex-concave min-max optimization and monotone variational inequalities. https://arxiv.org/abs/2007.04528

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