The Complexity of Min-Max Optimization with Product Constraints

We study the computational complexity of the problem of computing local min-max equilibria of games with a nonconvex-nonconcave utility function $f$. From the work of Daskalakis, Skoulakis, and Zampetakis [DSZ21], this problem was known to be hard in the restrictive case in which players are required to play strategies that are jointly constrained, leaving open the question of its complexity under more natural constraints. In this paper, we settle the question and show that the problem is PPAD-hard even under product constraints and, in particular, over the hypercube.

💡 Research Summary

The paper investigates the computational complexity of finding local min‑max equilibria—formalized as Gradient Descent‑Ascent (GDA) fixed points—for a smooth, non‑convex non‑concave function f defined on the product domain