We study the problem of finding min-max solutions for smooth two-input objective functions. While classic results show average-iterate convergence rates for various algorithms, nonconvex applications such as training Generative Adversarial Networks require \emph{last-iterate} convergence guarantees, which are more difficult to prove. It has been an open prob