On Finding Local Nash Equilibria (and only Local Nash Equilibria) in Zero-Sum Games

We propose local symplectic surgery , a two-timescale procedure for finding local Nash equilibria in two-player zero-sum games. We first show that previous gradient-based algorithms cannot guarantee convergence to local Nash equilibria due to the existence of non-Nash stationary points. By taking advantage of the differential structure of the game, we construct an algorithm for which the local Nash equilibria are the only attracting fixed points. Further, we show that the algorithm exhibits no oscillatory behavior in neighborhoods of equilibria and that it has the same per-iteration complexity as other recently proposed algorithms. Furthermore, we give convergence rates in structured classes of zero-sum games. We conclude by validating the algorithm on two numerical examples: a toy example with multiple Nash equilibria and a non-Nash equilibrium, and the training of a small generative adversarial network (GAN).