Before revision: We present a first-order algorithm for nonconvex-nonconcave min-max optimization problems such as those that arise in training GANs . Our algorithm provably converges in time polynomial in the dimension and smoothness parameters of the loss function .
After revision: We propose a variant of the standard min-max optimization problems such as those that arise in training GANs . Our algorithm provably converges in time polynomial in the dimension and smoothness parameters of the loss function .