  Generative adversarial networks (GANs) are a widely used framework for
learning generative models. Wasserstein GANs (WGANs), one of the most
successful variants of GANs, require solving a minmax optimization problem to
global optimality, but are in practice successfully trained using stochastic
gradient descent-ascent. In this paper, we show that, when the generator is a
one-layer network, stochastic gradient descent-ascent converges to a global
solution with polynomial time and sample complexity.
