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Many modern machine learning algorithms such as generative adversarialnetworks (GANs) and adversarial training can be formulated as minimaxoptimization. Gradient descent ascent (GDA) is the most commonly used algorithmdue to its simplicity. However, GDA can converge to non-optimal minimax points.We propose a new minimax optimization framework, GDA-AM, that views theGDAdynamics as a fixed-point iteration and solves it using Anderson Mixing tocon-verge to the local minimax. It addresses the diverging issue ofsimultaneous GDAand accelerates the convergence of alternating GDA. We showtheoretically that the algorithm can achieve global convergence for bilinearproblems under mild conditions. We also empirically show that GDA-AMsolves avariety of minimax problems and improves GAN training on several datasets

GDA-AM: On the effectiveness of solving minimax optimization via Anderson Acceleration