Minmax optimization, especially in its general nonconvex-nonconcave formulation, has found extensive applications in modern machine learning frameworks such as generative adversarial networks (GAN), adversarial training and multi-agent reinforcement learning. Gradient-based algorithms, in particular gradient descent ascent (GDA), are widely used in practice to solve these problems. Despite the practical popularity of GDA, however, its theoretical behavior has been considered highly undesirable. Indeed, apart from possiblity of non-convergence, recent results [Daskalakis and Panageas, 2018 , Mazumdar and Ratliff, 2018 , Adolphs et al., 2018 show that even when GDA converges, its stable limit points can be points that are not local Nash equilibria, thus not game-theoretically meaningful. In this paper, we initiate a discussion on the proper optimality measures for minmax optimization, and introduce a new notion of local optimality-local minmax-as a more suitable alternative to the notion of local Nash equilibrium. We establish favorable properties of local minmax points, and show, most importantly, that as the ratio of the ascent step size to the descent step size goes to infinity, stable limit points of GDA are exactly local minmax points up to some degenerate points, demonstrating that all stable limit points of GDA have a game-theoretic meaning for minmax problems.
In a related work, REF proposes a new notion of local optimality, called local minimax, designed for general sequential machine learning games.
Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal
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