We study the iteration complexity of the optimistic gradient descent-ascent (OGDA) method and the extragradient (EG) method for finding a saddle point of a convex-concave unconstrained min-max problem. To do so, we first show that both OGDA and EG can be interpreted as approximate variants ...
In this section, we focus on analyzing the performance of optimistic gradient descent ascent (OGDA) for solving a general smooth convex-concave saddle point problem. It has been shown that the OGDA method recovers the convergence rate of the proximal point for both strongly convex-strongly concav...
We examine the convergence properties of Optimistic Gradient Ascent (OGA) in these games. We prove that the time-average behavior of such online learning dynamics exhibits O (1 /T ) rate convergence to the set of Nash Equilibria. Moreover, we show that the day-to-day behavior also ...
CONVERGENCE RATE OF O(1/k) FOR OPTIMISTIC GRADIENT AND EXTRAGRADIENT METHODS IN SMOOTH CONVEX-CONCAVE SADDLE POINT PROBLEMS We study the iteration complexity of the optimistic gradient descent-ascent (OGDA) method and the extragradient (EG) method for finding a saddle point of a... AOAEPS Mokht...