Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: We provide several applications of Optimistic Mirror Descent, an online learning algorithm based on the idea<lb>of predictable sequences. First, we recover theMirror Prox algorithm for offline optimization, prove an extension<lb>to Hölder-smooth functions, and apply the results to saddle-point type problems. Next, we prove that a version<lb>of Optimistic Mirror Descent (which has a close relation to the Exponential Weights algorithm) can be used by<lb>two strongly-uncoupled players in a finite zero-summatrix game to converge to the minimax equilibrium at the<lb>rate ofO((logT )/T ). This addresses a question of Daskalakis et al [6]. Further, we consider a partial information<lb>version of the problem. We then apply the results to convex programming and exhibit a simple algorithm for the<lb>approximateMax Flow problem.

Optimization, Learning, and Games with Predictable Sequences