Online Min-Max Optimization: From Individual Regrets to Cumulative Saddle Points

We propose and study an online version of min-max optimization based on cumulative saddle points under a variety of performance measures beyond convex-concave settings. After first observing the incompatibility of (static) Nash equilibrium (SNE-Reg${}_T$) with individual regrets even for strongly convex-strongly concave functions, we propose an alternate static duality gap (SDual-Gap${}_T$) inspired by the online convex optimization (OCO) framework that is compatible with the individual regrets . We provide algorithms that achieve sub-linear regret bounds for (SDual-Gap${}_T$) and the individual regrets and a novel dynamic saddle point regret (DSP-Reg${}_T$), which we suggest naturally represents a min-max version of the dynamic regret in OCO. We derive our bounds for (SDual-Gap${}_T$) and DSP-Reg${}_T$ under strong convexity-strong concavity and a min-max notion of exponential concavity (min-max EC), and in addition we establish a class of functions satisfying min-max EC that captures a two-player variant of the classic portfolio selection problem. Finally, for a dynamic notion of regret compatible with individual regrets, we derive bounds under a two-sided Polyak-ojasiewicz (PL) condition.