Decentralized Saddle-Point Problems with Different Constants of Strong Convexity and Strong Concavity

Understand

Large-scale saddle-point problems arise in such machine learning tasks as GANs and linear models with affine constraints.

• In this paper, we study distributed saddle-point problems (SPP) with strongly-convex-strongly-concave smooth objectives that have different strong convexity and strong concavity parameters of composite terms, which correspond to min and max variables, and bilinear saddle-point part.
• We consider two types of first-order oracles: deterministic (returns gradient) and stochastic (returns unbiased stochastic gradient).
• Our method works in both cases and takes several consensus steps between oracle calls.