DOT and DOP: Linearly Convergent Algorithms for Finding Fixed Points of Multiagent Operators

This article investigates the distributed fixed-point finding problem for a global operator over a directed and unbalanced multiagent network, where the global operator is quasi-nonexpansive and only partially accessible to each individual agent. Two cases are addressed, i.e., the global operator is sum separable and block separable. For this first case, the global operator is the sum of local operators, which are assumed to be Lipschitz, and each local operator is privately known to each individual agent. To deal with this scenario, a distributed (or decentralized) algorithm, called distributed quasi-averaged operator tracking algorithm (DOT), is proposed and analyzed, and it is shown that the algorithm can converge to a fixed point of the global operator at a linear rate under a linear regularity condition, which is strictly weaker than the strong convexity assumption on cost functions in existing convex optimization literature. For the second scenario, the global operator is composed of a group of local block mappings which are Lipschitz and can be accessed only by each individual agent. In this setup, a distributed algorithm, called distributed quasi-averaged operator playing algorithm (DOP), is developed and shown to be linearly convergent to a fixed point of the global operator under the linear regularity condition. The above studied problems provide a unified framework for many interesting problems. As examples, the proposed DOT and DOP are exploited to cope with distributed optimization and multiplayer games under partial-decision information. Finally, numerical examples are presented to corroborate the theoretical results.