Divide-and-coordinate: DCOPs by agreement

In this paper we investigate an approach to provide approximate, anytime algorithms for DCOPs that can provide quality guarantees. At this aim, we propose the divide-and-coordinate (DaC) approach. Such approach amounts to solving a DCOP by iterating (1) a divide stage in which agents divide the DCOP into a set of simpler local subproblems and solve them; and (2) a coordinate stage in which agents exchange local information that brings them closer to an agreement. Next, we formulate a novel algorithm, the Divide and Coordinate Subgradient Algorithm (DaCSA), a computational realization of DaC based on Lagrangian decompositions and the dual subgradient method. By relying on the DaC approach, DaCSA provides bounded approximate solutions. We empirically evaluate DaCSA showing that it is competitive with other state-ofthe-art DCOP approximate algorithms and can eventually outperform them while providing useful quality guarantees.