  Distributed Pseudo-tree Optimization Procedure (DPOP) is a well-known message
passing algorithm that has been used to provide optimal solutions of
Distributed Constraint Optimization Problems (DCOPs) -- a framework that is
designed to optimize constraints in cooperative multi-agent systems. The
traditional DCOP formulation does not consider those constraints that must be
satisfied (also known as hard constraints), rather it concentrates only on soft
constraints. However, the presence of both types of constraints are observed in
a number of applications, such as Distributed Radio Link Frequency Assignment
and Distributed Event Scheduling, etc. Although the combination of these types
of constraints is recently incorporated in DPOP to solve DCOPs, scalability
remains an issue for them as finding an optimal solution is NP-hard.
Additionally, in DPOP, the agents are arranged as a DFS pseudo-tree. Recently
it has been observed that the constructed pseudo-trees in this way often come
to be chain-like and greatly impair the algorithm's performance. To address
these issues, we develop an algorithm that speeds up the DPOP algorithm by
reducing the size of the messages exchanged and increasing parallelism in the
pseudo tree. Our empirical evidence suggests that our approach outperforms the
state-of-the-art algorithms by a significant margin.
