An optimal multiprocessor combinatorial auction solver

A combinatorial auction is an auction that permits bidders to bid on bundles of goods rather than just a single item. Unfortunately, winner determination for combinatorial auctions is known to be NPhard. In this paper, we propose a distributed algorithm to compute optimal solutions to this problem. The algorithm uses nagging, a technique for parallelizing search in heterogeneous distributed computing environments. Here, we show how nagging can be used to parallelize a branch-and-bound algorithm for this problem, and provide empirical results supporting both the performance advantage of nagging over more traditional partitioning methods as well as the superior scalability of nagging to larger numbers of processors.