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Randomized parallel algorithms for backtrack search and branch-and-bound computation
Richard M. Karp (University of California, Berkeley), Yanjun Zhang (University of California, Berkeley)
Abstract: Universal randomized methods for parallelizing sequential backtrack search and branch-and-bound computation are presented.These methods execute on message-passing multiprocessor systems, and require no global data structures or complex communication protocols.For backtrack search, it is shown that, uniformly on all instances, the method described in this paper is likely to yield a speed-up within a small constant factor from optimal, when all solutions to the problem instance are required.For branch-and-bound computation, it is shown that, uniformly on all instances, the execution time of this method is unlikely to exceed a certain inherent lower bound by more than a constant factor.These randomized methods demonstrate the effectiveness of randomization in distributed parallel computation.