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Parallelizing Constraint Programming with Learning

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Integration of AI and OR Techniques in Constraint Programming (CPAIOR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9676))

Abstract

Parallel Constraint Programming (CP) solvers typically split the search space in disjoint subspaces, and run solvers independently on these. This may induce significant overhead when solving optimization problems. Parallel Boolean Satisfiability (SAT) solvers typically run a portfolio of solvers, all solving the same problem but sharing some limited learnt clause information. In this paper we consider parallelizing a lazy clause generation (LCG) constraint programming solver, which is a constraint programming solver with learning. Since it is both a kind of CP solver and a kind of SAT solver it is not clear which approach to parallelization is likely to be most effective. We give examples of very different kinds of optimization problems we wish to parallelize and show that a hybrid approach to parallelization can provide a robust and high performing parallel LCG solver.

T. Ehlers—Supported by a fellowship within the FITweltweit program of the German Academic Exchange Service (DAAD).

P.J. Stuckey—NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.

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Notes

  1. 1.

    https://github.com/geoffchu/chuffed.

  2. 2.

    http://www.minizinc.org/challenge2013/probs/cargo/challenge04_1s_626.dzn.

  3. 3.

    http://www.minizinc.org/challenge2014/probs/mqueens/n12.dzn.

  4. 4.

    The exact set of instances is available at people.unimelb.edu.au/pstuckey/pchuffed.

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Acknowledgements

The authors would like to thank Graeme Gange for the fruitful discussions, and Prof. Dirk Nowotka for providing the computational resources for the experiments conducted in this paper.

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Authors and Affiliations

  1. Department of Computer Science, Kiel University, 24098, Kiel, Germany

    Thorsten Ehlers

  2. Department of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia

    Peter J. Stuckey

  3. Victoria Laboratory, National ICT Australia, Melbourne, Australia

    Peter J. Stuckey

Authors
  1. Thorsten Ehlers
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  2. Peter J. Stuckey
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Correspondence to Thorsten Ehlers .

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Editors and Affiliations

  1. Université Laval, Quebec, Québec, Canada

    Claude-Guy Quimper

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Ehlers, T., Stuckey, P.J. (2016). Parallelizing Constraint Programming with Learning. In: Quimper, CG. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2016. Lecture Notes in Computer Science(), vol 9676. Springer, Cham. https://doi.org/10.1007/978-3-319-33954-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-33954-2_11

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