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Parallel Algorithm Design for Branch and Bound

In: Tutorials on Emerging Methodologies and Applications in Operations Research

Author

Listed:
  • David A. Bader

    (University of New Mexico)

  • William E. Hart

    (Sandia National Laboratories)

  • Cynthia A. Phillips

    (Sandia National Laboratories)

Abstract

Large and/or computationally expensive optimization problems sometimes require parallel or high-performance computing systems to achieve reasonable running times. This chapter gives an introduction to parallel computing for those familiar with serial optimization. We present techniques to assist the posting of serial optimization codes to parallel systems and discuss more fundamentally parallel approaches to optimization. We survey the state-of-the-art in distributed and shared-memory architectures and give an overview of the programming models appropriate for efficient algorithms on these platforms. As concrete examples, we discuss the design of parallel branch-and-bound algorithms for mixed-integer programming on a distributed-memory system, quadratic assignment problem on a grid architecture, and maximum parsimony in evolutionary trees on a sharedmemory system.

Suggested Citation

  • David A. Bader & William E. Hart & Cynthia A. Phillips, 2005. "Parallel Algorithm Design for Branch and Bound," International Series in Operations Research & Management Science, in: H J. G (ed.), Tutorials on Emerging Methodologies and Applications in Operations Research, chapter 0, pages 5-1-5-44, Springer.
    • Handle: RePEc:spr:isochp:978-0-387-22827-3_5
    DOI: 10.1007/0-387-22827-6_5
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    Citations

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    Cited by:

    1. Lluís-Miquel Munguía & Shabbir Ahmed & David A. Bader & George L. Nemhauser & Yufen Shao, 2018. "Alternating criteria search: a parallel large neighborhood search algorithm for mixed integer programs," Computational Optimization and Applications, Springer, vol. 69(1), pages 1-24, January.
    2. Hossein Babaei & Sina Alemohammad & Richard Baraniuk, 2021. "Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust," Papers 2110.13262, arXiv.org, revised Aug 2022.
    3. Aniko Bartos & Botond Bertok, 2019. "Parameter tuning for a cooperative parallel implementation of process-network synthesis algorithms," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(2), pages 551-572, June.
    4. Jan Gmys, 2022. "Exactly Solving Hard Permutation Flowshop Scheduling Problems on Peta-Scale GPU-Accelerated Supercomputers," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2502-2522, September.

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