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Maximum-entropy sampling and the Boolean quadric polytope

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  • Kurt M. Anstreicher

    (University of Iowa)

Abstract

We consider a bound for the maximum-entropy sampling problem (MESP) that is based on solving a max-det problem over a relaxation of the Boolean quadric polytope (BQP). This approach to MESP was first suggested by Christoph Helmberg over 15 years ago, but has apparently never been further elaborated or computationally investigated. We find that the use of a relaxation of BQP that imposes semidefiniteness and a small number of equality constraints gives excellent bounds on many benchmark instances. These bounds can be further tightened by imposing additional inequality constraints that are valid for the BQP. Duality information associated with the BQP-based bounds can be used to fix variables to 0/1 values, and also as the basis for the implementation of a “strong branching” strategy. A branch-and-bound algorithm using the BQP-based bounds solves some benchmark instances of MESP to optimality for the first time.

Suggested Citation

  • Kurt M. Anstreicher, 2018. "Maximum-entropy sampling and the Boolean quadric polytope," Journal of Global Optimization, Springer, vol. 72(4), pages 603-618, December.
    • Handle: RePEc:spr:jglopt:v:72:y:2018:i:4:d:10.1007_s10898-018-0662-x
    DOI: 10.1007/s10898-018-0662-x
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    References listed on IDEAS

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    1. Chun-Wa Ko & Jon Lee & Maurice Queyranne, 1995. "An Exact Algorithm for Maximum Entropy Sampling," Operations Research, INFORMS, vol. 43(4), pages 684-691, August.
    2. Kim-Chuan Toh & Michael J. Todd & Reha H. Tütüncü, 2012. "On the Implementation and Usage of SDPT3 – A Matlab Software Package for Semidefinite-Quadratic-Linear Programming, Version 4.0," International Series in Operations Research & Management Science, in: Miguel F. Anjos & Jean B. Lasserre (ed.), Handbook on Semidefinite, Conic and Polynomial Optimization, chapter 0, pages 715-754, Springer.
    3. ANSTREICHER, Kurt M. & FAMPA, Marcia & LEE, Jon & WILLIAMS, Joy, 1999. "Using continuous nonlinear relaxations to solve constrained maximum-entropy sampling problems," LIDAM Reprints CORE 1412, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. ANSTREICHER, Kurt M. & FAMPA, Marcia & LEE , Jon & WILLIAMS, Joy, 2001. "Maximum-entropy remote sampling," LIDAM Reprints CORE 1494, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Jon Lee, 1998. "Constrained Maximum-Entropy Sampling," Operations Research, INFORMS, vol. 46(5), pages 655-664, October.
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    Cited by:

    1. Hessa Al-Thani & Jon Lee, 2020. "An R Package for Generating Covariance Matrices for Maximum-Entropy Sampling from Precipitation Chemistry Data," SN Operations Research Forum, Springer, vol. 1(3), pages 1-21, September.
    2. Zhongzhu Chen & Marcia Fampa & Jon Lee, 2023. "On Computing with Some Convex Relaxations for the Maximum-Entropy Sampling Problem," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 368-385, March.
    3. Kurt M. Anstreicher, 2020. "Efficient Solution of Maximum-Entropy Sampling Problems," Operations Research, INFORMS, vol. 68(6), pages 1826-1835, November.

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