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Using continuous nonlinear relaxations to solve constrained maximum-entropy sampling problems

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  • ANSTREICHER, Kurt M.
  • FAMPA, Marcia
  • LEE, Jon
  • WILLIAMS, Joy

Abstract

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Suggested Citation

  • 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).
    • Handle: RePEc:cor:louvrp:1412
    DOI: 10.1007/s101070050055
    Note: In : Mathematical Programming, 85(2), 221-240, 1999
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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. Kurt M. Anstreicher, 2020. "Efficient Solution of Maximum-Entropy Sampling Problems," Operations Research, INFORMS, vol. 68(6), pages 1826-1835, November.
    3. Kurt M. Anstreicher, 2018. "Maximum-entropy sampling and the Boolean quadric polytope," Journal of Global Optimization, Springer, vol. 72(4), pages 603-618, December.
    4. 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.
    5. HOFFMAN, Alan & LEE, Jon & WILLIAMS, Joy, 2000. "New upper bounds for maximum-entropy sampling," LIDAM Discussion Papers CORE 2000012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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