IDEAS home Printed from https://ideas.repec.org/p/cor/louvco/2000012.html
   My bibliography  Save this paper

New upper bounds for maximum-entropy sampling

Author

Listed:
  • HOFFMAN, Alan
  • LEE, Jon
  • WILLIAMS, Joy

Abstract

We develop and experiment with new upper bounds for the constrained maximum-entropy sampling problem. Our partition bounds are based on Fischer's inequality. Further new upper bounds combine the use of Fischer's inequality with previously developed bounds. We demonstrate this in detail by using the partitioning idea to strengthen the spectral bounds of Ko, Lee and Queyranne and of Lee. Computational evidence suggests that these bounds may be useful in solving problems to optimality in a branch-and-bound framework.

Suggested Citation

  • 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).
    • Handle: RePEc:cor:louvco:2000012
    as

    Download full text from publisher

    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp2000.html
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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).
    3. Jon Lee, 1998. "Constrained Maximum-Entropy Sampling," Operations Research, INFORMS, vol. 46(5), pages 655-664, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Goldengorin, Boris, 2009. "Maximization of submodular functions: Theory and enumeration algorithms," European Journal of Operational Research, Elsevier, vol. 198(1), pages 102-112, October.
    6. repec:dgr:rugsom:99a17 is not listed on IDEAS
    7. Boris Goldengorin & Gerard Sierksma & Gert A. Tijssen & Michael Tso, 1999. "The Data-Correcting Algorithm for the Minimization of Supermodular Functions," Management Science, INFORMS, vol. 45(11), pages 1539-1551, November.
    8. Goldengorin, Boris & Tijssen, Gert A. & Tso, Michael, 1999. "The maximization of submodular functions : old and new proofs for the correctness of the dichotomy algorithm," Research Report 99A17, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    9. Yildiz, Anil & Mern, John & Kochenderfer, Mykel J. & Howland, Michael F., 2023. "Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit," Renewable Energy, Elsevier, vol. 216(C).
    10. Linda Altieri & Daniela Cocchi, 2021. "Spatial Sampling for Non‐compact Patterns," International Statistical Review, International Statistical Institute, vol. 89(3), pages 532-549, December.
    11. Xuesong Zhou & George F. List, 2010. "An Information-Theoretic Sensor Location Model for Traffic Origin-Destination Demand Estimation Applications," Transportation Science, INFORMS, vol. 44(2), pages 254-273, May.
    12. Theodore T. Allen & Olivia K. Hernand & Abdullah Alomair, 2020. "Optimal Off-line Experimentation for Games," Decision Analysis, INFORMS, vol. 17(4), pages 277-298, December.
    13. Boyang Shang & Daniel W. Apley & Sanjay Mehrotra, 2023. "Diversity Subsampling: Custom Subsamples from Large Data Sets," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 161-182, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cor:louvco:2000012. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alain GILLIS (email available below). General contact details of provider: https://edirc.repec.org/data/coreebe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.