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An Outer-Approximation Algorithm for Maximum-Entropy Sampling

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Combinatorial Optimization (ISCO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13526))

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Abstract

We apply the well-known MINLO outer-approximation algorithm (OA) to the maximum-entropy sampling problem (MESP), using the linx and NLP convex relaxations for MESP. We enhance our approach using disjunctive cuts.

M. Fampa was supported in part by CNPq grants 305444/2019-0 and 434683/2018-3. J. Lee was supported in part by AFOSR grant FA9550-19-1-0175 and ONR grant N00014-21-1-2135.

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Authors and Affiliations

  1. Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

    Marcia Fampa

  2. University of Michigan, Ann Arbor, MI, USA

    Jon Lee

Authors
  1. Marcia Fampa
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  2. Jon Lee
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Correspondence to Marcia Fampa .

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Editors and Affiliations

  1. ESSEC Business School of Paris, Cergy Pontoise Cedex, France

    Ivana Ljubić

  2. IBM TJ Watson Research Center, Yorktown Heights, NY, USA

    Francisco Barahona

  3. Georgia Institute of Technology, Atlanta, GA, USA

    Santanu S. Dey

  4. Université Paris-Dauphine, Paris, France

    A. Ridha Mahjoub

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Fampa, M., Lee, J. (2022). An Outer-Approximation Algorithm for Maximum-Entropy Sampling. In: Ljubić, I., Barahona, F., Dey, S.S., Mahjoub, A.R. (eds) Combinatorial Optimization. ISCO 2022. Lecture Notes in Computer Science, vol 13526. Springer, Cham. https://doi.org/10.1007/978-3-031-18530-4_10

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  • DOI: https://doi.org/10.1007/978-3-031-18530-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18529-8

  • Online ISBN: 978-3-031-18530-4

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