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A new branch-and-bound algorithm for the maximum edge-weighted clique problem

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  • San Segundo, Pablo
  • Coniglio, Stefano
  • Furini, Fabio
  • Ljubić, Ivana

Abstract

We study the maximum edge-weighted clique problem, a problem related to the maximum (vertex-weighted) clique problem which asks for finding a complete subgraph (i.e., a clique) of maximum total weight on its edges. The problem appears in a wide range of applications, including bioinformatics, material science, computer vision, robotics, and many more. In this work, we propose a new combinatorial branch-and-bound algorithm for the problem which relies on a novel bounding procedure capable of pruning a very large amount of nodes of the branch-and-bound tree. Extensive computational experiments on random and structured graphs, encompassing standard benchmarks used in the literature as well as recently introduced real-world large-scale graphs, show that our new algorithm outperforms the state-of-the-art by several orders of magnitude on many instances.

Suggested Citation

  • San Segundo, Pablo & Coniglio, Stefano & Furini, Fabio & Ljubić, Ivana, 2019. "A new branch-and-bound algorithm for the maximum edge-weighted clique problem," European Journal of Operational Research, Elsevier, vol. 278(1), pages 76-90.
    • Handle: RePEc:eee:ejores:v:278:y:2019:i:1:p:76-90
    DOI: 10.1016/j.ejor.2019.03.047
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    1. Seyedmohammadhossein Hosseinian & Dalila B. M. M. Fontes & Sergiy Butenko & Marco Buongiorno Nardelli & Marco Fornari & Stefano Curtarolo, 2017. "The Maximum Edge Weight Clique Problem: Formulations and Solution Approaches," Springer Optimization and Its Applications, in: Sergiy Butenko & Panos M. Pardalos & Volodymyr Shylo (ed.), Optimization Methods and Applications, pages 217-237, Springer.
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    12. Maciej Rysz & Mohammad Mirghorbani & Pavlo Krokhmal & Eduardo L. Pasiliao, 2014. "On risk-averse maximum weighted subgraph problems," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 167-185, July.
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    15. Park, Kyungchul & Lee, Kyungsik & Park, Sungsoo, 1996. "An extended formulation approach to the edge-weighted maximal clique problem," European Journal of Operational Research, Elsevier, vol. 95(3), pages 671-682, December.
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    Cited by:

    1. San Segundo, Pablo & Furini, Fabio & León, Rafael, 2022. "A new branch-and-filter exact algorithm for binary constraint satisfaction problems," European Journal of Operational Research, Elsevier, vol. 299(2), pages 448-467.
    2. Furini, Fabio & Ljubić, Ivana & San Segundo, Pablo & Zhao, Yanlu, 2021. "A branch-and-cut algorithm for the Edge Interdiction Clique Problem," European Journal of Operational Research, Elsevier, vol. 294(1), pages 54-69.
    3. Coniglio, Stefano & Furini, Fabio & San Segundo, Pablo, 2021. "A new combinatorial branch-and-bound algorithm for the Knapsack Problem with Conflicts," European Journal of Operational Research, Elsevier, vol. 289(2), pages 435-455.
    4. San Segundo, Pablo & Furini, Fabio & Álvarez, David & Pardalos, Panos M., 2023. "CliSAT: A new exact algorithm for hard maximum clique problems," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1008-1025.
    5. Stefano Coniglio & Stefano Gualandi, 2022. "Optimizing over the Closure of Rank Inequalities with a Small Right-Hand Side for the Maximum Stable Set Problem via Bilevel Programming," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1006-1023, March.
    6. Nasirian, Farzaneh & Mahdavi Pajouh, Foad & Balasundaram, Balabhaskar, 2020. "Detecting a most closeness-central clique in complex networks," European Journal of Operational Research, Elsevier, vol. 283(2), pages 461-475.
    7. Ferrarini, Luca & Gualandi, Stefano, 2022. "Total Coloring and Total Matching: Polyhedra and Facets," European Journal of Operational Research, Elsevier, vol. 303(1), pages 129-142.
    8. Melo, Rafael A. & Queiroz, Michell F. & Santos, Marcio C., 2021. "A matheuristic approach for the b-coloring problem using integer programming and a multi-start multi-greedy randomized metaheuristic," European Journal of Operational Research, Elsevier, vol. 295(1), pages 66-81.

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