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A branch-price-and-cut algorithm for the minimum evolution problem

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  • Catanzaro, Daniele
  • Aringhieri, Roberto
  • Di Summa, Marco
  • Pesenti, Raffaele

Abstract

We investigate the Minimum Evolution Problem (MEP), an NP-hard network design problem arising from computational biology. The MEP consists in finding a weighted unrooted binary tree having n leaves, minimal length, and such that the sum of the edge weights belonging to the unique path between each pair of leaves is greater than or equal to a prescribed value. We study the polyhedral combinatorics of the MEP and investigate its relationships with the Balanced Minimum Evolution Problem. We develop an exact solution approach for the MEP based on a nontrivial combination of a parallel branch-price-and-cut scheme and a non-isomorphic enumeration of all possible solutions to the problem. Computational experiments show that the new solution approach outperforms the best mixed integer linear programming formulation for the MEP currently described in the literature. Our results give a perspective on the combinatorics of the MEP and suggest new directions for the development of future exact solution approaches that may turn out useful in practical applications. We also show that the MEP is statistically consistent.

Suggested Citation

  • Catanzaro, Daniele & Aringhieri, Roberto & Di Summa, Marco & Pesenti, Raffaele, 2015. "A branch-price-and-cut algorithm for the minimum evolution problem," European Journal of Operational Research, Elsevier, vol. 244(3), pages 753-765.
    • Handle: RePEc:eee:ejores:v:244:y:2015:i:3:p:753-765
    DOI: 10.1016/j.ejor.2015.02.019
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    1. Shmuel Sattath & Amos Tversky, 1977. "Additive similarity trees," Psychometrika, Springer;The Psychometric Society, vol. 42(3), pages 319-345, September.
    2. Daniele Catanzaro & Martine Labbé & Raffaele Pesenti & Juan-José Salazar-González, 2012. "The Balanced Minimum Evolution Problem," INFORMS Journal on Computing, INFORMS, vol. 24(2), pages 276-294, May.
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    Cited by:

    1. Fortz, Bernard & Oliveira, Olga & Requejo, Cristina, 2017. "Compact mixed integer linear programming models to the minimum weighted tree reconstruction problem," European Journal of Operational Research, Elsevier, vol. 256(1), pages 242-251.
    2. Catanzaro, Daniele & Frohn, Martin & Pesenti, Raffaele, 2021. "A Massively Parallel Exact Solution Algorithm for the Balanced Minimum Evolution Problem," LIDAM Discussion Papers CORE 2021023, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Gasparin, Andrea & Camerota Verdù, Federico Julian & Catanzaro, Daniele, 2023. "An evolution strategy approach for the Balanced Minimum Evolution Problem," LIDAM Discussion Papers CORE 2023021, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Olga Fajarda & Cristina Requejo, 2022. "MIP model-based heuristics for the minimum weighted tree reconstruction problem," Operational Research, Springer, vol. 22(3), pages 2305-2342, July.
    5. Catanzaro, Daniele & Frohn, Martin & Gascuel, Olivier & Pesenti, Raffaele, 2022. "A tutorial on the balanced minimum evolution problem," European Journal of Operational Research, Elsevier, vol. 300(1), pages 1-19.
    6. Catanzaro, Daniele & Frohn, Martin & Pesenti, Raffaele, 2021. "On Numerical Stability and Statistical Consistency of the Balanced Minimum Evolution Problem," LIDAM Discussion Papers CORE 2021026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Catanzaro, Daniele & Frohn, Martin & Gascuel, Olivier & Pesenti, Raffaele, 2023. "A Massively Parallel Exact Solution Algorithm for the Balanced Minimum Evolution Problem," LIDAM Discussion Papers CORE 2023001, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Catanzaro, Daniele & Frohn, Martin & Gascuel, Olivier & Pesenti, Raffaele, 2021. "A Tutorial on the Balanced Minimum Evolution Problem," LIDAM Discussion Papers CORE 20210, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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