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Mixed-integer programming techniques for the minimum sum-of-squares clustering problem

Author

Listed:
  • Jan Pablo Burgard

    (Trier University)

  • Carina Moreira Costa

    (Trier University)

  • Christopher Hojny

    (Eindhoven University of Technology)

  • Thomas Kleinert

    (Quantagonia GmbH)

  • Martin Schmidt

    (Trier University)

Abstract

The minimum sum-of-squares clustering problem is a very important problem in data mining and machine learning with very many applications in, e.g., medicine or social sciences. However, it is known to be NP-hard in all relevant cases and to be notoriously hard to be solved to global optimality in practice. In this paper, we develop and test different tailored mixed-integer programming techniques to improve the performance of state-of-the-art MINLP solvers when applied to the problem—among them are cutting planes, propagation techniques, branching rules, or primal heuristics. Our extensive numerical study shows that our techniques significantly improve the performance of the open-source MINLP solver SCIP. Consequently, using our novel techniques, we can solve many instances that are not solvable with SCIP without our techniques and we obtain much smaller gaps for those instances that can still not be solved to global optimality.

Suggested Citation

  • Jan Pablo Burgard & Carina Moreira Costa & Christopher Hojny & Thomas Kleinert & Martin Schmidt, 2023. "Mixed-integer programming techniques for the minimum sum-of-squares clustering problem," Journal of Global Optimization, Springer, vol. 87(1), pages 133-189, September.
    • Handle: RePEc:spr:jglopt:v:87:y:2023:i:1:d:10.1007_s10898-022-01267-4
    DOI: 10.1007/s10898-022-01267-4
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    References listed on IDEAS

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