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Fitting piecewise linear continuous functions

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  • Toriello, Alejandro
  • Vielma, Juan Pablo

Abstract

We consider the problem of fitting a continuous piecewise linear function to a finite set of data points, modeled as a mathematical program with convex objective. We review some fitting problems that can be modeled as convex programs, and then introduce mixed-binary generalizations that allow variability in the regions defining the best-fit function’s domain. We also study the additional constraints required to impose convexity on the best-fit function.

Suggested Citation

  • Toriello, Alejandro & Vielma, Juan Pablo, 2012. "Fitting piecewise linear continuous functions," European Journal of Operational Research, Elsevier, vol. 219(1), pages 86-95.
    • Handle: RePEc:eee:ejores:v:219:y:2012:i:1:p:86-95
    DOI: 10.1016/j.ejor.2011.12.030
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    References listed on IDEAS

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    Cited by:

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    5. Jon Lee & Daphne Skipper & Emily Speakman & Luze Xu, 2023. "Gaining or Losing Perspective for Piecewise-Linear Under-Estimators of Convex Univariate Functions," Journal of Optimization Theory and Applications, Springer, vol. 196(1), pages 1-35, January.
    6. John Alasdair Warwicker & Steffen Rebennack, 2022. "A Comparison of Two Mixed-Integer Linear Programs for Piecewise Linear Function Fitting," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1042-1047, March.
    7. Ploussard, Quentin, 2024. "Piecewise linear approximation with minimum number of linear segments and minimum error: A fast approach to tighten and warm start the hierarchical mixed integer formulation," European Journal of Operational Research, Elsevier, vol. 315(1), pages 50-62.
    8. Juan Pablo Vielma, 2018. "Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139," Management Science, INFORMS, vol. 64(10), pages 4721-4734, October.
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    15. Noam Goldberg & Steffen Rebennack & Youngdae Kim & Vitaliy Krasko & Sven Leyffer, 2021. "MINLP formulations for continuous piecewise linear function fitting," Computational Optimization and Applications, Springer, vol. 79(1), pages 223-233, May.
    16. David Lucas dos Santos Abreu & Erlon Cristian Finardi, 2022. "Continuous Piecewise Linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems," Energies, MDPI, vol. 15(5), pages 1-23, February.
    17. Wu, Yaqing & Maravelias, Christos T., 2024. "Piecewise linear trees as surrogate models for system design and planning under high-frequency temporal variability," European Journal of Operational Research, Elsevier, vol. 315(2), pages 541-552.
    18. Steffen Rebennack & Vitaliy Krasko, 2020. "Piecewise Linear Function Fitting via Mixed-Integer Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 507-530, April.
    19. Jan Szczegielniak & Krzysztof J Latawiec & Jacek Łuniewski & Rafał Stanisławski & Katarzyna Bogacz & Marcin Krajczy & Marek Rydel, 2018. "A study on nonlinear estimation of submaximal effort tolerance based on the generalized MET concept and the 6MWT in pulmonary rehabilitation," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-18, February.
    20. Huaiyu Zhu & Yun Pan & Kwang-Ting Cheng & Ruohong Huan, 2018. "A lightweight piecewise linear synthesis method for standard 12-lead ECG signals based on adaptive region segmentation," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-22, October.
    21. Emilio Carrizosa & Vanesa Guerrero & Dolores Romero Morales, 2023. "On mathematical optimization for clustering categories in contingency tables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 407-429, June.
    22. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.

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