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Fractional 0–1 programs: links between mixed-integer linear and conic quadratic formulations

Author

Listed:
  • Erfan Mehmanchi

    (University of Pittsburgh)

  • Andrés Gómez

    (University of Southern California)

  • Oleg A. Prokopyev

    (University of Pittsburgh)

Abstract

This paper focuses on methods that improve the performance of solution approaches for multiple-ratio fractional 0–1 programs (FPs) in their general structure. In particular, we explore the links between equivalent mixed-integer linear programming and conic quadratic programming reformulations of FPs. Thereby, we show that integrating the ideas behind these two types of reformulations of FPs allows us to push further the limits of the current state-of-the-art results and tackle larger-size problems. We perform extensive computational experiments to compare the proposed approaches against the current reformulations from the literature.

Suggested Citation

  • Erfan Mehmanchi & Andrés Gómez & Oleg A. Prokopyev, 2019. "Fractional 0–1 programs: links between mixed-integer linear and conic quadratic formulations," Journal of Global Optimization, Springer, vol. 75(2), pages 273-339, October.
    • Handle: RePEc:spr:jglopt:v:75:y:2019:i:2:d:10.1007_s10898-019-00817-7
    DOI: 10.1007/s10898-019-00817-7
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    Cited by:

    1. Jose Joaquin del Pozo-Antúnez & Francisco Fernández-Navarro & Horacio Molina-Sánchez & Antonio Ariza-Montes & Mariano Carbonero-Ruz, 2021. "The Machine-Part Cell Formation Problem with Non-Binary Values: A MILP Model and a Case of Study in the Accounting Profession," Mathematics, MDPI, vol. 9(15), pages 1-16, July.
    2. Erfan Mehmanchi & Andrés Gómez & Oleg A. Prokopyev, 2021. "Solving a class of feature selection problems via fractional 0–1 programming," Annals of Operations Research, Springer, vol. 303(1), pages 265-295, August.

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