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An interior-point method for generalized linear-fractional programming

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  • NESTEROV, Y.
  • NEMIROVSKII, A.

Abstract

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Suggested Citation

  • Nesterov, Y. & Nemirovskii, A., 1995. "An interior-point method for generalized linear-fractional programming," LIDAM Reprints CORE 1168, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:1168
    DOI: 10.1007/BF01585557
    Note: In : Mathematical Programming, 69, 177-204, 1995
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    Cited by:

    1. Milan Hladík & Michal Černý & Jaromír Antoch, 2020. "EIV regression with bounded errors in data: total ‘least squares’ with Chebyshev norm," Statistical Papers, Springer, vol. 61(1), pages 279-301, February.
    2. Hou, Zhisong & Liu, Sanyang, 2023. "A spatial branch-reduction-bound algorithm for solving generalized linear fractional problems globally," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Hladík, Milan & Sitarz, Sebastian, 2013. "Maximal and supremal tolerances in multiobjective linear programming," European Journal of Operational Research, Elsevier, vol. 228(1), pages 93-101.
    4. Hladík, Milan, 2016. "Robust optimal solutions in interval linear programming with forall-exists quantifiers," European Journal of Operational Research, Elsevier, vol. 254(3), pages 705-714.
    5. Illes, Tibor & Szirmai, Akos & Terlaky, Tamas, 1999. "The finite criss-cross method for hyperbolic programming," European Journal of Operational Research, Elsevier, vol. 114(1), pages 198-214, April.
    6. Bo Zhang & YueLin Gao & Xia Liu & XiaoLi Huang, 2022. "An Outcome-Space-Based Branch-and-Bound Algorithm for a Class of Sum-of-Fractions Problems," Journal of Optimization Theory and Applications, Springer, vol. 192(3), pages 830-855, March.
    7. Hladík, Milan, 2010. "Generalized linear fractional programming under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 205(1), pages 42-46, August.
    8. T Drezner & Z Drezner & P Kalczynski, 2011. "A cover-based competitive location model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 100-113, January.

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