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An Efficient Algorithm for Solving Convex–Convex Quadratic Fractional Programs

Author

Listed:
  • R. Yamamoto

    (Chuo University
    MTB Investment Technology Institute Company)

  • H. Konno

    (Chuo University)

Abstract

This paper is concerned with an efficient algorithm for solving a convex-convex type quadratic fractional program whose objective function is defined as the ratio of two convex quadratic functions and whose constraints are linear. This is a typical nonconcave maximization problem with multiple local maxima. The algorithm to be proposed here is a combination of (i) the classical Dinkelbach approach, (ii) the integer programming approach for solving nonconvex quadratic programming problems and (iii) the standard nonlinear programming algorithm. It will be shown that an exact algorithm which is a combination of (i) and (ii) above can solve problems much larger than those solved by an earlier algorithm based on a branch and bound algorithm. It addition, the combination of (i)–(iii) can solve much larger problems within a practical amount of time.

Suggested Citation

  • R. Yamamoto & H. Konno, 2007. "An Efficient Algorithm for Solving Convex–Convex Quadratic Fractional Programs," Journal of Optimization Theory and Applications, Springer, vol. 133(2), pages 241-255, May.
    • Handle: RePEc:spr:joptap:v:133:y:2007:i:2:d:10.1007_s10957-007-9188-y
    DOI: 10.1007/s10957-007-9188-y
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    References listed on IDEAS

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    1. Lo, Andrew W. & Mackinlay, A. Craig, 1997. "Maximizing Predictability In The Stock And Bond Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 1(1), pages 102-134, January.
    2. Siegfried Schaible, 1976. "Fractional Programming. II, On Dinkelbach's Algorithm," Management Science, INFORMS, vol. 22(8), pages 868-873, April.
    3. Werner Dinkelbach, 1967. "On Nonlinear Fractional Programming," Management Science, INFORMS, vol. 13(7), pages 492-498, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. H. Konno & K. Tsuchiya & R. Yamamoto, 2007. "Minimization of the Ratio of Functions Defined as Sums of the Absolute Values," Journal of Optimization Theory and Applications, Springer, vol. 135(3), pages 399-410, December.
    2. M. Barkhagen & S. García & J. Gondzio & J. Kalcsics & J. Kroeske & S. Sabanis & A. Staal, 2023. "Optimising portfolio diversification and dimensionality," Journal of Global Optimization, Springer, vol. 85(1), pages 185-234, January.
    3. Michael Pinelis & David Ruppert, 2023. "Maximizing Portfolio Predictability with Machine Learning," Papers 2311.01985, arXiv.org.
    4. Vandana Goyal & Namrata Rani & Deepak Gupta, 2022. "FGP approach to quadratically constrained multi-objective quadratic fractional programming with parametric functions," OPSEARCH, Springer;Operational Research Society of India, vol. 59(2), pages 594-602, June.
    5. Hiroshi Konno & Yuuhei Morita & Rei Yamamoto, 2010. "A maximal predictability portfolio using absolute deviation reformulation," Computational Management Science, Springer, vol. 7(1), pages 47-60, January.

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