Skip to main content
Log in

Interval division and linearization algorithm for minimax linear fractional program

  • Original Paper
  • Published:
Numerical Algorithms Aims and scope Submit manuscript

Abstract

This paper constructs an interval partition linearization algorithm for solving minimax linear fractional programming (MLFP) problem. In this algorithm, MLFP is converted and decomposed into a series of linear programs by dividing the outer 1-dimensional space of the equivalent problem (EP) into polynomially countable intervals. To improve the computational efficiency of the algorithm, two new acceleration techniques are introduced, in which the regions in outer space where the optimal solution of EP does not exist are largely deleted. In addition, the global convergence of the proposed algorithm is summarized, and its computational complexity is illustrated to reveal that it is a fully polynomial time approximation scheme. Finally, the numerical results demonstrate that the proposed algorithm is feasible and effective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Data availability

The data in Tables 1, 2, 3, 4, 5, and 6 are obtained by recompiling the pseudo-code in [26, 27] and experimenting in the same operating environment as our algorithm.

References

  1. Xu, C., Xu, X.M., Wang, H.F.: The fractional minimal cost flow problem on network. Optim. Lett. 5(2), 307–317 (2011)

    Article  MathSciNet  Google Scholar 

  2. Avriel, M., Diewert, W.E., Schaible, S., Zang, I.: Generalized concavity. Concepts and Methods in Science and Engineering. Plenum Press, New York (1998)

    Google Scholar 

  3. Charnes, A., Cooper, W.W.: Programming with linear fractional functionals. Nav. Res. Log. 10(1), 273–274 (1963)

    Article  Google Scholar 

  4. Dinkelbach, W.: On nonlinear fractional programming. Manage. Sci. 13(7), 492–498 (1967)

    Article  MathSciNet  Google Scholar 

  5. Ozkok, B.: An iterative algorithm to solve a linear fractional programming problem. Comput. Ind. Eng. 140(3), 106234–11062347 (2020)

    Article  Google Scholar 

  6. Schaible, S., Ibaraki, T.: Fractional programming. Eur. J. Oper. Res. 12(4), 325–338 (1983)

    Article  MathSciNet  Google Scholar 

  7. Tofallis, C.: Fractional programming: theory, methods and applications. J Oper Res Soc 49(8), 895–895 (1998)

    Article  Google Scholar 

  8. Feng, Q.G., Mao, H.P., Jiao, H.W.: A feasible method for a class of mathematical problems in manufacturing system. Key Engin. Mater. 460, 806–809 (2011). https://doi.org/10.4028/www.scientific.net/KEM.460-461.806

    Article  Google Scholar 

  9. Lu, X., Shi, W., Zhou, W.: Decomposition based least squares iterative estimation algorithm for two-input single-output output error systems. J. Franklin I. 351(12), 5511–5522 (2014)

    Article  MathSciNet  Google Scholar 

  10. Balasubramaniam, P., Lakshmanan, S.: Delay-interval-dependent robust-stability criteria for neutral stochastic neural networks with polytopic and linear fractional uncertainties. Int. J. Comput. Math. 88(10), 2001–2015 (2011)

    Article  MathSciNet  Google Scholar 

  11. Ding, F.: Two-stage least squares based iterative estimation algorithm for CARARMA system modeling. Appl. Math. Model. 37(7), 4798–4808 (2013)

    Article  MathSciNet  Google Scholar 

  12. Ding, F., Ding, J.: Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling. Appl. Math. Model. 37(4), 1694–1704 (2012)

    Article  MathSciNet  Google Scholar 

  13. Ding, F., Liu, X., Chu, J.: Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle. IET Control Theory Appl. 7(2), 176–184 (2013)

    Article  MathSciNet  Google Scholar 

  14. Wang, W., Li, J., Ding, R.: Maximum likelihood parameter algorithm for controlled autoregressive autoregressive models. Int. J. Comput. Math. 88(16), 3458–3467 (2011)

    Article  MathSciNet  Google Scholar 

  15. Crouzeix, J.P., Ferland, J.A., Schaible, S.: An algorithm for generalized fractional programs. J. Optim. Theory Appl. 47(1), 35–49 (1985)

    Article  MathSciNet  Google Scholar 

  16. Benadada, Y., Ferland, J.A.: Partial linearization for generalized fractional programming. Z. Oper. Res. 32(2), 101–106 (1988)

    MathSciNet  Google Scholar 

  17. Phuong, N., Tuy, H.: A unified monotonic approach to generalized linear fractional programming. J. Global Optim. 26(3), 229–259 (2003)

    Article  MathSciNet  Google Scholar 

  18. Freund, R.W., Jarre, F.: An interior-point method for fractional programs with convex constraints. Math. Program. 85(1), 125–161 (1995)

    Google Scholar 

  19. Barros, A.I., Frenk, H.: Generalized fractional programming and cutting plane algorithms. J. Optim. Theory Appl. 87(1), 103–120 (1995)

    Article  MathSciNet  Google Scholar 

  20. Roubi, A.: Method of centers for generalized fractional programming. J. Optim. Theory Appl. 107(1), 123–143 (2000)

    Article  MathSciNet  Google Scholar 

  21. Borde, J., Crouzeix, J.P.: Convergence of a Dinkelbach-type algorithm in generalized fractional programming. Math. Method. Oper. Res. 31(1), 31–54 (1987)

    Article  MathSciNet  Google Scholar 

  22. Lin, J.Y., Sheu, R.L.: Modified Dinkelbach-type algorithm for generalized fractional programs with infinitely many ratios. J. Optim. Theory Appl. 126(2), 323–343 (2005)

    Article  MathSciNet  Google Scholar 

  23. Gugat, M.: Prox-regularization methods for generalized fractional programming. J. Optim. Theory Appl. 99(3), 691–722 (1998)

    Article  MathSciNet  Google Scholar 

  24. Feng, Q., Jiao, H., Mao, H., Chen, Y.: A deterministic algorithm for min-max and max-min linear fractional programming problems. Intern. J. Comput. Intel. Sys. 4(2), 134–141 (2011)

    Google Scholar 

  25. Jiao, H.W., Liu, B.B.: Solving min-max linear fractional programs based on image space branch-and-bound scheme. Chaos Soliton. Fract. 164(1), 112682 (2022)

    Article  MathSciNet  Google Scholar 

  26. Jiao, H.W., Liu, S.Y.: A new linearization technique for minimax linear fractional programming. Intern. J. Comp. Math. 91(8), 1730–1743 (2014)

    Article  MathSciNet  Google Scholar 

  27. Wang, C.F., Jiang, Y., Shen, P.P.: A new branch-and-bound algorithm for solving minimax linear fractional programming. J. Math. 38(1), 113–123 (2018)

    Google Scholar 

  28. Zhao, Y., Liu, S., Jiao, H.: A new branch and bound algorithm for minimax ratios problems. Open Math. 15(X1), 840–851 (2017)

    Article  MathSciNet  Google Scholar 

  29. Boualam, H., Roubi, A.: Dual algorithms based on the proximal bundle method for solving convex minimax fractional programs. J. Ind. Manag. Optim. 15(4), 840–851 (2019)

    Article  MathSciNet  Google Scholar 

  30. Chen, H., Schaible, S., Sheu, R.: Generic algorithm for generalized fractional programming. J. Optim. Theory Appl. 141(1), 93–105 (2009)

    Article  MathSciNet  Google Scholar 

  31. Boualam, H., Roubi, A.: Dual method of centers for solving generalized fractional programs. J. Global Optim. 69(2), 387–426 (2017)

    Article  MathSciNet  Google Scholar 

  32. Ghazi, A., Roubi, A.: A DC approach for minimax fractional optimization programs with ratios of convex functions. Optim. Methods Softw. 2020(4), 1–19 (2020)

    Google Scholar 

  33. Gupta, S., Dangar, D.: On second-order duality for nondifferentiable minimax fractional programming. J. Comput. Appl. Math. 255, 878–886 (2014). https://doi.org/10.1016/j.cam.2013.06.044

    Article  MathSciNet  Google Scholar 

  34. Antczak, T., Zalmai, G.: Second order \(\Phi,\rho -V\)-invexity and duality for semi-infinite minimax fractional programming. Appl. Math. Comput. 227, 831–856 (2014). https://doi.org/10.1016/j.amc.2013.10.050

    Article  MathSciNet  Google Scholar 

  35. Sahinidis, N.: BARON: a general purpose global optimization software package. J. Global Optim. 8(2), 201–205 (1996)

    Article  MathSciNet  Google Scholar 

Download references

Funding

This research is supported by the National Natural Science Foundation of China under Grant (11961001), the Construction Project of first-class subjects in Ningxia higher Education (NXYLXK2017B09), and the major proprietary funded project of North Minzu University (ZDZX201901).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuelin Gao.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, B., Gao, Y., Liu, X. et al. Interval division and linearization algorithm for minimax linear fractional program. Numer Algor 95, 839–858 (2024). https://doi.org/10.1007/s11075-023-01591-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11075-023-01591-0

Keywords

Navigation