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Discrete Filled Function Method for Discrete Global Optimization

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Abstract

A discrete filled function method is developed in this paper to solve discrete global optimization problems over “strictly pathwise connected domains.” Theoretical properties of the proposed discrete filled function are investigated and a solution algorithm is proposed. Numerical experiments reported in this paper on several test problems with up to 200 variables have demonstrated the applicability and efficiency of the proposed method.

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References

  1. A. Beck and M. Teboulle, “Global optimality conditions for quadratic optimization problems with binary constraints,” SIAM Journal on Optimization, vol. 11, no. 1, pp. 179–188, 2000.

    Article  Google Scholar 

  2. W. Conley, Computer Optimization Techniques, Petrocelli Books Inc.: New York, 1980.

    Google Scholar 

  3. M.W. Cooper, “A survey of methods for pure nonlinear programming,” Management Science, vol. 27, no. 3, pp. 353–361, 1981.

    Google Scholar 

  4. M.W. Cooper, “Nonlinear integer programming for various forms of constraints,” Naval Research Logistics Quarterly, vol. 29, no. 4, pp. 585–592, 1983.

    Google Scholar 

  5. L.C.W. Dixon, J. Gomulka, and S.E. Herson, “Reflection on global optimization problems,” in Optimization in Action: Proceedings of the Conference on Optimization in Action held at the University of Bristol in January 1975, L.C.W. Dixon (Ed.), New York, 1976 pp. 398–435.

  6. M.L. Fisher, “The Lagrangian relaxation method for solving integer programming problems,” Management Science, vol. 27, no. 1, pp. 1–18, 1981.

    Google Scholar 

  7. R.-P. Ge, “A filled function method for finding a global minimizer of a function of several variables,” Mathematical Programming, vol. 46, no. 2, pp. 191–204, 1990.

    Article  Google Scholar 

  8. R.-P. Ge and C.-B. Huang, “A continuous approach to nonlinear integer programming,” Applied Mathematics and Computation, vol. 34, no. 1, pp. 39–60, 1989.

    Article  Google Scholar 

  9. R.-P. Ge and Y.-F. Qin, “A class of filled functions for finding global minimizers of a function of several variables,” Journal of Optimization Theory and Applications, vol. 54, no. 2, pp. 241–252, 1987.

    Article  Google Scholar 

  10. A.M. Geoffirion, “Lagrangian relaxation for integer programming,” Mathematical Programming Study, vol. 2, pp. 82–114, 1974.

    Google Scholar 

  11. A.A. Goldstein and J.F. Price, “On descent from local minima,” Mathematics of Computation, vol. 25, no. 115, pp. 569–574, 1971.

    Google Scholar 

  12. O.K. Gupta and A. Ravindran, “Branch and bound experiments in convex nonlinear integer programming,” Management Science, vol. 31, no. 12, pp. 1533–1546, 1985.

    Google Scholar 

  13. Q.-M. Han and J.-Y. Han, “Revised filled function methods for global optimization,” Applied Mathematics and Computation, vol. 119, nos. 2/3, pp. 217–228, 2001.

    Article  Google Scholar 

  14. W. Hock and K. Schittkowski, Test Examples for Nonlinear Programming Codes, Springer-Verlag, New York, 1981.

    Google Scholar 

  15. F. Körner, “A new branching rule for the branch and bound algorithm for solving nonlinear integer programming problems,” BIT, vol. 28, no. 3, pp. 701–708, 1988.

    Google Scholar 

  16. A.V. Levy and A. Montalvo, “The tunneling algorithm for the global minimization of functions,” SIAM Journal on Scientific and Statistical Computing, vol. 6, no. 1, pp. 15–29, 1985.

    Article  Google Scholar 

  17. D. Li and X.-L. Sun, “Success guarantee of dual search in integer programming: pth power Lagrangian Method,” Journal of Global Optimization, vol. 18, pp. 235–254, 2000.

    Article  CAS  Google Scholar 

  18. D. Li and D.J. White, “pth power Lagrangian Method for Integer Programming,” Annals of Operations Research, vol. 98, pp. 151–170, 2000.

    Article  Google Scholar 

  19. V.V. Litinetski and B.M. Abramzon, “MARS—A multi-start adaptive random search method for global constrained optimization in engineering applications,” Engineering Optimization, vol. 30, pp. 125–154, 1998.

    Google Scholar 

  20. X. Liu, “Finding global minima with a computable filled function,” Journal of Global Optimization, vol. 19, no. 2, pp. 151–161, 2001.

    Article  Google Scholar 

  21. R. Luus and T.H.I. Jaakola, “Optimization by direct search and systematic reduction of the size of the search region,” AIChE Journal, vol. 19, no. 4, pp. 760–765, 1973.

    Article  CAS  Google Scholar 

  22. C. Mohan and H.T. Nguyen, “A controlled random search technique incorporating the simulated annealing concept for solving integer and mixed integer global optimization problems,” Computational Optimization and Applications, vol. 14, pp. 103–132, 1999.

    Article  MathSciNet  Google Scholar 

  23. J.J. Moré, B.S. Garbow, and K.E. Hillstrom, “Testing unconstrained optimization software,” ACM Transactions on Mathematical Software, vol. 7, no. 1, pp. 17–41, 1981.

    Article  Google Scholar 

  24. W.L. Price, “Global optimization by controlled random search,” Journal of Optimization: Theory and Applications, vol. 40, pp. 333–348, 1983.

    Article  Google Scholar 

  25. K. Schittkowski, More Test Examples for Nonlinear Programming Codes, Springer-Verlag, 1987.

  26. X.-L. Sun and D. Li, “Asymptotic strong duality for bounded integer programming: A logarithmic-exponential dual formulation,” Mathematics of Operations Research, vol. 25, pp. 625–644, 2000.

    Article  Google Scholar 

  27. Z. Xu, H.-X. Huang, P.M. Pardalos, and C.-X. Xu, “Filled functions for unconstrained global optimization,” Journal of Global Optimization, vol. 20, no. 1, pp. 49–65, 2001.

    Article  Google Scholar 

  28. X.Q. Yang and C.J. Goh, “A nonlinear Lagrangian function for discrete optimization problems,” in From Local to Global Optimization, A. Migdalas, P. M. Pardalos, and P. Värbrand (Eds.), Kluwer Academic Publishers, Dordrecht, 2001.

    Google Scholar 

  29. L.-S. Zhang, F. Gao, and W.-X. Zhu, “Nonlinear integer programming and global optimization,” Journal of Computational Mathematics, vol. 17, no. 2, pp. 179–190, 1999.

    Google Scholar 

  30. L.-S. Zhang, C.-K. Ng, D. Li, and W.-W. Tian, “A new filled function method for global optimization,” Journal of Global Optimization, vol. 28, no. 1, pp. 17–43, 2004.

    Article  CAS  Google Scholar 

  31. Q. Zheng and D.-M. Zhuang, “‘Integral global minimization: Algorithms, implementations and numerical tests,” Journal of Global Optimization, vol. 7, no. 4, pp. 421–454, 1995.

    Article  MathSciNet  Google Scholar 

  32. W.-X. Zhu, “An approximate algorithm for nonlinear integer programming,” Applied Mathematics and Computation, vol. 93, nos. 2/3, pp. 183–193, 1998.

    Article  Google Scholar 

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Correspondence to Duan Li.

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Ng, CK., Zhang, LS., Li, D. et al. Discrete Filled Function Method for Discrete Global Optimization. Comput Optim Applic 31, 87–115 (2005). https://doi.org/10.1007/s10589-005-0985-7

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  • DOI: https://doi.org/10.1007/s10589-005-0985-7

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