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Branch-reduction-bound algorithm for generalized geometric programming

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  • Peiping Shen
  • Xiaoai Li

Abstract

This article presents a branch-reduction-bound algorithm for globally solving the generalized geometric programming problem. To solve the problem, an equivalent monotonic optimization problem whose objective function is just a simple univariate is proposed by exploiting the particularity of this problem. In contrast to usual branch-and-bound methods, in the algorithm the upper bound of the subproblem in each node is calculated easily by arithmetic expressions. Also, a reduction operation is introduced to reduce the growth of the branching tree during the algorithm search. The proposed algorithm is proven to be convergent and guarantees to find an approximative solution that is close to the actual optimal solution. Finally, numerical examples are given to illustrate the feasibility and efficiency of the present algorithm. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Peiping Shen & Xiaoai Li, 2013. "Branch-reduction-bound algorithm for generalized geometric programming," Journal of Global Optimization, Springer, vol. 56(3), pages 1123-1142, July.
    • Handle: RePEc:spr:jglopt:v:56:y:2013:i:3:p:1123-1142
    DOI: 10.1007/s10898-012-9933-0
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    References listed on IDEAS

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    1. El Barmi, Hammou & Dykstra, Richard L., 1994. "Restricted multinomial maximum likelihood estimation based upon Fenchel duality," Statistics & Probability Letters, Elsevier, vol. 21(2), pages 121-130, September.
    2. Jung-Fa Tsai & Ming-Hua Lin, 2011. "An Efficient Global Approach for Posynomial Geometric Programming Problems," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 483-492, August.
    3. Qu, Shaojian & Zhang, Kecun & Wang, Fusheng, 2008. "A global optimization using linear relaxation for generalized geometric programming," European Journal of Operational Research, Elsevier, vol. 190(2), pages 345-356, October.
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    Citations

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    Cited by:

    1. Jiao, Hongwei & Liu, Sanyang & Lu, Nan, 2015. "A parametric linear relaxation algorithm for globally solving nonconvex quadratic programming," Applied Mathematics and Computation, Elsevier, vol. 250(C), pages 973-985.
    2. Tseng, Chung-Li & Zhan, Yiduo & Zheng, Qipeng P. & Kumar, Manish, 2015. "A MILP formulation for generalized geometric programming using piecewise-linear approximations," European Journal of Operational Research, Elsevier, vol. 245(2), pages 360-370.
    3. Peiping Shen & Kaimin Wang & Ting Lu, 2020. "Outer space branch and bound algorithm for solving linear multiplicative programming problems," Journal of Global Optimization, Springer, vol. 78(3), pages 453-482, November.
    4. Shen, Peiping & Zhu, Zeyi & Chen, Xiao, 2019. "A practicable contraction approach for the sum of the generalized polynomial ratios problem," European Journal of Operational Research, Elsevier, vol. 278(1), pages 36-48.

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