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An Outcome Space Branch-and-Bound Algorithm for a Class of Linear Multiplicative Programming Problems

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Advances in Global Optimization

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 95))

Abstract

This article presents an outcome space branch-and-bound algorithm for globally solving a class of linear multiplicative programming problem. In this algorithm, the lower bound is found by solving a separable relaxation programming problem. A convex quadratic programming problem is constructed so as to improve the ability to set the upper bound. The convergence of the algorithm is proved. Numerical experiments are reported to show the feasibility and effectiveness of the proposed algorithm.

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Acknowledgements

The work is supported by the Foundation of National Natural Science China (11161001) and by the Scientific Project Foundation of Beifang University of Nationalities (2013XY Z025).

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Authors and Affiliations

  1. Institute of Information & System Science, Beifang University of Nationalities, Yinchuan, 750021, China

    Yuelin Gao, Nihong Zhang & Xiaohua Ma

  2. School of Mathematics and Computer Science, Ningxia University, Yinchuan, 750021, China

    Yuelin Gao

Authors
  1. Yuelin Gao
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  2. Nihong Zhang
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  3. Xiaohua Ma
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Corresponding author

Correspondence to Yuelin Gao .

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Editors and Affiliations

  1. School of Science, Information Technology and Engineering, Federation University Australia, Ballarat, Victoria, Australia

    David Gao

  2. School Science, Inform., Tech. & Engg., Federation University Australia, Ballarat, Victoria, Australia

    Ning Ruan

  3. Dept. of Mathematical Sciences, Tsinghua University, Beijing, China

    Wenxun Xing

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Gao, Y., Zhang, N., Ma, X. (2015). An Outcome Space Branch-and-Bound Algorithm for a Class of Linear Multiplicative Programming Problems. In: Gao, D., Ruan, N., Xing, W. (eds) Advances in Global Optimization. Springer Proceedings in Mathematics & Statistics, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-08377-3_5

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