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An enhanced logarithmic method for signomial programming with discrete variables

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  • Li, Han-Lin
  • Fang, Shu-Cherng
  • Huang, Yao-Huei
  • Nie, Tiantian

Abstract

Signomial programming problems with discrete variables (SPD) appear widely in real-life applications, but they are hard to solve. This paper proposes an enhanced logarithmic method to reformulate the SPD problem as a mixed 0-1 linear program (MILP) with a minimum number of binary variables and inequality constraints. Both of the theoretical analysis and numerical results strongly support its superior performance to other state-of-the-art linearization methods. We also extend the proposed method to linearize some more complicated problems involving product and fractional terms in discrete and continuous variables.

Suggested Citation

  • Li, Han-Lin & Fang, Shu-Cherng & Huang, Yao-Huei & Nie, Tiantian, 2016. "An enhanced logarithmic method for signomial programming with discrete variables," European Journal of Operational Research, Elsevier, vol. 255(3), pages 922-934.
    • Handle: RePEc:eee:ejores:v:255:y:2016:i:3:p:922-934
    DOI: 10.1016/j.ejor.2016.05.063
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    References listed on IDEAS

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    Cited by:

    1. F. J. Hwang & Yao-Huei Huang, 2021. "An effective logarithmic formulation for piecewise linearization requiring no inequality constraint," Computational Optimization and Applications, Springer, vol. 79(3), pages 601-631, July.

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