On generalized geometric programming problems with non-positive variables
- 作者：Jung-Fa Tsai ; Ming-Hua Lin ; Yi-Chung Hu
- 关键词：Global optimization ; Generalized geometric programming ; Non-positive variables ; Convexification
- 刊名：European Journal of Operational Research
- 出版年：2007
- 期刊代码：58_03772217
- 类别：et
- 出版时间：1 April 2007
- 卷：178
- 期：1
- 页码：10-19
- 文件大小：183 K
摘要
Generalized geometric programming (GGP) problems occur frequently in engineering design and management. Some exponential-based decomposition methods have been developed for solving global optimization of GGP problems. However, the use of logarithmic/exponential transformations restricts these methods to handle the problems with strictly positive variables. This paper proposes a technique for treating non-positive variables with integer powers in GGP problems. By means of variable transformation, the GGP problem with non-positive variables can be equivalently solved with another one having positive variables. In addition, we present some computationally efficient convexification rules for signomial terms to enhance the efficiency of the optimization approach. Numerical examples are presented to demonstrate the usefulness of the proposed method in GGP problems with non-positive variables.