Abstract and Applied Analysis

A Global Optimization Algorithm for Signomial Geometric Programming Problem

Xue-Ping Hou, Pei-Ping Shen, and Yong-Qiang Chen

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Abstract

This paper presents a global optimization algorithm for solving the signomial geometric programming (SGP) problem. In the algorithm, by the straight forward algebraic manipulation of terms and by utilizing a transformation of variables, the initial nonconvex programming problem (SGP) is first converted into an equivalent monotonic optimization problem and then is reduced to a sequence of linear programming problems, based on the linearizing technique. To improve the computational efficiency of the algorithm, two range reduction operations are combined in the branch and bound procedure. The proposed algorithm is convergent to the global minimum of the (SGP) by means of the subsequent solutions of a series of relaxation linear programming problems. And finally, the numerical results are reported to vindicate the feasibility and effectiveness of the proposed method.

Article information

Source
Abstr. Appl. Anal., Volume 2014, Special Issue (2014), Article ID 163263, 12 pages.

Dates
First available in Project Euclid: 2 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412278799

Digital Object Identifier
doi:10.1155/2014/163263

Mathematical Reviews number (MathSciNet)
MR3193489

Zentralblatt MATH identifier
07021841

Citation

Hou, Xue-Ping; Shen, Pei-Ping; Chen, Yong-Qiang. A Global Optimization Algorithm for Signomial Geometric Programming Problem. Abstr. Appl. Anal. 2014, Special Issue (2014), Article ID 163263, 12 pages. doi:10.1155/2014/163263. https://projecteuclid.org/euclid.aaa/1412278799


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