Branch-reduction-bound algorithm for generalized geometric programming

Peiping Shen () and Xiaoai Li

Journal of Global Optimization, 2013, vol. 56, issue 3, 1123-1142

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

Keywords: Generalized geometric programming; Global optimization; Monotonic function; Reduction operation (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10898-012-9933-0

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