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Scientific Programming
Volume 2016 (2016), Article ID 3204368, 9 pages
Research Article

Global Optimization for Solving Linear Multiplicative Programming Based on a New Linearization Method

1Department of Mathematics, College of Sciences, Shanghai University, Shanghai 200444, China
2Department of Mathematics, Henan Normal University, Xinxiang 453007, China

Received 22 April 2016; Revised 22 July 2016; Accepted 31 July 2016

Academic Editor: Fabrizio Riguzzi

Copyright © 2016 Chun-Feng Wang and Yan-Qin Bai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a new global optimization algorithm for solving a class of linear multiplicative programming (LMP) problem. First, a new linear relaxation technique is proposed. Then, to improve the convergence speed of our algorithm, two pruning techniques are presented. Finally, a branch and bound algorithm is developed for solving the LMP problem. The convergence of this algorithm is proved, and some experiments are reported to illustrate the feasibility and efficiency of this algorithm.