Global algorithm for solving linear multiplicative programming problems

2019 ◽  
Vol 14 (3) ◽  
pp. 693-710 ◽  
Author(s):  
Peiping Shen ◽  
Bingdi Huang
Keyword(s):  
Multiplicative Programming ◽  
Linear Multiplicative Programming ◽  
Global Algorithm

scholarly journals Global Algorithm for Generalized Affine Multiplicative Programming Problem

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 162245-162253 ◽  
Author(s):  
Jingben Yin ◽  
Hongwei Jiao ◽  
Youlin Shang
Keyword(s):  
Programming Problem ◽  
Multiplicative Programming ◽  
Global Algorithm

scholarly journals An Efficient Outer Space Algorithm for Generalized Linear Multiplicative Programming Problem

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 80629-80637 ◽  
Author(s):  
Hongwei Jiao ◽  
Wenjie Wang ◽  
Rongjiang Chen ◽  
Youlin Shang ◽  
Jingben Yin
Keyword(s):  
Programming Problem ◽  
Outer Space ◽  
Multiplicative Programming ◽  
Linear Multiplicative Programming

scholarly journals Global Optimization for Generalized Linear Multiplicative Programming Using Convex Relaxation

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yingfeng Zhao ◽  
Ting Zhao
Keyword(s):  
Global Optimization ◽  
Branch And Bound ◽  
Recent Literature ◽  
Convex Relaxation ◽  
Relaxation Method ◽  
Engineering Practice ◽  
Multiplicative Programming ◽  
Wide Range ◽  
Linear Multiplicative Programming ◽  
Convergence And Optimality

Applications of generalized linear multiplicative programming problems (LMP) can be frequently found in various areas of engineering practice and management science. In this paper, we present a simple global optimization algorithm for solving linear multiplicative programming problem (LMP). The algorithm is developed by a fusion of a new convex relaxation method and the branch and bound scheme with some accelerating techniques. Global convergence and optimality of the algorithm are also presented and extensive computational results are reported on a wide range of problems from recent literature and GLOBALLib. Numerical experiments show that the proposed algorithm with a new convex relaxation method is more efficient than usual branch and bound algorithm that used linear relaxation for solving the LMP.

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