A Deterministic Global Optimization Method for Solving Generalized Linear Multiplicative Programming Problem with Multiplicative Constraints

Bo ZHANG, Yue-lin GAO

Abstract


This paper presents a deterministic global optimization algorithm for solving generalized linear multiplicative programming problem with multiplicative constraints (GLMP). By utilizing equivalent transformation and linear relaxation method, a linear relaxation programming (LRP) of equivalent problem (GLMPH) is established. In the algorithm, lower and upper bounds are simultaneously obtained by solving some linear relaxation programming problems (LRP). Global convergence has been proved and results of some sample examples and a small random experiment show that the proposed algorithm is feasible and efficient.

Keywords


Global optimization, Linear multiplicative programming, Linear relaxation, Branch and bound


DOI
10.12783/dtcse/amms2018/26189

Full Text:

PDF

Refbacks

  • There are currently no refbacks.