A global optimization algorithm for linear fractional programming

摘要

In this paper, we present an efficient branch and bound method for general linear fractional problem (GFP). First, by using a transformation technique, an equivalent problem (EP) of GFP is derived, then by exploiting structure of EP, a linear relaxation programming (LRP) of EP is obtained. To implement the algorithm, the main computation involve solving a sequence of linear programming problem, which can be solved efficiently. The proposed algorithm is convergent to the global maximum through the successive refinement of the solutions of a series of linear programming problems. Numerical experiments are reported to show the feasibility of our algorithm.