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A global optimization algorithm for linear fractional programming
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DOI：10.1016/j.amc.2008.06.045.png)
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Abstract
En 中文
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. (C) 2008 Elsevier Inc. All rights reserved.
Keywords:
global optimization
linear relaxation
branch and bound
fractional programming
sum-of-ratios
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3.4
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2.3W
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3.3W