首页|Branch and Bound Algorithm for Globally Solving Minimax Linear Fractional Programming
Branch and Bound Algorithm for Globally Solving Minimax Linear Fractional Programming
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In this paper,we study the minimax linear fractional programming problem on a non-empty bounded set,called problem(MLFP),and we design a branch and bound algorithm to find a globally optimal solution of(MLFP).Firstly,we convert the problem(MLFP)to a problem(EP2)that is equivalent to it.Secondly,by applying the convex relaxation technique to problem(EP2),a convex quadratic relaxation problem(CQRP)is obtained.Then,the overall framework of the algorithm is given and its convergence is proved,the worst-case iteration number is also estimated.Finally,experimental data are listed to illustrate the effectiveness of the algorithm.
Minimax linear fractional programmingGlobal optimal solutionBranch and bound
WANG Hui-man、SHEN Pei-ping、LIANG Yu-xin
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School of Mathematics and Statistics,North China University of Water Resources and Electric Power,Zhengzhou 450046,China
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