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论文类型：期刊论文

发表时间：2011-01-01

发表刊物：INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS

收录刊物：SCIE

卷号：88

期号：3

页面范围：508-516

ISSN号：0020-7160

关键字：inverse optimization; linear programming; perturbation approach; inexact Newton method; quadratic convergence

摘要：We consider an inverse linear programming (LP) problem in which the parameters in both the objective function and the constraint set of a given LP problem need to be adjusted as little as possible so that a known feasible solution becomes the optimal one. We formulate this problem as a linear complementarity constrained minimization problem. With the help of the smoothed Fischer-Burmeister function, we propose a perturbation approach to solve the inverse problem and demonstrate its global convergence. An inexact Newton method is constructed to solve the perturbed problem and numerical results are reported to show the effectiveness of the approach.