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论文类型：期刊论文
发表时间：2009-11-01
发表刊物：JOURNAL OF COMPUTATIONAL MATHEMATICS
收录刊物：SCIE、Scopus
卷号：27
期号：6
页面范围：787-801
ISSN号：0254-9409
关键字：Fischer-Burmeister function; Smoothing Newton method; Inverse optimization; Quadratic programming; Convergence rate
摘要：We consider an inverse quadratic programming (IQP) problem in which the parameters in the objective function of a given quadratic programming (QP) problem are adjusted as little as possible so that a known feasible solution becomes the optimal one. This problem can be formulated as a minimization problem with a positive semidefinite cone constraint and its dual (denoted IQD(A, b)) is a semismoothly differentiable (SC(1)) convex programming problem with fewer variables than the original one. In this paper a smoothing Newton method is used for getting a Karush-Kuhn-Tucker point of IQD(A, b). The proposed method needs to solve only one linear system per iteration and achieves quadratic convergence. Numerical experiments are reported to show that the smoothing Newton method is effective for solving this class of inverse quadratic programming problems.