中文摘要：

我们考虑在目的参数给定的二次的编程(QP ) 在工作的一个反的二次的编程(IQP ) 问题问题几乎没被调整以便一个已知的可行解决方案成为最佳的。这个问题能与积极 semidefinite 锥限制作为一个最小化问题被提出并且它的双(表示 IQD (一， b )) 一 semismoothly 可辨(SC [1 ]) 有更少变量的凸的编程问题比原来的。IQD 在一个变光滑的牛顿方法被用于得到 Karush-Kuhn-Tucker 的这份报纸指(一， b ) 。建议方法需要每重复解决仅仅一个线性系统并且完成二次的集中。数字实验被报导证明变光滑的牛顿方法为解决反的二次的编程问题的这个班是有效的。[从作者抽象]

英文摘要：

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.