关键词: 二次规划, 逆问题, 光滑函数, 信赖域共轭梯度法

Abstract: In order to solve inverse quadratic programming problems effectively, a smoothing trust region Newton-CG method to solve its dual sub problem is proposed.  Augmented Lagrange method is used to solve the dual problem and then the dual sub problem is conversed into a continuous unconstrained optimization problem through introducing a smoothing function. The trust region method and the conjugate gradient method are combined to design the algorithm flow of inverse quadratic programming problems. It is proved that this method is effective by numerical experiments, and compared with Newton method, it is more suitable for solving large scale problems.

Key words: quadratic programming, inverse programming problems, smooth function, trust-region Newton-CG method