Article ID: | iaor20011553 |
Country: | Netherlands |
Volume: | 124 |
Issue: | 1 |
Start Page Number: | 151 |
End Page Number: | 158 |
Publication Date: | Jul 2000 |
Journal: | European Journal of Operational Research |
Authors: | Pinar Mustafa . |
Keywords: | programming: convex |
In this paper a simple derivation of duality is presented for convex quadratic programs with a convex quadratic constraint. This problem arises in a number of applications including trust region subproblems of nonlinear programming, regularized solution of ill-posed least squares problems, and ridge regression problems in statistical analysis. In general, the dual problem is a concave maximization problem with a linear equality constraint. We apply the duality result to: (1) the trust region subproblem, (2) the smoothing of empirical functions, and (3) to piecewise quadratic trust region subproblems arising in nonlinear robust Huber M-estimation problems in statistics. The results are obtained from a straightforward application of Lagrange duality.