An interior Newton method for quadratic programming

We propose a new (interior) approach for the general quadratic programming problem. We establish that the new method has strong convergence properties: the generated sequence converges globally to a point satisfying the second-order necessary optimality conditions, and the rate of convergence is 2-step quadratic if the limit point is a strong local minimizer. Published alternative interior approaches do not share such strong convergence properties for the nonconvex case. We also report on the results of preliminary numerical experiments: the results indicate that the proposed method has considerable practical potential.

Authors and Affiliations

1. Computer Science Department and Center for Applied Mathematics, Cornell University, Ithaca, NY 14853, USA, , , , , , US

   Thomas F. Coleman

2. Center for Applied Mathematics, Cornell University, Ithaca, NY 14853, USA, Current address: Department of Mathematics, University of North Texas, Denton, TX 75067, USA, , , , , , US

   Jianguo Liu

Received October 11, 1993 / Revised version received February 20, 1996 Published online July 19, 1999

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