We consider numerical methods for finding (weak) second-order critical points for large-scale non-convex quadratic programming problems. We describe two new methods. The first is of the active-set variety. Although convergent from any starting point, it is intended primarily for the case where a good estimate of the optimal active set can be predicted. The second is an interior-point trust-region type, and has proved capable of solving problems involving up to half a million unknowns and constraints. The solution of a key equality constrained subproblem, common to both methods, is described. The results of comparative tests on a large set of convex and non-convex quadratic programming examples are given.
|titre||Trends in Industrial and Applied Mathematics|
|rédacteurs en chef||A Siddiqi, M Kocvara|
|Lieu de publication||Dordrecht (NL)|
|Nombre de pages||31|
|Etat de la publication||Publié - 2002|