Abstract
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.
Original language | English |
---|---|
Title of host publication | Trends in Industrial and Applied Mathematics |
Editors | A Siddiqi, M Kocvara |
Place of Publication | Dordrecht (NL) |
Publisher | Kluwer academic |
Pages | 149-179 |
Number of pages | 31 |
Publication status | Published - 2002 |