Madsen, K.Nielsen, H. B.Pınar, M. Ç.2016-02-082016-02-0819990025-5610http://hdl.handle.net/11693/25150We consider the strictly convex quadratic programming problem with bounded variables. A dual problem is derived using Lagrange duality. The dual problem is the minimization of an unconstrained, piecewise quadratic function. It involves a lower bound of λ1 , the smallest eigenvalue of a symmetric, positive definite matrix, and is solved by Newton iteration with line search. The paper describes the algorithm and its implementation including estimation of λ1, how to get a good starting point for the iteration, and up- and downdating of Cholesky factorization. Results of extensive testing and comparison with other methods for constrained QP are given. © Springer-Verlag 1999.EnglishBound constrained quadratic programmingCondition estimation Newton iteration factorization updateHuber's M-estimatorArticle10.1007/s101070050049