Bound constrained quadratic programming via piecewise quadratic functions
Date
1999
Authors
Madsen, K.
Nielsen, H. B.
Pınar, M. Ç.
Editor(s)
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
BUIR Usage Stats
2
views
views
12
downloads
downloads
Citation Stats
Series
Abstract
We 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.
Source Title
Mathematical Programming, Series B
Publisher
Springer-Verlag
Course
Other identifiers
Book Title
Degree Discipline
Degree Level
Degree Name
Citation
Permalink
Published Version (Please cite this version)
Language
English