Bound constrained quadratic programming via piecewise quadratic functions
| dc.citation.epage | 156 | en_US |
| dc.citation.issueNumber | 1 | en_US |
| dc.citation.spage | 135 | en_US |
| dc.citation.volumeNumber | 85 | en_US |
| dc.contributor.author | Madsen, K. | en_US |
| dc.contributor.author | Nielsen, H. B. | en_US |
| dc.contributor.author | Pınar, M. Ç. | en_US |
| dc.date.accessioned | 2016-02-08T10:40:01Z | |
| dc.date.available | 2016-02-08T10:40:01Z | |
| dc.date.issued | 1999 | en_US |
| dc.department | Department of Industrial Engineering | en_US |
| dc.description.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. | en_US |
| dc.identifier.doi | 10.1007/s101070050049 | en_US |
| dc.identifier.issn | 0025-5610 | |
| dc.identifier.uri | http://hdl.handle.net/11693/25150 | |
| dc.language.iso | English | en_US |
| dc.publisher | Springer-Verlag | en_US |
| dc.relation.isversionof | https://doi.org/10.1007/s101070050049 | en_US |
| dc.source.title | Mathematical Programming, Series B | en_US |
| dc.subject | Bound constrained quadratic programming | en_US |
| dc.subject | Condition estimation Newton iteration factorization update | en_US |
| dc.subject | Huber's M-estimator | en_US |
| dc.title | Bound constrained quadratic programming via piecewise quadratic functions | en_US |
| dc.type | Article | en_US |
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