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dc.contributor.authorMadsen, K.en_US
dc.contributor.authorNielsen, H. B.en_US
dc.contributor.authorPınar, M. Ç.en_US
dc.date.accessioned2016-02-08T10:40:01Z
dc.date.available2016-02-08T10:40:01Z
dc.date.issued1999en_US
dc.identifier.issn0025-5610
dc.identifier.urihttp://hdl.handle.net/11693/25150
dc.description.abstractWe 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.language.isoEnglishen_US
dc.source.titleMathematical Programming, Series Ben_US
dc.relation.isversionofhttps://doi.org/10.1007/s101070050049en_US
dc.subjectBound constrained quadratic programmingen_US
dc.subjectCondition estimation Newton iteration factorization updateen_US
dc.subjectHuber's M-estimatoren_US
dc.titleBound constrained quadratic programming via piecewise quadratic functionsen_US
dc.typeArticleen_US
dc.departmentDepartment of Industrial Engineering
dc.citation.spage135en_US
dc.citation.epage156en_US
dc.citation.volumeNumber85en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1007/s101070050049en_US
dc.publisherSpringer-Verlagen_US


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