On the efficient determination of Hessian matrix sparsity pattern : algorithms and data structures

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dc.contributor.authorSultana, Marzia
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorHossain, Shahadat
dc.date.accessioned2016-09-09T15:58:24Z
dc.date.available2016-09-09T15:58:24Z
dc.date.issued2016
dc.degree.levelMastersen_US
dc.description.abstractEvaluation of the Hessian matrix of a scalar function is a subproblem in many numerical optimization algorithms. For large-scale problems often the Hessian matrix is sparse and structured, and it is preferable to exploit such information when available. Using symmetry in the second derivative values of the components it is possible to detect the sparsity pattern of the Hessian via products of the Hessian matrix with specially chosen direction vectors. We use graph coloring methods and employ efficient sparse data structures to implement the sparsity pattern detection algorithms.en_US
dc.embargoNoen_US
dc.identifier.urihttps://hdl.handle.net/10133/4601
dc.language.isoen_CAen_US
dc.proquest.subject0405en_US
dc.proquest.subject0642en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta : University of Lethbridge, Dept. of Mathematics and Computer Scienceen_US
dc.publisher.departmentDepartment of Mathematics and Computer Scienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectalgorithmic differentiation toolsen_US
dc.subjectblack-box gradienten_US
dc.subjectdirection vectorsen_US
dc.subjectgraph coloringen_US
dc.subjectgreedy CPR algorithmen_US
dc.subjectsparsity patternsen_US
dc.titleOn the efficient determination of Hessian matrix sparsity pattern : algorithms and data structuresen_US
dc.typeThesisen_US
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