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dc.contributor.supervisor Hossain, Shahadat
dc.contributor.author Sultana, Marzia
dc.contributor.author University of Lethbridge. Faculty of Arts and Science
dc.date.accessioned 2016-09-09T15:58:24Z
dc.date.available 2016-09-09T15:58:24Z
dc.date.issued 2016
dc.identifier.uri https://hdl.handle.net/10133/4601
dc.description.abstract Evaluation 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.language.iso en_CA en_US
dc.publisher Lethbridge, Alta : University of Lethbridge, Dept. of Mathematics and Computer Science en_US
dc.relation.ispartofseries Thesis (University of Lethbridge. Faculty of Arts and Science) en_US
dc.subject algorithmic differentiation tools en_US
dc.subject black-box gradient en_US
dc.subject direction vectors en_US
dc.subject graph coloring en_US
dc.subject greedy CPR algorithm en_US
dc.subject sparsity patterns en_US
dc.title On the efficient determination of Hessian matrix sparsity pattern : algorithms and data structures en_US
dc.type Thesis en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Mathematics and Computer Science en_US
dc.degree.level Masters en_US
dc.proquest.subject 0405 en_US
dc.proquest.subject 0642 en_US
dc.proquestyes Yes en_US
dc.embargo No en_US


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