Iterative Solver Selection Techniques for Sparse Linear Systems

dc.contributor.advisor	Norris, Boyana
dc.contributor.author	Sood, Kanika
dc.date.accessioned	2019-09-18T19:29:48Z
dc.date.available	2019-09-18T19:29:48Z
dc.date.issued	2019-09-18
dc.description.abstract	Scientific and engineering applications are dominated by linear algebra and depend on scalable solutions of sparse linear systems. For large problems, preconditioned iterative methods are a popular choice. High-performance numerical libraries offer a variety of preconditioned Newton-Krylov methods for solving sparse problems. However, the selection of a well-performing Krylov method remains to be the user’s responsibility. This research presents the technique for choosing well-performing parallel sparse linear solver methods, based on the problem characteristics and the amount of communication involved in the Krylov methods	en_US
dc.identifier.uri	https://hdl.handle.net/1794/24931
dc.language.iso	en_US
dc.publisher	University of Oregon
dc.rights	All Rights Reserved.
dc.subject	machine learning	en_US
dc.subject	numerical library	en_US
dc.subject	PETSc	en_US
dc.subject	solver selection	en_US
dc.subject	sparse linear systems	en_US
dc.title	Iterative Solver Selection Techniques for Sparse Linear Systems
dc.type	Electronic Thesis or Dissertation
thesis.degree.discipline	Department of Computer and Information Science
thesis.degree.grantor	University of Oregon
thesis.degree.level	doctoral
thesis.degree.name	Ph.D.

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