{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81889"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81889","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Efficient Iterative Methods for Saddle Point Problems","abstract":"This thesis investigates efficient iterative methods for a type of saddle-point problem, namely the generalized Stokes problem, which arises frequently in the simulation of time-dependent Navier-Stokes equations for incompressible fluid flow. These systems are indefinite due to a set of linear constraints on the velocity, causing difficulty for most preconditioners and iterative methods. A multilevel algorithm is proposed for the solution of such systems, which uses a novel technique for the construction of a basis for the space satisfying the constraints. The proposed algorithm achieves faster convergence on account of implicit preconditioning of the linear system, and can be implemented efficiently on parallel processors. Along with a scalable parallel implementation described in the thesis, the multilevel algorithm yields a competitive parallel preconditioned iterative method for the solution of these problems.","abstract_html":"This thesis investigates efficient iterative methods for a type of saddle-point problem, namely the generalized Stokes problem, which arises frequently in the simulation of time-dependent Navier-Stokes equations for incompressible fluid flow. These systems are indefinite due to a set of linear constraints on the velocity, causing difficulty for most preconditioners and iterative methods. A multilevel algorithm is proposed for the solution of such systems, which uses a novel technique for the construction of a basis for the space satisfying the constraints. The proposed algorithm achieves faster convergence on account of implicit preconditioning of the linear system, and can be implemented efficiently on parallel processors. Along with a scalable parallel implementation described in the thesis, the multilevel algorithm yields a competitive parallel preconditioned iterative method for the solution of these problems.","abstract_has_math":false,"creators":["Sarin, Vivek"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Sameh, A."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:20:52Z","date_published":"2015-09-25T20:20:52Z","updated_at":"2026-07-22T22:26:17Z","subjects":["Computer Science"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI9737241"],"render_values":[{"text":"(MiAaPQ)AAI9737241","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81889","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sameh, A."]},{"key":"dc:creator","label":"Author","values":["Sarin, Vivek"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:20:52Z","10000-01-01","1997"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81889","(MiAaPQ)AAI9737241"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis investigates efficient iterative methods for a type of saddle-point problem, namely the generalized Stokes problem, which arises frequently in the simulation of time-dependent Navier-Stokes equations for incompressible fluid flow. These systems are indefinite due to a set of linear constraints on the velocity, causing difficulty for most preconditioners and iterative methods. A multilevel algorithm is proposed for the solution of such systems, which uses a novel technique for the construction of a basis for the space satisfying the constraints. The proposed algorithm achieves faster convergence on account of implicit preconditioning of the linear system, and can be implemented efficiently on parallel processors. Along with a scalable parallel implementation described in the thesis, the multilevel algorithm yields a competitive parallel preconditioned iterative method for the solution of these problems.","Made available in DSpace on 2015-09-25T20:20:52Z (GMT). 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These systems are indefinite due to a set of linear constraints on the velocity, causing difficulty for most preconditioners and iterative methods. A multilevel algorithm is proposed for the solution of such systems, which uses a novel technique for the construction of a basis for the space satisfying the constraints. The proposed algorithm achieves faster convergence on account of implicit preconditioning of the linear system, and can be implemented efficiently on parallel processors. Along with a scalable parallel implementation described in the thesis, the multilevel algorithm yields a competitive parallel preconditioned iterative method for the solution of these problems.","Made available in DSpace on 2015-09-25T20:20:52Z (GMT). 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