Dissertation/ Thesis
An improved implementation of sparsity detection of sparse derivative matrices
| Title: | An improved implementation of sparsity detection of sparse derivative matrices |
|---|---|
| Authors: | Jesmin, Tasnuba, University of Lethbridge. Faculty of Arts and Science |
| Contributors: | Hossain, Shahadat |
| Publisher Information: | Universtiy of Lethbridge, Department of Mathematics and Computer Science Department of Mathematics and Computer Science Arts and Science |
| Publication Year: | 2018 |
| Collection: | University of Lethbridge Institutional Repository |
| Subject Terms: | Jacobians, Combinatorial optimization, Sparse matrices -- Data processing, Graph coloring, Parallel programs (Computer programs), Matix devrivatives, sparse data structure, CPR algorithm, sparse derivative matrices, Jacobian matrix, multilevel algorithm, parallel implementation |
| Description: | Optimization is a crucial branch of research with application in numerous domain. Determination of sparsity is a vital stream of optimization research with potentials for improvement. Manual determination of sparsity structure of Jacobian matrix for a large problem is complicated and highly error-prone. The main motivation of this research is to propose an efficient algorithm which can effectively detect and represent sparsity of unknown Jacobian matrices. Automated sparsity detection algorithms find an optimal or near-optimal solution, which reduces time and space complexity for large scale data. Our proposed approach efficiently generates symmetric pattern utilizing band matrix and reduces the number of gradient evaluation. For efficient solution, we integrate our approach with existing pattern detection process. Greedy coloring algorithm is used for column portioning and multilevel algorithm with voting scheme is implemented for detection of sparsity pattern. Finally, parallel computation is used to reduce processing time of the overall approach. ; S.G.S. Deans Scholarship- University of Lethbridge, Alberta Innovates Technology Futures Graduate Student Scholarship(AITF) |
| Document Type: | thesis |
| File Description: | application/pdf |
| Language: | English |
| Relation: | Thesis (University of Lethbridge. Faculty of Arts and Science); https://hdl.handle.net/10133/5266 |
| Availability: | https://hdl.handle.net/10133/5266 |
| Accession Number: | edsbas.1D6FC540 |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hdl.handle.net/10133/5266# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.1D6FC540 RelevancyScore: 724 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 724.436645507813 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: An improved implementation of sparsity detection of sparse derivative matrices – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jesmin%2C+Tasnuba%22">Jesmin, Tasnuba</searchLink><br /><searchLink fieldCode="AR" term="%22University+of+Lethbridge%2E+Faculty+of+Arts+and+Science%22">University of Lethbridge. Faculty of Arts and Science</searchLink> – Name: Author Label: Contributors Group: Au Data: Hossain, Shahadat – Name: Publisher Label: Publisher Information Group: PubInfo Data: Universtiy of Lethbridge, Department of Mathematics and Computer Science<br />Department of Mathematics and Computer Science<br />Arts and Science – Name: DatePubCY Label: Publication Year Group: Date Data: 2018 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Lethbridge Institutional Repository – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Jacobians%22">Jacobians</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+optimization%22">Combinatorial optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+matrices+--+Data+processing%22">Sparse matrices -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+coloring%22">Graph coloring</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programs+%28Computer+programs%29%22">Parallel programs (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Matix+devrivatives%22">Matix devrivatives</searchLink><br /><searchLink fieldCode="DE" term="%22sparse+data+structure%22">sparse data structure</searchLink><br /><searchLink fieldCode="DE" term="%22CPR+algorithm%22">CPR algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22sparse+derivative+matrices%22">sparse derivative matrices</searchLink><br /><searchLink fieldCode="DE" term="%22Jacobian+matrix%22">Jacobian matrix</searchLink><br /><searchLink fieldCode="DE" term="%22multilevel+algorithm%22">multilevel algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22parallel+implementation%22">parallel implementation</searchLink> – Name: Abstract Label: Description Group: Ab Data: Optimization is a crucial branch of research with application in numerous domain. Determination of sparsity is a vital stream of optimization research with potentials for improvement. Manual determination of sparsity structure of Jacobian matrix for a large problem is complicated and highly error-prone. The main motivation of this research is to propose an efficient algorithm which can effectively detect and represent sparsity of unknown Jacobian matrices. Automated sparsity detection algorithms find an optimal or near-optimal solution, which reduces time and space complexity for large scale data. Our proposed approach efficiently generates symmetric pattern utilizing band matrix and reduces the number of gradient evaluation. For efficient solution, we integrate our approach with existing pattern detection process. Greedy coloring algorithm is used for column portioning and multilevel algorithm with voting scheme is implemented for detection of sparsity pattern. Finally, parallel computation is used to reduce processing time of the overall approach. ; S.G.S. Deans Scholarship- University of Lethbridge, Alberta Innovates Technology Futures Graduate Student Scholarship(AITF) – Name: TypeDocument Label: Document Type Group: TypDoc Data: thesis – Name: Format Label: File Description Group: SrcInfo Data: application/pdf – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: Thesis (University of Lethbridge. Faculty of Arts and Science); https://hdl.handle.net/10133/5266 – Name: URL Label: Availability Group: URL Data: https://hdl.handle.net/10133/5266 – Name: AN Label: Accession Number Group: ID Data: edsbas.1D6FC540 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.1D6FC540 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Jacobians Type: general – SubjectFull: Combinatorial optimization Type: general – SubjectFull: Sparse matrices -- Data processing Type: general – SubjectFull: Graph coloring Type: general – SubjectFull: Parallel programs (Computer programs) Type: general – SubjectFull: Matix devrivatives Type: general – SubjectFull: sparse data structure Type: general – SubjectFull: CPR algorithm Type: general – SubjectFull: sparse derivative matrices Type: general – SubjectFull: Jacobian matrix Type: general – SubjectFull: multilevel algorithm Type: general – SubjectFull: parallel implementation Type: general Titles: – TitleFull: An improved implementation of sparsity detection of sparse derivative matrices Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jesmin, Tasnuba – PersonEntity: Name: NameFull: University of Lethbridge. Faculty of Arts and Science – PersonEntity: Name: NameFull: Hossain, Shahadat IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2018 Identifiers: – Type: issn-locals Value: edsbas |
| ResultId | 1 |