Jacobian sparsity detection using Bloom filters.
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| Title: | Jacobian sparsity detection using Bloom filters. |
|---|---|
| Authors: | Hovland, Paul D.1 (AUTHOR) hovland@anl.gov |
| Source: | Optimization Methods & Software. Apr2026, Vol. 41 Issue 2, p295-307. 13p. |
| Subjects: | Sparse matrices |
| Abstract: | Determining Jacobian sparsity structure is an important step in the efficient computation of sparse Jacobians. We introduce a new method for determining Jacobian sparsity patterns by combining bit vector probing with Bloom filters. We further refine Bloom filter probing by combining it with hierarchical probing to yield a highly effective strategy for Jacobian sparsity pattern determination. [ABSTRACT FROM AUTHOR] |
| Copyright of Optimization Methods & Software is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193364602 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Jacobian sparsity detection using Bloom filters. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hovland%2C+Paul+D%2E%22">Hovland, Paul D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hovland@anl.gov</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Optimization+Methods+%26+Software%22">Optimization Methods & Software</searchLink>. Apr2026, Vol. 41 Issue 2, p295-307. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sparse+matrices%22">Sparse matrices</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Determining Jacobian sparsity structure is an important step in the efficient computation of sparse Jacobians. We introduce a new method for determining Jacobian sparsity patterns by combining bit vector probing with Bloom filters. We further refine Bloom filter probing by combining it with hierarchical probing to yield a highly effective strategy for Jacobian sparsity pattern determination. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Optimization Methods & Software is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193364602 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10556788.2023.2285486 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 295 Subjects: – SubjectFull: Sparse matrices Type: general Titles: – TitleFull: Jacobian sparsity detection using Bloom filters. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hovland, Paul D. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10556788 Numbering: – Type: volume Value: 41 – Type: issue Value: 2 Titles: – TitleFull: Optimization Methods & Software Type: main |
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