A probabilistic algorithm for Optimal Linear Arrangements.
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| Title: | A probabilistic algorithm for Optimal Linear Arrangements. |
|---|---|
| Authors: | Berend, D.1 (AUTHOR) berend@bgu.ac.il, Mamana, S.1,2 (AUTHOR) shakema1@ac.sce.ac.il |
| Source: | Discrete Applied Mathematics. Oct2026, Vol. 391, p231-248. 18p. |
| Subjects: | Conditional expectations, Weighted graphs, Linear orderings, Deterministic algorithms, Algorithms, Monte Carlo method |
| Abstract: | The Optimal Linear Arrangement (OLA) problem seeks a vertex ordering of a graph that minimizes the sum of edge lengths, a fundamental challenge in graph layout, with applications in VLSI design, network optimization, and data visualization. We propose a basic randomized algorithm and enhance it using the method of conditional expectations to derive a deterministic algorithm with guaranteed performance bounds. Our theoretical analysis includes a concentration result showing that for random graphs, the optimal OLA value concentrates around the expected value of a random arrangement. Additionally, we extend the problem to weighted graphs and demonstrate the effectiveness of our algorithms through empirical evaluations. [ABSTRACT FROM AUTHOR] |
| Copyright of Discrete Applied Mathematics is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194551726 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A probabilistic algorithm for Optimal Linear Arrangements. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Berend%2C+D%2E%22">Berend, D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> berend@bgu.ac.il</i><br /><searchLink fieldCode="AR" term="%22Mamana%2C+S%2E%22">Mamana, S.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> shakema1@ac.sce.ac.il</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Discrete+Applied+Mathematics%22">Discrete Applied Mathematics</searchLink>. Oct2026, Vol. 391, p231-248. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Conditional+expectations%22">Conditional expectations</searchLink><br /><searchLink fieldCode="DE" term="%22Weighted+graphs%22">Weighted graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+orderings%22">Linear orderings</searchLink><br /><searchLink fieldCode="DE" term="%22Deterministic+algorithms%22">Deterministic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The Optimal Linear Arrangement (OLA) problem seeks a vertex ordering of a graph that minimizes the sum of edge lengths, a fundamental challenge in graph layout, with applications in VLSI design, network optimization, and data visualization. We propose a basic randomized algorithm and enhance it using the method of conditional expectations to derive a deterministic algorithm with guaranteed performance bounds. Our theoretical analysis includes a concentration result showing that for random graphs, the optimal OLA value concentrates around the expected value of a random arrangement. Additionally, we extend the problem to weighted graphs and demonstrate the effectiveness of our algorithms through empirical evaluations. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Discrete Applied Mathematics is the property of Elsevier B.V. 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=194551726 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.dam.2026.05.001 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 231 Subjects: – SubjectFull: Conditional expectations Type: general – SubjectFull: Weighted graphs Type: general – SubjectFull: Linear orderings Type: general – SubjectFull: Deterministic algorithms Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Monte Carlo method Type: general Titles: – TitleFull: A probabilistic algorithm for Optimal Linear Arrangements. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Berend, D. – PersonEntity: Name: NameFull: Mamana, S. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 10 Text: Oct2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 0166218X Numbering: – Type: volume Value: 391 Titles: – TitleFull: Discrete Applied Mathematics Type: main |
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