SSS Algorithms for Max-Cut.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	SSS Algorithms for Max-Cut.
Συγγραφείς:	Gentile, Claudio¹ (AUTHOR) gentile@iasi.cnr.it, Rinaldi, Giovanni¹ (AUTHOR) rinaldi@iasi.cnr.it, Salgado, Esteban² (AUTHOR) esteban.salgadov@usm.cl
Πηγή:	INFORMS Journal on Computing. May/Jun2026, Vol. 38 Issue 3, p878-904. 27p.
Θεματικοί όροι:	*Dynamic programming, Heuristic algorithms, Combinatorial optimization, Quantum annealing, Sampling (Process)
Περίληψη:	The subgraph sampling scheme (SSS) is a technique originally introduced for Markov random fields. It is a powerful tool for designing heuristic algorithms for max-cut, quadratic unconstrained binary optimization (QUBO), and other optimization problems. The first application of SSS in combinatorial optimization, combined with dynamic programming, is in Selby's heuristic. This algorithm is shown to outperform quantum annealing for solving max-cut problems on chimera graphs. Leveraging SSS, we introduce two new algorithms. One is designed to handle general graphs, whereas the other is specifically tailored for toroidal grid graphs. To assess the effectiveness of these algorithms, we conducted a comprehensive evaluation. We used the same methodology, test bed, and set of 37 well-established heuristics for max-cut and QUBO problems as described in a recent study of Dunning, Gupta, and Silberholz. Notably, all three SSS-based algorithms consistently achieve top rankings in terms of performance. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by the funding program Horizon 2020 - Excellent Science - Marie Skłodowska-Curie Actions of the European Commission (Grant MINOA- Mixed-Integer Non-Linear Optimization Applications/764759]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0812) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0812). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]
	Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences 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.)
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Items	– Name: Title Label: Title Group: Ti Data: SSS Algorithms for Max-Cut. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gentile%2C+Claudio%22">Gentile, Claudio</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gentile@iasi.cnr.it</i><br /><searchLink fieldCode="AR" term="%22Rinaldi%2C+Giovanni%22">Rinaldi, Giovanni</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rinaldi@iasi.cnr.it</i><br /><searchLink fieldCode="AR" term="%22Salgado%2C+Esteban%22">Salgado, Esteban</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> esteban.salgadov@usm.cl</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22INFORMS+Journal+on+Computing%22">INFORMS Journal on Computing</searchLink>. May/Jun2026, Vol. 38 Issue 3, p878-904. 27p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+optimization%22">Combinatorial optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+annealing%22">Quantum annealing</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+%28Process%29%22">Sampling (Process)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The subgraph sampling scheme (SSS) is a technique originally introduced for Markov random fields. It is a powerful tool for designing heuristic algorithms for max-cut, quadratic unconstrained binary optimization (QUBO), and other optimization problems. The first application of SSS in combinatorial optimization, combined with dynamic programming, is in Selby's heuristic. This algorithm is shown to outperform quantum annealing for solving max-cut problems on chimera graphs. Leveraging SSS, we introduce two new algorithms. One is designed to handle general graphs, whereas the other is specifically tailored for toroidal grid graphs. To assess the effectiveness of these algorithms, we conducted a comprehensive evaluation. We used the same methodology, test bed, and set of 37 well-established heuristics for max-cut and QUBO problems as described in a recent study of Dunning, Gupta, and Silberholz. Notably, all three SSS-based algorithms consistently achieve top rankings in terms of performance. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by the funding program Horizon 2020 - Excellent Science - Marie Skłodowska-Curie Actions of the European Commission (Grant MINOA- Mixed-Integer Non-Linear Optimization Applications/764759]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0812) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0812). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences 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.)
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RecordInfo	BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/ijoc.2024.0812 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 878 Subjects: – SubjectFull: Dynamic programming Type: general – SubjectFull: Heuristic algorithms Type: general – SubjectFull: Combinatorial optimization Type: general – SubjectFull: Quantum annealing Type: general – SubjectFull: Sampling (Process) Type: general Titles: – TitleFull: SSS Algorithms for Max-Cut. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gentile, Claudio – PersonEntity: Name: NameFull: Rinaldi, Giovanni – PersonEntity: Name: NameFull: Salgado, Esteban IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May/Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10919856 Numbering: – Type: volume Value: 38 – Type: issue Value: 3 Titles: – TitleFull: INFORMS Journal on Computing Type: main
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