SolverSet: A Large-scale Benchmark Dataset for the Auto-selection of the Optimal Combination of Iterative Solvers and Preconditioners.
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| Title: | SolverSet: A Large-scale Benchmark Dataset for the Auto-selection of the Optimal Combination of Iterative Solvers and Preconditioners. |
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| Authors: | Xiong, Hantao1 (AUTHOR) xionghantao512@hnu.edu.cn, Yang, Wangdong1 (AUTHOR) yangwangdong@hnu.edu.cn, Lin, Shengle1 (AUTHOR) lsl036@hnu.edu.cn, He, Weiqing1 (AUTHOR) heweiqing@hnu.edu.cn, Li, Keqin2 (AUTHOR) lkl@hnu.edu.cn, Li, Kenli1 (AUTHOR) lik@newpaltz.edu |
| Source: | Journal of Circuits, Systems & Computers. 5/30/2026, Vol. 35 Issue 9, p1-26. 26p. |
| Subjects: | Iterative methods (Mathematics), Sparse matrices, Deep learning, Machine learning, Model validation |
| Abstract: | The combination of iterative solvers and preconditioners is the mainstream approach for solving sparse linear systems of the form A x = b , which are fundamental to many scientific and engineering applications. However, the automatic selection (auto-selection) of the optimal solver–preconditioner combination remains a challenging task. Although machine learning or deep learning methods have been explored for this purpose, their performance has been limited due to the lack of standardized, large-scale datasets. To address this issue, we introduce a large-scale benchmark dataset called SolverSet, designed for the auto-selection of the optimal combination of iterative solvers and preconditioners. We develop a matrix generation tool to produce a wide variety of large-scale sparse matrices, based on which we construct the SolverSet dataset. This dataset now comprises 12,651 large-scale matrices and their corresponding sparse linear systems, effectively overcoming the limitations of prior work — namely, limited size and a small number of systems — making it highly suitable for automatic selection modeling. We evaluate several baseline methods on SolverSet to validate its effectiveness and usability. Furthermore, we analyze key features of sparse linear systems and propose a deep learning based model named DL-Solver to predict the optimal solver–preconditioner combination for a given system. Experimental results demonstrate that SolverSet serves as a valuable benchmark for auto-selection research, and DL-Solver outperforms state-of-the-art method in predictive performance on the test set, achieving improvements of 2.14%, 3.47%, 2.71% and 3.16% in accuracy, macro-precision, macro-recall and macro-F1 score, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company 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: 192256754 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: SolverSet: A Large-scale Benchmark Dataset for the Auto-selection of the Optimal Combination of Iterative Solvers and Preconditioners. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiong%2C+Hantao%22">Xiong, Hantao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xionghantao512@hnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Wangdong%22">Yang, Wangdong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yangwangdong@hnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Shengle%22">Lin, Shengle</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lsl036@hnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22He%2C+Weiqing%22">He, Weiqing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> heweiqing@hnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Keqin%22">Li, Keqin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> lkl@hnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Kenli%22">Li, Kenli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lik@newpaltz.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Circuits%2C+Systems+%26+Computers%22">Journal of Circuits, Systems & Computers</searchLink>. 5/30/2026, Vol. 35 Issue 9, p1-26. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+matrices%22">Sparse matrices</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The combination of iterative solvers and preconditioners is the mainstream approach for solving sparse linear systems of the form A x = b , which are fundamental to many scientific and engineering applications. However, the automatic selection (auto-selection) of the optimal solver–preconditioner combination remains a challenging task. Although machine learning or deep learning methods have been explored for this purpose, their performance has been limited due to the lack of standardized, large-scale datasets. To address this issue, we introduce a large-scale benchmark dataset called SolverSet, designed for the auto-selection of the optimal combination of iterative solvers and preconditioners. We develop a matrix generation tool to produce a wide variety of large-scale sparse matrices, based on which we construct the SolverSet dataset. This dataset now comprises 12,651 large-scale matrices and their corresponding sparse linear systems, effectively overcoming the limitations of prior work — namely, limited size and a small number of systems — making it highly suitable for automatic selection modeling. We evaluate several baseline methods on SolverSet to validate its effectiveness and usability. Furthermore, we analyze key features of sparse linear systems and propose a deep learning based model named DL-Solver to predict the optimal solver–preconditioner combination for a given system. Experimental results demonstrate that SolverSet serves as a valuable benchmark for auto-selection research, and DL-Solver outperforms state-of-the-art method in predictive performance on the test set, achieving improvements of 2.14%, 3.47%, 2.71% and 3.16% in accuracy, macro-precision, macro-recall and macro-F1 score, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company 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: 10.1142/S0218126626500209 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Iterative methods (Mathematics) Type: general – SubjectFull: Sparse matrices Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Model validation Type: general Titles: – TitleFull: SolverSet: A Large-scale Benchmark Dataset for the Auto-selection of the Optimal Combination of Iterative Solvers and Preconditioners. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiong, Hantao – PersonEntity: Name: NameFull: Yang, Wangdong – PersonEntity: Name: NameFull: Lin, Shengle – PersonEntity: Name: NameFull: He, Weiqing – PersonEntity: Name: NameFull: Li, Keqin – PersonEntity: Name: NameFull: Li, Kenli IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 05 Text: 5/30/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02181266 Numbering: – Type: volume Value: 35 – Type: issue Value: 9 Titles: – TitleFull: Journal of Circuits, Systems & Computers Type: main |
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