Dual adaptive stochastic block projection algorithm for solving convex feasibility problem in support vector machines.
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| Title: | Dual adaptive stochastic block projection algorithm for solving convex feasibility problem in support vector machines. |
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| Authors: | Li, Chunmei1 (AUTHOR) lichunmei@guet.edu.cn, Chen, Bangjun1 (AUTHOR) 744630120@qq.com, Duan, Xuefeng1 (AUTHOR) guidian520@126.com |
| Source: | Computational Mathematics & Modeling. Jun2026, Vol. 37 Issue 2, p303-315. 13p. |
| Subjects: | Support vector machines, Feasibility problem (Mathematical optimization), Extrapolation, Algorithms, Iterative methods (Mathematics) |
| Abstract: | In this paper, we consider a class of convex feasibility problem, which arises in support vector machines. Based on the stochastic block projection algorithm with adaptive extrapolation, we design a dual adaptive stochastic block projection algorithm to solve this problem. The convergence analysis of the new algorithm is given. Finally, some numerical examples demonstrate that our proposed algorithm is feasible and effective. [ABSTRACT FROM AUTHOR] |
| Copyright of Computational Mathematics & Modeling is the property of Springer Nature 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: 195094529 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dual adaptive stochastic block projection algorithm for solving convex feasibility problem in support vector machines. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Chunmei%22">Li, Chunmei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lichunmei@guet.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Bangjun%22">Chen, Bangjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 744630120@qq.com</i><br /><searchLink fieldCode="AR" term="%22Duan%2C+Xuefeng%22">Duan, Xuefeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> guidian520@126.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computational+Mathematics+%26+Modeling%22">Computational Mathematics & Modeling</searchLink>. Jun2026, Vol. 37 Issue 2, p303-315. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Feasibility+problem+%28Mathematical+optimization%29%22">Feasibility problem (Mathematical optimization)</searchLink><br /><searchLink fieldCode="DE" term="%22Extrapolation%22">Extrapolation</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, we consider a class of convex feasibility problem, which arises in support vector machines. Based on the stochastic block projection algorithm with adaptive extrapolation, we design a dual adaptive stochastic block projection algorithm to solve this problem. The convergence analysis of the new algorithm is given. Finally, some numerical examples demonstrate that our proposed algorithm is feasible and effective. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computational Mathematics & Modeling is the property of Springer Nature 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: 10.1007/s10598-026-09689-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 303 Subjects: – SubjectFull: Support vector machines Type: general – SubjectFull: Feasibility problem (Mathematical optimization) Type: general – SubjectFull: Extrapolation Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Iterative methods (Mathematics) Type: general Titles: – TitleFull: Dual adaptive stochastic block projection algorithm for solving convex feasibility problem in support vector machines. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Chunmei – PersonEntity: Name: NameFull: Chen, Bangjun – PersonEntity: Name: NameFull: Duan, Xuefeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1046283X Numbering: – Type: volume Value: 37 – Type: issue Value: 2 Titles: – TitleFull: Computational Mathematics & Modeling Type: main |
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