Parallel computational issues of an interior point method for solving large bound-constrained quadratic programming problems
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| Title: | Parallel computational issues of an interior point method for solving large bound-constrained quadratic programming problems |
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| Authors: | D’Apuzzo, M.1,2 marco.dapuzzo@unina2.it, Marino, M.2,3 |
| Source: | Parallel Computing. Apr2003, Vol. 29 Issue 4, p467. 17p. |
| Subjects: | Quadratic programming, Linear algebra |
| Abstract: | This paper deals with a parallel implementation of an interior point algorithm for solving sparse convex quadratic programs with bound constraints. The parallelism is introduced at the linear algebra level. Concerning the solution of the linear system arising at each step of the considered algorithm, we use an iterative approach based on the conjugate gradient method and on a block diagonal preconditioning technique. Moreover, we apply an incomplete Cholesky factorization with limited memory into each block, in order to put together the high degree of parallelism of diagonal preconditioning techniques and the greater effectiveness of incomplete factorizations procedures. The goal is to obtain an efficient parallel interior point solver for general sparse problems. Results of computational experiments carried out on an IBM SP parallel system by using randomly generated very sparse problems without a particular structure are presented. Such results show that the considered inner iterative approach allows to obtain a constant CPU time reduction as the number of processors used increases. [Copyright &y& Elsevier] |
| Copyright of Parallel Computing 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: 9289454 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Parallel computational issues of an interior point method for solving large bound-constrained quadratic programming problems – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22D’Apuzzo%2C+M%2E%22">D’Apuzzo, M.</searchLink><relatesTo>1,2</relatesTo><i> marco.dapuzzo@unina2.it</i><br /><searchLink fieldCode="AR" term="%22Marino%2C+M%2E%22">Marino, M.</searchLink><relatesTo>2,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Parallel+Computing%22">Parallel Computing</searchLink>. Apr2003, Vol. 29 Issue 4, p467. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Quadratic+programming%22">Quadratic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+algebra%22">Linear algebra</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper deals with a parallel implementation of an interior point algorithm for solving sparse convex quadratic programs with bound constraints. The parallelism is introduced at the linear algebra level. Concerning the solution of the linear system arising at each step of the considered algorithm, we use an iterative approach based on the conjugate gradient method and on a block diagonal preconditioning technique. Moreover, we apply an incomplete Cholesky factorization with limited memory into each block, in order to put together the high degree of parallelism of diagonal preconditioning techniques and the greater effectiveness of incomplete factorizations procedures. The goal is to obtain an efficient parallel interior point solver for general sparse problems. Results of computational experiments carried out on an IBM SP parallel system by using randomly generated very sparse problems without a particular structure are presented. Such results show that the considered inner iterative approach allows to obtain a constant CPU time reduction as the number of processors used increases. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Parallel Computing 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/S0167-8191(03)00017-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 467 Subjects: – SubjectFull: Quadratic programming Type: general – SubjectFull: Linear algebra Type: general Titles: – TitleFull: Parallel computational issues of an interior point method for solving large bound-constrained quadratic programming problems Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: D’Apuzzo, M. – PersonEntity: Name: NameFull: Marino, M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2003 Type: published Y: 2003 Identifiers: – Type: issn-print Value: 01678191 Numbering: – Type: volume Value: 29 – Type: issue Value: 4 Titles: – TitleFull: Parallel Computing Type: main |
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