Multiprocessor sparse SVD algorithms and applications:
Abstract: "In this thesis, we develop four numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture. We particularly consider the SVD of unstructured sparse matrices in which the number of rows may be substantially larger o...
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Urbana, Ill.
1990
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| Series: | Center for Supercomputing Research and Development <Urbana, Ill.>: CSRD report
1049 |
| Subjects: | |
| Summary: | Abstract: "In this thesis, we develop four numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture. We particularly consider the SVD of unstructured sparse matrices in which the number of rows may be substantially larger or smaller than the number of columns. On vector machines, considerable progress has been made over past 10 years in developing robust algorithms for the solution of the sparse symmetric eigenvalue problem using subspace iteration and the Lanczos scheme, with or without a re-orthogonalization strategy Our intent is to extend and refine this knowledge for computing the sparse singular value decomposition on shared memory parallel computers. We emphasize Lanczos, block-Lanczos, subspace iteration, and the trace minimization methods for determining several of the largest (or smallest) singular triplets (singular values and corresponding left-and- right-singular vectors) for sparse matrices. The target architectures for implementations of such methods include the Alliant FX/80 and the Cray- 2S/4128 This algorithmic research is particularly motivated by recent information-retrieval techniques in which high-rank approximations to large sparse term-document matrices are needed, and by nonlinear inverse problems arising from seismic reflection tomography applications. |
| Item Description: | Zugl.: Urbana, Ill., Univ., Diss., 1991 |
| Physical Description: | 173 S. |
Staff View
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| 520 | 3 | |a Abstract: "In this thesis, we develop four numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture. We particularly consider the SVD of unstructured sparse matrices in which the number of rows may be substantially larger or smaller than the number of columns. On vector machines, considerable progress has been made over past 10 years in developing robust algorithms for the solution of the sparse symmetric eigenvalue problem using subspace iteration and the Lanczos scheme, with or without a re-orthogonalization strategy | |
| 520 | 3 | |a Our intent is to extend and refine this knowledge for computing the sparse singular value decomposition on shared memory parallel computers. We emphasize Lanczos, block-Lanczos, subspace iteration, and the trace minimization methods for determining several of the largest (or smallest) singular triplets (singular values and corresponding left-and- right-singular vectors) for sparse matrices. The target architectures for implementations of such methods include the Alliant FX/80 and the Cray- 2S/4128 | |
| 520 | 3 | |a This algorithmic research is particularly motivated by recent information-retrieval techniques in which high-rank approximations to large sparse term-document matrices are needed, and by nonlinear inverse problems arising from seismic reflection tomography applications. | |
| 650 | 4 | |a Matrices | |
| 650 | 4 | |a Multiprocessors | |
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| id | DE-604.BV008979086 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:27:51Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-005929846 |
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| physical | 173 S. |
| publishDate | 1990 |
| publishDateSearch | 1990 |
| publishDateSort | 1990 |
| record_format | marc |
| series | Center for Supercomputing Research and Development <Urbana, Ill.>: CSRD report |
| series2 | Center for Supercomputing Research and Development <Urbana, Ill.>: CSRD report |
| spelling | Berry, Michael W. Verfasser (DE-588)128412976 aut Multiprocessor sparse SVD algorithms and applications by Michael Waitsel Berry Reportnr.: UILU ENG 90 8031 Urbana, Ill. 1990 173 S. txt rdacontent n rdamedia nc rdacarrier Center for Supercomputing Research and Development <Urbana, Ill.>: CSRD report 1049 Zugl.: Urbana, Ill., Univ., Diss., 1991 Abstract: "In this thesis, we develop four numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture. We particularly consider the SVD of unstructured sparse matrices in which the number of rows may be substantially larger or smaller than the number of columns. On vector machines, considerable progress has been made over past 10 years in developing robust algorithms for the solution of the sparse symmetric eigenvalue problem using subspace iteration and the Lanczos scheme, with or without a re-orthogonalization strategy Our intent is to extend and refine this knowledge for computing the sparse singular value decomposition on shared memory parallel computers. We emphasize Lanczos, block-Lanczos, subspace iteration, and the trace minimization methods for determining several of the largest (or smallest) singular triplets (singular values and corresponding left-and- right-singular vectors) for sparse matrices. The target architectures for implementations of such methods include the Alliant FX/80 and the Cray- 2S/4128 This algorithmic research is particularly motivated by recent information-retrieval techniques in which high-rank approximations to large sparse term-document matrices are needed, and by nonlinear inverse problems arising from seismic reflection tomography applications. Matrices Multiprocessors (DE-588)4113937-9 Hochschulschrift gnd-content Center for Supercomputing Research and Development <Urbana, Ill.>: CSRD report 1049 (DE-604)BV008930033 1049 |
| spellingShingle | Berry, Michael W. Multiprocessor sparse SVD algorithms and applications Center for Supercomputing Research and Development <Urbana, Ill.>: CSRD report Matrices Multiprocessors |
| subject_GND | (DE-588)4113937-9 |
| title | Multiprocessor sparse SVD algorithms and applications |
| title_alt | Reportnr.: UILU ENG 90 8031 |
| title_auth | Multiprocessor sparse SVD algorithms and applications |
| title_exact_search | Multiprocessor sparse SVD algorithms and applications |
| title_full | Multiprocessor sparse SVD algorithms and applications by Michael Waitsel Berry |
| title_fullStr | Multiprocessor sparse SVD algorithms and applications by Michael Waitsel Berry |
| title_full_unstemmed | Multiprocessor sparse SVD algorithms and applications by Michael Waitsel Berry |
| title_short | Multiprocessor sparse SVD algorithms and applications |
| title_sort | multiprocessor sparse svd algorithms and applications |
| topic | Matrices Multiprocessors |
| topic_facet | Matrices Multiprocessors Hochschulschrift |
| volume_link | (DE-604)BV008930033 |
| work_keys_str_mv | AT berrymichaelw multiprocessorsparsesvdalgorithmsandapplications AT berrymichaelw reportnruilueng908031 |