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| Hauptseite > Publikationsdatenbank > Computing the bidiagonal SVD using multiple relatively robust representations > print |
| Hauptseite > Publikationsdatenbank > Computing the bidiagonal SVD using multiple relatively robust representations > print |
| 001 | 57019 | ||
| 005 | 20180211175802.0 | ||
| 024 | 7 | _ | |2 DOI |a 10.1137/050628301 |
| 024 | 7 | _ | |2 WOS |a WOS:000243280600002 |
| 037 | _ | _ | |a PreJuSER-57019 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 510 |
| 084 | _ | _ | |2 WoS |a Mathematics, Applied |
| 100 | 1 | _ | |a Willems, P. R. |b 0 |u FZJ |0 P:(DE-Juel1)VDB68518 |
| 245 | _ | _ | |a Computing the bidiagonal SVD using multiple relatively robust representations |
| 260 | _ | _ | |a Philadelphia, Pa. |b Soc. |c 2006 |
| 300 | _ | _ | |a 907 - 926 |
| 336 | 7 | _ | |a Journal Article |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a article |2 DRIVER |
| 440 | _ | 0 | |a SIAM Journal on Matrix Analysis and Applications |x 0895-4798 |0 17201 |y 4 |v 28 |
| 500 | _ | _ | |a Record converted from VDB: 12.11.2012 |
| 520 | _ | _ | |a We describe the design and implementation of a new algorithm for computing the singular value decomposition (SVD) of a real bidiagonal matrix. This algorithm uses ideas developed by Grosser and Lang that extend Parlett's and Dhillon's multiple relatively robust representations (MRRR) algorithm for the tridiagonal symmetric eigenproblem. One key feature of our new implementation is that k singular triplets can be computed using only O(nk) storage units and floating point operations, where n is the dimension of the matrix. The algorithm will be made available as routine xBDSCR in the upcoming new release of the LAPACK library. |
| 536 | _ | _ | |a Scientific Computing |c P41 |2 G:(DE-HGF) |0 G:(DE-Juel1)FUEK411 |x 0 |
| 588 | _ | _ | |a Dataset connected to Web of Science |
| 650 | _ | 7 | |a J |2 WoSType |
| 653 | 2 | 0 | |2 Author |a bidiagonal singular value decomposition |
| 653 | 2 | 0 | |2 Author |a tridiagonal symmetric eigenproblem |
| 653 | 2 | 0 | |2 Author |a MRRR algorithm |
| 653 | 2 | 0 | |2 Author |a coupling relations |
| 653 | 2 | 0 | |2 Author |a LAPACK library |
| 700 | 1 | _ | |a Lang, B. |b 1 |0 P:(DE-HGF)0 |
| 700 | 1 | _ | |a Vömel, C. |b 2 |0 P:(DE-HGF)0 |
| 773 | _ | _ | |a 10.1137/050628301 |g Vol. 28, p. 907 - 926 |p 907 - 926 |q 28<907 - 926 |0 PERI:(DE-600)1468407-x |t SIAM journal on matrix analysis and applications |v 28 |y 2006 |x 0895-4798 |
| 856 | 7 | _ | |u http://dx.doi.org/10.1137/050628301 |
| 909 | C | O | |o oai:juser.fz-juelich.de:57019 |p VDB |
| 913 | 1 | _ | |k P41 |v Scientific Computing |l Supercomputing |b Schlüsseltechnologien |0 G:(DE-Juel1)FUEK411 |x 0 |
| 914 | 1 | _ | |a Nachtrag |y 2006 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0010 |a JCR/ISI refereed |
| 920 | 1 | _ | |k ZAM |l Zentralinstitut für Angewandte Mathematik |d 31.12.2007 |g ZAM |0 I:(DE-Juel1)VDB62 |x 1 |
| 970 | _ | _ | |a VDB:(DE-Juel1)89714 |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a ConvertedRecord |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
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