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000057019 0247_ $$2DOI$$a10.1137/050628301
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000057019 084__ $$2WoS$$aMathematics, Applied
000057019 1001_ $$0P:(DE-Juel1)VDB68518$$aWillems, P. R.$$b0$$uFZJ
000057019 245__ $$aComputing the bidiagonal SVD using multiple relatively robust representations
000057019 260__ $$aPhiladelphia, Pa.$$bSoc.$$c2006
000057019 300__ $$a907 - 926
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000057019 440_0 $$017201$$aSIAM Journal on Matrix Analysis and Applications$$v28$$x0895-4798$$y4
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000057019 520__ $$aWe describe the design and implementation of a new algorithm for computing the singular value decomposition (SVD) of a real bidiagonal matrix. This algorithm uses ideas developed by Grosser and Lang that extend Parlett's and Dhillon's multiple relatively robust representations (MRRR) algorithm for the tridiagonal symmetric eigenproblem. One key feature of our new implementation is that k singular triplets can be computed using only O(nk) storage units and floating point operations, where n is the dimension of the matrix. The algorithm will be made available as routine xBDSCR in the upcoming new release of the LAPACK library.
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000057019 65320 $$2Author$$abidiagonal singular value decomposition
000057019 65320 $$2Author$$atridiagonal symmetric eigenproblem
000057019 65320 $$2Author$$aMRRR algorithm
000057019 65320 $$2Author$$acoupling relations
000057019 65320 $$2Author$$aLAPACK library
000057019 7001_ $$0P:(DE-HGF)0$$aLang, B.$$b1
000057019 7001_ $$0P:(DE-HGF)0$$aVömel, C.$$b2
000057019 773__ $$0PERI:(DE-600)1468407-x$$a10.1137/050628301$$gVol. 28, p. 907 - 926$$p907 - 926$$q28<907 - 926$$tSIAM journal on matrix analysis and applications$$v28$$x0895-4798$$y2006
000057019 8567_ $$uhttp://dx.doi.org/10.1137/050628301
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000057019 9131_ $$0G:(DE-Juel1)FUEK411$$bSchlüsseltechnologien$$kP41$$lSupercomputing$$vScientific Computing$$x0
000057019 9141_ $$aNachtrag$$y2006
000057019 915__ $$0StatID:(DE-HGF)0010$$aJCR/ISI refereed
000057019 9201_ $$0I:(DE-Juel1)VDB62$$d31.12.2007$$gZAM$$kZAM$$lZentralinstitut für Angewandte Mathematik$$x1
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