A fast randomized algorithm for the approximation of matrices

We introduce a randomized procedure that, given an m × n matrix A and a positive integer k, approximates A with a matrix Z of rank k. The algorithm relies on applying a structured l × m random matrix R to each column of A, where l is an integer near to, but greater than, k. The structure of R allows...

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Veröffentlicht in:	Applied and computational harmonic analysis Jg. 25; H. 3; S. 335 - 366
Hauptverfasser:	Woolfe, Franco, Liberty, Edo, Rokhlin, Vladimir, Tygert, Mark
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.11.2008
Schlagworte:	Algorithm Fast Lanczos Low rank Matrix Randomized SVD SVD QR Randomized Matrix Lanczos Fast Algorithm Low rank
ISSN:	1063-5203, 1096-603X
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Abstract	We introduce a randomized procedure that, given an m × n matrix A and a positive integer k, approximates A with a matrix Z of rank k. The algorithm relies on applying a structured l × m random matrix R to each column of A, where l is an integer near to, but greater than, k. The structure of R allows us to apply it to an arbitrary m × 1 vector at a cost proportional to m log ( l ) ; the resulting procedure can construct a rank- k approximation Z from the entries of A at a cost proportional to m n log ( k ) + l 2 ( m + n ) . We prove several bounds on the accuracy of the algorithm; one such bound guarantees that the spectral norm ‖ A − Z ‖ of the discrepancy between A and Z is of the same order as max { m , n } times the ( k + 1 ) st greatest singular value σ k + 1 of A, with small probability of large deviations. In contrast, the classical pivoted “ QR” decomposition algorithms (such as Gram–Schmidt or Householder) require at least kmn floating-point operations in order to compute a similarly accurate rank- k approximation. In practice, the algorithm of this paper runs faster than the classical algorithms, even when k is quite small or large. Furthermore, the algorithm operates reliably independently of the structure of the matrix A, can access each column of A independently and at most twice, and parallelizes naturally. Thus, the algorithm provides an efficient, reliable means for computing several of the greatest singular values and corresponding singular vectors of A. The results are illustrated via several numerical examples.
AbstractList	We introduce a randomized procedure that, given an m × n matrix A and a positive integer k, approximates A with a matrix Z of rank k. The algorithm relies on applying a structured l × m random matrix R to each column of A, where l is an integer near to, but greater than, k. The structure of R allows us to apply it to an arbitrary m × 1 vector at a cost proportional to m log ( l ) ; the resulting procedure can construct a rank- k approximation Z from the entries of A at a cost proportional to m n log ( k ) + l 2 ( m + n ) . We prove several bounds on the accuracy of the algorithm; one such bound guarantees that the spectral norm ‖ A − Z ‖ of the discrepancy between A and Z is of the same order as max { m , n } times the ( k + 1 ) st greatest singular value σ k + 1 of A, with small probability of large deviations. In contrast, the classical pivoted “ QR” decomposition algorithms (such as Gram–Schmidt or Householder) require at least kmn floating-point operations in order to compute a similarly accurate rank- k approximation. In practice, the algorithm of this paper runs faster than the classical algorithms, even when k is quite small or large. Furthermore, the algorithm operates reliably independently of the structure of the matrix A, can access each column of A independently and at most twice, and parallelizes naturally. Thus, the algorithm provides an efficient, reliable means for computing several of the greatest singular values and corresponding singular vectors of A. The results are illustrated via several numerical examples.
Author	Tygert, Mark Liberty, Edo Woolfe, Franco Rokhlin, Vladimir
Author_xml	– sequence: 1 givenname: Franco surname: Woolfe fullname: Woolfe, Franco email: francis.woolfe@yale.edu – sequence: 2 givenname: Edo surname: Liberty fullname: Liberty, Edo email: edo.liberty@yale.edu – sequence: 3 givenname: Vladimir surname: Rokhlin fullname: Rokhlin, Vladimir – sequence: 4 givenname: Mark surname: Tygert fullname: Tygert, Mark email: mark.tygert@yale.edu
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Snippet	We introduce a randomized procedure that, given an m × n matrix A and a positive integer k, approximates A with a matrix Z of rank k. The algorithm relies on...
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SubjectTerms	Algorithm Fast Lanczos Low rank Matrix Randomized SVD
Title	A fast randomized algorithm for the approximation of matrices
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