Householder QR Factorization With Randomization for Column Pivoting (HQRRP)
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Otros documentos de la autoría: MARTINSSON, GUNNAR; Quintana-Ortí, Gregorio; Heavner, Nathan; Van de Geijn, Robert A.
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Título
Householder QR Factorization With Randomization for Column Pivoting (HQRRP)Fecha de publicación
2017Editor
Society for Industrial and Applied MathematicsISSN
1064-8275; 1095-7197Cita bibliográfica
Martinsson, P. G., Quintana OrtÍ, G., Heavner, N., & van de Geijn, R. (2017). Householder QR Factorization With Randomization for Column Pivoting (HQRRP). SIAM Journal on Scientific Computing, 39(2), C96-C115.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://epubs.siam.org/doi/abs/10.1137/16M1081270Versión
info:eu-repo/semantics/submittedVersionResumen
A fundamental problem when adding column pivoting to the Householder QR fac-
torization is that only about half of the computation can be cast in terms of high performing matrix-
matrix multiplications, which greatly ... [+]
A fundamental problem when adding column pivoting to the Householder QR fac-
torization is that only about half of the computation can be cast in terms of high performing matrix-
matrix multiplications, which greatly limits the bene ts that can be derived from so-called blocking
of algorithms. This paper describes a technique for selecting groups of pivot vectors by means of
randomized projections. It is demonstrated that the asymptotic
op count for the proposed method
is 2mn2 �����(2=3)n3 for an m n matrix, identical to that of the best classical unblocked Householder
QR factorization algorithm (with or without pivoting). Experiments demonstrate acceleration in
speed of close to an order of magnitude relative to the geqp3 function in LAPACK, when executed
on a modern CPU with multiple cores. Further, experiments demonstrate that the quality of the
randomized pivot selection strategy is roughly the same as that of classical column pivoting. The
described algorithm is made available under Open Source license and can be used with LAPACK or
libflame. [-]
Publicado en
SIAM Journal on Scientific Computing, 2017, vol. 39, no 2, p. C96-C115.Proyecto de investigación
The research reported was supported by DARPA, under the contract N66001-13-1-4050, and by the NSF, under the contracts DMS-1407340, DMS-1620472, and ACI-1148125/1340293. Any opinions, ndings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily re ect the views of the National Science Foundation (NSF).Derechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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