Fast Parallel Randomized QR with Column Pivoting Algorithms for Reliable Low-Rank Matrix Approximations

Factorizing large matrices by QR with column pivoting (QRCP) is substantially more expensive than QR without pivoting, owing to communication costs required for pivoting decisions. In contrast, randomized QRCP (RQRCP) algorithms have proven themselves empirically to be highly competitive with high-p...

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Vydané v:	Proceedings, 24th IEEE International Conference on High Performance Computing : 18-21 December 2017, Jaipur, India s. 233 - 242
Hlavní autori:	Xiao, Jianwei, Gu, Ming, Langou, Julien
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.12.2017
Predmet:	Algorithm design and analysis Approximation algorithms Decorrelation distributed computing low-rank approximation Matrix decomposition QR factorization randomization spectrum-revealing Reliability theory
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