Randomized algorithms in numerical linear algebra

This survey provides an introduction to the use of randomization in the design of fast algorithms for numerical linear algebra. These algorithms typically examine only a subset of the input to solve basic problems approximately, including matrix multiplication, regression and low-rank approximation....

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Veröffentlicht in:Acta numerica Jg. 26; S. 95 - 135
Hauptverfasser: Kannan, Ravindran, Vempala, Santosh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge, UK Cambridge University Press 01.05.2017
Schlagworte:
Algorithms
Computer science
Linear algebra
Matrices (mathematics)
Principal components analysis
Random variables
Randomization
Set theory
Sparsity
Theory
Trends
ISSN:0962-4929, 1474-0508
Online-Zugang:Volltext
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Zusammenfassung:This survey provides an introduction to the use of randomization in the design of fast algorithms for numerical linear algebra. These algorithms typically examine only a subset of the input to solve basic problems approximately, including matrix multiplication, regression and low-rank approximation. The survey describes the key ideas and gives complete proofs of the main results in the field. A central unifying idea is sampling the columns (or rows) of a matrix according to their squared lengths.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0962-4929
1474-0508
DOI:10.1017/S0962492917000058