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research article

Randomized Extended Kaczmarz For Solving Least Squares

Zouzias, Anastasios
•
Freris, Nikolaos M.
2013
Siam Journal On Matrix Analysis And Applications

We present a randomized iterative algorithm that exponentially converges in the mean square to the minimum l(2)-norm least squares solution of a given linear system of equations. The expected number of arithmetic operations required to obtain an estimate of given accuracy is proportional to the squared condition number of the system multiplied by the number of nonzero entries of the input matrix. The proposed algorithm is an extension of the randomized Kaczmarz method that was analyzed by Strohmer and Vershynin.

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Type
research article
DOI
10.1137/120889897
Web of Science ID

WOS:000321043700023

Author(s)
Zouzias, Anastasios
•
Freris, Nikolaos M.
Date Issued

2013

Publisher

Society for Industrial and Applied Mathematics

Published in
Siam Journal On Matrix Analysis And Applications
Volume

34

Issue

2

Start page

773

End page

793

Subjects

linear least squares

•

minimum-length solution

•

sparse matrix

•

overdetermined system

•

underdetermined system

•

iterative method

•

random sampling

•

LAPACK

•

randomized algorithms

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LCAV  
Available on Infoscience
October 1, 2013
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/95936
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