Bidiagonalization

Known as: Bi-diagonalization 
Bidiagonalization is one of unitary (orthogonal) matrix decompositions such that U* A V = B, where U and V are unitary (orthogonal) matrices… (More)
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2012
2012
Householder bidiagonalization is the first step of Singular Value Decomposition (SVD) - an important algorithm in numerical… (More)
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2009
2009
With the increasing use of high-resolution multimedia streams and large image and video archives in many of today’s research and… (More)
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2008
2008
On cache based computer architectures using current standard algorithms, Householder bidiagonalization requires a significant… (More)
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2007
2007
The L-curve is often applied to determine a suitable value of the regularization parameter when solving ill-conditioned linear… (More)
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2007
2007
Two new algorithms for one-sided bidiagonalization are presented. The first is a block version which improves execution time by… (More)
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2007
2007
This paper presents an O(mn log m) algorithm for bidiagonalizing a Hankel matrix. An m×n Hankel matrix is reduced to a real… (More)
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2006
2006
The problem of computing a few of the largest or smallest singular values and associated singular vectors of a large matrix… (More)
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Highly Cited
2005
Highly Cited
2005
New restarted Lanczos bidiagonalization methods for the computation of a few of the largest or smallest singular values of a… (More)
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Highly Cited
2000
Highly Cited
2000
Low-rank approximation of large and/or sparse matrices is important in many applications, and the singular value decomposition… (More)
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Highly Cited
1982
Highly Cited
1982
An iterative method is given for solving Ax ~ffi b and minU Ax b 112, where the matrix A is large and sparse. The method is based… (More)
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