Results 251 to 260 of about 46,819 (293)

Downdating the Singular Value Decomposition

SIAM Journal on Matrix Analysis and Applications, 1995
Let \(A\) be a matrix with known singular values and left and/or right singular vectors, and let \(A'\) be the matrix obtained by deleting a row from \(A\). Computing the singular value decomposition (SVD) of \(A'\) from the SVD of \(A\) is called the downdating singular value decomposition problem (DSVDP).
Ming Gu 0002, Stanley C. Eisenstat
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Continuation of Singular Value Decompositions

Mediterranean Journal of Mathematics, 2005
In this work we consider computing a smooth path for a (block) singular value decomposition of a full rank matrix valued function. We give new theoretical results and then introduce and implement several algorithms to compute a smooth path. We illustrate performance of the algorithms with a few numerical examples.
L. DIECI L, GASPARO, MARIA GRAZIA, PAPINI, ALESSANDRA   +2 more
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Generalizations of the Singular Value and QR-Decompositions

SIAM Journal on Matrix Analysis and Applications, 1992
The authors present multimatrix generalizations of some well-known orthogonal rank factorizations and show how the idea of a QR- decomposition (QRD), a URV- decomposition (URVD), and a singular value decomposition (SVD), which has become an important tool in the analysis and numerical solution of numerous problems, especially since the development of ...
Bart De Moor, Paul Van Dooren
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The hyperbolic singular value decomposition and applications

Proceedings of the 32nd Midwest Symposium on Circuits and Systems, 1990
A new generalization of singular value decomposition (SVD), the hyperbolic SVD, is advanced, and its existence is established under mild restrictions. Two algorithms for effecting this decomposition are discussed. The new decomposition has applications in downdating in problems where the solution depends on the eigenstructure of the normal equations ...
Ruth Onn, Allan O. Steinhardt, Adam W. Bojanczyk   +2 more
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Generalizing the Singular Value Decomposition

SIAM Journal on Numerical Analysis, 1976
Two generalizations of the singular value decomposition are given. These generalizations provided a unified way of regarding certain matrix problems and the numerical techniques which are used to s...
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The QLP Approximation to the Singular Value Decomposition

SIAM Journal on Scientific Computing, 1999
A new decomposition, termed the pivoted QLP-decomposition, of a matrix is introduced. It is a postprocessing step to the pivoted QR-decomposition, used to determine the rank of a matrix. It is simply a further pivoted QR-decomposition of \(R^T\) of the first decomposition.
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Functional Tensor Singular Value Decomposition

SIAM Journal on Scientific Computing
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chuan Wang 0001, Xi-Le Zhao, Yu-Bang Zheng, Ben-Zheng Li, Michael K. Ng 0001   +4 more
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Singular Value Decomposition

1994
Many numerical methods used in application areas such as signal processing, estimation, and control are based on the singular value decomposition (SVD) of matrices. The SVD is widely used in least squares estimation, systems approximations, and numerical linear algebra.
Uwe Helmke, John B. Moore
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The Singular Value Decomposition

2020
The factorization is easily verified. Here \( \mathit\mathbf{U}=\mathit\mathbf{V}=\frac{1}{\sqrt{2}} \left[ \begin{array}{cc} 1 & 1 \\ 1 & -1 \end{array} \right], \)which is clearly unitary. Since in addition the matrix in the middle is diagonal, it follows that this is both a spectral and a singular value decomposition.
Tom Lyche, Georg Muntingh, Øyvind Ryan
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Nonlinear singular value decomposition

2017
Linear functions are widely used and well-understood. For example, to solve f(x) = 0 or f(x) = λx, with linear f, we can rely on matrix decompositions (singular value decomposition (SVD), eigenvalue decomposition (EVD), etc.). On the other hand, having nonlinear multivariate vector functions (multiple input-multiple output static nonlinearities), it is
Ishteva, Mariya Kamenova, Dreesen, Philippe   +1 more
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