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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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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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Nonlinear singular value decomposition
2017Linear 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 +1 more
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The singular value decomposition
1987The purpose of this section is to prove the singular value decomposition for compact operators in Hilbert spaces. This decomposition shows that compact operators have a simple spectrum and illustrates in a transparent way the ill-posedness of equations which are governed by compact operators (equations of the first kind).
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On Singular Value Decomposition
This study investigates the algebraic and geometric structures underlying Singular Value Decomposition (SVD), one of the most fundamental matrix factorizations in linear algebra with extensive applications in both theoretical and applied mathematics. The research aims to define SVD and derive its formal construction, identify the algebraic properties ...openaire +1 more source
The Singular Value Decomposition and Applications⁎
2010FABRIZIO GABBIANI, STEVEN J. COX
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Quaternion higher-order singular value decomposition and its applications in color image processing
Information Fusion, 2023Jifei Miao, Kit Ian Kou
exaly