The Singular Value Decomposition

Richard L. Branham
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Richard L. Branham: Jefe Area Matematicas y Del Centro Regional de Investigaciones Científicas y Tecnológicas

Chapter Chapter 8 in Scientific Data Analysis, 1990, pp 199-232 from Springer

Abstract: Abstract The singular value decomposition (SVD), closely related to matrix eigenvalue-eigenvector decompositions, is a powerful tool for analyzing linear systems. Like all mathematical tools it has its legitimate uses, but it can also be abused, of which we will have more to say in Section 8.4.

Keywords: Singular Value Decomposition; Normal Equation; Null Space; CHOLESKY Factor; Orthogonal Matrice (search for similar items in EconPapers)
Date: 1990
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DOI: 10.1007/978-1-4612-3362-6_8

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