A Bidiagonal Matrix Determines Its Hyperbolic SVD to Varied Relative Accuracy

DOI10.1137/S0895479803424980zbMath1081.15005OpenAlexW1975899256MaRDI QIDQ5317639

Beresford N. Parlett

Publication date: 19 September 2005

Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/s0895479803424980

zbMATH Keywords

eigenvectorcondition numberspectral decompositionbidiagonal matrixhyperbolic singular value decomposition (SVD)indefinite tridiagonal symmetric matrix

Mathematics Subject Classification ID

Factorization of matrices (15A23) Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Eigenvalues, singular values, and eigenvectors (15A18) Conditioning of matrices (15A12)

Related Items (3)

A note on the hyperbolic singular value decomposition without hyperexchange matrices ⋮ A note on generating finer‐grain parallelism in a representation tree ⋮ Relative Perturbation Analysis for Eigenvalues and Singular Values of Totally Nonpositive Matrices

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