On computing the eigenvectors of a class of structured matrices

DOI10.1016/j.cam.2005.03.048zbMath1086.65035OpenAlexW1988378097MaRDI QIDQ818220

Raf Vandebril, Marc Van Barel, Nicola Mastronardi, Ellen Van Camp

Publication date: 24 March 2006

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2005.03.048

zbMATH Keywords

algorithmnumerical exampleseigenvalueseigenvectorstridiagonal matrixsymmetric matrixorthogonalityinverse iterationGram-Schmidt proceduresemiseparable matrix

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15)

Related Items (2)

A fast algorithm for the recursive calculation of dominant singular subspaces ⋮ Fast and stable QR eigenvalue algorithms for generalized companion matrices and secular equations

Cites Work

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• Current inverse iteration software can fail
• Fernando's solution to Wilkinson's problem: An application of double factorization
• Multiple representations to compute orthogonal eigenvectors of symmetric tridiagonal matrices
• The orthogonal Rayleigh quotient iteration (ORQI) method
• Inner deflation for symmetric tridiagonal matrices
• On computing of arbitrary positive integer powers for one type of symmetric tridiagonal matrices of even order. I
• An implicit QR algorithm for symmetric semiseparable matrices
• Orthogonal Eigenvectors and Relative Gaps
• Convergence of GMRES for Tridiagonal Toeplitz Matrices
• An Orthogonal Similarity Reduction of a Matrix into Semiseparable Form

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