Computing eigenvectors of block tridiagonal matrices based on twisted block factorizations
DOI10.1016/j.cam.2011.07.010zbMath1245.65044OpenAlexW2149612201WikidataQ39455342 ScholiaQ39455342MaRDI QIDQ432800
Michael Moldaschl, Gerhard König, Wilfried N. Gansterer
Publication date: 4 July 2012
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.2011.07.010
algorithmsnumerical examplesinverse iterationblock tridiagonal matrixeigenvector computationtwisted block factorizationtwisted factorization
Computational methods for sparse matrices (65F50) Numerical computation of eigenvalues and eigenvectors of matrices (65F15)
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Uses Software
Cites Work
- Fernando's solution to Wilkinson's problem: An application of double factorization
- Multiple representations to compute orthogonal eigenvectors of symmetric tridiagonal matrices
- For tridiagonals \(T\) replace \(T\) with \(LDL\)
- Relatively robust representations of symmetric tridiagonals
- An implementation of the dqds algorithm (positive case)
- Twisted factorization of a banded matrix
- High-performance bidiagonal reduction using tile algorithms on homogeneous multicore architectures
- Accurate Singular Values of Bidiagonal Matrices
- The design and implementation of the MRRR algorithm
- LAPACK Users' Guide
- Computing an Eigenvector with Inverse Iteration
- On Computing an Eigenvector of a Tridiagonal Matrix. Part I: Basic Results
- A framework for symmetric band reduction
- Computing Approximate Eigenpairs of Symmetric Block Tridiagonal Matrices
- An extension of the divide-and-conquer method for a class of symmetric block-tridiagonal eigenproblems
- Block tridiagonalization of "effectively" sparse symmetric matrices
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