The Matrix Eigenvalue Problem: GR and Krylov Subspace Methods

By David S. Watkins. 2007

239 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/be4b8c80cf2a1cd84feb8451bd6e680ec2446578

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In Computational Theory and Mathematics

1. D. Steven Mackey
2. Volker Mehrmann
3. Christian Mehl
4. Niloufer Mackey
5. Peter Bhlmann
6. Michael J. Kane
7. M. J. van der Laan
8. Mims EPrint

Citations in the corpus

• Wavelets on Graphs via Spectral Graph Theory (2009, unknown): “Computing extremal eigenvalues of a self-adjoint operator is a well-studied problem, and efficient algorithms exist that access L only through matrix-vector multiplication, notably Arnoldi iteration or the Jacobi–Davidson method [45,46].”
• Handbook of Big Data (2016, background): “However, this implementation is prone to round-off errors (see [5,32]); therefore, a mathematically equivalent implementation less prone to round-off errors was developed.”
• Jordan structures of alternating matrix polynomials (2010, methodology): “Once a linearization has been obtained, one may employ standard numerical techniques for the solution of the corresponding eigenvalue problem [4, 12, 28].”