A framework for analyzing nonlinear eigenproblems and parametrized linear systems
By Laurence Grammont, Nicholas John Higham, Françoise Tisseur. 2011
46 citations recorded by the source corpus.
Subfields: Computational Mathematics, Computational Theory and Mathematics, Numerical Analysis
View Paper: https://www.semanticscholar.org/paper/f0013b3398ebf2d5b0424e30c1eb2d7775db7f3c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mathematics
- Christine Tobler
- Daniel Kressner
- D. Steven Mackey
- Froilán M. Dopico
- Fernando De Terán
- André Uschmajew
- Bart Vandereycken
- Dmitry V. Savostyanov
In Computational Theory and Mathematics
- Christine Tobler
- Daniel Kressner
- Froilán M. Dopico
- Fernando De Terán
- D. Steven Mackey
- André Uschmajew
- Bart Vandereycken
- Valeria Simoncini
In Numerical Analysis
- Froilán M. Dopico
- D. Steven Mackey
- Fernando De Terán
- Daniel Kressner
- Christine Tobler
- Volker Mehrmann
Citations in the corpus
- Spectral equivalence of matrix polynomials and the index sum theorem (2014, methodology): “Using linearizations has long been the traditional means of solving polynomial eigenvalue problems [18, 55], but recently the conversion of a matrix polynomial into a quadratic polynomial with the same spectral structure (to be called a quadratification) has been proposed as a viable alternative met”
- Low-Rank Tensor Krylov Subspace Methods for Parametrized Linear Systems (2011, background): “These approaches can be easily extended to polynomial dependence on a single parameter by means of (exact) linearization, see [19, 22, 42].”