%0 Book Section %1 mackey2015polynomial %A Mackey, D. Steven %A Mackey, Niloufer %A Tisseur, Francoise %B Numerical Algebra, Matrix Theory, Differential-Algebraic Equations and Control Theory: Festschrift in Honor of Volker Mehrmann %C Cham %D 2015 %E Benner, Peter %E Bollhöfer, Matthias %E Kressner, Daniel %E Mehl, Christian %E Stykel, Tatjana %I Springer %K 15a18-eigenvalues-singular-values-and-eigenvectors %P 319--348 %R 10.1007/978-3-319-15260-8_12 %T Polynomial Eigenvalue Problems: Theory, Computation, and Structure %U https://link.springer.com/chapter/10.1007%2F978-3-319-15260-8_12 %X Matrix polynomial eigenproblems arise in many application areas, both directly and as approximations for more general nonlinear eigenproblems. One of the most common strategies for solving a polynomial eigenproblem is via a linearization, which replaces the matrix polynomial by a matrix pencil with the same spectrum, and then computes with the pencil. Many matrix polynomials arising from applications have additional algebraic structure, leading to symmetries in the spectrum that are important for any computational method to respect. Thus it is useful to employ a structured linearization for a matrix polynomial with structure. This essay surveys the progress over the last decade in our understanding of linearizations and their construction, both with and without structure, and the impact this has had on numerical practice. %@ 978-3-319-15260-8

@inbook{mackey2015polynomial, abstract = {Matrix polynomial eigenproblems arise in many application areas, both directly and as approximations for more general nonlinear eigenproblems. One of the most common strategies for solving a polynomial eigenproblem is via a linearization, which replaces the matrix polynomial by a matrix pencil with the same spectrum, and then computes with the pencil. Many matrix polynomials arising from applications have additional algebraic structure, leading to symmetries in the spectrum that are important for any computational method to respect. Thus it is useful to employ a structured linearization for a matrix polynomial with structure. This essay surveys the progress over the last decade in our understanding of linearizations and their construction, both with and without structure, and the impact this has had on numerical practice.}, added-at = {2021-07-09T06:21:05.000+0200}, address = {Cham}, author = {Mackey, D. Steven and Mackey, Niloufer and Tisseur, Fran{\c{c}}oise}, biburl = {https://www.bibsonomy.org/bibtex/278242d1acbe9daa7c25049e7dbf41935/gdmcbain}, booktitle = {Numerical Algebra, Matrix Theory, Differential-Algebraic Equations and Control Theory: Festschrift in Honor of Volker Mehrmann}, description = {Polynomial Eigenvalue Problems: Theory, Computation, and Structure | SpringerLink}, doi = {10.1007/978-3-319-15260-8_12}, editor = {Benner, Peter and Bollh{\"o}fer, Matthias and Kressner, Daniel and Mehl, Christian and Stykel, Tatjana}, interhash = {36ae4e040ad77f15b36a1df4e2f01ccd}, intrahash = {78242d1acbe9daa7c25049e7dbf41935}, isbn = {978-3-319-15260-8}, keywords = {15a18-eigenvalues-singular-values-and-eigenvectors}, pages = {319--348}, publisher = {Springer}, timestamp = {2021-07-09T06:30:43.000+0200}, title = {Polynomial Eigenvalue Problems: Theory, Computation, and Structure}, url = {https://link.springer.com/chapter/10.1007%2F978-3-319-15260-8_12}, year = 2015 }