Improving performance of contour integral-based nonlinear eigensolvers with infinite GMRES
[EN] In this work, the infinite GMRES algorithm, recently proposed by [Jarlebring and Correnty, SIAM J. Matrix Anal. Appl., 43 (2022), pp. 1382--1405] is employed in contour integralbased nonlinear eigensolvers, avoiding the computation of costly factorizations at each quadrature node to solve the l...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::160c960e82ec27720c7ce888d639b0ca |
| Acceso en línea: | https://riunet.upv.es/handle/10251/234927 |
| Access Level: | acceso abierto |
| Palabra clave: | Krylov methods Infinite Arnoldi Nonlinear eigenvalue problem Contour integration Companion linearization |
| Sumario: | [EN] In this work, the infinite GMRES algorithm, recently proposed by [Jarlebring and Correnty, SIAM J. Matrix Anal. Appl., 43 (2022), pp. 1382--1405] is employed in contour integralbased nonlinear eigensolvers, avoiding the computation of costly factorizations at each quadrature node to solve the linear systems efficiently. Several techniques are applied to make the infinite GMRES memory-friendly, computationally efficient, and numerically stable in practice. More specifically, we analyze the relationship between polynomial eigenvalue problems and their scaled linearizations, and provide a novel weighting strategy which can significantly accelerate the convergence of infinite GMRES in this particular context. We also adopt the technique of TOAR to infinite GMRES to reduce the memory footprint. Theoretical analysis and numerical experiments are provided to illustrate the efficiency of the proposed algorithm. |
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