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Journal of Applied Mathematics and Computing

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22.03.2021 | Original Research

A model reduction method in large scale dynamical systems using an extended-rational block Arnoldi method

verfasst von: M. A. Hamadi, K. Jbilou, A. Ratnani

Erschienen in: Journal of Applied Mathematics and Computing | Ausgabe 1/2022

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Abstract

In this paper, we propose a new block Krylov-type subspace method for model reduction in large scale dynamical systems. We project the initial problem onto a new subspace, generated as a combination of rational and polynomial block Krylov subspaces. Simple algebraic properties are given and expressions of the error between the original and reduced transfer functions are established. Furthermore, we present an adaptive strategy of the interpolation points that will be used in the construction of our new block Krylov subspace. We also show how this method can be used to extract an approximate low rank solution of large-scale Lyapunov equations. Numerical results are reported on some benchmark examples to confirm the performance of our method compared with other known methods.

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Titel: A model reduction method in large scale dynamical systems using an extended-rational block Arnoldi method
verfasst von: M. A. Hamadi
K. Jbilou
A. Ratnani
Publikationsdatum: 22.03.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Applied Mathematics and Computing / Ausgabe 1/2022
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI: https://doi.org/10.1007/s12190-021-01521-0

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