On Krylov Subspace Implementation of ParaExp for Linear Switched Systems

A direct time-parallel time domain simulation algorithm based on ParaExp with Krylov subspace implementation for the large-scale linear switched systems (LSSs) is studied in this paper. It is built upon the superposition principle that the LSSs can be decoupled into independent subsystems consisting of homogeneous and inhomogeneous subproblems. The states of these independent subsystems are approximated by the residual-based shift-and-invert (SAI) Krylov subspace methods, and this time-parallel algorithm can thus be regarded as a parallel Krylov subspace approximation algorithm for LSSs. We demonstrate the efficiency of the proposed algorithm with numerical experiments.

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Publication Details

Journal
IEEE Control Systems Letters
Published
2025-01-01
DOI
https://doi.org/10.1109/lcsys.2025.3595189
Primary Topic
Matrix Theory and Algorithms
Type
article
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article

On Krylov Subspace Implementation of ParaExp for Linear Switched Systems

Weiwei Li, Ren-Hao Zhang, Yao‐Lin Jiang
IEEE Control Systems Letters
Matrix Theory and Algorithms
article

On Krylov Subspace Implementation of ParaExp for Linear Switched Systems

Weiwei Li, Ren-Hao Zhang, Yao‐Lin Jiang
article en

Abstract

A direct time-parallel time domain simulation algorithm based on ParaExp with Krylov subspace implementation for the large-scale linear switched systems (LSSs) is studied in this paper. It is built upon the superposition principle that the LSSs can be decoupled into independent subsystems consisting of homogeneous and inhomogeneous subproblems. The states of these independent subsystems are approximated by the residual-based shift-and-invert (SAI) Krylov subspace methods, and this time-parallel algorithm can thus be regarded as a parallel Krylov subspace approximation algorithm for LSSs. We demonstrate the efficiency of the proposed algorithm with numerical experiments.

IEEE Control Systems LettersVol. 9
Xi'an Jiaotong University (CN), University of South China (CN)
Openalex Percentile: Top 79%
Matrix Theory and Algorithms
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