A Structured Quasi-Arnoldi procedure for model order reduction of second-order systems
Resource
LINEAR ALGEBRA AND ITS APPLICATIONS, 436(8), 2780-2794
Journal
Linear Algebra and its Applications
Pages
2780-2794
Date Issued
2012
Date
2012
Author(s)
Li, Yung-Ta
Bai, Zhaojun
Lin, Wen-Wei
Su, Yangfeng
Abstract
Existing Krylov subspace-based structure-preserving model order reduction methods for the second-order systems proceed in two stages. The first stage is to generate a basis matrix of the underlying Krylov subspace. The second stage is to employ an explicit subspace projection to obtain a reduced-order model with a moment-matching property. An open problem is how to avoid explicit projection so that it will be efficient for truly large scale systems. In addition, it is also desired that a structure-preserving reduced system of order n matches maximum 2n moments. In this paper we propose a new procedure to compute a so-called Structured Quasi-Arnoldi (SQA) decomposition. Once the SQA decomposition is computed, a structure-preserving reduced-order model can be defined immediately from the decomposition without the explicit subspace projection. Furthermore, the reduced model of order n matches maximum 2n moments. Numerical examples demonstrate that the transpose-free SQA-based reduced model is compatible with the two-sided structure-preserving explicit projection methods and is more accurate than the one-sided structure-preserving explicit projection methods due to the higher number of matched moments. © 2011 Elsevier Inc. All rights reserved.
Type
journal article
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