文献类型：期刊文献

英文题名：Model-order reduction of large-scale kth-order linear dynamical systems via a kth-order Arnoldi method

作者：Lin, Yiqin[3];Bao, Liang[4];Wei, Yimin[1,2]

机构：[1]Fudan Univ, Inst Math, Sch Math Sci, Shanghai 200433, Peoples R China;[2]Fudan Univ, Minist Educ, Key Lab Math Nonlinear Sci, Shanghai 200433, Peoples R China;[3]Hunan Univ Sci & Engn, Dept Math & Computat Sci, Yongzhou, Peoples R China;[4]E China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China

年份：2010

卷号：87

期号：2

起止页码：435

外文期刊名：INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000273011200015)】；

基金：Y. Lin was supported by Scientific Research Startup Foundation of Hunan University of Science and Engineering. L. Bao was supported by Scientific Research Startup Foundation of East China University of Science and Technology under grant YK0157110. Y. Wei was supported by the National Natural Science Foundation of China and Shanghai Education Committee. The authors would like to thank Professor Z. Bai for providing them with the examples and two referees for their helpful suggestions, which greatly improved the paper.

语种：英文

外文关键词：high-order linear system; kth-order Krylov subspace; kth-order Arnoldi procedure; model-order reduction; polynomial eigenvalue problem

摘要：In this paper, we first introduce a kth-order Krylov subspace G(n)(A(j); u) based on a square matrix sequence {A(j)} and a vector u. Then we present a kth-order Arnoldi procedure for generating an orthonormal basis of G(n)(A(j); u). By applying the projection technique, we derive a structure-preserving kth-order Arnoldi method for reduced-order modelling of the large-scale kth-order linear dynamical system. Applications to polynomial eigenvalue problems are also included. Numerical experiments report the effectiveness of this method.