关键词: 模型降阶, Krylov子空间方法, 重正交化, Krylov-Schur重启技术

Abstract: Krylov subspace method is one of the typical model reduction methods, in which Arnoldi model reduction method is the basic method. Re-orthogonalizational Arnoldi algorithm is proposed to obtain r step Arnoldi decomposition. Next, this paper restarts Krylov-Schur process and drives Arnoldi model reduction method based on implicitly restarted Krylov-Schur technology to reduce the large scale linearly time invariant systems. By this method, it can obtain a stable order-reduced system with higher accuracy, which can improve the drawback of Krylov subspace methods. Finally, simulations of a linearly time invariant system will be conducted to illustrate the effectiveness of the proposed method.

Key words: model-order reduction, Krylov subspace methods, re-orthogonalization；implicitly restarted Krylov-Schur technology