Laguerre-SVD reduced order modeling

L. Knockaert, D. Zutter
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引用次数: 4

Abstract

Circuit simulation tasks, such as the accurate prediction of the behavior of large RLGC interconnects, generally requires the solution of very large linear networks. In recent years, this has led to the development of reduced order modeling technologies such as Pade via Lanczos (Feldmann and Freund, 1995), block Arnoldi (Boley, 1994) and passive reduced-order interconnect macromodeling (PRIMA) (Odabasioglu et al., 1998). In this paper, a reduced order modeling technique based on a system description in terms of orthonormal Laguerre functions, together with a Krylov subspace decomposition via singular value decomposition is presented. The link with Pade approximation, the block Arnoldi algorithm and the singular value decomposition (SVD) (Golub and Van Loan, 1996) permits a simple and stable implementation of the algorithm.
Laguerre-SVD降阶建模
电路仿真任务,如准确预测大型RLGC互连的行为,通常需要求解非常大的线性网络。近年来,这导致了低阶建模技术的发展,如Pade via Lanczos (Feldmann and Freund, 1995)、block Arnoldi (Boley, 1994)和被动低阶互连宏观建模(PRIMA) (Odabasioglu et al., 1998)。本文提出了一种基于正交Laguerre函数的系统描述和基于奇异值分解的Krylov子空间分解的降阶建模技术。与Pade近似、块Arnoldi算法和奇异值分解(SVD) (Golub和Van Loan, 1996)的联系使得该算法的实现简单而稳定。
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