Fast positive-real balanced truncation via quadratic alternating direction implicit iteration

Balanced truncation (BT), as applied to date in model order reduction (MOR), is known for its superior accuracy and computable error bounds. Positive-real BT (PRBT) is a particular BT procedure that preserves passivity and stability and imposes no structural constraints on the original state space. However, PRBT requires solving two algebraic Riccati equations (AREs), whose computational complexity limits its practical use in large-scale systems. This paper introduces a novel quadratic extension of the alternating direction implicit (ADI) iteration, which is called quadratic ADI (QADI), that efficiently solves an ARE. A Cholesky factor version of QADI, which is called CEQADI, exploits low-rank matrices and further accelerates PRBT. © 2007 IEEE.published_or_final_versio

A moment-matching scheme for the passivity-preserving model order reduction of indefinite descriptor systems with possible polynomial parts

Passivity-preserving model order reduction (MOR) of descriptor systems (DSs) is highly desired in the simulation of VLSI interconnects and on-chip passives. One popular method is PRIMA, a Krylov-subspace projection approach which preserves the passivity of positive semidefinite (PSD) structured DSs. However, system passivity is not guaranteed by PRIMA when the system is indefinite. Furthermore, the possible polynomial parts of singular systems are normally not captured. For indefinite DSs, positive-real balanced truncation (PRBT) can generate passive reduced-order models (ROMs), whose main bottleneck lies in solving the dual expensive generalized algebraic Riccati equations (GAREs). This paper presents a novel moment-matching MORfor indefinite DSs, which preserves both the system passivity and, if present, also the improper polynomial part. This method only requires solving one GARE, therefore it is cheaper than existing PRBT schemes. On the other hand, the proposed algorithm is capable of preserving the passivity of indefinite DSs, which is not guaranteed by traditional moment-matching MORs. Examples are finally presented showing that our method is superior to PRIMA in terms of accuracy. ©2011 IEEE.published_or_final_versionThe 16th Asia and South Pacific Design Automation Conference (ASP-DAC 2011), Yokohama, Japan, 25-28 January 2011. In Proceedings of the 16th ASP-DAC, 2011, p. 49-54, paper 1C-

Efficient positive-real balanced truncation of symmetric systems via cross-riccati equations

We present a highly efficient approach for realizing a positive-real balanced truncation (PRBT) of symmetric systems. The solution of a pair of dual algebraic Riccati equations in conventional PRBT, whose cost constrains practical large-scale deployment, is reduced to the solution of one cross-Riccati equation (XRE). The cross-Riccatian nature of the solution then allows a simple construction of PRBT projection matrices, using a Schur decomposition, without actual balancing. An invariant subspace method and a modified quadratic alternating-direction-implicit iteration scheme are proposed to efficiently solve the XRE. A low-rank variant of the latter is shown to offer a remarkably fast PRBT speed over the conventional implementations. The XRE-based framework can be applied to a large class of linear passive networks, and its effectiveness is demonstrated through numerical examples. © 2008 IEEE.published_or_final_versio

Model order reduction for neutral systems by moment matching

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A numerical comparison of solvers for large-scale, continuous-time algebraic Riccati equations and LQR problems

In this paper, we discuss numerical methods for solving large-scale continuous-time algebraic Riccati equations. These methods have been the focus of intensive research in recent years, and significant progress has been made in both the theoretical understanding and efficient implementation of various competing algorithms. There are several goals of this manuscript: first, to gather in one place an overview of different approaches for solving large-scale Riccati equations, and to point to the recent advances in each of them. Second, to analyze and compare the main computational ingredients of these algorithms, to detect their strong points and their potential bottlenecks. And finally, to compare the effective implementations of all methods on a set of relevant benchmark examples, giving an indication of their relative performance

Methodik zur Integration von Vorwissen in die Modellbildung

Das Buch zeigt, wie Vorwissen über Eigenschaften dynamischer Systeme und über Funktionen in die mathematische Modellbildung integriert werden kann. Hierzu wird im ersten Teil der Arbeit das verbale Vorwissen mathematisch formuliert. Der zweite Teil beschreibt vier Zugängen, um die entstehenden restringierten Probleme zu lösen. Zahlreiche Beispiele, Tabellen und Zusammenstellungen vervollständigen das Buch