Model Reduction Software in the SLICOT Library

Varga, A.

doi:10.1007/978-1-4615-1471-8_7

Cited by 42 publications

(34 citation statements)

References 16 publications

(28 reference statements)

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“…In SLICOT wird dies entweder durch additive Zerlegung derÜbertragungsfunktion in den stabilen und instabilen Anteil oder durch teilerfremde Faktorisierung (mit stabilen Faktoren) und anschliessender Anwendung der Methoden für stabile LTI-Systeme auf die resultierenden stabilen Anteile derÜbertragungsfunktion realisiert, siehe z.B. [37].…”

Section: Performance-resultateunclassified

Die SLICOT-Toolboxen für MatlabThe SLICOT Toolboxes for Matlab

Benner¹,

Kreßner

Sima³

et al. 2010

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SLICOT ist eine umfangreiche Softwarebibliothek zur numerischen Behandlung von Fragestellungen aus der System-und Regelungstheorie, die mit dem Ziel entwickelt wurde, hohe Leistungsfähigkeit mit Robustheit, Verlässlichkeit, sowie Benutzerfreundlichkeit zu vereinen. Dies wird mittels einer Kombination von Fortran-Kernroutinen und Matlab-bzw. Scilab-Schnittstellen erreicht. In dieser Ubersicht soll der Funktionsumfang der folgenden SLICOT-Toolboxen beschrieben und erläutert werden: (1) Grundaufgaben der System-und Regelungstheorie, (2) Systemidentifizierung, (3) Modell-und Reglerreduktion. Der Einsatz der Toolboxen in der Praxis wird durch verschiedene Beispiele veranschaulicht.SLICOT is a comprehensive numerical software package for control systems analysis and design. While based on highly performant Fortran routines, Matlab and Scilab interfaces provide convenient alternative access for users. In this survey, we summarize the functionality contained in the three SLICOT toolboxes for (1) basic tasks in systems and control, (2) system identification, and (3) model and controller reduction. Several examples illustrate the use of these toolboxes for addressing frequent computational tasks.

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Section: Performance-resultateunclassified

Die SLICOT-Toolboxen für MatlabThe SLICOT Toolboxes for Matlab

Benner¹,

Kreßner

Sima³

et al. 2010

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Self Cite

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“…There exist many different methods that can be used for model reduction of stable systems [2]. However, those working with the state-space representation of the system are usually more adequate for a parallel implementation.…”

Section: Model Reduction Of Stable Systemsmentioning

confidence: 99%

“…However, later work showed that, for some systems, when large distribution block sizes are used, the accuracy of the reduced models is far from that obtained using the equivalent sequential routines of SLICOT [2].…”

Section: Introductionmentioning

confidence: 99%

Improving accuracy of parallel SLICOT model reduction routines for stable systems

Guerrero-Lopez

Román

2015

2015 23rd Mediterranean Conference on Control and Automation (MED)

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Abstract-This paper shows part of the work carried out to develop parallel versions of the SLICOT routines for model reduction of stable systems. In particular, the routines that have been parallelised are those based on the solution of Lyapunov equations. The goal is to be able to work with larger unreduced models and also to obtain better performance in the reduction process. New routines have been developed using standard libraries to improve portability and efficiency. A preliminary version was released previously by the authors, which achieved high performance. However, accuracy improvements have been necessary in order to make the new routines similar to the sequential ones in this aspect. Routines presented in this paper preserve good performance obtained by the previous parallel implementation while maintaining high accuracy of sequential SLICOT routines.

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“…It should be emphasized that the methods just described for solving 2.4and 2.5 significantly differ from standard methods used in the MATLAB toolboxes or SLICOT [26]. First, the proposed LR-ADI iteration for the solution of the dual Lyapunov equation exploits the sparsity in the coefficient matrix.…”

Section: An Analogous Iteration Involving Amentioning

confidence: 99%

Parallel Algorithms for Balanced Truncation Model Reduction of Sparse Systems

Badía

Benner

Mayo

et al. 2006

Applied Parallel Computing. State of the Art in Scientific Computing

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Abstract. We describe the parallelization of an efficient algorithm for balanced truncation that allows to reduce models with state-space dimension up to O(10 5 ). The major computational task in this approach is the solution of two largescale sparse Lyapunov equations, performed via a coupled LR-ADI iteration with (super-)linear convergence. Experimental results on a cluster of Intel Xeon processors illustrate the efficacy of our parallel model reduction algorithm.

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Model Reduction Software in the SLICOT Library

Cited by 42 publications

References 16 publications

Die SLICOT-Toolboxen für MatlabThe SLICOT Toolboxes for Matlab

Die SLICOT-Toolboxen für MatlabThe SLICOT Toolboxes for Matlab

Improving accuracy of parallel SLICOT model reduction routines for stable systems

Parallel Algorithms for Balanced Truncation Model Reduction of Sparse Systems

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