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Boris Lohmann, Behnam Salimbahrami

Order Reduction using Krylov Subspace Methods

In the modelling of dynamic systems, increasing accuracy requirements and the usage of software tools lead to models of high order. These models can significantly be simplified by model reduction. Krylov Subspace Methods allow reducing even very high order models with several ten thousands of state variables. This paper gives an introduction into the basic concepts, presents the most important algorithms, and gives a short outlook into open questions.

at – Automatisierungstechnik, Oldenbourg Wissenschaftsverlag

Print ISSN: 0178-2312
Volume: 52, 01/2004
Pages: 030

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