State-Space Truncation Methods for Parallel Model Reduction of Large-Scale Systems

Please always quote using this URN: urn:nbn:de:0296-matheon-651

We discuss a parallel library of efficient algorithms for model reduction of largescale systems with state-space dimension up to O(104). We survey the numerical algorithms underlying the implementation of the chosen model reduction methods. The approach considered here is based on state-space truncation of the system matrices and includes absolute and relative error methods for both stable and unstable systems. In contrast to serial implementations of these methods, we employ Newton-type iterative algorithms for the solution of the major computational tasks. Experimental results report the numerical accuracy and the parallel performance of our approach on a cluster of Intel Pentium II processors.

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Metadaten
Author:Peter Benner, Enrique S. Quintana-Ortí, Gregorio Quintana-Ortí
URN:urn:nbn:de:0296-matheon-651
Referee:Fredi Tröltzsch
Language:English
Date of first Publication:30.01.2004
Preprint Number:Matheon Preprint #59