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.
Author: | Peter Benner, Enrique S. Quintana-Ortí, Gregorio Quintana-Ortí |
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URN: | urn:nbn:de:0296-matheon-651 |
Referee: | Fredi Tröltzsch |
Document Type: | Preprint, Research Center Matheon |
Language: | English |
Date of first Publication: | 2004/01/30 |
Release Date: | 2004/01/28 |
Preprint Number: | 59 |