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 |
|---|---|
| URN: | urn:nbn:de:0296-matheon-651 |
| Referee: | Fredi Tröltzsch |
| Language: | English |
| Date of first Publication: | 30.01.2004 |
| Preprint Number: | Matheon Preprint #59 |


