Computing Optimal Hankel Norm Approximations of Large-Scale Systems
Please always quote using this URN:urn:nbn:de:0296-matheon-1182
- We discuss an efficient algorithm for optimal Hankel norm approximation of large-scale systems and an implementation which allows to reduce models of order up to O(104) using parallel computing techniques. The major computational tasks in this approach are the computation of a minimal balanced realization, involving the solution of two Lyapunov equations, and the additive decomposition of a transfer function via block diagonalization. We will illustrate that these computational tasks can all be performed using iterative schemes for the matrix sign function. Numerical experiments on a cluster of Linux PCs show the efficiency of our methods.
Author: | Peter Benner, Enrique S. Quintana-Ortí, Gregorio Quintana-Ortí |
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URN: | urn:nbn:de:0296-matheon-1182 |
Referee: | Volker Mehrmann |
Document Type: | Preprint, Research Center Matheon |
Language: | English |
Date of first Publication: | 2004/04/15 |
Release Date: | 2004/04/14 |
Preprint Number: | 116 |