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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.

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Metadaten
Author:Peter Benner, Enrique S. Quintana-Ortí, Gregorio Quintana-Ortí
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
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