- Tytuł:
- Iterative Model Reduction of Large State-Space Systems
- Autorzy:
- Huhtanen, M.
- Powiązania:
- https://bibliotekanauki.pl/articles/908302.pdf
- Data publikacji:
- 1999
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
redukcja modeli
metoda iteracyjna
równanie macierzowe Lyapunova
kondycjonowanie
operator Hankela
model reduction
iterative methods
Lyapunov matrix equations
Hankel operator
preconditioning
Hankel singular values - Opis:
- There exist criteria for reducing the order of a large state-space model based on the accuracy of the approximate solutions to the Lyapunov matrix equations and the Hankel operator. Iterative solution techniques for the Lyapunov equations with the Arnoldi method have been proposed in a number of papers. In this paper we derive error bounds for approximations to the solutions to the Lyapunov equations as well as for the Hankel operator that indicate how to precondition while solving these equations iteratively.These bounds show that the error depends on three terms: First, on the amount of invariance of the constructed subspace for A, second, on the eigenvalues of A at least in proportion to 1/|Re l|, and third, under a certain condition on projectors P_l=W_lW_l* ,on the factor min_{X in C^{l x p}}|| B-( l I-A)W_lX|| for l on a path G surrounding the spectrum of A. Consequently, in order to compensate for those parts of the spectrum where 1/|Re l| is not small, preconditioning or an inverse iteration is needed to keep the sizes of the matrices used in construction of a reduced-order model moderate.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 1999, 9, 2; 245-263
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł