2010.11: Two efficient SVD/Krylov algorithms for model order reduction of large scale systems
2010.11: Younès Chahlaoui (2011) Two efficient SVD/Krylov algorithms for model order reduction of large scale systems. Electronic Transactions On Numerical Analysis (ETNA), 38. pp. 113-145. ISSN 1068-9613
This is the latest version of this eprint.
Full text available as:
|PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
We present two efficient algorithms to produce a reduced order model of a time-invariant linear dynamical system by approximate balanced truncation. Attention is focused on the use of the structure and the iterative construction via Krylov subspaces of both controllability and observability matrices to compute low-rank approximations of the Gramians or the Hankel operator. This allows us to take advantage of any sparsity in the system matrices and indeed the cost of our two algorithms is only linear in the system dimension. Both algorithms efficiently produce good low-rank approximations (in the least square sense) of the Cholesky factor of each Gramian and the Hankel operator. The second algorithm works directly on the Hankel operator, and it has the advantage that it is independent of the chosen realization. Moreover it is also an approximate Hankel norm method. The two reduced order models produced by our methods are guaranteed to be stable and balanced. We study the convergence of our iterative algorithms and the properties of the fixed point iteration. We also discuss the stopping criteria and the choice of the reduced order.
|Subjects:||MSC 2000 > 15 Linear and multilinear algebra; matrix theory|
MSC 2000 > 65 Numerical analysis
MSC 2000 > 93 Systems theory; control
|Deposited By:||Dr Younes Chahlaoui|
|Deposited On:||29 June 2011|
Available Versions of this Item
- Two efficient SVD/Krylov algorithms for model order reduction of large scale systems (deposited 29 June 2011) [Currently Displayed]