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

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Abstract

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.

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• Two efficient SVD/Krylov algorithms for model order reduction of large scale systems (deposited 29 June 2011) [Currently Displayed]
  • Two efficient SVD/Krylov algorithms for model order reduction of large scale systems (deposited 14 January 2010)

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