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An iterative SVD-Krylov based method for model reduction of large-scale dynamical systems
Proceedings of the 44th IEEE Conference on Decision and Control
In this paper, we propose a model reduction algorithm for approximation of large-scale linear time-invariant dynamical systems. The method is a two-sided projection combining features of the singular value decomposition (SVD)-based and the Krylov-based model reduction techniques. While the SVD-side of the projection depends on the observability gramian, the Krylov-side is obtained via iterative rational Krylov steps. The reduced model is asymptotically stable, matches certain moments and solves
doi:10.1109/cdc.2005.1583106
fatcat:qij3fljfunbthfcubqc7rumm3q