Summary
Two main approaches are known for the reduced order modelling of linear time-invariant systems: Krylov subspace based and SVD based approximation methods. Krylov subspace based methods have large scale applicability, but do not have a global error bound. SVD based methods do have a global error bound, but require full space matrix computations and hence have limited large scale applicability. In this paper features and short-comings of both types of methods will be addressed. Furthermore, ideas for improvements will be discussed and the possible application of Jacobi-Davidson style methods such as JDQR and JDQZ for model reduction will be considered.
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Rommes, J. (2006). Reduced Order Models for Eigenvalue Problems. In: Di Bucchianico, A., Mattheij, R., Peletier, M. (eds) Progress in Industrial Mathematics at ECMI 2004. Mathematics in Industry, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28073-1_26
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DOI: https://doi.org/10.1007/3-540-28073-1_26
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