Premium
Combining Krylov subspace methods and identification‐based methods for model order reduction
Author(s) -
Heres P. J.,
Deschrijver D.,
Schilders W. H. A.,
Dhaene T.
Publication year - 2007
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.644
Subject(s) - krylov subspace , subspace topology , generalized minimal residual method , orthonormal basis , model order reduction , computer science , reduction (mathematics) , mathematics , algorithm , mathematical optimization , artificial intelligence , iterative method , physics , projection (relational algebra) , geometry , quantum mechanics
Abstract Many different techniques to reduce the dimensions of a model have been proposed in the near past. Krylov subspace methods are relatively cheap, but generate non‐optimal models. In this paper a combination of Krylov subspace methods and orthonormal vector fitting (OVF) is proposed. In that way a compact model for a large model can be generated. In the first step, a Krylov subspace method reduces the large model to a model of medium size, then a compact model is derived with OVF as a second step. Copyright © 2007 John Wiley & Sons, Ltd.