Combining Krylov subspace methods and identification‐based methods for model order reduction | Zendy

Heres P. J. | Zendy; Deschrijver D. | Zendy; Schilders W. H. A. | Zendy; Dhaene T. | Zendy

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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

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.

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