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2007•International Journal of Numerical Modelling Electronic Networks Devices and FieldsOpen access

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

P. J. Heres, Dirk Deschrijver, W.H.A. Schilders, Tom Dhaene

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Abstract

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.

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What this paper is about

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.

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Available abstract

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.

Key concepts: Krylov subspace, Subspace topology, Generalized minimal residual method, Orthonormal basis, Model order reduction, Computer science, Reduction (mathematics), Applied mathematics

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