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Reduced-Order Modeling Techniques Based on Krylov Subspaces and Their Use in Circuit Simulation

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Applied and Computational Control, Signals, and Circuits

Abstract

In recent years reduced-order modeling techniques based on Krylov-subspace iterations especially the Lanczos algorithm and the Arnoldi process have become popular tools to tackle the large-scale time-invariant linear dynamical systems that arise in the simulation of electronic circuits. This chapter reviews the main ideas of reduced-order modeling techniques based on Krylov subspaces and describes their use in circuit simulation.

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  1. Roland W. Freund
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  1. Department of Mathematical Sciences, Northern Illinois University, DeKalb, IL, 60115, USA

    Biswa Nath Datta

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Freund, R.W. (1999). Reduced-Order Modeling Techniques Based on Krylov Subspaces and Their Use in Circuit Simulation. In: Datta, B.N. (eds) Applied and Computational Control, Signals, and Circuits. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-0571-5_9

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  • DOI: https://doi.org/10.1007/978-1-4612-0571-5_9

  • Publisher Name: Birkhäuser, Boston, MA

  • Print ISBN: 978-1-4612-6822-2

  • Online ISBN: 978-1-4612-0571-5

  • eBook Packages: Springer Book Archive

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