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An adaptive singular value decomposition algorithm and its application to adaptive realization

  • Session 11 Numerical Methods
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Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 63))

Abstract

In this paper we present an algorithm ASVD for the computation of the singular value decomposition (SVD). The method presented is a power method for calculating the largest triplets of the SVD of a matrix A when multiplication is "cheap". The method used bears a lot of similitude with the power method for finding the eigenvalues of a symmetric matrix M. The triplets are found one after another and also some deflation techniques (orthogonalization) are used. The algorithm can take profit of the SVD of slightly different matrices and it is based on the geometric properties of the SVD. Tests have shown that it is more efficient than Golub's algorithm if only the dominant part of the SVD of a long sequence of slowly varying matrices is needed. Also storage efficiency is obtained whenever the matrices are structured.

The paper describes the basic ASVD algorithm, its numerical properties using the shift mechanism, an acceleration method, a computer implementation and its use in adaptive state space realization of noisy impulse responses. It is expected that the new ASVD algorithm and its many strategies will be useful in the domain of signal processing, system theory and automatic control, where SVD is becoming more and more an important concept.

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A. Bensoussan J. L. Lions

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© 1984 Springer-Verlag

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Vandewalle, J., Staar, J., De Moor, B., Lauwers, J. (1984). An adaptive singular value decomposition algorithm and its application to adaptive realization. In: Bensoussan, A., Lions, J.L. (eds) Analysis and Optimization of Systems. Lecture Notes in Control and Information Sciences, vol 63. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0006275

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  • DOI: https://doi.org/10.1007/BFb0006275

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-13552-4

  • Online ISBN: 978-3-540-39010-7

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