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On properties and structure of the analytic singular value decomposition

Stephan Weiss, Ian K. Proudler, Giovanni Barbarino, Jennifer Pestana, John G. McWhirter

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Abstract

We investigate the singular value decomposition (SVD) of a rectangular matrix A(z) of functions that are analytic on an annulus that includes at least the unit circle. Such matrices occur, e.g., as matrices of transfer functions representing broadband multiple-input multiple-output systems. Our analysis is based on findings for the analytic SVD applicable to continuous time systems, and on the analytic eigenvalue decomposition. Using these, we establish two potentially overlapping cases where analyticity of the SVD factors is denied. Firstly, from a structural point of view, multiplexed systems require oversampling by the multiplexing factor in order to admit an analytic solution. Secondly, from an algebraic perspective, we state under which condition spectral zeroes of any singular value require additional oversampling by a factor of two if an analytic solution is to be found. In all other cases, an analytic matrix admits an analytic
SVD, whereby the singular values are unique up to a permutation, and the left- and right-singular vectors are coupled through a joint ambiguity w.r.t.~an arbitrary allpass function. We demonstrate how some state-of-the-art polynomial matrix decomposition algorithms approximate this solution, motivating the need for dedicated algorithms.
Original languageEnglish
Pages (from-to) 2260-2275
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume72
Early online date11 Apr 2024
DOIs
Publication statusPublished - 8 May 2024

Funding

This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/S000631/1 and in part by the MOD University Defence Research Collaboration in Signal Processing. The work of G. Barbarino was supported in part by Alfred Kordelinin Säätiö under Grant 210122, and in part by the European Union (ERC consolidator Grant, eLinoR, 101085607). G. Barbarino is member of the Research Group GNCS (Gruppo Nazionale per il Calcolo Scientifico) of INdAM (Istituto Nazionale di Alta Matematica).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • matrix decomposition
  • signal processing algorithms
  • vectors
  • approximation algorithms
  • singular value decomposition
  • indexes

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