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Bounds on the Spectral Norm and the Nuclear Norm of a Tensor Based on Tensor Partitions

Published: 01 January 2016 Publication History

Abstract

It is known that computing the spectral norm and the nuclear norm of a tensor is NP-hard in general. In this paper, we provide neat bounds for the spectral norm and the nuclear norm of a tensor based on tensor partitions. The spectral norm (respectively, the nuclear norm) can be lower and upper bounded by manipulating the spectral norms (respectively, the nuclear norms) of its subtensors. The bounds are sharp in general. When a tensor is partitioned into its matrix slices, our inequalities provide polynomial-time worst-case approximation bounds for computing the spectral norm and the nuclear norm of the tensor.

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Cited By

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  • (2024)On the tensor spectral -norm and its higher order power methodCalcolo: a quarterly on numerical analysis and theory of computation10.1007/s10092-024-00588-y61:3Online publication date: 13-Jun-2024
  • (2023)Further results on tensor nuclear normsCalcolo: a quarterly on numerical analysis and theory of computation10.1007/s10092-023-00528-260:3Online publication date: 15-Jun-2023

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Information

Published In

cover image SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Matrix Analysis and Applications  Volume 37, Issue 4
2016
407 pages
ISSN:0895-4798
DOI:10.1137/sjmael.37.4
Issue’s Table of Contents

Publisher

Society for Industrial and Applied Mathematics

United States

Publication History

Published: 01 January 2016

Author Tags

  1. tensor norm
  2. spectral norm
  3. nuclear norm
  4. tensor partition
  5. block tensor
  6. approximation bound

Author Tags

  1. 15A60
  2. 15A69
  3. 90C59
  4. 68Q17

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Cited By

View all
  • (2024)On the tensor spectral -norm and its higher order power methodCalcolo: a quarterly on numerical analysis and theory of computation10.1007/s10092-024-00588-y61:3Online publication date: 13-Jun-2024
  • (2023)Further results on tensor nuclear normsCalcolo: a quarterly on numerical analysis and theory of computation10.1007/s10092-023-00528-260:3Online publication date: 15-Jun-2023

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