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International Conference on Learning and Intelligent Optimization

LION 2017: Learning and Intelligent Optimization pp 326–330Cite as

Global Optimization Challenges in Structured Low Rank Approximation

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10556)

Abstract

In this paper, we investigate the complexity of the numerical construction of the so-called Hankel structured low-rank approximation (HSLRA). Briefly, HSLRA is the problem of finding a rank r approximation of a given Hankel matrix, which is also of Hankel structure.

Keywords

  • Structured low rank approximation
  • Hankel matrices
  • Time series analysis

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References

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Acknowledgements

This work was supported by the project No. 15-11-30022 “Global optimization, supercomputing computations, and applications” of the Russian Science Foundation.

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Authors and Affiliations

  1. School of Mathematics, Cardiff University, Cardiff, CF24 4AG, UK

    Jonathan Gillard & Anatoly Zhigljavsky

  2. Lobachevsky Nizhny Novgorod State University, 23 Prospekt Gagarina, 603950, Nizhny Novgorod, Russia

    Anatoly Zhigljavsky

Authors
  1. Jonathan Gillard
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  2. Anatoly Zhigljavsky
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Corresponding author

Correspondence to Jonathan Gillard .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Calabria, Rende, Italy

    Dmitri E. Kvasov

  3. University of Calabria, Rende, Italy

    Yaroslav D. Sergeyev

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Gillard, J., Zhigljavsky, A. (2017). Global Optimization Challenges in Structured Low Rank Approximation. In: Battiti, R., Kvasov, D., Sergeyev, Y. (eds) Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science(), vol 10556. Springer, Cham. https://doi.org/10.1007/978-3-319-69404-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-69404-7_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69403-0

  • Online ISBN: 978-3-319-69404-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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