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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)


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.


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

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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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Correspondence to Jonathan Gillard .

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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.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69403-0

  • Online ISBN: 978-3-319-69404-7

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