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Low-Rank Matrix Recovery with Ky Fan 2-k-Norm

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Optimization of Complex Systems: Theory, Models, Algorithms and Applications (WCGO 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 991))

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Abstract

We propose Ky Fan 2-k-norm-based models for the non-convex low-rank matrix recovery problem. A general difference of convex algorithm (DCA) is developed to solve these models. Numerical results show that the proposed models achieve high recoverability rates.

This work is partially supported by the Alan Turing Fellowship of the first author.

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Author information

Authors and Affiliations

  1. Operations Group, Warwick Business School, University of Warwick, Coventry, CV4 7AL, UK

    Xuan Vinh Doan

  2. The Alan Turing Institute, British Library, 96 Euston Road, London, NW1 2DB, UK

    Xuan Vinh Doan

  3. Combinatorics and Optimization, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada

    Stephen Vavasis

Authors
  1. Xuan Vinh Doan
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  2. Stephen Vavasis
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Corresponding author

Correspondence to Xuan Vinh Doan .

Editor information

Editors and Affiliations

  1. Computer science and Applications Department, LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai An Le Thi

  2. Computer Science and Applications Department, LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai Minh Le

  3. Laboratory of Mathematics, National Institute for Applied Sciences (INSA)-Rouen Normadie, Saint-Étienne-du-Rouvray Cedex, France

    Tao Pham Dinh

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Doan, X.V., Vavasis, S. (2020). Low-Rank Matrix Recovery with Ky Fan 2-k-Norm. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_32

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