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

  • Xuan Vinh DoanEmail author
  • Stephen Vavasis
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


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.


Rank minimization Ky Fan 2-k-norm Matrix recovery 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Operations Group, Warwick Business SchoolUniversity of WarwickCoventryUK
  2. 2.The Alan Turing Institute, British LibraryLondonUK
  3. 3.Combinatorics and OptimizationUniversity of WaterlooWaterlooCanada

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