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Low-rank matrix recovery with Ky Fan 2-k-norm

Author

Listed:
  • Xuan Vinh Doan

    (University of Warwick
    Alan Turing Institute, British Library)

  • Stephen Vavasis

    (University of Waterloo)

Abstract

Low-rank matrix recovery problem is difficult due to its non-convex properties and it is usually solved using convex relaxation approaches. In this paper, we formulate the non-convex low-rank matrix recovery problem exactly using novel Ky Fan 2-k-norm-based models. A general difference of convex functions algorithm (DCA) is developed to solve these models. A proximal point algorithm (PPA) framework is proposed to solve sub-problems within the DCA, which allows us to handle large instances. Numerical results show that the proposed models achieve high recoverability rates as compared to the truncated nuclear norm method and the alternating bilinear optimization approach. The results also demonstrate that the proposed DCA with the PPA framework is efficient in handling larger instances.

Suggested Citation

  • Xuan Vinh Doan & Stephen Vavasis, 2022. "Low-rank matrix recovery with Ky Fan 2-k-norm," Journal of Global Optimization, Springer, vol. 82(4), pages 727-751, April.
    • Handle: RePEc:spr:jglopt:v:82:y:2022:i:4:d:10.1007_s10898-021-01031-0
    DOI: 10.1007/s10898-021-01031-0
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