4.6 Article

A simple primal-dual algorithm for nuclear norm and total variation regularization

Journal

NEUROCOMPUTING
Volume 289, Issue -, Pages 1-12

Publisher

ELSEVIER
DOI: 10.1016/j.neucom.2017.12.056

Keywords

Primal-dual method; Saddle-point problem; Nuclear norm; Total variation

Funding

  1. NNSF of China [11361018, 11461015]
  2. Guangxi Natural Science Foundation [2014GXNSFFA118001]
  3. Innovation Project of Guangxi and GUET Graduate Education [YJCXB201502]
  4. U.S. NSF [IIS-1423056, CMMI-1434401, CNS-1405985]
  5. NSF CAREER [IIS-1553687]

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We propose a simple primal-dual method for nuclear norm plus total variation minimization problems. A predictor-corrector scheme to the dual variable is used in our algorithm. Convergence of the method is proved and convergence rate which is O(1/N) in the ergodic sense is also discussed, where N denotes the iteration number. Numerical results including tensor completion, parallel magnetic resonance imaging and dynamic magnetic resonance imaging demonstrate the efficiency of the new algorithm. (C) 2018 Elsevier B.V. All rights reserved.

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