A simple primal-dual algorithm for nuclear norm and total variation regularization
作者: Zhibin ZhuJiawen YaoZheng XuJunzhou HuangBenxin Zhang
作者单位: 1School of Mathematics and Computational Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin 541004, China
2Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
刊名: Neurocomputing, 2018, Vol.289 , pp.1-12
来源数据库: Elsevier Journal
DOI: 10.1016/j.neucom.2017.12.056
关键词: Primal-dual methodSaddle-point problemNuclear normTotal variation
原始语种摘要: Abstract(#br)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.
全文获取路径: Elsevier  (合作)
影响因子:1.634 (2012)

  • regularization 正则化
  • variation 变异
  • primal 最初
  • algorithm 算法
  • simple 单纯的
  • minimization 最小化
  • imaging 图像形成
  • demonstrate 说明
  • iteration 迭代
  • point