Sparse and low-rank matrix decomposition via alternating direction method

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Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm (2012, background): “2) in [8, 33] and the closely related sparse and low-rank matrix decomposition in [34].”
Robust Principal Component Analysis on Graphs (2015, methodology): “Algorithm 1 is a special case of Alternating Directions method [18], [2].”
Alternating Direction Algorithms for 1-Problems in Compressive Sensing (2009, methodology): “An ADM scheme has been proposed and studied for this problem in [55].”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, unknown): “Many efforts have been recently concentrated to develop low-computational algorithms for solving PCP (Accelerated Proximal Gradient (APG) [164], Augmented Lagrange Multiplier (ALM) [165], Alternating Direction Method (ADM) [310]), to develop incremental algorithms of PCP to update the low-rank and s”
Value function approximation via low-rank models (2015, methodology): “We choose D mn=4 k M k 1 , as suggested in [24], and terminate the algorithm when k M (cid:0) L (cid:0) S k F (cid:20) ı k M k F , with ı D 10 5 .”
Robust principal component analysis? (2009, methodology): “Algorithm 1 is a special case of a more general class of augmented Lagrange multiplier algorithms known as alternating directions methods [51].”