A Note on the Alternating Direction Method of Multipliers

We consider the linearly constrained separable convex programming, whose objective function is separable into m individual convex functions without coupled variables. The alternating direction method of multipliers has been well studied in the literature for the special case m=2, while it remains open whether its convergence can be extended to the general case m≥3. This note shows the global convergence of this extension when the involved functions are further assumed to be strongly convex.

[1]  Michael K. Ng,et al.  Solving Constrained Total-variation Image Restoration and Reconstruction Problems via Alternating Direction Methods , 2010, SIAM J. Sci. Comput..

[2]  Robert R. Meyer,et al.  A variable-penalty alternating directions method for convex optimization , 1998, Math. Program..

[3]  D. Gabay Applications of the method of multipliers to variational inequalities , 1983 .

[4]  P. Tseng Applications of splitting algorithm to decomposition in convex programming and variational inequalities , 1991 .

[5]  Bingsheng He,et al.  Solving Large-Scale Least Squares Semidefinite Programming by Alternating Direction Methods , 2011, SIAM J. Matrix Anal. Appl..

[6]  Masao Fukushima,et al.  Application of the alternating direction method of multipliers to separable convex programming problems , 1992, Comput. Optim. Appl..

[7]  John Wright,et al.  RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images , 2012, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Su Zhang,et al.  A modified alternating direction method for convex quadratically constrained quadratic semidefinite programs , 2010, Eur. J. Oper. Res..

[9]  Raymond H. Chan,et al.  Alternating Direction Method for Image Inpainting in Wavelet Domains , 2011, SIAM J. Imaging Sci..

[10]  Xiaoming Yuan,et al.  Sparse and low-rank matrix decomposition via alternating direction method , 2013 .

[11]  Xiaoming Yuan,et al.  Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations , 2011, SIAM J. Optim..

[12]  Gabriele Steidl,et al.  Deblurring Poissonian images by split Bregman techniques , 2010, J. Vis. Commun. Image Represent..

[13]  Marc Teboulle,et al.  A proximal-based decomposition method for convex minimization problems , 1994, Math. Program..

[14]  Xiaoming Yuan,et al.  Matrix completion via an alternating direction method , 2012 .

[15]  Dimitri P. Bertsekas,et al.  On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators , 1992, Math. Program..

[16]  Junfeng Yang,et al.  Alternating Direction Algorithms for 1-Problems in Compressive Sensing , 2009, SIAM J. Sci. Comput..

[17]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[18]  Bingsheng He,et al.  Linearized Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming , 2011 .

[19]  B. Mercier,et al.  A dual algorithm for the solution of nonlinear variational problems via finite element approximation , 1976 .

[20]  M. Fortin,et al.  Augmented Lagrangian methods : applications to the numerical solution of boundary-value problems , 1983 .

[21]  Bingsheng He,et al.  A new inexact alternating directions method for monotone variational inequalities , 2002, Math. Program..

[22]  James G. Nagy,et al.  Structured linear algebra problems in adaptive optics imaging , 2011, Adv. Comput. Math..

[23]  Andrzej Ruszczynski,et al.  Proximal Decomposition Via Alternating Linearization , 1999, SIAM J. Optim..

[24]  F. Facchinei,et al.  Finite-Dimensional Variational Inequalities and Complementarity Problems , 2003 .