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In this paper we propose a randomized primal-dual proximal block coordinate updating framework for a general multi-block convex optimization model with coupled objective function and linear constraints.
Sur l’approximation, par eléments finis d’ordre un, et la résolution, par pénalisation-dualité d’une classe de problèmes de dirichlet non linéaires
R. Glowinski and A. Marrocco · 1975
Earlier work this paper cites.
A dual algorithm for the solution of nonlinear variational problems via finite element approximation
D. Gabay and B. Mercier · 1976
Earlier work this paper cites.
Augmented Lagrangian and operator-splitting methods in nonlinear mechanics
R. Glowinski and P. Le Tallec · 1989
Earlier work this paper cites.
Large-scale extended linear-quadratic programming and multistage optimization
R. T. Rockafellar · 1991
Earlier work this paper cites.
On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
J. Eckstein and D. Bertsekas · 1992
Earlier work this paper cites.
A first-order primal-dual algorithm for convex problems with applications to imaging
A. Chambolle and T. Pock · 2011
Earlier work this paper cites.
Recovering low-rank and sparse components of matrices from incomplete and noisy observations
M. Tao and X. Yuan · 2011
Earlier work this paper cites.
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Earlier work this paper cites.
Alternating direction method with gaussian back substitution for separable convex programming
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Earlier work this paper cites.
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B. He, M. Tao, and X. Yuan · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Y. Nesterov · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
Local linear convergence of the alternating direction method of multipliers on quadratic or linear programs
D. Boley · 2013
Earlier work this paper cites.
On the convergence analysis of the alternating direction method of multipliers with three blocks
C. Chen, Y. Shen, and Y. You · 2013
Earlier work this paper cites.
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R. D. Monteiro and B. F. Svaiter · 2013
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The direct extension of admm for three-block separable convex minimization models is convergent when one function is strongly convex
X. Cai, D. Han, and X. Yuan · 2014
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Randomized first-order methods for saddle point optimization
C. Dang and G. Lan · 2014
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On the information-adaptive variants of the ADMM: an iteration complexity perspective
A convergent 3-block semi-proximal ADMM for convex minimization problems with one strongly convex block
M. Li, D. Sun, and K.-C. Toh · 2015
Later among the works it cites.
On the global linear convergence of the admm with multiblock variables
T. Lin, S. Ma, and S. Zhang · 2015
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On the sublinear convergence rate of multi-block admm
T. Lin, S. Ma, and S. Zhang · 2015
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On the complexity analysis of randomized block-coordinate descent methods
Z. Lu and L. Xiao · 2015
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A convergent 3-block semiproximal alternating direction method of multipliers for conic programming with 4-type constraints
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The direct extension of admm for multi-block convex minimization problems is not necessarily convergent
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