Convergence analysis of L-ADMM for multi-block linear-constrained separable convex minimization problem

DOI10.1007/s40305-015-0084-0zbMath1332.90195OpenAlexW1115208862MaRDI QIDQ259135

Hai-Bin Zhang, Hui-Min Pei, Jun-Kai Feng, Cao-Zong Cheng

Publication date: 11 March 2016

Published in: Journal of the Operations Research Society of China (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40305-015-0084-0

zbMATH Keywords

linearizationalternating direction method of multipliersseparable convex minimizationsublinear convergence

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Decomposition methods (49M27)

Related Items

Hybrid Jacobian and Gauss--Seidel Proximal Block Coordinate Update Methods for Linearly Constrained Convex Programming, Asynchronous parallel primal-dual block coordinate update methods for affinely constrained convex programs

Cites Work

• A note on the convergence of alternating proximal gradient method
• Ergodic convergence to a zero of the sum of monotone operators in Hilbert space
• A dual algorithm for the solution of nonlinear variational problems via finite element approximation
• A proximal-based deomposition method for compositions method for convex minimization problems
• A new inexact alternating directions method for monotone variational inequalities
• Linearized alternating direction method of multipliers with Gaussian back substitution for separable convex programming
• Alternating proximal gradient method for convex minimization
• Splitting Algorithms for the Sum of Two Nonlinear Operators
• The Linearized Alternating Direction Method of Multipliers for Dantzig Selector
• Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
• A Linearized Alternating Direction Method of Multipliers with Substitution Procedure
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent