Block-wise ADMM with a relaxation factor for multiple-block convex programming
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Publication:2422129
DOI10.1007/s40305-017-0186-yzbMath1424.90204OpenAlexW2791264501MaRDI QIDQ2422129
Ming-Hua Xu, Xiao-Ming Yuan, Bing-sheng He
Publication date: 18 June 2019
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/s40305-017-0186-y
convex programmingalternating direction method of multipliersoperator splitting methodsFortin and Glowinski's relaxation factor
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Cites Work
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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Gradient methods for minimizing composite functions
- Alternating direction augmented Lagrangian methods for semidefinite programming
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- A dual algorithm for the solution of nonlinear variational problems via finite element approximation
- Some convergence properties of a method of multipliers for linearly constrained monotone variational inequalities
- Proximal alternating directions method for structured variational inequalities
- Multiplier and gradient methods
- On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
- A Strictly Contractive Peaceman--Rachford Splitting Method for Convex Programming
- The Numerical Solution of Parabolic and Elliptic Differential Equations
- Splitting Algorithms for the Sum of Two Nonlinear Operators
- Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
- Finite-Dimensional Variational Inequalities and Complementarity Problems
- Block-wise Alternating Direction Method of Multipliers for Multiple-block Convex Programming and Beyond
- On Alternating Direction Methods of Multipliers: A Historical Perspective
- The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent
