Linearized generalized ADMM-based algorithm for multi-block linearly constrained separable convex programming in real-world applications

DOI10.1016/j.cam.2023.115632MaRDI QIDQ6126060

Zhenrong Lu, Bangzhong Zhang, Jinlin Li, Jian He

Publication date: 9 April 2024

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

zbMATH Keywords

global convergenceconvex optimizationgeneralized alternating direction method of multiplierscalibrating the correlation matricesworst case convergence rate

Mathematics Subject Classification ID

Mathematical programming (90Cxx) Communication, information (94Axx) Numerical methods for mathematical programming, optimization and variational techniques (65Kxx)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Generalized peaceman-Rachford splitting method for separable convex programming with applications to image processing
• An algorithm twisted from generalized ADMM for multi-block separable convex minimization models
• Combining Lagrangian decomposition and excessive gap smoothing technique for solving large-scale separable convex optimization problems
• Structured linear algebra problems in adaptive optics imaging
• Subgradient methods for saddle-point problems
• 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
• Parallel alternating direction multiplier decomposition of convex programs
• A new inexact alternating directions method for monotone variational inequalities
• Linearized alternating direction method of multipliers with Gaussian back substitution for separable convex programming
• A faster generalized ADMM-based algorithm using a sequential updating scheme with relaxed step sizes for multiple-block linearly constrained separable convex programming
• \(O(1/t)\) complexity analysis of the generalized alternating direction method of multipliers
• A generalization of linearized alternating direction method of multipliers for solving two-block separable convex programming
• A proximal point algorithm revisit on the alternating direction method of multipliers
• Generalized alternating direction method of multipliers: new theoretical insights and applications
• Iteration-complexity analysis of a generalized alternating direction method of multipliers
• A proximal Peaceman-Rachford splitting method for compressive sensing
• Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming
• A Strictly Contractive Peaceman--Rachford Splitting Method for Convex Programming
• Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
• Robust principal component analysis?
• Inexact Alternating Direction Methods for Image Recovery
• The Numerical Solution of Parabolic and Elliptic Differential Equations
• Splitting Algorithms for the Sum of Two Nonlinear Operators
• Distributed Spectrum Management Algorithms for Multiuser DSL Networks
• Sparsity and Smoothness Via the Fused Lasso
• The Linearized Alternating Direction Method of Multipliers for Dantzig Selector
• An Inexact Perturbed Path-Following Method for Lagrangian Decomposition in Large-Scale Separable Convex Optimization

This page was built for publication: Linearized generalized ADMM-based algorithm for multi-block linearly constrained separable convex programming in real-world applications