A parallel splitting ALM-based algorithm for separable convex programming
type: Item
instance of: scholarly article
title: A parallel splitting ALM-based algorithm for separable convex programming
zbMATH Open document ID: 1481.90256
author: Shengjie Xu
author: Bingsheng He
published in: Computational Optimization and Applications
publication date: 2021-12-08T00:00:00Z
Mathematics Subject Classification ID: 90C25
Mathematics Subject Classification ID: 90C06
Mathematics Subject Classification ID: 90C33
zbMATH DE Number: 7441213
zbMATH Keywords: convex programming
zbMATH Keywords: augmented Lagrangian method
zbMATH Keywords: Jacobian decomposition
zbMATH Keywords: parallel computing
zbMATH Keywords: operator splitting methods
describes a project that uses: RASL
MaRDI profile type: Publication
full work available at URL: s10589-021-00321-3
OpenAlex ID: W3202887397
cites work: Generalized symmetric ADMM for separable convex optimization
cites work: First-Order Methods in Optimization
cites work: Q4077092
cites work: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
cites work: Latent variable graphical model selection via convex optimization
cites work: The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent
cites work: Finite-Dimensional Variational Inequalities and Complementarity Problems
cites work: A dual algorithm for the solution of nonlinear variational problems via finite element approximation
cites work: Q3694624
cites work: Convergence rates for an inexact ADMM applied to separable convex optimization
cites work: Q4690837
cites work: Study on the Splitting Methods for Separable Convex Optimization in a Unified Algorithmic Framework
cites work: On Full Jacobian Decomposition of the Augmented Lagrangian Method for Separable Convex Programming
cites work: Optimal proximal augmented Lagrangian method and its application to full Jacobian splitting for multi-block separable convex minimization problems
cites work: Optimally linearizing the alternating direction method of multipliers for convex programming
cites work: Alternating direction method with Gaussian back substitution for separable convex programming
cites work: A splitting method for separable convex programming
cites work: Convergence Rate Analysis for the Alternating Direction Method of Multipliers with a Substitution Procedure for Separable Convex Programming
cites work: Convergence analysis of primal-dual algorithms for a saddle-point problem: from contraction perspective
cites work: On the \(O(1/n)\) convergence rate of the Douglas-Rachford alternating direction method
cites work: A class of ADMM-based algorithms for three-block separable convex programming
cites work: Multiplier and gradient methods
cites work: Proximal Decomposition Via Alternating Linearization
cites work: Q5618030
cites work: Q5590418
cites work: Monotone Operators and the Proximal Point Algorithm
cites work: Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
cites work: Sparsity and Smoothness Via the Fused Lasso
DOI: 10.1007/S10589-021-00321-3
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description: scientific article; zbMATH DE number 7441213