A parallel splitting ALM-based algorithm for separable convex programming

DOI10.1007/s10589-021-00321-3zbMath1481.90256OpenAlexW3202887397MaRDI QIDQ2057225

Shengjie Xu, Bing-sheng He

Publication date: 8 December 2021

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-021-00321-3

zbMATH Keywords

convex programmingparallel computingaugmented Lagrangian methodJacobian decompositionoperator splitting methods

Mathematics Subject Classification ID

Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33)

Uses Software

• RASL

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Latent variable graphical model selection via convex optimization
• A class of ADMM-based algorithms for three-block separable convex programming
• A dual algorithm for the solution of nonlinear variational problems via finite element approximation
• Generalized symmetric ADMM for separable convex optimization
• Convergence rates for an inexact ADMM applied to separable convex optimization
• Optimally linearizing the alternating direction method of multipliers for convex programming
• Multiplier and gradient methods
• On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
• Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming
• Convergence Analysis of Primal-Dual Algorithms for a Saddle-Point Problem: From Contraction Perspective
• Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
• On Full Jacobian Decomposition of the Augmented Lagrangian Method for Separable Convex Programming
• Monotone Operators and the Proximal Point Algorithm
• First-Order Methods in Optimization
• Sparsity and Smoothness Via the Fused Lasso
• Proximal Decomposition Via Alternating Linearization
• Finite-Dimensional Variational Inequalities and Complementarity Problems
• Study on the Splitting Methods for Separable Convex Optimization in a Unified Algorithmic Framework
• Optimal proximal augmented Lagrangian method and its application to full Jacobian splitting for multi-block separable convex minimization problems
• A splitting method for separable convex programming
• Convergence Rate Analysis for the Alternating Direction Method of Multipliers with a Substitution Procedure for Separable Convex Programming
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent

This page was built for publication: A parallel splitting ALM-based algorithm for separable convex programming