On the information-adaptive variants of the ADMM: an iteration complexity perspective
type: Item
instance of: scholarly article
title: On the information-adaptive variants of the ADMM: an iteration complexity perspective
zbMATH Open document ID: 1394.90447
author: Xiang Gao
author: Bo Jiang
author: Shuzhong Zhang
published in: Journal of Scientific Computing
publication date: 2018-08-29T00:00:00Z
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90C25
Mathematics Subject Classification ID: 68Q25
Mathematics Subject Classification ID: 62L20
zbMATH DE Number: 6928673
zbMATH Keywords: alternating direction method of multipliers (ADMM)
zbMATH Keywords: iteration complexity
zbMATH Keywords: stochastic approximation
zbMATH Keywords: first-order method
zbMATH Keywords: direct method
MaRDI profile type: Publication
full work available at URL: s10915-017-0621-6
OpenAlex ID: W2772692493
cites work: Q3151174
cites work: Local Linear Convergence of the Alternating Direction Method of Multipliers on Quadratic or Linear Programs
cites work: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
cites work: A first-order primal-dual algorithm for convex problems with applications to imaging
cites work: The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent
cites work: First-Order Methods for Sparse Covariance Selection
cites work: Convergence Rate Analysis of Several Splitting Schemes
cites work: Sparsity and Smoothness Via the Fused Lasso
cites work: On the global and linear convergence of the generalized alternating direction method of multipliers
cites work: On the Numerical Solution of Heat Conduction Problems in Two and Three Space Variables
cites work: A simple algorithm for a class of nonsmooth convex-concave saddle-point problems
cites work: On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
cites work: stochastic quasigradient methods and their application to system optimization†
cites work: Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization I: A Generic Algorithmic Framework
cites work: Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization, II: Shrinking Procedures and Optimal Algorithms
cites work: Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming
cites work: Accelerated gradient methods for nonconvex nonlinear and stochastic programming
cites work: Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
cites work: Q3995612
cites work: Q4144736
cites work: Local Linear Convergence of the Alternating Direction Method of Multipliers for Quadratic Programs
cites work: On Full Jacobian Decomposition of the Augmented Lagrangian Method for Separable Convex Programming
cites work: A new inexact alternating directions method for monotone variational inequalities
cites work: Convergence Rate Analysis for the Alternating Direction Method of Multipliers with a Substitution Procedure for Separable Convex Programming
cites work: On the proximal Jacobian decomposition of ALM for multiple-block separable convex minimization problems and its relationship to ADMM
cites work: On the \(O(1/n)\) convergence rate of the Douglas-Rachford alternating direction method
cites work: On non-ergodic convergence rate of Douglas-Rachford alternating direction method of multipliers
cites work: An optimal method for stochastic composite optimization
cites work: An extragradient-based alternating direction method for convex minimization
cites work: On the Global Linear Convergence of the ADMM with MultiBlock Variables
cites work: On the sublinear convergence rate of multi-block ADMM
cites work: Iteration-Complexity of Block-Decomposition Algorithms and the Alternating Direction Method of Multipliers
cites work: Prox-Method with Rate of Convergence O(1/t) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems
cites work: Robust Stochastic Approximation Approach to Stochastic Programming
cites work: Q3967358
cites work: Random gradient-free minimization of convex functions
cites work: Inexact Alternating Direction Methods for Image Recovery
cites work: New method of stochastic approximation type
cites work: Acceleration of Stochastic Approximation by Averaging
cites work: A Stochastic Approximation Method
cites work: A method of aggregate stochastic subgradients with on-line stepsize rules for convex stochastic programming problems
cites work: Asymptotic Distribution of Stochastic Approximation Procedures
cites work: Online Learning and Online Convex Optimization
cites work: Rate of Convergence Analysis of Decomposition Methods Based on the Proximal Method of Multipliers for Convex Minimization
cites work: The solution path of the generalized lasso
cites work: Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions
DOI: 10.1007/S10915-017-0621-6
description: scientific article; zbMATH DE number 6928673