DISTRIBUTED PROXIMAL-GRADIENT METHOD FOR CONVEX OPTIMIZATION WITH INEQUALITY CONSTRAINTS

DOI10.1017/S1446181114000273zbMath1315.90029MaRDI QIDQ5174338

Qiang Long, Xiangyu Wang, Changzhi Wu, Jueyou Li, Zhi-You Wu

Publication date: 17 February 2015

Published in: The ANZIAM Journal (Search for Journal in Brave)

zbMATH Keywords

convex optimizationdistributed algorithmexact penalty function methodproximal-gradient method

Mathematics Subject Classification ID

Convex programming (90C25)

Related Items (5)

A fast dual proximal-gradient method for separable convex optimization with linear coupled constraints ⋮ Stochastic mirror descent method for distributed multi-agent optimization ⋮ Incremental gradient-free method for nonsmooth distributed optimization ⋮ Distributed optimization methods for nonconvex problems with inequality constraints over time-varying networks ⋮ An inexact dual fast gradient-projection method for separable convex optimization with linear coupled constraints

Cites Work

• Unnamed Item
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Gradient methods for minimizing composite functions
• Min-max optimal control of linear systems with uncertainty and terminal state constraints
• A new exact penalty function method for continuous inequality constrained optimization problems
• Incremental proximal methods for large scale convex optimization
• Distributed average consensus with least-mean-square deviation
• Subgradient methods for saddle-point problems
• A proximal alternating direction method for \(\ell_{2,1}\)-norm least squares problem in multi-task feature learning
• A penalty function algorithm with objective parameters for nonlinear mathematical programming
• Relaxed augmented Lagrangian-based proximal point algorithms for convex optimization with linear constraints
• On an exact penalty function method for semi-infinite programming problems
• Fast linear iterations for distributed averaging
• Minimizing control variation in nonlinear optimal control
• Incremental Subgradient Methods for Nondifferentiable Optimization
• Necessary and sufficient conditions for a penalty method to be exact
• Distributed Subgradient Methods for Multi-Agent Optimization
• Constrained Consensus and Optimization in Multi-Agent Networks
• On Distributed Convex Optimization Under Inequality and Equality Constraints
• Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling

This page was built for publication: DISTRIBUTED PROXIMAL-GRADIENT METHOD FOR CONVEX OPTIMIZATION WITH INEQUALITY CONSTRAINTS