A distributed hierarchical algorithm for multi-cluster constrained optimization

DOI10.1016/j.automatica.2016.11.029zbMath1355.93006OpenAlexW2573876578MaRDI QIDQ510122

Changyun Wen, Guoqi Li, Fanghong Guo, Jian-feng Mao, Yong-Duan Song

Publication date: 16 February 2017

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2016.11.029

zbMATH Keywords

distributed optimizationprojected gradientsequential communicationvirtual agent

Mathematics Subject Classification ID

Hierarchical systems (93A13) Decentralized systems (93A14) Large-scale systems (93A15) Agent technology and artificial intelligence (68T42)

Related Items (9)

A fixed-time convergent algorithm for distributed convex optimization in multi-agent systems ⋮ Distributed optimal resource allocation over strongly connected digraphs: a surplus-based approach ⋮ Static group synchronization of second-order multi-agent systems via pinning control ⋮ Multi-cluster distributed optimization via random sleep strategy ⋮ Hierarchical constrained consensus algorithm over multi-cluster networks ⋮ Distributed Nash equilibrium computation in aggregative games: an event-triggered algorithm ⋮ On exponential stability of switched homogeneous positive systems of degree one ⋮ Parallel alternating direction method of multipliers ⋮ Fast finite-time adaptive stabilization of high-order uncertain nonlinear system with an asymmetric output constraint

Cites Work

• Accelerated gradient methods and dual decomposition in distributed model predictive control
• Distributed gradient algorithm for constrained optimization with application to load sharing in power systems
• Distributed multi-agent optimization subject to nonidentical constraints and communication delays
• Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication
• Incremental Subgradient Methods for Nondifferentiable Optimization
• A Randomized Incremental Subgradient Method for Distributed Optimization in Networked Systems
• 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
• Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization

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