GradConsensus: Linearly Convergent Algorithm for Reducing Disagreement in Multi-Agent Optimization
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
2. Enphase Energy, Austin, TX, USA
3. Google, Inc., Mountain View, CA, USA
Funder
Advanced Research Projects Agency-Energy OPEN
Rapidly Viable Sustained Grid
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/6488902/10382160/10273441.pdf?arnumber=10273441
Reference40 articles.
1. On the complexity of decentralized decision making and detection problems
2. Distributed Subgradient Methods for Multi-Agent Optimization
3. Distributed Subgradient Methods for Convex Optimization Over Random Networks
4. Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
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1. A Linear Push-Pull Average Consensus Algorithm for Delay-Prone Networks;2024 European Control Conference (ECC);2024-06-25
2. Noise Resilient Distributed Average Consensus Over Directed Graphs;IEEE Transactions on Signal and Information Processing over Networks;2023
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