Abstract
We study a distributed computation model for optimizing a sum of convex objective functions corresponding to multiple agents. For solving this (not necessarily smooth) optimization problem, we consider a subgradient method that is distributed among the agents. The method involves every agent minimizing his/her own objective function while exchanging information locally with other agents in the network over a time-varying topology. We provide convergence results and convergence rate estimates for the subgradient method. Our convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.
Original language | English (US) |
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Pages (from-to) | 48-61 |
Number of pages | 14 |
Journal | IEEE Transactions on Automatic Control |
Volume | 54 |
Issue number | 1 |
DOIs | |
State | Published - 2009 |
Externally published | Yes |
Keywords
- Convex optimization
- Cooperative control
- Distributed optimization
- Multi-agent network
- Subgradient method
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering