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An Event-Triggered Approach for Gradient Tracking in Consensus-Based Distributed Optimization
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DOI：10.1109/TNSE.2021.3122927.png)
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摘要
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This paper is concerned with a communication-efficient algorithm update scheme for solving distributed convex optimization problems by introducing a distributed event-triggered approach. Compared with real-time consensus-based distributed optimization algorithms in the literature, this paper focuses on extending the real-time gradient tracking scheme and proposes a novel distributed event-triggering condition to reduce the frequency of information exchange between agents in a network. The proposed event-triggered approach for consensus-based distributed optimization algorithms not only avoids the real-time consecutive communication and the coordinated computation between agents but reduces the computation load of algorithm execution. Furthermore, the proposed event-triggering condition depends on only local states from neighbors at only their event times and does not require a global and homogeneous sampling period. In addition, this paper analytically shows that the proposed consensus-based distributed optimization algorithm based on an event-triggered approach can also converge to the exact optimal solution with a linear convergence rate even if the real-time consecutive communication between agents is replaced with sporadic communication.
Keyword:
Optimization
Real-time systems
Convergence
Machine learning algorithms
Symmetric matrices
Costs
Information exchange
Consensus-based distributed optimization
gradient tracking
event-triggered approach
multi-agent networks
期刊
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IF:
7.9
论文数:
2.6K
被引数:
10.0K
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