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An Event-Based Delayed Projection Row-Stochastic Method for Distributed Constrained Optimization Over Time-Varying Graphs
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DOI：10.1109/TSMC.2024.3458972.png)
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Abstract
En 中文
This article investigates the distributed constrained optimization problem with event-triggered communication over time-varying weight-unbalanced directed graphs. A more generalized network model is considered where the communication topology may be variable and unbalanced over time, the information flows across agents are subject to time-varying communication delays, and agents are not required to know their out-degree information accurately. To address the above challenges, we propose a novel discrete-time distributed event-triggered delay subgradient algorithm. To facilitate convergence analysis, a consensus-only virtual agent technique is employed, dynamically adjusting its state (active or asleep) to ensure a delay-free information flow among agents. Additionally, an augmentation approach is proposed to ensure that the augmented time-varying weight matrix is row-stochastic. It is shown that the agents' local decision variables converge to the same optimal solution, in the case of reasonable communication delays and event-triggering thresholds. Numerical examples show the efficiency of the proposed algorithm.
Keywords:
Optimization
Delays
Linear programming
Topology
Heuristic algorithms
Directed graphs
Real-time systems
Communication delays
constrained optimization
event-triggered communication
projected subgradient algorithm
time-varying unbalanced directed graph
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W