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Distributed Event-Triggered Subgradient Method for Convex Optimization with a Common Constraint Set

机译:具有公共约束集的凸优化问题的分布式事件触发次梯度方法

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This paper proposes a discrete-time event-triggered subgradient algorithm to minimize a sum of local cost functions under a common constraint set. In the proposed method, each agent sends its state to a neighbor agent based on the edge-based event-triggering mechanism. We consider a consensus-based subgradient algorithm with a projection to the constraint set so that states of all agents asymptotically converge to an optimal solution. Simulation results confirm that the proposed event-triggered subgradient algorithm can effectively reduce the number of communications compared with the time-triggered algorithm.
机译:本文提出了一种离散事件触发的次梯度算法,以最小化公共约束集下的局部成本函数之和。在提出的方法中,每个代理都基于基于边缘的事件触发机制将其状态发送给邻居代理。我们考虑一种基于共识的次梯度算法,该算法具有对约束集的投影,以便所有主体的状态渐近收敛至最优解。仿真结果表明,与时间触发算法相比,该事件触发子梯度算法可以有效减少通信量。

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