We propose a distributed method to solve a multi-agent optimization problem with strongly convex cost function and equality coupling constraints. The method is based on Nesterov's accelerated gradient approach and works over stochastically time-varying communication networks. We consider the standard assumptions of Nesterov's method and show that the sequence of the expected dual values converge toward the optimal value with the rate of $\mathcal{O}(1/k^2)$. Furthermore, we provide a simulation study of solving an optimal power flow problem with a well-known benchmark case.

中文翻译：

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随机网络上的加速多智能体优化方法

我们提出了一种分布式方法来解决具有强凸成本函数和等式耦合约束的多代理优化问题。该方法基于 Nesterov 的加速梯度方法，适用于随机时变的通信网络。我们考虑 Nesterov 方法的标准假设，并表明期望对偶值的序列以 $\mathcal{O}(1/k^2)$ 的速率向最优值收敛。此外，我们提供了一个用众所周知的基准案例解决最优潮流问题的模拟研究。