论文标题
随机网络加速的多代理优化方法
Accelerated Multi-Agent Optimization Method over Stochastic Networks
论文作者
论文摘要
我们提出了一种分布式方法来解决具有强烈凸成本函数和平等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，并在随机时间变化的通信网络上起作用。我们考虑了Nesterov方法的标准假设，并表明预期双值的序列以$ \ Mathcal {O}（O}（1/K^2）$的速率收敛到最佳值。此外，我们提供了一个模拟研究，以解决众所周知的基准案例解决最佳功率流问题。
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.
