This paper presents a decentralized algorithm for non-convex optimization over tree-structured networks. We assume that each node of this network can solve small-scale optimization problems and communicate approximate value functions with its neighbors based on a novel multi-sweep communication protocol. In contrast to existing parallelizable optimization algorithms for non-convex optimization, the nodes of the network are neither synchronized nor assign any central entity. None of the nodes needs to know the whole topology of the network, but all nodes know that the network is tree-structured. We discuss conditions under which locally quadratic convergence rates can be achieved. The method is illustrated by running the decentralized asynchronous multi-sweep protocol on a radial AC power network case study.

本文提出了一种用于树状网络上非凸优化的分散算法。我们假设该网络的每个节点都可以解决小规模的优化问题，并基于一种新颖的多扫描通信协议与其邻居通信近似值函数。与用于非凸优化的现有可并行优化算法相比，网络的节点既不同步也不分配任何中央实体。没有节点需要知道网络的整个拓扑，但是所有节点都知道网络是树状结构的。我们讨论了可以实现局部二次收敛速度的条件。通过在径向交流电网案例研究中运行分散式异步多扫描协议来说明该方法。