论文标题
基于二元性的分布式优化与多群集网络中的通信延迟
Duality-Based Distributed Optimization With Communication Delays in Multi-Cluster Networks
论文作者
论文摘要
在这项工作中，我们考虑在具有多个代理簇的多代理网络中解决分布式优化问题（DOP）。在每个集群中，代理人管理由可能的非平滑成分组成的可分离成本功能，旨在达成集群共同决策的协议。全球成本函数被认为是与群体决策上的仿射耦合约束相关的个人成本函数的总和。为了解决这个问题，双重问题是通过Fenchel共轭概念提出的。然后，基于基于群集的部分和混合共识方案，提出了异步分布式双近端梯度（ASYN-DDPG）算法，仅需要代理与邻居与邻居进行通信延迟进行交流。提供了崇高的收敛结果，并通过在模拟中解决社会福利优化问题来验证所提出的算法的可行性。
In this work, we consider solving a distributed optimization problem (DOP) in a multi-agent network with multiple agent clusters. In each cluster, the agents manage separable cost functions composed of possibly non-smooth components and aim to achieve an agreement on a common decision of the cluster. The global cost function is considered as the sum of the individual cost functions associated with affine coupling constraints on the clusters' decisions. To solve this problem, the dual problem is formulated by the concept of Fenchel conjugate. Then an asynchronous distributed dual proximal gradient (Asyn-DDPG) algorithm is proposed based on a cluster-based partial and mixed consensus protocol, by which the agents are only required to communicate with their neighbors with communication delays. An ergodic convergence result is provided, and the feasibility of the proposed algorithm is verified by solving a social welfare optimization problem in the simulation.
