论文标题
分散式凸优化超过时变图形：调查
Decentralized convex optimization over time-varying graphs: a survey
论文作者
论文摘要
在分布式学习，信号处理和各种分布式控制问题中，分散优化在时间变化的网络上具有广泛的应用。分布式系统的代理本地持有优化目标，并可以通过不时变化的网络与直接邻居进行通信。在本文中，我们调查了最先进的结果，并描述了超过时变图的优化技术。我们还概述了该领域的开放问题，并为将来的工作提出了假设和方向。
Decentralized optimization over time-varying networks has a wide range of applications in distributed learning, signal processing and various distributed control problems. The agents of the distributed system locally hold optimization objectives and can communicate to their immediate neighbors over a network that changes from time to time. In this paper, we survey state-of-the-art results and describe the techniques for optimization over time-varying graphs. We also give an overview of open questions in the field and formulate hypotheses and directions for future work.
