Distributed Optimisation without Central Coordination

本项目将为发现和分析分布式计算系统优化问题的迭代方法奠定数学基础。分布式优化中的大多数方法不是为分布式计算设计的，而是针对特定目的进行调整的。通过建立单调算子分裂的最新进展，该项目期望为分布式系统专门设计的分散优化算法开发一种数学理论。该框架预计将产生一套算法，每个算法都针对特定的网络配置进行定制。该项目将为分布式机器学习应用（如联邦学习）提供显著的好处。

This project will develop the mathematical foundations for discovery and analysis of iterative methods for optimisation problems in distributed computing systems. Most methods in distributed optimisation were not designed for distributed computing, rather they were adapted for purpose post-hoc. By building on recent advances in monotone operator splitting, this project expects to develop a mathematical theory for decentralised optimisation algorithms specially designed for distributed systems. The framework is expected to produce a suite of algorithms, each customised to exploit a specific network configuration. The project will provide significant benefits in distributed machine learning applications such as federated learning.