FAST ASYNCHRONOUS DECENTRALIZED OPTIMIZATION: ALLOWING MULTIPLE MASTERS
Meng Ma, Jineng Ren, Georgios B. Giannakis, Jarvis Haupt · 2018
The present paper deals with asynchronous decentralized optimization over networks. Pertinent algorithms are either centralized relying on a specific topology, where a single master connects all workers, or decentralized devoid of any master by only exchanging information between single-hop neighbors. The present work bridges the gap of existing approaches with a novel hybrid framework that is capable of accommodating multiple masters. Moreover, it enables considerable acceleration of decentralized approaches without physically deploying masters, thus making it possible to achieve a desirable tradeoff between convergence and communication/computation complexity by tuning the configuration. Numerical tests showcase advantages over decentralized counterparts.