Decentralized convex optimization over time-varying graphs: a survey

Understand

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.