Decentralized resource allocation via dual consensus ADMM

G. Banjac, F. Rey, P. Goulart and J. Lygeros

in American Control Conference (ACC), Philadelphia, PA, USA, July 2019.
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  author = {G. Banjac and F. Rey and P. Goulart and J. Lygeros},
  title = {Decentralized resource allocation via dual consensus {ADMM}},
  booktitle = {American Control Conference (ACC)},
  year = {2019},
  url = {},
  doi = {10.23919/ACC.2019.8814988}

We consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links. The goal is to minimize the sum of agent-specific convex objective functions, while the agents’ decisions are coupled via a convex conic constraint. We propose two methods based on the alternating direction method of multipliers (ADMM) in which agents exchange information only with their neighbors. Each agent requires only its own data for updating its decision and no global information on the network structure. Moreover, both methods are fully parallelizable and decentralized. Numerical results demonstrate the effectiveness of the proposed methods.