A Proximal Dual Consensus ADMM Method for Multi-Agent Constrained Optimization

@article{Chang2016APD,
  title={A Proximal Dual Consensus ADMM Method for Multi-Agent Constrained Optimization},
  author={Tsung-Hui Chang},
  journal={IEEE Transactions on Signal Processing},
  year={2016},
  volume={64},
  pages={3719-3734}
}
This paper considers a convex optimization problem with a globally coupled linear equality constraint and local polyhedron constraints and develops efficient distributed optimization methods. The considered problem has many engineering applications. Due to the polyhedron constraints, agents in the existing methods have to deal with polyhedron constrained subproblems at each iteration. One of the key challenges is that projection onto a polyhedron set is not trivial, which prohibits the agents… CONTINUE READING
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