Weighted ADMM for Fast Decentralized Network Optimization

@article{Ling2016WeightedAF,
  title={Weighted ADMM for Fast Decentralized Network Optimization},
  author={Qing Ling and Yaohua Liu and Wei Shi and Zhi Tian},
  journal={IEEE Transactions on Signal Processing},
  year={2016},
  volume={64},
  pages={5930-5942}
}
In this paper, we propose a weighted alternating direction method of multipliers (ADMM) to solve the consensus optimization problem over a decentralized network. In the proposed algorithm, every node holds its local objective function, exchanges its current iterate with a subset of neighbors, carries on local computation, and eventually reaches an optimal and consensual solution that minimizes the summation of the local objective functions. Compared with the conventional ADMM that is popular in… CONTINUE READING

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