Adaptive Penalty-Based Distributed Stochastic Convex Optimization

@article{Towfic2014AdaptivePD,
  title={Adaptive Penalty-Based Distributed Stochastic Convex Optimization},
  author={Zaid J. Towfic and Ali H. Sayed},
  journal={IEEE Transactions on Signal Processing},
  year={2014},
  volume={62},
  pages={3924-3938}
}
In this work, we study the task of distributed optimization over a network of learners in which each learner possesses a convex cost function, a set of affine equality constraints, and a set of convex inequality constraints. We propose a fully distributed adaptive diffusion algorithm based on penalty methods that allows the network to cooperatively optimize the global cost function, which is defined as the sum of the individual costs over the network, subject to all constraints. We show that… CONTINUE READING
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