Distributed stochastic approximation for constrained and unconstrained optimization

@article{Bianchi2011DistributedSA,
  title={Distributed stochastic approximation for constrained and unconstrained optimization},
  author={Pascal Bianchi and J{\'e}r{\'e}mie Jakubowicz},
  journal={ArXiv},
  year={2011},
  volume={abs/1104.2773}
}
  • Pascal Bianchi, Jérémie Jakubowicz
  • Published 2011
  • Computer Science, Mathematics
  • ArXiv
  • In this paper, we analyze the convergence of a distributed Robbins-Monro algorithm for both constrained and unconstrained optimization in multi-agent systems. The algorithm searches local minima of a (nonconvex) objective function which is supposed to coincide with a sum of local utility functions of the agents. The algorithm under study consists of two steps: a local stochastic gradient descent at each agent and a gossip step that drives the network of agents to a consensus. It is proved that… CONTINUE READING

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