Distributed Gradient Methods with Variable Number of Working Nodes

@article{Jakoveti2016DistributedGM,
title={Distributed Gradient Methods with Variable Number of Working Nodes},
author={Du{\vs}an Jakoveti{\'c} and Dragana Bajovi{\'c} and Natasa Krejic and Nata{\vs}a Krklec Jerinki{\'c}},
journal={IEEE Transactions on Signal Processing},
year={2016},
volume={64},
pages={4080-4095}
}
We consider distributed optimization where N nodes in a connected network minimize the sum of their local costs subject to a common constraint set. We propose a distributed projected gradient method where each node, at each iteration k, performs an update (is active) with probability pk, and stays idle (is inactive) with probability 1-pk. Whenever active, each node performs an update by weight-averaging its solution estimate with the estimates of its active neighbors, taking a negative gradient… Expand
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