A fast distributed proximal-gradient method
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Ozdaglar_A fast.pdf
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Author(s)
Chen, Annie I.
Ozdaglar, Asuman E.
Date Issued
October 2012
Journal
Proceedings of the 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Chen, Annie I., and Asuman Ozdaglar. “A Fast Distributed Proximal-Gradient Method.” 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (October 2012).
Version
Author's final manuscript
Abstract
We present a distributed proximal-gradient method for optimizing the average of convex functions, each of which is the private local objective of an agent in a network with time-varying topology. The local objectives have distinct differentiable components, but they share a common nondifferentiable component, which has a favorable structure suitable for effective computation of the proximal operator. In our method, each agent iteratively updates its estimate of the global minimum by optimizing its local objective function, and exchanging estimates with others via communication in the network. Using Nesterov-type acceleration techniques and multiple communication steps per iteration, we show that this method converges at the rate 1/k (where k is the number of communication rounds between the agents), which is faster than the convergence rate of the existing distributed methods for solving this problem. The superior convergence rate of our method is also verified by numerical experiments.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/Allerton.2012.6483273
