A lower bound for distributed averaging algorithms on the line graph
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We derive lower bounds on the convergence speed of a widely used class of distributed averaging algorithms. In particular, we prove that any distributed averaging algorithm whose state consists of a single real number and whose (possibly nonlinear) update function satisfies a natural smoothness condition has a worst case running time of at least on the order of n[superscript 2] on a line network of n nodes. Our results suggest that increased memory or expansion of the state space is crucial for improving the running times of distributed averaging algorithms.
Date issued
2011-02Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Proceedings of the 49th IEEE Conference on Decision and Control (CDC), 2010
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Tsitsiklis, John N. and Alexander Olshevsky. "A lower bound for distributed averaging algorithms on the line graph." Proceedings of the 2010 IEEE Conference on Decision and Control (CDC): 4523-4528. © 2010 IEEE.
Version: Final published version
ISBN
978-1-4244-7745-6
ISSN
0743-1546