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arXiv · 1003.5941

A lower bound for distributed averaging algorithms

Alex Olshevsky·John N. Tsitsiklis
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Abstract

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 $n^2$ on a 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.

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Alex Olshevsky, John N. Tsitsiklis. 2010-03-30. A lower bound for distributed averaging algorithms. https://arxiv.org/abs/1003.5941

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