稀疏割下的分布平均
Distributed Averaging in the presence of a Sparse Cut
摘要：
我们考虑一个图的平均问题，该图有一个稀疏的切割分离两个内部连通良好的子图。虽然已经有大量的工作致力于分布式平均算法，几乎所有的算法只涉及{ \it凸}更新。在本文中，我们建议{ \it非凸}更新可以导致显着的改善。我们这样做，展示一个分散的算法，图与一个稀疏的削减，使用非凸平均值，并具有平均时间，可以显着小于平均时间的已知分布式算法，如\cite{tsitsiklis, Boyd}。我们使用随机优势来证明这一结果的方式，可能是独立的利益。
英文摘要：
We consider the question of averaging on a graph that has one sparse cut separating two subgraphs that are internally well connected. While there has been a large body of work devoted to algorithms for distributed averaging, nearly all algorithms involve only {\it convex} updates. In this paper, we suggest that {\it non-convex} updates can lead to significant improvements. We do so by exhibiting a decentralized algorithm for graphs with one sparse cut that uses non-convex averages and has an averaging time that can be significantly smaller than the averaging time of known distributed algorithms, such as those of \cite{tsitsiklis, Boyd}. We use stochastic dominance to prove this result in a way that may be of independent interest.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
