广义大都市加速权下平均一致性的收敛性

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI:10.1109/ICASSP.2014.6854643

V. Schwarz, Gabor Hannak, G. Matz

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引用次数: 24

摘要

平均一致性是一种被广泛研究的分布式平均方法。平均一致性的收敛性取决于平均权值。常用重量设计的例子是Metropolis-Hastings (MH)重量和恒重。在本文中，我们提供了一个完整的收敛分析广义MH权重设计，包括传统的MH作为特殊情况。更具体地说，我们给出了收敛的充要条件。一个主要结论是，除非底层网络是正则二部图，否则具有MH权值的AC保证收敛。

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On the convergence of average consensus with generalized metropolis-hasting weights

Average consensus is a well-studied method for distributed averaging. The convergence properties of average consensus depend on the averaging weights. Examples for commonly used weight designs are Metropolis-Hastings (MH) weights and constant weights. In this paper, we provide a complete convergence analysis for a generalized MH weight design that encompasses conventional MH as special case. More specifically, we formulate sufficient and necessary conditions for convergence. A main conclusion is that AC with MH weights is guaranteed to converge unless the underlying network is a regular bipartite graph.

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2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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