Results for estimating the convergence rate of nonstationary distributed consensus algorithms are provided, on the basis of qualitative (mainly topological) as well as basic quantitative information (lower-bounds on the matrix entries). The results appear to be tight in a number of instances and are illustrated through simple as well as more sophisticated examples. The main idea is to follow propagation of information along certain spanning-trees which arise in the communication graph.
No takes yet. Share an insight, caveat, or question.
Angeli et al. (2009) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: