Examination of Average Consensus with Maximum-degree Weights and Metropolis-Hastings Algorithm in Regular Bipartite Graphs

Kenyeres, Martin; Kenyeres, Jozef

doi:10.1109/iceta57911.2022.9974744

Cited by 1 publication

(2 citation statements)

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“…As identified in our recent paper [52], MD and LD do not converge in d-regular bipartite graphs as the third convergence condition is broken in this topology. However, as shown in [53], the inner states of both algorithms oscillate between two values instead of approaching infinitely high values (as shown in [52], the divergence usually results in the second case). Despite the mentioned divergence, it is, thus, meaningful to also include these two algorithms in our analyses since the consensus can theoretically be achieved due to the mentioned oscillation and the application of a stopping criterion.…”

Section: Our Contributionmentioning

confidence: 97%

“…In addition, a comprehensive spectral analysis justifying the divergence of the algorithm is provided in that paper. In [53], a comprehensive analysis of MD and GMH with the optimal mixing parameter (LD) is presented. In the paper, it is shown how the inner states oscillate in graphs of various connectivities, and it is further identified that the mean square error of the estimates cannot drop below a certain threshold value.…”

Section: Distributed Consensus Algorithms In D-regular Bipartite Graphsmentioning

confidence: 99%

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Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study

Kenyeres

2023

Future Internet

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Consensus-based data aggregation in d-regular bipartite graphs poses a challenging task for the scientific community since some of these algorithms diverge in this critical graph topology. Nevertheless, one can see a lack of scientific studies dealing with this topic in the literature. Motivated by our recent research concerned with this issue, we provide a comparative study of frequently applied consensus algorithms for distributed averaging in d-regular bipartite graphs in this paper. More specifically, we examine the performance of these algorithms with bounded execution in this topology in order to identify which algorithm can achieve the consensus despite no reconfiguration and find the best-performing algorithm in these graphs. In the experimental part, we apply the number of iterations required for consensus to evaluate the performance of the algorithms in randomly generated regular bipartite graphs with various connectivities and for three configurations of the applied stopping criterion, allowing us to identify the optimal distributed consensus algorithm for this graph topology. Moreover, the obtained experimental results presented in this paper are compared to other scientific manuscripts where the analyzed algorithms are examined in non-regular non-bipartite topologies.

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Section: Our Contributionmentioning

confidence: 97%

Section: Distributed Consensus Algorithms In D-regular Bipartite Graphsmentioning

confidence: 99%

Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study

Kenyeres

2023

Future Internet

Self Cite

View full text Add to dashboard Cite

show abstract

Examination of Average Consensus with Maximum-degree Weights and Metropolis-Hastings Algorithm in Regular Bipartite Graphs

Cited by 1 publication

References 24 publications

Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study

Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study

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