Optimal Memory Scheme for Accelerated Consensus Over Multi-Agent Networks [article]

Jiahao Dai, Jing-Wen Yi, Li Chai
<span title="2021-12-14">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The consensus over multi-agent networks can be accelerated by introducing agent's memory to the control protocol. In this paper, a more general protocol with the node memory and the state deviation memory is designed. We aim to provide the optimal memory scheme to accelerate consensus. The contributions of this paper are three: (i) For the one-tap memory scheme, we demonstrate that the state deviation memory is useless for the optimal convergence. (ii) In the worst case, we prove that it is a
more &raquo; ... in to add any tap of the state deviation memory, and the one-tap node memory is sufficient to achieve the optimal convergence. (iii) We show that the two-tap state deviation memory is effective on some special networks, such as star networks. Numerical examples are listed to illustrate the validity and correctness of the obtained results.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.07108v1">arXiv:2112.07108v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iwob4bj32fh7lgp2cmkcd46vwq">fatcat:iwob4bj32fh7lgp2cmkcd46vwq</a> </span>
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