论文标题
边缘共识的图理论优化
Graph-theoretic optimization for edge consensus
论文作者
论文摘要
我们考虑在Edge Laplacian所描述的此类共识网络的最小表示下，优化了优化$ \ MATHCAL {H} _2 $加权，时间缩放的共识网络的规范。我们表明，贪婪的算法可用于找到最小值-UM \ Mathcal {H} _2 $ norm Spanning树，以及如何选择边缘以优化$ \ MATHCAL {H} _2 $ NORD时，将边缘添加回到跨越树时。在Edge共识的情况下，考虑图中的所有边缘，我们表明，图中慢节点之间的边缘提供了$ \ Mathcal {H} _2 $ NORM的最小增加。
We consider network structures that optimize the $\mathcal{H}_2$ norm of weighted, time scaled consensus networks, under a minimal representation of such consensus networks described by the edge Laplacian. We show that a greedy algorithm can be used to find the minimum-$\mathcal{H}_2$ norm spanning tree, as well as how to choose edges to optimize the $\mathcal{H}_2$ norm when edges are added back to a spanning tree. In the case of edge consensus with a measurement model considering all edges in the graph, we show that adding edges between slow nodes in the graph provides the smallest increase in the $\mathcal{H}_2$ norm.
