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This simplification relates the minimum-length scheduling problem to the problem of obtaining a maximal matching in a non-bipartite graph [11].
From previous work, it is known that the minimal number of nodes required is related to the maximal matching in the network, which can be computed with a reasonable complexity.
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The MAM algorithm has to be only slightly modified to determine an MDM: instead of preferring to include edges with a lower assortative weight (to maximize the assortative index of the maximal matching), we need to include the uncovered edge with the largest assortative weight (to minimize the assortative index of the maximal matching) in each iteration.
Let M be a maximal matching in G.
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Recently, Khan et al.[ 18] proposed using sparse suffix arrays for finding maximal matches in large sequence data along with an application on short read alignment.
In this paper, we investigate how the class of maximal matching algorithms deployed in switches with a speedup of less than two can be modified to take into account the varying packet sizes.
We primarily use Gephi for visualization; the implementations of the maximal matching algorithms proposed in this paper have been done from scratch through object-oriented programming in Java.
The maximal matching algorithms developed in this paper take the approach of preferring to include edges that cover a smaller number of adjacent edges so that the number of independent edges determined could be as large as possible.
To clearly Solidthe interference in Figure 7(d), we color a vertex in black if it is intedgesed by the actine edge in the upper wheel, and in gray if it is interfered by the active edge in the lower wheel.
At this stage, we have found a maximal matching of the vertices in the graph.
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