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New bounds on the barycenter heuristic for bipartite graph drawing

机译:二分图绘制的重心启发式的新界限

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The barycenter heuristic is often used to solve the NP-hard two-layer edge crossing minimization problem. It is well known that the barycenter heuristic can give solutions as bad as Ω() times the optimum, where n is the number of nodes in the graph. However, the example used in the proof has many isolated nodes. Makinen [ETCS Bull. 70 (2000) 156-158] conjectured that A better performance ratio is possible if isolated nodes are not present. We show that the performance ratio for the barycenter Heuristic is still Ω() even for connected bipartite graphs. We also prove a tight constant ratio for the barycenter heuristic On bounded-degree graphs. The performance ratio is d-1, where d is the maximum degree of a node in the layer that can be Permuted.
机译:重心启发法通常用于解决NP难的两层边交叉最小化问题。众所周知,重心试探法可提供的质量差是最优值的Ω(/ n)倍,其中n是图中的节点数。但是,证明中使用的示例具有许多隔离的节点。 Makinen [ETCS公牛。 70(2000)156-158]推测,如果不存在孤立的节点,则可以实现更好的性能比。我们显示,即使对于连接的二部图,重心启发式算法的性能比仍为Ω(/ n)。我们还证明了重心启发式On有界图的紧常数比。性能比为d-1,其中d是可置换层中节点的最大程度。

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