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The K-partitioning problem: Formulations and branch-and-cut

机译:k分区问题:配方和分支和切割

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The K-partitioning problem consists in partitioning the nodes of a complete graph G = (V, E) with weights on the edges in exactly K clusters such that the sum of the weights of the edges inside the clusters is minimized. For this problem, we propose two node-cluster formulations adapted from the literature on similar problems as well as two edge-representative formulations. We introduced the first edge-representative formulation in a previous work while the second is obtained by adding an additional set of edge variables. We compare the structure of the polytopes of the two edge-representative formulations and identify a new family of facet-defining inequalities. The quality of the linear relaxation and the resolution times of the four formulations are compared on various data sets. We provide bounds on the relaxation values of the node-cluster formulations which may account for their low performances. Finally, we propose a branch-and-cut strategy, based on the edge-representative formulations, which performs even better.
机译:k分区问题在于将具有恰好k簇的边缘上的重量分区的完整图G =(v,e)的节点,使得集群内部的边缘的权重的总和最小化。对于这个问题,我们提出了一种从文献中的两个节点簇配方,就像类似问题以及两个边缘代表性的配方。我们在先前的工作中引入了第一边缘代表性的制定,而第二组是通过添加附加的边缘变量而获得的。我们比较两个边缘代表制剂的多种子区的结构,并识别新的面部定义不平等系列。在各种数据集上比较线性松弛的质量和四种配方的分辨率。我们在节点集群配方的放松值上提供限制,这可能会考虑其低性能。最后,我们提出了一种基于边缘代表制剂的分支和裁减策略,其表现得更好。

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