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arXiv 1801.09407cs.DM
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基于频率二次多项式的TSP稀疏图计算方法

A Method to Compute the Sparse Graphs for Traveling Salesman Problem Based on Frequency Quadrilaterals

Yong Wang, Jeffrey Remmel

摘要:

针对旅行商问题(TSP)的稀疏图问题,设计了一种基于频率四边形的迭代算法,以降低TSP算法的计算时间。在每个计算周期,该算法首先计算输入图G(V,E)中具有包含e的N个频率四边形的边e的平均频率\bar{f}(e)。然后,具有低频率的1/3 |E|边缘被消除,以生成具有较少数目的边缘的输出图。该算法可以迭代多次,并以很高的概率保持原来的最佳哈密顿循环。实验结果表明,该算法计算的稀疏图的O(nlog_2n)的边缘包含原来的最佳哈密尔顿圈的TSP的TSPLIB中的大多数实例。算法的时间复杂度为O(Nn^2)。

英文摘要:

In this paper, an iterative algorithm is designed to compute the sparse graphs for traveling salesman problem (TSP) according to the frequency quadrilaterals so that the computation time of the algorithms for TSP will be lowered. At each computation cycle, the algorithm first computes the average frequency \bar{f}(e) of an edge e with N frequency quadrilaterals containing e in the input graph G(V,E). Then the 1/3|E| edges with low frequency are eliminated to generate the output graph with a smaller number of edges. The algorithm can be iterated several times and the original optimal Hamiltonian cycle is preserved with a high probability. The experiments demonstrate the algorithm computes the sparse graphs with the O(nlog_2n) edges containing the original optimal Hamiltonian cycle for most of the TSP instances in the TSPLIB. The computation time of the iterative algorithm is O(Nn^2).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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