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Deeper Local Search for Better Approximation on Maximum Internal Spanning Trees

机译:更深入的本地搜索,以在最大内部生成树上获得更好的近似值

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Spanning tree has been fundamental in the research of graph algorithms. In this paper, we study the optimization problem MaxIST, which maximizes the number of internal nodes in a spanning tree of a given graph, and is a generalization of the famous Hamiltonian-Path problem. We present a polynomial-time approximation algorithm based on a deep local search strategy, identify combinatorial structures that support thorough analysis on the spanning trees resulted from such deep local search strategies, and prove that our algorithm has an approximation ratio 1.5 for the MaxIST problem, improving the previous best approximation algorithm of ratio 5/3 for the problem.
机译:生成树一直是图算法研究的基础。在本文中,我们研究了优化问题MaxIST,它使给定图的生成树中的内部节点数最大化,并且是对著名的汉密尔顿路径问题的推广。我们提出了一种基于深度局部搜索策略的多项式时间近似算法,确定了支持对此类深度局部搜索策略产生的生成树进行深入分析的组合结构,并证明了我们的算法对MaxIST问题的近似率为1.5,针对该问题改进了比率5/3的先前最佳近似算法。

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