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On The Hardness Of Range Assignment Problems

机译:论范围分配的难点

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We investigate the computational hardness of the connectivity, the strong connectivity, and the broadcast type of range assignment problems in R~2 and R~3. We present new reductions for the connectivity problem, which are easily adapted to suit the other two problems. All reductions are considerably simpler than the technically quite involved ones used in earlier works on these problems. Using our constructions, we can for the first time prove NP-hardness of these problems for all real distance-power gradients α > 0 (resp. α > 1 for broadcast) in 2D, and prove APX-hardness of all three problems in 3D for all α > 1. Our reductions yield improved lower bounds on the approximation ratios for all problems where APX-hardness was known before already. In particular, we derive the overall first APX-hardness proof for broadcast. This was an open problem posed in earlier work in this area, as was the question whether (strong) connectivity remains NP-hard for α = 1. In addition, we give the first hardness results for so-called well-spread instances. On the positive side, we prove that two natural greedy algorithms are 2-approximations for (strong) connectivity, and show that the factor 2 is tight in R~2 for α > 1. We also analyze the performance guarantee of the well-known MST-heuristic as a function of the input size.
机译:我们研究了R〜2和R〜3中的连通性,强连通性和范围分配问题的广播类型的计算难度。我们针对连通性问题提出了新的解决方案,可以轻松地使其适应其他两个问题。所有减少都比早期解决这些问题的技术上相当复杂的减少要简单得多。使用我们的构造,我们可以首次证明所有问题的NP硬度都适用于2D的所有实际距离功率梯度α> 0(对于广播而言分别为α> 1),并可以证明3D的所有这三个问题的APX硬度对于所有α> 1的情况。对于以前已知APX硬度的所有问题,我们的减少都可以提高近似率的下限。特别是,我们导出了广播的第一个APX硬度证明。这是该领域早期工作中存在的一个开放性问题,对于(α)= 1,(强)连通性是否仍为NP-硬质的问题也是一个问题。此外,我们给出了所谓的良好扩散实例的第一硬质结果。在积极方面,我们证明了两个自然贪婪算法是(强)连通性的2个近似值,并证明当α> 1时,因子2在R〜2中是紧的。我们还分析了众所周知的性能保证MST启发式作为输入大小的函数。

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