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Brief Announcement: A Note on Hardness of Diameter Approximation

机译:简要公告:关于直径近似硬度的注释

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We revisit the hardness of approximating the diameter of a network. In the CONGEST model, ~Omega(n) rounds are necessary to compute the diameter [Frischknecht et al. SODA'12]. Abboud et al. [DISC 2016] extended this result to sparse graphs and, at a more fine-grained level, showed that, for any integer 1 <= l <= polylog(n) , distinguishing between networks of diameter 4l + 2 and 6l + 1 requires ~Omega(n) rounds. We slightly tighten this result by showing that even distinguishing between diameter 2l + 1 and 3l + 1 requires ~Omega(n) rounds. The reduction of Abboud et al. is inspired by recent conditional lower bounds in the RAM model, where the orthogonal vectors problem plays a pivotal role. In our new lower bound, we make the connection to orthogonal vectors explicit, leading to a conceptually more streamlined exposition. This is suited for teaching both the lower bound in the CONGEST model and the conditional lower bound in the RAM model.
机译:我们重新审视近似网络直径的硬度。在CONGEST模型中,必须使用〜Omega(n)个回合来计算直径[Frischknecht等。 SODA'12]。 Abboud等。 [DISC 2016]将此结果扩展为稀疏图,并且在更细粒度的级别上显示,对于任何1 <= l <= polylog(n)的整数,区分直径为4l + 2和6l +1的网络都需要〜Ω(n)个回合。通过显示甚至区分直径2l +1和3l +1,也需要〜Omega(n)个回合,从而略微收紧了该结果。 Abboud等人的减少。受到最近RAM模型中条件下界的启发,其中正交向量问题起着关键作用。在新的下限中,我们使与正交向量的连接更明确,从而在概念上更简化了说明。这既适合于教授CONGEST模型中的下限,也可以用于教导RAM模型中的条件下限。

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