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Near-Optimal Lower Bounds on Quantifier Depth and Weisfeiler–Leman Refinement Steps

机译:量词深度和Weisfeiler-Leman精炼步骤的接近最佳下界

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We prove near-optimal trade-offs for quantifier depth versus number of variables in first-order logic by exhibiting pairs of n-element structures that can be distinguished by a k-variable first-order sentence but where every such sentence requires quantifier depth at least nΩ(k/ log k). Our trade-offs also apply to first-order counting logic, and by the known connection to the k-dimensional Weisfeiler-Leman algorithm imply near-optimal lower bounds on the number of refinement iterations. A key component in our proof is the hardness condensation technique recently introduced by [Razborov '16] in the context of proof complexity. We apply this method to reduce the domain size of relational structures while maintaining the quantifier depth required to distinguish them.
机译:我们通过展示可以用k变量一阶句子区分的n元素结构对来证明量词深度与一阶逻辑中变量数量的接近最佳的权衡,但是每个这样的句子都需要量词深度为至少n Ω(k / log k) 。我们的权衡也适用于一阶计数逻辑,并且通过与k维Weisfeiler-Leman算法的已知连接,隐含了细化迭代次数的接近最佳下限。我们证明中的关键组成部分是[Razborov '16]最近在证明复杂性的背景下引入的硬度浓缩技术。我们应用此方法来减小关系结构的域大小,同时保持区分它们所需的量词深度。

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