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Nearly Optimal Separations Between Communication (or Query) Complexity and Partitions

机译:通信(或查询)复杂性和分区之间几乎最佳分离

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We show a nearly quadratic separation between deterministic communication complexity and the logarithm of the partition number, which is essentially optimal. This improves upon a recent power 1.5 separation of G??s, Pitassi, and Watson (FOCS 2015). In query complexity, we establish a nearly quadratic separation between deterministic (and even randomized) query complexity and subcube partition complexity, which is also essentially optimal. We also establish a nearly power 1.5 separation between quantum query complexity and subcube partition complexity, the first superlinear separation between the two measures. Lastly, we show a quadratic separation between quantum query complexity and one-sided subcube partition complexity. Our query complexity separations use the recent cheat sheet framework of Aaronson, Ben-David, and Kothari. Our query functions are built up in stages by alternating function composition with the cheat sheet construction. The communication complexity separation follows from "lifting" the query separation to communication complexity.
机译:我们表明确定性通信的复杂性和分区号,这基本上是最佳的对数之间的近二次分离。这改善了在G的??的,Pitassi,和Watson(FOC​​S 2015)最近的功率1.5分离。在查询的复杂性,我们建立了确定性的(甚至是随机),查询的复杂性和子多维数据集分区的复杂性,这也基本上是最优之间的接近二次分离。我们还建立量子查询的复杂性和子多维数据集分区的复杂性,这两项措施之间的第一超线性分离之间的近1.5电源分离。最后,我们显示量子查询的复杂度和片面子立方体分区复杂度之间的二次分离。我们查询的复杂分离使用阿伦森,本 - 大卫,科塔里近期小抄框架。我们的查询功能由交替的函数组合物与备忘单结构建立在阶段。从“提升”,查询分离到复杂的通信的通信复杂的分离如下所述。

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