通信(或查询)复杂性与分区之间几乎最佳的分离

Robin Kothari
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引用次数: 27

摘要

我们展示了确定性通信复杂度和分区数的对数之间的近似二次分离,这是本质上最优的。这在最近Goos、Pitassi和Watson的1.5功率分离(FOCS 2015)的基础上得到了改进。在查询复杂度方面,我们在确定性(甚至是随机化)查询复杂度和子立方体分区复杂度之间建立了接近二次的分离,这也是最优的。我们还在量子查询复杂度和子立方体分区复杂度之间建立了接近1.5次幂的分离,这是两个度量之间的第一个超线性分离。最后,我们展示了量子查询复杂度和单侧子立方体分区复杂度之间的二次分离。我们的查询复杂度分离使用了Aaronson、Ben-David和Kothari最近的小抄表框架。我们的查询函数是通过交替的函数组合和小抄构造来分阶段构建的。通信复杂度分离是从查询分离“提升”到通信复杂度分离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nearly optimal separations between communication (or query) complexity and partitions
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 Goos, 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.
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