高维塔尔斯基定点的随机下界

Simina Brânzei, Reed Phillips, Nicholas Recker
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引用次数: 0

摘要

克纳斯特-塔尔斯基定理(Knaster-Tarski theorem)又称塔尔斯基定理,它保证了定义在完整网格上的每个单调函数都有一个定点。我们分析了在边长为 $n$ 的 $k$ 维网格上根据 $\leq$ 关系找到这样一个定点的查询复杂度:\具体来说,有一个未知的单调函数 $f:{0,1,\ldots, n-1\}^k\to \{0,1,\ldots,n-1\}^k$,算法必须查询一个顶点 $v$ 来学习 $f(v)$。我们的主要结果是为边长为 $n$ 的 $k$ 维网格提供了一个随机下限:$Omega\left( k + \frac{k\cdot \{log{n}}\{log{k}} \right)$,当 $k$ 相对于 $n$ 较大时,这个下限在高维中几乎是最优的。作为推论,我们将布尔超立方$\{0,1\}^k$上的随机和确定性查询复杂度表征为$\Theta(k)$。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Randomized Lower Bounds for Tarski Fixed Points in High Dimensions
The Knaster-Tarski theorem, also known as Tarski's theorem, guarantees that every monotone function defined on a complete lattice has a fixed point. We analyze the query complexity of finding such a fixed point on the $k$-dimensional grid of side length $n$ under the $\leq$ relation. Specifically, there is an unknown monotone function $f: \{0,1,\ldots, n-1\}^k \to \{0,1,\ldots, n-1\}^k$ and an algorithm must query a vertex $v$ to learn $f(v)$. Our main result is a randomized lower bound of $\Omega\left( k + \frac{k \cdot \log{n}}{\log{k}} \right)$ for the $k$-dimensional grid of side length $n$, which is nearly optimal in high dimensions when $k$ is large relative to $n$. As a corollary, we characterize the randomized and deterministic query complexity on the Boolean hypercube $\{0,1\}^k$ as $\Theta(k)$.
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