arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2102.07342math.COcs.DM
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随机超图偏差的相变

The Phase Transition of Discrepancy in Random Hypergraphs

Calum MacRury, Tomáš Masařík, Leilani Pai, Xavier Pérez-Giménez

AI总结:

受 Beck-Fiala 猜想启发,本文研究了边独立与边相关两类随机超图模型的偏差问题,通过部分着色引理证明了偏差随边数变化从 $\Theta(\sqrt{d})$ 到 $o(\sqrt{d})$ 的相变,并给出了渐近匹配的上下界。

摘要:

受 Beck-Fiala 猜想的启发,我们研究了在具有 $n$ 个顶点和 $m$ 条边的两个相关随机超图模型中的偏差问题。在第一个(边独立)模型中,随机超图 $H_1$ 的构造方式是固定参数 $p$,并允许 $n$ 个顶点中的每一个以概率 $p$ 独立地加入 $m$ 条边中的每一条。在 $pn \rightarrow \infty$ 且 $pm \rightarrow \infty$ 的参数范围内,我们证明了高概率下(w.h.p.),当 $m = O(n)$ 时 $H_1$ 的偏差至少为 $\Omega(2^{-n/m} \sqrt{pn})$;当 $m \gg n$ 时至少为 $\Omega(\sqrt{pn \log\gamma})$,其中 $\gamma= \min\{ m/n, pn\}$。在第二个(边相关)模型中,$d$ 是固定的,$H_2$ 的每个顶点独立且均匀随机地恰好加入 $d$ 条边。我们通过推广用于边独立模型(令 $p=d/m$)的技术,为该模型获得了类似的结果。即,对于 $d \rightarrow \infty$ 且 $dn/m \rightarrow \infty$,我们证明高概率下当 $m = O(n)$ 时 $H_{2}$ 的偏差至少为 $\Omega(2^{-n/m} \sqrt{dn/m})$;当 $m \gg n$ 时至少为 $\Omega(\sqrt{(dn/m) \log\gamma})$,其中 $\gamma=\min\{m/n, dn/m\}$。此外,在 $m \gg n$ 的稠密状态下,我们获得了两个模型中偏差的渐近匹配上界(当 $p=d/m$ 时)。具体而言,我们应用 Lovett 和 Meka 的部分着色引理,证明高概率下 $H_{1}$ 和 $H_{2}$ 各自的偏差为 $O( \sqrt{dn/m} \log(m/n))$,前提是 $d \rightarrow \infty$、$d n/m \rightarrow \infty$ 且 $m \gg n$。该结果是算法性的,结合 Bansal 和 Meka 的工作,刻画了随着 $m$ 从 $m=\Theta(n)$ 变为 $m \gg n$,每个随机超图模型的偏差如何从 $\Theta(\sqrt{d})$ 相变至 $o(\sqrt{d})$。

英文摘要:

Motivated by the Beck-Fiala conjecture, we study the discrepancy problem in two related models of random hypergraphs on $n$ vertices and $m$ edges. In the first (edge-independent) model, a random hypergraph $H_1$ is constructed by fixing a parameter $p$ and allowing each of the $n$ vertices to join each of the $m$ edges independently with probability $p$. In the parameter range in which $pn \rightarrow \infty$ and $pm \rightarrow \infty$, we show that with high probability (w.h.p.) $H_1$ has discrepancy at least $Ω(2^{-n/m} \sqrt{pn})$ when $m = O(n)$, and at least $Ω(\sqrt{pn \logγ})$ when $m \gg n$, where $γ= \min\{ m/n, pn\}$. In the second (edge-dependent) model, $d$ is fixed and each vertex of $H_2$ independently joins exactly $d$ edges uniformly at random. We obtain analogous results for this model by generalizing the techniques used for the edge-independent model with $p=d/m$. Namely, for $d \rightarrow \infty$ and $dn/m \rightarrow \infty$, we prove that w.h.p. $H_{2}$ has discrepancy at least $Ω(2^{-n/m} \sqrt{dn/m})$ when $m = O(n)$, and at least $Ω(\sqrt{(dn/m) \logγ})$ when $m \gg n$, where $γ=\min\{m/n, dn/m\}$. Furthermore, we obtain nearly matching asymptotic upper bounds on the discrepancy in both models (when $p=d/m$), in the dense regime of $m \gg n$. Specifically, we apply the partial colouring lemma of Lovett and Meka to show that w.h.p. $H_{1}$ and $H_{2}$ each have discrepancy $O( \sqrt{dn/m} \log(m/n))$, provided $d \rightarrow \infty$, $d n/m \rightarrow \infty$ and $m \gg n$. This result is algorithmic, and together with the work of Bansal and Meka characterizes how the discrepancy of each random hypergraph model transitions from $Θ(\sqrt{d})$ to $o(\sqrt{d})$ as $m$ varies from $m=Θ(n)$ to $m \gg n$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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