具有 i.i.d. 根的随机多项式的高斯-卢卡斯定理的渐近改进

Sean O'Rourke, Noah Williams
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摘要

如果 $p:\mathbb{C}\到 \mathbb{C}$ 是一个非常数多项式,高斯-卢卡斯定理断言其临界点包含在其根的凸壳中。我们考虑的情况是,当 $p$ 是阶数为 $n$ 的随机多项式时,其根是从复平面中一个径向对称、紧凑支撑的可能度量 $\mu$ 中独立选择的。我们证明,最大(幅度)临界点与 $p$ 的最大根密切相关。这使我们能够计算最大临界点在阶数 $n$ 趋于无穷大时的渐近波动。我们证明,波动的极限分布可以用高斯分布或重尾稳定分布来描述,这取决于 $\mu$ 在其支持边缘附近的行为。作为推论,我们得到了随机多项式的高斯-卢卡斯定理的渐近修正。
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An asymptotic refinement of the Gauss-Lucas Theorem for random polynomials with i.i.d. roots
If $p:\mathbb{C} \to \mathbb{C}$ is a non-constant polynomial, the Gauss--Lucas theorem asserts that its critical points are contained in the convex hull of its roots. We consider the case when $p$ is a random polynomial of degree $n$ with roots chosen independently from a radially symmetric, compactly supported probably measure $\mu$ in the complex plane. We show that the largest (in magnitude) critical points are closely paired with the largest roots of $p$. This allows us to compute the asymptotic fluctuations of the largest critical points as the degree $n$ tends to infinity. We show that the limiting distribution of the fluctuations is described by either a Gaussian distribution or a heavy-tailed stable distribution, depending on the behavior of $\mu$ near the edge of its support. As a corollary, we obtain an asymptotic refinement to the Gauss--Lucas theorem for random polynomials.
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