随机半径

IF 0.9 3区 数学 Q2 MATHEMATICS
Manjunath Krishnapur , Erik Lundberg , Koushik Ramachandran
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引用次数: 0

摘要

本文研究的是当定义多项式 p 是随机的,即 p 的零点是从紧凑支持的概率量 μ 中独立采样时的多项式内半径。如果由 μ 生成的对数势 Uμ 的负集是非空的,那么内径就会以压倒性的概率(随着 p 的阶数 n 趋于无穷大)自下而上地以一个正常数为界。此外,如果 Uμ 的负集额外包含 μ 的支持,则内径有一个确定的极限。我们还提供了关于 μ 的条件,保证以压倒性的概率将∞包含在 n 个指数小的磁盘的联合中。这就部分地解决了埃尔德斯、赫尔佐格和皮拉尼安提出的关于∞的面积的(确定性)问题。另一方面,当从单位圆中独立均匀地抽取零点时,我们证明了半径在分布上收敛于取值在(0,1/2)的随机变量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inradius of random lemniscates

A classically studied geometric property associated to a complex polynomial p is the inradius (the radius of the largest inscribed disk) of its (filled) lemniscate Λ{z:|p(z)|<1}.

In this paper, we study the lemniscate inradius when the defining polynomial p is random, namely, with the zeros of p sampled independently from a compactly supported probability measure μ. If the negative set of the logarithmic potential Uμ generated by μ is non-empty, then the inradius is bounded from below by a positive constant with overwhelming probability (as the degree n of p tends to infinity). Moreover, the inradius has a deterministic limit if the negative set of Uμ additionally contains the support of μ.

We also provide conditions on μ guaranteeing that the lemniscate is contained in a union of n exponentially small disks with overwhelming probability. This leads to a partial solution to a (deterministic) problem concerning the area of lemniscates posed by Erdös, Herzog, and Piranian.

On the other hand, when the zeros are sampled independently and uniformly from the unit circle, then we show that the inradius converges in distribution to a random variable taking values in (0,1/2).

We also consider the characteristic polynomial of a Ginibre random matrix whose lemniscate we show is close to the unit disk with overwhelming probability.

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来源期刊
CiteScore
1.90
自引率
11.10%
发文量
55
审稿时长
6-12 weeks
期刊介绍: The Journal of Approximation Theory is devoted to advances in pure and applied approximation theory and related areas. These areas include, among others: • Classical approximation • Abstract approximation • Constructive approximation • Degree of approximation • Fourier expansions • Interpolation of operators • General orthogonal systems • Interpolation and quadratures • Multivariate approximation • Orthogonal polynomials • Padé approximation • Rational approximation • Spline functions of one and several variables • Approximation by radial basis functions in Euclidean spaces, on spheres, and on more general manifolds • Special functions with strong connections to classical harmonic analysis, orthogonal polynomial, and approximation theory (as opposed to combinatorics, number theory, representation theory, generating functions, formal theory, and so forth) • Approximation theoretic aspects of real or complex function theory, function theory, difference or differential equations, function spaces, or harmonic analysis • Wavelet Theory and its applications in signal and image processing, and in differential equations with special emphasis on connections between wavelet theory and elements of approximation theory (such as approximation orders, Besov and Sobolev spaces, and so forth) • Gabor (Weyl-Heisenberg) expansions and sampling theory.
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