Modeling Complex Root Motion of Real Random Polynomials under Differentiation

A. Galligo
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引用次数: 2

Abstract

In this paper, we consider nonlocal, nonlinear partial differential equations to model anisotropic dynamics of complex root sets of random polynomials under differentiation. These equations aim to generalise the recent PDE obtained by Stefan Steinerberger (2019) in the real case, and the PDE obtained by Sean O'Rourke and Stefan Steinerberger (2020) in the radial case, which amounts to work in 1D. These PDEs approximate dynamics of the complex roots for random polynomials of sufficiently high degree n. The unit of the time t corresponds to n differentiations, and the increment Δt corresponds to 1/n. The general situation in 2D, in particular for complex roots of real polynomials, was not yet addressed. The purpose of this paper is to present a first attempt in that direction. We assume that the roots are distributed according to a regular distribution with a local homogeneity property (defined in the text), and that this property is maintained under differentiation. This allows us to derive a system of two coupled equations to model the motion. Our system could be interesting for other applications. The paper is illustrated with examples computed with the Maple system.
微分下实随机多项式复根运动的建模
本文考虑非局部非线性偏微分方程来模拟随机多项式复根集在微分作用下的各向异性动力学。这些方程旨在推广Stefan Steinerberger(2019)最近在实际情况下获得的偏微分方程,以及Sean O'Rourke和Stefan Steinerberger(2020)在径向情况下获得的偏微分方程,相当于一维的功。这些偏微分方程近似于n次足够高的随机多项式的复根的动力学。时间单位t对应于n次微分,增量Δt对应于1/n。二维中的一般情况,特别是实多项式的复根,还没有解决。本文的目的是在这个方向上进行第一次尝试。我们假设根按照正则分布分布,具有局部同质性(在文中定义),并且在微分下保持这种性质。这使我们能够推导出一个由两个耦合方程组成的系统来模拟运动。我们的系统可能对其他应用程序很有趣。文中用Maple系统的算例进行了说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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