Taking the Convoluted Out of Bernoulli Convolutions: A Discrete Approach
Taking the Convoluted Out of Bernoulli Convolutions: A Discrete Approach
复制标题
从伯努利卷积中去除卷积：一种离散方法
作者：
Neil J. Calkin;Julia Davis;Michelle Delcourt;Zebediah Engberg;Jobby Jacob;Kevin James
在本文中，我们考虑一个离散版本的伯努利卷积问题，传统上研究通过功能分析。我们讨论了几个创新的算法计算的序列与这种新的方法。特别是，这些算法帮助我们收集关于最大值的数据。通过研究一系列相关多项式，我们可以深入了解序列本身的局部行为。这项工作是作为克莱姆森大学REU的一部分完成的，这是一个NSF资助的项目。
In this paper we consider a discrete version of the Bernoulli convolution problem traditionally studied via functional analysis. We discuss several innovative algorithms for computing the sequences with this new approach. In particular, these algorithms assist us in gathering data regarding the maximum values. By looking at a family of associated polynomials, we gain insight on the local behavior of the sequence itself. This work was completed as part of the Clemson University REU, an NSF funded program.