{"title":"A Geometric Approach to Approximating the Limit Set of Eigenvalues for Banded Toeplitz Matrices","authors":"Teodor Bucht, Jacob S. Christiansen","doi":"10.1137/23m1587804","DOIUrl":null,"url":null,"abstract":"SIAM Journal on Matrix Analysis and Applications, Volume 45, Issue 3, Page 1573-1598, September 2024. <br/> Abstract. This article is about finding the limit set for banded Toeplitz matrices. Our main result is a new approach to approximate the limit set [math], where [math] is the symbol of the banded Toeplitz matrix. The new approach is geometrical and based on the formula [math], where [math] is a scaling factor, i.e., [math], and [math] denotes the spectrum. We show that the full intersection can be approximated by the intersection for a finite number of [math]’s and that the intersection of polygon approximations for [math] yields an approximating polygon for [math] that converges to [math] in the Hausdorff metric. Further, we show that one can slightly expand the polygon approximations for [math] to ensure that they contain [math]. Then, taking the intersection yields an approximating superset of [math] which converges to [math] in the Hausdorff metric and is guaranteed to contain [math]. Combining the established algebraic (root-finding) method with our approximating superset, we are able to give an explicit bound on the Hausdorff distance to the true limit set. We implement the algorithm in Python and test it. It performs on par to and better in some cases than existing algorithms. We argue, but do not prove, that the average time complexity of the algorithm is [math], where [math] is the number of [math]’s and [math] is the number of vertices for the polygons approximating [math]. Further, we argue that the distance from [math] to both the approximating polygon and the approximating superset decreases as [math] for most of [math], where [math] is the number of elementary operations required by the algorithm.","PeriodicalId":49538,"journal":{"name":"SIAM Journal on Matrix Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM Journal on Matrix Analysis and Applications","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1137/23m1587804","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
SIAM Journal on Matrix Analysis and Applications, Volume 45, Issue 3, Page 1573-1598, September 2024. Abstract. This article is about finding the limit set for banded Toeplitz matrices. Our main result is a new approach to approximate the limit set [math], where [math] is the symbol of the banded Toeplitz matrix. The new approach is geometrical and based on the formula [math], where [math] is a scaling factor, i.e., [math], and [math] denotes the spectrum. We show that the full intersection can be approximated by the intersection for a finite number of [math]’s and that the intersection of polygon approximations for [math] yields an approximating polygon for [math] that converges to [math] in the Hausdorff metric. Further, we show that one can slightly expand the polygon approximations for [math] to ensure that they contain [math]. Then, taking the intersection yields an approximating superset of [math] which converges to [math] in the Hausdorff metric and is guaranteed to contain [math]. Combining the established algebraic (root-finding) method with our approximating superset, we are able to give an explicit bound on the Hausdorff distance to the true limit set. We implement the algorithm in Python and test it. It performs on par to and better in some cases than existing algorithms. We argue, but do not prove, that the average time complexity of the algorithm is [math], where [math] is the number of [math]’s and [math] is the number of vertices for the polygons approximating [math]. Further, we argue that the distance from [math] to both the approximating polygon and the approximating superset decreases as [math] for most of [math], where [math] is the number of elementary operations required by the algorithm.
期刊介绍:
The SIAM Journal on Matrix Analysis and Applications contains research articles in matrix analysis and its applications and papers of interest to the numerical linear algebra community. Applications include such areas as signal processing, systems and control theory, statistics, Markov chains, and mathematical biology. Also contains papers that are of a theoretical nature but have a possible impact on applications.