Computing Truncated Joint Approximate Eigenbases for Model Order Reduction
Computing Truncated Joint Approximate Eigenbases for Model Order Reduction
复制标题
计算截断的联合近似特征库以减少模型阶数
DOI：
10.11128/arep.17.a17209
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Vides, Fredy
中科院分区：
文献类型：
--
作者：
Loring, Terry;Vides, Fredy
在本文中，一些元素的理论和算法相应的存在性和可计算性的近似联合特征对有限集合的矩阵模型降阶的应用程序，提出。更具体地说，给定一个有限的集合的厄米特矩阵，一个正整数，和一个集合的复数，。首先，我们研究了一组向量的可计算性，使得对于每个向量，然后我们提出了一个模型降阶过程的基础上截断联合近似特征基计算与上述技术。给出了一些原型算法和数值例子。
In this document, some elements of the theory and algorithmics corresponding to the existence and computability of approximate joint eigenpairs for finite collections of matrices with applications to model order reduction, are presented. More specifically, given a finite collectionof Hermitian matrices in, a positive integer, and a collection of complex numbersfor,. First, we study the computability of a set ofvectors, such thatfor each, then we present a model order reduction procedure based on the truncated joint approximate eigenbases computed with the aforementioned techniques. Some prototypical algorithms together with some numerical examples are presented as well.