自动化学报2023，Vol.49Issue(12)：2639-2644,6.DOI:10.16383/j.aas.c200399
基于加权矩阵的多维广义特征值并行分解算法
Multiple Generalized Eigenvalue Decomposi-tion Algorithm in Parallel Based on Weighted Matrix
摘要
Abstract
In order to overcome the disadvantages of sequential algorithms,such as poor real time,a multiple generalized eigen-value decomposition algorithm is proposed based on weighted matrix method.Unlike sequential algorithms,the proposed al-gorithm is able to estimate multiple generalized eigenvectors in parallel only through one iteration procedure.The stationary point analysis shows that the algorithm reaches convergence state if and only if the state matrix is equal to the desired generalized eigenvectors.The self-stabilization characteristics of the pro-posed algorithm is proved by comparing the state matrix module values of adjacent moments.The proposed algorithm parameters are simple to select and easy to implement in practice.Numeric-al simulation and example application further verify the parallel-ism,self-stability and practicality of the algorithm.关键词
广义特征值分解/加权矩阵/并行分解/多维估计Key words
Generalized eigenvalue decomposition/weighted matrix/parallel decomposition/multiple estimation引用本文复制引用
高迎彬,徐中英..基于加权矩阵的多维广义特征值并行分解算法[J].自动化学报,2023,49(12):2639-2644,6.基金项目
国家自然科学基金(62106242,62273354)资助Supported by National Natural Science Foundation of China(62106242,62273354) （62106242,62273354）
