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数学与物理
应用数学进展
Vol. 6 No. 3 (May 2017)
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随机矩阵非1特征值的新包含集
New Inclusion Sets of Eigenvalue Different from 1 for a Stochastic Matrix
DOI:
10.12677/AAM.2017.63043
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被引量
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作者:
王笑笑
:云南大学,数学与统计学院,云南 昆明
关键词:
随机矩阵
;
双α1-矩阵
;
特征值包含集
;
Stochastic Matrix
;
Double α1-Matrices
;
Eigenvalue Inclusion Set
摘要:
利用双α-型特征值包含定理及修正矩阵理论,给出随机矩阵两个新的非1特征值包含集,并由此得到随机矩阵非奇异的两个新的充分条件。数值算例表明,所得结果优于几个现有结果。
Abstract:
Two new inclusion sets are given to localize all eigenvalues different from 1 for stochastic matrices by using the double α-eigenvalue inclusion theorem and the theory of modified matrices, and then two new nonsingular sufficient conditions of stochastic matrices are obtained. Numerical examples are given to illustrate that the proposed sets are better than the sets were obtained from the existing literatures.
文章引用:
王笑笑. 随机矩阵非1特征值的新包含集[J]. 应用数学进展, 2017, 6(3): 376-381.
https://doi.org/10.12677/AAM.2017.63043
参考文献
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Li, C.Q. and Li, Y.T. (2011) Generalizations of Brauer’s Localization Theorem. The Electronic Journal of Linear Algebra, 22, 1168-1178.
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Cvetkovic, L. (2006) H-Matrix Theory vs. Eigenvalue Localization. Numerical Algorithms, 42, 229-245.
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