Block Decompositions and Applications of Generalized Reflexive Matrices  

作　　者：Hsin-Chu Chen 

机构地区：[1]Department of Cyber-Physical Systems, Clark Atlanta University, Atlanta, GA, USA

出　　处：《Advances in Linear Algebra & Matrix Theory》2018年第3期122-133,共12页线性代数与矩阵理论研究进展（英文）

摘　　要：Generalize reflexive matrices are a special class of matrices ?that have the relation where? and ?are some generalized reflection matrices. The nontrivial cases ( or ) of this class of matrices occur very often in many scientific and engineering applications. They are also a generalization of centrosymmetric matrices and reflexive matrices. The main purpose of this paper is to present block decomposition schemes for generalized reflexive matrices of various types and to obtain their decomposed explicit block-diagonal structures. The decompositions make use of unitary equivalence transformations and, therefore, preserve the singular values of the matrices. They lead to more efficient sequential computations and at the same time induce large-grain parallelism as a by-product, making themselves computationally attractive for large-scale applications. A numerical example is employed to show the usefulness of the developed explicit decompositions for decoupling linear least-square problems whose coefficient matrices are of this class into smaller and independent subproblems.Generalize reflexive matrices are a special class of matrices ?that have the relation where? and ?are some generalized reflection matrices. The nontrivial cases ( or ) of this class of matrices occur very often in many scientific and engineering applications. They are also a generalization of centrosymmetric matrices and reflexive matrices. The main purpose of this paper is to present block decomposition schemes for generalized reflexive matrices of various types and to obtain their decomposed explicit block-diagonal structures. The decompositions make use of unitary equivalence transformations and, therefore, preserve the singular values of the matrices. They lead to more efficient sequential computations and at the same time induce large-grain parallelism as a by-product, making themselves computationally attractive for large-scale applications. A numerical example is employed to show the usefulness of the developed explicit decompositions for decoupling linear least-square problems whose coefficient matrices are of this class into smaller and independent subproblems.

关 键 词：GENERALIZED REFLEXIVE MATRICES REFLEXIVE MATRICES CENTROSYMMETRIC MATRICES GENERALIZED SIMULTANEOUS DIAGONALIZATION SIMULTANEOUS DIAGONALIZATION Linear Least-Square Problems 

分 类 号：O1[理学—数学]