Least-squares solutions of generalized inverse eigenvalue problem over Hermitian–Hamiltonian matrices with a submatrix constraint

In this paper, a gradient-based iterative algorithm is proposed for finding the least-squares solutions of the following constrained generalized inverse eigenvalue problem: given X ∈ C n × m , Λ = diag ( λ 1 , λ 2 , … , λ m ) ∈ C m × m , find A ∗ , B ∗ ∈ C n × n , such that ‖ A X - B X Λ ‖ is minimi...

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Vydáno v:Computational & applied mathematics Ročník 37; číslo 1; s. 593 - 603
Hlavní autoři: Cai, Jing, Chen, Jianlong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.03.2018
Springer Nature B.V
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ISSN:0101-8205, 2238-3603, 1807-0302
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Abstract In this paper, a gradient-based iterative algorithm is proposed for finding the least-squares solutions of the following constrained generalized inverse eigenvalue problem: given X ∈ C n × m , Λ = diag ( λ 1 , λ 2 , … , λ m ) ∈ C m × m , find A ∗ , B ∗ ∈ C n × n , such that ‖ A X - B X Λ ‖ is minimized, where A ∗ , B ∗ are Hermitian–Hamiltonian except for a special submatrix. For any initial constrained matrices, a solution pair ( A ∗ , B ∗ ) can be obtained in finite iteration steps by this iterative algorithm in the absence of roundoff errors. The least-norm solution can be obtained by choosing a special kind of initial matrix pencil. In addition, the unique optimal approximation solution to a given matrix pencil in the solution set of the above problem can also be obtained. A numerical example is given to show the efficiency of the proposed algorithm.
AbstractList In this paper, a gradient-based iterative algorithm is proposed for finding the least-squares solutions of the following constrained generalized inverse eigenvalue problem: given X∈Cn×m, Λ=diag(λ1,λ2,…,λm)∈Cm×m, find A∗,B∗∈Cn×n, such that ‖AX-BXΛ‖ is minimized, where A∗,B∗ are Hermitian–Hamiltonian except for a special submatrix. For any initial constrained matrices, a solution pair (A∗,B∗) can be obtained in finite iteration steps by this iterative algorithm in the absence of roundoff errors. The least-norm solution can be obtained by choosing a special kind of initial matrix pencil. In addition, the unique optimal approximation solution to a given matrix pencil in the solution set of the above problem can also be obtained. A numerical example is given to show the efficiency of the proposed algorithm.
In this paper, a gradient-based iterative algorithm is proposed for finding the least-squares solutions of the following constrained generalized inverse eigenvalue problem: given X ∈ C n × m , Λ = diag ( λ 1 , λ 2 , … , λ m ) ∈ C m × m , find A ∗ , B ∗ ∈ C n × n , such that ‖ A X - B X Λ ‖ is minimized, where A ∗ , B ∗ are Hermitian–Hamiltonian except for a special submatrix. For any initial constrained matrices, a solution pair ( A ∗ , B ∗ ) can be obtained in finite iteration steps by this iterative algorithm in the absence of roundoff errors. The least-norm solution can be obtained by choosing a special kind of initial matrix pencil. In addition, the unique optimal approximation solution to a given matrix pencil in the solution set of the above problem can also be obtained. A numerical example is given to show the efficiency of the proposed algorithm.
Author Chen, Jianlong
Cai, Jing
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crossref_primary_10_1007_s40314_020_01380_8
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Cites_doi 10.1016/j.amc.2007.05.035
10.1080/03081087.2014.922969
10.1007/BFb0039443
10.1007/s11741-004-0055-x
10.1016/j.cam.2008.05.015
10.1016/0024-3795(88)90223-6
10.1093/imamci/2.4.335
10.1137/140972494
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Issue 1
Keywords Hermitian–Hamiltonian matrix
65J22
Optimal approximation
Submatrix constraint
Generalized inverse eigenvalue problem
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Snippet In this paper, a gradient-based iterative algorithm is proposed for finding the least-squares solutions of the following constrained generalized inverse...
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SubjectTerms Algorithms
Applications of Mathematics
Applied physics
Computational mathematics
Computational Mathematics and Numerical Analysis
Eigenvalues
Generalized inverse
Iterative algorithms
Iterative methods
Least squares
Mathematical Applications in Computer Science
Mathematical Applications in the Physical Sciences
Mathematics
Mathematics and Statistics
Matrix
Roundoff error
Title Least-squares solutions of generalized inverse eigenvalue problem over Hermitian–Hamiltonian matrices with a submatrix constraint
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