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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computational & applied mathematics Ročník 37; číslo 1; s. 593 - 603
Hlavní autori: Cai, Jing, Chen, Jianlong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.03.2018
Springer Nature B.V
Predmet:
ISSN:0101-8205, 2238-3603, 1807-0302
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0101-8205
2238-3603
1807-0302
DOI:10.1007/s40314-016-0363-3