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AbstractAbstract
[en] 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 , , find , such that is minimized, where are Hermitian–Hamiltonian except for a special submatrix. For any initial constrained matrices, a solution pair 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.
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Copyright (c) 2018 SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional; Country of input: International Atomic Energy Agency (IAEA)
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Journal Article
Journal
Computational and Applied Mathematics; ISSN 0101-8205; ; v. 37(1); p. 593-603
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