Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10225877 | Computers & Mathematics with Applications | 2018 | 17 Pages |
Abstract
In this paper, we consider a class of constrained matrix quadratic inverse eigenvalue problem and its optimal approximation problem. It is proved that the proposed algorithm always converge to the generalized Hamiltonian solutions with a submatrix constraint of Problem 1.1 within finite iterative steps in the absence of roundoff error. In addition, by choosing a special kind of initial matrices, it is shown that the minimum norm solution of Problem 1.1 can be obtained consequently. At last, for a given matrix group in the solution set of Problem 1.1, it is proved that the unique optimal approximation solution of Problem 1.2 can be also obtained. Some numerical results are reported to demonstrate the efficiency of our algorithm.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Jia Tang, Linjie Chen, Changfeng Ma,