Name
Title | ||
|---|---|---|
An iterative method for obtaining the Least squares solutions of quadratic inverse eigenvalue problems over generalized Hamiltonian matrix with submatrix constraints. |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.camwa.2018.07.014 | Computers & Mathematics with Applications |
Keywords | Field | DocType |
Generalized Hamiltonian solution,Matrix quadratic inverse eigenvalue problem,Optimal approximation problem,Minimum-norm solution group | Least squares,Matrix (mathematics),Round-off error,Mathematical analysis,Iterative method,Solution set,Hamiltonian matrix,Eigenvalues and eigenvectors,Mathematics,Matrix group | Journal |
Volume | Issue | ISSN |
76 | 7 | 0898-1221 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia Tang | 1 | 9 | 2.76 |
| Linjie Chen | 2 | 14 | 1.64 |
| Changfeng Ma | 3 | 197 | 29.63 |