Name
Title | ||
|---|---|---|
Matrix LSQR algorithm for structured solutions to quaternionic least squares problem. |
Abstract | ||
|---|---|---|
In this paper, we employ matrix LSQR algorithm to deal with quaternionic least squares problem in order to find the minimum norm solutions with kinds of special structures, and propose a strategy to accelerate convergence rate of the algorithm via right–left preconditioning of the coefficient matrices. We mainly focus on analyzing the minimum norm η-Hermitian solution and the minimum norm η-biHermitian solution to the quaternionic least squares problem, η∈{i,j,k}. Other structured solutions also can be obtained using the proposed technique. A number of numerical experiments are performed to show the efficiency of the preconditioned matrix LSQR algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.camwa.2018.10.023 | Computers & Mathematics with Applications |
Keywords | Field | DocType |
Quaternionic least squares,η-Hermitian matrix,Real representation,Matrix LSQR algorithm,Structured preconditioner | Least squares,Matrix (mathematics),Algorithm,Minimum norm,Rate of convergence,Hermitian matrix,Mathematics | Journal |
Volume | Issue | ISSN |
77 | 3 | 0898-1221 |
Citations | PageRank | References |
0 | 0.34 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sitao Ling | 1 | 39 | 6.01 |
| Zhigang Jia | 2 | 43 | 9.02 |
| Xin Lu | 3 | 586 | 27.15 |
| Bing Yang | 4 | 44 | 8.37 |