Name
Abstract | ||
|---|---|---|
Quaternionic least squares QLS is an efficient method for solving approximate problems in quaternionic quantum theory. Based on Paige's algorithms LSQR and residual-reducing version of LSQR proposed in Paige and Saunders [LSQR: An algorithm for sparse linear equations and sparse least squares, ACM Trans. Math. Softw. 81 1982, pp. 43–71], we provide two matrix iterative algorithms for finding solution with the least norm to the QLS problem by making use of structure of real representation matrices. Numerical experiments are presented to illustrate the efficiency of our algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1080/00207160.2012.739684 | Int. J. Comput. Math. |
Keywords | Field | DocType |
sparse linear equation,approximate problem,acm trans,numerical experiment,algorithms lsqr,matrix iterative algorithm,least-squares problem,efficient method,quaternionic quantum theory,qls problem,squares qls,iterative algorithm | Least squares,Real representation,Linear equation,Mathematical optimization,Algebra,Quantum mechanics,Iterative method,Quaternion matrix,Matrix (mathematics),Algorithm,Mathematics | Journal |
Volume | Issue | ISSN |
90 | 3 | 0020-7160 |
Citations | PageRank | References |
1 | 0.35 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sitao Ling | 1 | 39 | 6.01 |
| Zhigang Jia | 2 | 43 | 9.02 |