Name
Abstract | ||
|---|---|---|
In this letter, we propose a novel iterative method for computing generalized inverse, based on a novel KKT formulation. The proposed iterative algorithm requires making four matrix and vector multiplications at each iteration and thus has low computational complexity. The proposed method is proved to be globally convergent without any condition. Furthermore, for fast computing generalized inverse, we present an acceleration scheme based on the proposed iterative method. The global convergence of the proposed acceleration algorithm is also proved. Finally, the effectiveness of the proposed iterative algorithm is evaluated numerically. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1162/NECO_a_00549 | Neural Computation |
Keywords | Field | DocType |
proposed iterative algorithm,generalized inverse,proposed acceleration algorithm,proposed iterative method,proposed method,novel iterative method,acceleration scheme,novel KKT formulation,global convergence,low computational complexity | Convergence (routing),Mathematical optimization,Iterative method,Matrix (mathematics),Generalized inverse,Acceleration,Karush–Kuhn–Tucker conditions,Mathematics,Computational complexity theory | Journal |
Volume | Issue | ISSN |
26 | 2 | 1530-888X |
Citations | PageRank | References |
7 | 0.44 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youshen Xia | 1 | 1795 | 123.60 |
| Tianping Chen | 2 | 3095 | 250.77 |
| Jinjun Shan | 3 | 8 | 1.15 |