A novel iterative method for computing generalized inverse
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...
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| Veröffentlicht in: | Neural computation Jg. 26; H. 2; S. 449 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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United States
01.02.2014
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| ISSN: | 1530-888X, 1530-888X |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Xia, Youshen Chen, Tianping Shan, Jinjun |
| Author_xml | – sequence: 1 givenname: Youshen surname: Xia fullname: Xia, Youshen email: ysxia2001@yahoo.com organization: College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350002, China ysxia2001@yahoo.com – sequence: 2 givenname: Tianping surname: Chen fullname: Chen, Tianping – sequence: 3 givenname: Jinjun surname: Shan fullname: Shan, Jinjun |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24206382$$D View this record in MEDLINE/PubMed |
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| Title | A novel iterative method for computing generalized inverse |
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