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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Bibliographic Details
Published in:	Neural computation Vol. 26; no. 2; p. 449
Main Authors:	Xia, Youshen, Chen, Tianping, Shan, Jinjun
Format:	Journal Article
Language:	English
Published:	United States 01.02.2014
Subjects:	Algorithms Computer Simulation - trends
ISSN:	1530-888X, 1530-888X
Online Access:	Get more information
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1530-888X 1530-888X
DOI:	10.1162/NECO_a_00549