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Sparse Recovery by Semi-Iterative Hard Thresholding Algorithm
Joint Authors
Zhou, Xueqin
Jing, Mingli
Feng, Xiang-Chu
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-02-13
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
We propose a computationally simple and efficient method for sparse recovery termed as the semi-iterative hard thresholding (SIHT).
Unlike the existing iterative-shrinkage algorithms, which rely crucially on using negative gradient as the search direction, the proposed algorithm uses the linear combination of the current gradient and directions of few previous steps as the search direction.
Compared to other iterative shrinkage algorithms, the performances of the proposed method show a clear improvement in iterations and error in noiseless, whilst the computational complexity does not increase.
American Psychological Association (APA)
Zhou, Xueqin& Feng, Xiang-Chu& Jing, Mingli. 2013. Sparse Recovery by Semi-Iterative Hard Thresholding Algorithm. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1009596
Modern Language Association (MLA)
Zhou, Xueqin…[et al.]. Sparse Recovery by Semi-Iterative Hard Thresholding Algorithm. Mathematical Problems in Engineering No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-1009596
American Medical Association (AMA)
Zhou, Xueqin& Feng, Xiang-Chu& Jing, Mingli. Sparse Recovery by Semi-Iterative Hard Thresholding Algorithm. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1009596
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1009596