Accelerating monotone fast iterative shrinkage–thresholding algorithm with sequential subspace optimization for sparse recovery

The present paper focuses on accelerating monotone fast iterative shrinkage–thresholding algorithm (MFISTA) that is popular to solve the basis pursuit denoising problem for sparse recovery. Inspired by a recent work that accelerates MFISTA with line search, we alternatively use a much more effective...

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Bibliographic Details
Published in:	Signal, image and video processing Vol. 14; no. 4; pp. 771 - 780
Main Author:	Zhu, Tao
Format:	Journal Article
Language:	English
Published:	London Springer London 01.06.2020 Springer Nature B.V
Subjects:	Algorithms Computer Imaging Computer Science Image Processing and Computer Vision Mathematical analysis Multimedia Information Systems Noise reduction Optimization Original Paper Pattern Recognition and Graphics Recovery Shrinkage Signal,Image and Speech Processing Subspaces Vision Monotone fast iterative shrinkage–thresholding algorithm Sparse recovery Acceleration Sequential subspace optimization
ISSN:	1863-1703, 1863-1711
Online Access:	Get full text
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