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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 speed-up option, termed sequential subspace optimization. Furthermore, instead of manually setting the number of previous propagation directions in the subspace beforehand, we propose an adaptive method to set it. Additionally, for approximating the absolute value function, we analyze the superiority of a smooth version used in this paper over the one recommended in a previous work, and give an analytical closed-form expression for the shrinkage operator corresponding to the smooth approximation. The experiments presented here show that the proposed method achieves faster convergence speeds in terms of iteration and run-time.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-019-01603-4