Partial gradient optimal thresholding algorithms for a class of sparse optimization problems
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Title
Partial gradient optimal thresholding algorithms for a class of sparse optimization problems
Authors
Keywords
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Journal
JOURNAL OF GLOBAL OPTIMIZATION
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-02-23
DOI
10.1007/s10898-022-01143-1
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- (2022) Nan Meng et al. Journal of the Operations Research Society of China
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- (2016) Jean-Luc Bouchot et al. APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
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- (2016) Yunsong Liu et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
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- (2014) Jeffrey D. Blanchard et al. NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS
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- (2011) Simon Foucart SIAM JOURNAL ON NUMERICAL ANALYSIS
- Accelerated iterative hard thresholding
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- (2010) T. Blumensath et al. IEEE Journal of Selected Topics in Signal Processing
- Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit
- (2010) Deanna Needell et al. IEEE Journal of Selected Topics in Signal Processing
- Iterative hard thresholding for compressed sensing
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- (2009) Wei Dai et al. IEEE TRANSACTIONS ON INFORMATION THEORY
- Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing
- (2009) D. Donoho et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
- (2008) D. Needell et al. APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
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- (2008) Emmanuel J. Candès et al. JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS
- Iterative Thresholding for Sparse Approximations
- (2008) Thomas Blumensath et al. JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS
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- (2007) Massimo Fornasier et al. APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
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