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Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems
Nan Meng
, Yun Bin Zhao
Research output
:
Journal Publication
›
Article
›
peer-review
4
Citations (Scopus)
Overview
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Dive into the research topics of 'Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems'. Together they form a unique fingerprint.
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Engineering
Simulation Result
100%
Optimisation Problem
100%
Optimization Method
100%
Nonlinear Optimization
100%
Metrics
100%
Compressive Sensing
100%
Sparse Signal
100%
Sensing Algorithm
100%
Restricted Isometry Property
100%
Keyphrases
Threshold Method
100%
Sparse Optimization Problem
100%
Threshold Algorithm
100%
Newton-type
100%
Optimal Thresholding
100%
Signal Recovery
50%
Result-oriented
25%
Gradient Method
25%
Sensing Matrix
25%
Compressed Sensing
25%
Newton's Method
25%
Error Metrics
25%
Sparse Signal
25%
Sensing Algorithm
25%
Optim
25%
Synthetic Signals
25%
Restricted Isometry Property
25%
Nonlinear Optimization Model
25%
Performance Guarantee
25%
Computer Science
Optimization Problem
100%
Gradient Method
100%
Compressive Sensing
100%
Mathematics
Thresholding
100%
Matrix (Mathematics)
12%
Type Method
12%