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Computable Performance Bounds on Sparse Recovery
Gongguo Tang, Arye Nehorai
Department of Electrical & Systems Engineering
Roy and Diana Vagelos Division of Biology & Biomedical Sciences (DBBS)
DBBS - Computational and Systems Biology
DBBS - Neurosciences
DBBS - Biomedical Informatics and Data Science
Research output
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Article
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peer-review
6
Scopus citations
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Keyphrases
Quality Measures
100%
Computable
100%
Performance Bounds
100%
Sparse Recovery
100%
Tight
25%
Noise Free
25%
Optimization Problem
25%
Design Optimization
25%
Numerical Experiments
25%
Number of Measurements
25%
Linear Programming
25%
Large Classes
25%
Error Bound
25%
Convergence Guarantee
25%
Second-order Cone Programming
25%
Sensing Matrix
25%
Non-degenerate
25%
Sparsity Level
25%
Global Convergence
25%
Recovery Error
25%
1-minimization
25%
Random Measurement Matrix
25%
Polynomial-time Algorithm
25%
Restricted Isometry
25%
Sparse Recovery Algorithm
25%
Mathematics
Matrix (Mathematics)
100%
Wide Range
50%
Numerical Experiment
50%
Error Bound
50%
Sufficient Condition
50%
Polynomial Time
50%
Linear Program
50%
Computer Science
Quality Measure
100%
Performance Bound
100%
Optimization Problem
25%
Sparsity
25%
Sufficient Condition
25%
Linear Program
25%
Recovery Algorithm
25%
polynomial-time algorithm
25%
Global Convergence
25%
Engineering
Quality Measure
100%
Optimisation Problem
25%
Numerical Experiment
25%
Sparsity
25%
Sufficient Condition
25%
Linear Program
25%
Error Bound
25%
Polynomial Time
25%
Isometry
25%