Compressed Sensing
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Summary
The methodology for updating weights proposed in this work consists in considering those weights as Lagrange multipliers, being able in this way to apply classical Lagrange relaxation algorithms for the update process.
- Published
- 2014-01-01
- Cited by
- 4,098
- References
- 85
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- https://api.semanticscholar.org/CorpusID:30603419
References
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- Iterative Thresholding for Sparse Approximations
- Random Projections of Smooth Manifolds
- Atomic Decomposition by Basis Pursuit
- A single-pixel terahertz imaging system based on compressed sensing
- Signal recovery and the large sieve
- Experimental comparison of efficient tomography schemes for a six-qubit state.
- Convex Optimization in Julia
- Compressed Sensing for Wideband Cognitive Radios
- A simple message-passing algorithm for compressed sensing
- Permutationally invariant quantum tomography.
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