Compressive Sensing and Structured Random Matrices

@inproceedings{Hausdorff2009CompressiveSA,
  title={Compressive Sensing and Structured Random Matrices},
  author={Holger Rauhut Hausdorff},
  year={2009}
}
  • Holger Rauhut Hausdorff
  • Published 2009
These notes give a mathematical introduction to compressive sensing focusing on recovery using `1-minimization and structured random matrices. An emphasis is put on techniques for proving probabilistic estimates for condition numbers of structured random matrices. Estimates of this type are key to providing conditions that ensure exact or approximate recovery of sparse vectors using `1-minimization. 
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