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Compressive Sensing and Structured Random Matrices

By Holger Rauhut

Abstract

These notes give a mathematical introduction to compressive sensing focusing on recovery using ℓ1-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 ℓ1-minimization

Topics: inequalities, bounded orthogonal systems
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.185.3754
Provided by: CiteSeerX
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