Journal
SIAM REVIEW
Volume 55, Issue 3, Pages 547-566Publisher
SIAM PUBLICATIONS
DOI: 10.1137/110837681
Keywords
compressed sensing; restricted isometry property; random matrices; linear programming; sparse signal recovery
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This article offers an accessible but rigorous and essentially self-contained account of some of the central ideas in compressed sensing, aimed at nonspecialists and undergraduates who have had linear algebra and some probability. The basic premise is first illustrated by considering the problem of detecting a few defective items in a large set. We then build up the mathematical framework of compressed sensing to show how combining efficient sampling methods with elementary ideas from linear algebra and a bit of approximation theory, optimization, and probability allows the estimation of unknown quantities with far less sampling of data than traditional methods.
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