Sparse Recovery Using Sparse Matrices

Sparse Recovery Using Sparse Matrices

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In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals. We discuss applications t...

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Bibliographic Details
Main Authors: Gilbert, Anna (Author), Indyk, Piotr (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers, 2012-05-24T18:31:47Z.
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Summary:In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals. We discuss applications to several areas, including compressive sensing, data stream computing, and group testing.
Statens naturvidenskabelige forskningsrad (Denmark)
Center for Massive Data Algorithmics (MADALGO)
David & Lucile Packard Foundation (Fellowship)
National Science Foundation (U.S.) (grant CCF-0728645)
National Science Foundation (U.S.) (grant CCF-0910765)
National Science Foundation (U.S.) (grant DMS-0547744)

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