期刊
FOUNDATIONS OF COMPUTATIONAL MATHEMATICS
卷 9, 期 3, 页码 317-334出版社
SPRINGER
DOI: 10.1007/s10208-008-9031-3
关键词
Signal recovery algorithms; Restricted isometry condition; Uncertainty principle; Basis pursuit; Compressed sensing; Orthogonal matching pursuit; Signal recovery; Sparse approximation
资金
- Alfred P. Sloan Foundation
- NSF [0401032, 0652617]
- Direct For Mathematical & Physical Scien [0401032, 0918623] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [0401032, 0918623] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0652617] Funding Source: National Science Foundation
This paper seeks to bridge the two major algorithmic approaches to sparse signal recovery from an incomplete set of linear measurements-L-1-minimization methods and iterative methods (Matching Pursuits). We find a simple regularized version of Orthogonal Matching Pursuit (ROMP) which has advantages of both approaches: the speed and transparency of OMP and the strong uniform guarantees of L-1-minimization. Our algorithm, ROMP, reconstructs a sparse signal in a number of iterations linear in the sparsity, and the reconstruction is exact provided the linear measurements satisfy the uniform uncertainty principle.
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