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- Title
A fast and accurate algorithm for ℓ minimization problems in compressive sampling.
- Authors
Chen, Feishe; Shen, Lixin; Suter, Bruce; Xu, Yuesheng
- Abstract
An accurate and efficient algorithm for solving the constrained ℓ-norm minimization problem is highly needed and is crucial for the success of sparse signal recovery in compressive sampling. We tackle the constrained ℓ-norm minimization problem by reformulating it via an indicator function which describes the constraints. The resulting model is solved efficiently and accurately by using an elegant proximity operator-based algorithm. Numerical experiments show that the proposed algorithm performs well for sparse signals with magnitudes over a high dynamic range. Furthermore, it performs significantly better than the well-known algorithm NESTA (a shorthand for Nesterov's algorithm) and DADM (dual alternating direction method) in terms of the quality of restored signals and the computational complexity measured in the CPU-time consumed.
- Subjects
COMPUTER algorithms; COMPRESSED sensing; SIGNAL processing; SPARSE approximations; PROXIMITY matrices; COMPUTATIONAL complexity
- Publication
EURASIP Journal on Advances in Signal Processing, 2015, Vol 2015, Issue 1, p1
- ISSN
1687-6172
- Publication type
Article
- DOI
10.1186/s13634-015-0247-5