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https://doi.org/10.1090/S0025-5718-08-02189-3
Title: | Linearized Bregman iterations for compressed sensing | Authors: | Cai, J.-F. Osher, S. Shen, Z. |
Issue Date: | Jul-2009 | Citation: | Cai, J.-F., Osher, S., Shen, Z. (2009-07). Linearized Bregman iterations for compressed sensing. Mathematics of Computation 78 (267) : 1515-1536. ScholarBank@NUS Repository. https://doi.org/10.1090/S0025-5718-08-02189-3 | Abstract: | Finding a solution of a linear equation Au = f with various minimization properties arises from many applications. One such application is compressed sensing, where an efficient and robust-to-noise algorithm to find a minimal ℓ 1 norm solution is needed. This means that the algorithm should be tailored for large scale and completely dense matrices A, while Au and A T u can be computed by fast transforms and the solution we seek is sparse. Recently, a simple and fast algorithm based on linearized Bregman iteration was proposed in [28,32] for this purpose. This paper is to analyze the convergence of linearized Bregman iterations and the minimization properties of their limit. Based on our analysis here, we derive also a new algorithm that is proven to be convergent with a rate. Furthermore, the new algorithm is simple and fast in approximating a minimal ℓ 1 norm solution of Au = f as shown by numerical simulations. Hence, it can be used as another choice of an efficient tool in compressed sensing. © 2008 American Mathematical Society. | Source Title: | Mathematics of Computation | URI: | http://scholarbank.nus.edu.sg/handle/10635/115795 | ISSN: | 00255718 | DOI: | 10.1090/S0025-5718-08-02189-3 |
Appears in Collections: | Staff Publications |
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