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An Efficient Adaptive Iteratively Reweighted l1 Algorithm for Elastic lq Regularization

DOI: 10.4236/apm.2016.67036, PP. 498-506

Keywords: Compressed Sensing, Elastic font-size:20pt,lq Minimization&searchField=keyword">">lq Minimization, Nonconvex Optimization, Convergence, Critical Point

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Abstract:

In this paper, we propose an efficient adaptive iteratively reweighted l1 algorithm (A-IRL1 algorithm) for solving the elastic lq regularization problem. We prove that the sequence generated by the A-IRL1 algorithm is convergent for any rational \"\" and the limit is a critical point of the elastic lq regularization problem. Under certain conditions, we present an error bound for the limit point of convergent sequence.

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