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An Efficient Adaptive Iteratively Reweighted l1 Algorithm for Elastic lq Regularization
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作者 Yong Zhang Wanzhou Ye 《Advances in Pure Mathematics》 2016年第7期498-506,共9页
In this paper, we propose an efficient adaptive iteratively reweighted l<sub>1</sub> algorithm (A-IRL1 algorithm) for solving the elastic l<sub>q</sub> regularization problem. We prove that the... In this paper, we propose an efficient adaptive iteratively reweighted l<sub>1</sub> algorithm (A-IRL1 algorithm) for solving the elastic l<sub>q</sub> 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 l<sub>q</sub> regularization problem. Under certain conditions, we present an error bound for the limit point of convergent sequence. 展开更多
关键词 Compressed Sensing elastic style="font-family:Mistral font-size:20pt ">lq Minimization Nonconvex Optimization Convergence Critical Point
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