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Foucart, S. and Lai, M. (2009) Sparsest Solutions of Underdetermined Linear Systems via l_q Minimization for 0http://dx.doi.org/10.1016/j.acha.2008.09.001

has been cited by the following article:

• TITLE: An Efficient Adaptive Iteratively Reweighted l1 Algorithm for Elastic lq Regularization

  AUTHORS: Yong Zhang, Wanzhou Ye

  KEYWORDS: Compressed Sensing, Elastic lq Minimization, Nonconvex Optimization, Convergence, Critical Point

  JOURNAL NAME: Advances in Pure Mathematics, Vol.6 No.7, June 16, 2016

  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.