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.