Recently,the l_(p)minimization problem(p∈(0,1))for sparse signal recovery has been studied a lot because of its efficiency.In this paper,we propose a general smoothing algorithmic framework based on the entropy funct...Recently,the l_(p)minimization problem(p∈(0,1))for sparse signal recovery has been studied a lot because of its efficiency.In this paper,we propose a general smoothing algorithmic framework based on the entropy function for solving a class of l_(p)minimization problems,which includes the well-known unconstrained l_(2)-l_(p)problem as a special case.We show that any accumulation point of the sequence generated by the proposed algorithm is a stationary point of the l_(p)minimization problem,and derive a lower bound for the nonzero entries of the stationary point of the smoothing problem.We implement a specific version of the proposed algorithm which indicates that the entropy function-based algorithm is effective.展开更多
基金supported by the National Natural Science Foundation of China(Nos.11171252,11431002).
文摘Recently,the l_(p)minimization problem(p∈(0,1))for sparse signal recovery has been studied a lot because of its efficiency.In this paper,we propose a general smoothing algorithmic framework based on the entropy function for solving a class of l_(p)minimization problems,which includes the well-known unconstrained l_(2)-l_(p)problem as a special case.We show that any accumulation point of the sequence generated by the proposed algorithm is a stationary point of the l_(p)minimization problem,and derive a lower bound for the nonzero entries of the stationary point of the smoothing problem.We implement a specific version of the proposed algorithm which indicates that the entropy function-based algorithm is effective.
