Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a nonconvex and nonsmooth method for compressed sensing and low-rank matrix completion is studied. The proposed model is formulated as nonconvex regularized least square optimization problem. At first, an alternating minimization scheme is developed in which the problem can be decomposed into three subproblems, two of them are convex and the remaining one is smooth. Then, the convergence of the sequence which is generated by the alternating minimization algorithm is proved. In addition, some recovery guarantees are also analyzed. Finally, various numerical simulations are performed to test the efficiency of the method.
['Zhuo-Xu Cui', 'Qibin Fan']
I am writing a research paper on a idea A Nonconvex Nonsmooth Regularization Method for Compressed Sensing and Low-Rank Matrix Completion
[{"from": "human", "value": "I am writing a research paper on a idea A Nonconvex Nonsmooth Regularization Method for Compressed Sensing and Low-Rank Matrix Completion"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a nonconvex and nonsmooth method for compressed sensing and low-rank matrix completion is studied. The proposed model is formulated as nonconvex regularized least square optimization problem. At first, an alternating minimization scheme is developed in which the problem can be decomposed into three subproblems, two of them are convex and the remaining one is smooth. Then, the convergence of the sequence which is generated by the alternating minimization algorithm is proved. In addition, some recovery guarantees are also analyzed. Finally, various numerical simulations are performed to test the efficiency of the method."}]