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【01.13 8#108】Alternating direction method of multipliers for robust low rank matrix completion


报告题目:Alternating direction method of multipliers for robust low rank matrix completion


报告学者:张超 
 

报告者单位北京交通大学


报告时间2020年1月13日下午14:40---15:20


报告地点第八教学楼108


摘要:In this paper, we propose a novel robust low rank matrix completion model, which adds the $\mbox{L}_{2,1}$-norm penalty to the rank function in the objective function in order to alleviate the row structured noise. The first-order necessary optimality condition of the new model is given. We adapt the alternating direction method of multipliers (ADMM) to solve the nonconvex and discontinuous model directly and show its convergence under certain assumptions. Finally, extensive numerical results on artificial and real datasets show that the ADMM can efficiently solve our model, and provide the results that own the low rank structure of the matrix and the accuracy better than the state-of-the-art methods.

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