算法
低秩近似
压缩传感
稀疏逼近
块(置换群论)
秩(图论)
基质(化学分析)
计算机科学
数学
稀疏矩阵
匹配追踪
矩阵范数
作者
Peng Wang,Chengde Lin,Xiaobo Yang,Shengwu Xiong
出处
期刊:Journal of Applied Analysis and Computation
[Wilmington Scientific Publisher, LLC]
日期:2020-01-01
卷期号:10 (3): 1024-1037
被引量:1
摘要
Recovering low-rank and sparse matrix from a given matrix arises in many applications, such as image processing, video background substraction, and so on. The 3-block alternating direction method of multipliers (ADMM) has been applied successfully to solve convex problems with 3-block variables. However, the existing sufficient conditions to guarantee the convergence of the 3-block ADMM usually require the penalty parameter $ \gamma $ to satisfy a certain bound, which may affect the performance of solving the large scale problem in practice. In this paper, we propose the 3-block ADMM to recover low-rank and sparse matrix from noisy observations. In theory, we prove that the 3-block ADMM is convergent when the penalty parameters satisfy a certain condition and the objective function value sequences generated by 3-block ADMM converge to the optimal value. Numerical experiments verify that proposed method can achieve higher performance than existing methods in terms of both efficiency and accuracy.
科研通智能强力驱动
Strongly Powered by AbleSci AI