图书馆数据存储中的重球最优阈值算法

Heavy-ball-based optimal thresholding algorithms for library data storage

对图书馆数据存储中的压缩感知问题的数值算法进行研究.将传统梯度下降法多步扩展的重球加速法与一种新的阈值技术——最优s阈值相结合,提出了用于求解压缩感知问题的两种算法:基于重球的最优s阈值算法和基于重球的最优s阈值追踪算法.证明了算法产生的迭代点序列全局收敛到压缩感知问题的一个解.

This paper mainly focuses on the numerical algorithm of compressed sensing problem,which is used for library data storage.By merging heavy-ball acceleration method which is a multi-step extension of the traditional gradient descent method and a new thresholding technique-optimal s-thresholding,we propose two algorithms to solve the compressed sensing problem,namely,heavy-ball-based optimal s-thresholding algorithm(HBOT)and heavy-ball-based optimal s-thresholding pursuit algorithm(HBOTP).It is proved that the sequence of iteration points generated by the algorithms converges globally to a solution of the compressed sensing problem.