用随机奇异值分解算法求解矩阵恢复问题

摘要

In this paper, we investigate the large low-rank matrix completion problem. By using randomized singular value decomposition (RSVD) algorithm, we compute singular values of sparse matrix. Compared to the Lanczos method, the computational time is greatly reduced with the same error. The algorithm also can be used to solve the relatively low rank matrix.%本文研究了大型低秩矩阵恢复问题.利用随机奇异值分解(RSVD)算法,对稀疏矩阵做奇异值分解.该算法与Lanczos方法相比,在误差精度一致的同时运算时间大大降低,且该算法对相对低秩矩阵也有效.