The QLP decomposition is one of the effective algorithms to approximate singular value decomposition (SVD) in numerical linear algebra. In this paper, we propose some single-pass randomized QLP decomposition algorithms for computing the low-rank matrix approximation. Compared with the deterministic QLP decomposition, the complexity of the proposed algorithms does not increase significantly and the system matrix needs to be accessed only once. Therefore, our algorithms are very suitable for a large matrix stored outside of memory or generated by stream data. In the error analysis, we give the bounds of matrix approximation error and singular value approximation error. Numerical experiments also reported to verify our results.

翻译：QLP 分解是数值线性代数中近似单值分解( SVD) 的有效算法之一。 在本文中, 我们建议为计算低级矩阵近似值而使用一些单个随机的 QLP 分解算法。 与确定式 QLP 分解法相比, 拟议的算法的复杂性没有显著增加, 系统矩阵只需要一次访问。 因此, 我们的算法非常适合存储在内存之外或由流数据生成的大型矩阵。 在错误分析中, 我们给出矩阵近似误差和单值近似差的界限。 数字实验还报告验证我们的结果 。