We first propose a concise singular value decomposition of dual matrices. Then, the randomized version of the decomposition is presented. It can significantly reduce the computational cost while maintaining the similar accuracy. We analyze the theoretical properties and illuminate the numerical performance of the randomized algorithm.

翻译：我们首先提出一种简洁的对偶矩阵奇异值分解方法。随后，给出该分解的随机化版本。该算法能在保持相近精度的同时显著降低计算成本。我们分析了该随机化算法的理论性质，并阐明了其数值性能。