  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.
