Randomized SVD is a powerful method to accurately compute low-rank SVD for large matrices, such as the additive and dominance matrices derived from data sets with many observations (individuals)1. Fitting a mixed model that contains large matrices A and D is computationally expensive if not prohibitive for very large matrices. Using randomized SVD is an alternative that speeds up computations without loosing accuracy in the predictions.
