A generalization of the randomized singular value decomposition. (arXiv:2105.13052v3 [math.NA] UPDATED)

The randomized singular value decomposition (SVD) is a popular and effective
algorithm for computing a near-best rank $k$ approximation of a matrix $A$
using matrix-vector products with standard Gaussian vectors. Here, we
generalize the randomized SVD to multivariate Gaussian vectors, allowing one to
incorporate prior knowledge of $A$ into the algorithm. This enables us to
explore the continuous analogue of the randomized SVD for Hilbert--Schmidt (HS)
operators using operator-function products with functions drawn from a Gaussian
process (GP). We then …

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