  We discuss how to evaluate the proximal operator of a convex and increasing
function of a nuclear norm, which forms the key computational step in several
first-order optimization algorithms such as (accelerated) proximal gradient
descent and ADMM. Various special cases of the problem arise in low-rank matrix
completion, dropout training in deep learning and high-order low-rank tensor
recovery, although they have all been solved on a case-by-case basis. We
provide an unified and efficiently computable procedure for solving this
problem.
