“…The most direct approach for SVT is applying full SVD through svd and then soft-threshold the singular values. This approach is in practice used in many matrix learning problems according to the distributed code, e.g., Kalofolias, Bresson, Bronstein, and Vandergheynst (2014); Chi et al (2013); Parikh and Boyd (2013) ;Yang, Wang, Zhang, and Zhao (2013);Zhou, Liu, Wan, and Yu (2014); Zhou and Li (2014); Zhang et al (2017); Otazo, Candès, and Sodickson (2015); Goldstein, Studer, and Baraniuk (2015), to name a few. However, the built-in function svd is for full SVD of a dense matrix, and hence is very time-consuming and computationally expensive for large-scale problems.…”