Truncated SVD of extended precision matrix

[jd-foster]

I saw this earlier post:

Truncated Singular Value Decomposition

Hi. Given a matrix M I would like to compute its SVD truncated to rank k. I think this is possible without doing the full SVD. For example, Python has this: sklearn.decomposition.TruncatedSVD — scikit-learn 1.1.2 documentation. The code I am currently using to do this is given below. The problem is that it computes SVD first, and then throws out the extra rows/columns, which can be quite costly if k is much smaller than rank of M. Is there a function in Julia to do this? using LinearAlgebra "re…

The recommendation is TSVD.jl.