Github user iyounus commented on the pull request:
https://github.com/apache/spark/pull/11610#issuecomment-196960794
I'm a bit confused about the use of DGELSD. As far as I can tell, it
requires matrix A itself. But in the current implementation, we're decomposing
A^T.A on the driver.
To find the inverse of A^T.A, I only need the matrix V and singular values
from SVD decomposition: A^T.A = V \Sigma^2 V^T. I can construct this using
eigenvalues and eigenvectors of A^T.A which I can do on the driver. Then,
finding the inverse is trivial. Is this what we're actually trying to do?
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