Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/11610#issuecomment-197199401
Locally, we are solving `A^T A x = A^T b`. In a rank deficient case, we can
compute the min-length least squares solution that also minimizes `\| x \|_2`,
which is unique and returned by DGELSD. This is the same as eigen decomposition
because `A^T A` is symmetric. We can try that too.
Ideally, we should solve the least squares problems using tall-skinny
QR/SVD to get better stability. But it is a little tricky for sparse data. So I
would suggest solving the normal equation locally with SVD.
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