Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/964#issuecomment-47681084
@vrilleup Just checked Matlabâs svd and svds. I donât remember I have
used options.{tol, maxit} before. I wonder whether this is useful to expose to
users. I did use RCOND before because I needed to compute very accurate
solution. But that work was purely academic. In MLlibâs implementation, we
take the A^T A approach, which couldnât give us very accurate small singular
values if the matrix is ill-conditioned. So this is not useful either. My
suggestion for the type signature is simply:
~~~
def computeSVD(k: Int, computeU: Boolean)
~~~
Letâs estimate the complexity of the dense approach and the iterative
approach and decide which to use internally. We can open advanced options
later, e.g. rcond, iter, method: {"dense", "arpack"}, etc. What do you think?
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