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- May 02 14:21ernstbrunr51 commented #278
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- May 14 2019 01:30
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- Mar 05 2019 05:46Raviteja1996 opened #274

Hi Jack, thanks for merging my PR. I've got some issues with COMPACT_SVD. In https://github.com/elemental/Elemental/blob/master/src/lapack_like/spectral/SVD.cpp#L75, the default tol is used to cull 'low enough but not necessarily zero' singular values. Is this the intended behaviour? Lines 277, 469, 771 and 1126 of src/lapack_like/spectral/BidiagSVD.cpp seem to contradict this, and do not make use of the APosterioriThreshold function.

@AidanGG Sorry, I hadn't checked this gitter in a while for temporary reasons. The

`COMPACT_SVD`

is indeed meant to have that behavior: the finite-precision reduction to bidiagonal form introduces a perturbation of magnitude similar to `eps || A ||_2 max(m,n)`

, and so one should ignore singular values below this amount.
any cases of SVD not converging should be a bug: do you have a reproducing example?

there are several SVD algorithms (e.g., Divide and Conquer and QR), so it is unlikely that multiple of them are not converging for the same matrix

and you are correct that ScaLAPACK does not support 64-bit integers (and the problem is in the library itself, not Elemental)

I've been working on getting the LP solvers to work on all of the http://www.netlib.org/lp/data/ examples using a symmetric-indefinite solver and hadn't been checking in as much as I should have

Is there any reason why we can’t further generalize elastic net to penalize l1*||Gx||_1 + l2*||Gx||_2 for a square matrix G.

or even two matrices G,H (where in my case G=H)

Looking at the EN code, it looks like I need to just patch up the corresponding bits of the objective function

and H can be an arbitrary matrix. Not sure how to do the same thing for G here.. but, I don’t need it.

it's still a QP

my guess is that it would take about 10 minutes

ScaLAPACK uses the QR algorithm

have you tested Elemental's QR algorithm on it?

yes

Also, did you verify that ScaLAPACK returned with

`INFO=0`

?