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- May 14 01:33poulson closed #276
- May 14 01:33poulson commented #276
- May 14 01:30
poulson on master

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- May 14 01:24
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- May 10 21:37jedbrown commented #276
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- Feb 11 21:53BenBrock commented #228
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- Jan 23 23:57adambaskerville commented #273

err, actually I did implement

`Reshape`

DistMatrix<T,El::CIRC,El::CIRC> C_copy(C.Height(),C.Width(),g);

C_copy=C;

C_copy.Resize(C_copy.Height()*C_copy.Width(),1);

C_copy=C;

C_copy.Resize(C_copy.Height()*C_copy.Width(),1);

The

`CIRC,CIRC`

distribution puts the entire matrix on one process
i started working on it last year but didn't find much material online.

What CMake configuration flags did you use?

the usual issue is that you need to make sure your

`PYTHONPATH`

includes the Python installation directory
Also

`DYLD_LIBRARY_PATH`

needs to be set: http://libelemental.org/documentation/dev/build.html#mac-os-x
The python files were not installed in the right folder as a package under python3.5/site-packages but in the top-level folder

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