Re: New keyword name for linalg.lu

Yes indeed that's something I checked but in sparse case there is no
possibility of full array return due to the size constraints hence, say in
SuperLU, they only have "perm_r" and "perm_c" integer 1D arrays anyways for
row and column permutation indices. Also permutation index is a thing in
combinatorics so I have

p_as_vector = False/True
return_indices = False/True

so far that doesn't sound confusing and also not that ugly.


On Mon, May 22, 2023 at 8:37 AM Todd Bailey <[email protected]>
wrote:

> Conceptually this seems related to sparse matrices so I wonder if there is
> some helpful terminology there.  Your 1D option would return “column
> indices” for the non-zero entries in each row of the 2D option.
>
> Best,
> Todd
>
> On 21 May 2023, at 16:08, Ilhan Polat <[email protected]> wrote:
>
> [snip]
>
> when
>
> P, L, U = scipy.linalg.lu(A)
>
> is run, currently, P is returning a full 2D array. If A is a tall array
> say, (25, 5) then P is necessarily (25, 25). And it is just a permutaiton
> matrix, a row shuffled np.eye(25). Instead, you can ask with this new
> keyword to return that shuffle pattern. as a 1D array and hence P becomes
> (25, ) array.
> [snip]
> Could you please offer some alternatives even just for inspiration?
>
> Best,
> ilhan
>
>
>
> On Tue, Apr 25, 2023 at 9:34 AM Jake Bowhay <[email protected]> wrote:
>
>> It would be nice to add a quick note to the docs explaining when/which
>> you should use. Currently both state "Compute pivoted LU decomposition of a
>> matrix." which while true isn't very helpful for a user trying to decide
>> which function to pick!
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