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 <shoppert@baileywick.plus.com> 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 <ilhanpolat@gmail.com> 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 <jb9.bowhay@gmail.com> 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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