Re: New keyword name for linalg.lu

That breaks practically all LU code out there so we have to keep the array
return for quite a while before we attempt deprecating it.

On Mon, May 22, 2023 at 3:33 PM Christian Lorentzen <[email protected]>
wrote:

> Hi
> How about returning the most efficient one, probably a 1-dim ndarray and
> then providing good examples and/or utility functions for this returned
> object/ndarray? This way, the LU function does not get overloaded.
>
> Best
> Christian
>
> Am 22.05.2023 um 08:58 schrieb Ilhan Polat <[email protected]>:
>
> 
> 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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