Cost Problem

[xcTorres]

Thank you for your library, it is very fast. But when I calculate this matrix the result seems like not true.
-625.000 2187.500 -156.25 1000000
-2500.000 1000000 -2500.00 -2500.0
-1015.625 - 1015.625 1000000 1000000
1000000 1000000 1000000 1000000
the answer is x = [2, 3, 1, 0] , y = [3, 2, 0, 1],
But the true result should be x = [0, 2, 1, 3].
The method I used is lapjv(extend_cost=False)

[emanuele]

Hi, could you try to avoid negative costs? Just add 2500.00 or more to your matrix and retry the computation.

…

On Fri, Jan 10, 2020 at 7:33 AM Torres ***@***.***> wrote: Thank you for your library, it is very fast. But when I calculate this matrix the result seems like not true. -625.000 2187.500 -156.25 1000000 -2500.000 1000000 -2500.00 -2500.0 -1015.625 - 1015.625 1000000 1000000 1000000 1000000 1000000 1000000 the answer is x = [2, 3, 1, 0] , y = [3, 2, 0, 1], But the true result should be x = [0, 2, 1, 3]. The method I used is lapjv(extend_cost=False) — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#19?email_source=notifications&email_token=AAAQ55RQ5BSQ2PZHPFWBPM3Q5AJCRA5CNFSM4KFDCXU2YY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4IFIFWCQ>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAAQ55X3JRSNI5MUX45XNQDQ5AJCRANCNFSM4KFDCXUQ> .

[xcTorres]

maybe it is not the reason. Actually 1000000 is a large num used to fill np.inf in my case.
For this matrix in which there is no negatives, but the assign result is also not correct.

[ 0.4 1.0 0.5 1000000
0.0 1000000 0.0 0.0
0.3166 0.3166 1000000 1000000
1000000 1000000 1000000 1000000 ]

By the way, if I use np.inf directly, the result is also wrong. But if using 100,000 to fill np.inf, the result is true.

[jvlmdr]

It seems to be due to the constant LARGE = 1000000 defined here:

lap/lap/lapjv.h

Line 4 in c2b6309

#define LARGE 1000000

I was able to make the test pass with this change:
master...jvlmdr:issue19

However, I didn't remove LARGE completely because I didn't take the time to understand how it is employed in lapmod. It seems to be exported to python.

[gatagat]

The issue here is that for the extension to a square matrix to work, costs are expected to be less than the constant LARGE.

You can preprocess the input cost as follows:

from lap import LARGE
cost[np.inf(cost)] = LARGE-1

Ideally lapjv/lapmod should take an optional check_bounds argument that would check the input cost and raise a verbose exception if there are any values greater or equal than LARGE.