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Very slow computation time compared to other methods #300
Description
Hi,
I'm trying to compute short vectors of thousands of lattices in small dimension using SageMaths (Jupyter) on Windows 11.
I tried the function SVP as used in the example files:
from fpylll import LLL, IntegerMatrix, SVP
A = IntegerMatrix.from_matrix([
[-1, -1, 0, -2, 0, 0, -1, 0, 1, 1, -1],
[ 2, 0, 0, 0, 1, 0, -1, 1, 0, 2, 1],
[ 0, -1, -2, 2, 0, 0, 0, -2, 2, -1, -1],
[-1, -1, -1, 1, 1, -1, 2, 0, 2, -1, 2],
[-1, 1, -1, 0, 1, 2, -2, 1, -1, 2, -1],
[-1, 1, -1, -1, 0, 0, 1, 3, 0, 0, -2],
[ 1, 0, 0, 0, 0, 1, -2, 1, 0, -3, -1],
[-2, -2, 0, 2, -1, 0, 0, 1, 0, 2, 0],
[-1, 3, 1, 1, 1, -2, 0, 0, 1, 1, 2],
[-1, 1, 3, 2, 1, 1, 2, 0, 2, 0, -1],
[ 0, 2, 1, 0, -3, 3, -1, 1, 3, -1, -1],
])
t=time.time()
u=SVP.shortest_vector(A, method='proved')
print(time.time()-t,'s')
The time taken is flabbergastingly long : 0.39350318908691406 s.
I have another method, using Fincke-Pohst algorithm implemented in PariGP (qfminim) and that can be used within Sage. Its execution time takes 0.0020112991333007812 s. I wonder why fpylll is so much slower than this.
Why is fpylll so slow and how to get it to work faster ?
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