Possible memory leak issue #8
Description
I've been trying to use this package to solve a very large (1M x 1M cost matrix, though very sparse) linear assignment problem. doing this causes a segmentation fault even when passing cardinality_check=False.
To get around this, I cut up my problem into many smaller problems of varying sizes (10kx10k to 300x300) because this is suboptimal but feasible for my use-case. My program will start to solve the smaller problems, but will eventually crash, but never at one particular problem.
To isolate the problem, I wrote the following code to remove the other elements of my code:
import numpy as np
from scipy.sparse import random as sparse_random
import sslap
size = 1000
density = 0.01
loop_count = 100
for i in range(loop_count):
dense_matrix = sparse_random(size, size, density=density, format='coo', data_rvs=np.random.rand).todense()
result = sslap.auction_solve(dense_matrix, cardinality_check=False, fast=True)
print(f"Loop {i + 1} completed")
When this is done, memory usage climbs very quickly. Our compute has 200gb ram and a larger swap, and it quickly exceeds this.
Notably this only happens when passing a sparse matrix, and not a dense matrix. I believe it still happens with the dense matrix but it appears to be much much slower.