The Assignment Problem, a NumPy function?

There is now a numpy implementation of the munkres algorithm in scikit-learn under sklearn/utils/linear_assignment_.py its only dependency is numpy. I tried it with some approximately 20x20 matrices, and it seems to be about 4 times as fast as the one linked to in the question. cProfiler shows 2.517 seconds vs 9.821 seconds for 100 iterations.

I was hoping that the newer scipy.optimize.linear_sum_assignment would be fastest, but (perhaps not surprisingly) the Cython library (which does not have pip support) is significantly faster, at least for my use case:

UPDATE: using munkres v1.1.2 and scipy v1.5.0 achieves the following results:

$ python -m timeit -s "from scipy.optimize import linear_sum_assignment; import numpy as np; np.random.seed(0); c = np.random.rand(20,30)" "a,b = linear_sum_assignment(c)"10000 loops, best of 5: 32.8 usec per loop$ python -m timeit -s "from munkres import Munkres; import numpy as np;  np.random.seed(0); c = np.random.rand(20,30); m = Munkres()" "a = m.compute(c)"100 loops, best of 5: 2.41 msec per loop$ python -m timeit -s "from scipy.optimize import linear_sum_assignment; import numpy as np; np.random.seed(0);" "c = np.random.rand(20,30); a,b = linear_sum_assignment(c)"5000 loops, best of 5: 51.7 usec per loop$ python -m timeit -s "from munkres import Munkres; import numpy as np;  np.random.seed(0)" "c = np.random.rand(20,30); m = Munkres(); a = m.compute(c)"10 loops, best of : 26 msec per loop

No, NumPy contains no such function. Combinatorial optimization is outside of NumPy's scope. It may be possible to do it with one of the optimizers in scipy.optimize but I have a feeling that the constraints may not be of the right form.

NetworkX probably also includes algorithms for assignment problems.