Allow default quadratic assignment options

src/pyrecest/_backend/_shared_numpy/linalg.py

@@ -70,7 +70,7 @@ def sqrtm(x):
     return _np.vectorize(_scipy.linalg.sqrtm, signature="(n,m)->(n,m)")(x)
 
 
-def quadratic_assignment(a, b, options):
+def quadratic_assignment(a, b, options=None):
     return list(_scipy.optimize.quadratic_assignment(a, b, options=options).col_ind)
 
 

src/pyrecest/_backend/pytorch/linalg.py

@@ -166,7 +166,7 @@ def matrix_rank(a, tol=None, hermitian=False, *, rtol=None, atol=None, **kwargs)
     return _torch.linalg.matrix_rank(a, atol=atol, rtol=rtol, hermitian=hermitian)
 
 
-def quadratic_assignment(a, b, options):
+def quadratic_assignment(a, b, options=None):
     return list(
         _scipy.optimize.quadratic_assignment(
             _as_numpy_no_grad(a), _as_numpy_no_grad(b), options=options

tests/test_quadratic_assignment_defaults.py

@@ -0,0 +1,54 @@
+import importlib.util
+
+import numpy as np
+
+from pyrecest._backend import numpy as numpy_backend
+
+pytorch_backend = None
+if importlib.util.find_spec("torch") is not None:
+    from pyrecest._backend import pytorch as pytorch_backend
+
+
+def _problem_matrices():
+    adjacency = np.array(
+        [
+            [0.0, 2.0, 1.0],
+            [2.0, 0.0, 3.0],
+            [1.0, 3.0, 0.0],
+        ]
+    )
+    permuted = adjacency[[1, 2, 0]][:, [1, 2, 0]]
+    return adjacency, permuted
+
+
+def test_numpy_quadratic_assignment_accepts_default_options():
+    adjacency, permuted = _problem_matrices()
+
+    assignment = numpy_backend.linalg.quadratic_assignment(adjacency, permuted)
+
+    assert sorted(assignment) == [0, 1, 2]
+
+
+def test_numpy_quadratic_assignment_still_accepts_options_dict():
+    adjacency, permuted = _problem_matrices()
+
+    assignment = numpy_backend.linalg.quadratic_assignment(
+        adjacency,
+        permuted,
+        options={"maximize": False},
+    )
+
+    assert sorted(assignment) == [0, 1, 2]
+
+
+def test_pytorch_quadratic_assignment_accepts_default_options():
+    if pytorch_backend is None:
+        return
+    adjacency, permuted = _problem_matrices()
+
+    assignment = pytorch_backend.linalg.quadratic_assignment(
+        pytorch_backend.array(adjacency),
+        pytorch_backend.array(permuted),
+    )
+
+    assert sorted(assignment) == [0, 1, 2]