diff --git a/numpy/linalg/_linalg.py b/numpy/linalg/_linalg.py
index 52a2ffb8f50b..7471db7e1fe2 100644
--- a/numpy/linalg/_linalg.py
+++ b/numpy/linalg/_linalg.py
@@ -2011,6 +2011,7 @@ def cond(x, p=None):
# contain nans in the entries where inversion failed.
_assert_stacked_square(x)
t, result_t = _commonType(x)
+ result_t = _realType(result_t) # condition number is always real
signature = 'D->D' if isComplexType(t) else 'd->d'
with errstate(all='ignore'):
invx = _umath_linalg.inv(x, signature=signature)
diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py
index 8ad1c3ed6d16..4cc9ac7a5496 100644
--- a/numpy/linalg/tests/test_linalg.py
+++ b/numpy/linalg/tests/test_linalg.py
@@ -793,15 +793,28 @@ def do(self, a, b, tags):
class TestCond(CondCases):
- def test_basic_nonsvd(self):
+ @pytest.mark.parametrize('is_complex', [False, True])
+ def test_basic_nonsvd(self, is_complex):
# Smoketest the non-svd norms
A = array([[1., 0, 1], [0, -2., 0], [0, 0, 3.]])
+ if is_complex:
+ # Since A is linearly scaled, the condition number should not change
+ A = A * (1 + 1j)
assert_almost_equal(linalg.cond(A, inf), 4)
assert_almost_equal(linalg.cond(A, -inf), 2 / 3)
assert_almost_equal(linalg.cond(A, 1), 4)
assert_almost_equal(linalg.cond(A, -1), 0.5)
assert_almost_equal(linalg.cond(A, 'fro'), np.sqrt(265 / 12))
+ @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+ @pytest.mark.parametrize('norm_ord', [1, -1, 2, -2, 'fro', np.inf, -np.inf])
+ def test_cond_dtypes(self, dtype, norm_ord):
+ # Check that the condition number is computed in the same dtype
+ # as the input matrix
+ A = array([[1., 0, 1], [0, -2., 0], [0, 0, 3.]], dtype=dtype)
+ out_type = get_real_dtype(dtype)
+ assert_equal(linalg.cond(A, p=norm_ord).dtype, out_type)
+
def test_singular(self):
# Singular matrices have infinite condition number for
# positive norms, and negative norms shouldn't raise
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