Restrict changes to proper norms.

Author: carlosgmartin

numpy/linalg/_linalg.py

@@ -2541,7 +2541,7 @@ def lstsq(a, b, rcond=None):
     return wrap(x), wrap(resids), rank, s
 
 
-def _multi_svd_norm(x, row_axis, col_axis, op, initial):
+def _multi_svd_norm(x, row_axis, col_axis, op, initial=None):
     """Compute a function of the singular values of the 2-D matrices in `x`.
 
     This is a private utility function used by `numpy.linalg.norm()`.
@@ -2765,7 +2765,7 @@ def norm(x, ord=None, axis=None, keepdims=False):
         if ord == inf:
             return abs(x).max(axis=axis, keepdims=keepdims, initial=0)
         elif ord == -inf:
-            return abs(x).min(axis=axis, keepdims=keepdims, initial=inf)
+            return abs(x).min(axis=axis, keepdims=keepdims)
         elif ord == 0:
             # Zero norm
             return (
@@ -2799,7 +2799,7 @@ def norm(x, ord=None, axis=None, keepdims=False):
         if ord == 2:
             ret = _multi_svd_norm(x, row_axis, col_axis, amax, 0)
         elif ord == -2:
-            ret = _multi_svd_norm(x, row_axis, col_axis, amin, inf)
+            ret = _multi_svd_norm(x, row_axis, col_axis, amin)
         elif ord == 1:
             if col_axis > row_axis:
                 col_axis -= 1
@@ -2811,11 +2811,11 @@ def norm(x, ord=None, axis=None, keepdims=False):
         elif ord == -1:
             if col_axis > row_axis:
                 col_axis -= 1
-            ret = add.reduce(abs(x), axis=row_axis).min(axis=col_axis, initial=inf)
+            ret = add.reduce(abs(x), axis=row_axis).min(axis=col_axis)
         elif ord == -inf:
             if row_axis > col_axis:
                 row_axis -= 1
-            ret = add.reduce(abs(x), axis=col_axis).min(axis=row_axis, initial=inf)
+            ret = add.reduce(abs(x), axis=col_axis).min(axis=row_axis)
         elif ord in [None, 'fro', 'f']:
             ret = sqrt(add.reduce((x.conj() * x).real, axis=axis))
         elif ord == 'nuc':

numpy/linalg/tests/test_linalg.py

@@ -2376,21 +2376,11 @@ def test_matrix_norm_empty():
     for shape in [(0, 2), (2, 0), (0, 0)]:
         for dtype in [np.float64, np.float32, np.int32]:
             x = np.zeros(shape, dtype)
-
             assert_equal(np.linalg.matrix_norm(x, ord="fro"), 0)
             assert_equal(np.linalg.matrix_norm(x, ord="nuc"), 0)
-
-            assert_equal(np.linalg.matrix_norm(x, ord=2), 0)
-            assert_equal(np.linalg.matrix_norm(x, ord=-2), np.inf)
-
             assert_equal(np.linalg.matrix_norm(x, ord=1), 0)
-            y = np.inf if x.shape[1] == 0 else 0
-            assert_equal(np.linalg.matrix_norm(x, ord=-1), y)
-
+            assert_equal(np.linalg.matrix_norm(x, ord=2), 0)
             assert_equal(np.linalg.matrix_norm(x, ord=np.inf), 0)
-            z = np.inf if x.shape[0] == 0 else 0
-            assert_equal(np.linalg.matrix_norm(x, ord=-np.inf), z)
-
 
 def test_vector_norm():
     x = np.arange(9).reshape((3, 3))
@@ -2413,8 +2403,6 @@ def test_vector_norm():
 def test_vector_norm_empty():
     for dtype in [np.float64, np.float32, np.int32]:
         x = np.zeros(0, dtype)
-        assert_equal(np.linalg.vector_norm(x, ord=0), 0)
         assert_equal(np.linalg.vector_norm(x, ord=1), 0)
         assert_equal(np.linalg.vector_norm(x, ord=2), 0)
         assert_equal(np.linalg.vector_norm(x, ord=np.inf), 0)
-        assert_equal(np.linalg.vector_norm(x, ord=-np.inf), np.inf)