clarify svd documentation

`u @ np.diag(s) @ vh` can only reproduce the original matrix when `full_matrices` is `False`, otherwise dimension does not match.

1 files changed, 3 insertions, 2 deletions

diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py
index 6b0294659..f0954b996 100644
--- a/numpy/linalg/linalg.py
+++ b/numpy/linalg/linalg.py
@@ -1478,8 +1478,9 @@ def svd(a, full_matrices=True, compute_uv=True, hermitian=False):
     """
     Singular Value Decomposition.
 
-    When `a` is a 2D array, it is factorized as ``u @ np.diag(s) @ vh
-    = (u * s) @ vh``, where `u` and `vh` are 2D unitary arrays and `s` is a 1D
+    When `a` is a 2D array, and when `full_matrices` is `False`,
+    it is factorized as ``u @ np.diag(s) @ vh = (u * s) @ vh``,
+    where `u` and `vh` are 2D unitary arrays and `s` is a 1D
     array of `a`'s singular values. When `a` is higher-dimensional, SVD is
     applied in stacked mode as explained below.