diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/linalg/linalg.py | 11 |
1 files changed, 6 insertions, 5 deletions
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index f0954b996..d3acc5938 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -1478,11 +1478,12 @@ def svd(a, full_matrices=True, compute_uv=True, hermitian=False): """ Singular Value Decomposition. - 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. + When `a` is a 2D array, and ``full_matrices=False``, then it is + factorized as ``u @ np.diag(s) @ vh = (u * s) @ vh``, where + `u` and the Hermitian transpose of `vh` are 2D arrays with + orthonormal columns 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. Parameters ---------- |