DOC: Refine SVD documentation (#9845)

1 files changed, 89 insertions, 48 deletions

diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py
index 060a3ecd4..bfd6ac28e 100644
--- a/numpy/linalg/linalg.py
+++ b/numpy/linalg/linalg.py
@@ -1300,31 +1300,40 @@ def svd(a, full_matrices=True, compute_uv=True):
     """
     Singular Value Decomposition.
 
-    Factors the matrix `a` as ``u * np.diag(s) * v``, where `u` and `v`
-    are unitary and `s` is a 1-d array of `a`'s singular values.
+    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
+    array of `a`'s singular values. When `a` is higher-dimensional, SVD is
+    applied in stacked mode as explained below.
 
     Parameters
     ----------
     a : (..., M, N) array_like
-        A real or complex matrix of shape (`M`, `N`) .
+        A real or complex array with ``a.ndim >= 2``.
     full_matrices : bool, optional
-        If True (default), `u` and `v` have the shapes (`M`, `M`) and
-        (`N`, `N`), respectively.  Otherwise, the shapes are (`M`, `K`)
-        and (`K`, `N`), respectively, where `K` = min(`M`, `N`).
+        If True (default), `u` and `vh` have the shapes ``(..., M, M)`` and
+        ``(..., N, N)``, respectively.  Otherwise, the shapes are
+        ``(..., M, K)`` and ``(..., K, N)``, respectively, where
+        ``K = min(M, N)``.
     compute_uv : bool, optional
-        Whether or not to compute `u` and `v` in addition to `s`.  True
+        Whether or not to compute `u` and `vh` in addition to `s`.  True
         by default.
 
     Returns
     -------
     u : { (..., M, M), (..., M, K) } array
-        Unitary matrices. The actual shape depends on the value of
-        ``full_matrices``. Only returned when ``compute_uv`` is True.
+        Unitary array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`. The size of the last two dimensions
+        depends on the value of `full_matrices`. Only returned when
+        `compute_uv` is True.
     s : (..., K) array
-        The singular values for every matrix, sorted in descending order.
-    v : { (..., N, N), (..., K, N) } array
-        Unitary matrices. The actual shape depends on the value of
-        ``full_matrices``. Only returned when ``compute_uv`` is True.
+        Vector(s) with the singular values, within each vector sorted in
+        descending order. The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    vh : { (..., N, N), (..., K, N) } array
+        Unitary array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`. The size of the last two dimensions
+        depends on the value of `full_matrices`. Only returned when
+        `compute_uv` is True.
 
     Raises
     ------
@@ -1334,48 +1343,79 @@ def svd(a, full_matrices=True, compute_uv=True):
     Notes
     -----
 
-    .. versionadded:: 1.8.0
-
-    Broadcasting rules apply, see the `numpy.linalg` documentation for
-    details.
-
-    The decomposition is performed using LAPACK routine _gesdd
+    .. versionchanged:: 1.8.0
+       Broadcasting rules apply, see the `numpy.linalg` documentation for
+       details.
+
+    The decomposition is performed using LAPACK routine ``_gesdd``.
+
+    SVD is usually described for the factorization of a 2D matrix :math:`A`.
+    The higher-dimensional case will be discussed below. In the 2D case, SVD is
+    written as :math:`A = U S V^H`, where :math:`A = a`, :math:`U= u`,
+    :math:`S= \\mathtt{np.diag}(s)` and :math:`V^H = vh`. The 1D array `s`
+    contains the singular values of `a` and `u` and `vh` are unitary. The rows
+    of `vh` are the eigenvectors of :math:`A^H A` and the columns of `u` are
+    the eigenvectors of :math:`A A^H`. In both cases the corresponding
+    (possibly non-zero) eigenvalues are given by ``s**2``.
+
+    If `a` has more than two dimensions, then broadcasting rules apply, as
+    explained in :ref:`routines.linalg-broadcasting`. This means that SVD is
+    working in "stacked" mode: it iterates over all indices of the first
+    ``a.ndim - 2`` dimensions and for each combination SVD is applied to the
+    last two indices. The matrix `a` can be reconstructed from the
+    decomposition with either ``(u * s[..., None, :]) @ vh`` or
+    ``u @ (s[..., None] * vh)``. (The ``@`` operator can be replaced by the
+    function ``np.matmul`` for python versions below 3.5.)
+
+    If `a` is a ``matrix`` object (as opposed to an ``ndarray``), then so are
+    all the return values.
 
-    The SVD is commonly written as ``a = U S V.H``.  The `v` returned
-    by this function is ``V.H`` and ``u = U``.
+    Examples
+    --------
+    >>> a = np.random.randn(9, 6) + 1j*np.random.randn(9, 6)
+    >>> b = np.random.randn(2, 7, 8, 3) + 1j*np.random.randn(2, 7, 8, 3)
 
-    If ``U`` is a unitary matrix, it means that it
-    satisfies ``U.H = inv(U)``.
+    Reconstruction based on full SVD, 2D case:
 
-    The rows of `v` are the eigenvectors of ``a.H a``. The columns
-    of `u` are the eigenvectors of ``a a.H``.  For row ``i`` in
-    `v` and column ``i`` in `u`, the corresponding eigenvalue is
-    ``s[i]**2``.
+    >>> u, s, vh = np.linalg.svd(a, full_matrices=True)
+    >>> u.shape, s.shape, vh.shape
+    ((9, 9), (6,), (6, 6))
+    >>> np.allclose(a, np.dot(u[:, :6] * s, vh))
+    True
+    >>> smat = np.zeros((9, 6), dtype=complex)
+    >>> smat[:6, :6] = np.diag(s)
+    >>> np.allclose(a, np.dot(u, np.dot(smat, vh)))
+    True
 
-    If `a` is a `matrix` object (as opposed to an `ndarray`), then so
-    are all the return values.
+    Reconstruction based on reduced SVD, 2D case:
 
-    Examples
-    --------
-    >>> a = np.random.randn(9, 6) + 1j*np.random.randn(9, 6)
+    >>> u, s, vh = np.linalg.svd(a, full_matrices=False)
+    >>> u.shape, s.shape, vh.shape
+    ((9, 6), (6,), (6, 6))
+    >>> np.allclose(a, np.dot(u * s, vh))
+    True
+    >>> smat = np.diag(s)
+    >>> np.allclose(a, np.dot(u, np.dot(smat, vh)))
+    True
 
-    Reconstruction based on full SVD:
+    Reconstruction based on full SVD, 4D case:
 
-    >>> U, s, V = np.linalg.svd(a, full_matrices=True)
-    >>> U.shape, V.shape, s.shape
-    ((9, 9), (6, 6), (6,))
-    >>> S = np.zeros((9, 6), dtype=complex)
-    >>> S[:6, :6] = np.diag(s)
-    >>> np.allclose(a, np.dot(U, np.dot(S, V)))
+    >>> u, s, vh = np.linalg.svd(b, full_matrices=True)
+    >>> u.shape, s.shape, vh.shape
+    ((2, 7, 8, 8), (2, 7, 3), (2, 7, 3, 3))
+    >>> np.allclose(b, np.matmul(u[..., :3] * s[..., None, :], vh))
+    True
+    >>> np.allclose(b, np.matmul(u[..., :3], s[..., None] * vh))
     True
 
-    Reconstruction based on reduced SVD:
+    Reconstruction based on reduced SVD, 4D case:
 
-    >>> U, s, V = np.linalg.svd(a, full_matrices=False)
-    >>> U.shape, V.shape, s.shape
-    ((9, 6), (6, 6), (6,))
-    >>> S = np.diag(s)
-    >>> np.allclose(a, np.dot(U, np.dot(S, V)))
+    >>> u, s, vh = np.linalg.svd(b, full_matrices=False)
+    >>> u.shape, s.shape, vh.shape
+    ((2, 7, 8, 3), (2, 7, 3), (2, 7, 3, 3))
+    >>> np.allclose(b, np.matmul(u * s[..., None, :], vh))
+    True
+    >>> np.allclose(b, np.matmul(u, s[..., None] * vh))
     True
 
     """
@@ -1401,11 +1441,11 @@ def svd(a, full_matrices=True, compute_uv=True):
                 gufunc = _umath_linalg.svd_n_s
 
         signature = 'D->DdD' if isComplexType(t) else 'd->ddd'
-        u, s, vt = gufunc(a, signature=signature, extobj=extobj)
+        u, s, vh = gufunc(a, signature=signature, extobj=extobj)
         u = u.astype(result_t, copy=False)
         s = s.astype(_realType(result_t), copy=False)
-        vt = vt.astype(result_t, copy=False)
-        return wrap(u), s, wrap(vt)
+        vh = vh.astype(result_t, copy=False)
+        return wrap(u), s, wrap(vh)
     else:
         if m < n:
             gufunc = _umath_linalg.svd_m
@@ -1417,6 +1457,7 @@ def svd(a, full_matrices=True, compute_uv=True):
         s = s.astype(_realType(result_t), copy=False)
         return s
 
+
 def cond(x, p=None):
     """
     Compute the condition number of a matrix.