# scipy.linalg.svd

scipy.linalg.svd(a, full_matrices=True, compute_uv=True, overwrite_a=False, check_finite=True, lapack_driver='gesdd')

  Singular Value Decomposition.

  Factorizes the matrix a into two unitary matrices `U` and `Vh`, and a 1-D array `s` of singular values (real, non-negative) such that `a == U @ S @ Vh`, where `S` is a suitably shaped matrix of zeros with main diagonal `s`.

  Parameters
    a(M, N) array_like

      Matrix to decompose.

    full_matrices bool, optional

      If True (default), U and Vh are of shape `(M, M)`, `(N, N)`. If False, the shapes are `(M, K)` and `(K, N)`, where `K = min(M, N)`.

    compute_uv bool, optional

      Whether to compute also `U` and `Vh` in addition to `s`. Default is True.

    overwrite_a bool, optional

      Whether to overwrite a; may improve performance. Default is False.

    check_finite bool, optional

      Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

    lapack_driver{‘gesdd’, ‘gesvd’}, optional

      Whether to use the more efficient divide-and-conquer approach (`'gesdd'`) or general rectangular approach (`'gesvd'`) to compute the SVD. MATLAB and Octave use the `'gesvd'` approach. Default is `'gesdd'`.

      New in version 0.18.

  Returns
    U ndarray

      Unitary matrix having left singular vectors as columns. Of shape `(M, M)` or `(M, K)`, depending on full_matrices.

    s ndarray

      The singular values, sorted in non-increasing order. Of shape (K,), with `K = min(M, N)`.

    Vh ndarray

      Unitary matrix having right singular vectors as rows. Of shape `(N, N)` or `(K, N)` depending on full_matrices.

    For `compute_uv=False`, only `s` is returned.
  Raises
    LinAlgError

      If SVD computation does not converge.

  See also

  `svdvals`

    Compute singular values of a matrix.

  `diagsvd`

    Construct the Sigma matrix, given the vector s.

  Examples

      >>> from scipy import linalg
      >>> from numpy.random import default_rng
      >>> rng = default_rng()
      >>> m, n = 9, 6
      >>> a = rng.standard_normal((m, n)) + 1.j*rng.standard_normal((m, n))
      >>> U, s, Vh = linalg.svd(a)
      >>> U.shape,  s.shape, Vh.shape
      ((9, 9), (6,), (6, 6))

  Reconstruct the original matrix from the decomposition:

      >>> sigma = np.zeros((m, n))
      >>> for i in range(min(m, n)):
      ...     sigma[i, i] = s[i]
      >>> a1 = np.dot(U, np.dot(sigma, Vh))
      >>> np.allclose(a, a1)
      True

  Alternatively, use `full_matrices=False` (notice that the shape of `U` is then `(m, n)` instead of `(m, m)`):

      >>> U, s, Vh = linalg.svd(a, full_matrices=False)
      >>> U.shape, s.shape, Vh.shape
      ((9, 6), (6,), (6, 6))
      >>> S = np.diag(s)
      >>> np.allclose(a, np.dot(U, np.dot(S, Vh)))
      True

      >>> s2 = linalg.svd(a, compute_uv=False)
      >>> np.allclose(s, s2)
      True

scipy.linalg.lu_solve scipy.linalg.svdvals