# scipy.linalg.interpolative.svd

scipy.linalg.interpolative.svd(A, eps_or_k, rand=True)

  Compute SVD of a matrix via an ID.

  An SVD of a matrix A is a factorization:

      A = numpy.dot(U, numpy.dot(numpy.diag(S), V.conj().T))

  where U and V have orthonormal columns and S is nonnegative.

  The SVD can be computed to any relative precision or rank (depending on the value of eps_or_k).

  See also `interp_decomp` and `id_to_svd`.

  Parameters
    A `numpy.ndarray` or `scipy.sparse.linalg.LinearOperator`

      Matrix to be factored, given as either a `numpy.ndarray` or a `scipy.sparse.linalg.LinearOperator` with the matvec and rmatvec methods (to apply the matrix and its adjoint).

    eps_or_k float or int

      Relative error (if eps_or_k < 1) or rank (if eps_or_k >= 1) of approximation.

    rand bool, optional

      Whether to use random sampling if A is of type `numpy.ndarray` (randomized algorithms are always used if A is of type `scipy.sparse.linalg.LinearOperator`).

  Returns
    U `numpy.ndarray`

      Left singular vectors.

    S `numpy.ndarray`

      Singular values.

    V `numpy.ndarray`

      Right singular vectors.

scipy.linalg.interpolative.id_to_svd scipy.linalg.interpolative.estimate_spectral_norm