`scipy.linalg.interpolative:svd`

Signature

def svd ( A , eps_or_k , rand = True , rng = None )

Compute SVD of a matrix via an ID.

An SVD of a matrix A is a factorization

A = U @ np.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).

A : :class:`numpy.ndarray` or :class:`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).
rng : `numpy.random.Generator`, optional: Pseudorandom number generator state. When rng is None, a new numpy.random.Generator is created using entropy from the operating system. Types other than numpy.random.Generator are passed to numpy.random.default_rng to instantiate a Generator. If rand is False, the argument is ignored.