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
eps_or_k : float or int
rand : bool, optional
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Returns: | U : numpy.ndarray
S : numpy.ndarray
V : numpy.ndarray
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