# scipy.linalg.interpolative.svd¶

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

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: 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). Left singular vectors. Singular values. Right singular vectors.

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