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_decompandid_to_svd.- Parameters
- A
numpy.ndarrayorscipy.sparse.linalg.LinearOperator Matrix to be factored, given as either a
numpy.ndarrayor ascipy.sparse.linalg.LinearOperatorwith the matvec and rmatvec methods (to apply the matrix and its adjoint).- eps_or_kfloat or int
Relative error (if eps_or_k < 1) or rank (if eps_or_k >= 1) of approximation.
- randbool, optional
Whether to use random sampling if A is of type
numpy.ndarray(randomized algorithms are always used if A is of typescipy.sparse.linalg.LinearOperator).
- A
- Returns
- U
numpy.ndarray Left singular vectors.
- S
numpy.ndarray Singular values.
- V
numpy.ndarray Right singular vectors.
- U