def idzp_asvd(eps, A): """ Compute SVD of a complex matrix to a specified relative precision using random sampling. :param eps: Relative precision. :type eps: float :param A: Matrix. :type A: :class:`numpy.ndarray` :return: Left singular vectors. :rtype: :class:`numpy.ndarray` :return: Right singular vectors. :rtype: :class:`numpy.ndarray` :return: Singular values. :rtype: :class:`numpy.ndarray` """ A = np.asfortranarray(A) m, n = A.shape n2, winit = _id.idz_frmi(m) w = np.empty( max((min(m, n) + 1)*(3*m + 5*n + 11) + 8*min(m, n)**2, (2*n + 1)*(n2 + 1)), dtype=np.complex128, order='F') k, iU, iV, iS, w, ier = _id.idzp_asvd(eps, A, winit, w) if ier: raise _RETCODE_ERROR U = w[iU-1:iU+m*k-1].reshape((m, k), order='F') V = w[iV-1:iV+n*k-1].reshape((n, k), order='F') S = w[iS-1:iS+k-1] return U, V, S
def idzp_asvd(eps, A): """ Compute SVD of a complex matrix to a specified relative precision using random sampling. :param eps: Relative precision. :type eps: float :param A: Matrix. :type A: :class:`numpy.ndarray` :return: Left singular vectors. :rtype: :class:`numpy.ndarray` :return: Right singular vectors. :rtype: :class:`numpy.ndarray` :return: Singular values. :rtype: :class:`numpy.ndarray` """ A = np.asfortranarray(A) m, n = A.shape n2, winit = _id.idz_frmi(m) w = np.empty( max((min(m, n) + 1)*(3*m + 5*n + 11) + 8*min(m, n)**2, (2*n + 1)*(n2 + 1)), dtype=np.complex128, order='F') k, iU, iV, iS, w, ier = _id.idzp_asvd(eps, A, winit, w) if ier: raise _RETCODE_ERROR U = w[iU-1:iU+m*k-1].reshape((m, k), order='F') V = w[iV-1:iV+n*k-1].reshape((n, k), order='F') S = w[iS-1:iS+k-1] return U, V, S