def _add(self, op1, op2, operator_name, hints): return linear_operator_diag.LinearOperatorDiag( diag=op1.diag_part() + op2.diag_part(), is_non_singular=hints.is_non_singular, is_self_adjoint=hints.is_self_adjoint, is_positive_definite=hints.is_positive_definite, name=operator_name)
def _cholesky_diag(diag_operator): return linear_operator_diag.LinearOperatorDiag(math_ops.sqrt( diag_operator.diag), is_non_singular=True, is_self_adjoint=True, is_positive_definite=True, is_square=True)
def _inverse_diag(diag_operator): return linear_operator_diag.LinearOperatorDiag( 1. / diag_operator.diag, is_non_singular=diag_operator.is_non_singular, is_self_adjoint=diag_operator.is_self_adjoint, is_positive_definite=diag_operator.is_positive_definite, is_square=True)
def _matmul_linear_operator_diag(linop_a, linop_b): return linear_operator_diag.LinearOperatorDiag( diag=linop_a.diag * linop_b.diag, is_non_singular=registrations_util.combined_non_singular_hint( linop_a, linop_b), is_self_adjoint=registrations_util.combined_commuting_self_adjoint_hint( linop_a, linop_b), is_positive_definite=( registrations_util.combined_commuting_positive_definite_hint( linop_a, linop_b)), is_square=True)
def _adjoint_diag(diag_operator): diag = diag_operator.diag if np.issubdtype(diag.dtype, np.complexfloating): diag = math_ops.conj(diag) return linear_operator_diag.LinearOperatorDiag( diag=diag, is_non_singular=diag_operator.is_non_singular, is_self_adjoint=diag_operator.is_self_adjoint, is_positive_definite=diag_operator.is_positive_definite, is_square=True)
def _matmul_linear_operator_diag_scaled_identity_left( linop_scaled_identity, linop_diag): return linear_operator_diag.LinearOperatorDiag( diag=linop_diag.diag * linop_scaled_identity.multiplier, is_non_singular=registrations_util.combined_non_singular_hint( linop_diag, linop_scaled_identity), is_self_adjoint=registrations_util.combined_commuting_self_adjoint_hint( linop_diag, linop_scaled_identity), is_positive_definite=( registrations_util.combined_commuting_positive_definite_hint( linop_diag, linop_scaled_identity)), is_square=True)
def _set_diag_operators(self, diag_update, is_diag_update_positive): """Set attributes self._diag_update and self._diag_operator.""" if diag_update is not None: self._diag_operator = linear_operator_diag.LinearOperatorDiag( self._diag_update, is_positive_definite=is_diag_update_positive) else: if tensor_shape.dimension_value(tensor_shape.TensorShape(self.u.shape)[-1]) is not None: r = tensor_shape.dimension_value(tensor_shape.TensorShape(self.u.shape)[-1]) else: r = array_ops.shape(self.u)[-1] self._diag_operator = linear_operator_identity.LinearOperatorIdentity( num_rows=r, dtype=self.dtype)