def stationary_values(self, method='doubling'):
        """
        Computes the matrix :math:`P` and scalar :math:`d` that represent
        the value function

        .. math::

             V(x) = x' P x + d

        in the infinite horizon case.  Also computes the control matrix
        :math:`F` from :math:`u = - Fx`. Computation is via the solution
        algorithm as specified by the `method` option (default to the
        doubling algorithm) (see the documentation in
        `matrix_eqn.solve_discrete_riccati`).

        Parameters
        ----------
        method : str, optional(default='doubling')
            Solution method used in solving the associated Riccati
            equation, str in {'doubling', 'qz'}.

        Returns
        -------
        P : array_like(float)
            P is part of the value function representation of
            :math:`V(x) = x'Px + d`
        F : array_like(float)
            F is the policy rule that determines the choice of control
            in each period.
        d : array_like(float)
            d is part of the value function representation of
            :math:`V(x) = x'Px + d`

        """
        # === simplify notation === #
        Q, R, A, B, N, C = self.Q, self.R, self.A, self.B, self.N, self.C

        # === solve Riccati equation, obtain P === #
        A0, B0 = np.sqrt(self.beta) * A, np.sqrt(self.beta) * B
        P = solve_discrete_riccati(A0, B0, R, Q, N, method=method)

        # == Compute F == #
        S1 = Q + self.beta * dot(B.T, dot(P, B))
        S2 = self.beta * dot(B.T, dot(P, A)) + N
        F = solve(S1, S2)

        # == Compute d == #
        d = self.beta * np.trace(dot(P, dot(C, C.T))) / (1 - self.beta)

        # == Bind states and return values == #
        self.P, self.F, self.d = P, F, d

        return P, F, d