Compute agent 1's best value-maximizing response F, given K.

        Parameters
        ----------
        K : array_like(float, ndim=2)
            A j x n array
        method : str, optional(default='doubling')
            Solution method used in solving the associated Riccati
            equation, str in {'doubling', 'qz'}.

        Returns
        -------
        F : array_like(float, ndim=2)
            The policy function for a given K
        P : array_like(float, ndim=2)
            The value function for a given K

def K_to_F(self, K, method='doubling'):
        """
        Compute agent 1's best value-maximizing response F, given K.

        Parameters
        ----------
        K : array_like(float, ndim=2)
            A j x n array
        method : str, optional(default='doubling')
            Solution method used in solving the associated Riccati
            equation, str in {'doubling', 'qz'}.

        Returns
        -------
        F : array_like(float, ndim=2)
            The policy function for a given K
        P : array_like(float, ndim=2)
            The value function for a given K

        """
        A1 = self.A + dot(self.C, K)
        B1 = self.B
        Q1 = self.Q
        R1 = self.R - self.beta * self.theta * dot(K.T, K)
        lq = LQ(Q1, R1, A1, B1, beta=self.beta)
        P, F, d = lq.stationary_values(method=method)

        return F, P