r"""

        Given K and F, compute the value of deterministic entropy, which
        is

        .. math::

            \sum_t \beta^t x_t' K'K x_t`

        with

        .. math::

            x_{t+1} = (A - BF + CK) x_t

        Parameters
        ----------
        F : array_like(float, ndim=2)
            The policy function, a k x n array
        K : array_like(float, ndim=2)
            The worst case matrix, a j x n array
        x0 : array_like(float, ndim=1)
            The initial condition for state

        Returns
        -------
        e : scalar(int)
            The deterministic entropy

def compute_deterministic_entropy(self, F, K, x0):
        r"""

        Given K and F, compute the value of deterministic entropy, which
        is

        .. math::

            \sum_t \beta^t x_t' K'K x_t`

        with

        .. math::

            x_{t+1} = (A - BF + CK) x_t

        Parameters
        ----------
        F : array_like(float, ndim=2)
            The policy function, a k x n array
        K : array_like(float, ndim=2)
            The worst case matrix, a j x n array
        x0 : array_like(float, ndim=1)
            The initial condition for state

        Returns
        -------
        e : scalar(int)
            The deterministic entropy

        """
        H0 = dot(K.T, K)
        C0 = np.zeros((self.n, 1))
        A0 = self.A - dot(self.B, F) + dot(self.C, K)
        e = var_quadratic_sum(A0, C0, H0, self.beta, x0)

        return e