docs: 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