docs: def var_quadratic_sum(A, C, H, beta, x0):
      r"""
      Computes the expected discounted quadratic sum
  
      .. math::
  
          q(x_0) = \mathbb{E} \Big[ \sum_{t=0}^{\infty} \beta^t x_t' H x_t \Big]
  
      Here :math:`{x_t}` is the VAR process :math:`x_{t+1} = A x_t + C w_t`
      with :math:`{x_t}` standard normal and :math:`x_0` the initial condition.
  
      Parameters
      ----------
      A : array_like(float, ndim=2)
          The matrix described above in description.  Should be n x n
      C : array_like(float, ndim=2)
          The matrix described above in description.  Should be n x n
      H : array_like(float, ndim=2)
          The matrix described above in description.  Should be n x n
      beta: scalar(float)
          Should take a value in (0, 1)
      x_0: array_like(float, ndim=1)
          The initial condtion. A conformable array (of length n, or with
          n rows)
  
      Returns
      -------
      q0: scalar(float)
          Represents the value :math:`q(x_0)`
  
      Remarks: The formula for computing :math:`q(x_0)` is
      :math:`q(x_0) = x_0' Q x_0 + v`
      where
  
          * :math:`Q` is the solution to :math:`Q = H + \beta A' Q A`, and
          * :math:`v = \frac{trace(C' Q C) \beta}{(1 - \beta)}`
  
      """
      # == Make sure that A, C, H and x0 are array_like == #
  
      A, C, H = list(map(np.atleast_2d, (A, C, H)))
      x0 = np.atleast_1d(x0)
      # == Start computations == #
      Q = scipy.linalg.solve_discrete_lyapunov(sqrt(beta) * A.T, H)
      cq = dot(dot(C.T, Q), C)
      v = np.trace(cq) * beta / (1 - beta)
      q0 = dot(dot(x0.T, Q), x0) + v
  
      return q0