title: Convex optimization problem does not follow DCP rules
body: <p>I am trying to solve the following optimization problem using cvxpy:</p>
  
  <p>x and delta_x are (1,N) row vectors. A is a (N,N) symmetric matrix and b is a scalar. I am trying to find a y, such that it minimizes the sum of squares of (y - delta_x) with the constraint (x+y).A.(x+y).T - b = 0. Below is my attempt to solve it.   </p>
  
  <pre><code>x = np.reshape(np.ravel(x_data.T), (1, -1))
  delta_x = np.reshape(np.ravel(delta.T), (1, -1))
  y = cp.Variable(delta_x.shape)
  objective = cp.Minimize(cp.sum_squares(y - delta_x))
  constraints = [cp.matmul(cp.matmul(x + y, A), (x + y).T) == (b*b)]
  prob = cp.Problem(objective, constraints)
  result = prob.solve()
  </code></pre>
  
  <p>I keep getting the error 'cvxpy.error.DCPError: Problem does not follow DCP rules'.  </p>
  
  <p>I followed the rules stated in the answer <a href="https://stackoverflow.com/questions/44343243/constraints-do-not-follow-dcp-rules-in-cvxpy">here</a>, but I don't understand how to construct the proper cvxpy minimization Problem. Any help would be greatly appreciated. 
  Thanks! </p>