  We show that the max entropy algorithm can be derandomized (with respect to a
particular objective function) to give a deterministic $3/2-\epsilon$
approximation algorithm for metric TSP for some $\epsilon > 10^{-36}$.
  To obtain our result, we apply the method of conditional expectation to an
objective function constructed in prior work which was used to certify that the
expected cost of the algorithm is at most $3/2-\epsilon$ times the cost of an
optimal solution to the subtour elimination LP. The proof in this work involves
showing that the expected value of this objective function can be computed in
polynomial time (at all stages of the algorithm's execution).
