  In this letter, we propose a data-driven warm start approach, empowered by
artificial neural networks, to boost the efficiency of convex relaxations in
optimal gas flow. Case studies show that this approach significantly decreases
the number of iterations for the convex-concave procedure algorithm, and
optimality and feasibility of the solution can still be guaranteed.
