  In this paper, we provide a novel strategy for solving Traveling Salesman
Problem, which is a famous combinatorial optimization problem studied intensely
in the TCS community. In particular, we consider the imitation learning
framework, which helps a deterministic algorithm making good choices whenever
it needs to, resulting in a speed up while maintaining the exactness of the
solution without suffering from the unpredictability and a potential large
deviation.
  Furthermore, we demonstrate a strong generalization ability of a graph neural
network trained under the imitation learning framework. Specifically, the model
is capable of solving a large instance of TSP faster than the baseline while
has only seen small TSP instances when training.
