A Generative Graph Method to Solve the Travelling Salesman Problem
  The Travelling Salesman Problem (TSP) is a challenging graph task in
combinatorial optimization that requires reasoning about both local node
neighborhoods and global graph structure. In this paper, we propose to use the
novel Graph Learning Network (GLN), a generative approach, to approximately
solve the TSP. GLN model learns directly the pattern of TSP instances as
training dataset, encodes the graph properties, and merge the different node
embeddings to output node-to-node an optimal tour directly or via graph search
technique that validates the final tour. The preliminary results of the
proposed novel approach proves its applicability to this challenging problem
providing a low optimally gap with significant computation saving compared to
the optimal solution.
