  End-to-end training of neural network solvers for graph combinatorial
optimization problems such as the Travelling Salesperson Problem (TSP) have
seen a surge of interest recently, but remain intractable and inefficient
beyond graphs with few hundreds of nodes. While state-of-the-art
learning-driven approaches for TSP perform closely to classical solvers when
trained on trivially small sizes, they are unable to generalize the learnt
policy to larger instances at practical scales. This work presents an
end-to-end neural combinatorial optimization pipeline that unifies several
recent papers in order to identify the inductive biases, model architectures
and learning algorithms that promote generalization to instances larger than
those seen in training. Our controlled experiments provide the first principled
investigation into such zero-shot generalization, revealing that extrapolating
beyond training data requires rethinking the neural combinatorial optimization
pipeline, from network layers and learning paradigms to evaluation protocols.
Additionally, we analyze recent advances in deep learning for routing problems
through the lens of our pipeline and provide new directions to stimulate future
research.
