  Data-driven approaches have been proven effective in solving combinatorial
optimization problems over graphs such as the traveling salesman problems and
the vehicle routing problem. The rationale behind such methods is that the
input instances may follow distributions with salient patterns that can be
leveraged to overcome the worst-case computational hardness. For optimization
problems over graphs, the common practice of neural combinatorial solvers
consumes the inputs in the form of adjacency matrices. In this paper, we
explore a vision-based method that is conceptually novel: can neural models
solve graph optimization problems by \textit{taking a look at the graph
pattern}? Our results suggest that the performance of such vision-based methods
is not only non-trivial but also comparable to the state-of-the-art
matrix-based methods, which opens a new avenue for developing data-driven
optimization solvers.
