Graph convolutional neural networks for the travelling salesman problem - Joshi, Chaitanya Krishna (2019)
Combinatorial optimization problems, also called NP-hard problems, are practical constraint satisfaction problems that are impossible to solve optimally at large scales. In practice, handcrafted heuristic algorithms are able to solve problems with up to a million variables and constraints. These algorithms are the backbone of modern industries such as transportation, supply chain, and scheduling. However, coming up with good heuristics requires significant specialized knowledge. A recent line of work investigates using deep neural networks to directly learn these heuristics from problem instances instead.