Neural Algorithmic Reasoning for Combinatorial Optimisation
This addresses the challenge of improving neural network solutions for combinatorial optimization problems, but it is incremental as it builds on existing neural algorithmic reasoning methods.
The paper tackles solving NP-hard combinatorial optimization problems by pre-training neural models on relevant algorithms before training on problem instances, achieving superior performance compared to non-algorithmically informed deep learning models.
Solving NP-hard/complete combinatorial problems with neural networks is a challenging research area that aims to surpass classical approximate algorithms. The long-term objective is to outperform hand-designed heuristics for NP-hard/complete problems by learning to generate superior solutions solely from training data. Current neural-based methods for solving CO problems often overlook the inherent "algorithmic" nature of the problems. In contrast, heuristics designed for CO problems, e.g. TSP, frequently leverage well-established algorithms, such as those for finding the minimum spanning tree. In this paper, we propose leveraging recent advancements in neural algorithmic reasoning to improve the learning of CO problems. Specifically, we suggest pre-training our neural model on relevant algorithms before training it on CO instances. Our results demonstrate that by using this learning setup, we achieve superior performance compared to non-algorithmically informed deep learning models.