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.

翻译：解决NP难/完全组合问题的神经网络方法是一个旨在超越经典近似算法的挑战性研究领域。其长期目标是通过从训练数据中学习生成更优解决方案，从而超越针对NP难/完全问题手工设计的启发式算法。当前基于神经网络的组合优化方法常忽略问题固有的"算法"本质。相比之下，针对组合优化问题（如旅行商问题）设计的启发式算法通常利用成熟算法（如最小生成树算法）。本文提出利用神经算法推理的最新进展来改进组合优化问题的学习。具体而言，我们建议在针对组合优化实例训练之前，先对相关算法进行预训练。实验结果表明，采用这种学习框架，我们比未受算法启发的深度学习模型取得了更优的性能。