COMPASS：10万规模下的有序聚类路由
COMPASS: Ordered Clustered Routing at 100K Scale
- NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
COMPASS算法结合搜索与学习加速路由，利用聚类结构解决大规模有序聚类旅行商问题，可扩展至10万节点，性能优于现有方法。
AI中文摘要：
大规模路由问题通常需要按指定顺序访问节点簇，由此产生了有序聚类旅行商问题（OCTSP）。独立优化每个簇看似自然，但会忽略非局部依赖关系。我们提出了用于OCTSP的COMPASS算法，该算法通过编排并行子求解器，将搜索与学习加速路由相结合。COMPASS没有质量上限，其解随计算量增加而持续改进。它利用聚类结构，可在随簇大小而非实例大小呈指数增长的时间内达到精确解。实验上，COMPASS始终优于其他方法。与常见的大规模路由求解器不同，COMPASS使用通用距离矩阵，不限于坐标输入。我们展示了在10万个合成节点和2.85万个真实电商节点上的扩展能力。据我们所知，后者是报道的基于非对称距离的最大规模路由解，比现有ATSP基准大9倍。
英文摘要：
Large-scale routing often requires visiting clusters of nodes in a prescribed order, giving rise to the Ordered Clustered Traveling Salesman Problem (OCTSP). Optimizing each cluster independently seems natural, but misses non-local dependencies. We introduce the COMPASS algorithm for OCTSP, which combines search with learning-accelerated routing by orchestrating parallel sub-solvers. COMPASS has no quality ceiling and its solutions keep improving with compute. It exploits the clustered structure, and can reach exact solutions in time exponential in cluster size rather than instance size. Empirically, COMPASS consistently outperforms alternative methods. Unlike common large-scale routing solvers, COMPASS consumes general distance matrices and is not limited to coordinate inputs. We demonstrate scaling to 100K synthetic nodes and to 28.5K real e-commerce nodes. To our knowledge, the latter is the largest reported routing solution over asymmetric distances, 9x beyond established ATSP benchmarks.
