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arXiv Machine Learning By Ido Greenberg, Hugo Linsenmaier, Piotr Sielski, Shie Mannor, Alex Fender, Gal Chechik, Eli Meirom

COMPASS: Ordered Clustered Routing at 100K Scale

Read the original on arXiv Machine Learning →

The paper introduces COMPASS, an algorithm for the Ordered Clustered Traveling Salesman Problem (OCTSP) that combines search with learning-accelerated routing through parallel sub-solvers. COMPASS improves solutions continuously with more compute, exploits clustered structure to achieve exact solutions in time exponential in cluster size, and outperforms existing methods. It works with general distance matrices, not just coordinate inputs, and scales to 100,000 synthetic nodes and 28,500 real e-commerce nodes, representing the largest reported routing solution over asymmetric distances.

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COMPASS: Ordered Clustered Routing at 100K Scale

COMPASS is a new algorithm for the Ordered Clustered Traveling Salesman Problem (OCTSP) that combines search with learning‑accelerated routing by orchestrating parallel sub‑solvers. It exploits the clustered structure to achieve exact solutions in time exponential in cluster size rather than instance size, and its solutions improve continuously with more compute. Empirically, COMPASS outperforms existing methods and scales to 100K synthetic nodes and 28.5K real e‑commerce nodes, representing the largest reported routing solution over asymmetric distances—9× beyond established ATSP benchmarks.

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