An Amortized Efficiency Threshold for Comparing Neural and Heuristic Solvers in Combinatorial Optimization

In three linesPaper evaluating energy efficiency of neural vs heuristic combinatorial solvers. Defines Amortized Efficiency Threshold (AET): deployment volume where neural network training cost breaks even. On CVRP (n=50), attention-based solver from Kool et al. (2019) reaches energy parity at ~4560 deployed instances. Per-instance neural-to-heuristic ratio: 2.29e-3.

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