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

A common critique of neural combinatorial-optimization solvers is that they are less energy-efficient than CPU metaheuristics, given the operational energy cost of training them on GPUs. This paper examines the inferential step from "training is expensive" to "neural solvers are net-inefficient", wh...