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This paper proposes a method using reservoir computing to recycle computational processes of dynamic programming for combinatorial optimization problems, achieving improved approximation accuracy and reduced computation time on traveling salesman and subset sum problems.
This page describes the spacefilling curve heuristic for generating approximate solutions to the Traveling Salesman Problem, emphasizing its speed, simplicity, and practical applications in routing, logistics, and map drawing.
This paper proposes C2TSP, an end-to-end unsupervised learning method for the Traveling Salesman Problem that learns a tractable distribution over near-tour structures using a connected-by-construction Gibbs family, incorporating implicit differentiation and certificate-guided sharpening to preserve interpretable Hamiltonian structure.
Proposes GES, a learning-based graph sparsification method for Euclidean TSP that adaptively prunes up to 95% of edges while maintaining solution quality within 1% of optimal, demonstrating strong generalization.