A two-stage ML sparsifier for TSP candidate graphs combines alpha-Nearest and POPMUSIC for high recall then trains a model to cut density while preserving coverage across distance types and instance sizes up to 500.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Defines edge frequencies over optimal i-paths in K_n to characterize OHC edges and gives a DP algorithm for exact TSP in O(n² i_d⁴ 2^{i_d}) time with i_d = O(n^{4/7}).
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Machine Learning for Two-Stage Graph Sparsification for the Travelling Salesman Problem
A two-stage ML sparsifier for TSP candidate graphs combines alpha-Nearest and POPMUSIC for high recall then trains a model to cut density while preserving coverage across distance types and instance sizes up to 500.
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The frequency $K_i$s for symmetrical traveling salesman problem
Defines edge frequencies over optimal i-paths in K_n to characterize OHC edges and gives a DP algorithm for exact TSP in O(n² i_d⁴ 2^{i_d}) time with i_d = O(n^{4/7}).