AGDN is a new GNN framework using a MixScore matrix and anisotropic graph diffusion to outperform prior methods on TSP instances across sizes and distributions.
Keld Helsgaun
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3years
2026 3representative citing papers
Edge-aware decoder exposes transition-level quantities at decision time, cutting the ATSP-1000 gap from 4.13% to 2.73% over RADAR on SVD/Sinkhorn backbone.
A Transformer model trained via supervised learning on ILP solutions for a new nursing care taxi dispatch VRP variant reduces operating time by up to 8% on small instances while keeping constraint violations low.
citing papers explorer
-
AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network
AGDN is a new GNN framework using a MixScore matrix and anisotropic graph diffusion to outperform prior methods on TSP instances across sizes and distributions.
-
Edge-aware Decoding for Neural Asymmetric Routing
Edge-aware decoder exposes transition-level quantities at decision time, cutting the ATSP-1000 gap from 4.13% to 2.73% over RADAR on SVD/Sinkhorn backbone.
-
Optimizing Nursing Care Taxi Dispatch Leveraging Integer Linear Programming Solvers and Machine Learning
A Transformer model trained via supervised learning on ILP solutions for a new nursing care taxi dispatch VRP variant reduces operating time by up to 8% on small instances while keeping constraint violations low.