The paper introduces the Compositional Geometry Routing Problem and proposes DiCon, a differential-attention plus double-level contrastive learning solver that reports strong performance and generalization on mixed-geometry routing instances.
Learning to delegate for large-scale vehicle routing.Advances in Neural Information Processing Systems, 34:26198–26211
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The CARM module boosts neural routing solvers by adaptively modulating embeddings with constraint variables, enabling better use of global observations and improved performance on constrained VRPs.
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Learning to Solve Compositional Geometry Routing Problems
The paper introduces the Compositional Geometry Routing Problem and proposes DiCon, a differential-attention plus double-level contrastive learning solver that reports strong performance and generalization on mixed-geometry routing instances.
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Rethinking Constraint Awareness for Efficient State Embedding of Neural Routing Solver
The CARM module boosts neural routing solvers by adaptively modulating embeddings with constraint variables, enabling better use of global observations and improved performance on constrained VRPs.