AI RESEARCH
Feature Augmentation of GNNs for ILPs: Local Uniqueness Suffices
arXiv CS.LG
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ArXi:2509.21000v2 Announce Type: replace Integer Linear Programs (ILPs) are central to real-world optimizations but notoriously difficult to solve. Learning to Optimize (L2O) has emerged as a promising paradigm, with Graph Neural Networks (GNNs) serving as the standard backbone. However, standard anonymous GNNs are limited in expressiveness for ILPs, and the common enhancement of augmenting nodes with globally unique identifiers (UIDs) typically