Local d-hop uniqueness in GNN node features matches global UID expressiveness for ILP solving while providing stronger generalization.
Apollo-milp: An alter- nating prediction-correction neural solving framework for mixed-integer linear programming
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RL-SPH is a reinforcement learning start primal heuristic that independently produces feasible solutions for ILPs with non-binary integers at 100% rate and with 28.6× lower primal gap than prior start heuristics.
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Feature Augmentation of GNNs for ILPs: Local Uniqueness Suffices
Local d-hop uniqueness in GNN node features matches global UID expressiveness for ILP solving while providing stronger generalization.
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RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs
RL-SPH is a reinforcement learning start primal heuristic that independently produces feasible solutions for ILPs with non-binary integers at 100% rate and with 28.6× lower primal gap than prior start heuristics.