Research map / Graph learning / Graph algorithms and clustering
On Representing Mixed-Integer Linear Programs by Graph Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Restrict the dataset to unfoldable MILPs | GNN feasibility prediction · Unfoldable MILPs in D1 | feasibility prediction error | Helped |
| Use a standard GNN without random features on foldable MILPs | GNN feasibility prediction · Foldable MILPs in D2 | feasibility prediction error | Hurt |
| Append random vertex features | GNN feasibility prediction · Foldable MILPs in D2 | feasibility prediction error | Helped |
| Restrict the dataset to unfoldable instances | GNN optimal objective value prediction · Feasible instances in D1 | approximation error | Helped |
| Append random features to the graph | GNN optimal objective value prediction · Feasible instances in D2 | approximation error | Helped |
| Use a larger GNN | GNN optimal solution prediction · Feasible instances in D1 and D2 | solution approximation error | Mixed |
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