Research map / Learning theory and algorithms / Combinatorial and search optimization
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add an explicit search-tree state that gates candidate-variable representations (ablation) | NoTree branching policy · Heterogeneous MILP test instances | top-1 accuracy: 64.02 -> 83.70 | Helped |
| Add an explicit search-tree state that gates candidate-variable representations (ablation) | NoTree branching policy · Heterogeneous MILP test instances | top-5 accuracy: 88.51 -> 95.83 | Helped |
| Add an explicit search-tree state that gates candidate-variable representations (ablation) | NoTree branching policy · MILP test instances | B&B nodes (shifted geometric mean): 3068.96 -> 2239.47 | Helped |
| Use batch normalization (ablation) | NoTree · neos-476283 | B&B nodes: 66170.66 | Hurt |
Closest papers by meaning
- Generative Branching for Mixed-Integer Linear Programming (AAAI 2026 · similarity 0.94)
- Learning Branching Policies for MILPs with Proximal Policy Optimization (AAAI 2026 · similarity 0.93)
- CAMBranch: Contrastive Learning with Augmented MILPs for Branching (ICLR 2024 · similarity 0.91)
- Generalization Guarantees for Learning Score-Based Branch-and-Cut Policies in Integer Programming (NeurIPS 2025 · similarity 0.90)
- Search Strategy Generation for Branch and Bound Using Genetic Programming (AAAI 2025 · similarity 0.90)
- Rethinking the Capacity of Graph Neural Networks for Branching Strategy (NeurIPS 2024 · similarity 0.88)
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