Research map / Learning theory and algorithms / Combinatorial and search optimization
Learning to Branch in Mixed Integer Programming
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace pseudocost branching after 500 nodes with an instance-specific learned SVMrank model imitating strong branching | SB+PC · MIP instances, All | number of nodes: 70,455 -> 59,223 | Helped |
| learned branching (SB+ML) vs pseudocost branching | PC · MIP instances, All | unsolved instances: 66 -> 52 | Helped |
| learned branching (SB+ML) vs pseudocost branching | PC · MIP instances | total time: 10% slower on All; 28% and 14% faster on Medium and Hard | Mixed |
| learned branching (SB+ML) vs CPLEX default | CPLEX-D · MIP instances solved by both | number of nodes: only 3% more nodes than CPLEX-D | No clear effect |
| varying alpha in {0.1..0.5} and SVMrank C in {0.001..1} (ablation) | SB+ML · MIP instances | performance | No clear effect |
Closest papers by meaning
- Generalization Guarantees for Learning Score-Based Branch-and-Cut Policies in Integer Programming (NeurIPS 2025 · similarity 0.91)
- CAMBranch: Contrastive Learning with Augmented MILPs for Branching (ICLR 2024 · similarity 0.90)
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- Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut (NeurIPS 2024 · similarity 0.88)
- Search Strategy Generation for Branch and Bound Using Genetic Programming (AAAI 2025 · similarity 0.88)
- Learning Cut Generating Functions for Integer Programming (NeurIPS 2024 · similarity 0.88)
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