mlsanityData to 14 Oct 2026

CAMBranch: Contrastive Learning with Augmented MILPs for Branching

arXiv 2402.03647 ↗First posted 6 Feb 2024cs.LG cs.AIcited by 10

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Recent advancements have introduced machine learning frameworks to enhance the Branch and Bound (B&B) branching policies for solving Mixed Integer Linear Programming (MILP). These methods, primarily relying on imitation learning of Strong Branching, have shown superior performance. However, collecting expert samples for imitation learning, particularly for Strong Branching, is a time-consuming endeavor. To address this challenge, we propose Contrastive Learning with Augmented MILPs for Branching (CAMBranch), a framework that generates Augmented MILPs (AMILPs) by applying variable shifting to limited expert data from their original MILPs. This approach enables the acquisition of a considerable number of labeled expert samples. CAMBranch leverages both MILPs and AMILPs for imitation learning and employs contrastive learning to enhance the model's ability to capture MILP features, thereby improving the quality of branching decisions. Experimental results demonstrate that CAMBranch, trained with only 10% of the complete dataset, exhibits superior performance. Ablation studies further validate the effectiveness of our method.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 30 of its 54 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

30 of 54 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 3.

10 papers.

  1. 2024MILP-StuDio: MILP Instance Generation via Block Structure Decompositioncited by 17
  2. 2025Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programmingcited by 16
  3. 2024Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methodscited by 15
  4. 2024Multi-task Representation Learning for Mixed Integer Linear Programmingcited by 7
  5. 2024Learning by Analogy: Enhancing Few-Shot Prompting for Math Word Problem Solving with Computational Graph-Based Retrievalcited by 5
  6. 2025Dynamic Stratified Contrastive Learning with Upstream Augmentation for MILP Branchingcited by 1
  7. 2026MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solverscited by 1
  8. 2025FMIP: Joint Continuous-Integer Flow For Mixed-Integer Linear Programmingcited by 0
  9. 2026SHSP: Structure-Aware Hierarchical Solution Prediction for Mixed-Integer Linear Programmingcited by 0
  10. 2026Reformulation-Contrastive Learning for Mixed Integer Programscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning Branching Policies for MILPs with Proximal Policy Optimization
  2. 2025A Markov Decision Process for Variable Selection in Branch & Bound
  3. 2022Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories
  4. 2022Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning
  5. 2025Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization
  6. 2022An Improved Reinforcement Learning Algorithm for Learning to Branch
  7. 2026BiFE: Search-Efficient Discovery of CPU-Only Branching Policies via LLM-based Bi-Fidelity Evolution
  8. 2024SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for Learning to Branch