CAMBranch: Contrastive Learning with Augmented MILPs for Branching
This work addresses a bottleneck in MILP solving for optimization practitioners, but it is incremental as it builds on existing imitation learning frameworks.
The paper tackles the challenge of time-consuming expert data collection for imitation learning in Branch and Bound branching policies for Mixed Integer Linear Programming (MILP) by proposing CAMBranch, which generates augmented MILPs to increase labeled samples and uses contrastive learning to improve feature capture, resulting in superior performance with only 10% of the dataset.
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 \textbf{C}ontrastive Learning with \textbf{A}ugmented \textbf{M}ILPs for \textbf{Branch}ing (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.