Learning to branch with Tree-aware Branching Transformers

, with a binary tree representation. In this way, we can fully utilize features exploited from the parameterized B&B search trees and stronger branching policies can be attained thereby. The proposed models are evaluated on multiple benchmark instances and achieve a significant boost on performance, in terms of smaller B&B search trees and lower primal–dual integrals and gaps for harder problems within a given time limit. Ablation studies further validate the effectiveness of our method.