A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention

Peixin Huang; Yaoxin Wu; Yining Ma; Cathy Wu; Wei Zhang; Wen Song

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arXiv · 2601.04509

A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention

Peixin Huang·Yaoxin Wu·Yining Ma·Cathy Wu·Wei Zhang·Wen Song

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Abstract

Mixed-integer linear programming (MILP) is a foundational framework for combinatorial optimization across science and engineering, but remains hard to solve at scale due to NP-hardness. Recent learning-based methods typically model MILP instances as variable-constraint bipartite graphs and use Graph Neural Networks (GNNs) for representation learning, yet their locality limits representation power. We propose an attention-driven neural backbone that adopts an element-centric view of variables and constraints, with dual attention performing parallel intra-type self-attention and inter-type cross-attention. Across three representative tasks at the instance, element, and solving-state levels, our model consistently outperforms conventional GNN-based architectures, highlighting attention-based, element-centric modeling as a powerful foundation for learning-enhanced combinatorial optimization.

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Peixin Huang, Yaoxin Wu, Yining Ma, Cathy Wu, Wei Zhang, Wen Song. 2026-01-08. A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention. https://arxiv.org/abs/2601.04509

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