AI RESEARCH
Lagrangian Relaxation Score-based Generation for Mixed Integer linear Programming
arXiv CS.LG
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ArXi:2603.24033v1 Announce Type: new Predict-and-search (PaS) methods have shown promise for accelerating mixed-integer linear programming (MILP) solving. However, existing approaches typically assume variable independence and rely on deterministic single-point predictions, which limits solution diversityand often necessitates extensive downstream search for high-quality solutions. In this paper, we propose \textbf{SRG}, a generative framework based on Lagrangian relaxation-guided stochastic differential equations (SDEs), with theoretical guarantees on solution quality.