5 papers
EvoOptiGraph: Weakness-Driven Coevolution via Graph-Based Structural Generation for Optimization Modeling
Qingcan Kang, Mingyang Liu, Xiaojin Fu +3
Automating optimization modeling from natural language with large language models (LLMs) faces two key challenges. First, training corpora lack structural diversity. Second, data g…
GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs
Yidong Luo, Xuemin Chen, Chenguang Wang +3
Configuring the hyperparameters of Mixed-integer programming (MIP) solvers is a high-dimensional, instance-dependent optimization problem where suboptimal settings can degrade solv…
Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification
Haoyang Liu, Jie Wang, Boxuan Niu +8
Building mathematical optimization models is critical in operations research (OR), while it requires substantial human expertise. Recent advancements have utilized large language m…
Constraint Matters: Multi-Modal Representation for Reducing Mixed-Integer Linear programming
Jiajun Li, Yixuan Li, Ran Hou +8
Model reduction, which aims to learn a simpler model of the original mixed integer linear programming (MILP), can solve large-scale MILP problems much faster. Most existing model r…
Fast and Interpretable Mixed-Integer Linear Program Solving by Learning Model Reduction
Yixuan Li, Can Chen, Jiajun Li +6
By exploiting the correlation between the structure and the solution of Mixed-Integer Linear Programming (MILP), Machine Learning (ML) has become a promising method for solving lar…