most citedLeveraging Large Language Models for Solving Rare MIP Challenges

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025

A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions

Ziyang Xiao, Jingrong Xie, Lilin Xu +15

By virtue of its great utility in solving real-world problems, optimization modeling has been widely employed for optimal decision-making across various sectors, but it requires su…

cs.AI2025

Decision Information Meets Large Language Models: The Future of Explainable Operations Research

Yansen Zhang, Qingcan Kang, Wing Yin Yu +5

Operations Research (OR) is vital for decision-making in many industries. While recent OR methods have seen significant improvements in automation and efficiency through integratin…

cs.AI2024

BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving

Teng Wang, Wing-Yin Yu, Zhenqi He +8

LLMs exhibit advanced reasoning capabilities, offering the potential to transform natural language questions into mathematical models. However, existing open-source datasets in ope…

cs.CL2024★ 2 cited

Leveraging Large Language Models for Solving Rare MIP Challenges

Teng Wang, Wing-Yin Yu, Ruifeng She +3

Mixed Integer Programming (MIP) has been extensively applied in areas requiring mathematical solvers to address complex instances within tight time constraints. However, as the pro…

cs.CL2024

Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Prompts

Teng Wang, Zhenqi He, Wing-Yin Yu +2

With the advent of Large Language Models (LLMs), generating rule-based data for real-world applications has become more accessible. Due to the inherent ambiguity of natural languag…