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References (18)

[1]
OptiMind: Teaching LLMs to Think Like Optimization Experts
2025Zeyi Chen, Xinzhi Zhang et al.
[2]
Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling
2025Yitian Chen, Jingfan Xia et al.
[3]
OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling
2025Hongliang Lu, Zhonglin Xie et al.
[4]
OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale
2024Ali AhmadiTeshnizi, Wenzhi Gao et al.
[5]
OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling
2024Zhicheng YANG, Yinya Huang et al.
[6]
Self-Guiding Exploration for Combinatorial Problems
2024Zangir Iklassov, Yali Du et al.
[7]
Exploring Combinatorial Problem Solving with Large Language Models: A Case Study on the Travelling Salesman Problem Using GPT-3.5 Turbo
2024Mahmoud Masoud, Ahmed Abdelhay et al.
[8]
OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models
2024Ali AhmadiTeshnizi, Wenzhi Gao et al.
[9]
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
2024Haoran Ye, Jiarui Wang et al.
[10]
Chain-of-Experts: When LLMs Meet Complex Operations Research Problems
2024Ziyang Xiao, Dongxiang Zhang et al.
[11]
Mathematical discoveries from program search with large language models
2023Bernardino Romera-Paredes, M. Barekatain et al.
[12]
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
2023Carlos E. Jimenez, John Yang et al.
[13]
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
2023Rindranirina Ramamonjison, Timothy T. L. Yu et al.
[14]
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
2020Yeong-Dae Kwon, Jinho Choo et al.
[15]
The SCIP Optimization Suite 7.0
2020Ksenia Bestuzheva, Mathieu Besanccon et al.
[16]
Attention, Learn to Solve Routing Problems!
2018W. Kool, H. V. Hoof et al.
[17]
Pointer Networks
2015O. Vinyals, Meire Fortunato et al.
[18]
The Design of Approximation Algorithms
2011David P. Williamson, D. Shmoys

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