OptMATH

🚀 A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling - Revolutionizing LLM capabilities in mathematical optimization

OptMATH is a groundbreaking framework that revolutionizes optimization modeling through innovative bidirectional data synthesis, significantly enhancing mathematical optimization capabilities of large language models.

Authors: Hongliang Lu, Zhonglin Xie, Yaoyu Wu, Can Ren, Yuxuan Chen, Zaiwen Wen
Conference: 🏆 ICML 2025 (Top-tier ML Conference)
Paper: arXiv:2502.11102 | Code: GitHub Repository

🌟 Key Innovations

  • 🔄 Bidirectional Data Synthesis: Revolutionary approach combining forward and backward synthesis for comprehensive optimization modeling
  • 📊 Scalable Framework: Seamlessly handles diverse optimization problem types with unprecedented efficiency
  • 🎯 ICML 2025 Accepted: Recognized at the premier machine learning conference for its groundbreaking contributions
  • 🚀 Interactive Exploration: Rich visualization and analysis tools for deep insights into optimization landscapes
  • ⚡ Performance Breakthrough: Significant improvements in LLM optimization modeling capabilities
Explore Interactive Demo View Code

🎯 Impact & Applications

OptMATH bridges the gap between large language models and mathematical optimization, opening new frontiers in:

  • Automated Optimization Modeling: Transform natural language descriptions into mathematical models
  • Multi-domain Applications: Finance, logistics, engineering, and scientific computing
  • Educational Tools: Interactive learning environments for optimization concepts

🎓 This pioneering work was conducted at Peking University, advancing the state-of-the-art in LLM-driven mathematical optimization.

References