ScalingOpt - Optimization Community

Welcome to ScalingOpt (Optimization Community), a dedicated community for advancing efficient AI through optimization methods, scalable algorithms, and resource-aware model design.

If this repository has been helpful to you, please consider giving it a ⭐️ to show your support. Your support helps us reach more researchers and contributes to the growth of this resource. Thank you! ☺️

🌟 Introduction

ScalingOpt is a comprehensive platform dedicated to optimization algorithms for large-scale machine learning. As deep learning models grow increasingly complex and datasets become massive, choosing the right optimizer becomes crucial for achieving optimal performance and efficiency.

This platform provides:

• 📚 Extensive Optimizer Library: optimizers from foundational SGD to cutting-edge Adam-mini and Muon
• 🔬 Research Hub: research papers covering optimization theory and latest developments
• 🎓 Educational Resources: Tutorials, guides, and learning paths for all skill levels
• 🤝 Open Source Community: Collaborative environment for researchers and practitioners

🤝 Contributing

We welcome contributions from the optimization community! Here's how you can help:

📝 Add New Optimizers

1. Implement your optimizer in the Optimizers/ directory
2. Follow our coding standards and documentation guidelines
3. Submit a pull request with performance benchmarks

📚 Educational Content

1. Write tutorials or guides
2. Translate content to other languages
3. Improve existing documentation

🐛 Bug Reports & Feature Requests

• Use GitHub Issues for bug reports
• Suggest new features or improvements
• Help improve the website and user experience

🌐 Community

Join our growing community of optimization researchers and practitioners:

• GitHub Discussions: Technical discussions and Q&A
• Research Collaboration: Connect with other researchers
• Blog Posts: Share your optimization insights
• Tutorial Contributions: Help others learn optimization

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

We thank the optimization research community for their groundbreaking work and contributions. Special thanks to:

• All researchers who developed the optimization algorithms featured in this platform

---