ScalingOpt - Optimization Community

Welcome to ScalingOpt (Optimization Community), a meticulously curated collection of optimization algorithms implemented in PyTorch, designed to serve the diverse needs of the machine learning research community.

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

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