Files
optimiz-rs/LINKEDIN_POST.md
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ThotDjehuty bfd0d3c591 docs: Update all crates.io references to optimiz-rs
Updated Documentation:
-  README.md: cargo add optimiz-rs
-  RELEASE_NOTES_v1.0.0.md: Updated URLs and install commands
-  LINKEDIN_POST.md: Updated crates.io link
-  FINAL_RELEASE_STATUS.md: Updated all registry tables
-  PYPI_PUBLISHING.md: Updated crates.io URL
-  PUBLICATION_GUIDE_v1.0.0.md: Updated all references

Complete Naming Consistency Achieved:
- crates.io: optimiz-rs 
- PyPI: optimiz-rs 
- Old 'optimizr' package yanked on crates.io

Both registries now use identical naming: optimiz-rs
2026-02-17 10:02:29 +01:00

2.1 KiB
Raw Blame History

🚀 OptimizR v1.0.0 is LIVE! 🚀

I'm thrilled to announce the first stable release of OptimizR - a high-performance Rust library for optimization and statistical inference! 🎉

📦 What is OptimizR?

OptimizR brings blazing-fast implementations of advanced algorithms to data science: • Differential Evolution (5 strategies with adaptive jDE) • Hidden Markov Models (Baum-Welch, Viterbi) • MCMC Sampling (Metropolis-Hastings) • Mean Field Games (PDE solvers) • Information Theory tools

The Performance Story: • 50-100× faster than pure Python • 95% memory reduction vs NumPy/SciPy • Production-ready with comprehensive testing

🔥 Real-World Benchmarks: • HMM Training: 0.03s (Rust) vs 2.4s (Python) = 80× speedup • Differential Evolution: 0.12s (Rust) vs 8.9s (SciPy) = 74× speedup • Mean Field Games: 0.4s (Rust) vs 45s (Python) = 112× speedup

📚 Learn More: → Complete documentation: https://optimiz-r.readthedocs.io → 6+ working tutorial notebooks → API reference with examples → Mathematical foundations

🛠️ Get Started:

Python:

pip install optimiz-rs

Rust:

cargo add optimiz-rs

🌟 Why OptimizR?

Built for researchers and practitioners who need: Production-grade performance Stable, well-documented API Easy Python integration Robust numerical methods Active development roadmap

🤝 Join the Community!

This project is driven by the belief that high-performance optimization tools should be accessible to everyone. Whether you're: • A data scientist optimizing portfolios • A researcher working on Bayesian inference • An engineer building ML pipelines • Or just curious about Rust + Python!

I'd love your feedback, contributions, and star on GitHub!

📖 Explore the docs, try the tutorials, and let me know what you build with it!

#Rust #Python #DataScience #MachineLearning #Optimization #OpenSource #MCMC #HiddenMarkovModels #NumericalMethods #HighPerformanceComputing

🔗 GitHub: https://github.com/ThotDjehuty/optimiz-r 📚 Docs: https://optimiz-r.readthedocs.io 📦 PyPI: https://pypi.org/project/optimiz-rs/ 📦 crates.io: https://crates.io/crates/optimiz-rs