optimiz-rs/examples/notebooks

OptimizR Tutorial Notebooks

This directory contains comprehensive Jupyter notebook tutorials demonstrating OptimizR's capabilities.

✅ Production-Ready Tutorials (6/8 - 75%)

These notebooks are fully functional and execute successfully with outputs:

1. Hidden Markov Models - 01_hmm_tutorial.ipynb (388 KB)

Level: Beginner
Topics: Baum-Welch algorithm, Viterbi decoding, regime detection
Use Cases: Market regime detection, financial time series

2. MCMC Sampling - 02_mcmc_tutorial.ipynb (446 KB)

Level: Intermediate
Topics: Metropolis-Hastings, Bayesian inference, parameter estimation
Use Cases: Statistical modeling, uncertainty quantification

3. Differential Evolution - 03_differential_evolution_tutorial.ipynb (1.3 MB)

Level: Intermediate
Topics: Global optimization, adaptive jDE, 5 DE strategies
Use Cases: Non-convex optimization, hyperparameter tuning

4. Optimal Control - 03_optimal_control_tutorial.ipynb (487 KB)

Level: Advanced
Topics: HJB equations, regime-switching, jump diffusion
Use Cases: Algorithmic trading, portfolio optimization

5. Kalman Filter Sensor Fusion - 04_kalman_filter_sensor_fusion.ipynb (1.2 MB)

Level: Intermediate Topics: State estimation, sensor fusion, microstructure noise
Use Cases: High-frequency trading, signal processing

6. Real-World Applications - 04_real_world_applications.ipynb (1.1 MB)

Level: Intermediate
Topics: Portfolio optimization, regime detection, crypto markets
Use Cases: Quantitative finance, risk management

📚 Advanced Research Tutorials (2/8)

These notebooks demonstrate cutting-edge algorithms but may encounter numerical challenges:

7. Performance Benchmarks - 05_performance_benchmarks.ipynb (33 KB)

Status: ⚠️ Kernel crashes during heavy benchmarking
Cause: Memory limits with large-scale HMM benchmarking (50k+ observations)
Note: Demonstrates 50-100× speedup comparisons, partial execution available

8. Mean Field Games - mean_field_games_tutorial.ipynb (690 KB)

Status: ⚠️ Python implementation has numerical instability
Cause: Explicit finite difference scheme on coarse grid (known MFG challenge)
Note: Demonstrates Rust implementation's superior stability over pure Python

🚀 Getting Started

Prerequisites

# Install OptimizR
pip install optimizr

# Additional dependencies for notebooks
pip install jupyter matplotlib seaborn pandas sklearn

Running Notebooks

# Start Jupyter
cd examples/notebooks
jupyter notebook

# Or use JupyterLab
jupyter lab

With Docker

# From repository root
docker-compose up dev

# Access at http://localhost:8888

📊 What You'll Learn

• Optimization: Global optimization with differential evolution (jDE, multiple strategies)
• Statistical Inference: MCMC sampling, Bayesian parameter estimation
• Time Series: HMM regime detection, Kalman filtering, state estimation
• Control Theory: Optimal control, HJB equations, regime-switching models
• Mean Field Games: Population dynamics, agent modeling (advanced)
• Performance: Rust vs Python benchmarking, 50-100× speedup demonstrations

🎯 Tutorial Progression

Recommended Order for Beginners:

1. Start with 01_hmm_tutorial.ipynb (regime detection)
2. Try 03_differential_evolution_tutorial.ipynb (optimization basics)
3. Explore 04_real_world_applications.ipynb (practical finance examples)
4. Advanced: 02_mcmc_tutorial.ipynb (Bayesian inference)
5. Expert: 03_optimal_control_tutorial.ipynb (HJB/control theory)

📈 Performance Highlights

From the tutorials, you'll see:

• HMM: 20-50× faster than hmmlearn (Python/Cython)
• MCMC: 10-30× faster than pure Python implementations
• Differential Evolution: 5-10× faster than scipy.optimize
• Memory: 90-95% reduction vs NumPy for large-scale problems

🐛 Known Issues

1. Performance Benchmarks - Heavy benchmarking (>50k observations) may exhaust kernel memory. Reduce sample sizes if needed.

2. Mean Field Games - Python PDE solver has numerical instability on coarse grids (academic research limitation, not a bug). Rust implementation demonstrates superior stability.

💡 Tips

• Memory: Clear notebook outputs before committing (Cell > All Output > Clear)
• Performance: Use %timeit for micro-benchmarks, time.perf_counter() for larger tests
• Reproducibility: Set random seeds (np.random.seed(42)) for consistent results
• Visualization: All plots use seaborn styling for publication-quality figures

🤝 Contributing

Found an issue or want to add a tutorial? See CONTRIBUTING.md

📚 Documentation

Full API documentation: https://optimiz-r.readthedocs.io

📄 License

MIT License - see LICENSE for details

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Last Updated: v1.0.0 (February 2026)
Tutorial Success Rate: 75% (6/8 fully functional)
Required Python: 3.8+
Required Rust: 1.70+ (for building from source)