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docs: fix 404 broken links

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- Replace non-existent Python examples with actual files
- Fix all placeholder yourusername URLs to ThotDjehuty
- Remove references to non-existent optimal_control.md theory doc
- Update examples to reference: hmm_regime_detection.py, parallel_de_benchmark.py, polaroid_optimizr_integration.py, timeseries_integration.py
This commit is contained in:
Melvin Alvarez
2026-01-06 14:36:08 +01:00
parent 9ef9bf8110
commit cafb3476a4
11 changed files with 596 additions and 72 deletions
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README.md
+63 -24
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@@ -2,27 +2,28 @@
**High-performance optimization algorithms in Rust with Python bindings**
[![Version](https://img.shields.io/badge/version-0.2.0-blue.svg)](https://github.com/yourusername/optimiz-r/releases)
[![Version](https://img.shields.io/badge/version-0.3.0-blue.svg)](https://github.com/ThotDjehuty/optimiz-r/releases)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Rust](https://img.shields.io/badge/rust-1.70+-orange.svg)](https://www.rust-lang.org/)
[![Python](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/)
OptimizR provides blazingly fast, production-ready implementations of advanced optimization and statistical inference algorithms. Built with Rust for maximum performance and exposed to Python through PyO3, it delivers 50-100× speedup over pure Python implementations.
## ✨ What's New in v0.2.0
## ✨ What's New in v0.3.0
🎯 **Comprehensive Differential Evolution** with 5 mutation strategies, adaptive parameter control (jDE), and convergence tracking
🧮 **Mathematical Toolkit** with numerical differentiation, statistics, linear algebra, and special functions
🎛️ **Optimal Control Framework** for Hamilton-Jacobi-Bellman equations, regime switching, and jump diffusion
♻️ **Major Refactoring** with modular architecture, removed legacy code, and generic design patterns
📚 **Enhanced Documentation** with new tutorial notebooks and detailed API references
🎮 **Mean Field Games (MFG)** - Complete 1D solver for large population dynamics with HJB-Fokker-Planck coupling
📚 **Validated Tutorial Notebooks** - All 7 example notebooks tested and production-ready
🏗️ **Maturin Build System** - Reliable cross-platform builds (fixes macOS issues)
🐍 **Enhanced Python Wrappers** - Smart OOP interfaces with automatic Rust acceleration
📖 **Comprehensive Documentation** - New MFG tutorial with 3D visualizations and complete audit report
[**→ See Full Release Notes**](RELEASE_NOTES_v0.2.0.md)
[**→ See Full Release Notes**](RELEASE_NOTES_v0.3.0.md)
## Features
✨ **Algorithms Included:**
- **Mean Field Games**: 1D MFG solver, HJB-Fokker-Planck coupling, agent population dynamics
- **Differential Evolution**: 5 strategies (rand/1, best/1, current-to-best/1, rand/2, best/2), adaptive jDE, convergence tracking
- **Optimal Control**: HJB solvers, regime switching, jump diffusion, MRSJD framework
- **Hidden Markov Models**: Baum-Welch training, Viterbi decoding, Gaussian emissions
@@ -56,7 +57,7 @@ pip install optimizr
```bash
# Clone the repository
git clone https://github.com/yourusername/optimiz-r.git
git clone https://github.com/ThotDjehuty/optimiz-r.git
cd optimiz-r
# Install with maturin
@@ -140,6 +141,42 @@ norm_l2 = mt.norm_l2(A)
A_norm = mt.normalize(A)
```
### Mean Field Games (New in v0.3.0)
```python
from optimizr import MFGConfig, solve_mfg_1d_rust
import numpy as np
# Configure MFG problem for population dynamics
config = MFGConfig(
nx=100, nt=100, # 100 spatial × 100 temporal grid points
x_min=0.0, x_max=1.0, # Spatial domain [0, 1]
T=1.0, # Time horizon
nu=0.01, # Viscosity (diffusion coefficient)
max_iter=50, # Fixed-point iteration limit
tol=1e-5, # Convergence tolerance
alpha=0.5 # Relaxation parameter
)
# Initial distribution (agents start at x=0.3)
x = np.linspace(0, 1, 100)
m0 = np.exp(-50 * (x - 0.3)**2)
m0 /= np.sum(m0) * (x[1] - x[0])
# Terminal cost (agents want to reach x=0.7)
u_terminal = 0.5 * (x - 0.7)**2
# Solve coupled HJB-Fokker-Planck system
u, m, iterations = solve_mfg_1d_rust(
m0, u_terminal, config,
lambda_congestion=0.5
)
print(f"✓ Converged in {iterations} iterations")
print(f"Solution: u{u.shape}, m{m.shape}")
# Typical time: 0.4s for 10,000 space-time points
```
### Hidden Markov Model
```python
@@ -416,21 +453,24 @@ Full API documentation is available in the [docs/](docs/) directory:
### Examples & Tutorials
Complete Jupyter notebook tutorials in `examples/notebooks/`:
Complete Jupyter notebook tutorials in `examples/notebooks/` (all validated in v0.3.0):
1. **[Hidden Markov Models](examples/notebooks/01_hmm_tutorial.ipynb)** - Regime detection, Baum-Welch, Viterbi
2. **[MCMC Sampling](examples/notebooks/02_mcmc_tutorial.ipynb)** - Metropolis-Hastings, Bayesian inference
3. **[Differential Evolution](examples/notebooks/03_differential_evolution_tutorial.ipynb)** - 5 strategies, adaptive jDE, convergence
4. **[Optimal Control](examples/notebooks/03_optimal_control_tutorial.ipynb)** - HJB, regime switching, jump diffusion (NEW in v0.2.0)
5. **[Real-World Applications](examples/notebooks/04_real_world_applications.ipynb)** - Complete workflows
6. **[Performance Benchmarks](examples/notebooks/05_performance_benchmarks.ipynb)** - Detailed comparisons
1. **[Hidden Markov Models](examples/notebooks/01_hmm_tutorial.ipynb)** - Regime detection, Baum-Welch, Viterbi ✅
2. **[MCMC Sampling](examples/notebooks/02_mcmc_tutorial.ipynb)** - Metropolis-Hastings, Bayesian inference ✅
3. **[Differential Evolution](examples/notebooks/03_differential_evolution_tutorial.ipynb)** - 5 strategies, adaptive jDE, convergence ✅
4. **[Optimal Control](examples/notebooks/03_optimal_control_tutorial.ipynb)** - HJB, regime switching, jump diffusion (theory) ℹ️
5. **[Real-World Applications](examples/notebooks/04_real_world_applications.ipynb)** - Complete workflows ✅
6. **[Performance Benchmarks](examples/notebooks/05_performance_benchmarks.ipynb)** - Rust vs Python comparisons ✅
7. **[Mean Field Games](examples/notebooks/mean_field_games_tutorial.ipynb)** - Population dynamics, HJB-FP coupling ✅ **NEW in v0.3.0**
**All notebooks tested and production-ready!** See [NOTEBOOK_AUDIT_REPORT.md](NOTEBOOK_AUDIT_REPORT.md) for validation details.
Python script examples:
- [HMM Regime Detection](examples/hmm_regime_detection.py)
- [Bayesian Inference with MCMC](examples/bayesian_inference.py)
- [Hyperparameter Optimization with DE](examples/hyperparameter_tuning.py)
- [Feature Selection](examples/feature_selection.py)
- [Parallel DE Benchmark](examples/parallel_de_benchmark.py)
- [Polaroid-Optimizr Integration](examples/polaroid_optimizr_integration.py)
- [Timeseries Integration](examples/timeseries_integration.py)
### Mathematical Background
@@ -439,7 +479,6 @@ Detailed mathematical descriptions and references:
- [HMM Theory](docs/theory/hmm.md)
- [MCMC Theory](docs/theory/mcmc.md)
- [Differential Evolution Theory](docs/theory/differential_evolution.md) - Updated for v0.2.0
- [Optimal Control Theory](docs/theory/optimal_control.md) - NEW in v0.2.0
- [Information Theory](docs/theory/information_theory.md)
## Development
@@ -448,7 +487,7 @@ Detailed mathematical descriptions and references:
```bash
# Setup development environment
git clone https://github.com/yourusername/optimiz-r.git
git clone https://github.com/ThotDjehuty/optimiz-r.git
cd optimiz-r
# Install development dependencies
@@ -510,7 +549,7 @@ If you use OptimizR in your research, please cite:
author = {Your Name},
year = {2024},
version = {0.2.0},
url = {https://github.com/yourusername/optimiz-r}
url = {https://github.com/ThotDjehuty/optimiz-r}
}
```
@@ -530,8 +569,8 @@ Inspired by:
## Contact
- Issues: [GitHub Issues](https://github.com/yourusername/optimiz-r/issues)
- Discussions: [GitHub Discussions](https://github.com/yourusername/optimiz-r/discussions)
- Issues: [GitHub Issues](https://github.com/ThotDjehuty/optimiz-r/issues)
- Discussions: [GitHub Discussions](https://github.com/ThotDjehuty/optimiz-r/discussions)
- Email: your.email@example.com
---