Commit Graph

2 Commits

Author SHA1 Message Date
Melvin Alvarez 6b084ae396 docs: add execution outputs to working notebooks
Successfully executed and saved outputs for:
- 01_hmm_tutorial.ipynb (HMM regime detection examples)
- 03_optimal_control_tutorial.ipynb (Optimal control and Kalman filtering)

These notebooks now demonstrate working code with real outputs,
validating documentation examples for v1.0.0 release.

Note: Several notebooks require API updates to match current library:
- 02_mcmc_tutorial.ipynb - mcmc_sample API changed
- 03_differential_evolution_tutorial.ipynb - parameter names changed
- 04_kalman_filter_sensor_fusion.ipynb - syntax errors
- 04_real_world_applications.ipynb - requires investigation
- 05_performance_benchmarks.ipynb - requires investigation
- mean_field_games_tutorial.ipynb - requires investigation

These will be fixed in follow-up commits.
2026-02-16 16:59:21 +01:00
Melvin Avarez 79f51e4775 Release v0.2.0: Comprehensive DE, Mathematical Toolkit, Optimal Control
Major Features:
• Comprehensive Differential Evolution with 5 strategies (rand1, best1, currenttobest1, rand2, best2)
• Adaptive jDE algorithm for self-tuning F and CR parameters
• Convergence tracking with history records and early stopping
• Mathematical toolkit module (780 lines): gradient, hessian, jacobian, statistics, linear algebra
• Optimal control framework: HJB solvers, regime switching, jump diffusion, MRSJD
• Sparse optimization: Sparse PCA, Box-Tao decomposition, ADMM, Elastic Net
• Rayon parallelization infrastructure (ready for pure Rust objectives)

Performance:
• 74-88× speedup for DE vs SciPy
• 50-100× speedup overall vs pure Python

Refactoring & Cleanup:
• Removed 5 legacy files (de_refactored.rs, hmm_legacy.rs, hmm_refactored.rs, mcmc_legacy.rs, mcmc_refactored.rs)
• Modular architecture with trait-based design
• Generic implementations (no domain-specific code)
• Updated Python bindings for new DE API
• Fixed ALL compilation warnings (0 errors, 0 warnings)

Documentation:
• Updated README with v0.2.0 features and benchmarks
• Created RELEASE_NOTES_v0.2.0.md (comprehensive changelog)
• New optimal control tutorial notebook (03_optimal_control_tutorial.ipynb)
• Updated API examples in README
• Created test_release.py for release validation

Version Bumps:
• Cargo.toml: 0.1.0 → 0.2.0
• pyproject.toml: 0.1.0 → 0.2.0
• python/__init__.py: 0.1.0 → 0.2.0

Breaking Changes:
• DE API: mutation_factor/crossover_rate → f/cr
• DE API: use_adaptive_jde → adaptive
• DE API: strategy names simplified (e.g., 'rand/1/bin' → 'rand1')
• DE returns: (x, fun) tuple instead of dict-like object

Known Items (Post-Release):
• Mathematical toolkit functions available in Rust but not yet exposed to Python
• MCMC Python wrapper needs API update to match new Rust implementation
• Tutorial notebooks need DE API updates

Tests: 34 Rust tests passing, core Python functionality validated with test_release.py
2025-12-10 18:54:32 +01:00