Five deterministic test failures rooted out (all pre-existing on main):
- mrsjd + regime_switching + hjb_solver: pointwise Jacobi iteration on the
stationary HJB diverges (sigma^2/dx^2 >> rho, residual -> NaN). Replaced
with implicit-in-space Kushner-Dupuis upwind discretisation solved by the
Thomas algorithm (unconditionally stable); removed now-unneeded
under-relaxation; NaN-safe convergence checks (!(r < tol)).
- hjb_solver: equation had no source term, so V = 0 and the V' = +-1
boundaries were grid artifacts. Added quadratic tracking payoff and
singular-control obstacle projection -> symmetric boundaries.
- regime_switching two_regime_model: zero running term made cross-regime
value comparison meaningless; running payoff f(x) = x makes the
higher-drift regime strictly more valuable.
- ou_estimator test: stderr(kappa) ~ sqrt(2*kappa/T) was 140% of kappa with
n=500 and an unseeded rng; now seeded StdRng + T ~ 80y (stderr ~ 22%).
- hurst test: fixture passed alternating +-1 LEVELS; R/S input convention
is the increment series -> seeded iid +-1 increments.
- hmm doctest: placeholder example executed empty data -> rust,no_run.
Tests: 139/139 (129 lib + 10 doc).
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
ThotDjehuty