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

src/lib.rs

@@ -24,70 +24,79 @@
 //! - `sparse_optimization`: Sparse PCA, Box-Tao, Elastic Net
 //! - `risk_metrics`: Portfolio risk analysis and Hurst exponent
 
+#[cfg(feature = "python-bindings")]
 use pyo3::prelude::*;
+#[cfg(feature = "python-bindings")]
 use pyo3::types::PyModule;
 
 // Core modules with trait-based architecture
 pub mod core;
 pub mod functional;
+pub mod maths_toolkit; // Mathematical utilities
 
-// New modular structure (recommended)
+// Modular structure (trait-based, generic)
+pub mod de;
 pub mod hmm;
 pub mod mcmc;
-pub mod de;
-pub mod sparse_optimization;
+pub mod optimal_control;
 pub mod risk_metrics;
+pub mod sparse_optimization;
 
-// Legacy modules for backward compatibility
-mod hmm_legacy;
-mod mcmc_legacy;
-mod hmm_refactored;
-mod mcmc_refactored;
-mod de_refactored;
+// Python bindings for legacy compatibility
+#[cfg(feature = "python-bindings")]
 mod differential_evolution;
+#[cfg(feature = "python-bindings")]
 mod grid_search;
+#[cfg(feature = "python-bindings")]
 mod information_theory;
 
 /// OptimizR Python module
+#[cfg(feature = "python-bindings")]
 #[pymodule]
 fn _core(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
     // ===== New Modular API (Recommended) =====
-    
+
     // HMM functions (modular structure)
     m.add_class::<hmm::HMMParams>()?;
     m.add_function(wrap_pyfunction!(hmm::fit_hmm, m)?)?;
     m.add_function(wrap_pyfunction!(hmm::viterbi_decode, m)?)?;
-    
+
     // MCMC functions (modular structure)
     m.add_function(wrap_pyfunction!(mcmc::mcmc_sample, m)?)?;
     m.add_function(wrap_pyfunction!(mcmc::adaptive_mcmc_sample, m)?)?;
-    
+
     // DE functions (modular structure - uses de_refactored for now)
     m.add_class::<de::DEResult>()?;
     m.add_function(wrap_pyfunction!(de::differential_evolution, m)?)?;
-    
-    // ===== Legacy API (Backward Compatible) =====
-    
-    // Legacy optimization functions
-    m.add_function(wrap_pyfunction!(differential_evolution::differential_evolution, m)?)?;
+
+    // ===== Additional Algorithms =====
+
+    // Optimization functions
+    m.add_function(wrap_pyfunction!(
+        differential_evolution::differential_evolution,
+        m
+    )?)?;
     m.add_function(wrap_pyfunction!(grid_search::grid_search, m)?)?;
-    
+
     // Information theory functions
     m.add_function(wrap_pyfunction!(information_theory::mutual_information, m)?)?;
     m.add_function(wrap_pyfunction!(information_theory::shannon_entropy, m)?)?;
-    
+
     // ===== New Optimization Algorithms =====
-    
+
     // Sparse optimization functions
     m.add_function(wrap_pyfunction!(sparse_optimization::sparse_pca_py, m)?)?;
-    m.add_function(wrap_pyfunction!(sparse_optimization::box_tao_decomposition_py, m)?)?;
+    m.add_function(wrap_pyfunction!(
+        sparse_optimization::box_tao_decomposition_py,
+        m
+    )?)?;
     m.add_function(wrap_pyfunction!(sparse_optimization::elastic_net_py, m)?)?;
-    
+
     // Risk metrics functions
     m.add_function(wrap_pyfunction!(risk_metrics::hurst_exponent_py, m)?)?;
     m.add_function(wrap_pyfunction!(risk_metrics::compute_risk_metrics_py, m)?)?;
     m.add_function(wrap_pyfunction!(risk_metrics::estimate_half_life_py, m)?)?;
     m.add_function(wrap_pyfunction!(risk_metrics::bootstrap_returns_py, m)?)?;
-    
+
     Ok(())
 }