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/optimal_control/mod.rs

@@ -0,0 +1,71 @@
+//! Optimal Control Module
+//! ======================
+//!
+//! Generic optimal control algorithms for dynamical systems.
+//!
+//! # Features
+//!
+//! - Generic HJB PDE solver with finite differences
+//! - Viscosity solutions for non-smooth value functions
+//! - Upwind schemes for numerical stability
+//! - Parallel processing support
+//!
+//! # Mathematical Foundation
+//!
+//! ## Hamilton-Jacobi-Bellman Equation
+//!
+//! For a general stochastic process dX_t = μ(X_t)dt + σ(X_t)dW_t,
+//! the HJB equation is:
+//!
+//! ρV(x) = sup_u [μ(x,u)V'(x) + (σ²(x,u)/2)V''(x) + L(x,u)]
+//!
+//! where:
+//! - V(x) is the value function
+//! - ρ is the discount rate
+//! - u is the control
+//! - L(x,u) is the running cost
+//!
+//! ## Viscosity Solutions
+//!
+//! Handle non-smooth value functions via:
+//! 1. Finite difference discretization
+//! 2. Upwind schemes for stability
+//! 3. Iterative convergence to viscosity solution
+
+pub mod hjb_solver;
+pub mod jump_diffusion;
+pub mod mrsjd;
+pub mod regime_switching;
+pub mod viscosity;
+
+pub use hjb_solver::{HJBConfig, HJBResult, HJBSolver};
+pub use jump_diffusion::{
+    JumpDiffusionConfig, JumpDiffusionResult, JumpDiffusionSolver, JumpDistribution,
+};
+pub use mrsjd::{MRSJDConfig, MRSJDResult, MRSJDSolver, RegimeJumpParameters};
+pub use regime_switching::{
+    RegimeParameters, RegimeSwitchingConfig, RegimeSwitchingResult, RegimeSwitchingSolver,
+};
+pub use viscosity::{ViscosityConfig, ViscositySolver};
+
+use thiserror::Error;
+
+#[derive(Error, Debug)]
+pub enum OptimalControlError {
+    #[error("Numerical error: {0}")]
+    NumericalError(String),
+
+    #[error("Convergence failed: {0}")]
+    ConvergenceError(String),
+
+    #[error("Invalid parameters: {0}")]
+    InvalidParameters(String),
+
+    #[error("Insufficient data: need at least {0} points")]
+    InsufficientData(usize),
+
+    #[error("Matrix computation error: {0}")]
+    MatrixError(String),
+}
+
+pub type Result<T> = std::result::Result<T, OptimalControlError>;