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

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "optimizr"
-version = "0.1.0"
+version = "0.2.0"
 edition = "2021"
 authors = ["Your Name <your.email@example.com>"]
 description = "High-performance optimization algorithms in Rust with Python bindings"
@@ -15,8 +15,8 @@ name = "optimizr"
 crate-type = ["cdylib", "rlib"]
 
 [dependencies]
-pyo3 = { version = "0.21", features = ["extension-module", "abi3-py38"] }
-numpy = "0.21"
+pyo3 = { version = "0.21", features = ["extension-module", "abi3-py38"], optional = true }
+numpy = { version = "0.21", optional = true }
 rand = "0.8"
 rand_distr = "0.4"
 ndarray = "0.15"
@@ -28,7 +28,8 @@ ordered-float = "4.2"
 statrs = "0.17"
 
 [features]
-default = []
+default = ["python-bindings"]
+python-bindings = ["pyo3", "numpy"]
 parallel = []
 
 [dev-dependencies]