Melvin Avarez
79f51e4775
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
43 lines
1.0 KiB
TOML
43 lines
1.0 KiB
TOML
[package]
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name = "optimizr"
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version = "0.2.0"
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edition = "2021"
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authors = ["Your Name <your.email@example.com>"]
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description = "High-performance optimization algorithms in Rust with Python bindings"
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license = "MIT"
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repository = "https://github.com/yourusername/optimiz-r"
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keywords = ["optimization", "machine-learning", "statistics", "numerical", "scientific"]
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categories = ["algorithms", "science", "mathematics"]
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readme = "README.md"
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[lib]
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name = "optimizr"
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crate-type = ["cdylib", "rlib"]
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[dependencies]
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pyo3 = { version = "0.21", features = ["extension-module", "abi3-py38"], optional = true }
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numpy = { version = "0.21", optional = true }
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rand = "0.8"
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rand_distr = "0.4"
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ndarray = "0.15"
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ndarray-linalg = { version = "0.16", features = ["openblas-system"] }
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num-traits = "0.2"
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rayon = "1.8"
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thiserror = "1.0"
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ordered-float = "4.2"
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statrs = "0.17"
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[features]
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default = ["python-bindings"]
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python-bindings = ["pyo3", "numpy"]
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parallel = []
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[dev-dependencies]
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criterion = "0.5"
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approx = "0.5"
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[profile.release]
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opt-level = 3
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lto = true
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codegen-units = 1
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