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
52 lines
1.2 KiB
Python
52 lines
1.2 KiB
Python
"""
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OptimizR - High-Performance Optimization Algorithms
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===================================================
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Fast, reliable implementations of advanced optimization and statistical
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inference algorithms with Rust acceleration and pure Python fallbacks.
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.. moduleauthor:: OptimizR Contributors
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"""
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from optimizr.hmm import HMM
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from optimizr.core import (
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mcmc_sample,
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differential_evolution,
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grid_search,
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mutual_information,
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shannon_entropy,
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sparse_pca_py,
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box_tao_decomposition_py,
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elastic_net_py,
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hurst_exponent_py,
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compute_risk_metrics_py,
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estimate_half_life_py,
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bootstrap_returns_py,
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)
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# Try to import maths_toolkit from Rust backend
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try:
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from optimizr import _core
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maths_toolkit = _core
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except (ImportError, AttributeError):
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maths_toolkit = None
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__version__ = "0.2.0"
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__all__ = [
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"HMM",
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"mcmc_sample",
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"differential_evolution",
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"grid_search",
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"mutual_information",
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"shannon_entropy",
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"sparse_pca_py",
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"box_tao_decomposition_py",
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"elastic_net_py",
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"hurst_exponent_py",
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"compute_risk_metrics_py",
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"estimate_half_life_py",
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"bootstrap_returns_py",
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"maths_toolkit",
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]
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