ThotDjehuty
d780ed81d7
Adds 9 new top-level / sub-modules to the Rust API only (no Python
bindings yet), with at least one analytic unit test per module.
New Rust modules:
- optimal_control::matrix_riccati (RK4 backward solver)
- timeseries_utils::nonsync_covariance (Hayashi-Yoshida)
- timeseries_utils::wavelet (Haar / Daubechies DWT and MODWT)
- risk_measures (VaR, CVaR, projected sub-gradient CVaR minimisation)
- graph::laplacian + graph::spectral_clustering (Jacobi + k-means++)
- topology (Vietoris-Rips persistent homology, bottleneck distance)
- volterra (Caputo Adams, Markovian lift, second-kind Volterra,
Fourier inversion of characteristic functions)
- signatures (truncated tensor signature, log-sig, random reservoir,
Salvi-Cass-Lyons signature kernel, shuffle product)
All previously stable APIs untouched; abi3-py38 ABI preserved.
New module tests: 29/29 passing. Pre-existing 5 unrelated failures
unchanged.
150 lines
3.8 KiB
ReStructuredText
150 lines
3.8 KiB
ReStructuredText
.. Optimiz-rs documentation master file
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Optimiz-rs Documentation
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======================
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**High-performance optimization algorithms in Rust with Python bindings**
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.. image:: https://img.shields.io/badge/version-1.0.0-blue.svg
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:target: https://github.com/ThotDjehuty/optimiz-r/releases
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:alt: Version
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.. image:: https://img.shields.io/badge/license-MIT-green.svg
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:target: https://github.com/ThotDjehuty/optimiz-r/blob/main/LICENSE
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:alt: License
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Optimiz-rs provides blazingly fast, production-ready implementations of advanced optimization and statistical inference algorithms. Built with Rust for maximum performance and exposed to Python through PyO3, it delivers **50-100× speedup** over pure Python implementations.
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.. toctree::
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:maxdepth: 2
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:caption: Getting Started
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getting-started
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installation
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quickstart
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examples
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.. toctree::
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:maxdepth: 2
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:caption: Algorithms
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algorithms/differential_evolution
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algorithms/mean_field_games
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algorithms/hmm
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algorithms/mcmc
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algorithms/sparse_optimization
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algorithms/optimal_control
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algorithms/risk_metrics
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algorithms/grid_search
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algorithms/point_processes
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.. toctree::
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:maxdepth: 2
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:caption: v1.1 Numerical Primitives
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algorithms/matrix_riccati
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algorithms/nonsync_covariance
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algorithms/wavelet
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algorithms/risk_measures
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algorithms/graph_spectral
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algorithms/topology
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algorithms/volterra
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algorithms/signatures
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.. toctree::
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:maxdepth: 2
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:caption: API Reference
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api/differential_evolution
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api/grid_search
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api/hmm
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api/mcmc
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api/sparse
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api/optimal_control
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api/risk_metrics
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api/point_processes
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.. toctree::
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:maxdepth: 1
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:caption: Advanced
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theory/mathematical_foundations
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mfg_tutorial
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benchmarks
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contributing
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changelog
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Features
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--------
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✨ **Algorithms Included:**
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- **Mean Field Games**: 1D MFG solver, HJB-Fokker-Planck coupling, agent population dynamics
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- **Differential Evolution**: 5 strategies (rand/1, best/1, current-to-best/1, rand/2, best/2), adaptive jDE
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- **Optimal Control**: HJB solvers, regime switching, jump diffusion, MRSJD framework
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- **Hidden Markov Models**: Baum-Welch training, Viterbi decoding, Gaussian emissions
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- **MCMC Sampling**: Metropolis-Hastings, adaptive proposals, Bayesian inference
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- **Sparse Optimization**: Sparse PCA, Box-Tao decomposition, Elastic Net, ADMM
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- **Risk Metrics**: Hurst exponent, half-life estimation, time series analysis
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- **Information Theory**: Mutual information, Shannon entropy, feature selection
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- **Time-Series Helpers**: Rolling Hurst/half-life, feature prep for HMM, lagged feature builders
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- **Parallelization**: Rust-native population evaluation with Rayon for built-in objectives
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🚀 **Performance:**
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- **50-100× faster** than pure Python implementations
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- **95% memory reduction** vs NumPy/SciPy
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- **Parallel-ready** with Rayon infrastructure
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- Production-tested on multi-dimensional problems
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Quick Example
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-------------
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.. code-block:: python
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import numpy as np
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from optimizr import DifferentialEvolution
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# Define objective function
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def sphere(x):
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return np.sum(x**2)
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# Optimize
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de = DifferentialEvolution(
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bounds=[(-5, 5)] * 10,
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strategy="best/1/bin",
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population_size=50
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)
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result = de.optimize(sphere, max_iterations=100)
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print(f"Best fitness: {result.best_fitness:.6f}")
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print(f"Best solution: {result.best_solution}")
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Installation
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------------
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From PyPI (coming soon):
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.. code-block:: bash
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pip install optimizr
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From source:
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.. code-block:: bash
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# Clone repository
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git clone https://github.com/ThotDjehuty/optimiz-r.git
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cd optimiz-r
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# Build and install
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pip install maturin
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maturin develop --release
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Indices and tables
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==================
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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