Optimiz-rs Documentation¶

High-performance optimization algorithms in Rust with Python bindings

Version License PyPI crates.io

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 50-100Ă— speedup over pure Python implementations.

🚀 Quick Integration with Polarway — Combine Optimiz-rs algorithms with Polarway’s high-performance DataFrame engine for streaming time-series analytics:

🔄 Time-Series Regime Detection

Polarway streams OHLCV data → Optimiz-rs HMM classifies market regimes

đź“Š Bayesian Inference at Scale

Polarway distributed storage → Optimiz-rs MCMC for probabilistic modeling

⚡ Strategy Optimization

Polarway hybrid storage → Optimiz-rs Differential Evolution (74-88× speedup)

🧠 Mean Field Games

Polarway time-series → Optimiz-rs MFG for agent population dynamics

Getting Started

Algorithms

v1.1 Numerical Primitives

v2.0 Generic Stochastic Control & PDE

API Reference

Advanced

Features¶

✨ Algorithms Included:

  • Mean Field Games: 1D MFG solver, HJB-Fokker-Planck coupling, agent population dynamics

  • Differential Evolution: 5 strategies (rand/1, best/1, current-to-best/1, rand/2, best/2), adaptive jDE

  • Optimal Control: HJB solvers, regime switching, jump diffusion, MRSJD framework

  • Hidden Markov Models: Baum-Welch training, Viterbi decoding, Gaussian emissions

  • MCMC Sampling: Metropolis-Hastings, adaptive proposals, Bayesian inference

  • Sparse Optimization: Sparse PCA, Box-Tao decomposition, Elastic Net, ADMM

  • Risk Metrics: Hurst exponent, half-life estimation, time series analysis

  • Information Theory: Mutual information, Shannon entropy, feature selection

  • Time-Series Helpers: Rolling Hurst/half-life, feature prep for HMM, lagged feature builders

  • Parallelization: Rust-native population evaluation with Rayon for built-in objectives

🚀 Performance:

  • 50-100Ă— faster than pure Python implementations

  • 95% memory reduction vs NumPy/SciPy

  • Parallel-ready with Rayon infrastructure

  • Production-tested on multi-dimensional problems

Quick Example¶

import numpy as np
from optimizr import DifferentialEvolution

# Define objective function
def sphere(x):
    return np.sum(x**2)

# Optimize
de = DifferentialEvolution(
    bounds=[(-5, 5)] * 10,
    strategy="best/1/bin",
    population_size=50
)
result = de.optimize(sphere, max_iterations=100)

print(f"Best fitness: {result.best_fitness:.6f}")
print(f"Best solution: {result.best_solution}")

Installation¶

From PyPI:

pip install optimiz-rs

From source:

# Clone repository
git clone https://github.com/ThotDjehuty/optimiz-r.git
cd optimiz-r

# Build and install
pip install maturin
maturin develop --release

Indices and tables¶