Optimiz-rs Changelog

Product updates and releases across the Optimiz-rs optimization library, Rust crates, Python bindings (optimiz-rs on PyPI), and documentation, in reverse-chronological order.

February 2026

Optimiz-rs v1.0.0 — First Stable Release

Released February 16, 2026 — Semantic Versioning Begins

📦 Distribution

  • Published to crates.io: cargo add optimiz-rs

  • Published to PyPI: pip install optimiz-rs (package name: optimiz-rs)

  • Stable API commitment: Semantic versioning from v1.0.0 forward

  • Production ready: Comprehensive testing and validation

🚀 Stable Features

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

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

  • MCMC Sampling: Metropolis-Hastings, adaptive proposals, convergence diagnostics

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

  • Mathematical Toolkit: Numerical differentiation, statistics, linear algebra, information theory

  • Grid Search: Exhaustive parameter space exploration

⚡ Performance Benchmarks

Algorithm

Problem

OptimizR (Rust)

Python Baseline

Speedup

Differential Evolution

Rosenbrock 10D

0.12s

8.9s (SciPy)

74×

HMM Training

1000 obs, 3 states

0.03s

2.4s (hmmlearn)

80×

Mean Field Games

100×100 grid

0.4s

45s (Pure Python)

112×

🔧 Breaking Changes from v0.3.0

  • Cargo features: python-bindings moved from default to opt-in

    • Rust-only users: No changes needed

    • Python users: No impact (maturin auto-enables)

    • Explicit Rust library users: Add features = ["python-bindings"] to Cargo.toml

🐛 Bug Fixes

  • Fixed linking errors when using as Rust-only library

  • Fixed PyInit__core symbol warning in maturin builds

  • Resolved flate2 yanked dependency warning

📚 Documentation

January 2025

Optimiz-rs v0.3.0 — Mean Field Games & Maturin Build

Released January 4, 2025 — Major Feature Release

✨ New Features

Mean Field Games (MFG) Framework

  • Complete 1D MFG solver with HJB backward + Fokker-Planck forward

  • Fixed-point iteration for coupled equations

  • Upwind finite difference schemes with Neumann boundaries

  • Convergence diagnostics and stability guarantees

  • Performance: 0.4s for 100×100 grid, 50 iterations (112× vs pure Python)

  • Tutorial notebook: mean_field_games_tutorial.ipynb with 3D visualizations

Maturin Build System

  • Replaced cargo with maturin for reliable cross-platform builds

  • Works on macOS (fixes linker issues)

  • Creates proper Python wheels for abi3 (Python ≥ 3.8)

  • Editable installs with maturin develop

  • Better integration with Python packaging ecosystem

Python Wrapper Architecture

  • Two-layer design: Rust core (PyO3) + Python OOP wrappers

  • User-friendly interfaces (scikit-learn style)

  • Automatic Rust acceleration with graceful Python fallback

  • Example: HMM class wraps _rust_fit_hmm() and _rust_viterbi()

📚 Documentation & Validation

  • All 7 example notebooks audited and tested ✅

  • New MFG tutorial notebook fully working (12/12 cells)

  • Fixed 04_real_world_applications.ipynb (removed invalid random_state)

  • New documentation: MFG_TUTORIAL_COMPLETE.md, NOTEBOOK_AUDIT_REPORT.md, COMPLETE_NOTEBOOK_PROOF.md

🐛 Bug Fixes

  • MFGConfig: Removed ny parameter for 1D problems (was 2D-only)

  • HMM: Removed non-existent random_state parameter

  • macOS build: Resolved via maturin migration

  • Numerical stability: MFG solver handles large gradients without overflow

  • Convergence reporting: Fixed misleading “converged” message

December 2025

Optimiz-rs v0.2.0 — Comprehensive Differential Evolution & Mathematical Toolkit

Released December 10, 2025

🎉 Major Additions

Comprehensive Differential Evolution

  • 5 mutation strategies: rand/1, best/1, current-to-best/1, rand/2, best/2

  • Adaptive jDE: Self-adapting F ∈ [0.1, 1.0] and CR ∈ [0, 1] per individual

  • Convergence tracking: best fitness, mean/std, diversity metrics, early stopping

  • Rich result object with history, generations, function evaluations

  • Performance: 74-88× speedup vs pure Python across benchmark suite

Mathematical Toolkit Module (maths_toolkit)

  • Numerical differentiation: gradient, hessian, jacobian

  • Statistics: mean, variance, skewness, kurtosis, autocorrelation, correlation matrix

  • Linear algebra: norms, normalization, trace, outer product, condition number

  • Numerical integration: trapezoidal, Simpson’s rule

  • Interpolation: linear, 1D grid interpolation

  • Special functions: sigmoid, softplus, relu, soft_threshold, bounds checking

Optimal Control Framework

  • Generic HJB solver for continuous-time optimal control

  • Regime switching systems with Markov chains

  • Jump diffusion processes (Lévy, compound Poisson)

  • MRSJD: Combined Markov regime switching + jump diffusion

  • Finite difference schemes: upwind, value iteration, policy iteration

  • Generic applications: temperature control, inventory, robot navigation, resource allocation

🏗️ Architecture Refactoring

  • Removed legacy code: hmm_legacy.rs, mcmc_legacy.rs, de_refactored.rs

  • Modular structure: core, functional, maths_toolkit, differential_evolution, sparse_optimization, risk_metrics, optimal_control/, hmm/, mcmc/, de/

  • All algorithms now domain-agnostic (finance code moved to application layer)

🚀 Performance

Problem

Dimensions

Python

Rust

Speedup

Sphere

10

12.3s

0.14s

88×

Rosenbrock

10

15.2s

0.18s

84×

Rosenbrock

20

62.5s

0.71s

88×

Rastrigin

10

18.7s

0.22s

85×

Rastrigin

20

72.1s

0.84s

86×

Portfolio

50

145.0s

1.95s

74×

Memory: 95% reduction vs NumPy/SciPy across all dimensions

🔧 Breaking Changes

  • DE API: New parameters (strategy, adaptive, track_history)

  • Result objects: Rich objects replacing simple tuples

  • Module imports: Clean imports from optimizr and optimizr.de

  • Removed: de_refactored, legacy HMM/MCMC modules

Earlier Releases

Optimiz-rs v0.1.x — Initial Development

  • Core HMM implementation (Baum-Welch, Viterbi)

  • MCMC sampling (Metropolis-Hastings)

  • Basic differential evolution (single strategy)

  • Python bindings via PyO3

  • Initial benchmark infrastructure

Cross-Project Integration

Time-Series Analysis with Polarway

Optimiz-rs provides statistical primitives that complement Polarway’s high-performance DataFrame engine:

Polarway (DataFrame)

Optimiz-rs (Algorithms)

OHLCV resampling & rolling windows

Hurst exponent, half-life estimation

VWAP/TWAP calculations

Regime detection (HMM)

Distributed time-series storage

MCMC for Bayesian inference

gRPC streaming for real-time data

Differential evolution for strategy optimization

Hybrid storage (Parquet + DuckDB)

Mean Field Games for market dynamics

Integration Pattern:

import polarway as pw
from optimizr import HMM, DifferentialEvolution, mutual_information

# 1. Load data via Polarway (streaming, distributed)
client = pw.connect("localhost:50051")
df = client.scan_parquet("s3://bucket/trades/*.parquet")

# 2. Feature engineering with Polarway
features = df.with_columns([
    pw.col("returns").rolling_std(20).alias("vol_20"),
    pw.col("volume").rolling_mean(50).alias("vol_avg_50"),
])

# 3. Regime detection with Optimiz-rs
hmm = HMM(n_states=3)
regimes = hmm.fit_predict(features.select("returns").collect().to_numpy())

# 4. Strategy optimization
de = DifferentialEvolution(bounds=[(-1, 1)] * 10, strategy="currenttobest1", adaptive=True)
result = de.optimize(lambda x: -sharpe_ratio(x, features, regimes))

Distributed Topological Data Analysis

Optimiz-rs v1.1+ (planned) will include topological primitives for distributed analysis:

  • Persistent homology for time-series shape analysis

  • Graph spectral methods for network topology

  • Signature methods for path-dependent data

  • Wavelet transforms for multi-scale analysis

Polarway’s distributed computing framework (polarway-distributed) will provide the execution layer for running these algorithms at scale across multiple nodes.

Migration Guides

From Version

To Version

Guide

v0.3.x

v1.0.0

Cargo Feature Flags

v0.2.x

v0.3.0

Add MFG imports

v0.1.x

v0.2.0

DE API Migration