fcmaes-rust 0.1.2

Native Rust implementation of fast, parallel, gradient-free optimization.

fcmaes-rust for Python

fcmaes-rust provides Python access to fast, parallel, gradient-free optimization algorithms implemented in native Rust. The optimizer core is pure Rust and does not compile, link, or load the historical C++ backend.

Installation

python -m pip install fcmaes-rust

The initial release supports CPython 3.11 through 3.13 on the platforms for which wheels are listed on PyPI.

Quick check

import fcmaes_rust

print(fcmaes_rust.__version__)
print(fcmaes_rust.phase1_build_info())

Optimizers

The package exposes one-shot and ask/tell interfaces for Differential Evolution, active CMA-ES, CR-FM-NES, PGPE and BiteOpt, plus Dual Annealing, MODE, MAP-Elites, the Diversifier, independent retry, coordinated retry and weighted multi-objective retry.

The functions currently return low-level NumPy arrays, tuples or dictionaries. See the Python binding guide for signatures, examples, parallelism behavior and result layouts. SciPy is a runtime dependency because retry callbacks receive a public scipy.optimize.Bounds object.

import numpy as np
import fcmaes_rust

def sphere(x):
    return float(np.dot(x, x))

dim = 4
empty = np.empty(0, dtype=np.float64)
x, value, evaluations, iterations, stop = fcmaes_rust.optimize_de(
    sphere,
    dim,
    np.full(dim, -5.0),
    np.full(dim, 5.0),
    empty,
    empty,
    np.empty(0, dtype=np.bool_),
    seed=7,
    max_evaluations=4_000,
    popsize=20,
)
print(value, evaluations, x)

Python callbacks must reacquire the GIL. Native Rust objectives offer the strongest parallel scaling; cheap pure-Python callbacks are usually callback-bound.

Project links

• Source repository
• Documentation
• Issue tracker
• Release history

License

MIT