anneal

Anneal

Start here. Bound-constrained global optimization with a single budget knob, or classical simulated-annealing presets you can swap without rewriting a driver.

Simulated-annealing components on the eindir typed primitives. One surface, many drivers: classical presets, Bayesian pilot+mixer, generalized Langevin equation (GLE) colored noise, rank-1 additive independence, quasi-Monte Carlo (QMC) polish, device/ensemble scale. All obey the same five-component algebra (Obj / Cool / Neigh / Move / Accept) and four composition laws checked at construction.


Docs	https://anneal.rgoswami.me
License	MIT
Software DOI	https://zenodo.org/doi/10.5281/zenodo.10672746
Paper reproducibility	https://github.com/HaoZeke/anneal_repro — Zenodo 10.5281/zenodo.20672621
History	Continuous development since 2023-02 (see git log); multi-author `CITATION.cff`

Install

pip install anneal

Full stack (pinned Rust + Python + docs):

pixi install

Start here (budget-only portfolio)

The intended stand-alone tool for most users: pass an objective, box bounds, and a work-unit budget (objective and gradient evaluations share the counter).

import numpy as np
from anneal import global_optimize

def rastrigin(x):
    return 10.0 * len(x) + np.sum(x * x - 10.0 * np.cos(2.0 * np.pi * x))

low, high = np.full(5, -5.0), np.full(5, 5.0)
out = global_optimize(rastrigin, low, high, budget=4000, seed=0)
print(out["best_val"], out["best_pos"])

Runnable copies:

Script: examples/quickstart_portfolio.py
Notebook: examples/notebooks/01_quickstart.ipynb
Website quickstart + four tutorials: https://anneal.rgoswami.me

Classical presets (same driver, different slots)

from anneal import Boltzmann, Fast, Gsa, run

h = run(rastrigin, low, high, Boltzmann(t_init=5.0, sigma=0.5),
        n_epochs=40, steps_per_epoch=50, seed=1)
print(h.best_val)

Optional arms (additive independence + QMC polish)

import numpy as np
from anneal import additive_independence, qmc_polish

def rastrigin(x):
    return 10.0 * len(x) + np.sum(x*x - 10.0 * np.cos(2.0 * np.pi * x))

def grad_rastrigin(x):
    return 2.0 * x + 20.0 * np.pi * np.sin(2.0 * np.pi * x)

low = np.full(5, -5.0)
high = np.full(5, 5.0)

# Values-only rank-1 independence (no gradient)
res = additive_independence(rastrigin, low, high, max_fevals=3000, seed=7)

# Polish with gradient
refined = qmc_polish(rastrigin, grad_rastrigin, low, high,
                     n_starts=32, max_fevals_per_start=50, seed=0, top_k=1)
print(refined["best_val"])

Full docs, tutorials (classical, Bayesian pilot+mixer, GLE, polish+device), algebra, how-tos, and reference at https://anneal.rgoswami.me .

Development

pixi install
pixi run -e python python-test
pixi run -e docs docs-export
pixi run -e docs docs-build

See pixi.toml and docs/export.el (modeled on rgpycrumbs/rsx-rs patterns).

License and citation

MIT (see LICENSE.txt). Citation: CITATION.cff or the software Zenodo DOI. Multi-author software citation lists six authors. Project history since February 2023. Reproducibility package for paper tables and figures: HaoZeke/anneal_repro (Zenodo 10.5281/zenodo.20672621).

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0.7.2

Aug 1, 2026

0.7.1

Jul 18, 2026

0.7.0

Jul 18, 2026

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0.6.0

Jul 18, 2026

0.5.0

Jul 5, 2026

0.1.0

Feb 17, 2024

0.0.8

Feb 24, 2023

0.0.7

Feb 23, 2023

0.0.6

Feb 23, 2023

0.0.5

Feb 23, 2023

0.0.4

Feb 21, 2023

0.0.3

Feb 21, 2023

0.0.2

Feb 6, 2023

0.0.1

Feb 5, 2023

anneal 0.6.0

Anneal

Install

Start here (budget-only portfolio)

Classical presets (same driver, different slots)

Optional arms (additive independence + QMC polish)

Development

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1 maintainer

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