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

Cluster search: the measured configuration

Config::recommended(n) is the measured configuration for cluster global minimisation: composed surface relocations paying one acceptance test for a whole excursion, Normal-Gamma Thompson allocation over move arms rewarded by the depth reached, and tabu on stall. The algorithm is the contribution; its generality is measured, not assumed: one configuration across four Lennard-Jones morphologies and, with the rigid-group move library, across molecular clusters under tight-binding and machine-learned potentials, where an engine swap reproduces the known functional ordering of the water hexamer with nothing about water in the search. Accuracy means reaching the literature global minima, which it does on every solved run to 1e-6. Efficiency is reported under evaluation-matched comparisons as the honest protocol: against reference GMIN under its own documented settings, this stack solves the 75- and 98-point double-funnel systems that the reference does not reach at the same number of potential calls, with Beta-Binomial posterior comparisons and Bayes factors throughout. Config::for_cluster(n) keeps the plain Wales-Doye protocol as the comparison baseline, and every mechanism that measured null or harmful is recorded with its failure mechanism rather than shipped.

use anneal_core::methods::cluster_hopping::{optimize, Config, Ledger};

let cfg = Config::recommended(38);
let mut ledger = Ledger::new(400_000);
// supply `relax` closing over your objective; see examples/lj_cluster_search.rs

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:

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).

Project links

Data verified by PyPI on Aug 17, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.9.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for rgoswami from gravatar.com rgoswami

Credits

PyPI data
Data sourced directly from PyPI's database.

Author: Rohit Goswami

Maintainer: Rohit Goswami

GitHub Statistics

Data verified by PyPI on Aug 17, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

cutest gpu plotter testing

Tags

optimization algorithms simulated-annealing

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

anneal-0.8.0.tar.gz (3.7 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

anneal-0.8.0-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.10+manylinux: glibc 2.17+ x86-64

anneal-0.8.0-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.10+manylinux: glibc 2.17+ ARM64

anneal-0.8.0-cp310-abi3-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

anneal-0.8.0-cp310-abi3-macosx_10_12_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.10+macOS 10.12+ x86-64

File details

Details for the file anneal-0.8.0.tar.gz.

File metadata

  • Download URL: anneal-0.8.0.tar.gz
  • Upload date:
  • Size: 3.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for anneal-0.8.0.tar.gz
Algorithm Hash digest
SHA256 3913591555dc4e35561f7879fc096c8d3cd0c4fc23506fe29b5c62a9a38f8e6d
MD5 0cca31df3e0994e89c9f494c8ad67301
BLAKE2b-256 70381a3934835aa4506c01aed42842a075c0c081fd3d5185f080b7714f92cbd6

See more details on using hashes here.

Provenance

The following attestation bundles were made for anneal-0.8.0.tar.gz:

Publisher: release.yml on HaoZeke/anneal

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file anneal-0.8.0-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for anneal-0.8.0-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d1c9a4fc706a36f5d91f8c4f8aed9604c7b2edf57c3633d160f83ccfe9ecd432
MD5 248b39f6469089cc4fab23f1de3751aa
BLAKE2b-256 fb78d8f45cac81b93ef8005d746c085bb9745049d14bcf22588ccb7fa02563ab

See more details on using hashes here.

Provenance

The following attestation bundles were made for anneal-0.8.0-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: release.yml on HaoZeke/anneal

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file anneal-0.8.0-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for anneal-0.8.0-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b360e5707260cda6f1c983034d4108e44ecc1fd80868dcc1f3322276396fdaa9
MD5 e55db44a4f7f9f146902792a3117f1f7
BLAKE2b-256 a6088fe88a4e6d0f5b2ce5e643b36e04ed301a1c9e48c3109d4cba0b626ee417

See more details on using hashes here.

Provenance

The following attestation bundles were made for anneal-0.8.0-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: release.yml on HaoZeke/anneal

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file anneal-0.8.0-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for anneal-0.8.0-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6baf93520ca0f95b23de8e46e8bbb827386d616e2c668da1efd426568232b0f2
MD5 897806d245782e918714e5373d310614
BLAKE2b-256 8f818c7e6e060257680b3dc31d554e508738b10a2d414e5d82e002d6549aec16

See more details on using hashes here.

Provenance

The following attestation bundles were made for anneal-0.8.0-cp310-abi3-macosx_11_0_arm64.whl:

Publisher: release.yml on HaoZeke/anneal

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file anneal-0.8.0-cp310-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for anneal-0.8.0-cp310-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 9db10c172d73c4dc46ccd0cb81f717e6cc40a791d64cfa8bb32b38d72efc1efd
MD5 2826204e42de0927ce7e96c87d9ccf63
BLAKE2b-256 c8b52c6c239b842bec8952687c7623e6a2ace8d82e76225153636561ee59ca17

See more details on using hashes here.

Provenance

The following attestation bundles were made for anneal-0.8.0-cp310-abi3-macosx_10_12_x86_64.whl:

Publisher: release.yml on HaoZeke/anneal

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page