> :warning: *dwave-neal* is deprecated in favor of `dwave-samplers <https://github.com/dwavesystems/dwave-samplers>`_.
.. image:: https://img.shields.io/pypi/v/dwave-neal.svg
:target: https://pypi.org/project/dwave-neal
.. image:: https://codecov.io/gh/dwavesystems/dwave-neal/branch/master/graph/badge.svg
:target: https://codecov.io/gh/dwavesystems/dwave-neal
.. image:: https://readthedocs.com/projects/d-wave-systems-dwave-neal/badge/?version=latest
:target: https://docs.ocean.dwavesys.com/projects/neal/en/latest/?badge=latest
.. image:: https://circleci.com/gh/dwavesystems/dwave-neal.svg?style=svg
:target: https://circleci.com/gh/dwavesystems/dwave-neal
dwave-neal
==========
.. index-start-marker
An implementation of a simulated annealing sampler.
A simulated annealing sampler can be used for approximate Boltzmann sampling or
heuristic optimization. This implementation approaches the equilibrium
distribution by performing updates at a sequence of increasing beta values,
``beta_schedule``, terminating at the target beta. Each spin is updated once
in a fixed order per point in the beta_schedule according to a Metropolis-
Hastings update. When beta is large the target distribution concentrates, at
equilibrium, over ground states of the model. Samples are guaranteed to match
the equilibrium for long 'smooth' beta schedules.
For more information, see Kirkpatrick, S.; Gelatt Jr, C. D.; Vecchi, M. P.
(1983). "Optimization by Simulated Annealing". Science. 220 (4598): 671–680
Example Usage
-------------
.. code-block:: python
import neal
sampler = neal.SimulatedAnnealingSampler()
h = {0: -1, 1: -1}
J = {(0, 1): -1}
sampleset = sampler.sample_ising(h, J)
.. index-end-marker
Installation
------------
.. installation-start-marker
To install:
.. code-block:: bash
pip install dwave-neal
To build from source:
.. code-block:: bash
pip install -r requirements.txt
python setup.py build_ext --inplace
python setup.py install
.. installation-end-marker
License
-------
Released under the Apache License 2.0. See LICENSE file.
Contributing
============
Ocean's `contributing guide <https://docs.ocean.dwavesys.com/en/stable/contributing.html>`_
has guidelines for contributing to Ocean packages.
Raw data
{
"_id": null,
"home_page": "https://github.com/dwavesystems/dwave-neal",
"name": "dwave-neal",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "",
"author": "D-Wave Systems Inc.",
"author_email": "wbernoudy@dwavesys.com",
"download_url": "https://files.pythonhosted.org/packages/84/7c/e368bbddb111958f64d4fdd58d4a0e01f8dc16d56fe89c7abdb028fbb029/dwave-neal-0.6.0.tar.gz",
"platform": null,
"description": "> :warning: *dwave-neal* is deprecated in favor of `dwave-samplers <https://github.com/dwavesystems/dwave-samplers>`_.\n\n.. image:: https://img.shields.io/pypi/v/dwave-neal.svg\n :target: https://pypi.org/project/dwave-neal\n\n.. image:: https://codecov.io/gh/dwavesystems/dwave-neal/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/dwavesystems/dwave-neal\n\n.. image:: https://readthedocs.com/projects/d-wave-systems-dwave-neal/badge/?version=latest\n :target: https://docs.ocean.dwavesys.com/projects/neal/en/latest/?badge=latest\n\n.. image:: https://circleci.com/gh/dwavesystems/dwave-neal.svg?style=svg\n :target: https://circleci.com/gh/dwavesystems/dwave-neal\n\ndwave-neal\n==========\n\n.. index-start-marker\n\nAn implementation of a simulated annealing sampler.\n\nA simulated annealing sampler can be used for approximate Boltzmann sampling or\nheuristic optimization. This implementation approaches the equilibrium\ndistribution by performing updates at a sequence of increasing beta values,\n``beta_schedule``, terminating at the target beta. Each spin is updated once\nin a fixed order per point in the beta_schedule according to a Metropolis-\nHastings update. When beta is large the target distribution concentrates, at\nequilibrium, over ground states of the model. Samples are guaranteed to match\nthe equilibrium for long 'smooth' beta schedules.\n\nFor more information, see Kirkpatrick, S.; Gelatt Jr, C. D.; Vecchi, M. P.\n(1983). \"Optimization by Simulated Annealing\". Science. 220 (4598): 671\u2013680\n\nExample Usage\n-------------\n\n.. code-block:: python\n\n import neal\n\n sampler = neal.SimulatedAnnealingSampler()\n\n h = {0: -1, 1: -1}\n J = {(0, 1): -1}\n sampleset = sampler.sample_ising(h, J)\n\n.. index-end-marker\n\nInstallation\n------------\n\n.. installation-start-marker\n\nTo install:\n\n.. code-block:: bash\n\n pip install dwave-neal\n\nTo build from source:\n\n.. code-block:: bash\n\n pip install -r requirements.txt\n python setup.py build_ext --inplace\n python setup.py install\n\n.. installation-end-marker\n\nLicense\n-------\n\nReleased under the Apache License 2.0. See LICENSE file.\n\nContributing\n============\n\nOcean's `contributing guide <https://docs.ocean.dwavesys.com/en/stable/contributing.html>`_\nhas guidelines for contributing to Ocean packages.\n",
"bugtrack_url": null,
"license": "Apache 2.0",
"summary": "General Ising graph simulated annealing solver",
"version": "0.6.0",
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "0c369b3dd9232192d8557b187856c880",
"sha256": "8b7d89f0c52de6ac80e0f580ec272f6409b1cf9edb12250d22429425a13bd935"
},
"downloads": -1,
"filename": "dwave_neal-0.6.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "0c369b3dd9232192d8557b187856c880",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 8730,
"upload_time": "2022-11-25T23:44:35",
"upload_time_iso_8601": "2022-11-25T23:44:35.533747Z",
"url": "https://files.pythonhosted.org/packages/64/f2/32c45bcc42196f69f101549abf35fc6019ef3488c8d4082fbcab22d77274/dwave_neal-0.6.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "2566e8ea06108de3d88ed4b670471def",
"sha256": "8ce51fee3339195df1ab69920fdb5afc496b5fd945e487fad3547c983d90c564"
},
"downloads": -1,
"filename": "dwave-neal-0.6.0.tar.gz",
"has_sig": false,
"md5_digest": "2566e8ea06108de3d88ed4b670471def",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 6418,
"upload_time": "2022-11-25T23:44:37",
"upload_time_iso_8601": "2022-11-25T23:44:37.850129Z",
"url": "https://files.pythonhosted.org/packages/84/7c/e368bbddb111958f64d4fdd58d4a0e01f8dc16d56fe89c7abdb028fbb029/dwave-neal-0.6.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-11-25 23:44:37",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "dwavesystems",
"github_project": "dwave-neal",
"travis_ci": false,
"coveralls": true,
"github_actions": false,
"circle": true,
"requirements": [
{
"name": "numpy",
"specs": [
[
"==",
"1.19.1"
]
]
},
{
"name": "numpy",
"specs": [
[
"==",
"1.21.4"
]
]
},
{
"name": "cython",
"specs": [
[
"==",
"0.29.24"
]
]
},
{
"name": "dimod",
"specs": [
[
"==",
"0.9.11"
]
]
},
{
"name": "dimod",
"specs": [
[
"==",
"0.10.10"
]
]
}
],
"lcname": "dwave-neal"
}
JSON
