> :warning: **Note**: *dwave-greedy* is deprecated in favor of `dwave-samplers <https://github.com/dwavesystems/dwave-samplers>`_.
.. image:: https://circleci.com/gh/dwavesystems/dwave-greedy.svg?style=svg
:target: https://circleci.com/gh/dwavesystems/dwave-greedy
:alt: Linux/MacOS/Windows build status
.. image:: https://codecov.io/gh/dwavesystems/dwave-greedy/branch/master/graph/badge.svg?token=ZkZo09uAl7
:target: https://codecov.io/gh/dwavesystems/dwave-greedy
:alt: Code coverage
.. image:: https://readthedocs.com/projects/d-wave-systems-dwave-greedy/badge/?version=latest
:target: https://docs.ocean.dwavesys.com/projects/greedy/en/latest/
:alt: Documentation status
.. image:: https://badge.fury.io/py/dwave-greedy.svg
:target: https://badge.fury.io/py/dwave-greedy
:alt: Latest version on PyPI
.. image:: https://img.shields.io/pypi/pyversions/dwave-greedy.svg?style=flat
:target: https://pypi.org/project/dwave-greedy/
:alt: PyPI - Python Version
============
dwave-greedy
============
.. index-start-marker
An implementation of a steepest descent solver for binary quadratic models.
Steepest descent is the discrete analogue of gradient descent, but the best
move is computed using a local minimization rather rather than computing a
gradient. At each step, we determine the dimension along which to descend based
on the highest energy drop caused by a variable flip.
.. code-block:: python
>>> import greedy
...
>>> solver = greedy.SteepestDescentSolver()
>>> sampleset = solver.sample_ising({0: 2, 1: 2}, {(0, 1): -1})
...
>>> print(sampleset)
0 1 energy num_oc.
0 -1 -1 -5.0 1
['SPIN', 1 rows, 1 samples, 2 variables]
.. index-end-marker
Installation
============
.. installation-start-marker
Install from a package on PyPI:
.. code-block:: bash
pip install dwave-greedy
.. installation-end-marker
Examples
========
.. example-start-marker
Simple frustrated Ising triangle:
.. code-block:: python
import dimod
import greedy
# Construct a simple problem
bqm = dimod.BQM.from_qubo({'ab': 1, 'bc': 1, 'ca': 1})
# Instantiate the sampler
sampler = greedy.SteepestDescentSampler()
# Solve the problem
result = sampler.sample(bqm)
Large RAN1_ sparse problem (requires NetworkX_ package):
.. code-block:: python
import dimod
import greedy
import networkx
# Generate random Erdős-Rényi sparse graph with 10% density
graph = networkx.fast_gnp_random_graph(n=1000, p=0.1)
# Generate RAN1 problem on the sparse graph
bqm = dimod.generators.random.ran_r(r=1, graph=graph)
# Instantiate the sampler
sampler = greedy.SteepestDescentSampler()
# Run steepest descent for 100 times, each time from a random state
sampleset = sampler.sample(bqm, num_reads=100)
# Print the best energy
print(min(sampleset.record.energy))
.. example-end-marker
License
=======
Released under the Apache License 2.0. See `<LICENSE>`_ file.
.. _NetworkX: https://networkx.github.io/
.. _RAN1: https://docs.ocean.dwavesys.com/en/stable/docs_dimod/reference/generated/dimod.generators.ran_r.html
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-greedy",
"name": "dwave-greedy",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "",
"author": "D-Wave Systems Inc.",
"author_email": "tools@dwavesys.com",
"download_url": "https://files.pythonhosted.org/packages/54/e4/e1c79ae2cb50275434d314906518f45e0c156377f74a7bf0e8928bf5ddfb/dwave-greedy-0.3.0.tar.gz",
"platform": null,
"description": "> :warning: **Note**: *dwave-greedy* is deprecated in favor of `dwave-samplers <https://github.com/dwavesystems/dwave-samplers>`_.\n\n.. image:: https://circleci.com/gh/dwavesystems/dwave-greedy.svg?style=svg\n :target: https://circleci.com/gh/dwavesystems/dwave-greedy\n :alt: Linux/MacOS/Windows build status\n\n.. image:: https://codecov.io/gh/dwavesystems/dwave-greedy/branch/master/graph/badge.svg?token=ZkZo09uAl7\n :target: https://codecov.io/gh/dwavesystems/dwave-greedy\n :alt: Code coverage\n\n.. image:: https://readthedocs.com/projects/d-wave-systems-dwave-greedy/badge/?version=latest\n :target: https://docs.ocean.dwavesys.com/projects/greedy/en/latest/\n :alt: Documentation status\n\n.. image:: https://badge.fury.io/py/dwave-greedy.svg\n :target: https://badge.fury.io/py/dwave-greedy\n :alt: Latest version on PyPI\n\n.. image:: https://img.shields.io/pypi/pyversions/dwave-greedy.svg?style=flat\n :target: https://pypi.org/project/dwave-greedy/\n :alt: PyPI - Python Version\n\n\n============\ndwave-greedy\n============\n\n.. index-start-marker\n\nAn implementation of a steepest descent solver for binary quadratic models.\n\nSteepest descent is the discrete analogue of gradient descent, but the best\nmove is computed using a local minimization rather rather than computing a\ngradient. At each step, we determine the dimension along which to descend based\non the highest energy drop caused by a variable flip.\n\n.. code-block:: python\n\n >>> import greedy\n ...\n >>> solver = greedy.SteepestDescentSolver()\n >>> sampleset = solver.sample_ising({0: 2, 1: 2}, {(0, 1): -1})\n ...\n >>> print(sampleset)\n 0 1 energy num_oc.\n 0 -1 -1 -5.0 1\n ['SPIN', 1 rows, 1 samples, 2 variables]\n\n.. index-end-marker\n\n\nInstallation\n============\n\n.. installation-start-marker\n\nInstall from a package on PyPI:\n\n.. code-block:: bash\n\n pip install dwave-greedy\n\n.. installation-end-marker\n\n\nExamples\n========\n\n.. example-start-marker\n\nSimple frustrated Ising triangle:\n\n.. code-block:: python\n\n import dimod\n import greedy\n\n # Construct a simple problem\n bqm = dimod.BQM.from_qubo({'ab': 1, 'bc': 1, 'ca': 1})\n\n # Instantiate the sampler\n sampler = greedy.SteepestDescentSampler()\n\n # Solve the problem\n result = sampler.sample(bqm)\n\nLarge RAN1_ sparse problem (requires NetworkX_ package):\n\n.. code-block:: python\n\n import dimod\n import greedy\n import networkx\n\n # Generate random Erd\u0151s-R\u00e9nyi sparse graph with 10% density\n graph = networkx.fast_gnp_random_graph(n=1000, p=0.1)\n\n # Generate RAN1 problem on the sparse graph\n bqm = dimod.generators.random.ran_r(r=1, graph=graph)\n\n # Instantiate the sampler\n sampler = greedy.SteepestDescentSampler()\n\n # Run steepest descent for 100 times, each time from a random state\n sampleset = sampler.sample(bqm, num_reads=100)\n\n # Print the best energy\n print(min(sampleset.record.energy))\n\n.. example-end-marker\n\n\nLicense\n=======\n\nReleased under the Apache License 2.0. See `<LICENSE>`_ file.\n\n\n.. _NetworkX: https://networkx.github.io/\n.. _RAN1: https://docs.ocean.dwavesys.com/en/stable/docs_dimod/reference/generated/dimod.generators.ran_r.html\n\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": "Ocean-compatible collection of greedy/brute-force solvers/samplers",
"version": "0.3.0",
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "3206483b0c76d35b599edd1d7335b714",
"sha256": "34c2d24bf6d4f4d3cee532dbc85c79b7faa4845a7bd51435fc4114d077095f6f"
},
"downloads": -1,
"filename": "dwave_greedy-0.3.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "3206483b0c76d35b599edd1d7335b714",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 10115,
"upload_time": "2022-11-25T23:48:52",
"upload_time_iso_8601": "2022-11-25T23:48:52.443023Z",
"url": "https://files.pythonhosted.org/packages/02/91/bbc44323850dcf76f20721ab567a210680e8c16caa090e8831a386c86b34/dwave_greedy-0.3.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "356686ab74666b6a42b6e402bf199041",
"sha256": "806c02d6c3f260ef5bf063d4932f43dfb52a0ea00f9db0b0d34d9b15691a011a"
},
"downloads": -1,
"filename": "dwave-greedy-0.3.0.tar.gz",
"has_sig": false,
"md5_digest": "356686ab74666b6a42b6e402bf199041",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 7815,
"upload_time": "2022-11-25T23:48:54",
"upload_time_iso_8601": "2022-11-25T23:48:54.532871Z",
"url": "https://files.pythonhosted.org/packages/54/e4/e1c79ae2cb50275434d314906518f45e0c156377f74a7bf0e8928bf5ddfb/dwave-greedy-0.3.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-11-25 23:48:54",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "dwavesystems",
"github_project": "dwave-greedy",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"circle": true,
"requirements": [
{
"name": "numpy",
"specs": [
[
"==",
"1.19.5"
]
]
},
{
"name": "numpy",
"specs": [
[
"==",
"1.21.4"
]
]
},
{
"name": "dimod",
"specs": [
[
"==",
"0.10.10"
]
]
},
{
"name": "cython",
"specs": [
[
"==",
"0.29.26"
]
]
},
{
"name": "importlib-metadata",
"specs": [
[
">=",
"1.0"
]
]
},
{
"name": "wheel",
"specs": [
[
">=",
"0.30.0"
]
]
},
{
"name": "setuptools",
"specs": [
[
">=",
"56.2.0"
]
]
}
],
"lcname": "dwave-greedy"
}
JSON
