semvec/pypi-packages huggingface dataset:
dwave-tabu 0.5.0
Optimized Tabu solver for QUBOs
License: Apache 2.0
Requires Python: >=3.7

> :warning: **Note**: *dwave-tabu* is deprecated in favor of `dwave-samplers <https://github.com/dwavesystems/dwave-samplers>`_.

.. image:: https://circleci.com/gh/dwavesystems/dwave-tabu.svg?style=svg
    :target: https://circleci.com/gh/dwavesystems/dwave-tabu
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.. image:: https://codecov.io/gh/dwavesystems/dwave-tabu/branch/master/graph/badge.svg?token=uoMkg6WvKy
    :target: https://codecov.io/gh/dwavesystems/dwave-tabu
    :alt: Code coverage

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    :target: https://docs.ocean.dwavesys.com/projects/d-wave-systems-dwave-tabu/en/latest/?badge=latest
    :alt: Documentation Status

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==========
dwave-tabu
==========

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A C/C++ implementation of the `MST2 multistart tabu search algorithm
<https://link.springer.com/article/10.1023/B:ANOR.0000039522.58036.68>`_
for quadratic unconstrained binary optimization (QUBO) problems with a
`dimod sampler <https://docs.ocean.dwavesys.com/en/stable/docs_dimod/reference/sampler_composites/api.html#dimod.Sampler>`_
Python interface.

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Installation
============

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Install from a wheel on PyPI::

    pip install dwave-tabu

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Example
=======

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This example solves a two-variable Ising model.

>>> from tabu import TabuSampler
>>> response = TabuSampler().sample_ising({'a': -0.5, 'b': 1.0}, {('a', 'b'): -1})

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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.
dwave-neal 0.6.0
General Ising graph simulated annealing solver
License: Apache 2.0
Requires Python: >=3.7

> :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
==========

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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)

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Installation
------------

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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

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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.