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Map constraint satisfaction problems with binary variables to binary quadratic models.
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README.rst

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dwavebinarycsp

Library to construct a binary quadratic model from a constraint satisfaction problem with small constraints over binary variables.

Example Usage

import dwavebinarycsp
import dimod

csp = dwavebinarycsp.factories.random_2in4sat(8, 4)  # 8 variables, 4 clauses

bqm = dwavebinarycsp.stitch(csp)

resp = dimod.ExactSolver().sample(bqm)

for sample, energy in resp.data(['sample', 'energy']):
    print(sample, csp.check(sample), energy)

Installation

To install:

pip install dwavebinarycsp

To build from source:

pip install -r requirements.txt
python setup.py install

License

Released under the Apache License 2.0. See LICENSE file.

Contribution

See CONTRIBUTING.rst file.

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