https://github.com/dwavesystems/dwave-greedy
Greedy binary quadratic model solvers.
https://github.com/dwavesystems/dwave-greedy
Last synced: 7 months ago
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Greedy binary quadratic model solvers.
- Host: GitHub
- URL: https://github.com/dwavesystems/dwave-greedy
- Owner: dwavesystems
- License: apache-2.0
- Created: 2019-08-02T10:10:41.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-11-25T23:48:12.000Z (over 3 years ago)
- Last Synced: 2025-10-21T20:55:35.013Z (7 months ago)
- Language: Python
- Homepage: https://docs.ocean.dwavesys.com/projects/greedy/
- Size: 149 KB
- Stars: 5
- Watchers: 3
- Forks: 13
- Open Issues: 2
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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============
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
or install from source:
.. code-block:: bash
pip install git+https://github.com/dwavesystems/dwave-greedy.git#egg=dwave-greeedy
Note: installation from source involves a "cythonization" step. To install
project requirements automatically, make sure to use a PEP-517 compliant pip,
e.g. ``pip>=10.0``.
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
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 ``_ 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 `_
has guidelines for contributing to Ocean packages.