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.
>>> 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]Install from a package on PyPI:
pip install dwave-greedyor install from source:
pip install git+https://github.com/dwavesystems/dwave-greedy.git#egg=dwave-greeedyNote: 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:
pip install -r requirements.txt
python setup.py build_ext --inplace
python setup.py installSimple frustrated Ising triangle:
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):
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))Released under the Apache License 2.0. See LICENSE file.
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