Samplers¶
The dwavegreedy package currently includes just one sampler,
SteepestDescentSampler
, which is an alias for
SteepestDescentSolver
.
A sampler accepts a binary quadratic model (BQM) and returns variable assignments. Samplers generally try to find minimizing values but can also sample from distributions defined by the BQM.
SteepestDescentSolver¶
Class¶

class
SteepestDescentSolver
[source]¶ Steepest descent solver/sampler 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.
Solves convex problems to optimality.
Number of downhill runs (samples produced) is determined by
num_reads
, number ofinitial_states
, or a combination of the two, depending on theinitial_states_generator
.For a given input model’s graph \(G = (V, E)\), \(V\) being a set of graph vertices and \(E\) a set of edges, runtime complexity of the underlying C++ implementation is \(O(E)\) for initialization phase and \(O(V)\) per downhill step.
In the
large_sparse_opt
mode, runtime complexity on sparse graphs is \(O(V*logV)\) for initialization and \(O(max\_degree * logV)\) per downhill step.Aliased as
SteepestDescentSampler
.Examples
Solve a simple Ising problem:
>>> import greedy ... >>> sampler = greedy.SteepestDescentSampler() >>> samples = sampler.sample_ising({0: 2, 1: 2}, {(0, 1): 1}) ... >>> print(samples) 0 1 energy num_oc. 0 1 1 5.0 1 ['SPIN', 1 rows, 1 samples, 2 variables]
Postprocesses samples generated by another sampler (simulated annealing in this example):
import neal import dimod import greedy import networkx bqm = dimod.generators.ran_r(5, networkx.complete_graph('abc')) samples = neal.SimulatedAnnealingSampler().sample(bqm) postprocessed = greedy.SteepestDescentSampler().sample(bqm, initial_states=samples)
For additional examples, see
sample()
.
Attributes¶
Values for parameters accepted by the sampling methods. 

Keyword arguments accepted by the sampling methods. 
Methods¶

Sample from a binary quadratic model. 

Sample from an Ising model using the implemented sample method. 
Sample from a QUBO using the implemented sample method. 
SteepestDescentSampler¶
Class¶

SteepestDescentSampler
¶ alias of
greedy.sampler.SteepestDescentSolver