Higher-Order Composites#

The dimod package includes several example higher-order composed samplers.

HigherOrderComposite#

class HigherOrderComposite(child_sampler)[source]#

Convert a binary quadratic model sampler to a binary polynomial sampler.

Energies of the returned samples do not include the penalties.

Parameters:: sampler (dimod.Sampler) – A dimod sampler

Example

This example uses HigherOrderComposite to instantiate a composed sampler that submits a simple Ising problem to a sampler. The composed sampler creates a binary quadratic model (BQM) from a higher order problem.

>>> sampler = dimod.HigherOrderComposite(dimod.ExactSolver())
>>> h = {0: -0.5, 1: -0.3, 2: -0.8}
>>> J = {(0, 1, 2): -1.7}
>>> sampleset = sampler.sample_hising(h, J, discard_unsatisfied=True)
>>> set(sampleset.first.sample.values()) == {1}
True

Properties#

`HigherOrderComposite.child`	The child sampler.
`HigherOrderComposite.children`	A list containing the wrapped sampler.
`HigherOrderComposite.parameters`	A dict where keys are the keyword parameters accepted by the sampler methods and values are lists of the properties relevant to each parameter.
`HigherOrderComposite.properties`	A dict containing any additional information about the sampler.

Methods#

`HigherOrderComposite.sample_poly`(poly[, ...])	Sample from the given binary polynomial.
`HigherOrderComposite.sample_hising`(h, J, ...)	Sample from a higher-order Ising model.
`HigherOrderComposite.sample_hubo`(H, **kwargs)	Sample from a higher-order unconstrained binary optimization problem.

PolyFixedVariableComposite#

class PolyFixedVariableComposite(child_sampler)[source]#

Composite that fixes variables of a problem.

Fixes variables of a binary polynomial and modifies linear and k-local terms accordingly. Returned samples include the fixed variable.

Parameters:: sampler (dimod.PolySampler) – A dimod polynomial sampler.

Examples

This example uses PolyFixedVariableComposite to instantiate a composed sampler that submits a simple high-order Ising problem to a sampler. The composed sampler fixes a variable and modifies linear and k-local terms biases.

>>> h = {1: -1.3, 2: 1.2, 3: -3.4, 4: -0.5}
>>> J = {(1, 4): -0.6, (1, 2, 3): 0.2, (1, 2, 3, 4): -0.1}
>>> poly = dimod.BinaryPolynomial.from_hising(h, J, offset=0)
>>> sampler = dimod.PolyFixedVariableComposite(dimod.ExactPolySolver())
>>> sampleset = sampler.sample_poly(poly, fixed_variables={3: -1, 4: 1})

Properties#

`PolyFixedVariableComposite.child`	The child sampler.
`PolyFixedVariableComposite.children`	List of child samplers that that are used by this composite.
`PolyFixedVariableComposite.parameters`	A dict where keys are the keyword parameters accepted by the sampler methods and values are lists of the properties relevant to each parameter.
`PolyFixedVariableComposite.properties`	A dict containing any additional information about the sampler.

Methods#

`PolyFixedVariableComposite.sample_poly`(poly)	Sample from the provided binary quadratic model.
`PolyFixedVariableComposite.sample_hising`(h, ...)	Sample from a higher-order Ising model.
`PolyFixedVariableComposite.sample_hubo`(H, ...)	Sample from a higher-order unconstrained binary optimization problem.

PolyScaleComposite#

class PolyScaleComposite(child)[source]#

Composite to scale biases of a binary polynomial.

Parameters:: child (PolySampler) – A binary polynomial sampler.

Examples

>>> linear = {'a': -4.0, 'b': -4.0}
>>> quadratic = {('a', 'b'): 3.2, ('a', 'b', 'c'): 1}
>>> sampler = dimod.PolyScaleComposite(dimod.HigherOrderComposite(dimod.ExactSolver()))
>>> response = sampler.sample_hising(linear, quadratic, scalar=0.5,
...                ignored_terms=[('a','b')])

Properties#

`PolyScaleComposite.child`	The child sampler.
`PolyScaleComposite.children`	The child sampler in a list
`PolyScaleComposite.parameters`	A dict where keys are the keyword parameters accepted by the sampler methods and values are lists of the properties relevant to each parameter.
`PolyScaleComposite.properties`	A dict containing any additional information about the sampler.

Methods#

`PolyScaleComposite.sample_poly`(poly[, ...])	Scale and sample from the given binary polynomial.
`PolyScaleComposite.sample_hising`(h, J, **kwargs)	Sample from a higher-order Ising model.
`PolyScaleComposite.sample_hubo`(H, **kwargs)	Sample from a higher-order unconstrained binary optimization problem.

PolyTruncateComposite#

class PolyTruncateComposite(child_sampler, n, sorted_by='energy', aggregate=False)[source]#

Composite that truncates returned samples.

Post-processing is expensive and sometimes one might want to only treat the lowest-energy samples. This composite layer allows one to pre-select the samples within a multi-composite pipeline.

Parameters:

child_sampler (dimod.PolySampler) – A dimod binary polynomial sampler.
n (int) – Maximum number of rows in the returned sample set.
sorted_by (str/None, optional, default='energy') – Selects the record field used to sort the samples before truncating. Note that sample order is maintained in the underlying array.
aggregate (bool, optional, default=False) – If True, aggregate the samples before truncating.

Note

If aggregate is True, SampleSet.record.num_occurrences are accumulated but no other fields are.

Properties#

`PolyTruncateComposite.child`	The child sampler.
`PolyTruncateComposite.children`	List of child samplers that that are used by this composite.
`PolyTruncateComposite.parameters`	A dict where keys are the keyword parameters accepted by the sampler methods and values are lists of the properties relevant to each parameter.
`PolyTruncateComposite.properties`	A dict containing any additional information about the sampler.

Methods#

`PolyTruncateComposite.sample_poly`(poly, **kwargs)	Sample from the binary polynomial and truncate output.
`PolyTruncateComposite.sample_hising`(h, J, ...)	Sample from a higher-order Ising model.
`PolyTruncateComposite.sample_hubo`(H, **kwargs)	Sample from a higher-order unconstrained binary optimization problem.