# Source code for dwave_networkx.algorithms.cover

```
# Copyright 2018 D-Wave Systems Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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# ================================================================================================
from dwave_networkx.algorithms.independent_set import maximum_weighted_independent_set
from dwave_networkx.utils import binary_quadratic_model_sampler
__all__ = ['min_weighted_vertex_cover', 'min_vertex_cover', 'is_vertex_cover']
[docs]@binary_quadratic_model_sampler(2)
def min_weighted_vertex_cover(G, weight=None, sampler=None, lagrange=2.0, **sampler_args):
"""Returns an approximate minimum weighted vertex cover.
Defines a QUBO with ground states corresponding to a minimum weighted
vertex cover and uses the sampler to sample from it.
A vertex cover is a set of vertices such that each edge of the graph
is incident with at least one vertex in the set. A minimum weighted
vertex cover is the vertex cover of minimum total node weight.
Parameters
----------
G : NetworkX graph
weight : string, optional (default None)
If None, every node has equal weight. If a string, use this node
attribute as the node weight. A node without this attribute is
assumed to have max weight.
sampler
A binary quadratic model sampler. A sampler is a process that
samples from low energy states in models defined by an Ising
equation or a Quadratic Unconstrained Binary Optimization
Problem (QUBO). A sampler is expected to have a 'sample_qubo'
and 'sample_ising' method. A sampler is expected to return an
iterable of samples, in order of increasing energy. If no
sampler is provided, one must be provided using the
`set_default_sampler` function.
lagrange : optional (default 2)
Lagrange parameter to weight constraints versus objective.
sampler_args
Additional keyword parameters are passed to the sampler.
Returns
-------
vertex_cover : list
List of nodes that the form a the minimum weighted vertex cover, as
determined by the given sampler.
Notes
-----
Samplers by their nature may not return the optimal solution. This
function does not attempt to confirm the quality of the returned
sample.
https://en.wikipedia.org/wiki/Vertex_cover
https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization
References
----------
Based on the formulation presented in [AL]_
"""
indep_nodes = set(maximum_weighted_independent_set(G, weight, sampler, lagrange, **sampler_args))
return [v for v in G if v not in indep_nodes]
[docs]@binary_quadratic_model_sampler(1)
def min_vertex_cover(G, sampler=None, lagrange=2.0, **sampler_args):
"""Returns an approximate minimum vertex cover.
Defines a QUBO with ground states corresponding to a minimum
vertex cover and uses the sampler to sample from it.
A vertex cover is a set of vertices such that each edge of the graph
is incident with at least one vertex in the set. A minimum vertex cover
is the vertex cover of smallest size.
Parameters
----------
G : NetworkX graph
The graph on which to find a minimum vertex cover.
sampler
A binary quadratic model sampler. A sampler is a process that
samples from low energy states in models defined by an Ising
equation or a Quadratic Unconstrained Binary Optimization
Problem (QUBO). A sampler is expected to have a 'sample_qubo'
and 'sample_ising' method. A sampler is expected to return an
iterable of samples, in order of increasing energy. If no
sampler is provided, one must be provided using the
`set_default_sampler` function.
lagrange : optional (default 2)
Lagrange parameter to weight constraints versus objective.
sampler_args
Additional keyword parameters are passed to the sampler.
Returns
-------
vertex_cover : list
List of nodes that form a minimum vertex cover, as
determined by the given sampler.
Examples
--------
This example uses a sampler from
`dimod <https://github.com/dwavesystems/dimod>`_ to find a minimum vertex
cover for a Chimera unit cell. Both the horizontal (vertices 0,1,2,3) and
vertical (vertices 4,5,6,7) tiles connect to all 16 edges, so repeated
executions can return either set.
>>> import dwave_networkx as dnx
>>> import dimod
>>> sampler = dimod.ExactSolver() # small testing sampler
>>> G = dnx.chimera_graph(1, 1, 4)
>>> G.remove_node(7) # to give a unique solution
>>> dnx.min_vertex_cover(G, sampler, lagrange=2.0)
[4, 5, 6]
Notes
-----
Samplers by their nature may not return the optimal solution. This
function does not attempt to confirm the quality of the returned
sample.
References
----------
https://en.wikipedia.org/wiki/Vertex_cover
https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization
.. [AL] Lucas, A. (2014). Ising formulations of many NP problems.
Frontiers in Physics, Volume 2, Article 5.
"""
return min_weighted_vertex_cover(G, None, sampler, lagrange, **sampler_args)
[docs]def is_vertex_cover(G, vertex_cover):
"""Determines whether the given set of vertices is a vertex cover of graph G.
A vertex cover is a set of vertices such that each edge of the graph
is incident with at least one vertex in the set.
Parameters
----------
G : NetworkX graph
The graph on which to check the vertex cover.
vertex_cover :
Iterable of nodes.
Returns
-------
is_cover : bool
True if the given iterable forms a vertex cover.
Examples
--------
This example checks two covers for a graph, G, of a single Chimera
unit cell. The first uses the set of the four horizontal qubits, which
do constitute a cover; the second set removes one node.
>>> import dwave_networkx as dnx
>>> G = dnx.chimera_graph(1, 1, 4)
>>> cover = [0, 1, 2, 3]
>>> dnx.is_vertex_cover(G,cover)
True
>>> cover = [0, 1, 2]
>>> dnx.is_vertex_cover(G,cover)
False
"""
cover = set(vertex_cover)
return all(u in cover or v in cover for u, v in G.edges)
```