dwave_networkx.algorithms.matching.min_maximal_matching¶

min_maximal_matching
(G, sampler=None, **sampler_args)[source]¶ Returns an approximate minimum maximal matching.
Defines a QUBO with ground states corresponding to a minimum maximal matching and uses the sampler to sample from it.
A matching is a subset of edges in which no node occurs more than once. A maximal matching is one in which no edges from G can be added without violating the matching rule. A minimum maximal matching is the smallest maximal matching for G.
Parameters:  G (NetworkX graph) – The graph on which to find a minimum maximal matching.
 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.
 sampler_args – Additional keyword parameters are passed to the sampler.
Returns: matching – A minimum maximal matching of the graph.
Return type: Example
This example uses a sampler from dimod to find a minimum maximal matching for a Chimera unit cell.
>>> import dimod >>> sampler = dimod.ExactSolver() >>> G = dnx.chimera_graph(1, 1, 4) >>> matching = dnx.min_maximal_matching(G, 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.
References
[AL] Lucas, A. (2014). Ising formulations of many NP problems. Frontiers in Physics, Volume 2, Article 5.