weighted_one_edge_augmentation(G, avail, weight=None, partial=False)
This is a variant of the weighted MST problem.
An undirected graph.
For more details, see k_edge_augmentation
.
key to use to find weights if avail
is a set of 3-tuples. For more details, see k_edge_augmentation
.
If partial is True and no feasible k-edge-augmentation exists, then the augmenting edges minimize the number of connected components.
Finds the minimum weight set of edges to connect G if one exists.
Edges in the subset of avail chosen to connect G.
k_edge_augmentation
func
>>> G = nx.Graph([(1, 2), (2, 3), (4, 5)])
... G.add_nodes_from([6, 7, 8])
... # any edge not in avail has an implicit weight of infinity
... avail = [(1, 3), (1, 5), (4, 7), (4, 8), (6, 1), (8, 1), (8, 2)]
... sorted(weighted_one_edge_augmentation(G, avail)) [(1, 5), (4, 7), (6, 1), (8, 1)]
>>> # find another solution by giving large weights to edges in theSee :
... # previous solution (note some of the old edges must be used)
... avail = [(1, 3), (1, 5, 99), (4, 7, 9), (6, 1, 99), (8, 1, 99), (8, 2)]
... sorted(weighted_one_edge_augmentation(G, avail)) [(1, 5), (4, 7), (6, 1), (8, 2)]
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.connectivity.edge_augmentation.weighted_one_edge_augmentation
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