970 lines
37 KiB
Python
970 lines
37 KiB
Python
# Copyright 2016 NetworkX developers.
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# Copyright (C) 2004-2019 by
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# Aric Hagberg <hagberg@lanl.gov>
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# Dan Schult <dschult@colgate.edu>
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# Pieter Swart <swart@lanl.gov>
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#
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# Copyright (C) 2008 by
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# Joris van Rantwijk.
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#
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# Copyright (C) 2011 by
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# Nicholas Mancuso <nick.mancuso@gmail.com>
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#
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# All rights reserved.
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# BSD license.
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"""Functions for computing and verifying matchings in a graph."""
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from collections import Counter
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from itertools import combinations
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from itertools import repeat
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__all__ = ['is_matching', 'is_maximal_matching', 'is_perfect_matching',
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'max_weight_matching', 'maximal_matching']
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def maximal_matching(G):
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r"""Find a maximal matching in the graph.
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A matching is a subset of edges in which no node occurs more than once.
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A maximal matching cannot add more edges and still be a matching.
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Parameters
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----------
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G : NetworkX graph
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Undirected graph
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Returns
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-------
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matching : set
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A maximal matching of the graph.
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Notes
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-----
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The algorithm greedily selects a maximal matching M of the graph G
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(i.e. no superset of M exists). It runs in $O(|E|)$ time.
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"""
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matching = set()
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nodes = set()
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for u, v in G.edges():
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# If the edge isn't covered, add it to the matching
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# then remove neighborhood of u and v from consideration.
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if u not in nodes and v not in nodes and u != v:
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matching.add((u, v))
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nodes.add(u)
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nodes.add(v)
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return matching
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def matching_dict_to_set(matching):
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"""Converts a dictionary representing a matching (as returned by
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:func:`max_weight_matching`) to a set representing a matching (as
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returned by :func:`maximal_matching`).
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In the definition of maximal matching adopted by NetworkX,
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self-loops are not allowed, so the provided dictionary is expected
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to never have any mapping from a key to itself. However, the
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dictionary is expected to have mirrored key/value pairs, for
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example, key ``u`` with value ``v`` and key ``v`` with value ``u``.
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"""
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# Need to compensate for the fact that both pairs (u, v) and (v, u)
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# appear in `matching.items()`, so we use a set of sets. This way,
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# only the (frozen)set `{u, v}` appears as an element in the
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# returned set.
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return set((u, v) for (u, v) in set(map(frozenset, matching.items())))
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def is_matching(G, matching):
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"""Decides whether the given set or dictionary represents a valid
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matching in ``G``.
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A *matching* in a graph is a set of edges in which no two distinct
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edges share a common endpoint.
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Parameters
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----------
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G : NetworkX graph
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matching : dict or set
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A dictionary or set representing a matching. If a dictionary, it
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must have ``matching[u] == v`` and ``matching[v] == u`` for each
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edge ``(u, v)`` in the matching. If a set, it must have elements
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of the form ``(u, v)``, where ``(u, v)`` is an edge in the
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matching.
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Returns
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-------
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bool
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Whether the given set or dictionary represents a valid matching
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in the graph.
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"""
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if isinstance(matching, dict):
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matching = matching_dict_to_set(matching)
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# TODO This is parallelizable.
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return all(len(set(e1) & set(e2)) == 0
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for e1, e2 in combinations(matching, 2))
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def is_maximal_matching(G, matching):
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"""Decides whether the given set or dictionary represents a valid
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maximal matching in ``G``.
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A *maximal matching* in a graph is a matching in which adding any
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edge would cause the set to no longer be a valid matching.
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Parameters
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----------
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G : NetworkX graph
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matching : dict or set
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A dictionary or set representing a matching. If a dictionary, it
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must have ``matching[u] == v`` and ``matching[v] == u`` for each
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edge ``(u, v)`` in the matching. If a set, it must have elements
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of the form ``(u, v)``, where ``(u, v)`` is an edge in the
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matching.
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Returns
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-------
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bool
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Whether the given set or dictionary represents a valid maximal
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matching in the graph.
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"""
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if isinstance(matching, dict):
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matching = matching_dict_to_set(matching)
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# If the given set is not a matching, then it is not a maximal matching.
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if not is_matching(G, matching):
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return False
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# A matching is maximal if adding any unmatched edge to it causes
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# the resulting set to *not* be a valid matching.
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#
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# HACK Since the `matching_dict_to_set` function returns a set of
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# sets, we need to convert the list of edges to a set of sets in
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# order for the set difference function to work. Ideally, we would
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# just be able to do `set(G.edges()) - matching`.
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all_edges = set(map(frozenset, G.edges()))
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matched_edges = set(map(frozenset, matching))
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unmatched_edges = all_edges - matched_edges
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# TODO This is parallelizable.
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return all(not is_matching(G, matching | {e}) for e in unmatched_edges)
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def is_perfect_matching(G, matching):
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"""Decides whether the given set represents a valid perfect matching in
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``G``.
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A *perfect matching* in a graph is a matching in which exactly one edge
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is incident upon each vertex.
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Parameters
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----------
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G : NetworkX graph
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matching : dict or set
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A dictionary or set representing a matching. If a dictionary, it
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must have ``matching[u] == v`` and ``matching[v] == u`` for each
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edge ``(u, v)`` in the matching. If a set, it must have elements
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of the form ``(u, v)``, where ``(u, v)`` is an edge in the
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matching.
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Returns
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-------
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bool
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Whether the given set or dictionary represents a valid perfect
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matching in the graph.
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"""
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if isinstance(matching, dict):
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matching = matching_dict_to_set(matching)
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if not is_matching(G, matching):
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return False
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counts = Counter(sum(matching, ()))
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return all(counts[v] == 1 for v in G)
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def max_weight_matching(G, maxcardinality=False, weight='weight'):
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"""Compute a maximum-weighted matching of G.
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A matching is a subset of edges in which no node occurs more than once.
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The weight of a matching is the sum of the weights of its edges.
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A maximal matching cannot add more edges and still be a matching.
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The cardinality of a matching is the number of matched edges.
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Parameters
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----------
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G : NetworkX graph
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Undirected graph
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maxcardinality: bool, optional (default=False)
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If maxcardinality is True, compute the maximum-cardinality matching
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with maximum weight among all maximum-cardinality matchings.
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weight: string, optional (default='weight')
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Edge data key corresponding to the edge weight.
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If key not found, uses 1 as weight.
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Returns
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-------
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matching : set
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A maximal matching of the graph.
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Notes
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-----
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If G has edges with weight attributes the edge data are used as
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weight values else the weights are assumed to be 1.
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This function takes time O(number_of_nodes ** 3).
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If all edge weights are integers, the algorithm uses only integer
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computations. If floating point weights are used, the algorithm
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could return a slightly suboptimal matching due to numeric
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precision errors.
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This method is based on the "blossom" method for finding augmenting
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paths and the "primal-dual" method for finding a matching of maximum
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weight, both methods invented by Jack Edmonds [1]_.
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Bipartite graphs can also be matched using the functions present in
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:mod:`networkx.algorithms.bipartite.matching`.
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References
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----------
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.. [1] "Efficient Algorithms for Finding Maximum Matching in Graphs",
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Zvi Galil, ACM Computing Surveys, 1986.
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"""
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#
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# The algorithm is taken from "Efficient Algorithms for Finding Maximum
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# Matching in Graphs" by Zvi Galil, ACM Computing Surveys, 1986.
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# It is based on the "blossom" method for finding augmenting paths and
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# the "primal-dual" method for finding a matching of maximum weight, both
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# methods invented by Jack Edmonds.
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#
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# A C program for maximum weight matching by Ed Rothberg was used
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# extensively to validate this new code.
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#
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# Many terms used in the code comments are explained in the paper
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# by Galil. You will probably need the paper to make sense of this code.
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#
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class NoNode:
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"""Dummy value which is different from any node."""
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pass
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class Blossom:
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"""Representation of a non-trivial blossom or sub-blossom."""
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__slots__ = ['childs', 'edges', 'mybestedges']
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# b.childs is an ordered list of b's sub-blossoms, starting with
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# the base and going round the blossom.
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# b.edges is the list of b's connecting edges, such that
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# b.edges[i] = (v, w) where v is a vertex in b.childs[i]
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# and w is a vertex in b.childs[wrap(i+1)].
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# If b is a top-level S-blossom,
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# b.mybestedges is a list of least-slack edges to neighbouring
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# S-blossoms, or None if no such list has been computed yet.
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# This is used for efficient computation of delta3.
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# Generate the blossom's leaf vertices.
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def leaves(self):
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for t in self.childs:
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if isinstance(t, Blossom):
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for v in t.leaves():
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yield v
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else:
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yield t
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# Get a list of vertices.
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gnodes = list(G)
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if not gnodes:
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return set() # don't bother with empty graphs
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# Find the maximum edge weight.
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maxweight = 0
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allinteger = True
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for i, j, d in G.edges(data=True):
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wt = d.get(weight, 1)
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if i != j and wt > maxweight:
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maxweight = wt
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allinteger = allinteger and (str(type(wt)).split("'")[1]
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in ('int', 'long'))
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# If v is a matched vertex, mate[v] is its partner vertex.
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# If v is a single vertex, v does not occur as a key in mate.
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# Initially all vertices are single; updated during augmentation.
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mate = {}
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# If b is a top-level blossom,
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# label.get(b) is None if b is unlabeled (free),
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# 1 if b is an S-blossom,
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# 2 if b is a T-blossom.
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# The label of a vertex is found by looking at the label of its top-level
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# containing blossom.
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# If v is a vertex inside a T-blossom, label[v] is 2 iff v is reachable
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# from an S-vertex outside the blossom.
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# Labels are assigned during a stage and reset after each augmentation.
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label = {}
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# If b is a labeled top-level blossom,
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# labeledge[b] = (v, w) is the edge through which b obtained its label
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# such that w is a vertex in b, or None if b's base vertex is single.
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# If w is a vertex inside a T-blossom and label[w] == 2,
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# labeledge[w] = (v, w) is an edge through which w is reachable from
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# outside the blossom.
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labeledge = {}
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# If v is a vertex, inblossom[v] is the top-level blossom to which v
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# belongs.
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# If v is a top-level vertex, inblossom[v] == v since v is itself
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# a (trivial) top-level blossom.
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# Initially all vertices are top-level trivial blossoms.
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inblossom = dict(zip(gnodes, gnodes))
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# If b is a sub-blossom,
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# blossomparent[b] is its immediate parent (sub-)blossom.
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# If b is a top-level blossom, blossomparent[b] is None.
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blossomparent = dict(zip(gnodes, repeat(None)))
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# If b is a (sub-)blossom,
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# blossombase[b] is its base VERTEX (i.e. recursive sub-blossom).
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blossombase = dict(zip(gnodes, gnodes))
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# If w is a free vertex (or an unreached vertex inside a T-blossom),
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# bestedge[w] = (v, w) is the least-slack edge from an S-vertex,
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# or None if there is no such edge.
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# If b is a (possibly trivial) top-level S-blossom,
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# bestedge[b] = (v, w) is the least-slack edge to a different S-blossom
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# (v inside b), or None if there is no such edge.
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# This is used for efficient computation of delta2 and delta3.
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bestedge = {}
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# If v is a vertex,
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# dualvar[v] = 2 * u(v) where u(v) is the v's variable in the dual
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# optimization problem (if all edge weights are integers, multiplication
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# by two ensures that all values remain integers throughout the algorithm).
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# Initially, u(v) = maxweight / 2.
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dualvar = dict(zip(gnodes, repeat(maxweight)))
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# If b is a non-trivial blossom,
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# blossomdual[b] = z(b) where z(b) is b's variable in the dual
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# optimization problem.
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blossomdual = {}
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# If (v, w) in allowedge or (w, v) in allowedg, then the edge
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# (v, w) is known to have zero slack in the optimization problem;
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# otherwise the edge may or may not have zero slack.
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allowedge = {}
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# Queue of newly discovered S-vertices.
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queue = []
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# Return 2 * slack of edge (v, w) (does not work inside blossoms).
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def slack(v, w):
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return dualvar[v] + dualvar[w] - 2 * G[v][w].get(weight, 1)
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# Assign label t to the top-level blossom containing vertex w,
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# coming through an edge from vertex v.
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def assignLabel(w, t, v):
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b = inblossom[w]
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assert label.get(w) is None and label.get(b) is None
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label[w] = label[b] = t
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if v is not None:
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labeledge[w] = labeledge[b] = (v, w)
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else:
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labeledge[w] = labeledge[b] = None
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bestedge[w] = bestedge[b] = None
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if t == 1:
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# b became an S-vertex/blossom; add it(s vertices) to the queue.
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if isinstance(b, Blossom):
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queue.extend(b.leaves())
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else:
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queue.append(b)
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elif t == 2:
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# b became a T-vertex/blossom; assign label S to its mate.
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# (If b is a non-trivial blossom, its base is the only vertex
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# with an external mate.)
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base = blossombase[b]
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assignLabel(mate[base], 1, base)
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# Trace back from vertices v and w to discover either a new blossom
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# or an augmenting path. Return the base vertex of the new blossom,
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# or NoNode if an augmenting path was found.
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def scanBlossom(v, w):
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# Trace back from v and w, placing breadcrumbs as we go.
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path = []
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base = NoNode
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while v is not NoNode:
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# Look for a breadcrumb in v's blossom or put a new breadcrumb.
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b = inblossom[v]
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if label[b] & 4:
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base = blossombase[b]
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break
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assert label[b] == 1
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path.append(b)
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label[b] = 5
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# Trace one step back.
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if labeledge[b] is None:
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# The base of blossom b is single; stop tracing this path.
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assert blossombase[b] not in mate
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v = NoNode
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else:
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assert labeledge[b][0] == mate[blossombase[b]]
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v = labeledge[b][0]
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b = inblossom[v]
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assert label[b] == 2
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# b is a T-blossom; trace one more step back.
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v = labeledge[b][0]
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# Swap v and w so that we alternate between both paths.
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if w is not NoNode:
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v, w = w, v
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# Remove breadcrumbs.
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for b in path:
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label[b] = 1
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# Return base vertex, if we found one.
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return base
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# Construct a new blossom with given base, through S-vertices v and w.
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# Label the new blossom as S; set its dual variable to zero;
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# relabel its T-vertices to S and add them to the queue.
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def addBlossom(base, v, w):
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bb = inblossom[base]
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bv = inblossom[v]
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bw = inblossom[w]
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# Create blossom.
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b = Blossom()
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blossombase[b] = base
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blossomparent[b] = None
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blossomparent[bb] = b
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# Make list of sub-blossoms and their interconnecting edge endpoints.
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b.childs = path = []
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b.edges = edgs = [(v, w)]
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# Trace back from v to base.
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while bv != bb:
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# Add bv to the new blossom.
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blossomparent[bv] = b
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path.append(bv)
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edgs.append(labeledge[bv])
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assert label[bv] == 2 or (label[bv] == 1 and labeledge[
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bv][0] == mate[blossombase[bv]])
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# Trace one step back.
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v = labeledge[bv][0]
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bv = inblossom[v]
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# Add base sub-blossom; reverse lists.
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path.append(bb)
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path.reverse()
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edgs.reverse()
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# Trace back from w to base.
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while bw != bb:
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# Add bw to the new blossom.
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blossomparent[bw] = b
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path.append(bw)
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edgs.append((labeledge[bw][1], labeledge[bw][0]))
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assert label[bw] == 2 or (label[bw] == 1 and labeledge[
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bw][0] == mate[blossombase[bw]])
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# Trace one step back.
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w = labeledge[bw][0]
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bw = inblossom[w]
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# Set label to S.
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assert label[bb] == 1
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label[b] = 1
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labeledge[b] = labeledge[bb]
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# Set dual variable to zero.
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blossomdual[b] = 0
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# Relabel vertices.
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for v in b.leaves():
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if label[inblossom[v]] == 2:
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# This T-vertex now turns into an S-vertex because it becomes
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# part of an S-blossom; add it to the queue.
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queue.append(v)
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inblossom[v] = b
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# Compute b.mybestedges.
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bestedgeto = {}
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for bv in path:
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if isinstance(bv, Blossom):
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if bv.mybestedges is not None:
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# Walk this subblossom's least-slack edges.
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nblist = bv.mybestedges
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# The sub-blossom won't need this data again.
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bv.mybestedges = None
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else:
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# This subblossom does not have a list of least-slack
|
|
# edges; get the information from the vertices.
|
|
nblist = [(v, w)
|
|
for v in bv.leaves()
|
|
for w in G.neighbors(v)
|
|
if v != w]
|
|
else:
|
|
nblist = [(bv, w)
|
|
for w in G.neighbors(bv)
|
|
if bv != w]
|
|
for k in nblist:
|
|
(i, j) = k
|
|
if inblossom[j] == b:
|
|
i, j = j, i
|
|
bj = inblossom[j]
|
|
if (bj != b and label.get(bj) == 1 and
|
|
((bj not in bestedgeto) or
|
|
slack(i, j) < slack(*bestedgeto[bj]))):
|
|
bestedgeto[bj] = k
|
|
# Forget about least-slack edge of the subblossom.
|
|
bestedge[bv] = None
|
|
b.mybestedges = list(bestedgeto.values())
|
|
# Select bestedge[b].
|
|
mybestedge = None
|
|
bestedge[b] = None
|
|
for k in b.mybestedges:
|
|
kslack = slack(*k)
|
|
if mybestedge is None or kslack < mybestslack:
|
|
mybestedge = k
|
|
mybestslack = kslack
|
|
bestedge[b] = mybestedge
|
|
|
|
# Expand the given top-level blossom.
|
|
def expandBlossom(b, endstage):
|
|
# Convert sub-blossoms into top-level blossoms.
|
|
for s in b.childs:
|
|
blossomparent[s] = None
|
|
if isinstance(s, Blossom):
|
|
if endstage and blossomdual[s] == 0:
|
|
# Recursively expand this sub-blossom.
|
|
expandBlossom(s, endstage)
|
|
else:
|
|
for v in s.leaves():
|
|
inblossom[v] = s
|
|
else:
|
|
inblossom[s] = s
|
|
# If we expand a T-blossom during a stage, its sub-blossoms must be
|
|
# relabeled.
|
|
if (not endstage) and label.get(b) == 2:
|
|
# Start at the sub-blossom through which the expanding
|
|
# blossom obtained its label, and relabel sub-blossoms untili
|
|
# we reach the base.
|
|
# Figure out through which sub-blossom the expanding blossom
|
|
# obtained its label initially.
|
|
entrychild = inblossom[labeledge[b][1]]
|
|
# Decide in which direction we will go round the blossom.
|
|
j = b.childs.index(entrychild)
|
|
if j & 1:
|
|
# Start index is odd; go forward and wrap.
|
|
j -= len(b.childs)
|
|
jstep = 1
|
|
else:
|
|
# Start index is even; go backward.
|
|
jstep = -1
|
|
# Move along the blossom until we get to the base.
|
|
v, w = labeledge[b]
|
|
while j != 0:
|
|
# Relabel the T-sub-blossom.
|
|
if jstep == 1:
|
|
p, q = b.edges[j]
|
|
else:
|
|
q, p = b.edges[j - 1]
|
|
label[w] = None
|
|
label[q] = None
|
|
assignLabel(w, 2, v)
|
|
# Step to the next S-sub-blossom and note its forward edge.
|
|
allowedge[(p, q)] = allowedge[(q, p)] = True
|
|
j += jstep
|
|
if jstep == 1:
|
|
v, w = b.edges[j]
|
|
else:
|
|
w, v = b.edges[j - 1]
|
|
# Step to the next T-sub-blossom.
|
|
allowedge[(v, w)] = allowedge[(w, v)] = True
|
|
j += jstep
|
|
# Relabel the base T-sub-blossom WITHOUT stepping through to
|
|
# its mate (so don't call assignLabel).
|
|
bw = b.childs[j]
|
|
label[w] = label[bw] = 2
|
|
labeledge[w] = labeledge[bw] = (v, w)
|
|
bestedge[bw] = None
|
|
# Continue along the blossom until we get back to entrychild.
|
|
j += jstep
|
|
while b.childs[j] != entrychild:
|
|
# Examine the vertices of the sub-blossom to see whether
|
|
# it is reachable from a neighbouring S-vertex outside the
|
|
# expanding blossom.
|
|
bv = b.childs[j]
|
|
if label.get(bv) == 1:
|
|
# This sub-blossom just got label S through one of its
|
|
# neighbours; leave it be.
|
|
j += jstep
|
|
continue
|
|
if isinstance(bv, Blossom):
|
|
for v in bv.leaves():
|
|
if label.get(v):
|
|
break
|
|
else:
|
|
v = bv
|
|
# If the sub-blossom contains a reachable vertex, assign
|
|
# label T to the sub-blossom.
|
|
if label.get(v):
|
|
assert label[v] == 2
|
|
assert inblossom[v] == bv
|
|
label[v] = None
|
|
label[mate[blossombase[bv]]] = None
|
|
assignLabel(v, 2, labeledge[v][0])
|
|
j += jstep
|
|
# Remove the expanded blossom entirely.
|
|
label.pop(b, None)
|
|
labeledge.pop(b, None)
|
|
bestedge.pop(b, None)
|
|
del blossomparent[b]
|
|
del blossombase[b]
|
|
del blossomdual[b]
|
|
|
|
# Swap matched/unmatched edges over an alternating path through blossom b
|
|
# between vertex v and the base vertex. Keep blossom bookkeeping
|
|
# consistent.
|
|
def augmentBlossom(b, v):
|
|
# Bubble up through the blossom tree from vertex v to an immediate
|
|
# sub-blossom of b.
|
|
t = v
|
|
while blossomparent[t] != b:
|
|
t = blossomparent[t]
|
|
# Recursively deal with the first sub-blossom.
|
|
if isinstance(t, Blossom):
|
|
augmentBlossom(t, v)
|
|
# Decide in which direction we will go round the blossom.
|
|
i = j = b.childs.index(t)
|
|
if i & 1:
|
|
# Start index is odd; go forward and wrap.
|
|
j -= len(b.childs)
|
|
jstep = 1
|
|
else:
|
|
# Start index is even; go backward.
|
|
jstep = -1
|
|
# Move along the blossom until we get to the base.
|
|
while j != 0:
|
|
# Step to the next sub-blossom and augment it recursively.
|
|
j += jstep
|
|
t = b.childs[j]
|
|
if jstep == 1:
|
|
w, x = b.edges[j]
|
|
else:
|
|
x, w = b.edges[j - 1]
|
|
if isinstance(t, Blossom):
|
|
augmentBlossom(t, w)
|
|
# Step to the next sub-blossom and augment it recursively.
|
|
j += jstep
|
|
t = b.childs[j]
|
|
if isinstance(t, Blossom):
|
|
augmentBlossom(t, x)
|
|
# Match the edge connecting those sub-blossoms.
|
|
mate[w] = x
|
|
mate[x] = w
|
|
# Rotate the list of sub-blossoms to put the new base at the front.
|
|
b.childs = b.childs[i:] + b.childs[:i]
|
|
b.edges = b.edges[i:] + b.edges[:i]
|
|
blossombase[b] = blossombase[b.childs[0]]
|
|
assert blossombase[b] == v
|
|
|
|
# Swap matched/unmatched edges over an alternating path between two
|
|
# single vertices. The augmenting path runs through S-vertices v and w.
|
|
def augmentMatching(v, w):
|
|
for (s, j) in ((v, w), (w, v)):
|
|
# Match vertex s to vertex j. Then trace back from s
|
|
# until we find a single vertex, swapping matched and unmatched
|
|
# edges as we go.
|
|
while 1:
|
|
bs = inblossom[s]
|
|
assert label[bs] == 1
|
|
assert (
|
|
labeledge[bs] is None and blossombase[bs] not in mate)\
|
|
or (labeledge[bs][0] == mate[blossombase[bs]])
|
|
# Augment through the S-blossom from s to base.
|
|
if isinstance(bs, Blossom):
|
|
augmentBlossom(bs, s)
|
|
# Update mate[s]
|
|
mate[s] = j
|
|
# Trace one step back.
|
|
if labeledge[bs] is None:
|
|
# Reached single vertex; stop.
|
|
break
|
|
t = labeledge[bs][0]
|
|
bt = inblossom[t]
|
|
assert label[bt] == 2
|
|
# Trace one more step back.
|
|
s, j = labeledge[bt]
|
|
# Augment through the T-blossom from j to base.
|
|
assert blossombase[bt] == t
|
|
if isinstance(bt, Blossom):
|
|
augmentBlossom(bt, j)
|
|
# Update mate[j]
|
|
mate[j] = s
|
|
|
|
# Verify that the optimum solution has been reached.
|
|
def verifyOptimum():
|
|
if maxcardinality:
|
|
# Vertices may have negative dual;
|
|
# find a constant non-negative number to add to all vertex duals.
|
|
vdualoffset = max(0, -min(dualvar.values()))
|
|
else:
|
|
vdualoffset = 0
|
|
# 0. all dual variables are non-negative
|
|
assert min(dualvar.values()) + vdualoffset >= 0
|
|
assert len(blossomdual) == 0 or min(blossomdual.values()) >= 0
|
|
# 0. all edges have non-negative slack and
|
|
# 1. all matched edges have zero slack;
|
|
for i, j, d in G.edges(data=True):
|
|
wt = d.get(weight, 1)
|
|
if i == j:
|
|
continue # ignore self-loops
|
|
s = dualvar[i] + dualvar[j] - 2 * wt
|
|
iblossoms = [i]
|
|
jblossoms = [j]
|
|
while blossomparent[iblossoms[-1]] is not None:
|
|
iblossoms.append(blossomparent[iblossoms[-1]])
|
|
while blossomparent[jblossoms[-1]] is not None:
|
|
jblossoms.append(blossomparent[jblossoms[-1]])
|
|
iblossoms.reverse()
|
|
jblossoms.reverse()
|
|
for (bi, bj) in zip(iblossoms, jblossoms):
|
|
if bi != bj:
|
|
break
|
|
s += 2 * blossomdual[bi]
|
|
assert s >= 0
|
|
if mate.get(i) == j or mate.get(j) == i:
|
|
assert mate[i] == j and mate[j] == i
|
|
assert s == 0
|
|
# 2. all single vertices have zero dual value;
|
|
for v in gnodes:
|
|
assert (v in mate) or dualvar[v] + vdualoffset == 0
|
|
# 3. all blossoms with positive dual value are full.
|
|
for b in blossomdual:
|
|
if blossomdual[b] > 0:
|
|
assert len(b.edges) % 2 == 1
|
|
for (i, j) in b.edges[1::2]:
|
|
assert mate[i] == j and mate[j] == i
|
|
# Ok.
|
|
|
|
# Main loop: continue until no further improvement is possible.
|
|
while 1:
|
|
|
|
# Each iteration of this loop is a "stage".
|
|
# A stage finds an augmenting path and uses that to improve
|
|
# the matching.
|
|
|
|
# Remove labels from top-level blossoms/vertices.
|
|
label.clear()
|
|
labeledge.clear()
|
|
|
|
# Forget all about least-slack edges.
|
|
bestedge.clear()
|
|
for b in blossomdual:
|
|
b.mybestedges = None
|
|
|
|
# Loss of labeling means that we can not be sure that currently
|
|
# allowable edges remain allowable throughout this stage.
|
|
allowedge.clear()
|
|
|
|
# Make queue empty.
|
|
queue[:] = []
|
|
|
|
# Label single blossoms/vertices with S and put them in the queue.
|
|
for v in gnodes:
|
|
if (v not in mate) and label.get(inblossom[v]) is None:
|
|
assignLabel(v, 1, None)
|
|
|
|
# Loop until we succeed in augmenting the matching.
|
|
augmented = 0
|
|
while 1:
|
|
|
|
# Each iteration of this loop is a "substage".
|
|
# A substage tries to find an augmenting path;
|
|
# if found, the path is used to improve the matching and
|
|
# the stage ends. If there is no augmenting path, the
|
|
# primal-dual method is used to pump some slack out of
|
|
# the dual variables.
|
|
|
|
# Continue labeling until all vertices which are reachable
|
|
# through an alternating path have got a label.
|
|
while queue and not augmented:
|
|
|
|
# Take an S vertex from the queue.
|
|
v = queue.pop()
|
|
assert label[inblossom[v]] == 1
|
|
|
|
# Scan its neighbours:
|
|
for w in G.neighbors(v):
|
|
if w == v:
|
|
continue # ignore self-loops
|
|
# w is a neighbour to v
|
|
bv = inblossom[v]
|
|
bw = inblossom[w]
|
|
if bv == bw:
|
|
# this edge is internal to a blossom; ignore it
|
|
continue
|
|
if (v, w) not in allowedge:
|
|
kslack = slack(v, w)
|
|
if kslack <= 0:
|
|
# edge k has zero slack => it is allowable
|
|
allowedge[(v, w)] = allowedge[(w, v)] = True
|
|
if (v, w) in allowedge:
|
|
if label.get(bw) is None:
|
|
# (C1) w is a free vertex;
|
|
# label w with T and label its mate with S (R12).
|
|
assignLabel(w, 2, v)
|
|
elif label.get(bw) == 1:
|
|
# (C2) w is an S-vertex (not in the same blossom);
|
|
# follow back-links to discover either an
|
|
# augmenting path or a new blossom.
|
|
base = scanBlossom(v, w)
|
|
if base is not NoNode:
|
|
# Found a new blossom; add it to the blossom
|
|
# bookkeeping and turn it into an S-blossom.
|
|
addBlossom(base, v, w)
|
|
else:
|
|
# Found an augmenting path; augment the
|
|
# matching and end this stage.
|
|
augmentMatching(v, w)
|
|
augmented = 1
|
|
break
|
|
elif label.get(w) is None:
|
|
# w is inside a T-blossom, but w itself has not
|
|
# yet been reached from outside the blossom;
|
|
# mark it as reached (we need this to relabel
|
|
# during T-blossom expansion).
|
|
assert label[bw] == 2
|
|
label[w] = 2
|
|
labeledge[w] = (v, w)
|
|
elif label.get(bw) == 1:
|
|
# keep track of the least-slack non-allowable edge to
|
|
# a different S-blossom.
|
|
if bestedge.get(bv) is None or \
|
|
kslack < slack(*bestedge[bv]):
|
|
bestedge[bv] = (v, w)
|
|
elif label.get(w) is None:
|
|
# w is a free vertex (or an unreached vertex inside
|
|
# a T-blossom) but we can not reach it yet;
|
|
# keep track of the least-slack edge that reaches w.
|
|
if bestedge.get(w) is None or \
|
|
kslack < slack(*bestedge[w]):
|
|
bestedge[w] = (v, w)
|
|
|
|
if augmented:
|
|
break
|
|
|
|
# There is no augmenting path under these constraints;
|
|
# compute delta and reduce slack in the optimization problem.
|
|
# (Note that our vertex dual variables, edge slacks and delta's
|
|
# are pre-multiplied by two.)
|
|
deltatype = -1
|
|
delta = deltaedge = deltablossom = None
|
|
|
|
# Compute delta1: the minimum value of any vertex dual.
|
|
if not maxcardinality:
|
|
deltatype = 1
|
|
delta = min(dualvar.values())
|
|
|
|
# Compute delta2: the minimum slack on any edge between
|
|
# an S-vertex and a free vertex.
|
|
for v in G.nodes():
|
|
if label.get(inblossom[v]) is None and \
|
|
bestedge.get(v) is not None:
|
|
d = slack(*bestedge[v])
|
|
if deltatype == -1 or d < delta:
|
|
delta = d
|
|
deltatype = 2
|
|
deltaedge = bestedge[v]
|
|
|
|
# Compute delta3: half the minimum slack on any edge between
|
|
# a pair of S-blossoms.
|
|
for b in blossomparent:
|
|
if (blossomparent[b] is None and label.get(b) == 1 and
|
|
bestedge.get(b) is not None):
|
|
kslack = slack(*bestedge[b])
|
|
if allinteger:
|
|
assert (kslack % 2) == 0
|
|
d = kslack // 2
|
|
else:
|
|
d = kslack / 2.0
|
|
if deltatype == -1 or d < delta:
|
|
delta = d
|
|
deltatype = 3
|
|
deltaedge = bestedge[b]
|
|
|
|
# Compute delta4: minimum z variable of any T-blossom.
|
|
for b in blossomdual:
|
|
if (blossomparent[b] is None and label.get(b) == 2 and
|
|
(deltatype == -1 or blossomdual[b] < delta)):
|
|
delta = blossomdual[b]
|
|
deltatype = 4
|
|
deltablossom = b
|
|
|
|
if deltatype == -1:
|
|
# No further improvement possible; max-cardinality optimum
|
|
# reached. Do a final delta update to make the optimum
|
|
# verifyable.
|
|
assert maxcardinality
|
|
deltatype = 1
|
|
delta = max(0, min(dualvar.values()))
|
|
|
|
# Update dual variables according to delta.
|
|
for v in gnodes:
|
|
if label.get(inblossom[v]) == 1:
|
|
# S-vertex: 2*u = 2*u - 2*delta
|
|
dualvar[v] -= delta
|
|
elif label.get(inblossom[v]) == 2:
|
|
# T-vertex: 2*u = 2*u + 2*delta
|
|
dualvar[v] += delta
|
|
for b in blossomdual:
|
|
if blossomparent[b] is None:
|
|
if label.get(b) == 1:
|
|
# top-level S-blossom: z = z + 2*delta
|
|
blossomdual[b] += delta
|
|
elif label.get(b) == 2:
|
|
# top-level T-blossom: z = z - 2*delta
|
|
blossomdual[b] -= delta
|
|
|
|
# Take action at the point where minimum delta occurred.
|
|
if deltatype == 1:
|
|
# No further improvement possible; optimum reached.
|
|
break
|
|
elif deltatype == 2:
|
|
# Use the least-slack edge to continue the search.
|
|
(v, w) = deltaedge
|
|
assert label[inblossom[v]] == 1
|
|
allowedge[(v, w)] = allowedge[(w, v)] = True
|
|
queue.append(v)
|
|
elif deltatype == 3:
|
|
# Use the least-slack edge to continue the search.
|
|
(v, w) = deltaedge
|
|
allowedge[(v, w)] = allowedge[(w, v)] = True
|
|
assert label[inblossom[v]] == 1
|
|
queue.append(v)
|
|
elif deltatype == 4:
|
|
# Expand the least-z blossom.
|
|
expandBlossom(deltablossom, False)
|
|
|
|
# End of a this substage.
|
|
|
|
# Paranoia check that the matching is symmetric.
|
|
for v in mate:
|
|
assert mate[mate[v]] == v
|
|
|
|
# Stop when no more augmenting path can be found.
|
|
if not augmented:
|
|
break
|
|
|
|
# End of a stage; expand all S-blossoms which have zero dual.
|
|
for b in list(blossomdual.keys()):
|
|
if b not in blossomdual:
|
|
continue # already expanded
|
|
if (blossomparent[b] is None and label.get(b) == 1 and
|
|
blossomdual[b] == 0):
|
|
expandBlossom(b, True)
|
|
|
|
# Verify that we reached the optimum solution (only for integer weights).
|
|
if allinteger:
|
|
verifyOptimum()
|
|
|
|
return matching_dict_to_set(mate)
|