# Source code for networkx.algorithms.matching

```"""Functions for computing and verifying matchings in a graph."""
from collections import Counter
from itertools import combinations, repeat

import networkx as nx
from networkx.utils import not_implemented_for

__all__ = [
"is_matching",
"is_maximal_matching",
"is_perfect_matching",
"max_weight_matching",
"min_weight_matching",
"maximal_matching",
]

[docs]
@not_implemented_for("multigraph")
@not_implemented_for("directed")
@nx._dispatchable
def maximal_matching(G):
r"""Find a maximal matching in the graph.

A matching is a subset of edges in which no node occurs more than once.
A maximal matching cannot add more edges and still be a matching.

Parameters
----------
G : NetworkX graph
Undirected graph

Returns
-------
matching : set
A maximal matching of the graph.

Examples
--------
>>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (2, 4), (3, 5), (4, 5)])
>>> sorted(nx.maximal_matching(G))
[(1, 2), (3, 5)]

Notes
-----
The algorithm greedily selects a maximal matching M of the graph G
(i.e. no superset of M exists). It runs in \$O(|E|)\$ time.
"""
matching = set()
nodes = set()
for edge in G.edges():
# If the edge isn't covered, add it to the matching
# then remove neighborhood of u and v from consideration.
u, v = edge
if u not in nodes and v not in nodes and u != v:
nodes.update(edge)
return matching

def matching_dict_to_set(matching):
"""Converts matching dict format to matching set format

Converts a dictionary representing a matching (as returned by
:func:`max_weight_matching`) to a set representing a matching (as
returned by :func:`maximal_matching`).

In the definition of maximal matching adopted by NetworkX,
self-loops are not allowed, so the provided dictionary is expected
to never have any mapping from a key to itself. However, the
dictionary is expected to have mirrored key/value pairs, for
example, key ``u`` with value ``v`` and key ``v`` with value ``u``.

"""
edges = set()
for edge in matching.items():
u, v = edge
if (v, u) in edges or edge in edges:
continue
if u == v:
raise nx.NetworkXError(f"Selfloops cannot appear in matchings {edge}")
return edges

[docs]
@nx._dispatchable
def is_matching(G, matching):
"""Return True if ``matching`` is a valid matching of ``G``

A *matching* in a graph is a set of edges in which no two distinct
edges share a common endpoint. Each node is incident to at most one
edge in the matching. The edges are said to be independent.

Parameters
----------
G : NetworkX graph

matching : dict or set
A dictionary or set representing a matching. If a dictionary, it
must have ``matching[u] == v`` and ``matching[v] == u`` for each
edge ``(u, v)`` in the matching. If a set, it must have elements
of the form ``(u, v)``, where ``(u, v)`` is an edge in the
matching.

Returns
-------
bool
Whether the given set or dictionary represents a valid matching
in the graph.

Raises
------
NetworkXError
If the proposed matching has an edge to a node not in G.
Or if the matching is not a collection of 2-tuple edges.

Examples
--------
>>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (2, 4), (3, 5), (4, 5)])
>>> nx.is_maximal_matching(G, {1: 3, 2: 4})  # using dict to represent matching
True

>>> nx.is_matching(G, {(1, 3), (2, 4)})  # using set to represent matching
True

"""
if isinstance(matching, dict):
matching = matching_dict_to_set(matching)

nodes = set()
for edge in matching:
if len(edge) != 2:
raise nx.NetworkXError(f"matching has non-2-tuple edge {edge}")
u, v = edge
if u not in G or v not in G:
raise nx.NetworkXError(f"matching contains edge {edge} with node not in G")
if u == v:
return False
if not G.has_edge(u, v):
return False
if u in nodes or v in nodes:
return False
nodes.update(edge)
return True

[docs]
@nx._dispatchable
def is_maximal_matching(G, matching):
"""Return True if ``matching`` is a maximal matching of ``G``

A *maximal matching* in a graph is a matching in which adding any
edge would cause the set to no longer be a valid matching.

Parameters
----------
G : NetworkX graph

matching : dict or set
A dictionary or set representing a matching. If a dictionary, it
must have ``matching[u] == v`` and ``matching[v] == u`` for each
edge ``(u, v)`` in the matching. If a set, it must have elements
of the form ``(u, v)``, where ``(u, v)`` is an edge in the
matching.

Returns
-------
bool
Whether the given set or dictionary represents a valid maximal
matching in the graph.

Examples
--------
>>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (3, 4), (3, 5)])
>>> nx.is_maximal_matching(G, {(1, 2), (3, 4)})
True

"""
if isinstance(matching, dict):
matching = matching_dict_to_set(matching)
# If the given set is not a matching, then it is not a maximal matching.
edges = set()
nodes = set()
for edge in matching:
if len(edge) != 2:
raise nx.NetworkXError(f"matching has non-2-tuple edge {edge}")
u, v = edge
if u not in G or v not in G:
raise nx.NetworkXError(f"matching contains edge {edge} with node not in G")
if u == v:
return False
if not G.has_edge(u, v):
return False
if u in nodes or v in nodes:
return False
nodes.update(edge)
# A matching is maximal if adding any new edge from G to it
# causes the resulting set to match some node twice.
# Be careful to check for adding selfloops
for u, v in G.edges:
if (u, v) not in edges:
# could add edge (u, v) to edges and have a bigger matching
if u not in nodes and v not in nodes and u != v:
return False
return True

[docs]
@nx._dispatchable
def is_perfect_matching(G, matching):
"""Return True if ``matching`` is a perfect matching for ``G``

A *perfect matching* in a graph is a matching in which exactly one edge
is incident upon each vertex.

Parameters
----------
G : NetworkX graph

matching : dict or set
A dictionary or set representing a matching. If a dictionary, it
must have ``matching[u] == v`` and ``matching[v] == u`` for each
edge ``(u, v)`` in the matching. If a set, it must have elements
of the form ``(u, v)``, where ``(u, v)`` is an edge in the
matching.

Returns
-------
bool
Whether the given set or dictionary represents a valid perfect
matching in the graph.

Examples
--------
>>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (2, 4), (3, 5), (4, 5), (4, 6)])
>>> my_match = {1: 2, 3: 5, 4: 6}
>>> nx.is_perfect_matching(G, my_match)
True

"""
if isinstance(matching, dict):
matching = matching_dict_to_set(matching)

nodes = set()
for edge in matching:
if len(edge) != 2:
raise nx.NetworkXError(f"matching has non-2-tuple edge {edge}")
u, v = edge
if u not in G or v not in G:
raise nx.NetworkXError(f"matching contains edge {edge} with node not in G")
if u == v:
return False
if not G.has_edge(u, v):
return False
if u in nodes or v in nodes:
return False
nodes.update(edge)
return len(nodes) == len(G)

[docs]
@not_implemented_for("multigraph")
@not_implemented_for("directed")
@nx._dispatchable(edge_attrs="weight")
def min_weight_matching(G, weight="weight"):
"""Computing a minimum-weight maximal matching of G.

Use the maximum-weight algorithm with edge weights subtracted
from the maximum weight of all edges.

A matching is a subset of edges in which no node occurs more than once.
The weight of a matching is the sum of the weights of its edges.
A maximal matching cannot add more edges and still be a matching.
The cardinality of a matching is the number of matched edges.

This method replaces the edge weights with 1 plus the maximum edge weight
minus the original edge weight.

new_weight = (max_weight + 1) - edge_weight

then runs :func:`max_weight_matching` with the new weights.
The max weight matching with these new weights corresponds
to the min weight matching using the original weights.
Adding 1 to the max edge weight keeps all edge weights positive
and as integers if they started as integers.

You might worry that adding 1 to each weight would make the algorithm
favor matchings with more edges. But we use the parameter
`maxcardinality=True` in `max_weight_matching` to ensure that the
number of edges in the competing matchings are the same and thus
the optimum does not change due to changes in the number of edges.

Parameters
----------
G : NetworkX graph
Undirected graph

weight: string, optional (default='weight')
Edge data key corresponding to the edge weight.
If key not found, uses 1 as weight.

Returns
-------
matching : set
A minimal weight matching of the graph.

--------
max_weight_matching
"""
if len(G.edges) == 0:
return max_weight_matching(G, maxcardinality=True, weight=weight)
G_edges = G.edges(data=weight, default=1)
max_weight = 1 + max(w for _, _, w in G_edges)
InvG = nx.Graph()
edges = ((u, v, max_weight - w) for u, v, w in G_edges)
return max_weight_matching(InvG, maxcardinality=True, weight=weight)

[docs]
@not_implemented_for("multigraph")
@not_implemented_for("directed")
@nx._dispatchable(edge_attrs="weight")
def max_weight_matching(G, maxcardinality=False, weight="weight"):
"""Compute a maximum-weighted matching of G.

A matching is a subset of edges in which no node occurs more than once.
The weight of a matching is the sum of the weights of its edges.
A maximal matching cannot add more edges and still be a matching.
The cardinality of a matching is the number of matched edges.

Parameters
----------
G : NetworkX graph
Undirected graph

maxcardinality: bool, optional (default=False)
If maxcardinality is True, compute the maximum-cardinality matching
with maximum weight among all maximum-cardinality matchings.

weight: string, optional (default='weight')
Edge data key corresponding to the edge weight.
If key not found, uses 1 as weight.

Returns
-------
matching : set
A maximal matching of the graph.

Examples
--------
>>> G = nx.Graph()
>>> edges = [(1, 2, 6), (1, 3, 2), (2, 3, 1), (2, 4, 7), (3, 5, 9), (4, 5, 3)]
>>> sorted(nx.max_weight_matching(G))
[(2, 4), (5, 3)]

Notes
-----
If G has edges with weight attributes the edge data are used as
weight values else the weights are assumed to be 1.

This function takes time O(number_of_nodes ** 3).

If all edge weights are integers, the algorithm uses only integer
computations.  If floating point weights are used, the algorithm
could return a slightly suboptimal matching due to numeric
precision errors.

This method is based on the "blossom" method for finding augmenting
paths and the "primal-dual" method for finding a matching of maximum
weight, both methods invented by Jack Edmonds [1]_.

Bipartite graphs can also be matched using the functions present in
:mod:`networkx.algorithms.bipartite.matching`.

References
----------
.. [1] "Efficient Algorithms for Finding Maximum Matching in Graphs",
Zvi Galil, ACM Computing Surveys, 1986.
"""
#
# The algorithm is taken from "Efficient Algorithms for Finding Maximum
# Matching in Graphs" by Zvi Galil, ACM Computing Surveys, 1986.
# It is based on the "blossom" method for finding augmenting paths and
# the "primal-dual" method for finding a matching of maximum weight, both
# methods invented by Jack Edmonds.
#
# A C program for maximum weight matching by Ed Rothberg was used
# extensively to validate this new code.
#
# Many terms used in the code comments are explained in the paper
# by Galil. You will probably need the paper to make sense of this code.
#

class NoNode:
"""Dummy value which is different from any node."""

class Blossom:
"""Representation of a non-trivial blossom or sub-blossom."""

__slots__ = ["childs", "edges", "mybestedges"]

# b.childs is an ordered list of b's sub-blossoms, starting with
# the base and going round the blossom.

# b.edges is the list of b's connecting edges, such that
# b.edges[i] = (v, w) where v is a vertex in b.childs[i]
# and w is a vertex in b.childs[wrap(i+1)].

# If b is a top-level S-blossom,
# b.mybestedges is a list of least-slack edges to neighboring
# S-blossoms, or None if no such list has been computed yet.
# This is used for efficient computation of delta3.

# Generate the blossom's leaf vertices.
def leaves(self):
stack = [*self.childs]
while stack:
t = stack.pop()
if isinstance(t, Blossom):
stack.extend(t.childs)
else:
yield t

# Get a list of vertices.
gnodes = list(G)
if not gnodes:
return set()  # don't bother with empty graphs

# Find the maximum edge weight.
maxweight = 0
allinteger = True
for i, j, d in G.edges(data=True):
wt = d.get(weight, 1)
if i != j and wt > maxweight:
maxweight = wt
allinteger = allinteger and (str(type(wt)).split("'")[1] in ("int", "long"))

# If v is a matched vertex, mate[v] is its partner vertex.
# If v is a single vertex, v does not occur as a key in mate.
# Initially all vertices are single; updated during augmentation.
mate = {}

# If b is a top-level blossom,
# label.get(b) is None if b is unlabeled (free),
#                 1 if b is an S-blossom,
#                 2 if b is a T-blossom.
# The label of a vertex is found by looking at the label of its top-level
# containing blossom.
# If v is a vertex inside a T-blossom, label[v] is 2 iff v is reachable
# from an S-vertex outside the blossom.
# Labels are assigned during a stage and reset after each augmentation.
label = {}

# If b is a labeled top-level blossom,
# labeledge[b] = (v, w) is the edge through which b obtained its label
# such that w is a vertex in b, or None if b's base vertex is single.
# If w is a vertex inside a T-blossom and label[w] == 2,
# labeledge[w] = (v, w) is an edge through which w is reachable from
# outside the blossom.
labeledge = {}

# If v is a vertex, inblossom[v] is the top-level blossom to which v
# belongs.
# If v is a top-level vertex, inblossom[v] == v since v is itself
# a (trivial) top-level blossom.
# Initially all vertices are top-level trivial blossoms.
inblossom = dict(zip(gnodes, gnodes))

# If b is a sub-blossom,
# blossomparent[b] is its immediate parent (sub-)blossom.
# If b is a top-level blossom, blossomparent[b] is None.
blossomparent = dict(zip(gnodes, repeat(None)))

# If b is a (sub-)blossom,
# blossombase[b] is its base VERTEX (i.e. recursive sub-blossom).
blossombase = dict(zip(gnodes, gnodes))

# If w is a free vertex (or an unreached vertex inside a T-blossom),
# bestedge[w] = (v, w) is the least-slack edge from an S-vertex,
# or None if there is no such edge.
# If b is a (possibly trivial) top-level S-blossom,
# bestedge[b] = (v, w) is the least-slack edge to a different S-blossom
# (v inside b), or None if there is no such edge.
# This is used for efficient computation of delta2 and delta3.
bestedge = {}

# If v is a vertex,
# dualvar[v] = 2 * u(v) where u(v) is the v's variable in the dual
# optimization problem (if all edge weights are integers, multiplication
# by two ensures that all values remain integers throughout the algorithm).
# Initially, u(v) = maxweight / 2.
dualvar = dict(zip(gnodes, repeat(maxweight)))

# If b is a non-trivial blossom,
# blossomdual[b] = z(b) where z(b) is b's variable in the dual
# optimization problem.
blossomdual = {}

# If (v, w) in allowedge or (w, v) in allowedg, then the edge
# (v, w) is known to have zero slack in the optimization problem;
# otherwise the edge may or may not have zero slack.
allowedge = {}

# Queue of newly discovered S-vertices.
queue = []

# Return 2 * slack of edge (v, w) (does not work inside blossoms).
def slack(v, w):
return dualvar[v] + dualvar[w] - 2 * G[v][w].get(weight, 1)

# Assign label t to the top-level blossom containing vertex w,
# coming through an edge from vertex v.
def assignLabel(w, t, v):
b = inblossom[w]
assert label.get(w) is None and label.get(b) is None
label[w] = label[b] = t
if v is not None:
labeledge[w] = labeledge[b] = (v, w)
else:
labeledge[w] = labeledge[b] = None
bestedge[w] = bestedge[b] = None
if t == 1:
# b became an S-vertex/blossom; add it(s vertices) to the queue.
if isinstance(b, Blossom):
queue.extend(b.leaves())
else:
queue.append(b)
elif t == 2:
# b became a T-vertex/blossom; assign label S to its mate.
# (If b is a non-trivial blossom, its base is the only vertex
# with an external mate.)
base = blossombase[b]
assignLabel(mate[base], 1, base)

# Trace back from vertices v and w to discover either a new blossom
# or an augmenting path. Return the base vertex of the new blossom,
# or NoNode if an augmenting path was found.
def scanBlossom(v, w):
# Trace back from v and w, placing breadcrumbs as we go.
path = []
base = NoNode
while v is not NoNode:
# Look for a breadcrumb in v's blossom or put a new breadcrumb.
b = inblossom[v]
if label[b] & 4:
base = blossombase[b]
break
assert label[b] == 1
path.append(b)
label[b] = 5
# Trace one step back.
if labeledge[b] is None:
# The base of blossom b is single; stop tracing this path.
assert blossombase[b] not in mate
v = NoNode
else:
assert labeledge[b][0] == mate[blossombase[b]]
v = labeledge[b][0]
b = inblossom[v]
assert label[b] == 2
# b is a T-blossom; trace one more step back.
v = labeledge[b][0]
# Swap v and w so that we alternate between both paths.
if w is not NoNode:
v, w = w, v
for b in path:
label[b] = 1
# Return base vertex, if we found one.
return base

# Construct a new blossom with given base, through S-vertices v and w.
# Label the new blossom as S; set its dual variable to zero;
# relabel its T-vertices to S and add them to the queue.
def addBlossom(base, v, w):
bb = inblossom[base]
bv = inblossom[v]
bw = inblossom[w]
# Create blossom.
b = Blossom()
blossombase[b] = base
blossomparent[b] = None
blossomparent[bb] = b
# Make list of sub-blossoms and their interconnecting edge endpoints.
b.childs = path = []
b.edges = edgs = [(v, w)]
# Trace back from v to base.
while bv != bb:
# Add bv to the new blossom.
blossomparent[bv] = b
path.append(bv)
edgs.append(labeledge[bv])
assert label[bv] == 2 or (
label[bv] == 1 and labeledge[bv][0] == mate[blossombase[bv]]
)
# Trace one step back.
v = labeledge[bv][0]
bv = inblossom[v]
# Add base sub-blossom; reverse lists.
path.append(bb)
path.reverse()
edgs.reverse()
# Trace back from w to base.
while bw != bb:
# Add bw to the new blossom.
blossomparent[bw] = b
path.append(bw)
edgs.append((labeledge[bw][1], labeledge[bw][0]))
assert label[bw] == 2 or (
label[bw] == 1 and labeledge[bw][0] == mate[blossombase[bw]]
)
# Trace one step back.
w = labeledge[bw][0]
bw = inblossom[w]
# Set label to S.
assert label[bb] == 1
label[b] = 1
labeledge[b] = labeledge[bb]
# Set dual variable to zero.
blossomdual[b] = 0
# Relabel vertices.
for v in b.leaves():
if label[inblossom[v]] == 2:
# This T-vertex now turns into an S-vertex because it becomes
# part of an S-blossom; add it to the queue.
queue.append(v)
inblossom[v] = b
# Compute b.mybestedges.
bestedgeto = {}
for bv in path:
if isinstance(bv, Blossom):
if bv.mybestedges is not None:
# Walk this subblossom's least-slack edges.
nblist = bv.mybestedges
# The sub-blossom won't need this data again.
bv.mybestedges = None
else:
# 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):
# This is an obnoxiously complicated recursive function for the sake of
# a stack-transformation.  So, we hack around the complexity by using
# a trampoline pattern.  By yielding the arguments to each recursive
# call, we keep the actual callstack flat.

def _recurse(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.
yield s
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 neighboring 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
# neighbors; 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]

# Now, we apply the trampoline pattern.  We simulate a recursive
# callstack by maintaining a stack of generators, each yielding a
# sequence of function arguments.  We grow the stack by appending a call
# to _recurse on each argument tuple, and shrink the stack whenever a
# generator is exhausted.
stack = [_recurse(b, endstage)]
while stack:
top = stack[-1]
for s in top:
stack.append(_recurse(s, endstage))
break
else:
stack.pop()

# 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):
# This is an obnoxiously complicated recursive function for the sake of
# a stack-transformation.  So, we hack around the complexity by using
# a trampoline pattern.  By yielding the arguments to each recursive
# call, we keep the actual callstack flat.

def _recurse(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):
yield (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):
yield (t, w)
# Step to the next sub-blossom and augment it recursively.
j += jstep
t = b.childs[j]
if isinstance(t, Blossom):
yield (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

# Now, we apply the trampoline pattern.  We simulate a recursive
# callstack by maintaining a stack of generators, each yielding a
# sequence of function arguments.  We grow the stack by appending a call
# to _recurse on each argument tuple, and shrink the stack whenever a
# generator is exhausted.
stack = [_recurse(b, v)]
while stack:
top = stack[-1]
for args in top:
stack.append(_recurse(*args))
break
else:
stack.pop()

# 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 neighbors:
for w in G.neighbors(v):
if w == v:
continue  # ignore self-loops
# w is a neighbor 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.
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
# verifiable.
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)

```