joris
/
maximum-weight-matching
1
0
Fork 0
Code Pull Requests Activity

Implement heap-based tracking for delta4

This commit is contained in:
Joris van Rantwijk 2024-05-25 21:25:00 +02:00
parent 13b6b76d47
commit a23c38eb70
1 changed files with 79 additions and 31 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

102
python/mwmatching.py
View File

@ -34,8 +34,9 @@ def maximum_weight_matching(
no effect on the maximum-weight matching.
Edges with negative weight are ignored.
This function takes time O(n**3 + n*m*log(n)), where "n" is the number of vertices.
This function uses O(n + m) memory, where "m" is the number of edges.
This function takes time O(n**3 + n*m*log(n)),
where "n" is the number of vertices and "m" is the number of edges.
This function uses O(n + m) memory.
Parameters:
edges: List of edges, each edge specified as a tuple "(x, y, w)"
@ -447,6 +448,11 @@ class _NonTrivialBlossom(_Blossom):
# New blossoms start with dual variable 0.
self.dual_var: float = 0
# If this is a top-level T-blossom,
# "delta4_node" is the corresponding node in the delta4 queue.
# Otherwise "delta4_node" is None.
self.delta4_node: Optional[PriorityQueue.Node] = None
def vertices(self) -> list[int]:
"""Return a list of vertex indices contained in the blossom."""
@ -522,24 +528,31 @@ class _MatchingContext:
# Initially all vertices are trivial top-level blossoms.
self.vertex_top_blossom: list[_Blossom] = self.trivial_blossom.copy()
# Running sum of applied delta steps times 2.
self.delta_sum_2x: float = 0
# Queue containing edges between S-vertices in different top-level
# blossoms. The priority of each edge is equal to its slack
# plus two times the running sum of delta updates.
self.delta3_queue = PriorityQueue()
self.delta3_set: set[int] = set()
# Every vertex has a variable in the dual LPP.
#
# "vertex_dual_2x[x]" is 2 times the dual variable of vertex "x".
# Initial dual value of all vertices times 2.
# Multiplication by 2 ensures that the values are integers
# if all edge weights are integers.
#
# Vertex duals are initialized to half the maximum edge weight.
max_weight = max(w for (_x, _y, w) in graph.edges)
self.vertex_dual_2x: list[float] = num_vertex * [max_weight]
self.start_vertex_dual_2x = max(w for (_x, _y, w) in graph.edges)
# Every vertex has a variable in the dual LPP.
# "vertex_dual_2x[x]" is 2 times the dual variable of vertex "x".
self.vertex_dual_2x: list[float]
self.vertex_dual_2x = num_vertex * [self.start_vertex_dual_2x]
# Running sum of applied delta steps times 2.
self.delta_sum_2x: float = 0
# Queue containing edges between S-vertices in different top-level
# blossoms. The priority of an edge is its slack plus two times the
# running sum of delta updates.
self.delta3_queue: PriorityQueue[int] = PriorityQueue()
self.delta3_set: set[int] = set()
# Queue containing top-level non-trivial T-blossoms.
# The priority of a blossom is its dual plus two times the running
# sum of delta updates.
self.delta4_queue: PriorityQueue[_NonTrivialBlossom] = PriorityQueue()
# For each T-vertex or unlabeled vertex "x",
# "vertex_best_edge[x]" is the edge index of the least-slack edge
@ -612,9 +625,6 @@ class _MatchingContext:
for x in range(num_vertex):
self.vertex_best_edge[x] = -1
self.delta3_queue.clear()
self.delta3_set.clear()
def lset_add_vertex_edge(self, y: int, e: int, slack: float) -> None:
"""Add edge "e" from an S-vertex to unlabeled vertex or T-vertex "y".
@ -663,6 +673,8 @@ class _MatchingContext:
Marks all blossoms as unlabeled, clears the queue,
and resets tracking of least-slack edges.
This function takes time O(n).
"""
# Remove blossom labels.
@ -676,6 +688,14 @@ class _MatchingContext:
# Reset least-slack edge tracking.
self.lset_reset()
# Reset delta queues.
self.delta3_queue.clear()
self.delta3_set.clear()
self.delta4_queue.clear()
for ntb in self.nontrivial_blossom:
ntb.delta4_node = None
def trace_alternating_paths(self, x: int, y: int) -> _AlternatingPath:
"""Trace back through the alternating trees from vertices "x" and "y".
@ -809,6 +829,13 @@ class _MatchingContext:
if sub.label == _LABEL_T:
self.queue.extend(sub.vertices())
# Remove sub-blossoms from the delta4 queue.
for sub in subblossoms:
if ((isinstance(sub, _NonTrivialBlossom))
and (sub.delta4_node is not None)):
self.delta4_queue.delete(sub.delta4_node)
sub.delta4_node = None
@staticmethod
def find_path_through_blossom(
blossom: _NonTrivialBlossom,
@ -845,6 +872,10 @@ class _MatchingContext:
assert blossom.parent is None
assert blossom.label == _LABEL_T
# Remove expanded blossom from the delta4 queue.
assert blossom.delta4_node is not None
self.delta4_queue.delete(blossom.delta4_node)
# Convert sub-blossoms into top-level blossoms.
for sub in blossom.subblossoms:
assert sub.label == _LABEL_NONE
@ -871,6 +902,12 @@ class _MatchingContext:
sub.label = _LABEL_T
sub.tree_edge = blossom.tree_edge
# Insert T sub-blossom into the delta4 queue.
if isinstance(sub, _NonTrivialBlossom):
assert sub.delta4_node is None
prio = sub.dual_var + self.delta_sum_2x
sub.delta4_node = self.delta4_queue.insert(prio, sub)
# Walk through the expanded blossom from "sub" to the base vertex.
# Assign alternating S and T labels to the sub-blossoms and attach
# them to the alternating tree.
@ -895,6 +932,12 @@ class _MatchingContext:
sub.label = _LABEL_T
sub.tree_edge = path_edges[p+1]
# Insert T sub-blossom into the delta4 queue.
if isinstance(sub, _NonTrivialBlossom):
assert sub.delta4_node is None
prio = sub.dual_var + self.delta_sum_2x
sub.delta4_node = self.delta4_queue.insert(prio, sub)
# Delete the expanded blossom.
self.nontrivial_blossom.remove(blossom)
@ -1131,6 +1174,12 @@ class _MatchingContext:
by.label = _LABEL_T
by.tree_edge = (x, y)
# Insert T sub-blossom into the delta4 queue.
if isinstance(by, _NonTrivialBlossom):
assert by.delta4_node is None
prio = by.dual_var + self.delta_sum_2x
by.delta4_node = self.delta4_queue.insert(prio, by)
# Assign label S to the blossom that is mated to the T-blossom.
z = self.vertex_mate[by.base_vertex]
assert z != -1
@ -1265,17 +1314,14 @@ class _MatchingContext:
Returns:
Tuple (delta_type, delta_2x, delta_edge, delta_blossom).
"""
num_vertex = self.graph.num_vertex
delta_edge = -1
delta_blossom: Optional[_NonTrivialBlossom] = None
# Compute delta1: minimum dual variable of any S-vertex.
# All unmatched vertices have the same dual value, and this is
# the minimum value among all S-vertices.
delta_type = 1
delta_2x = min(
self.vertex_dual_2x[x]
for x in range(num_vertex)
if self.vertex_top_blossom[x].label == _LABEL_S)
delta_2x = self.start_vertex_dual_2x - self.delta_sum_2x
# Compute delta2: minimum slack of any edge between an S-vertex and
# an unlabeled vertex.
@ -1311,8 +1357,10 @@ class _MatchingContext:
self.delta3_set.remove(e)
# Compute delta4: half minimum dual variable of a top-level T-blossom.
for blossom in self.nontrivial_blossom:
if (blossom.label == _LABEL_T) and (blossom.parent is None):
if not self.delta4_queue.empty():
blossom = self.delta4_queue.find_min().data
assert blossom.label == _LABEL_T
assert blossom.parent is None
if blossom.dual_var <= delta_2x:
delta_type = 4
delta_2x = blossom.dual_var