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maximum-weight-matching
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Fix bugs and cleanup

This commit is contained in:
Joris van Rantwijk 2023-02-05 19:58:23 +01:00
parent 5827c0ccfa
commit f76623dd5f
1 changed files with 26 additions and 30 deletions
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56
python/maximum_weight_matching.py
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@ -978,9 +978,9 @@ def _substage_scan(
# Take a vertex from the queue.
v = stage_data.queue.pop()
vblossom = matching.vertex_blossom[v]
# Double-check that "v" is an S-vertex.
vblossom = matching.vertex_blossom[v]
assert stage_data.blossom_label[vblossom] == _LABEL_S
# Scan the edges that are incident on "v".
@ -992,6 +992,9 @@ def _substage_scan(
# Try to pull this edge into an alternating tree.
# Ignore edges that are internal to a blossom.
# Note: blossom index of vertex "v" may change during this loop,
# so we need to refresh it here.
vblossom = matching.vertex_blossom[v]
wblossom = matching.vertex_blossom[w]
if vblossom == wblossom:
continue
@ -1028,14 +1031,16 @@ def _substage_scan(
assert best_edge_set is not None
best_edge_set.append(e)
# Update tracking of least-slack edges between vertex "w" and
# any S-vertex. We do this even for edges that have zero slack.
# (If "w" is part of a T-blossom, it may become unlabeled later.
# At that point we will need this edge to relabel vertex "w".
# And we will find this edge through least-slack edge tracking.)
best_edge = stage_data.vertex_best_edge[w]
if best_edge < 0 or slack < matching.edge_slack(best_edge):
stage_data.vertex_best_edge[w] = e
if wlabel != _LABEL_S:
# Update tracking of least-slack edges from vertex "w" to
# any S-vertex. We do this only for T-vertices and unlabeled
# vertices. Edges which have zero slack are also tracked.
# If "w" is part of a T-blossom, it may become unlabeled
# later. At that point we will need a zero-slack edge to
# relabel vertex "w".
best_edge = stage_data.vertex_best_edge[w]
if best_edge < 0 or slack < matching.edge_slack(best_edge):
stage_data.vertex_best_edge[w] = e
# No further S vertices to scan, and no augmenting path found.
return None
@ -1150,17 +1155,9 @@ def _expand_t_blossom(
# path_nodes[i] has already been labeled T.
# We now assign labels to path_nodes[i+1] and path_nodes[i+2].
# Assign label S to path_nodes[i+1] and attach it to path_nodes[i].
sub = path_nodes[i+1]
stage_data.blossom_label[sub] = _LABEL_S
stage_data.blossom_link[sub] = path_edges[i]
# Put vertices in the newly labeled S-blossom in the queue.
# TODO : It feels like we have seen this code pattern a few times; try to generalize it.
if sub < num_vertex:
stage_data.queue.append(sub)
else:
stage_data.queue.extend(matching.blossom_vertices(sub))
# Assign label S to path_nodes[i+1].
(w, v) = path_edges[i]
_substage_assign_label_s(matching, stage_data, v)
# Assign label T to path_nodes[i+2] and attach it to path_nodes[i+1].
sub = path_nodes[i+2]
@ -1434,15 +1431,14 @@ def _apply_delta_step(
# Apply delta to dual variables of top-level non-trivial blossoms.
for b in range(num_vertex, 2 * num_vertex):
blabel = stage_data.blossom_label[b]
if blabel == _LABEL_S:
# S-blossom: add 2*delta to dual variable.
assert matching.blossom_parent[b] == -1
matching.get_blossom(b).half_dual_var += delta
elif blabel == _LABEL_T:
# T-blossom: subtract 2*delta from dual variable.
assert matching.blossom_parent[b] == -1
matching.get_blossom(b).half_dual_var -= delta
if matching.blossom_parent[b] == -1:
blabel = stage_data.blossom_label[b]
if blabel == _LABEL_S:
# S-blossom: add 2*delta to dual variable.
matching.get_blossom(b).half_dual_var += delta
elif blabel == _LABEL_T:
# T-blossom: subtract 2*delta from dual variable.
matching.get_blossom(b).half_dual_var -= delta
def _run_stage(matching: _PartialMatching) -> bool:
@ -1604,7 +1600,7 @@ def _verify_optimum(
# List blossoms that contain the edge (i, j).
edge_blossoms = []
for (bi, bj) in zip(iblossoms, jblossoms):
for (bi, bj) in zip(reversed(iblossoms), reversed(jblossoms)):
if bi != bj:
break
edge_blossoms.append(bi)