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This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

# Source code for networkx.algorithms.flow.preflowpush

```
# -*- coding: utf-8 -*-
"""
Highest-label preflow-push algorithm for maximum flow problems.
"""
__author__ = """ysitu <ysitu@users.noreply.github.com>"""
# Copyright (C) 2014 ysitu <ysitu@users.noreply.github.com>
# All rights reserved.
# BSD license.
from collections import deque
from itertools import islice
import networkx as nx
from networkx.algorithms.flow.utils import *
__all__ = ['preflow_push']
def preflow_push_impl(G, s, t, capacity, residual, global_relabel_freq,
value_only):
"""Implementation of the highest-label preflow-push algorithm.
"""
if s not in G:
raise nx.NetworkXError('node %s not in graph' % str(s))
if t not in G:
raise nx.NetworkXError('node %s not in graph' % str(t))
if s == t:
raise nx.NetworkXError('source and sink are the same node')
if global_relabel_freq is None:
global_relabel_freq = 0
if global_relabel_freq < 0:
raise nx.NetworkXError('global_relabel_freq must be nonnegative.')
if residual is None:
R = build_residual_network(G, capacity)
else:
R = residual
detect_unboundedness(R, s, t)
R_node = R.node
R_pred = R.pred
R_succ = R.succ
# Initialize/reset the residual network.
for u in R:
R_node[u]['excess'] = 0
for e in R_succ[u].values():
e['flow'] = 0
def reverse_bfs(src):
"""Perform a reverse breadth-first search from src in the residual
network.
"""
heights = {src: 0}
q = deque([(src, 0)])
while q:
u, height = q.popleft()
height += 1
for v, attr in R_pred[u].items():
if v not in heights and attr['flow'] < attr['capacity']:
heights[v] = height
q.append((v, height))
return heights
# Initialize heights of the nodes.
heights = reverse_bfs(t)
if s not in heights:
# t is not reachable from s in the residual network. The maximum flow
# must be zero.
R.graph['flow_value'] = 0
return R
n = len(R)
# max_height represents the height of the highest level below level n with
# at least one active node.
max_height = max(heights[u] for u in heights if u != s)
heights[s] = n
grt = GlobalRelabelThreshold(n, R.size(), global_relabel_freq)
# Initialize heights and 'current edge' data structures of the nodes.
for u in R:
R_node[u]['height'] = heights[u] if u in heights else n + 1
R_node[u]['curr_edge'] = CurrentEdge(R_succ[u])
def push(u, v, flow):
"""Push flow units of flow from u to v.
"""
R_succ[u][v]['flow'] += flow
R_succ[v][u]['flow'] -= flow
R_node[u]['excess'] -= flow
R_node[v]['excess'] += flow
# The maximum flow must be nonzero now. Initialize the preflow by
# saturating all edges emanating from s.
for u, attr in R_succ[s].items():
flow = attr['capacity']
if flow > 0:
push(s, u, flow)
# Partition nodes into levels.
levels = [Level() for i in range(2 * n - 1)]
for u in R:
if u != s and u != t:
level = levels[R_node[u]['height']]
if R_node[u]['excess'] > 0:
level.active.add(u)
else:
level.inactive.add(u)
def activate(v):
"""Move a node from the inactive set to the active set of its level.
"""
if v != s and v != t:
level = levels[R_node[v]['height']]
if v in level.inactive:
level.inactive.remove(v)
level.active.add(v)
def relabel(u):
"""Relabel a node to create an admissible edge.
"""
grt.add_work(len(R_succ[u]))
return min(R_node[v]['height'] for v, attr in R_succ[u].items()
if attr['flow'] < attr['capacity']) + 1
def discharge(u, is_phase1):
"""Discharge a node until it becomes inactive or, during phase 1 (see
below), its height reaches at least n. The node is known to have the
largest height among active nodes.
"""
height = R_node[u]['height']
curr_edge = R_node[u]['curr_edge']
# next_height represents the next height to examine after discharging
# the current node. During phase 1, it is capped to below n.
next_height = height
levels[height].active.remove(u)
while True:
v, attr = curr_edge.get()
if (height == R_node[v]['height'] + 1 and
attr['flow'] < attr['capacity']):
flow = min(R_node[u]['excess'],
attr['capacity'] - attr['flow'])
push(u, v, flow)
activate(v)
if R_node[u]['excess'] == 0:
# The node has become inactive.
levels[height].inactive.add(u)
break
try:
curr_edge.move_to_next()
except StopIteration:
# We have run off the end of the adjacency list, and there can
# be no more admissible edges. Relabel the node to create one.
height = relabel(u)
if is_phase1 and height >= n - 1:
# Although the node is still active, with a height at least
# n - 1, it is now known to be on the s side of the minimum
# s-t cut. Stop processing it until phase 2.
levels[height].active.add(u)
break
# The first relabel operation after global relabeling may not
# increase the height of the node since the 'current edge' data
# structure is not rewound. Use height instead of (height - 1)
# in case other active nodes at the same level are missed.
next_height = height
R_node[u]['height'] = height
return next_height
def gap_heuristic(height):
"""Apply the gap heuristic.
"""
# Move all nodes at levels (height + 1) to max_height to level n + 1.
for level in islice(levels, height + 1, max_height + 1):
for u in level.active:
R_node[u]['height'] = n + 1
for u in level.inactive:
R_node[u]['height'] = n + 1
levels[n + 1].active.update(level.active)
level.active.clear()
levels[n + 1].inactive.update(level.inactive)
level.inactive.clear()
def global_relabel(from_sink):
"""Apply the global relabeling heuristic.
"""
src = t if from_sink else s
heights = reverse_bfs(src)
if not from_sink:
# s must be reachable from t. Remove t explicitly.
del heights[t]
max_height = max(heights.values())
if from_sink:
# Also mark nodes from which t is unreachable for relabeling. This
# serves the same purpose as the gap heuristic.
for u in R:
if u not in heights and R_node[u]['height'] < n:
heights[u] = n + 1
else:
# Shift the computed heights because the height of s is n.
for u in heights:
heights[u] += n
max_height += n
del heights[src]
for u, new_height in heights.items():
old_height = R_node[u]['height']
if new_height != old_height:
if u in levels[old_height].active:
levels[old_height].active.remove(u)
levels[new_height].active.add(u)
else:
levels[old_height].inactive.remove(u)
levels[new_height].inactive.add(u)
R_node[u]['height'] = new_height
return max_height
# Phase 1: Find the maximum preflow by pushing as much flow as possible to
# t.
height = max_height
while height > 0:
# Discharge active nodes in the current level.
while True:
level = levels[height]
if not level.active:
# All active nodes in the current level have been discharged.
# Move to the next lower level.
height -= 1
break
# Record the old height and level for the gap heuristic.
old_height = height
old_level = level
u = next(iter(level.active))
height = discharge(u, True)
if grt.is_reached():
# Global relabeling heuristic: Recompute the exact heights of
# all nodes.
height = global_relabel(True)
max_height = height
grt.clear_work()
elif not old_level.active and not old_level.inactive:
# Gap heuristic: If the level at old_height is empty (a 'gap'),
# a minimum cut has been identified. All nodes with heights
# above old_height can have their heights set to n + 1 and not
# be further processed before a maximum preflow is found.
gap_heuristic(old_height)
height = old_height - 1
max_height = height
else:
# Update the height of the highest level with at least one
# active node.
max_height = max(max_height, height)
# A maximum preflow has been found. The excess at t is the maximum flow
# value.
if value_only:
R.graph['flow_value'] = R_node[t]['excess']
return R
# Phase 2: Convert the maximum preflow into a maximum flow by returning the
# excess to s.
# Relabel all nodes so that they have accurate heights.
height = global_relabel(False)
grt.clear_work()
# Continue to discharge the active nodes.
while height > n:
# Discharge active nodes in the current level.
while True:
level = levels[height]
if not level.active:
# All active nodes in the current level have been discharged.
# Move to the next lower level.
height -= 1
break
u = next(iter(level.active))
height = discharge(u, False)
if grt.is_reached():
# Global relabeling heuristic.
height = global_relabel(False)
grt.clear_work()
R.graph['flow_value'] = R_node[t]['excess']
return R
[docs]def preflow_push(G, s, t, capacity='capacity', residual=None,
global_relabel_freq=1, value_only=False):
"""Find a maximum single-commodity flow using the highest-label
preflow-push algorithm.
This function returns the residual network resulting after computing
the maximum flow. See below for details about the conventions
NetworkX uses for defining residual networks.
This algorithm has a running time of `O(n^2 \sqrt{m})` for `n` nodes and
`m` edges.
Parameters
----------
G : NetworkX graph
Edges of the graph are expected to have an attribute called
'capacity'. If this attribute is not present, the edge is
considered to have infinite capacity.
s : node
Source node for the flow.
t : node
Sink node for the flow.
capacity : string
Edges of the graph G are expected to have an attribute capacity
that indicates how much flow the edge can support. If this
attribute is not present, the edge is considered to have
infinite capacity. Default value: 'capacity'.
residual : NetworkX graph
Residual network on which the algorithm is to be executed. If None, a
new residual network is created. Default value: None.
global_relabel_freq : integer, float
Relative frequency of applying the global relabeling heuristic to speed
up the algorithm. If it is None, the heuristic is disabled. Default
value: 1.
value_only : bool
If False, compute a maximum flow; otherwise, compute a maximum preflow
which is enough for computing the maximum flow value. Default value:
False.
Returns
-------
R : NetworkX DiGraph
Residual network after computing the maximum flow.
Raises
------
NetworkXError
The algorithm does not support MultiGraph and MultiDiGraph. If
the input graph is an instance of one of these two classes, a
NetworkXError is raised.
NetworkXUnbounded
If the graph has a path of infinite capacity, the value of a
feasible flow on the graph is unbounded above and the function
raises a NetworkXUnbounded.
See also
--------
:meth:`maximum_flow`
:meth:`minimum_cut`
:meth:`edmonds_karp`
:meth:`ford_fulkerson`
:meth:`shortest_augmenting_path`
Notes
-----
The residual network :samp:`R` from an input graph :samp:`G` has the
same nodes as :samp:`G`. :samp:`R` is a DiGraph that contains a pair
of edges :samp:`(u, v)` and :samp:`(v, u)` iff :samp:`(u, v)` is not a
self-loop, and at least one of :samp:`(u, v)` and :samp:`(v, u)` exists
in :samp:`G`. For each node :samp:`u` in :samp:`R`,
:samp:`R.node[u]['excess']` represents the difference between flow into
:samp:`u` and flow out of :samp:`u`.
For each edge :samp:`(u, v)` in :samp:`R`, :samp:`R[u][v]['capacity']`
is equal to the capacity of :samp:`(u, v)` in :samp:`G` if it exists
in :samp:`G` or zero otherwise. If the capacity is infinite,
:samp:`R[u][v]['capacity']` will have a high arbitrary finite value
that does not affect the solution of the problem. This value is stored in
:samp:`R.graph['inf']`. For each edge :samp:`(u, v)` in :samp:`R`,
:samp:`R[u][v]['flow']` represents the flow function of :samp:`(u, v)` and
satisfies :samp:`R[u][v]['flow'] == -R[v][u]['flow']`.
The flow value, defined as the total flow into :samp:`t`, the sink, is
stored in :samp:`R.graph['flow_value']`. Reachability to :samp:`t` using
only edges :samp:`(u, v)` such that
:samp:`R[u][v]['flow'] < R[u][v]['capacity']` induces a minimum
:samp:`s`-:samp:`t` cut.
Examples
--------
>>> import networkx as nx
>>> from networkx.algorithms.flow import preflow_push
The functions that implement flow algorithms and output a residual
network, such as this one, are not imported to the base NetworkX
namespace, so you have to explicitly import them from the flow package.
>>> G = nx.DiGraph()
>>> G.add_edge('x','a', capacity=3.0)
>>> G.add_edge('x','b', capacity=1.0)
>>> G.add_edge('a','c', capacity=3.0)
>>> G.add_edge('b','c', capacity=5.0)
>>> G.add_edge('b','d', capacity=4.0)
>>> G.add_edge('d','e', capacity=2.0)
>>> G.add_edge('c','y', capacity=2.0)
>>> G.add_edge('e','y', capacity=3.0)
>>> R = preflow_push(G, 'x', 'y')
>>> flow_value = nx.maximum_flow_value(G, 'x', 'y')
>>> flow_value == R.graph['flow_value']
True
>>> # preflow_push also stores the maximum flow value
>>> # in the excess attribute of the sink node t
>>> flow_value == R.node['y']['excess']
True
>>> # For some problems, you might only want to compute a
>>> # maximum preflow.
>>> R = preflow_push(G, 'x', 'y', value_only=True)
>>> flow_value == R.graph['flow_value']
True
>>> flow_value == R.node['y']['excess']
True
"""
R = preflow_push_impl(G, s, t, capacity, residual, global_relabel_freq,
value_only)
R.graph['algorithm'] = 'preflow_push'
return R
```