251 lines
8.0 KiB
Python
251 lines
8.0 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Edmonds-Karp algorithm for maximum flow problems.
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"""
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__author__ = """ysitu <ysitu@users.noreply.github.com>"""
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# Copyright (C) 2014 ysitu <ysitu@users.noreply.github.com>
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# All rights reserved.
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# BSD license.
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import networkx as nx
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from networkx.algorithms.flow.utils import *
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__all__ = ['edmonds_karp']
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def edmonds_karp_core(R, s, t, cutoff):
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"""Implementation of the Edmonds-Karp algorithm.
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"""
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R_nodes = R.nodes
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R_pred = R.pred
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R_succ = R.succ
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inf = R.graph['inf']
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def augment(path):
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"""Augment flow along a path from s to t.
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"""
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# Determine the path residual capacity.
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flow = inf
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it = iter(path)
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u = next(it)
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for v in it:
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attr = R_succ[u][v]
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flow = min(flow, attr['capacity'] - attr['flow'])
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u = v
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if flow * 2 > inf:
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raise nx.NetworkXUnbounded(
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'Infinite capacity path, flow unbounded above.')
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# Augment flow along the path.
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it = iter(path)
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u = next(it)
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for v in it:
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R_succ[u][v]['flow'] += flow
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R_succ[v][u]['flow'] -= flow
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u = v
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return flow
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def bidirectional_bfs():
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"""Bidirectional breadth-first search for an augmenting path.
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"""
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pred = {s: None}
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q_s = [s]
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succ = {t: None}
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q_t = [t]
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while True:
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q = []
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if len(q_s) <= len(q_t):
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for u in q_s:
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for v, attr in R_succ[u].items():
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if v not in pred and attr['flow'] < attr['capacity']:
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pred[v] = u
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if v in succ:
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return v, pred, succ
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q.append(v)
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if not q:
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return None, None, None
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q_s = q
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else:
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for u in q_t:
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for v, attr in R_pred[u].items():
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if v not in succ and attr['flow'] < attr['capacity']:
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succ[v] = u
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if v in pred:
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return v, pred, succ
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q.append(v)
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if not q:
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return None, None, None
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q_t = q
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# Look for shortest augmenting paths using breadth-first search.
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flow_value = 0
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while flow_value < cutoff:
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v, pred, succ = bidirectional_bfs()
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if pred is None:
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break
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path = [v]
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# Trace a path from s to v.
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u = v
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while u != s:
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u = pred[u]
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path.append(u)
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path.reverse()
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# Trace a path from v to t.
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u = v
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while u != t:
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u = succ[u]
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path.append(u)
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flow_value += augment(path)
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return flow_value
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def edmonds_karp_impl(G, s, t, capacity, residual, cutoff):
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"""Implementation of the Edmonds-Karp algorithm.
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"""
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if s not in G:
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raise nx.NetworkXError('node %s not in graph' % str(s))
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if t not in G:
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raise nx.NetworkXError('node %s not in graph' % str(t))
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if s == t:
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raise nx.NetworkXError('source and sink are the same node')
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if residual is None:
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R = build_residual_network(G, capacity)
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else:
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R = residual
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# Initialize/reset the residual network.
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for u in R:
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for e in R[u].values():
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e['flow'] = 0
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if cutoff is None:
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cutoff = float('inf')
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R.graph['flow_value'] = edmonds_karp_core(R, s, t, cutoff)
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return R
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def edmonds_karp(G, s, t, capacity='capacity', residual=None, value_only=False,
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cutoff=None):
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"""Find a maximum single-commodity flow using the Edmonds-Karp algorithm.
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This function returns the residual network resulting after computing
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the maximum flow. See below for details about the conventions
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NetworkX uses for defining residual networks.
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This algorithm has a running time of $O(n m^2)$ for $n$ nodes and $m$
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edges.
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Parameters
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----------
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G : NetworkX graph
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Edges of the graph are expected to have an attribute called
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'capacity'. If this attribute is not present, the edge is
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considered to have infinite capacity.
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s : node
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Source node for the flow.
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t : node
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Sink node for the flow.
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capacity : string
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Edges of the graph G are expected to have an attribute capacity
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that indicates how much flow the edge can support. If this
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attribute is not present, the edge is considered to have
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infinite capacity. Default value: 'capacity'.
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residual : NetworkX graph
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Residual network on which the algorithm is to be executed. If None, a
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new residual network is created. Default value: None.
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value_only : bool
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If True compute only the value of the maximum flow. This parameter
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will be ignored by this algorithm because it is not applicable.
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cutoff : integer, float
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If specified, the algorithm will terminate when the flow value reaches
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or exceeds the cutoff. In this case, it may be unable to immediately
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determine a minimum cut. Default value: None.
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Returns
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-------
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R : NetworkX DiGraph
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Residual network after computing the maximum flow.
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Raises
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------
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NetworkXError
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The algorithm does not support MultiGraph and MultiDiGraph. If
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the input graph is an instance of one of these two classes, a
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NetworkXError is raised.
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NetworkXUnbounded
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If the graph has a path of infinite capacity, the value of a
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feasible flow on the graph is unbounded above and the function
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raises a NetworkXUnbounded.
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See also
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--------
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:meth:`maximum_flow`
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:meth:`minimum_cut`
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:meth:`preflow_push`
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:meth:`shortest_augmenting_path`
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Notes
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-----
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The residual network :samp:`R` from an input graph :samp:`G` has the
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same nodes as :samp:`G`. :samp:`R` is a DiGraph that contains a pair
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of edges :samp:`(u, v)` and :samp:`(v, u)` iff :samp:`(u, v)` is not a
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self-loop, and at least one of :samp:`(u, v)` and :samp:`(v, u)` exists
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in :samp:`G`.
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For each edge :samp:`(u, v)` in :samp:`R`, :samp:`R[u][v]['capacity']`
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is equal to the capacity of :samp:`(u, v)` in :samp:`G` if it exists
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in :samp:`G` or zero otherwise. If the capacity is infinite,
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:samp:`R[u][v]['capacity']` will have a high arbitrary finite value
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that does not affect the solution of the problem. This value is stored in
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:samp:`R.graph['inf']`. For each edge :samp:`(u, v)` in :samp:`R`,
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:samp:`R[u][v]['flow']` represents the flow function of :samp:`(u, v)` and
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satisfies :samp:`R[u][v]['flow'] == -R[v][u]['flow']`.
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The flow value, defined as the total flow into :samp:`t`, the sink, is
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stored in :samp:`R.graph['flow_value']`. If :samp:`cutoff` is not
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specified, reachability to :samp:`t` using only edges :samp:`(u, v)` such
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that :samp:`R[u][v]['flow'] < R[u][v]['capacity']` induces a minimum
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:samp:`s`-:samp:`t` cut.
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Examples
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--------
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>>> import networkx as nx
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>>> from networkx.algorithms.flow import edmonds_karp
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The functions that implement flow algorithms and output a residual
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network, such as this one, are not imported to the base NetworkX
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namespace, so you have to explicitly import them from the flow package.
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>>> G = nx.DiGraph()
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>>> G.add_edge('x','a', capacity=3.0)
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>>> G.add_edge('x','b', capacity=1.0)
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>>> G.add_edge('a','c', capacity=3.0)
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>>> G.add_edge('b','c', capacity=5.0)
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>>> G.add_edge('b','d', capacity=4.0)
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>>> G.add_edge('d','e', capacity=2.0)
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>>> G.add_edge('c','y', capacity=2.0)
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>>> G.add_edge('e','y', capacity=3.0)
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>>> R = edmonds_karp(G, 'x', 'y')
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>>> flow_value = nx.maximum_flow_value(G, 'x', 'y')
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>>> flow_value
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3.0
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>>> flow_value == R.graph['flow_value']
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True
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"""
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R = edmonds_karp_impl(G, s, t, capacity, residual, cutoff)
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R.graph['algorithm'] = 'edmonds_karp'
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return R
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