"""
Dinitz' algorithm for maximum flow problems.
"""
from collections import deque
import networkx as nx
from networkx.algorithms.flow.utils import build_residual_network
from networkx.utils import pairwise
__all__ = ["dinitz"]
[docs]
@nx._dispatchable(
edge_attrs={"capacity": float("inf")}, returns_graph=True, preserve_edge_attrs=True
)
def dinitz(G, s, t, capacity="capacity", residual=None, value_only=False, cutoff=None):
"""Find a maximum single-commodity flow using Dinitz' 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 $O(n^2 m)$ for $n$ nodes and $m$
edges [1]_.
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 or function (default= 'capacity')
If this is a string, then edge capacity will be accessed via the
edge attribute with this key (that is, the capacity of the edge
joining `u` to `v` will be ``G.edges[u, v][capacity]``). If no
such edge attribute exists, the capacity of the edge is assumed to
be infinite.
If this is a function, the capacity of an edge is the value
returned by the function. The function must accept exactly three
positional arguments: the two endpoints of an edge and the
dictionary of edge attributes for that edge. The function must
return a number or None to indicate a hidden edge.
residual : NetworkX graph
Residual network on which the algorithm is to be executed. If None, a
new residual network is created. Default value: None.
value_only : bool
If True compute only the value of the maximum flow. This parameter
will be ignored by this algorithm because it is not applicable.
cutoff : integer, float
If specified, the algorithm will terminate when the flow value reaches
or exceeds the cutoff. In this case, it may be unable to immediately
determine a minimum cut. Default value: None.
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:`preflow_push`
: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 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']`. If :samp:`cutoff` is not
specified, 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
--------
>>> from networkx.algorithms.flow import dinitz
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 = dinitz(G, "x", "y")
>>> flow_value = nx.maximum_flow_value(G, "x", "y")
>>> flow_value
3.0
>>> flow_value == R.graph["flow_value"]
True
References
----------
.. [1] Dinitz' Algorithm: The Original Version and Even's Version.
2006. Yefim Dinitz. In Theoretical Computer Science. Lecture
Notes in Computer Science. Volume 3895. pp 218-240.
https://doi.org/10.1007/11685654_10
"""
R = dinitz_impl(G, s, t, capacity, residual, cutoff)
R.graph["algorithm"] = "dinitz"
nx._clear_cache(R)
return R
def dinitz_impl(G, s, t, capacity, residual, cutoff):
if s not in G:
raise nx.NetworkXError(f"node {str(s)} not in graph")
if t not in G:
raise nx.NetworkXError(f"node {str(t)} not in graph")
if s == t:
raise nx.NetworkXError("source and sink are the same node")
if residual is None:
R = build_residual_network(G, capacity)
else:
R = residual
# Initialize/reset the residual network.
for u in R:
for e in R[u].values():
e["flow"] = 0
# Use an arbitrary high value as infinite. It is computed
# when building the residual network.
INF = R.graph["inf"]
if cutoff is None:
cutoff = INF
R_succ = R.succ
R_pred = R.pred
def breath_first_search():
parents = {}
vertex_dist = {s: 0}
queue = deque([(s, 0)])
# Record all the potential edges of shortest augmenting paths
while queue:
if t in parents:
break
u, dist = queue.popleft()
for v, attr in R_succ[u].items():
if attr["capacity"] - attr["flow"] > 0:
if v in parents:
if vertex_dist[v] == dist + 1:
parents[v].append(u)
else:
parents[v] = deque([u])
vertex_dist[v] = dist + 1
queue.append((v, dist + 1))
return parents
def depth_first_search(parents):
# DFS to find all the shortest augmenting paths
"""Build a path using DFS starting from the sink"""
total_flow = 0
u = t
# path also functions as a stack
path = [u]
# The loop ends with no augmenting path left in the layered graph
while True:
if len(parents[u]) > 0:
v = parents[u][0]
path.append(v)
else:
path.pop()
if len(path) == 0:
break
v = path[-1]
parents[v].popleft()
# Augment the flow along the path found
if v == s:
flow = INF
for u, v in pairwise(path):
flow = min(flow, R_pred[u][v]["capacity"] - R_pred[u][v]["flow"])
for u, v in pairwise(reversed(path)):
R_pred[v][u]["flow"] += flow
R_pred[u][v]["flow"] -= flow
# Find the proper node to continue the search
if R_pred[v][u]["capacity"] - R_pred[v][u]["flow"] == 0:
parents[v].popleft()
while path[-1] != v:
path.pop()
total_flow += flow
v = path[-1]
u = v
return total_flow
flow_value = 0
while flow_value < cutoff:
parents = breath_first_search()
if t not in parents:
break
this_flow = depth_first_search(parents)
if this_flow * 2 > INF:
raise nx.NetworkXUnbounded("Infinite capacity path, flow unbounded above.")
flow_value += this_flow
R.graph["flow_value"] = flow_value
return R
