Ford-Fulkerson algorithm
Encyclopedia
The Ford–Fulkerson Method (named for L. R. Ford, Jr.
L. R. Ford, Jr.
Lester Randolph Ford, Jr. is an American mathematician specializing in network flow problems. He is the son of mathematician Lester R. Ford, Sr..Ford's paper with D. R...

 and D. R. Fulkerson
D. R. Fulkerson
Delbert Ray Fulkerson was a mathematician who co-developed the Ford-Fulkerson algorithm, one of the most well-known algorithms to solve the maximum flow problem in networks....

) computes the maximum flow
Maximum flow problem
In optimization theory, the maximum flow problem is to find a feasible flow through a single-source, single-sink flow network that is maximum....

 in a flow network
Flow network
In graph theory, a flow network is a directed graph where each edge has a capacity and each edge receives a flow. The amount of flow on an edge cannot exceed the capacity of the edge. Often in Operations Research, a directed graph is called a network, the vertices are called nodes and the edges are...

. It was published in 1956. The name "Ford–Fulkerson" is often also used for the Edmonds–Karp algorithm, which is a specialization of Ford–Fulkerson.

The idea behind the algorithm
Algorithm
In mathematics and computer science, an algorithm is an effective method expressed as a finite list of well-defined instructions for calculating a function. Algorithms are used for calculation, data processing, and automated reasoning...

 is very simple: As long as there is a path from the source (start node) to the sink (end node), with available capacity on all edges in the path, we send flow along one of these paths. Then we find another path, and so on. A path with available capacity is called an augmenting path.

Algorithm

Let be a graph, and for each edge from to , let be the capacity and be the flow. We want to find the maximum flow from the source to the sink . After every step in the algorithm the following is maintained:
Capacity constraints: The flow along an edge can not exceed its capacity.
Skew symmetry: The net flow from to must be the opposite of the net flow from to (see example).
Flow conservation: \sum_{w \in V} f(u,w) = 0 That is, unless or . The net flow to a node is zero, except for the source, which "produces" flow, and the sink, which "consumes" flow.


This means that the flow through the network is a legal flow after each round in the algorithm. We define the residual network to be the network with capacity and no flow. Notice that it can happen that a flow from to is allowed in the residual
network, though disallowed in the original network: if and then
.

Algorithm Ford–Fulkerson
Inputs Graph with flow capacity , a source node , and a sink node
Output A flow from to which is a maximum
  1. for all edges
  2. While there is a path from to in , such that for all edges :
    1. Find
    2. For each edge
      1. (Send flow along the path)
      2. (The flow might be "returned" later)


The path in step 2 can be found with for example a breadth-first search
Breadth-first search
In graph theory, breadth-first search is a graph search algorithm that begins at the root node and explores all the neighboring nodes...

 or a depth-first search
Depth-first search
Depth-first search is an algorithm for traversing or searching a tree, tree structure, or graph. One starts at the root and explores as far as possible along each branch before backtracking....

 in . If you use the former, the algorithm is called Edmonds–Karp.

When no more paths in step 2 can be found, will not be able to reach in the residual
network. If is the set of nodes reachable by in the residual network, then the total
capacity in the original network of edges from to the remainder of is on the one hand
equal to the total flow we found from to ,
and on the other hand serves as an upper bound for all such flows.
This proves that the flow we found is maximal. See also Max-flow Min-cut theorem
Max-flow min-cut theorem
In optimization theory, the max-flow min-cut theorem states that in a flow network, the maximum amount of flow passing from the source to the sink is equal to the minimum capacity which when removed in a specific way from the network causes the situation that no flow can pass from the source to the...

.

Complexity

By adding the flow augmenting path to the flow already established in the graph, the maximum flow will be reached when no more flow augmenting paths can be found in the graph. However, there is no certainty that this situation will ever be reached, so the best that can be guaranteed is that the answer will be correct if the algorithm terminates. In the case that the algorithm runs forever, the flow might not even converge towards the maximum flow. However, this situation only occurs with irrational flow values. When the capacities are integers, the runtime of Ford-Fulkerson is bounded by (see big O notation
Big O notation
In mathematics, big O notation is used to describe the limiting behavior of a function when the argument tends towards a particular value or infinity, usually in terms of simpler functions. It is a member of a larger family of notations that is called Landau notation, Bachmann-Landau notation, or...

), where is the number of edges in the graph and is the maximum flow in the graph. This is because each augmenting path can be found in time and increases the flow by an integer amount which is at least .

A variation of the Ford–Fulkerson algorithm with guaranteed termination and a runtime independent of the maximum flow value is the Edmonds–Karp algorithm, which runs in time.

Integral example

The following example shows the first steps of Ford–Fulkerson in a flow network with 4 nodes, source and sink . This example shows the worst-case behaviour of the algorithm. In each step, only a flow of is sent across the network. If breadth-first-search were used instead, only two steps would be needed.
Path Capacity Resulting flow network
Initial flow network








After 1998 more steps …
Final flow network


Notice how flow is "pushed back" from to when finding the path .

Non-terminating example

Consider the flow network shown on the right, with source , sink , capacities of edges , and respectively , and and the capacity of all other edges some integer . The constant was chosen so, that . We use augmenting paths according to the following table, where , and .
Step | Augmenting path | Sent flow | Residual capacities
0
1
2
3
4
5


Note that after step 1 as well as after step 5, the residual capacities of edges , and are in the form , and , respectively, for some . This means that we can use augmenting paths , , and infinitely many times and residual capacities of these edges will always be in the same form. Total flow in the network after step 5 is . If we continue to use augmenting paths as above, the total flow converges to , while the maximum flow is . In this case, the algorithm never terminates and the flow doesn't even converge to the maximum flow.

Python implementation


class Edge(object):
def __init__(self, u, v, w):
self.source = u
self.sink = v
self.capacity = w
def __repr__(self):
return "%s->%s:%s" % (self.source, self.sink, self.capacity)

class FlowNetwork(object):
def __init__(self):
self.adj = {}
self.flow = {}

def add_vertex(self, vertex):
self.adj[vertex] = []

def get_edges(self, v):
return self.adj[v]

def add_edge(self, u, v, w=0):
if u

v:
raise ValueError("u

v")
edge = Edge(u,v,w)
redge = Edge(v,u,0)
edge.redge = redge
redge.redge = edge
self.adj[u].append(edge)
self.adj[v].append(redge)
self.flow[edge] = 0
self.flow[redge] = 0

def find_path(self, source, sink, path):
if source sink:
return path
for edge in self.get_edges(source):
residual = edge.capacity - self.flow[edge]
if residual > 0 and not (edge,residual) in path:
result = self.find_path( edge.sink, sink, path + [(edge,residual)] )
if result != None:
return result

def max_flow(self, source, sink):
path = self.find_path(source, sink, [])
while path != None:
flow = min(res for edge,res in path)
for edge,res in path:
self.flow[edge] += flow
self.flow[edge.redge] -= flow
path = self.find_path(source, sink, [])
return sum(self.flow[edge] for edge in self.get_edges(source))

Usage example

For the example flow network in maximum flow problem
Maximum flow problem
In optimization theory, the maximum flow problem is to find a feasible flow through a single-source, single-sink flow network that is maximum....

we do the following:


g=FlowNetwork
map(g.add_vertex, ['s','o','p','q','r','t'])
g.add_edge('s','o',3)
g.add_edge('s','p',3)
g.add_edge('o','p',2)
g.add_edge('o','q',3)
g.add_edge('p','r',2)
g.add_edge('r','t',3)
g.add_edge('q','r',4)
g.add_edge('q','t',2)
print g.max_flow('s','t')

Output: 5
External links
The source of this article is wikipedia, the free encyclopedia.  The text of this article is licensed under the GFDL.
 
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