1. Algorithm

2. Complexity

3. Integral example

4. Non-terminating example

5. Python implementation of Edmonds-Karp algorithm

6. Notes

7. References

8. See also

9. External links

The Ford–Fulkerson method or Ford–Fulkerson algorithm (FFA) is a greedy algorithm that computes the maximum flow in a flow network. It is sometimes called a "method" instead of an "algorithm" as the approach to finding augmenting paths in a residual graph is not fully specified^[1] or it is specified in several implementations with different running times.^[2] It was published in 1956 by L. R. Ford, Jr. and D. R. Fulkerson.^[3] The name "Ford–Fulkerson" is often also used for the Edmonds–Karp algorithm, which is a fully defined implementation of the Ford–Fulkerson method.

The idea behind the algorithm is as follows: 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 the 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 {{mvar|u}} to {{mvar|v}}, let be the capacity and be the flow. We want to find the maximum flow from the source {{mvar|s}} to the sink {{mvar|t}}. After every step in the algorithm the following is maintained:

Capacity constraints		The flow along an edge can not exceed its capacity.
Skew symmetry		u}} to {{mvar|v}} must be the opposite of the net flow from {{mvar|v}} to {{mvar|u}} (see example).
Flow conservation		The net flow to a node is zero, except for the source, which "produces" flow, and the sink, which "consumes" flow.
Value(f)		s}} must be equal to the flow arriving at {{mvar|t}}.

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 {{mvar|v}} to {{mvar|u}} is allowed in the residual

network, though disallowed in the original network: if and then .

Inputs Given a Network with flow capacity {{mvar|c}}, a source node {{mvar|s}}, and a sink node {{mvar|t}}

Output Compute a flow {{mvar|f}} from {{mvar|s}} to {{mvar|t}} of maximum value

1. for all edges
2. While there is a path {{mvar|p}} from {{mvar|s}} to {{mvar|t}} in , such that for all edges :
3. Find
4. For each edge
5. (Send flow along the path)
6. (The flow might be "returned" later)

The path in step 2 can be found with for example a breadth-first search (BFS) or a depth-first search in . If you use the former, the algorithm is called Edmonds–Karp.

When no more paths in step 2 can be found, {{mvar|s}} will not be able to reach {{mvar|t}} in the residual

network. If {{mvar|S}} is the set of nodes reachable by {{mvar|s}} in the residual network, then the total

capacity in the original network of edges from {{mvar|S}} to the remainder of {{mvar|V}} is on the one hand

equal to the total flow we found from {{mvar|s}} to {{mvar|t}},

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.

If the graph has multiple sources and sinks, we act as follows:

Suppose that and . Add a new source with an edge from to every node , with capacity . And add a new sink with an edge from every node to , with capacity . Then apply the Ford–Fulkerson algorithm.

Also, if a node {{mvar|u}} has capacity constraint , we replace this node with two nodes , and an edge , with capacity . Then apply the Ford–Fulkerson algorithm.

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), 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 of at least , with the upper bound .

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.^[4]

Python implementation of Edmonds-Karp algorithm

      def __init__(self,graph):        self.graph = graph  # residual graph        self.ROW = len(graph)      def BFS(self, s, t, parent):        '''Returns true if there is a path from source 's' to sink 't' in        residual graph. Also fills parent[] to store the path '''

        # Mark all the vertices as not visited        visited = [False] * (self.ROW)                 # Create a queue for BFS        queue = collections.deque()                 # Mark the source node as visited and enqueue it        queue.append(s)        visited[s] = True                 # Standard BFS Loop        while queue:            u = queue.popleft()                     # Get all adjacent vertices's of the dequeued vertex u            # If a adjacent has not been visited, then mark it            # visited and enqueue it            for ind, val in enumerate(self.graph[u]):                if (visited[ind] == False) and (val > 0):                    queue.append(ind)                    visited[ind] = True                    parent[ind] = u         # If we reached sink in BFS starting from source, then return        # true, else false        return visited[t]                 # Returns the maximum flow from s to t in the given graph    def EdmondsKarp(self, source, sink):         # This array is filled by BFS and to store path        parent = [-1] * (self.ROW)         max_flow = 0 # There is no flow initially         # Augment the flow while there is path from source to sink        while self.BFS(source, sink, parent):             # Find minimum residual capacity of the edges along the            # path filled by BFS. Or we can say find the maximum flow            # through the path found.            path_flow = float("Inf")            s = sink            while s != source:                path_flow = min(path_flow, self.graph[parent[s]][s])                s = parent[s]             # Add path flow to overall flow            max_flow += path_flow             # update residual capacities of the edges and reverse edges            # along the path            v = sink            while v !=  source:                u = parent[v]                self.graph[u][v] -= path_flow                self.graph[v][u] += path_flow                v = parent[v]         return max_flow

Notes

References

• {{cite book

• {{cite book |author1=George T. Heineman |author2=Gary Pollice |author3=Stanley Selkow | title= Algorithms in a Nutshell | publisher=Oreilly Media | year=2008 | chapter=Chapter 8:Network Flow Algorithms | pages = 226–250 | isbn=978-0-596-51624-6 }}
• {{cite book |author1=Jon Kleinberg |author2=Éva Tardos | title= Algorithm Design | publisher=

Pearson Education | year=2006 | chapter=Chapter 7:Extensions to the Maximum-Flow Problem | pages = 378–384 | isbn=0-321-29535-8 }}

• {{cite book |author=Samuel Gutekunst |title= ENGRI 1101 |publisher= Cornell University | year=2009 }}

See also

• Approximate max-flow min-cut theorem
• Turn restriction routing

External links

• A tutorial explaining the Ford–Fulkerson method to solve the max-flow problem
• Another Java animation
• Java Web Start application

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