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 total weight of the edges in the minimum cut, i.e. the smallest total weight of the edges which if removed would disconnect the source from the sink.

The max-flow min-cut theorem is a special case of the duality theorem for linear programs and can be used to derive Menger's theorem and the König–Egerváry theorem.

Definitions and statement

Let $N = (V, E)$ be a network (directed graph) with $s$ and $t \in V$ being the source and the sink of $N$ respectively.

Maximum flow

Definition. The capacity of an edge is a mapping $c : E \to R +$ , denoted by $c uv$ or $c (u, v)$ . It represents the maximum amount of flow that can pass through an edge.

Definition. A flow is a mapping $f : E \to R +$ , denoted by $f uv$ or $f (u, v)$ , subject to the following two constraints:

1. Capacity Constraint:

\forall (u,v)\in E:\qquad f_{uv}\leq c_{uv}

2. Conservation of Flows:

\forall v\in V\setminus \{s,t\}:\qquad \sum \nolimits _{\{u:(u,v)\in E\}}f_{uv}=\sum \nolimits _{\{u:(v,u)\in E\}}f_{vu}.

Definition. The value of flow is defined by

|f|=\sum \nolimits _{v\in V}f_{sv},

where $s$ is the source of $N$ . It represents the amount of flow passing from the source to the sink.

Maximum Flow Problem. Maximize

| f |

, that is, to route as much flow as possible from

s

t

Minimum cut

Definition. An s-t cut $C = (S, T)$ is a partition of $V$ such that $s \in S$ and $t \in T$ . The cut-set of $C$ is the set

\{(u,v)\in E\ :\ u\in S,v\in T\}.

Note that if the edges in the cut-set of $C$ are removed, $| f | = 0$ .

Definition. The capacity of an s-t cut is defined by

c(S,T)=\sum \nolimits _{(u,v)\in (S\times T)\cap E}c_{uv}=\sum \nolimits _{(i,j)\in E}c_{ij}d_{ij},

where $d_{ij}=1$ if $i\in S$ and $j\in T$ , 0 otherwise.

Minimum s-t Cut Problem. Minimize

c (S, T)

, that is, to determine

S

and

T

such that the capacity of the S-T cut is minimal.

Statement

Max-flow min-cut theorem. The maximum value of an s-t flow is equal to the minimum capacity over all s-t cuts.

Linear program formulation

The max-flow problem and min-cut problem can be formulated as two primal-dual linear programs.

Max-flow (Primal)	Min-cut (Dual)
maximize $\|f\|=\nabla _{s}$	minimize $\sum _{(i,j)\in E}c_{ij}d_{ij}$
subject to ${\begin{array}{rclr}f_{ij}&\leq &c_{ij}&(i,j)\in E\\\sum _{j:(j,i)\in E}f_{ji}-\sum _{j:(i,j)\in E}f_{ij}&\leq &0&i\in V,i\neq s,t\\\nabla _{s}+\sum _{j:(j,s)\in E}f_{js}-\sum _{j:(s,j)\in E}f_{sj}&\leq &0&\\-\nabla _{s}+\sum _{j:(j,t)\in E}f_{jt}-\sum _{j:(t,j)\in E}f_{tj}&\leq &0&\\f_{ij}&\geq &0&(i,j)\in E\\\end{array}}$	subject to ${\begin{array}{rclr}d_{ij}-p_{i}+p_{j}&\geq &0&(i,j)\in E\\p_{s}-p_{t}&\geq &1&\\p_{i}&\geq &0&i\in V\\d_{ij}&\geq &0&(i,j)\in E\end{array}}$

Max-flow (Primal)

Min-cut (Dual)

maximize $|f|=\nabla _{s}$

minimize $\sum _{(i,j)\in E}c_{ij}d_{ij}$

subject to

{\begin{array}{rclr}f_{ij}&\leq &c_{ij}&(i,j)\in E\\\sum _{j:(j,i)\in E}f_{ji}-\sum _{j:(i,j)\in E}f_{ij}&\leq &0&i\in V,i\neq s,t\\\nabla _{s}+\sum _{j:(j,s)\in E}f_{js}-\sum _{j:(s,j)\in E}f_{sj}&\leq &0&\\-\nabla _{s}+\sum _{j:(j,t)\in E}f_{jt}-\sum _{j:(t,j)\in E}f_{tj}&\leq &0&\\f_{ij}&\geq &0&(i,j)\in E\\\end{array}}

subject to

{\begin{array}{rclr}d_{ij}-p_{i}+p_{j}&\geq &0&(i,j)\in E\\p_{s}-p_{t}&\geq &1&\\p_{i}&\geq &0&i\in V\\d_{ij}&\geq &0&(i,j)\in E\end{array}}

Note that for the given s-t cut $C=(S,T)$ if $i\in S$ then $p_{i}=1$ and 0 otherwise. Therefore, $p_{s}$ should be 1 and $p_{t}$ should be zero. The equality in the max-flow min-cut theorem follows from the strong duality theorem in linear programming, which states that if the primal program has an optimal solution, x*, then the dual program also has an optimal solution, y*, such that the optimal values formed by the two solutions are equal.

Example

The figure on the right is a network having a value of flow of 7. The vertex in white and the vertices in grey form the subsets $S$ and $T$ of an s-t cut, whose cut-set contains the dashed edges. Since the capacity of the s-t cut is 7, which equals to the value of flow, the max-flow min-cut theorem tells us that the value of flow and the capacity of the s-t cut are both optimal in this network.

Application

Generalized max-flow min-cut theorem

In addition to edge capacity, consider there is capacity at each vertex, that is, a mapping $c : V \to R +$ , denoted by $c (v)$ , such that the flow $f$ has to satisfy not only the capacity constraint and the conservation of flows, but also the vertex capacity constraint

\forall v\in V\setminus \{s,t\}:\qquad \sum \nolimits _{\{i\in V\mid (iv)\in E\}}f_{iv}\leq c(v).

In other words, the amount of flow passing through a vertex cannot exceed its capacity. Define an s-t cut to be the set of vertices and edges such that for any path from s to t, the path contains a member of the cut. In this case, the capacity of the cut is the sum the capacity of each edge and vertex in it.

In this new definition, the generalized max-flow min-cut theorem states that the maximum value of an s-t flow is equal to the minimum capacity of an s-t cut in the new sense.

Menger's theorem

Project selection problem

	Project $r (p i)$	Equipment $c (q j)$
1	100	200	Project 1 requires equipments 1 and 2.
2	200	100	Project 2 requires equipment 2.
3	150	50	Project 3 requires equipment 3.

Image segmentation problem

History

The max-flow min-cut theorem was proven by P. Elias, A. Feinstein, and C.E. Shannon in 1956, and independently also by L.R. Ford, Jr. and D.R. Fulkerson in the same year.

Proof

Let $G = (V, E)$ be a network (directed graph) with $s$ and $t$ being the source and the sink of $G$ respectively.

Consider the flow $f$ computed for $G$ by Ford–Fulkerson algorithm. In the residual graph $(G f)$ obtained for $G$ (after the final flow assignment by Ford–Fulkerson algorithm), define two subsets of vertices as follows:

$A$ : the set of vertices reachable from $s$ in $G f$
$A c$ : the set of remaining vertices i.e. $V - A$

Claim. $value(f) = c (A, A c)$ , where the capacity of an s-t cut is defined by

c(S,T)=\sum \nolimits _{(u,v)\in S\times T}c_{uv}

Now, we know, $value(f)=f_{out}(A)-f_{in}(A^{c})$ for any subset of vertices, $A$ . Therefore, for $value(f) = c (A, A c)$ we need:

All outgoing edges from the cut must be fully saturated.
All incoming edges to the cut must have zero flow.

To prove the above claim we consider two cases:

In $G$ , there exists an outgoing edge $(x,y),x\in A,y\in A^{c}$ such that it is not saturated, i.e., $f (x, y) < c xy$ . This implies, that there exists a forward edge from $x$ to $y$ in $G f$ , therefore there exists a path from $s$ to $y$ in $G f$ , which is a contradiction. Hence, any outgoing edge $(x, y)$ is fully saturated.
In $G$ , there exists an incoming edge $(y,x),x\in A,y\in A^{c}$ such that it carries some non-zero flow, i.e., $f (x, y) > 0$ . This implies, that there exists a backward edge from $x$ to $y$ in $G f$ , therefore there exists a path from $s$ to $y$ in $G f$ , which is again a contradiction. Hence, any incoming edge $(x, y)$ must have zero flow.

Both of the above statements prove that the capacity of cut obtained in the above described manner is equal to the flow obtained in the network. Also, the flow was obtained by Ford-Fulkerson algorithm, so it is the max-flow of the network as well.

Also, since any flow in the network is always less than or equal to capacity of every cut possible in a network, the above described cut is also the min-cut which obtains the max-flow.

References

Eugene Lawler (2001). "4.5. Combinatorial Implications of Max-Flow Min-Cut Theorem, 4.6. Linear Programming Interpretation of Max-Flow Min-Cut Theorem". Combinatorial Optimization: Networks and Matroids. Dover. pp. 117–120. ISBN 0-486-41453-1.
Christos H. Papadimitriou, Kenneth Steiglitz (1998). "6.1 The Max-Flow, Min-Cut Theorem". Combinatorial Optimization: Algorithms and Complexity. Dover. pp. 120–128. ISBN 0-486-40258-4.
Vijay V. Vazirani (2004). "12. Introduction to LP-Duality". Approximation Algorithms. Springer. pp. 93–100. ISBN 3-540-65367-8.

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