SDSU CS660 Combinatorial Algorithms
Fall Semester, 1996
Network Flow

[To Lecture Notes Index]
San Diego State University -- This page last updated Nov 25, 1996
----------

Contents of Network Flow

  1. Reference
  2. Network Flow
  3. The Max-flow min-cut theorem
Network Flow Slide # 1

Reference


Chapter 27 of the text

Network Flow Slide # 2

Network Flow


A flow network G = (V, E) is a directed graph where each edge
(u, v) has a capacity c(u, v) >= 0


Source (s) - node with only outflows

Sink (t) - node with only inflows

Assume graph is connected


Network Flow Slide # 3
Multiple Sources/Sinks

Super Sources and Sinks 


Network Flow Slide # 4

A flow in G is a real-valued function f : V * V -> R such that:

Value of a flow f is

Positive Net Flow entering vertex v is

Positive Net Flow leaving vertex v is

f(a, b) = 9 f(b, a) = -9 c(b, a) = 0 c(a, d) = 0 f(a, d) = 0
Network Flow Slide # 5
Oh Boy More Notation

Implicit Summation Notation

Let then

X = {a, c}, Y = {b, d } then

f(X, Y) = f(a, b) + f(a, d) + f(c, b) + f(c, b)
= 12 + 0 + -9 + 14
= 17

Network Flow Slide # 6

Lemma. Let G = (V, E) be a flow network and f be a flow in G

a. If then f(X, X) = 0

b. If then f(X, Y) = -f(Y, X)

c. If then
and


proof of a
For every term f(x, y) we also have f(y, x)
Since f(x, y) = -f(y, x) we have f(X, X) = 0

Network Flow Slide # 7


Let G = (V, E) be a flow network and f a flow in G

residual capacity of (u, v) is cf(u, v) = c(u, v) - f(u, v)

residual network of G induced by f is Gf = (V, Ef) where
Ef = { (u, v) V * V : cf(u, v) > 0}
Residual Network 

Network Flow Slide # 8
NetworkFlow on Network



Residual NetworkFlow in Residual Network


Resultant Flow in Original Network 

Network Flow Slide # 9
 Let G = (V, E) be a flow network and f be a flow in G.

Let Gf = (V, Ef) be the residual network of G induced by f.

Let h be a flow in Gf.

Define f + h to by (f + h )(u, v) = f(u, v) + h(u, v)


Lemma. f + h is a flow in G with value |f + h|= |f|+|h|

proof of Capacity constraint

(f + h )(u, v)
= f(u, v) + h(u, v)
= -f(v, u) - h(v, u)
= -(f(v, u) + h(v, u))
= -(f + h )(v, u)


Network Flow Slide # 10
Augmenting Paths

Let G = (V, E) be a flow network and f be a flow in G.

Let Gf = (V, Ef) be the residual network of G induced by f.

An augmenting path p is a simple path from s to t in Gf

Residual capacity of p is the maximum amount of net flow that we can ship along the edges of p

cf(p) = min { cf(u, v) : (u, v) is on path p}
Residual NetworkFlow in Residual Network


Lemma. Let G = (V, E) be a flow network and f be a flow in G.

Let p be an augmenting path in Gf. Define hp : V * V -> R by:

Then hp is a flow in Gf with value |hp| = cf(p).

Network Flow Slide # 11

Corollary

Let G = (V, E) be a flow network and f be a flow in G.

Let p be an augmenting path in Gf. Define hp as above.

Let k = f + hp. Then k is a flow in G with value
|k| = |f|+ | hp | > |f|


Ford-Fulkerson Method to find Maximum Flow

Let G = (V, E) be a flow network

initialize flow f to 0

while there exits an augmenting path p
do add hp to f


return f
Network Flow Slide # 12
 

Network Flow Slide # 13

Let G = (V, E) be a flow network and f a flow in G

A cut (S, T) of G is a partition of V into S and T = V - S such that:
and


The net flow across the cut (S, T) is f(S, T)

The capacity of the cut (S, T) is c(S, T)

S = {s, c} T = { a, b, d, t}

f(S, T) = 3 + 6 - 4 + 8 = 13

c(S, T) = 16 + 10 + 14

Network Flow Slide # 14
Lemma

Let G = (V, E) be a flow network and f be a flow in G.

Let (S, T) be a cut of G. Then the net flow across (S, T) is
f(S, T) = |f|


proof:
f(S, T)
= f(S, V - S)
= f(S, V) - f(S, S)
= f(S, V)
= f(s, V) + f(S - s, V)
= f(s, V)
= |f|

Corollary The value of any flow f in a flow network G is bounded from above by the capacity of any cut of G

Network Flow Slide # 15

The Max-flow min-cut theorem


Let f be a flow in G = (V, E) be a flow network with source s and sink t.

The following are equivalent:

1. f is a maximum flow in G

2. The residual network Gf contains no augmenting paths

3. |f| = c(S, T) for some cut (S, T) of G.

Network Flow Slide # 16
Ford-Fulkerson Algorithm to find Maximum Flow

Let G = (V, E) be a flow network

for each edge (u, v) do
f[u, v] = 0
f[v, u] = 0


while there exits a path p from s to t in residual network Gf do
do cf(p) = min { cf(u, v) : (u, v) is on path p}
for each edge (u, v) in p
do
f[u, v] = f[u, v] + cf(p)
f[u, v] = -f[v, u]
return f

How long does it take? 

Network Flow Slide # 17
Edmonds-Karp Algorithm
Find shortest path from s to t in Ford-Fulkerson



Let G = (V, E) be a flow network and f be a flow in G.

Let Gf = (V, Ef) be the residual network of G induced by f.

Let p be shortest path from s to t in Gf.

Define the flow

Let df(x, y) be the shortest-path distance from x to y in Gf

Let dh(x, y) be the shortest-path distance from x to y in Gh

Network Flow Slide # 18
Lemma 27.8 For all v - {s, t} we have df(s, v) <= dh(s, v)

proof:
Assume that there exists v such that df(s, v) > dh(s, v)
Let v be the closes vertex to s for which this is true
Let sp be the shortest path from s to v in Gf
Let u be the vertex before v on sp and f[u, v] < c(u,v) then
df(s, v)
= df(s, u) + 1
<= dh(s, u) + 1
= dh(s, v)
So we must have f[u, v] = c(u, v)
Thus (u, v) is not in Ef which means (v, u) is in Ef
So df(s, u)
= df(s, v) + 1
That is flow goes from v to u
df(s, v)
= df(s, u) - 1
<= dh(s, u) - 1
= dh(s, v) - 2
< dh(s, v)

Network Flow Slide # 19

Theorem The Edmonds-Karp Algorithm will perform at most O(V*E) flow augmentations.

proof:
An edge (u, v) in a residual network Gf is critical on an augmenting path p if the residual capacity of p is the residual capacity of (u, v)
How many times can an edge be critical?
We have (draw picture)
df(s, v)
= df(s, u) + 1
dh(s, u)
= dh(s, v) + 1
So dh(s, u)
= dh(s, v) + 1
>= df(s, v) + 1
= df(s, u) + 2
Thus ( u, v) can be critical at most O(V) times
Since there are O(E) edges the total number of critical edges is O(E*V)


----------