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Why the flow is maximal (if there are no AP)? Definition. A cut is any S ⊆ V such that s ∈ S, t /∈ S. The capacity of the cut S is defined as c(S) =

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Definition. A cut is any S ⊆ V such that s ∈ S, t /∈ S.

The capacity of the cut S is defined as

c(S) =

x∈S.y /∈S,(x,y)∈A

u(x.y).

Theorem. We have vol(f) � c(S) for every feasible flow f and every cut S.

If f and S satisfy vol(f) = c(S) then the flow f is maximal.

1

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If there are no AP. . .

Theorem. If the labelling algorithm finds no augmenting paths then the given flow is maximal.

Proof. If LA stops finding no augmenting paths then we examine the set E ⊆ V that got the labels. Then s ∈ E, t /∈ E so E is a cut. We check that vol(f) = c(E):

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Does the whole algorithm works?

Lemma. If the initial data are integer-valued and we start from the initial flow with integer values then all the values of augmented flows remain integer.

Theorem. If all the capacities are integers then the Ford- Fulkerson algorithm stops after a finite number of steps.

Proof. Let

M =

x∈Out(s)c(s,x).

Then M is an upper bound of the volume of any feasible flow. At each step we augment the given flow by at least 1 so there will be no more then M steps.

Remark. We then perform at most 2M|A| operations du- ring the whole process.

Corollary. If u : A → Q+ ∪ {∞} then FF stops after a finite number of steps.

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Funny, isn’t it?

The last fact is not true for in case of real-valued capacities.

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Matching in bipartite graphs

Suppose that we have a graph G = (V, A) where V = S ∪ T and every arc in A is of the form (x, y), where x ∈ S and y ∈ T .

A matching in such a graph is an injective function g : D → T where D ⊆ S and (x, f(x)) ∈ A for every x ∈ D.

Problem. Given a bipartite graphs, find a maximal mat- ching in it; the one maximizing |D|.

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Matchings from flows

Given a bipartite graph G = (V, A), V = S ⊆ T etc. extend it to G as in the picture:

Observation. Every feasible flows in G defines a matching in G. Augmenting paths and labelling can be interpreted inside G.

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Algorithms prove theorems

Theorem (K¨onig’s). In a bipartite graph, the size of a maximal matching equals the minimal number of blocking vertices (B ⊆ V is blocking if every arc either starts in B or ends in it).

Hall’s marriage theorem. In a bipartite graph G = (V, A), where G = S ∪ T there is a matching of maximal size |S| if and only if for every I ⊆ S we have |G[I]| � |I|

(here G[I] = {y ∈ T : (x, y) ∈ A for some x ∈ I}).

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