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Algorithms

The Min Cut Max Flow Theorem

The most flow you can send equals the cheapest way to disconnect the sink.

5 min read · advanced · beat Platinum to climb

Two views of the same number

A cut splits the nodes into a side with the source and a side with the sink. The capacity of a cut is the total capacity of edges crossing from the source side to the sink side. The famous theorem says the maximum flow equals the minimum cut capacity.

Why they match

Any flow must cross every cut, so no flow can exceed the smallest cut capacity. That gives an upper bound. The deeper direction shows the bound is tight:

  • Run a maximum flow until no augmenting path remains.
  • Mark every node still reachable from the source in the residual graph.
  • Edges from marked to unmarked nodes are full and form a cut whose capacity equals the flow.

So the flow value and a cut value meet exactly, proving both are optimal.

Why it matters

The theorem turns optimization into structure: finding the most flow also reveals the cheapest set of edges whose removal separates source from sink. This duality underlies image segmentation, reliability analysis, and project selection.

Key idea

Maximum flow equals minimum cut, and the saturated edges leaving the source reachable set reveal that cheapest separating cut.

Check yourself

Answer to earn rating on the learn ladder.

1. What does the min cut max flow theorem state?

2. How do you recover the minimum cut after computing the flow?