Max-flow min-cut theorem
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In a finite capacitated network the largest feasible flow value equals the least capacity among all source–sink cuts.
mko-max-flow-min-cut-theorem · theorem · v0.10.0
Mathematical statement
Enumerate each vertex subset S containing the source and excluding the sink. Its capacity sums only directed edges from S to its complement; incoming edges are not counted. Every feasible flow value is at most every cut capacity. Complete cut enumeration determines the minimum cut. If a feasible flow and a cut have equal values, this bound immediately certifies both a maximum flow and a minimum cut. An arbitrary feasible flow or single cut is not automatically optimal. This slice does not claim integral flow, an augmenting-path algorithm proof or a general theorem proof from finitely many instances.
Representative formula (exact TeX)
F(N)=C(N)
Assumptions
- Use a complete finite directed network with nonnegative exact rational capacities and feasible flows.
- The vertex bound permits complete enumeration of all source–sink cuts.
Symbol scope
F— maximum flow valueN— finite flow networkC— minimum cut capacity
Intuition, definition, and misconceptions
Enumerate the complete family of legal source–sink cuts.
Check flow≤cut using capacity and conservation for every cut.
An equal-valued feasible pair supplies both optimality certificates.
Do not add incoming-edge capacity to a cut or infer optimality from an arbitrary feasible flow or cut.
Adopted derivation guide
All four cuts and an equal-value two-sided certificateThe original MKO is retained. The guide has its own append-only revision and verification boundaries, not a new Lean proof.
Prerequisites
- Finite flow network — Use feasibility and net flow value.
- Optimization problem — Distinguish maximum and minimum claims.
- Linear programming — Retain the general theorem background.
Verification boundary
Finite enumeration certifies only the specified network, not integrality or the correctness of a particular algorithm.
not_formalized
This does not mean the content is false; it means no replayable computational or formal evidence is registered yet.
Next obligation
Add augmenting paths and Ford–Fulkerson boundaries.
Same-source structured data
This page and the interactive view use the same object and existing language edition; this adds no proof or human review.
Canonical MKO JSON · Plain Markdown