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Menger's theorem

Menger's theorem is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Menger's theorem rather than just read about it. In short: In the mathematical discipline of graph theory, Menger's theorem says that in a finite graph, the size of a minimum cut set is equal to the maximum number of disjoint paths that can be found between any pair of vertices. Proved by Karl Menger in 1927, it characterizes the connectivity of a graph.

Menger's theorem — main illustration
Menger's theorem — illustration

Key takeaways

  • Menger's theorem belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Menger's theorem to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Menger's theorem from memory before moving on to harder problems.

Reference excerpt

In the mathematical discipline of graph theory, Menger's theorem says that in a finite graph, the size of a minimum cut set is equal to the maximum number of disjoint paths that can be found between any pair of vertices. Proved by Karl Menger in 1927, it characterizes the connectivity of a graph. It is generalized by the max-flow min-cut theorem, which is a weighted, edge version, and which in turn is a special case of the strong duality theorem for linear programs.

Edge connectivity The edge-connectivity version of Menger's theorem is as follows:

Let G be a finite undirected graph and x and y two distinct vertices. Then the size of the minimum edge cut for x and y (the minimum number of edges whose removal disconnects x and y) is equal to the maximum number of pairwise edge-disjoint paths from x to y. The implication for the graph G is the following version:

A graph is k-edge-connected (it remains connected after removing fewer than k edges) if and only if every pair of vertices has k edge-disjoint paths in between.

Vertex connectivity The vertex-connectivity statement of Menger's theorem is as follows:

Let G be a finite undirected graph and x and y two nonadjacent vertices. Then the size of the minimum vertex cut for x and y (the minimum number of vertices, distinct from x and y, whose removal disconnects x and y) is equal to the maximum number of pairwise internally disjoint paths from x to y. A consequence for the entire graph G is this version:

A graph is k-vertex-connected (it has more than k vertices and it remains connected after removing fewer than k vertices) if and only if every pair of vertices has at least k internally disjoint paths in between.

Directed graphs All these statements in both edge and vertex versions remain true in directed graphs (when considering directed paths).

Short proof Most direct proofs consider a more general statement to allow proving it by induction. It is also convenient to use definitions that include some degenerate cases. The following proof for undirected graphs works without change for directed graphs or multi-graphs, provided we take path to mean directed path. For sets of vertices A,B ⊂ G (not necessarily disjoint), an AB-path is a path in G with a starting vertex in A, a final vertex in B, and no internal vertices either in A or in B. We allow a path with a single vertex in A ∩ B and zero edges. An AB-separator of size k is a set S of k vertices (which may intersect A and B) such that G−S contains no AB-path. An AB-connector of size k is a union of k vertex-disjoint AB-paths.

Theorem: The minimum size of an AB-separator is equal to the maximum size of an AB-connector. In other words, if no k−1 vertices disconnect A from B, then there exist k disjoint paths from A to B. This variant implies the above vertex-connectivity statement: for x,y ∈ G in the previous section, apply the current theorem to G−{x,y} with A = N(x), B = N(y), the neighboring vertices of x,y. Then a set of vertices disconnecting x and y is the same thing as an AB-separator, and removing the end vertices in a set of independent xy-paths gives an AB-connector. Proof of the Theorem: Induction on the number of edges in G. For G with no edges, the minimum AB-separator is A ∩ B, which is itself an AB-connector consisting of single-vertex paths. For G having an edge e, we may assume by induction that the Theorem holds for G−e. If G−e has a minimal AB-separator of size k, then there is an AB-connector of size k in G−e, and hence in G.

Otherwise, let S be a AB-separator of G−e of size less than k, so that every AB-path in G contains a vertex of S or the edge e. The size of S must be k-1, since if it was less, S together with either endpoint of e would be a better AB-separator of G. In G−S there is an AB-path through e, since S alone is too small to be an AB-separator of G. Let v1 be the earlier and v2 be the later vertex of e on such a path. Then v1 is reachable from A but not from B in G−S−e, while v2 is reachable from B but not from A. Now, let S1 = S ∪ {v1}, and consider a minimum AS1-separator T in G−e. Since v2 is not reachable from A in G−S1, T is also an AS1-separator in G. Then T is also an AB-separator in G (because every AB-path intersects S1). Hence it has size at least k. By induction, G−e contains an AS1-connector C1 of size k. Because of its size, the endpoints of the paths in it must be exactly S1. Similarly, letting S2 = S ∪ {v2}, a minimum S2B-separator has size k, and there is an S2B-connector C2 of size k, with paths whose starting points are exactly S2. Furthermore, since S1 disconnects G, every path in C1 is internally disjoint from every path in C2, and we can define an AB-connector of size k in G by concatenating paths (k−1 paths through S and one path going through e=v1v2). Q.E.D.

Other proofs The directed edge version of the theorem easily implies the other versions. To infer the directed graph vertex version, it suffices to split each vertex v into two vertices v1, v2, with all ingoing edges going to v1, all outgoing edges going from v2, and an additional edge from v1 to v2. The directed versions of the theorem immediately imply undirected versions: it suffices to replace each edge of an undirected graph with a pair of directed edges (a digon). The directed edge version in turn follows from its weighted variant, the max-flow min-cut theorem. Its proofs are often correctness proofs for max flow algorithms. It is also a special case of the still more general (strong) duality theorem for linear programs. A formulation that for finite digraphs is equivalent to the above formulation is:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Menger's theorem

Start with the simplest possible case. Write down what Menger's theorem claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Menger's theorem before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Menger's theorem ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Menger's theorem

In research
Menger's theorem appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Menger's theorem in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Menger's theorem is common in secondary-school and first-year university syllabi. It links to neighbouring topics Graph connectivity, Network theory, Theorems in graph theory, so understanding it makes those chapters shorter.
In everyday life
Look for Menger's theorem outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Menger's theorem in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Menger's theorem means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Menger's theorem out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Menger's theorem in simple terms?

In the mathematical discipline of graph theory, Menger's theorem says that in a finite graph, the size of a minimum cut set is equal to the maximum number of disjoint paths that can be found between any pair of vertices. Proved by Karl Menger in 1927, it characterizes the connectivity of a graph.

Why does Menger's theorem matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Menger's theorem?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Menger's theorem.

Tags

  • Graph connectivity
  • Network theory
  • Theorems in graph theory

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