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Trémaux tree

Trémaux tree is a science 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 Trémaux tree rather than just read about it. In short: In graph theory, a Trémaux tree of an undirected graph G {\displaystyle G} is a type of spanning tree, generalizing depth-first search trees. They are defined by the property that every edge of G {\displaystyle G} connects an ancestor–descendant pair in the tree.

Trémaux tree — main illustration
Trémaux tree — illustration

Key takeaways

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

Reference excerpt

In graph theory, a Trémaux tree of an undirected graph G {\displaystyle G} is a type of spanning tree, generalizing depth-first search trees. They are defined by the property that every edge of G {\displaystyle G} connects an ancestor–descendant pair in the tree. Trémaux trees are named after Charles Pierre Trémaux, a 19th-century French author who used a form of depth-first search as a strategy for solving mazes. They have also been called normal spanning trees, especially in the context of infinite graphs. All depth-first search trees and all Hamiltonian paths are Trémaux trees. In finite graphs, every Trémaux tree is a depth-first search tree, but although depth-first search itself is inherently sequential, Trémaux trees can be constructed by a randomized parallel algorithm in the complexity class RNC. They can be used to define the tree-depth of a graph, and as part of the left-right planarity test for testing whether a graph is a planar graph. A characterization of Trémaux trees in the monadic second-order logic of graphs allows graph properties involving orientations to be recognized efficiently for graphs of bounded treewidth using Courcelle's theorem. Not every infinite connected graph has a Trémaux tree, and not every infinite Trémaux tree is a depth-first search tree. The graphs that have Trémaux trees can be characterized by forbidden minors. An infinite Trémaux tree must have exactly one infinite path for each end of the graph, and the existence of a Trémaux tree characterizes the graphs whose topological completions, formed by adding a point at infinity for each end, are metric spaces.

Definition and examples A Trémaux tree, for an undirected graph G {\displaystyle G} , is a spanning tree T {\displaystyle T} with the property that, for every edge u v {\displaystyle uv} in G {\displaystyle G} , one of the two endpoints u {\displaystyle u} and v {\displaystyle v} is an ancestor of the other. To be a spanning tree, it must only use edges of G {\displaystyle G} , and include every vertex, with a unique finite path between every pair of vertices. Additionally, to define the ancestor–descendant relation in this tree, one of its vertices must be designated as its root. If a finite graph has a Hamiltonian path, then rooting that path at one of its two endpoints produces a Trémaux tree. For such a path, every pair of vertices is an ancestor–descendant pair. In the graph shown below, the tree with edges 1–3, 2–3, and 3–4 is a Trémaux tree when it is rooted at vertex 1 or vertex 2: every edge of the graph belongs to the tree except for the edge 1–2, which (for these choices of root) connects an ancestor-descendant pair.

However, rooting the same tree at vertex 3 or vertex 4 produces a rooted tree that is not a Trémaux tree, because with this root 1 and 2 are no longer an ancestor and descendant of each other.

In finite graphs

Existence Every finite connected undirected graph has at least one Trémaux tree. One can construct such a tree by performing a depth-first search and connecting each vertex (other than the starting vertex of the search) to the earlier vertex from which it was discovered. The tree constructed in this way is known as a depth-first search tree. If u v {\displaystyle uv} is an arbitrary edge in the graph, and u {\displaystyle u} is the earlier of the two vertices to be reached by the search, then v {\displaystyle v} must belong to the subtree descending from u {\displaystyle u} in the depth-first search tree, because the search will necessarily discover v {\displaystyle v} while it is exploring this subtree, either from one of the other vertices in the subtree or, failing that, from u {\displaystyle u} directly. Every finite Trémaux tree can be generated as a depth-first search tree: If T {\displaystyle T} is a Trémaux tree of a finite graph, and a depth-first search explores the children in T {\displaystyle T} of each vertex prior to exploring any other vertices, it will necessarily generate T {\displaystyle T} as its depth-first search tree.

Parallel construction

It is P-complete to find the Trémaux tree that would be found by a sequential depth-first search algorithm, in which the neighbors of each vertex are searched in order by their identities. Nevertheless, it is possible to find a different Trémaux tree by a randomized parallel algorithm, showing that the construction of Trémaux trees belongs to the complexity class RNC. The algorithm is based on another randomized parallel algorithm, for finding minimum-weight perfect matchings in 0-1-weighted graphs. As of 1997, it remained unknown whether Trémaux tree construction could be performed by a deterministic parallel algorithm, in the complexity class NC. If matchings can be found in NC, then so can Trémaux trees.

Logical expression It is possible to express the property that a set T {\displaystyle T} of edges with a choice of root vertex r {\displaystyle r} forms a Trémaux tree, in the monadic second-order logic of graphs, and more specifically in the form of this logic called MSO2, which allows quantification over both vertex and edge sets. This property can be expressed as the conjunction of the following properties:

The graph is connected by the edges in T {\displaystyle T} . This can be expressed logically as the statement that, for every non-empty proper subset of the graph's vertices, there exists an edge in T {\displaystyle T} with exactly one endpoint in the given subset.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Trémaux tree

Start with the simplest possible case. Write down what Trémaux tree claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, 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 Trémaux tree 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 Trémaux tree 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 Trémaux tree

In research
Trémaux tree appears in science 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 Trémaux tree 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
Trémaux tree is common in secondary-school and first-year university syllabi. It links to neighbouring topics Graph minor theory, Graph theory objects, Infinite graphs, so understanding it makes those chapters shorter.
In everyday life
Look for Trémaux tree 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 Trémaux tree in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Trémaux tree 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 Trémaux tree out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Trémaux tree in simple terms?

In graph theory, a Trémaux tree of an undirected graph G {\displaystyle G} is a type of spanning tree, generalizing depth-first search trees. They are defined by the property that every edge of G {\displaystyle G} connects an ancestor–descendant pair in the tree.

Why does Trémaux tree matter?

Because it connects several science 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 Trémaux tree?

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 Trémaux tree.

Tags

  • Graph minor theory
  • Graph theory objects
  • Infinite graphs
  • Spanning tree

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