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Hamiltonian decomposition

Hamiltonian decomposition 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 Hamiltonian decomposition rather than just read about it. In short: In graph theory, a branch of mathematics, a Hamiltonian decomposition of a given graph is a partition of the edges of the graph into Hamiltonian cycles. Hamiltonian decompositions have been studied both for undirected graphs and for directed graphs.

Hamiltonian decomposition — main illustration
Hamiltonian decomposition — illustration

Key takeaways

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

Reference excerpt

In graph theory, a branch of mathematics, a Hamiltonian decomposition of a given graph is a partition of the edges of the graph into Hamiltonian cycles. Hamiltonian decompositions have been studied both for undirected graphs and for directed graphs. In the undirected case a Hamiltonian decomposition can also be described as a 2-factorization of the graph such that each factor is connected.

Necessary conditions For a Hamiltonian decomposition to exist in an undirected graph, the graph must be connected and regular of even degree. A directed graph with such a decomposition must be strongly connected and all vertices must have the same in-degree and out-degree as each other, but this degree does not need to be even.

Special classes of graphs

Complete graphs Every complete graph with an odd number n {\displaystyle n} of vertices has a Hamiltonian decomposition. This result, which is a special case of the Oberwolfach problem of decomposing complete graphs into isomorphic 2-factors, was attributed to Walecki by Édouard Lucas in 1892. The original formulation of the Oberwolfach problem asks how to make a seating chart for n {\displaystyle n} people over ( n − 1 ) / 2 {\displaystyle (n-1)/2} dinners in a given set of circular tables of different sizes, such that each participant sits next to each other exactly once. Walecki's theorem corresponds to the case where there is one single table. Walecki's original construction places n − 1 {\displaystyle n-1} of the vertices into a regular polygon, and covers the complete graph in this subset of vertices with ( n − 1 ) / 2 {\displaystyle (n-1)/2} Hamiltonian paths that zigzag across the polygon, with each path rotated from each other path by a multiple of π / ( n − 1 ) {\displaystyle \pi /(n-1)} . The paths can then all be completed to Hamiltonian cycles by connecting their ends through the remaining vertex. Expanding a vertex of a 2 k {\displaystyle 2k} -regular graph into a clique of 2 k {\displaystyle 2k} vertices, one for each endpoint of an edge at the replaced vertex, cannot change whether the graph has a Hamiltonian decomposition. The reverse of this expansion process, collapsing a clique to a single vertex, will transform any Hamiltonian decomposition in the larger graph into a Hamiltonian decomposition in the original graph. Conversely, Walecki's construction can be applied to the clique to expand any Hamiltonian decomposition of the smaller graph into a Hamiltonian decomposition of the expanded graph. One kind of analogue of a complete graph, in the case of directed graphs, is a tournament. This is a graph in which every pair of distinct vertices is connected by a single directed edge, from one to the other; for instance, such a graph may describe the outcome of a round-robin tournament in sports, where each competitor in the tournament plays each other competitor, and edges are directed from the loser of each game to the winner. Answering a conjecture by Paul Kelly from 1968, Daniela Kühn and Deryk Osthus proved in 2012 that every sufficiently large regular tournament has a Hamiltonian decomposition.

Planar graphs

For 4-regular planar graphs, additional necessary conditions can be derived from Grinberg's theorem. An example of a 4-regular planar graph that does not meet these conditions, and does not have a Hamiltonian decomposition, is given by the medial graph of the Herschel graph.

Prisms

The prism over a graph is its Cartesian product with the two-vertex complete graph. For instance, the prism over a cycle graph is the graph of a geometric prism. The 4-regular graphs obtained as prisms over 3-regular graphs have been particularly studied with respect to Hamiltonian decomposition. When the underlying 3-regular graph is 3-vertex-connected, the resulting 4-regular prism always has a Hamiltonian cycle and, in all examples that have been tested, a Hamiltonian decomposition. Based on this observation, Alspach and Rosenfeld conjectured in 1986 that all prisms over 3-regular 3-vertex-connected graphs have a Hamiltonian decomposition. Many classes of 3-regular 3-vertex-connected graphs are known to have prisms with Hamiltonian decompositions. In particular this occurs when the 3-regular graph is planar and bipartite, when it is a Halin graph, when it is itself a prism or Möbius ladder, or when it is a generalized Petersen graph of order divisible by four.

Symmetric graphs There are infinitely many vertex-transitive graphs (graphs in which every vertex is symmetric to every other vertex) that do not have a Hamiltonian decomposition. In particular this applies to the Cayley graphs whose vertices describe the elements of a group and whose elements describe multiplication by generators of the group. Infinitely many 6-regular Cayley graphs have no Hamiltonian decomposition, and there exist Cayley graphs of arbitrarily large even degree with no Hamiltonian decomposition. One way of constructing these graphs is to use repeated expansions by cliques, which preserve symmetry and cannot change the existence of a Hamiltonian decomposition.

… excerpt ends here. Continue reading the full article.

Illustrations

Hamiltonian decomposition: Walecki's Hamiltonian decomposition of the complete graph 
  
    
      
        
          K
          
            9
          
        
      
    
    {\displaystyle K_{9}}
Walecki's Hamiltonian decomposition of the complete graph K 9 {\displaystyle K_{9}}
Hamiltonian decomposition: The medial graph of the Herschel graph is a 4-regular planar graph with no Hamiltonian decomposition. The shaded regions correspond to the vertices of the underlying Herschel graph.
The medial graph of the Herschel graph is a 4-regular planar graph with no Hamiltonian decomposition. The shaded regions correspond to the vertices of the underlying Herschel graph.

Worked examples

Example 1 — a first encounter with Hamiltonian decomposition

Start with the simplest possible case. Write down what Hamiltonian decomposition 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 Hamiltonian decomposition 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 Hamiltonian decomposition 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 Hamiltonian decomposition

In research
Hamiltonian decomposition 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 Hamiltonian decomposition 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
Hamiltonian decomposition is common in secondary-school and first-year university syllabi. It links to neighbouring topics Graph theory objects, NP-complete problems, so understanding it makes those chapters shorter.
In everyday life
Look for Hamiltonian decomposition 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 Hamiltonian decomposition in 20 minutes

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

Frequently asked questions

What is Hamiltonian decomposition in simple terms?

In graph theory, a branch of mathematics, a Hamiltonian decomposition of a given graph is a partition of the edges of the graph into Hamiltonian cycles. Hamiltonian decompositions have been studied both for undirected graphs and for directed graphs.

Why does Hamiltonian decomposition 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 Hamiltonian decomposition?

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 Hamiltonian decomposition.

Tags

  • Graph theory objects
  • NP-complete problems

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