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Hopcroft–Karp algorithm

Hopcroft–Karp algorithm is a computer 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 Hopcroft–Karp algorithm rather than just read about it. In short: In computer science, the Hopcroft–Karp algorithm (sometimes more accurately called the Hopcroft–Karp–Karzanov algorithm) is an algorithm that takes a bipartite graph as input and produces a maximum-cardinality matching as output — a set of as many edges as possible with the property that no two edges share an endpoint. It runs in O ( | E | | V | ) {\displaystyle O(|E|{\sqrt {|V|}})} time in the worst case, where E {…

Hopcroft–Karp algorithm — main illustration
Hopcroft–Karp algorithm — illustration

Key takeaways

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

Reference excerpt

In computer science, the Hopcroft–Karp algorithm (sometimes more accurately called the Hopcroft–Karp–Karzanov algorithm) is an algorithm that takes a bipartite graph as input and produces a maximum-cardinality matching as output — a set of as many edges as possible with the property that no two edges share an endpoint. It runs in O ( | E | | V | ) {\displaystyle O(|E|{\sqrt {|V|}})} time in the worst case, where E {\displaystyle E} is set of edges in the graph, V {\displaystyle V} is set of vertices of the graph, and it is assumed that | E | = Ω ( | V | ) {\displaystyle |E|=\Omega (|V|)} . In the case of dense graphs the time bound becomes O ( | V | 2.5 ) {\displaystyle O(|V|^{2.5})} , and for sparse random graphs it runs in time O ( | E | log ⁡ | V | ) {\displaystyle O(|E|\log |V|)} with high probability. The algorithm was discovered by John Hopcroft and Richard Karp (1973) and independently by Alexander Karzanov (1973). As in previous methods for matching such as the Hungarian algorithm and the work of Edmonds (1965), the Hopcroft–Karp algorithm repeatedly increases the size of a partial matching by finding augmenting paths. These paths are sequences of edges of the graph, which alternate between edges in the matching and edges out of the partial matching, and where the initial and final edge are not in the partial matching. Finding an augmenting path allows us to increment the size of the partial matching, by simply toggling the edges of the augmenting path (putting in the partial matching those that were not, and vice versa). Simpler algorithms for bipartite matching, such as the Ford–Fulkerson algorithm‚ find one augmenting path per iteration: the Hopcroft-Karp algorithm instead finds a maximal set of shortest augmenting paths, so as to ensure that only O ( | V | ) {\displaystyle O({\sqrt {|V|}})} iterations are needed instead of O ( | V | ) {\displaystyle O(|V|)} iterations. The same performance of O ( | E | | V | ) {\displaystyle O(|E|{\sqrt {|V|}})} can be achieved to find maximum-cardinality matchings in arbitrary graphs, with the more complicated algorithm of Micali and Vazirani. The Hopcroft–Karp algorithm can be seen as a special case of Dinic's algorithm for the maximum-flow problem.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Hopcroft–Karp algorithm

Start with the simplest possible case. Write down what Hopcroft–Karp algorithm claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer 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 Hopcroft–Karp algorithm 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 Hopcroft–Karp algorithm 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 Hopcroft–Karp algorithm

In research
Hopcroft–Karp algorithm appears in computer 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 Hopcroft–Karp algorithm 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
Hopcroft–Karp algorithm is common in secondary-school and first-year university syllabi. It links to neighbouring topics Graph algorithms, Matching (graph theory), so understanding it makes those chapters shorter.
In everyday life
Look for Hopcroft–Karp algorithm 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 Hopcroft–Karp algorithm in 20 minutes

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

Frequently asked questions

What is Hopcroft–Karp algorithm in simple terms?

In computer science, the Hopcroft–Karp algorithm (sometimes more accurately called the Hopcroft–Karp–Karzanov algorithm) is an algorithm that takes a bipartite graph as input and produces a maximum-cardinality matching as output — a set of as many edges as possible with the property that no two edg…

Why does Hopcroft–Karp algorithm matter?

Because it connects several computer 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 Hopcroft–Karp algorithm?

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 Hopcroft–Karp algorithm.

Tags

  • Graph algorithms
  • Matching (graph theory)

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