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Gromov–Hausdorff convergence

Gromov–Hausdorff convergence 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 Gromov–Hausdorff convergence rather than just read about it. In short: In mathematics, Gromov–Hausdorff convergence, named after Mikhail Gromov and Felix Hausdorff, is a notion for convergence of metric spaces which is a generalization of Hausdorff distance. Gromov–Hausdorff distance The Gromov–Hausdorff distance was introduced by David Edwards in 1975, and it was later rediscovered and generalized by Mikhail Gromov in 1981.

Gromov–Hausdorff convergence — main illustration
Gromov–Hausdorff convergence — illustration

Key takeaways

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

Reference excerpt

In mathematics, Gromov–Hausdorff convergence, named after Mikhail Gromov and Felix Hausdorff, is a notion for convergence of metric spaces which is a generalization of Hausdorff distance.

Gromov–Hausdorff distance

The Gromov–Hausdorff distance was introduced by David Edwards in 1975, and it was later rediscovered and generalized by Mikhail Gromov in 1981. This distance measures how far two compact metric spaces are from being isometric. If X and Y are two compact metric spaces, then dGH (X, Y) is defined to be the infimum of all numbers dH(f(X), g(Y)) for all (compact) metric spaces M and all isometric embeddings f : X → M and g : Y → M. Here dH denotes Hausdorff distance between subsets in M and the isometric embedding is understood in the global sense, i.e. it must preserve all distances, not only infinitesimally small ones; for example no compact Riemannian manifold admits such an embedding into Euclidean space of the same dimension. The Gromov–Hausdorff distance turns the set of all isometry classes of compact metric spaces into a metric space, called Gromov–Hausdorff space, and it therefore defines a notion of convergence for sequences of compact metric spaces, called Gromov–Hausdorff convergence. A metric space to which such a sequence converges is called the Gromov–Hausdorff limit of the sequence.

Some properties of Gromov–Hausdorff space The Gromov–Hausdorff space is path-connected, complete, and separable. It is also geodesic, i.e., any two of its points are the endpoints of a minimizing geodesic. In the global sense, the Gromov–Hausdorff space is totally heterogeneous, i.e., its isometry group is trivial, but locally there are many nontrivial isometries.

Pointed Gromov–Hausdorff convergence The pointed Gromov–Hausdorff convergence is an analog of Gromov–Hausdorff convergence appropriate for non-compact spaces. A pointed metric space is a pair (X,p) consisting of a metric space X and point p in X. A sequence (Xn, pn) of pointed metric spaces converges to a pointed metric space (Y, p) if, for each R > 0, the sequence of closed R-balls around pn in Xn converges to the closed R-ball around p in Y in the usual Gromov–Hausdorff sense.

Applications The notion of Gromov–Hausdorff convergence was used by Gromov to prove that any discrete group with polynomial growth is virtually nilpotent (i.e. it contains a nilpotent subgroup of finite index). See Gromov's theorem on groups of polynomial growth. (Also see D. Edwards for an earlier work.) The key ingredient in the proof was the observation that for the Cayley graph of a group with polynomial growth a sequence of rescalings converges in the pointed Gromov–Hausdorff sense. Another simple and very useful result in Riemannian geometry is Gromov's compactness theorem, which states that the set of Riemannian manifolds with Ricci curvature ≥ c and diameter ≤ D is relatively compact in the Gromov–Hausdorff metric. The limit spaces are metric spaces. Additional properties on the length spaces have been proven by Cheeger and Colding. The Gromov–Hausdorff distance metric has been applied in the field of computer graphics and computational geometry to find correspondences between different shapes. It also has been applied in the problem of motion planning in robotics. The Gromov–Hausdorff distance has been used by Sormani to prove the stability of the Friedmann model in Cosmology. This model of cosmology is not stable with respect to smooth variations of the metric. In a special case, the concept of Gromov–Hausdorff limits is closely related to large-deviations theory.

See also Intrinsic flat distance

References

M. Gromov. Metric structures for Riemannian and non-Riemannian spaces, Birkhäuser (1999). ISBN 0-8176-3898-9 (translation with additional content).

Worked examples

Example 1 — a first encounter with Gromov–Hausdorff convergence

Start with the simplest possible case. Write down what Gromov–Hausdorff convergence 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 Gromov–Hausdorff convergence 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 Gromov–Hausdorff convergence 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 Gromov–Hausdorff convergence

In research
Gromov–Hausdorff convergence 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 Gromov–Hausdorff convergence 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
Gromov–Hausdorff convergence is common in secondary-school and first-year university syllabi. It links to neighbouring topics Convergence (mathematics), Metric geometry, Riemannian geometry, so understanding it makes those chapters shorter.
In everyday life
Look for Gromov–Hausdorff convergence 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 Gromov–Hausdorff convergence in 20 minutes

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

Frequently asked questions

What is Gromov–Hausdorff convergence in simple terms?

In mathematics, Gromov–Hausdorff convergence, named after Mikhail Gromov and Felix Hausdorff, is a notion for convergence of metric spaces which is a generalization of Hausdorff distance. Gromov–Hausdorff distance The Gromov–Hausdorff distance was introduced by David Edwards in 1975, and it was lat…

Why does Gromov–Hausdorff convergence 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 Gromov–Hausdorff convergence?

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 Gromov–Hausdorff convergence.

Tags

  • Convergence (mathematics)
  • Metric geometry
  • Riemannian geometry

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