Math 2.0

Dynamical systems and ergodic theory Definition

Unique ergodicity

Builds on Metric spaceCompact spaceBorel measureProbability measureInvariant measureKrylov–Bogolyubov theorem

Definition

Let XX be a compact metric space and let T ⁣:X→XT \colon X \to X be a continuous map. A Borel probability measure μ\mu on XX is TT-invariant if μ(T−1A)=μ(A)\mu(T^{-1}A) = \mu(A) for every Borel set A⊆XA \subseteq X. The map TT is uniquely ergodic if there is exactly one TT-invariant Borel probability measure on XX.

Remarks

By the Krylov–Bogolyubov theorem the set of invariant Borel probability measures is never empty, so unique ergodicity asks that it has a single element.

Example: let X=ℝ/ℤX = \R/\Z, let α\alpha be irrational and let T(x)=x+α mod 1T(x) = x + \alpha \bmod 1. If μ\mu is invariant, its Fourier coefficients μ^(h)=∫e−2πihx dμ(x)\hat\mu(h) = \int e^{-2\pi i h x} \, d\mu(x) satisfy μ^(h)=e−2πihαμ^(h)\hat\mu(h) = e^{-2\pi i h \alpha} \hat\mu(h). For h≠0h \ne 0 the factor e−2πihαe^{-2\pi i h \alpha} is not 11, so μ^(h)=0\hat\mu(h) = 0. A measure on the circle is determined by its Fourier coefficients, so μ\mu is Lebesgue measure, and TT is uniquely ergodic.

Non-example: the doubling map T(x)=2x mod 1T(x) = 2x \bmod 1 on ℝ/ℤ\R/\Z is continuous and fixes 00. Both the point mass at 00 and Lebesgue measure are invariant, so TT is not uniquely ergodic.

Equivalent form: TT is uniquely ergodic if and only if, for every continuous f ⁣:X→ℝf \colon X \to \R, the averages 1N∑n=0N−1f(Tnx)\frac{1}{N} \sum_{n=0}^{N-1} f(T^n x) converge uniformly in xx to a constant. The constant is then ∫f dμ\int f \, d\mu for the unique invariant measure μ\mu.

Reading path

To understand this page from the ground up, read these 14 pages in order. The foundations come first.

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Each page uses the pages above it. The colour of the bar on a box shows how strong the evidence is, and the box also gives it as text. The list has the same pages.
  • no evidence level
  1. Borel measure concept
  2. Compact set concept
  3. Compact space concept
  4. Complete metric space concept
  5. Continuous function concept
  6. Invariant measure concept
  7. Metric space concept
  8. Probability measure concept
  9. Separable space concept
  10. Tightness of measures concept
  11. Weak convergence of measures concept
  12. Prokhorov's theorem statement
  13. Krylov–Bogolyubov theorem statement
  14. Unique ergodicity definition

Further reading

  1. Web https://en.wikipedia.org/wiki/Ergodicity
About this page
TagKH07KHXJB6
URIm2://KH07KHXJB6
Kinddefinition
WritingW0 stub
MSC37A25, 37B05
DefinesUnique ergodicity
UsesMetric space, Compact space, Borel measure, Probability measure, Invariant measure, Krylov–Bogolyubov theorem

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