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Measure-preserving dynamical system

  (Redirected from Measure-preserving transformation)

In mathematics, a measure-preserving dynamical system is an object of study in the abstract formulation of dynamical systems, and ergodic theory in particular.

Contents

DefinitionEdit

A measure-preserving dynamical system is defined as a probability space and a measure-preserving transformation on it. In more detail, it is a system

 

with the following structure:

  •   is a set,
  •   is a σ-algebra over  ,
  •   is a probability measure, so that μ(X) = 1, and μ(∅) = 0,
  •   is a measurable transformation which preserves the measure  , i.e.,  .

This definition can be generalized to the case in which T is not a single transformation that is iterated to give the dynamics of the system, but instead is a monoid (or even a group) of transformations Ts : XX parametrized by sZ (or R, or N ∪ {0}, or [0, +∞)), where each transformation Ts satisfies the same requirements as T above. In particular, the transformations obey the rules:

  •  , the identity function on X;
  •  , whenever all the terms are well-defined;
  •  , whenever all the terms are well-defined.

The earlier, simpler case fits into this framework by definingTs = Ts for sN.

The existence of invariant measures for certain maps and Markov processes is established by the Krylov–Bogolyubov theorem.

ExamplesEdit

 
Example of a (Lebesgue measure) preserving map: T : [0,1) → [0,1),  

Examples include:

HomomorphismsEdit

The concept of a homomorphism and an isomorphism may be defined.

Consider two dynamical systems   and  . Then a mapping

 

is a homomorphism of dynamical systems if it satisfies the following three properties:

  1. The map   is measurable.
  2. For each  , one has  .
  3. For μ-almost all  , one has  .

The system   is then called a factor of  .

The map   is an isomorphism of dynamical systems if, in addition, there exists another mapping

 

that is also a homomorphism, which satisfies

  1. for μ-almost all  , one has  ;
  2. for ν-almost all  , one has  .

Hence, one may form a category of dynamical systems and their homomorphisms.

Generic pointsEdit

A point xX is called a generic point if the orbit of the point is distributed uniformly according to the measure.

Symbolic names and generatorsEdit

Consider a dynamical system  , and let Q = {Q1, ..., Qk} be a partition of X into k measurable pair-wise disjoint pieces. Given a point xX, clearly x belongs to only one of the Qi. Similarly, the iterated point Tnx can belong to only one of the parts as well. The symbolic name of x, with regards to the partition Q, is the sequence of integers {an} such that

 

The set of symbolic names with respect to a partition is called the symbolic dynamics of the dynamical system. A partition Q is called a generator or generating partition if μ-almost every point x has a unique symbolic name.

Operations on partitionsEdit

Given a partition Q = {Q1, ..., Qk} and a dynamical system  , we define T-pullback of Q as

 

Further, given two partitions Q = {Q1, ..., Qk} and R = {R1, ..., Rm}, we define their refinement as

 

With these two constructs we may define refinement of an iterated pullback

 

which plays crucial role in the construction of the measure-theoretic entropy of a dynamical system.

Measure-theoretic entropyEdit

The entropy of a partition Q is defined as[1][2]

 

The measure-theoretic entropy of a dynamical system   with respect to a partition Q = {Q1, ..., Qk} is then defined as

 

Finally, the Kolmogorov–Sinai or metric or measure-theoretic entropy of a dynamical system   is defined as

 

where the supremum is taken over all finite measurable partitions. A theorem of Yakov Sinai in 1959 shows that the supremum is actually obtained on partitions that are generators. Thus, for example, the entropy of the Bernoulli process is log 2, since almost every real number has a unique binary expansion. That is, one may partition the unit interval into the intervals [0, 1/2) and [1/2, 1]. Every real number x is either less than 1/2 or not; and likewise so is the fractional part of 2nx.

If the space X is compact and endowed with a topology, or is a metric space, then the topological entropy may also be defined.

See alsoEdit

ReferencesEdit

  1. ^ Ya.G. Sinai, (1959) "On the Notion of Entropy of a Dynamical System", Doklady of Russian Academy of Sciences 124, pp. 768–771.
  2. ^ Ya. G. Sinai, (2007) "Metric Entropy of Dynamical System"
  • Michael S. Keane, "Ergodic theory and subshifts of finite type", (1991), appearing as Chapter 2 in Ergodic Theory, Symbolic Dynamics and Hyperbolic Spaces, Tim Bedford, Michael Keane and Caroline Series, Eds. Oxford University Press, Oxford (1991). ISBN 0-19-853390-X (Provides expository introduction, with exercises, and extensive references.)
  • Lai-Sang Young, "Entropy in Dynamical Systems" (pdf; ps), appearing as Chapter 16 in Entropy, Andreas Greven, Gerhard Keller, and Gerald Warnecke, eds. Princeton University Press, Princeton, NJ (2003). ISBN 0-691-11338-6

ExamplesEdit

  • T. Schürmann and I. Hoffmann, The entropy of strange billiards inside n-simplexes. J. Phys. A28, page 5033ff, 1995. PDF-Dokument