- Topological entropy
In
mathematics , the topological entropy of a topologicaldynamical system is a nonnegative real number that measures the complexity of the system. Topological entropy was first introduced in 1965 by Adler, Konheim and McAndrew. Their definition was modelled after the definition of the Kolmogorov–Sinai, or metric, entropy. Later, Dinaburg and Rufus Bowen gave a different, equivalent definition reminiscent of theHausdorff dimension . The second definition clarified the meaning of the topological entropy: for a system given by aniterated function , the topological entropy represents the exponential growth rate of the number of distinguishable orbits of the iterates. An important variational principle relates the notions of topological and measure-theoretic entropy.Definition
A topological dynamical system consists of a
Hausdorff topological space "X" (usually assumed to be compact) and a continuous self-map "f". Its topological entropy is a nonnegative real number that can be defined in various ways, which are known to be equivalent.Definition of Adler, Konheim, and McAndrew
Let "X" be a
compact Hausdorff topological space. For any finite cover "C" of "X", let "H"("C") be thelogarithm (usually to base 2) of the smallest number of elements of "C" that cover "X". For two covers "C" and "D", let:
be their (minimal) common refinement, which consists of all the non-empty intersections of a set from "C" with a set from "D", and similarly for multiple covers. For any
continuous map "f": "X" → "X", the following limit exists::
Then the topological entropy of "f", denoted "h"("f"), is defined to be the
supremum of "H"("C", "f") over all possible finite covers "C".Interpretation
The parts of "C" may be viewed as symbols that (partially) describe the position of a point "x" in "X": all points "x" ∈ "C""i" are assigned the symbol "C""i" . Imagine that the position of "x" is (imperfectly) measured by a certain device and that each part of "C" corresponds to one possible outcome of the measurement. The integer then represents the minimal number of "words" of length "n" needed to encode the points of "X" according to the behavior of their first "n" − 1 iterates under "f", or, put differently, the total number of "scenarios" of the behavior of these iterates, as "seen" by the partition "C". Thus the topological entropy is the average (per iteration) amount of
information needed to describe long iterations of the map "f".Definition of Bowen and Dinaburg
This definition uses a metric on "X" (actually,
uniform structure would suffice).Let ("X", "d") be acompact metric space and "f": "X" → "X" be acontinuous map . For eachnatural number "n", a new metric "d""n" is defined on "X" by the formula:
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