In mathematics, the topological entropy of a topological dynamical system is a nonnegative real number that is a measure of 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, weaker definition reminiscent of the Hausdorff dimension. The second definition clarified the meaning of the topological entropy: for a system given by an iterated 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.
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 open cover C of X, let H(C) be the logarithm (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 of X.
The parts of C may be viewed as symbols that (partially) describe the position of a point x in X: all points x ∈ Ci are assigned the symbol Ci . 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). This is a weaker definition than that of Adler, Konheim, and McAndrew, as it requires additional, unnecessary structure on the topological space. However, in practice, the Bowen-Dinaburg topological entropy is usually much easier to calculate.
Given any ε > 0 and n ≥ 1, two points of X are ε-close with respect to this metric if their first n iterates are ε-close. This metric allows one to distinguish in a neighborhood of an orbit the points that move away from each other during the iteration from the points that travel together. A subset E of X is said to be (n, ε)-separated if each pair of distinct points of E is at least ε apart in the metric dn. Denote by N(n, ε) the maximum cardinality of an (n, ε)-separated set. The topological entropy of the map f is defined by
Since X is compact, N(n, ε) is finite and represents the number of distinguishable orbit segments of length n, assuming that we cannot distinguish points within ε of one another. A straightforward argument shows that the limit defining h(f) always exists in the extended real line (but could be infinite). This limit may be interpreted as the measure of the average exponential growth of the number of distinguishable orbit segments. In this sense, it measures complexity of the topological dynamical system (X, f). Rufus Bowen extended this definition of topological entropy in a way which permits X to be noncompact.
- Let be an expansive homeomorphism of a compact metric space and let be a topological generator. Then the topological entropy of relative to is equal to the topological entropy of , i.e.
- Let be a continuous transformation of a compact metric space , let be the measure-theoretic entropy of with respect to and is the set of all -invariant Borel probability measures. Then
- In general the maximum of the functions over the set M(X,f) is not attained, but if additionally the entropy map
- is upper semicontinuous, the measure of maximal entropy exists.
- If has a unique measure of maximal entropy , then is ergodic with respect to .
- Let by denote the full two-sided k-shift on symbols . Let denote the partition of into cylinders of length 1. Then is a partition of for all and the number of sets is respectively. The partitions are open covers and is a topological generator. Hence
- . The measure-theoretic entropy of the Bernoulli -measure is also . Hence it is a measure of maximal entropy. Further on it can be shown that no other measures of maximal entropy exist.
- Let be an irreducible matrix with entries in and let be the corresponding subshift of finite type. Then where is the largest positive eigenvalue of .
- Since X is compact, H(C) is always finite, even for an infinite cover C. The use of arbitrary covers yields the same value of entropy.
- Milnor–Thurston kneading theory
- For the measure of correlations in systems with topological order see Topological entanglement entropy
- Adler, R.L.; Konheim, Allan G.; McAndrew, M.H. (1965). "Topological entropy". Transactions of the American Mathematical Society 114 (2): 309–319. doi:10.2307/1994177. Zbl 0127.13102.
- Dmitri Anosov (2001), "T/t093040", in Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4
- Roy Adler, Tomasz Downarowicz, Michał Misiurewicz, Topological entropy at Scholarpedia
- Walters, Peter (1982). An introduction to ergodic theory. Graduate Texts in Mathematics 79. Springer-Verlag. ISBN 0-387-95152-0. Zbl 0475.28009.