In mathematics, the meandimension of a topological dynamical system is a non-negative extended real number that is a measure of the complexity of the system. Mean dimension was first introduced in 1999 by Gromov. Shortly after it was developed and studied systematically by Lindenstrauss and Weiss. In particular they proved the following key fact: a system with finite topological entropy has zero mean dimension. For various topological dynamical systems with infinite topological entropy, the mean dimension can be calculated or at least bounded from below and above. This allows mean dimension to be used to distinguish between systems with infinite topological entropy.
A topological dynamical system consists of a compact Hausdorff topological space and a continuous self-map. Let denote the collection of open finite covers of. For define its order by An open finite cover refines, denoted, if for every, there is so that. Let Note that in terms of this definition the Lebesgue covering dimension is defined by. Let be open finite covers of. The join of and is the open finite cover by all sets of the form where,. Similarly one can define the join of any finite collection of open covers of. The mean dimension is the non-negative extended real number: where
Definition in the metric case
If the compact Hausdorff topological space is metrizable and is a compatible metric, an equivalent definition can be given. For, let be the minimal non-negative integer, such that there exists an open finite cover of by sets of diameter less than such that any distinct sets from this cover have empty intersection. Note that in terms of this definition the Lebesgue covering dimension is defined by. Let The mean dimension is the non-negative extended real number:
Properties
Mean dimension is an invariant of topological dynamical systems taking values in.
If the Lebesgue covering dimension of the system is finite then its mean dimension vanishes, i.e..
If the topological entropy of the system is finite then its mean dimension vanishes, i.e..
