Metric mean dimension and the variational principle for actions of amenable groups
Rui Yang, Xiaoyao Zhou

TL;DR
This paper extends the classical variational principle to metric mean dimension for actions of amenable groups, introducing new measure-theoretic concepts and establishing fundamental relations between topological and measure-theoretic complexities.
Contribution
It defines a new measure-theoretic metric mean dimension for invariant measures and proves a classical-type variational principle, extending prior results to a broader setting.
Findings
Established a variational principle for metric mean dimension in amenable group actions.
Introduced infinite entropy dimensions for zero metric mean dimension systems.
Connected local and global metric mean dimensions via variational principles.
Abstract
Metric mean dimension is a dynamical counterpart of the box dimension in fractal geometry to characterize the topological complexity of infinite entropy systems. The classical variational principle states that topological entropy equals the supremum of measure-theoretic entropy over the set of invariant measures. Lindenstrauss and Tsukamoto proved that this variational principle fails for metric mean dimension in terms of rate-distortion dimensions. For the actions of amenable groups, we define a new measure-theoretic metric mean dimension for invariant measures and establish a classical-type variational principle for metric mean dimension. In particular, we extend the Lindenstrauss-Tsukamoto variational principles to the classical variational principle by defining modified rate-distortion dimensions. As applications, for systems with zero metric mean dimension, we introduce infinite…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMathematical Dynamics and Fractals · Statistical Mechanics and Entropy · Topological and Geometric Data Analysis
