On quantization and the classical variational principle for the metric mean dimension

TL;DR

This paper establishes a variational principle linking metric mean dimension to mean quantization dimension of invariant measures, providing a new way to analyze complex dynamical systems.

Contribution

It introduces the mean quantization dimension and proves a classical variational principle for metric mean dimension, enhancing understanding of systems with infinite entropy.

Findings

Metric mean dimension equals the maximum mean quantization dimension among invariant measures.

The variational principle allows for the exchange of limits and suprema in the context of metric mean dimension.

Katok and Shapira entropies satisfy the property, ensuring the existence of maximizing measures.

Abstract

This paper investigates the relationship between quantization of measures and metric mean dimension of topological dynamical systems. We introduce the concept of mean quantization dimension for invariant probability measures and establish a classical variational principle: the metric mean dimension of a dynamical system is equal to the maximum mean quantization dimension among all invariant measures. This approach effectively characterizes the complexity of systems with infinite entropy by identifying a measure that captures information across all scales; and yields a fundamental property that allows for the exchange of limits and suprema in the Lindenstrauss-Tsukamoto variational principles, a feat that most known entropy-like maps fail to achieve due to convexity. Nevertheless, we show that the Katok and Shapira entropies do satisfy this property and, therefore, a classical variational principle for the metric mean dimension, for which maximizing measures always exist.