Empirical variational principles for preimage entropies

Abstract

Preimage entropy measures the complexity generated by the inverse-image structure of a non-invertible dynamical system. For a continuous map $f:X\to X$ on a compact metric space, Hurley's pointwise topological preimage entropies $h_m(f)$ and $h_p(f)$ are natural invariants measuring the complexity of preimage sets. The question of whether they admit unconditional variational principles in terms of suitable measure-theoretic counterparts remains open. In this paper we resolve it by using empirical metric preimage entropies $h^*_{m,μ}(f)$ and $h^*_{p,μ}(f)$, defined by restricting preimage fibers to orbit segments whose empirical measures are close to a prescribed invariant measure $μ$. We prove the variational principles $$ h_m(f)=\sup_{μ\in\mathcal M_f(X)}h^*_{m,μ}(f), \qquad h_p(f)=\sup_{μ\in\mathcal M_f(X)}h^*_{p,μ}(f) $$ for every continuous map on a compact metric space. We also show that, in general, the set of all invariant measures in these formulas cannot be replaced by the set of ergodic invariant measures. We then compare the empirical entropies with the pointwise metric preimage entropy $h_{m,μ}(f)$. For every ergodic invariant measure $μ$, we prove $h^*_{p,μ}(f)\ge h_{m,μ}(f)$. Moreover, if $f$ has uniform separation of preimages, then for every ergodic invariant measure $μ$, $$ h^*_{m,μ}(f)=h^*_{p,μ}(f)=h_{m,μ}(f). $$ Examples show that $h_{m,μ}(f)$ is not comparable with the empirical quantities in general. We further introduce a resolving-partition property, weaker than uniform separation of preimages, under which the variational principle for $h_m(f)$ and $h_{m,μ}(f)$ holds. Finally, we establish corresponding variational principles for preimage pressure and give an example showing that uniform separation of preimages does not imply forward expansiveness.