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arXiv · 2406.01123

Ergodic optimization for continuous functions on non-Markov shifts

Mao Shinoda·Hiroki Takahasi·Kenichiro Yamamoto
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Abstract

Ergodic optimization aims to describe dynamically invariant probability measures that maximize the integral of a given function. For a wide class of intrinsically ergodic subshifts over a finite alphabet, we show that the space of continuous functions on the shift space splits into two subsets: one is a $G_\delta$ dense set for which all maximizing measures have `relatively small' entropy; the other is contained in the closure of the set of functions having uncountably many, fully supported ergodic measures with `relatively large' entropy. This result considerably generalizes and unifies the results of Morris (2010) and Shinoda (2018), and applies to a wide class of intrinsically ergodic non-Markov symbolic dynamics without Bowen's specification property, including any transitive piecewise monotonic interval map, some coded shifts and multidimensional $\beta$-transformations. Along with these examples of application, we provide an example of an intrinsically ergodic subshift with positive obstruction entropy to specification.

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Mao Shinoda, Hiroki Takahasi, Kenichiro Yamamoto. 2024-06-03. Ergodic optimization for continuous functions on non-Markov shifts. https://doi.org/10.1017/etds.2025.10206

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