Ergodic optimization for continuous functions on non-Markov shifts
| Autor: | Shinoda, Mao, Takahasi, Hiroki, Yamamoto, Kenichiro |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 27 pages, no figure |
| Databáze: | arXiv |
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