Ergodic optimization for continuous functions on non-Markov shifts

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 withrelatively 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.