2018
DOI: 10.1017/etds.2017.142
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Ergodic optimization in dynamical systems

Abstract: Abstract. Ergodic optimization is the study of problems relating to maximizing orbits and invariant measures, and maximum ergodic averages. An orbit of a dynamical system is called f -maximizing if the time average of the real-valued function f along the orbit is larger than along all other orbits, and an invariant probability measure is called fmaximizing if it gives f a larger space average than any other invariant probability measure. In this paper, we consider the main strands of ergodic optimization, begi… Show more

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Cited by 80 publications
(76 citation statements)
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References 156 publications
(283 reference statements)
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“…In traditional ergodic optimization, that is, the optimization of Birkhoff averages (see [Je1,Je2,Gar]), a maximizing set is a closed subset such that an invariant probability is maximizing if and only if its support lies on this subset. The existence of such sets is guaranteed in any context where a Mañé Lemma holds.…”
Section: Mather Setsmentioning
confidence: 99%
“…In traditional ergodic optimization, that is, the optimization of Birkhoff averages (see [Je1,Je2,Gar]), a maximizing set is a closed subset such that an invariant probability is maximizing if and only if its support lies on this subset. The existence of such sets is guaranteed in any context where a Mañé Lemma holds.…”
Section: Mather Setsmentioning
confidence: 99%
“…, p − 1} and consider the gap X a 0 ···a k−1 i (h n−1 ) of order k. Let x ∈ X a 0 ···a k−1 i (h n−1 ). From (10), (15) |f 0 (x) − f (x)| ≤ f 0 (X a 0 ···a k−1 i (h n−1 )) ≤ ε 2 .…”
Section: Approximation By Maps With Locally Constant Derivativesmentioning
confidence: 99%
“…|Df 0 (x) − Df (x)| ≤ ||Df 0 (x)| − τ | + |τ − |Df (x)|| ≤ ε 4From (12),(13),(14),(15),(16), f 0 − f C 1 ≤ ε follows. By Lemma 2.2, (E2) holds for f .…”
mentioning
confidence: 92%
“…We say a measure µ is a ground state of the potential ϕ if µ is the weak * limit of a sequence of equilibrium states µ tn ∈ ES(t n ϕ) for some sequence t n → ∞. It follows that every ground state µ of ϕ is a maximizing measure, that is µ maximizes the integral ϕdν among all invariant probability measures (see [16]). In the presence of a unique ground state (i.e.…”
mentioning
confidence: 99%