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首页> 外文期刊>Bulletin of the Brazilian Mathematical Society >Weak KAM methods and ergodic optimal problems for countable Markov shifts
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Weak KAM methods and ergodic optimal problems for countable Markov shifts

机译:可数Markov移位的弱KAM方法和遍历最优问题

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摘要

Let σ: Σ → Σ be the left shift acting on Σ, a one-sided Markov subshift on a countable alphabet. Our intention is to guarantee the existence of σ-invariant Borel probabilities that maximize the integral of a given locally Hölder continuous potential A: Σ → ℝ. Under certain conditions, we are able to show not only that A-maximizing probabilities do exist, but also that they are characterized by the fact their support lies actually in a particular Markov subshift on a finite alphabet. To that end, we make use of objects dual to maximizing measures, the so-called sub-actions (concept analogous to subsolutions of the Hamilton-Jacobi equation), and specially the calibrated sub-actions (notion similar to weak KAM solutions).
机译:令σ:Σ→Σ是作用于Σ的左移,这是可数字母上的单侧马尔可夫子移。我们的目的是保证存在σ不变的Borel概率,该概率使给定的局部Hölder连续势A:Σ→the的积分最大化。在某些条件下,我们不仅能够证明确实存在A最大化概率,而且它们的特征在于它们的支持实际上在于有限字母上的特定Markov子移位。为此,我们利用双重对象来最大化度量,即所谓的子作用(概念类似于汉密尔顿-雅各比方程的子解),尤其是经过校准的子作用(概念类似于弱KAM解决方案)。

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