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arXiv 1509.06347math.DSmath.OC
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关于遍历迁移的一个遍历定理

An Ergodic Theorem on Ergodic Transport

Joana Mohr, Rafael Rigão Souza

摘要:

本文提出一个遍历定理,它把J.Elton的一个定理推广到经典的代数形式和遍历输运.首先,我们讨论如何埃尔顿定理可以用来证明扩张映射的吉布斯测度。这样的表征将通过构造由迭代函数系统(IFS)定义的随机过程来完成,迭代函数系统的经验测量收敛于吉布斯测量,在这个意义上,在这个随机过程的结果中评估的任何测试函数的平均值收敛于这样的测试函数关于吉布斯测量的积分。这样我们就给出了一个计算函数积分的随机算法。在此之后,我们把我们的注意力遍历运输:给定两个集合$X$和$Ω$,$X$上的测量$μ$和$Ω$上的动态$T$,我们考虑$X \times Ω$上的概率测度的集合,其在第二个坐标上的投影是$T$不变的,而在第一个坐标上的投影是$μ$。这些措施被称为运输计划。我们称吉布斯计划为任何能使压力泛函最大化的运输计划,该压力泛函由添加到熵项的势函数定义。在经典的代数形式主义的情况下,我们适应埃尔顿定理定义一个随机过程(使用IFS)的经验措施收敛到吉布斯计划。我们提供的例子,并明确计算的情况下,$X$有两个元素和成本函数取决于$Ω$的两个第一坐标。

英文摘要:

Here we present an ergodic theorem which adapts a Theorem by J. Elton to the classical thermodynamical formalism and to ergodic transport. First, we discuss how Elton's theorem can be used to characterise Gibbs measures for expanding maps. Such characterisation will be done by constructing a stochastic process, defined by a iterated function system (IFS), whose empirical measure converges to the Gibbs measure, in the sense that the mean of any test function evaluated in the outcomes of this stochastic process converges to the integral of such test function with respect to the Gibbs measure. In this way we present a stochastic algorithm that compute integrals of functions. After this, we turn our attention to ergodic transport: given two sets $X$ and $Ω$, a measure $μ$ on $X$ and a dynamics $T$ on $Ω$, we consider the set of probability measures on $X \times Ω$ whose projections on the second coordinate are $T$-invariant, while the projections on the first coordinate are $μ$. Such measures are called transport plans. We call Gibbs plan any transport plan that maximizes a pressure functional that is defined by a potential function added to an entropy term. As in the classical thermodynamical formalism case, we adapt Elton's theorem to define a stochastic process (using a IFS) whose empirical measures converges to the Gibbs plan. We provide examples and show explicitly calculations in the case where $X$ has two elements and the cost function depends on the two first coordinates of $Ω$.

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