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arXiv 2409.03066math.DSmath.CO
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具有指定组合结构的伪阿诺索夫同胚的几何马尔可夫分割

Geometric Markov partitions for pseudo-Anosov homeomorphisms with prescribed combinatorics

Inti Cruz Diaz

AI总结:

本文研究伪阿诺索夫同胚的几何马尔可夫分割构造与细化,提出从单点出发的算法化构造,并引入二元细化、s和U边界细化及角点细化,为伪阿诺索夫同胚的拓扑共轭分类提供组合工具。

摘要:

本文重点研究可能包含脊线的伪阿诺索夫同胚的几何马尔可夫分割的构造与细化。我们引入一种系统方法来为这些同胚构造“适应马尔可夫分割”。我们的主要结果是对每一个广义伪阿诺索夫映射,从单个点出发算法化地构造“适应马尔可夫分割”。该算法应用于同胚的所谓“第一交点”,产生在迭代下表现良好的“原始马尔可夫分割”。我们还证明给定阶数的“原始几何类型”集合是有限的,从而为伪阿诺索夫同胚的分类提供了一个规范工具。随后,我们从已有分割出发构造新的几何马尔可夫分割,同时保持对其组合性质的控制并保留其几何类型。我们构造的第一个几何马尔可夫分割具有二元关联矩阵,这使得可以引入与任意马尔可夫分割的关联矩阵相关联的有限型子移位,称为“二元细化”。我们还描述了一个过程,该过程沿着由有限周期码集合指定的稳定和不稳定线段切割任意马尔可夫分割,称为s和U边界细化。最后,我们给出一种马尔可夫分割的算法构造,其中所有周期边界点都位于分割中矩形的角上,称为“角点细化”。这些马尔可夫分割及其内在组合性质在我们对伪阿诺索夫同胚进行拓扑共轭分类的算法化工作中起着关键作用。

英文摘要:

In this paper, we focus on constructing and refining geometric Markov partitions for pseudo-Anosov homeomorphisms that may contain spines. We introduce a systematic approach to constructing \emph{adapted Markov partitions} for these homeomorphisms. Our primary result is an algorithmic construction of \emph{adapted Markov partitions} for every generalized pseudo-Anosov map, starting from a single point. This algorithm is applied to the so-called \emph{first intersection points} of the homeomorphism, producing \emph{primitive Markov partitions} that behave well under iterations. We also prove that the set of \emph{primitive geometric types} of a given order is finite, providing a canonical tool for classifying pseudo-Anosov homeomorphisms. We then construct new geometric Markov partitions from existing ones, maintaining control over their combinatorial properties and preserving their geometric types. The first geometric Markov partition we construct has a binary incidence matrix, which allows for the introduction of the sub-shift of finite type associated with any Markov partition's incidence matrix -- this is known as the \emph{binary refinement}. We also describe a process that cuts any Markov partition along stable and unstable segments prescribed by a finite set of periodic codes, referred to as the $s$ and $U$-boundary refinements. Finally, we present an algorithmic construction of a Markov partition where all periodic boundary points are located at the corners of the rectangles in the partition, called the \emph{corner refinement}. Each of these Markov partitions and their intrinsic combinatorial properties plays a crucial role in our algorithmic classification of pseudo-Anosov homeomorphisms up to topological conjugacy.

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