图小消去理论中的优化
Optimization in graphical small cancellation theory
AI总结:
该研究聚焦图小消去理论,简化Osajda的概率方法,在构造含扩张图族的Gromov怪物群时显著减少生成元数量,推进相关群构造的优化。
摘要:
Gromov(2003)构造了有限生成群,其Cayley图包含给定无限序列中所有具有无界围长且直径与围长之比有界的expander graph(扩张图)。这些所谓的Gromov monster群(Gromov怪物群)提供了不能粗嵌入希尔伯特空间的有限生成群示例,还具备其他有趣性质。若Gromov构造中的图存在graphical small cancellation labelling(图小消去标记),则可得到类似的Cayley图示例,以等距子图的形式包含该族所有图。Osajda(2020)近期展示了如何用概率方法获得此类标记。本短文简化了Osajda的方法,显著减少了所得群的生成元数量。
英文摘要:
Gromov (2003) constructed finitely generated groups whose Cayley graphs contain all graphs from a given infinite sequence of expander graphs of unbounded girth and bounded diameter-to-girth ratio. These so-called Gromov monster groups provide examples of finitely generated groups that do not coarsely embed into Hilbert space, among other interesting properties. If graphs in Gromov's construction admit graphical small cancellation labellings, then one gets similar examples of Cayley graphs containing all the graphs of the family as isometric subgraphs. Osajda (2020) recently showed how to obtain such labellings using the probabilistic method. In this short note, we simplify Osajda's approach, decreasing the number of generators of the resulting group significantly.
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