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arXiv 1808.03992math.CO
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凸并可表示性与凸码

Convex Union Representability and Convex Codes

R. Amzi Jeffs, Isabella Novik

摘要:

我们介绍和调查$d$ -凸联盟表示复合物:出现的复合物作为神经的有限收集凸开集$\mathbb R^d$的工会也是凸的。陈,弗里克,和萧最近证明,这种复合物是可塌缩的,并要求所有可塌缩的复合物是凸联盟表示。我们反驳这一点,表明存在壳和可折叠的复合物,不是凸联盟表示,也存在非回避复合物,不是凸联盟表示。在这个过程中,我们建立了几个必要条件,一个复杂的是凸并表示,如:这样一个复杂的$Δ$塌缩到任何面对$Δ$的星,这亚历山大对偶的$Δ$也必须是可塌缩的,如果$Δ$的$k$方面包含所有自由面的$Δ$,那么$Δ$是$(k-1)$ -表示。我们还讨论了复形是凸并可表示的一些充分条件。凸并可表示性的概念与凸神经代码的研究密切相关。特别是,我们的研究结果提供了新的家庭的例子,非凸神经代码。

英文摘要:

We introduce and investigate $d$-convex union representable complexes: the complexes that arise as the nerve of a finite collection of convex open sets in $\mathbb R^d$ whose union is also convex. Chen, Frick, and Shiu recently proved that such complexes are collapsible and asked if all collapsible complexes are convex union representable. We disprove this by showing that there exist shellable and collapsible complexes that are not convex union representable; there also exist non-evasive complexes that are not convex union representable. In the process we establish several necessary conditions for a complex to be convex union representable such as: that such a complex $Δ$ collapses onto the star of any face of $Δ$, that the Alexander dual of $Δ$ must also be collapsible, and that if $k$ facets of $Δ$ contain all free faces of $Δ$, then $Δ$ is $(k-1)$-representable. We also discuss some sufficient conditions for a complex to be convex union representable. The notion of convex union representability is intimately related to the study of convex neural codes. In particular, our results provide new families of examples of non-convex neural codes.

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