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High-level methods for homotopy construction in associative n-categories

机译:关联n类中同伦构造的高级方法

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A combinatorial theory of associative n-categories has recently been proposed, with strictly associative and unital composition in all dimensions, and the weak structure arising as a notion of `homotopy' with a natural geometrical interpretation. Such a theory has the potential to serve as an attractive foundation for a computer proof assistant for higher category theory, since it allows composites to be uniquely described, and relieves proofs from the bureaucracy of associators, unitors and their coherence. However, this basic theory lacks a high-level way to construct homotopies, which would be intractable to build directly in complex situations; it is not therefore immediately amenable to implementation. We tackle this problem by describing a `contraction' operation, which algorithmically constructs complex homotopies that reduce the lengths of composite terms. This contraction procedure allows building of nontrivial proofs by repeatedly contracting subterms, and also allows the contraction of those proofs themselves, yielding in some cases single-step witnesses for complex homotopies. We prove correctness of this procedure by showing that it lifts connected colimits from a base category to a category of zigzags, a procedure which is then iterated to yield a contraction mechanism in any dimension. We also present homotopy.io, an online proof assistant that implements the theory of associative n-categories, and use it to construct a range of examples that illustrate this new contraction mechanism.
机译:最近有人提出了一种组合n类的组合理论,在所有维度上都具有严格的组合和单位组成,其弱结构作为“同伦”的概念出现,具有自然的几何解释。这样的理论有可能成为高级分类理论的计算机证明助手的诱人基础,因为它可以对复合材料进行独特的描述,并消除了关联者,联合者及其相关者的官僚主义。但是,该基本理论缺乏构建同伦同质的高级方法,要在复杂情况下直接构建同质异形是很难的。因此,它并不立即适合实施。我们通过描述一种“收缩”运算来解决此问题,该运算以算法方式构造出了可减少合成项长度的复杂同伦。这种收缩程序允许通过反复收缩子项来构建非平凡的证明,并且还允许收缩这些证明本身,在某些情况下,可以为复杂的同构产生单步证人。通过显示该过程将连接的限制范围从基本类别提升到曲折类别,我们证明了此过程的正确性,然后对该过程进行迭代以产生任意维度的收缩机制。我们还介绍了homotopy.io,它是一种在线证明助手,它实现了关联n类的理论,并用它来构造一系列示例来说明这种新的收缩机制。

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