arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1908.01212math.CTcs.LGcs.SE
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2-范畴中的张量演算类型化(I)

Typing Tensor Calculus in 2-Categories (I)

University of Oxford(牛津大学)

Fatimah Rita Ahmadi

AI总结:

本研究为开发适配函数式编程与并行计算的线性代数形式化框架,将矩阵、张量建模为半加性2-范畴中的态射,构建无指标带类型的线性代数框架,并扩展至幺半半加性2-范畴完成运算验证。

摘要:

为将线性代数中的计算形式化,以开发高效算法及适配函数式编程语言、更快速并行计算的框架,我们采用一种将矩阵等线性代数元素视为矩阵范畴$\boldsymbol{\text{Mat}_{k}}$中态射的方法。该框架通过将结果推广至任意幺半半加性范畴得到进一步扩展。为丰富这一视角并适配更高秩的矩阵(张量),我们定义了半加性2-范畴,其中矩阵$T_{ij}$被表示为1-态射,带四个指标的张量$T_{ijkl}$被表示为2-态射。这一形式化构建了一个无指标、带类型的线性代数框架,涵盖矩阵及最多含四个指标的张量。此外,我们将该框架扩展至幺半半加性2-范畴,并在Kapranov与Voevodsky提出的2Vec 2-范畴中演示了详细运算与向量化操作。

英文摘要:

To formalize calculations in linear algebra for the development of efficient algorithms and a framework suitable for functional programming languages and faster parallelized computations, we adopt an approach that treats elements of linear algebra, such as matrices, as morphisms in the category of matrices, $\mathbf{Mat_{k}}$. This framework is further extended by generalizing the results to arbitrary monoidal semiadditive categories. To enrich this perspective and accommodate higher-rank matrices (tensors), we define semiadditive 2-categories, where matrices $T_{ij}$ are represented as 1-morphisms, and tensors with four indices $T_{ijkl}$ as 2-morphisms. This formalization provides an index-free, typed linear algebra framework that includes matrices and tensors with up to four indices. Furthermore, we extend the framework to monoidal semiadditive 2-categories and demonstrate detailed operations and vectorization within the 2-category of 2Vec introduced by Kapranov and Voevodsky.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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