论文标题
加权切片等级和与Strassen光谱的最小值通信
Weighted slice rank and a minimax correspondence to Strassen's spectra
论文作者
论文摘要
对张量的结构和计算理解是更快的矩阵乘法算法,量子纠缠的解开以及CAP集问题的突破。 Strassen的渐近光谱程序(FOCS 1986)通过单调函数来表征最佳矩阵乘法算法。我们的工作进步并建立了张量研究的最新发展之间的新颖联系,即 - 张量的切片等级,从CAP集问题的解决方案中出现的张量的排名概念(Ann。Math。2017), - 和张量的量子功能(Stoc 2018),单调函数定义为在矩多形上的优化。 更确切地说,我们引入了切片等级的扩展名,我们称之为加权切片等级,并在渐近加权切片等级和量子功能之间开发了最小值的对应关系。加权切片等级封装了量子纠缠的两种概念。 该对应关系使我们能够给出量子功能的等级类型表征。而且,虽然量子功能的原始定义仅在复数上起作用,但这种新的特征可以扩展到所有字段。因此,除了对Strassen对复数的理论有了更深入的了解,我们还获得了其他领域的量子功能的建议。有限的场情况对于可以优化场的组合和算法问题至关重要。
Structural and computational understanding of tensors is the driving force behind faster matrix multiplication algorithms, the unraveling of quantum entanglement, and the breakthrough on the cap set problem. Strassen's asymptotic spectra program (FOCS 1986) characterizes optimal matrix multiplication algorithms through monotone functionals. Our work advances and makes novel connections among two recent developments in the study of tensors, namely - the slice rank of tensors, a notion of rank for tensors that emerged from the resolution of the cap set problem (Ann. of Math. 2017), - and the quantum functionals of tensors (STOC 2018), monotone functionals defined as optimizations over moment polytopes. More precisely, we introduce an extension of slice rank that we call weighted slice rank and we develop a minimax correspondence between the asymptotic weighted slice rank and the quantum functionals. Weighted slice rank encapsulates different notions of bipartiteness of quantum entanglement. The correspondence allows us to give a rank-type characterization of the quantum functionals. Moreover, whereas the original definition of the quantum functionals only works over the complex numbers, this new characterization can be extended to all fields. Thereby, in addition to gaining deeper understanding of Strassen's theory for the complex numbers, we obtain a proposal for quantum functionals over other fields. The finite field case is crucial for combinatorial and algorithmic problems where the field can be optimized over.