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arXiv 1705.09652cs.CCmath.RTquant-ph
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二乘二矩阵乘法的边界支撑秩为七

The border support rank of two-by-two matrix multiplication is seven

Markus Bläser, Matthias Christandl, Jeroen Zuiddam

摘要:

我们证明了对应于2 × 2矩阵乘法的张量的边界支撑秩在复数上为7。我们通过构造两个多项式来实现这一点,这两个多项式在所有格式为4 × 4 × 4且边界秩最多为6的复张量上为零,但不会在任何具有与2 × 2矩阵乘法张量相同支持的张量上同时为零。这扩展了Hauenstein,Ikenmeyer和Landsberg的工作。我们还给出了两个证明,2 × 2矩阵乘法张量的支持秩是7在任何域上:一个证明使用的结果De Groote说,这个张量的分解是唯一的直到dumping,另一个证明通过替代方法。这些结果回答了Cohn和Umans提出的一个问题。研究矩阵乘法张量的边界支持秩与矩阵乘法算法的设计有关,因为通过Cohn和Umans的构造,矩阵乘法张量的边界支持秩的上界导致矩阵乘法的计算复杂性的上界.此外,支持秩在量子通信复杂性中也有应用。

英文摘要:

We show that the border support rank of the tensor corresponding to two-by-two matrix multiplication is seven over the complex numbers. We do this by constructing two polynomials that vanish on all complex tensors with format four-by-four-by-four and border rank at most six, but that do not vanish simultaneously on any tensor with the same support as the two-by-two matrix multiplication tensor. This extends the work of Hauenstein, Ikenmeyer, and Landsberg. We also give two proofs that the support rank of the two-by-two matrix multiplication tensor is seven over any field: one proof using a result of De Groote saying that the decomposition of this tensor is unique up to sandwiching, and another proof via the substitution method. These results answer a question asked by Cohn and Umans. Studying the border support rank of the matrix multiplication tensor is relevant for the design of matrix multiplication algorithms, because upper bounds on the border support rank of the matrix multiplication tensor lead to upper bounds on the computational complexity of matrix multiplication, via a construction of Cohn and Umans. Moreover, support rank has applications in quantum communication complexity.

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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