Partial and weighted matrix multiplication

Péter Vrana
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Abstract

In a paper published in 1981, Sch\"onhage showed that large total matrix multiplications can be reduced to powers of partial matrix multiplication tensors, which correspond to the bilinear computation task of multiplying matrices with some of the entries fixed to be zero. It was left as an open problem to generalize the method to the case when the multiplication is also partial in the sense that only a subset of the entries need to be computed. We prove a variant of a more general case: reducing large weighted matrix multiplications to tensor powers of a partial matrix multiplication in the sense that every entry of the result is a partial version of the inner product of the corresponding row and column of the factors that would appear in the usual matrix product. The implication is that support rank upper bounds on partial matrix multiplication tensors in this general sense give upper bounds on the support rank exponent of matrix multiplication.
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部分矩阵乘法和加权矩阵乘法
Sch\"onhage 在 1981 年发表的一篇论文中指出,大型总矩阵乘法可以简化为部分矩阵乘法张量的幂次,这相当于部分项为零的矩阵乘法的双线性计算任务。如何将这一方法推广到乘法也是部分乘法的情况,即只需计算子集条目,这还是一个未决问题。我们证明了一种更普遍情况的变体:将大型加权矩阵乘法简化为部分矩阵乘法的张量幂,即结果的每个条目都是相应行列因子内积的部分版本,而这些因子会出现在通常的矩阵乘法中。这意味着,在这种一般意义上,部分矩阵乘法张量的支持等级上限给出了矩阵乘法的支持等级指数上限。
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