Probabilistic Tensors and Opportunistic Boolean Matrix Multiplication
Probabilistic Tensors and Opportunistic Boolean Matrix Multiplication
复制标题
概率张量和机会布尔矩阵乘法
DOI:
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发表时间:
2019
期刊:
影响因子:
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通讯作者:
P. Kaski
中科院分区:
文献类型:
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作者:
Matti Karppa;P. Kaski
We introduce probabilistic extensions of classical deterministic measures of algebraic complexity of a tensor, such as the rank and the border rank. We show that these probabilistic extensions satisfy various natural and algorithmically serendipitous properties, such as submultiplicativity under taking of Kronecker products. Furthermore, the probabilistic extensions enable improvements over their deterministic counterparts for specific tensors of interest, starting from the tensor (cid:104) 2 , 2 , 2 (cid:105) that represents 2 × 2 matrix multiplication. While it is well known that the (deterministic) tensor rank and border rank satisfy rk (cid:104) 2 , 2 , 2 (cid:105) = 7 and rk (cid:104) 2 , 2 , 2 (cid:105) = 7 [V. Strassen, Numer. Math. 13 (1969); J. E. Hopcroft and L. R. Kerr, SIAM J. Appl. Math. 20 (1971); S. Winograd, Linear Algebra Appl. 4 (1971); J. M. Landsberg, J. AMS 19 (2006)], we show that the probabilistic tensor rank and border rank satisfy (cid:101) rk (cid:104) 2 , 2 , 2 (cid:105) ≤ 6 + 6 7 and (cid:101) rk (cid:104) 2 , 2 , 2 (cid:105) ≤ 6 + 2 3 . By submultiplicativity, this leads immediately to novel randomized algorithm designs, such as algorithms for Boolean matrix multiplication as well as detecting and estimating the number of triangles in graphs. Our algorithms are opportunistic in the sense
DOI:
10.1561/0400000063
发表时间:
2013
期刊:
Found. Trends Theor. Comput. Sci.
影响因子:
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作者:
S. Jukna;I. Sergeev
通讯作者:
I. Sergeev