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  • 标题:On Multilinear Forms: Bias, Correlation, and Tensor Rank
  • 本地全文:下载
  • 作者:Abhishek Bhrushundi ; Prahladh Harsha ; Pooya Hatami
  • 期刊名称:Electronic Colloquium on Computational Complexity
  • 印刷版ISSN:1433-8092
  • 出版年度:2018
  • 卷号:2018
  • 出版社:Universität Trier, Lehrstuhl für Theoretische Computer-Forschung
  • 摘要:

    In this paper, we prove new relations between the bias of multilinear forms, the correlation between multilinear forms and lower degree polynomials, and the rank of tensors over G F (2) = 0 1 . We show the following results for multilinear forms and tensors.

    1. Correlation bounds : We show that a random d -linear form has exponentially low correlation with low-degree polynomials. More precisely, for d 2 o ( k ) , we show that a random d -linear form f ( X 1 X 2 X d ) : G F (2 ) k d G F (2) has correlation 2 − k (1 − o (1)) with any polynomial of degree at most d 10 .

    This result is proved by giving near-optimal bounds on the bias of random d -linear form, which is in turn proved by giving near-optimal bounds on the probability that a random rank- t d -linear form is identically zero.

    2. Tensor-rank vs Bias : We show that if a d -dimensional tensor has small rank, then the bias of the associated d -linear form is large. More precisely, given any d -dimensional tensor T : [ k ] [ k ] d times G F (2) of rank at most t , the bias of the associated d -linear form f T ( X 1 X d ) : = ( i 1 i d ) [ k ] d T ( i 1 i 2 i d ) X 1 i 1 X 1 i 2 X d i d is at least 1 − 1 2 d − 1 t . The above bias vs tensor-rank connection suggests a natural approach to proving nontrivial tensor-rank lower bounds for d = 3 . In particular, we use this approach to prove that the finite field multiplication tensor has tensor rank at least 3 52 k matching the best known lower bound for any explicit tensor in three dimensions over G F (2) .

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