DeepAI

# On Multilinear Forms: Bias, Correlation, and Tensor Rank

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 GF(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) : (GF(2)^k)^d → GF(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→ GF(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 most (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 GF(2).

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