Extensions of Autocorrelation Inequalities with Applications to Additive Combinatorics

Abstract

Barnard and Steinerberger ['Three convolution inequalities on the real line with connections to additive combinatorics', Preprint, 2019, arXiv:1903.08731] established the autocorrelation inequality Min_(0≤t≤1)∫_Rf(x)f(x+t) dx ≤ 0.411||f||²L¹, for fϵL¹(R), where the constant 0.4110.411 cannot be replaced by 0.370.37. In addition to being interesting and important in their own right, inequalities such as these have applications in additive combinatorics. We show that for f to be extremal for this inequality, we must have max min_(x₁∈R 0≤t≤1)[f(x₁−t)+f(x₁+t)] ≤ min_max(x₂∈ R0≤t≤1)[f(x₂−t)+f(x₂+t)]. Our central technique for deriving this result is local perturbation of f to increase the value of the autocorrelation, while leaving ||f||L¹|| unchanged. These perturbation methods can be extended to examine a more general notion of autocorrelation. Let d, n∈Z⁺, f∈L¹, A be a d×n matrix with real entries and columns a_i for 1≤i≤n and C be a constant. For a broad class of matrices A, we prove necessary conditions for f to extremise autocorrelation inequalities of the form Min_(t∈ [0,1]^d)∫R∏_(i=1)^n f(x+t⋅a_i)dx≤C||f||^nL¹.

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