An algebraic inverse theorem for the quadratic Littlewood-Offord problem, and an application to Ramsey graphs

IF 1 3区 数学 Q1 MATHEMATICS Discrete Analysis Pub Date : 2019-09-04 DOI:10.19086/DA.14351
Matthew Kwan, Lisa Sauermann
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引用次数: 5

Abstract

Consider a quadratic polynomial $f\left(\xi_{1},\dots,\xi_{n}\right)$ of independent Bernoulli random variables. What can be said about the concentration of $f$ on any single value? This generalises the classical Littlewood--Offord problem, which asks the same question for linear polynomials. As in the linear case, it is known that the point probabilities of $f$ can be as large as about $1/\sqrt{n}$, but still poorly understood is the "inverse" question of characterising the algebraic and arithmetic features $f$ must have if it has point probabilities comparable to this bound. In this paper we prove some results of an algebraic flavour, showing that if $f$ has point probabilities much larger than $1/n$ then it must be close to a quadratic form with low rank. We also give an application to Ramsey graphs, asymptotically answering a question of Kwan, Sudakov and Tran.
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二次Littlewood-Offord问题的代数逆定理及其在Ramsey图中的应用
考虑独立伯努利随机变量的二次多项式$f\left(\xi_{1},\dots,\xi_{n}\right)$。对于$f$在任意一个值上的浓度,我们能说些什么呢?这推广了经典的Littlewood—offford问题,后者对线性多项式提出了同样的问题。就像在线性情况下一样,我们知道$f$的点概率可以大到$1/\sqrt{n}$左右,但仍然很难理解的是,如果$f$具有与该边界相当的点概率,那么表征它必须具有的代数和算术特征的“逆”问题。在本文中,我们证明了一些代数性质的结果,表明如果$f$的点概率远大于$1/n$,那么它一定接近于低秩的二次型。我们也给出了Ramsey图的一个应用,渐近地回答了Kwan, Sudakov和Tran的问题。
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来源期刊
Discrete Analysis
Discrete Analysis Mathematics-Algebra and Number Theory
CiteScore
1.60
自引率
0.00%
发文量
1
审稿时长
17 weeks
期刊介绍: Discrete Analysis is a mathematical journal that aims to publish articles that are analytical in flavour but that also have an impact on the study of discrete structures. The areas covered include (all or parts of) harmonic analysis, ergodic theory, topological dynamics, growth in groups, analytic number theory, additive combinatorics, combinatorial number theory, extremal and probabilistic combinatorics, combinatorial geometry, convexity, metric geometry, and theoretical computer science. As a rough guideline, we are looking for papers that are likely to be of genuine interest to the editors of the journal.
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