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arXiv 1004.3967math.CO
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最优逆Littlewood-Offord定理

Optimal Inverse Littlewood-Offord theorems

Hoi Nguyen, Van Vu

摘要:

设eta_i是iid Bernoulli随机变量,取值为-1,1,概率为1/2。给定n个整数v_1,...,v_n,我们将集中概率定义为rho(V):= sup_{x} Pr(v_1 eta_1+.+ v_n eta_n=x)。Littlewood-Offord和Erdos在20世纪40年代的一个经典结果断言,如果v_i不为零,则rho(V)是O(n^{-1/2})。从那时起,许多研究人员通过假设V的各种额外限制获得了改进的边界。大约5年前,受随机矩阵问题的启发,Tao和Vu引入了逆Littlewood-Offord问题。在逆问题中,人们希望给出集合V的特征,假设rho(V)相对较大。在本文中,我们介绍了一种新的方法来攻击反问题。作为一个应用,我们加强了以前的结果陶和武,获得V的最佳表征。这立即意味着几个经典定理,如Sarkozy-Szemeredi和Halasz。该方法也适用于连续设置,并导致一个简单的证明β-网定理的陶和Vu,这在他们最近的研究中发挥了关键作用的随机矩阵。当V是阿贝尔无挠群的子集,eta_i是满足某些弱条件的自变量时,所有结果都推广到了一般情况.

英文摘要:

Let eta_i be iid Bernoulli random variables, taking values -1,1 with probability 1/2. Given a multiset V of n integers v_1,..., v_n, we define the concentration probability as rho(V) := sup_{x} Pr(v_1 eta_1+...+ v_n eta_n=x). A classical result of Littlewood-Offord and Erdos from the 1940s asserts that if the v_i are non-zero, then rho(V) is O(n^{-1/2}). Since then, many researchers obtained improved bounds by assuming various extra restrictions on V. About 5 years ago, motivated by problems concerning random matrices, Tao and Vu introduced the Inverse Littlewood-Offord problem. In the inverse problem, one would like to give a characterization of the set V, given that rho(V) is relatively large. In this paper, we introduce a new method to attack the inverse problem. As an application, we strengthen a previous result of Tao and Vu, obtaining an optimal characterization for V. This immediately implies several classical theorems, such as those of Sarkozy-Szemeredi and Halasz. The method also applies in the continuous setting and leads to a simple proof for the beta-net theorem of Tao and Vu, which plays a key role in their recent studies of random matrices. All results extend to the general case when V is a subset of an abelian torsion-free group and eta_i are independent variables satisfying some weak conditions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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