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

- Mathematics, Stanford University
- More about Matthew Kwan

- Mathematics, Stanford University
- More about Lisa Sauermann

*Discrete Analysis*, August. https://doi.org/10.19086/da.14351.

### Editorial introduction

An algebraic inverse theorem for the quadratic Littlewood-Offord problem, and an application to Ramsey graphs, Discrete Analysis 2020:12, 34 pp.

The Littlewood-Offord problem is the following general question. Let v1,…,vn be vectors in Rd of norm at least 1 and let B⊂Rd be a closed ball of diameter Δ. Of the 2n sums ∑i∈Evi, where E⊂{1,2,…,n}, how many can lie in B? Erdős observed that when d=1 and Δ<1, it is not possible for ∑i∈Evi and ∑i∈Fvi both to lie in B if E is a proper subset of F. Sperner’s theorem states that the largest collection of subsets of {1,2,…,n} with no set in the collection being a proper subset of another has size at most (n⌊n/2⌋) (with equality if one takes all sets of size ⌊n/2⌋ or alternatively all sets of size ⌈n/2⌉). He showed more generally that the best bound if B is an interval of length Δ is the sum of the sizes of the ⌊Δ⌋+1 largest binomial coefficients. It is easy to see that those results are best possible by considering the case where every vi is equal to 1. For general d, Kleitman proved in 1965 that if Δ<1 then the same bound of (n⌊n/2⌋) holds, and a good understanding for general Δ was achieved by Frankl and Füredi in 1988.

Of particular interest is the case Δ=0, where one is asking in how many ways the same value can be taken. It is convenient to rephrase this question in an equivalent probabilistic way. When d=1 the rephrased question asks the following. Let ξ1,…,ξn be independent Bernoulli random variables and let a1,…,an be real numbers greater than 1. What is the largest that a probability of the form P[∑iaiξi=t] can be? The result of Erdős states that it cannot be greater than 2−n(n⌊n/2⌋), which is of order n−1/2.

Sums of this kind come up naturally in the study of random matrices. Indeed, if M is a random n×n 01-matrix with all its entries independent, then the coordinates of the image of the vector (a1,…,an)T will all be of the above form. (In practice it is more convenient to study ±1-valued matrices, but the problems are essentially equivalent.) In a series of important papers, Tao and Vu realized that it helped them to understand the properties of sequences (a1,…,an) that come somewhere near achieving the Erdős bound, in the sense that the probability has a power dependence on n. They proved an *inverse* Littlewood-Offord theorem, showing that such sequences must have arithmetic relationships between the ai – their results in this direction are closely related to Freiman’s inverse theorem for sets with small sumsets.

This paper considers a generalization in another direction. The problem above can be expressed as a question about linear polynomials in n variables. If f is such a polynomial, how large can the probability that f(ξ1,…,ξn)=t be, given suitable assumptions about the coefficients? Once the question is phrased that way, it is natural to ask what happens if we replace “linear” by “quadratic”. The example (x1+⋯+xn)2 (with n even) shows that the probability that f(x)=0 can be as large as the maximum probability in the linear case, but it is notable that this example has very low rank: it is the square of a single linear polynomial. The paper proves that this phenomenon is more general: if a value is taken with probability significantly greater than n−1, then the polynomial must be close to a polynomial of low rank.

To see that n−1 is a natural bound, consider a quadratic ∑ni,j=1ϵijxixj, where the ϵij take the values ±1 independently at random. For each fixed choice of {0,1}-values for the xi, the variance of this sum will be of order the square of the number of xi that are equal to 1, which is typically of order n2. Therefore, the standard deviation is of order n, so there is a range of order n roughly equally likely integer values that the sum might take. Therefore we expect that if we make a typical choice for the ϵij, these values will be roughly equally likely as we range over sequences x with a given number of 1s, so they will occur with probability around n−1. Since the quadratic typically has full rank, we therefore cannot conclude anything about the rank from the existence of a value that occurs with this probability.

As this example shows, there are two phenomena at play here. As in the linear case, there are arithmetic properties of the set of coefficients, but in the quadratic case it also makes a great deal of difference how the coefficients are distributed amongst the pairs ij – in the case where the coefficients take just two values, the structure of the graph of pairs where one of the values is taken is very important.

In the light of this, it should not come as too much of a surprise that the results of this paper have graph-theoretic applications. A *Ramsey graph* is a graph with n vertices that contains no clique or independent set with more than Clogn vertices. The main examples of Ramsey graphs are random graphs, and there are are a number of results that aim to show that a Ramsey graph must be random-like in various ways. For instance, one class of such results states that there must be many different numbers of edges that an induced subgraph can have. The results of this paper are highly relevant, since the number of edges in an induced subgraph is in one-to-one correspondence with the value of the associated quadratic polynomial on the characteristic function of the vertex set of that subgraph, so the paper shows that if any number of edges occurs with probability significantly greater than n−1, then the graph must have quite strong “low rank structure”. The authors show that this structure is incompatible with the graph being Ramsey, thereby giving an asymptotic answer to a question of Kwan, Sudakov and Tran.

^{Matthew Kwan talking about the results in this paper.}