Optimal Probability Inequalities for Random Walks related to Problems in Extremal Combinatorics

Let S_n=X_1+...+X_n be a sum of independent symmetric random variables such that |X_{i}|\leq 1. Denote by W_n=\epsilon_{1}+...+\epsilon_{n} a sum of independent random variables such that \prob{\eps_i = \pm 1} = 1/2. We prove that \mathbb{P}{S_{n} \in A} \leq \mathbb{P}{cW_k \in A}, where A is either an interval of the form [x, \infty) or just a single point. The inequality is exact and the optimal values of c and k are given explicitly. It improves Kwapie\'n's inequality in the case of the Rademacher series. We also provide a new and very short proof of the Littlewood-Offord problem without using Sperner's Theorem. Finally, an extension to odd Lipschitz functions is given.Comment: 10 pages, submitte

Optimal Littlewood-Offord inequalities in groups

We prove several Littlewood-Offord type inequalities for arbitrary groups. In groups having elements of finite order the worst case scenario is provided by the simple random walk on a certain cyclic subgroup. The inequalities we obtain are optimal if the underlying group contains an element of certain order. It turns out that for torsion-free groups Erd\H{o}s's bound still holds. Our results strengthen and generalize some very recent results by Tiep and Vu

A non-uniform Littlewood-Offord inequality

Consider a sum $S_n=v_i\varepsilon_1+\cdots+v_n\varepsilon_{n}$, where $(v_i)^{n}_{i=1}$ are non-zero vectors in $\mathbb{R}^{d}$ and $(\varepsilon_i)^{n}_{i=1}$ are independent Rademacher random variables (i.e., $~{\mathbb{P}(\varepsilon_{i}=\pm 1)=1/2}$). The classical Littlewood-Offord problem asks for the best possible upper bound for $~{\sup_{x}\mathbb{P}(S_n = x)}$. In this paper we consider a non-uniform version of this problem. Namely, we obtain the optimal bound for $\mathbb{P}(S_n = x)$ in terms of the length of the vector $x\in \mathbb{R}^d$

Linear combinations of Rademacher random variables

For a fixed unit vector $a=(a_1,a_2,\ldots,a_n)\in S^{n-1}$, we consider the $2^n$ sign vectors $\varepsilon=(\varepsilon^1,\varepsilon^2,\ldots,\varepsilon^n)\in \{+1,-1\}^n$ and the corresponding scalar products $\varepsilon\cdot a=\sum_{i=1}^n \varepsilon^ia_i$. In this paper we will solve for $n=1,2,\ldots,9$ an old conjecture stating that of the $2^n$ sums of the form $\sum\pm a_i$ it is impossible that there are more with $|\sum_{i=1}^n \pm a_i|>1$ than there are with $|\sum_{i=1}^n \pm a_i|\leq1$. Although the problem has been solved completely in case the $a_i$'s are equal, the more general problem with possible non-equal $a_i$'s remains open for values of $n\geq 10$. The present method can also be used for $n\geq 10$, but unfortunately the technical difficulties seem to grow exponentially with $n$ and no "induction type of argument" has been found. The conjecture has an appealing reformulation in probability theory and in geometry. In probability theory the results lead to upper bounds which are much better than for instance Chebyshevnequalities.Comment: 15 page