  Let $X$ be a random variable and define its concentration function by
$$\mathcal{Q}_{h}(X)=\sup_{x\in \mathbb{R}}\mathbb{P}(X\in (x,x+h]).$$ For a
sum $S_n=X_1+\cdots+X_n$ of independent real-valued random variables the
Kolmogorov-Rogozin inequality states that $$\mathcal{Q}_{h}(S_n)\leq
C\left(\sum_{i=1}^{n}(1-\mathcal{Q}_{h}(X_i))\right)^{-\frac{1}{2}}.$$
  In this paper we give an optimal bound for $\mathcal{Q}_{h}(S_n)$ in terms of
$\mathcal{Q}_{h}(X_i)$, which settles a question posed by Leader and Radcliffe
in 1994. Moreover, we show that the extremal distributions are mixtures of two
uniform distributions each lying on an arithmetic progression.
