Starting from a question in probability, I arrived to the following optimization problem.
Let $I:=[0, 1],$ and let $A$ be a Lebesgue measurable subset of the $n$-dimensional cube, $A\subset I^n.$ Consider, correspondingly, the set $$\hat A:= \{x\in I^{n+1}:\, (x_1,\dots,x_n)\in A,\, (x_2,\dots,x_{n+1})\notin A\}=A\times I\,\cap\, I \times A^c.$$
Problem. Maximize the $(n+1)$-dimensional Lebesgue measure of $\hat A$ over all measurable $A\subset I^n$: $$\lambda_n:=\sup_{A\subset I^n}\vert\hat A\vert.$$
If $n=1,$ we have $|\hat A|=|A|(1-|A|),$ whence $\lambda_1=1/4.$ For $n=2$ the maximizing set is the triangle below the diagonal, giving $\lambda_2=1/3.$ The sequence $\lambda_n$ is increasing, and converges to $1/2.$ If $n$ is even, one finds $$\lambda_n=\frac{1}{2}\left(1-\frac{1}{n+1}\right).$$ (I will edit and provide the details of the computation at request). However, as a consequence of a computation by Trotter and Winkler (Ramsey theory and sequences of random variables, Probability, Combinatorics and Computing 7 (1998), 221-238), the formula can't hold true for all odd $n,$ for one has $\lambda_5>\frac{1}{2}\left(1-\frac{1}{6}\right)=5/12.$
I would be very grateful for any suggestion or reference useful to shed light on the case of odd $n.$
$\{0,1\}$? $\endgroup$