Rényi entropy and variance comparison for symmetric log-concave random variables

Abstract: We show that for any $\alpha&gt;0$ the R\'enyi entropy of order $\alpha$ is minimized, among all symmetric log-concave random variables with fixed variance, either for a uniform distribution or for a two sided exponential distribution. The first case occurs for $\alpha \in (0,\alpha<sup>*]$ and the second case for $\alpha \in [\alpha<sup>*,\infty)$, where $\alpha<sup>*$ satisfies the equation $$\frac{1}{\alpha^*-1}\log \alpha^*= \frac12 \log 6$$, that is $\alpha^* \approx 1.241$. Using those results, we prove that one-sided exponential distribution minimizes R\'enyi entropy of order $\alpha \geq 2$ among all log-concave random variables with fixed variance.