On the tightness of Gaussian concentration for convex functions

Abstract: The concentration of measure phenomenon in Gauss' space states that every $L$-Lipschitz map $f$ on $\mathbb R^n$ satisfies [ \gamma_{n} \left({ x : | f(x) - M_{f} | \geqslant t } \right) \leqslant 2 e^{ - \frac{t^2}{ 2L^2} }, \quad t>0, ] where $\gamma_{n} $ is the standard Gaussian measure on $\mathbb R^{n}$ and $M_{f}$ is a median of $f$. In this work, we provide necessary and sufficient conditions for when this inequality can be reversed, up to universal constants, in the case when $f$ is additionally assumed to be convex. In particular, we show that if the variance ${\rm Var}(f)$ (with respect to $\gamma_{n}$) satisfies $ \alpha L \leqslant \sqrt{ {\rm Var}(f) } $ for some $ 0<\alpha \leqslant 1$, then [ \gamma_{n} \left({ x : | f(x) - M_{f} | \geqslant t }\right) \geqslant c e^{ -C \frac{t^2}{ L^2} } , \quad t>0 ,] where $c,C>0$ are constants depending only on $\alpha$.