Sharpening the gap between $L^{1}$ and $L^{2}$ norms

TL;DR

This paper refines the classical Cauchy--Schwartz inequality by establishing a sharper bound involving $L^p$, $L^q$, and $L^1$ norms for random variables, with applications to Rademacher and exponential sums.

Contribution

It introduces a new inequality that tightens the relationship between $L^1$, $L^p$, and $L^q$ norms, extending classical bounds with explicit constants.

Findings

Derived a refined inequality relating $L^1$, $L^p$, and $L^q$ norms.

Applied the inequality to biased Rademacher sums.

Applied the inequality to exponential sums.

Abstract

We refine the classical Cauchy--Schwartz inequality $\|X\|_{1} \leq \|X\|_{2}$ by demonstrating that for any $p$ and $q$ with $q>p>2$, there exists a constant $C=C(p,q)$ such that $\|X\|_1 \leq 1 - C \Big{(}\|X\|_p^p - 1\Big{)}^{\frac{q-2}{q-p}}\Big{(}\|X\|_q^q - 1\Big{)}^{\frac{2-p}{q-p}}$ holds true for all Borel measurable random variables $X$ with $\|X\|_{2}=1$ and $\|X\|_{p}<\infty$. We illustrate two applications of this result: one for biased Rademacher sums and another for exponential sums.