Maximization of the efficiency of the first Dirichlet eigenfunction and improved eigenvalue inequalities

TL;DR

This paper improves bounds on the first Dirichlet eigenvalue and efficiency of the eigenfunction on convex domains, revealing new optimality results and asymptotic behaviors.

Contribution

It introduces sharper eigenvalue bounds using log-concavity estimates and identifies maximizers of efficiency among planar convex domains.

Findings

New sharp lower bounds for the first eigenvalue $\,\lambda_1$

Upper bounds for efficiency based on domain geometry

Existence of a maximizer of efficiency in planar convex domains

Abstract

We study the efficiency of the first Dirichlet eigenfunction $u$ on bounded convex domains $\Omega \subset \mathbb{R}^N$, defined as the ratio between the mean value of $u$ on $\Omega$ and its maximum value. By exploiting improved log-concavity estimates, we establish new sharp lower bounds for the first eigenvalue $\lambda_1$ and upper bounds for the efficiency in terms of the geometry of the domain, refining classical inequalities by Payne, Stakgold, and Hersch. Furthermore, we investigate the asymptotic behavior of the efficiency for elongating planar convex domains, making use of 1D limit profiles and Schr{\"o}dinger operators with convex potentials. As a main consequence of our analysis, we prove that among all planar convex domains the Payne-Stakgold upper bound is not optimal, and that there exists a maximizer of the efficiency.