Convex combinations of low eigenvalues, Fraenkel asymmetries and attainable sets

TL;DR

This paper investigates the minimization of convex combinations of the first two Dirichlet-Laplacian eigenvalues in fixed-measure open sets, exploring geometric properties of minimizers, their convexity, and the attainable spectral set boundary.

Contribution

It provides elementary geometric and symmetry-based methods to analyze minimizers of eigenvalue combinations and their convexity, including the role of Fraenkel asymmetries.

Findings

Balls are optimal in certain cases for eigenvalue combinations.

Minimizers can lose convexity depending on the convex combination.

The boundary of the attainable Dirichlet spectrum set is characterized.

Abstract

We consider the problem of minimizing convex combinations of the first two eigenvalues of the Dirichlet-Laplacian among open sets of $R^N$ of fixed measure. We show that, by purely elementary arguments, based on the minimality condition, it is possible to obtain informations on the geometry of the minimizers of convex combinations: we study, in particular, when these minimizers are no longer convex, and the optimality of balls. As an application of our results we study the boundary of the attainable set for the Dirichlet spectrum. Our techniques involve symmetry results \`a la Serrin, explicit constants in quantitative inequalities, as well as a purely geometrical problem: the minimization of the Fraenkel 2-asymmetry among convex sets of fixed measure.