Verification of some functional inequalities via polynomial optimization

TL;DR

This paper develops a polynomial optimization approach to verify functional inequalities relevant to PDEs, enabling the use of semidefinite programming for checking nonnegativity and optimizing parameters.

Contribution

It introduces a measure-theoretic lifting method that transforms functional inequalities into polynomial inequalities, extending existing moment relaxation techniques for PDE analysis.

Findings

Functional inequalities can be verified via semidefinite programming.

The approach allows optimization of functionals with tunable parameters.

The method extends moment relaxation strategies for PDEs.

Abstract

Motivated by the application of Lyapunov methods to partial differential equations (PDEs), we study functional inequalities of the form $f(I_1(u),\ldots,I_k(u))\geq 0$ where $f$ is a polynomial, $u$ is any function satisfying prescribed constraints, and $I_1(u),\ldots,I_k(u)$ are integral functionals whose integrands are polynomial in $u$, its derivatives, and the integration variable. We show that such functional inequalities can be strengthened into sufficient polynomial inequalities, which in principle can be checked via semidefinite programming using standard techniques for polynomial optimization. These sufficient conditions can be used also to optimize functionals with affine dependence on tunable parameters whilst ensuring their nonnegativity. Our approach relies on a measure-theoretic lifting of the original functional inequality, which extends both a recent moment relaxation strategy for PDE analysis and a dual approach to inequalities for integral functionals.