Proximal aiming in weak KAM theory with nonsmooth Lagrangian

TL;DR

This paper extends weak KAM theory to nonsmooth Lagrangians, constructing nearly optimal discontinuous feedback strategies via proximal aiming, and demonstrates the effective Hamiltonian can be found through linear programming.

Contribution

It introduces a novel approach to weak KAM theory for nonsmooth Lagrangians using proximal aiming and linear programming methods.

Findings

Constructed nearly optimal feedback strategies for nonsmooth Lagrangians.

Extended weak KAM theorem to nonsmooth settings.

Showed the effective Hamiltonian can be computed via linear programming.

Abstract

This work extends weak KAM theory to the case of a nonsmooth Lagrangian satisfying a superlinear growth condition. Using the solution of a weak KAM equation that is a stationary Hamilton-Jacobi equation and the proximal aiming method, we construct a family of discontinuous feedback strategies that are nearly optimal for every time interval. This result leads to an analogue of the weak KAM theorem. Additionally, as in classical weak KAM theory, we demonstrate that the effective Hamiltonian (Ma\~{n}\'{e} critical value) can be determined by solving a linear programming problem in the class of probability measures.