High-order filtered schemes for the Hamilton-Jacobi continuum limit of nondominated sorting
This work provides more accurate numerical methods for nondominated sorting, a key algorithm in multi-objective optimization used in machine learning and engineering.
The authors develop high-order filtered finite difference schemes for the Hamilton-Jacobi continuum limit of nondominated sorting, proving stability and convergence to the viscosity solution, with numerical simulations demonstrating improved accuracy over first-order methods.
We investigate high-order finite difference schemes for the Hamilton-Jacobi equation continuum limit of nondominated sorting. Nondominated sorting is an algorithm for sorting points in Euclidean space into layers by repeatedly removing minimal elements. It is widely used in multi-objective optimization, which finds applications in many scientific and engineering contexts, including machine learning. In this paper, we show how to construct filtered schemes, which combine high order possibly unstable schemes with first order monotone schemes in a way that guarantees stability and convergence while enjoying the additional accuracy of the higher order scheme in regions where the solution is smooth. We prove that our filtered schemes are stable and converge to the viscosity solution of the Hamilton-Jacobi equation, and we provide numerical simulations to investigate the rate of convergence of the new schemes.