Algorithm for overcoming the curse of dimensionality for certain non-convex Hamilton–Jacobi equations, projections and differential games

Abstract

Abstract : In this paper, we develop a method for solving a large class of non-convex Hamilton-Jacobi partial differential equations (HJ PDE). The method yields decoupled subproblems, which can be solved in an embarrassingly parallel fashion. The complexity of the resulting algorithm is polynomial in the problem dimension; hence, it overcomes the curse of dimensionality [1, 2]. We extend previous work in[6] and apply the Hopf formula to solve HJ PDE involving non-convex Hamiltonians. We propose an ADMM approach for finding the minimizer associated with the Hopf formula. Some explicit formulae of proximal maps, as well as newly-defined stretch operators, are used in the numerical solutions of ADMM subproblems. Our approach is expected to have wide applications in continuous dynamic games, control theory problems, and elsewhere.