Algorithm for Overcoming the Curse of Dimensionality For Time-Dependent Non-convex Hamilton-Jacobi Equations Arising From Optimal Control and Differential Games Problems

摘要

In this paper we develop a parallel method for solving possibly non-convex time-dependent Hamilton-Jacobi equations arising from optimal control and differential game problems. The subproblems are independent so they can be implemented in an embarrassingly parallel fashion, which usually has an ideal parallel speedup. The algorithm is proposed to overcome the curse of dimensionality (Bellman in Adaptive control processes: a guided tour. Princeton University Press, Princeton, 1961; Dynamic programming. Princeton University Press, Princeton, 1957) when solving HJ PDE . We extend previous work Chow et al. (Algorithm for overcoming the curse of dimensionality for certain non-convex Hamilton-Jacobi equations, Projections and differential games, UCLA CAM report, pp 16-27, 2016) and Darbon and Osher (Algorithms for overcoming the curse of dimensionality for certain Hamilton-Jacobi equations arising in control theory and elsewhere, UCLA CAM report, pp 15-50, 2015) and apply a generalized Hopf formula to solve HJ PDE involving time-dependent and perhaps non-convex Hamiltonians. We suggest a coordinate descent method for the minimization procedure in the Hopf formula. This method is preferable when even the evaluation of the function value itself requires some computational effort, and also when we handle higher dimensional optimization problem. For an integral with respect to time inside the generalized Hopf formula, we suggest using a numerical quadrature rule. Together with our suggestion to perform numerical differentiation to minimize the number of calculation procedures in each iteration step, we are bound to have numerical errors in our computations. These errors can be effectively controlled by choosing an appropriate mesh-size in time and the method does not use a mesh in space. The use of multiple initial guesses is suggested to overcome possibly multiple local extrema in the case when non-convex Hamiltonians are considered. Our method is expected to have wide application in control theory and differential game problems, and elsewhere.