High-resolution alternating evolution schemes for hyperbolic conservation laws and Hamilton-Jacobi equations

The novel approximation system introduced by Liu is an accurate approximation to systems of hyperbolic conservation laws. We develop a class of global and local alternating evolution (AE) schemes for one- and two-dimensional hyperbolic conservation law and one-dimensional Hamilton-Jacobi equations, where we take advantage of the high accuracy of the AE approximation. The nature of solutions having singularities, which is generic to these equations in handled using the AE methodology. The numerical scheme is constructed from the AE system by sampling over alternating computational grid points. Higher order accuracy is achieved by a combination of high-order polynomial reconstruction and a stable Runge-Kutta discretization in time. Local AE schemes are made possible by letting the scale parameter [epsilon] reflect the local distribution of nonlinear waves. The AE schemes have the advantage of easier formulation and implementation, and efficient computation of the solution. Theoretical numerical stability is proved mainly for the first and second order schemes of hyperbolic conservation law and Hamilton-Jacobi equations. In the case of hyperbolic conservation law, we have also shown that the numerical solutions converge to the weak solution. The designed methods have the advantage of being Riemann solver free, and the performs comparably to the finite volume/difference methods currently used. A series of numerical tests illustrates the capacity and accuracy of our method in describing the solutions

Domain Decomposition for Stochastic Optimal Control

This work proposes a method for solving linear stochastic optimal control (SOC) problems using sum of squares and semidefinite programming. Previous work had used polynomial optimization to approximate the value function, requiring a high polynomial degree to capture local phenomena. To improve the scalability of the method to problems of interest, a domain decomposition scheme is presented. By using local approximations, lower degree polynomials become sufficient, and both local and global properties of the value function are captured. The domain of the problem is split into a non-overlapping partition, with added constraints ensuring $C^1$ continuity. The Alternating Direction Method of Multipliers (ADMM) is used to optimize over each domain in parallel and ensure convergence on the boundaries of the partitions. This results in improved conditioning of the problem and allows for much larger and more complex problems to be addressed with improved performance.Comment: 8 pages. Accepted to CDC 201

Optimal control of the propagation of a graph in inhomogeneous media

We study an optimal control problem for viscosity solutions of a Hamilton–Jacobi equation describing the propagation of a one-dimensional graph with the control being the speed function. The existence of an optimal control is proved together with an approximate controllability result in the $H^{-1}$-norm. We prove convergence of a discrete optimal control problem based on a monotone finite difference scheme and describe some numerical results