Optimal feedback control arises in different areas such as aerospace engineering chemical
processing resource economics etc. In this context the application of dynamic programming
techniques leads to the solution of fully nonlinear Hamilton-Jacobi-Bellman equations. This
book presents the state of the art in the numerical approximation of Hamilton-Jacobi-Bellman
equations including post-processing of Galerkin methods high-order methods boundary
treatment in semi-Lagrangian schemes reduced basis methods comparison principles for
viscosity solutions max-plus methods and the numerical approximation of Monge-Ampère
equations. This book also features applications in the simulation of adaptive controllers and
the control of nonlinear delay differential equations. Contents From a monotone probabilistic
scheme to a probabilistic max-plus algorithm for solving Hamilton-Jacobi-Bellman equations
Improving policies for Hamilton-Jacobi-Bellman equations by postprocessing Viability approach
to simulation of an adaptive controller Galerkin approximations for the optimal control of
nonlinear delay differential equations Efficient higher order time discretization schemes for
Hamilton-Jacobi-Bellman equations based on diagonally implicit symplectic Runge-Kutta methods
Numerical solution of the simple Monge-Ampere equation with nonconvex Dirichlet data on
nonconvex domains On the notion of boundary conditions in comparison principles for viscosity
solutions Boundary mesh refinement for semi-Lagrangian schemes A reduced basis method for the
Hamilton-Jacobi-Bellman equation within the European Union Emission Trading Scheme
