Temporal Parallelization of Dynamic Programming and Linear Quadratic Control

This article proposes a general formulation for temporal parallelization of dynamic programming optimal control problems. We derive the elements and associative operators to be able use parallel scans solve these problems with logarithmic time complexity rather than linear complexity. apply this methodology finite state spaces, quadratic tracking problems, class nonlinear The computational benefits methods are demonstrated via numerical simulations run on graphics processing unit.