  Li, Chen, Tai & E. (J. Machine Learning Research, 2018) have proposed a
regularization of the forward-backward sweep iteration for solving the
Pontryagin maximum principle in optimal control problems. The authors prove the
global convergence of the iteration in the continuous time case. In this
article we show that their proof can be extended to the case of numerical
discretization by symplectic Runge-Kutta pairs. We demonstrate the convergence
with a simple numerical experiment.
