  PANOC is an algorithm for nonconvex optimization that has recently gained
popularity in real-time control applications due to its fast, global
convergence. The present work proposes a variant of PANOC that makes use of
Gauss-Newton directions to accelerate the method. Furthermore, we show that
when applied to optimal control problems, the computation of this Gauss-Newton
step can be cast as a linear quadratic regulator (LQR) problem, allowing for an
efficient solution through the Riccati recursion. Finally, we demonstrate that
the proposed algorithm is more than twice as fast as the traditional L-BFGS
variant of PANOC when applied to an optimal control benchmark problem, and that
the performance scales favorably with increasing horizon length.
