论文标题
高斯·诺顿（Gauss-Newton）遇到panoc：一种非线性最佳控制
Gauss-Newton meets PANOC: A fast and globally convergent algorithm for nonlinear optimal control
论文作者
论文摘要
PANOC是一种非convex优化的算法，由于其快速，全球收敛，最近在实时控制应用中广受欢迎。目前的工作提出了一种PANOC的变体，该变体利用高斯 - 纽顿方向加速了该方法。此外，我们表明，当应用于最佳控制问题时，该高斯 - 纽顿步骤的计算可以作为线性二次调节器（LQR）问题施放，从而可以通过Riccati递归进行有效的解决方案。最后，我们证明，当应用于最佳控制基准问题时，提出的算法的速度是Panoc的传统L-BFGS变体的两倍以上，并且性能随着地平线长度的增加而呈优惠率。
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.
