论文标题
部分可观测时空混沌系统的无模型预测
ProxNLP: a primal-dual augmented Lagrangian solver for nonlinear programming in Robotics and beyond
论文作者
论文摘要
数学优化是现代机器人技术和控制的几个方面背后的主力。在这些应用中，重点是受限的优化，以及在多种多样（例如经典矩阵谎言组）上工作的能力，以及对鲁棒性和速度的特定要求。近年来，增强的拉格朗日方法由于其稳健性和灵活性，与（不精确的）近端方法的连接以及与牛顿或牛顿牛顿牛顿方法的互操作性而引起了复兴。在续集中，我们提出了原始的二偶增强Lagrangian方法，用于在歧管上不平等问题，我们在最近的工作中介绍了这些方法，以及适合在机器人应用程序及其他地区使用的有效C ++实现。
Mathematical optimization is the workhorse behind several aspects of modern robotics and control. In these applications, the focus is on constrained optimization, and the ability to work on manifolds (such as the classical matrix Lie groups), along with a specific requirement for robustness and speed. In recent years, augmented Lagrangian methods have seen a resurgence due to their robustness and flexibility, their connections to (inexact) proximal-point methods, and their interoperability with Newton or semismooth Newton methods. In the sequel, we present primal-dual augmented Lagrangian method for inequality-constrained problems on manifolds, which we introduced in our recent work, as well as an efficient C++ implementation suitable for use in robotics applications and beyond.
