Conference Papers Year : 2022

ProxNLP: a primal-dual augmented Lagrangian solver for nonlinear programming in Robotics and beyond

Abstract

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.
Fichier principal
Vignette du fichier
icra22_legged_workshop_proxnlp_final.pdf (2) Télécharger le fichier
main.pdf (2) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03680510 , version 1 (28-05-2022)
hal-03680510 , version 2 (30-09-2022)

Identifiers

Cite

Wilson Jallet, Antoine Bambade, Nicolas Mansard, Justin Carpentier. ProxNLP: a primal-dual augmented Lagrangian solver for nonlinear programming in Robotics and beyond. 6th Legged Robots Workshop, May 2022, Philadelphia, Pennsylvania, United States. ⟨hal-03680510v2⟩
262 View
340 Download

Altmetric

Share

More